From 258d87dc53319b8f98c564366a7e46e4531b92c4 Mon Sep 17 00:00:00 2001 From: tiffanychu90 Date: Fri, 5 Apr 2024 18:19:55 +0000 Subject: [PATCH 01/36] add refactor concepts, notes --- bus_procurement_cost/refactor_notes.md | 31 ++++++++++++++++++++++++++ 1 file changed, 31 insertions(+) create mode 100644 bus_procurement_cost/refactor_notes.md diff --git a/bus_procurement_cost/refactor_notes.md b/bus_procurement_cost/refactor_notes.md new file mode 100644 index 000000000..b1a76ad15 --- /dev/null +++ b/bus_procurement_cost/refactor_notes.md @@ -0,0 +1,31 @@ +# Refactor Notes +* This is soft skill I want you to hone: what is the simplest way you can explain the analysis, and then back that statement up with code. +* There is a lot of "variables"...aka code to generate the values you need in the summary text. The balance is heavily tipped that way. Basically 200 lines of code are dedicated to creating variables. There are 6 datasets that you read in, sometimes multiple times. + * Do you read each DGS dataset in twice because one is pre-cleaning, and one is post-cleaning? Same for FTA and TIRCP? + * What is the 7th dataset that is the merged one? +* Challenge: for your Markdown paragraphs, can you write just 1 line of code for every variable you need? You can import your dfs once at the top. + * Refer to the Google Doc to see the desired table you want to create + +## Outline +**Question:** how much do transit agencies pay to procure buses? +**Ideal Table:** transit agency / grant recipients with the bus types (size, propulsion), unit bus cost, and number of buses purchased. + +Clear code can be read like a story. Each sentence can be developed with functions. The story of your analysis is roughly this: + +1. I have 3 datasets, one for each grant, FTA, TIRCP, DGS. + * Do these 3 datasets share columns? Can I put them all into 1 final table? + * What column do I need to add to distiguish between them? + * When I finish step 1, do I need to have 3 cleaned dfs or can I have just one? + * If I need 3 cleaned dfs, can I use a naming pattern to easily grab them later? (`fta_cleaned, tircp_cleaned, dgs_cleaned`) +2. I have a long bus description of text, and I need to grab this information to populate my columns: bus size, bus propulsion, unit bus cost, number purchased. + * clean up description, remove extraneous spaces, make it all lower case + * I need a function (or two) for tagging all the bus propulsion types + * I need a function (or two) for tagging all the bus size types + * ... maybe for unit bus cost or number purchased? + * I used the bus propulsion function and tagged out the 5 types + * I used the bus size function and tagged out the 3 types. +3. I have to populate some numeric columns +4. I have to clean up outliers after finding z-scores. + * Do you want to drop them? Do you want to keep them and add a column called `outlier`? How have you been using it in your summary statement? +5. Other cleaning, maybe agency names, etc etc +6. Save out my cleaned df, and I will use this for all my charts, captions, paragraphs, etc. \ No newline at end of file From 50b56d70d2d42c9db9c38fbd3386b8def6e679f6 Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Thu, 13 Jun 2024 20:41:19 +0000 Subject: [PATCH 02/36] started NB for all refactor work --- bus_procurement_cost/refactor_bus_cost.ipynb | 6 ++++++ 1 file changed, 6 insertions(+) create mode 100644 bus_procurement_cost/refactor_bus_cost.ipynb diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb new file mode 100644 index 000000000..363fcab7e --- /dev/null +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} From 0326a436df0d19b8f4e2b60030fdc42fb3979ddc Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Thu, 13 Jun 2024 20:46:52 +0000 Subject: [PATCH 03/36] renamed old NBs to seperate from current work --- ...ipynb => OLD_FTA_bus_grant_analysis.ipynb} | 0 bus_procurement_cost/OLD_cost_per_bus.ipynb | 388 ++++++++++++++++++ ...> OLD_dgs_usage_report_bus_analysis.ipynb} | 0 ...sis.ipynb => OLD_tircp_bus_analysis.ipynb} | 0 bus_procurement_cost/refactor_bus_cost.ipynb | 31 +- 5 files changed, 417 insertions(+), 2 deletions(-) rename bus_procurement_cost/{FTA_bus_grant_analysis.ipynb => OLD_FTA_bus_grant_analysis.ipynb} (100%) create mode 100644 bus_procurement_cost/OLD_cost_per_bus.ipynb rename bus_procurement_cost/{dgs_usage_report_bus_analysis.ipynb => OLD_dgs_usage_report_bus_analysis.ipynb} (100%) rename bus_procurement_cost/{tircp_bus_analysis.ipynb => OLD_tircp_bus_analysis.ipynb} (100%) diff --git a/bus_procurement_cost/FTA_bus_grant_analysis.ipynb b/bus_procurement_cost/OLD_FTA_bus_grant_analysis.ipynb similarity index 100% rename from bus_procurement_cost/FTA_bus_grant_analysis.ipynb rename to bus_procurement_cost/OLD_FTA_bus_grant_analysis.ipynb diff --git a/bus_procurement_cost/OLD_cost_per_bus.ipynb b/bus_procurement_cost/OLD_cost_per_bus.ipynb new file mode 100644 index 000000000..8443f10df --- /dev/null +++ b/bus_procurement_cost/OLD_cost_per_bus.ipynb @@ -0,0 +1,388 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "411091ad-2d69-485a-9b08-c009d5566940", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from fta_data_cleaner import gcs_path\n", + "from cost_per_bus_utils import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "81da1fc0-17e9-4623-a7cf-d163f6ca1eee", + "metadata": {}, + "outputs": [], + "source": [ + "pd.set_option('display.max_rows', None)\n", + "pd.set_option('display.max_columns', None)\n", + "pd.set_option('display.max_colwidth', None)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4548178c-1d1b-4128-8cc8-42a3837892ea", + "metadata": {}, + "outputs": [], + "source": [ + "all_bus = pd.read_parquet(\n", + " \"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/cpb_analysis_data_merge.parquet\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b9aded29-aa16-4bcb-8b1e-4a6c95ca952c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "89" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(all_bus)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "287c09d4-0df4-4be0-880b-94bb863b2122", + "metadata": {}, + "outputs": [], + "source": [ + "all_bus[\"source\"].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b800c7b2-d467-4291-adbb-940a8240ad47", + "metadata": {}, + "outputs": [], + "source": [ + "# need initial dataset numbers?\n", + "# ## projects from FTA, ## projects from TICP, ## projects from dgs\n", + "# of which, only ## were bus only projects \n", + "\n", + "all_fta = \"fta_all_projects_clean.parquet\"\n", + "all_tircp = \"clean_tircp_project.parquet\"\n", + "all_dgs = \"dgs_agg_clean.parquet\"\n", + "\n", + "#data going into notebook\n", + "fta_bus_data = \"fta_bus_cost_clean.parquet\"\n", + "tircp_bus_data = \"clean_tircp_project_bus_only.parquet\"\n", + "dgs_bus_data = \"dgs_agg_w_options_clean.parquet\" #cost of bus + options.\n", + "\n", + "fta1 = pd.read_parquet(f\"{gcs_path}{all_fta}\")\n", + "tircp1 = pd.read_parquet(f\"{gcs_path}{all_tircp}\")\n", + "dgs1 = pd.read_parquet(f\"{gcs_path}{all_dgs}\")\n", + "\n", + "fta2 = pd.read_parquet(f\"{gcs_path}{fta_bus_data}\")\n", + "tircp2 = pd.read_parquet(f\"{gcs_path}{tircp_bus_data}\")\n", + "dgs2 = pd.read_parquet(f\"{gcs_path}{dgs_bus_data}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "703a1753-a828-438b-92c3-2c945dadf785", + "metadata": {}, + "outputs": [], + "source": [ + "display(\n", + " len(fta1),\n", + " len(fta2),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "83e34c14-1e08-4aa4-8d72-2c3c454516d2", + "metadata": {}, + "outputs": [], + "source": [ + "display(\n", + " len(tircp1),\n", + " len(tircp2)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80dac111-63c3-462e-851a-b0d424ba1019", + "metadata": {}, + "outputs": [], + "source": [ + "len(fta1)+len(tircp1)+len(dgs1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a974fda7-b6ab-4806-a4ef-3077bfcc1beb", + "metadata": {}, + "outputs": [], + "source": [ + "display(\n", + " len(dgs1),\n", + " len(dgs2)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ec995ef-9ea4-47c4-8191-4c52c6d7b946", + "metadata": {}, + "outputs": [], + "source": [ + "display(\n", + " dgs1[\"source\"].value_counts(),\n", + " dgs2[\"source\"].value_counts()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a4f560b-4fdf-4fae-b7fe-e20e019082cf", + "metadata": {}, + "outputs": [], + "source": [ + "display(\n", + " dgs1[dgs1[\"source\"] == \"17c\"].sort_values(by = \"ordering_agency_name\"),\n", + " dgs2[dgs2[\"source\"] == \"17c\"].sort_values(by = \"ordering_agency_name\")\n", + ")\n", + "# dgs2 had duplicate agencies \n", + "# overall, use dgs1 (dgs_agg_clean.parquet) to get count of dgs projects" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c23d567a-e71f-48c0-b5e7-005cc18819f1", + "metadata": {}, + "outputs": [], + "source": [ + "#so final count of total projects is \n", + "# fta all projects, tircp all projects, dgs agg clean\n", + "\n", + "\n", + "all_fta = pd.read_parquet(\"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/fta_all_projects_clean.parquet\")\n", + "all_tircp = pd.read_parquet(\"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/clean_tircp_project.parquet\")\n", + "all_dgs = pd.read_parquet(\"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/dgs_agg_clean.parquet\")\n", + "\n", + "count_all_fta = len(pd.read_parquet(\"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/fta_all_projects_clean.parquet\"))\n", + "count_all_tircp = len(pd.read_parquet(\"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/clean_tircp_project.parquet\"))\n", + "count_all_dgs = len(pd.read_parquet(\"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/dgs_agg_clean.parquet\"))\n", + "\n", + "count_all_projects = count_all_fta+count_all_tircp+count_all_dgs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "718a5c84-1c78-40a5-9651-24034fa22fd0", + "metadata": {}, + "outputs": [], + "source": [ + "count_of_all_projects" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f846fe68-f87b-4eda-b098-f5d07b21215a", + "metadata": {}, + "outputs": [], + "source": [ + "def all_project_counter(fta_file: str, tircp_file:str, dgs_file: str) -> int:\n", + " \"\"\"\n", + " function to count all the projects from fta, tircp and dgs files.\n", + " use to find the total number of projects and the total number of bus only projects\n", + " \"\"\"\n", + " gcs_path = \"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/\"\n", + "\n", + " \n", + " all_fta = len(pd.read_parquet(f\"{gcs_path}{fta_file}\"))\n", + " all_tircp = len(pd.read_parquet(f\"{gcs_path}{tircp_file}\"))\n", + " all_dgs = len(pd.read_parquet(f\"{gcs_path}{dgs_file}\"))\n", + " \n", + " count_all_projects = all_fta+all_tircp+all_dgs\n", + " \n", + " return count_all_projects" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "32adf818-b4ed-40b4-ac52-298e97c1842e", + "metadata": {}, + "outputs": [], + "source": [ + "all_project_count = all_project_counter(\n", + " fta_file = \"fta_all_projects_clean.parquet\",\n", + " tircp_file = \"clean_tircp_project.parquet\",\n", + " dgs_file = \"dgs_agg_clean.parquet\"\n", + ")\n", + "\n", + "bus_only_project_count = all_project_counter(\n", + " fta_file = \"fta_bus_cost_clean.parquet\",\n", + " tircp_file = \"clean_tircp_project_bus_only.parquet\",\n", + " dgs_file = \"dgs_agg_clean.parquet\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "9f8aedf5-c69e-4be7-ad4d-bfa796c3d5f6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "289" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "87" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(all_project_count,\n", + " bus_only_project_count\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7a2b20d7-4e9a-413e-9c73-077027f89465", + "metadata": {}, + "outputs": [], + "source": [ + "all_fta = \"fta_all_projects_clean.parquet\"\n", + "all_tircp = \"clean_tircp_project.parquet\"\n", + "all_dgs = \"dgs_agg_clean.parquet\"" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "c39e1bb0-306f-4a21-8d17-e3775a804be7", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "bus_only_count_fta = len(pd.read_parquet(\"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/fta_bus_cost_clean.parquet\"))\n", + "bus_only_count_tircp = len(pd.read_parquet(\"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/clean_tircp_project_bus_only.parquet\"))\n", + "bus_only_count_dgs = len(pd.read_parquet(\"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/dgs_agg_clean.parquet\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "6b1aa66f-da1a-4155-b2d7-b4424d1bc7c7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "43" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "9" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "35" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(\n", + " bus_only_fta,\n", + " bus_only_tircp,\n", + " bus_only_dgs\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "32068f95-df74-40e4-a4af-dc7719f22fc7", + "metadata": {}, + "source": [ + "## FIX NUMBER OF BUSES" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02065103-933e-4ab5-a43a-2954213ebdc1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/bus_procurement_cost/dgs_usage_report_bus_analysis.ipynb b/bus_procurement_cost/OLD_dgs_usage_report_bus_analysis.ipynb similarity index 100% rename from bus_procurement_cost/dgs_usage_report_bus_analysis.ipynb rename to bus_procurement_cost/OLD_dgs_usage_report_bus_analysis.ipynb diff --git a/bus_procurement_cost/tircp_bus_analysis.ipynb b/bus_procurement_cost/OLD_tircp_bus_analysis.ipynb similarity index 100% rename from bus_procurement_cost/tircp_bus_analysis.ipynb rename to bus_procurement_cost/OLD_tircp_bus_analysis.ipynb diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index 363fcab7e..96d3ab656 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -1,6 +1,33 @@ { - "cells": [], - "metadata": {}, + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "c2bb63c6-6457-4433-aa9d-0136b2690464", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, "nbformat": 4, "nbformat_minor": 5 } From da829dd992dded3952d8f2b9f7358b2d6b1a5e57 Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Thu, 13 Jun 2024 22:40:10 +0000 Subject: [PATCH 04/36] started new bus_cost_utils.py to start dropping in shared functions. also started adding updated scripts to draft NB --- bus_procurement_cost/bus_cost_utils.py | 233 +++++ ...us_utils.py => cost_per_bus_nb_scripts.py} | 0 bus_procurement_cost/refactor_bus_cost.ipynb | 904 +++++++++++++++++- 3 files changed, 1135 insertions(+), 2 deletions(-) create mode 100644 bus_procurement_cost/bus_cost_utils.py rename bus_procurement_cost/{cost_per_bus_utils.py => cost_per_bus_nb_scripts.py} (100%) diff --git a/bus_procurement_cost/bus_cost_utils.py b/bus_procurement_cost/bus_cost_utils.py new file mode 100644 index 000000000..7f29a26a0 --- /dev/null +++ b/bus_procurement_cost/bus_cost_utils.py @@ -0,0 +1,233 @@ +import pandas as pd + +GCS_PATH = "gs://calitp-analytics-data/data-analyses/bus_procurement_cost/" + +def new_prop_finder(description: str) -> str: + """ + function that matches keywords from each propulsion type list against the item description col, returns a standardized prop type + now includes variable that make description input lowercase. + to be used with .assign() + """ + + BEB_list = [ + "battery electric", + "BEBs paratransit buses" + ] + + cng_list = [ + "cng", + "compressed natural gas" + ] + + electric_list = [ + "electric buses", + "electric commuter", + "electric", + ] + + FCEB_list = [ + "fuel cell", + "hydrogen", + #"fuel cell electric", + #"hydrogen fuel cell", + #"fuel cell electric bus", + #"hydrogen electric bus", + ] + + # low emission (hybrid) + hybrid_list = [ + #"diesel electric hybrids", + #"diesel-electric hybrids", + #"hybrid electric", + #"hybrid electric buses", + #"hybrid electrics", + "hybrids", + "hybrid", + ] + + # low emission (propane) + propane_list = [ + #"propane buses", + #"propaned powered vehicles", + "propane", + ] + + mix_beb_list = [ + "2 BEBs and 4 hydrogen fuel cell buses", + ] + + mix_lowe_list = [ + "diesel and gas", + ] + + mix_zero_low_list = [ + "15 electic, 16 hybrid", + "4 fuel cell / 3 CNG", + "estimated-cutaway vans (PM- award will not fund 68 buses", + "1:CNGbus ;2 cutaway CNG buses", + ] + + zero_e_list = [ + #"zero emission buses", + #"zero emission electric", + #"zero emission vehicles", + "zero-emission", + "zero emission", + ] + + item_description = description.lower().replace("‐", " ").strip() + + if any(word in item_description for word in BEB_list) and not any( + word in item_description for word in ["diesel", "hybrid", "fuel cell"] + ): + return "BEB" + + elif any(word in item_description for word in FCEB_list): + return "FCEB" + + elif any(word in item_description for word in hybrid_list): + return "low emission (hybrid)" + + elif any(word in item_description for word in mix_beb_list): + return "mix (BEB and FCEB)" + + elif any(word in item_description for word in mix_lowe_list): + return "mix (low emission)" + + elif any(word in item_description for word in mix_zero_low_list): + return "mix (zero and low emission)" + + elif any(word in item_description for word in zero_e_list): + return "zero-emission bus (not specified)" + + elif any(word in item_description for word in propane_list): + return "low emission (propane)" + + elif any(word in item_description for word in electric_list): + return "electric (not specified)" + + elif any(word in item_description for word in cng_list): + return "CNG" + + else: + return "not specified" + +def new_bus_size_finder(description: str) -> str: + """ + Similar to prop_type_find, matches keywords to item description col and return standardized bus size type. + now includes variable that make description input lowercase. + To be used with .assign() + """ + + articulated_list = [ + "60 foot", + "articulated", + ] + + standard_bus_list = [ + "30 foot", + "35 foot", + "40 foot", + "40ft", + "45 foot", + "standard", + ] + + cutaway_list = [ + "cutaway", + ] + + other_bus_size_list = ["feeder bus"] + + otr_bus_list = [ + "coach style", + "over the road", + ] + + item_description = description.lower().replace("-", " ").strip() + + if any(word in item_description for word in articulated_list): + return "articulated" + + elif any(word in item_description for word in standard_bus_list): + return "standard/conventional (30ft-45ft)" + + elif any(word in item_description for word in cutaway_list): + return "cutaway" + + elif any(word in item_description for word in otr_bus_list): + return "over-the-road" + + elif any(word in item_description for word in other_bus_size_list): + return "other" + + else: + return "not specified" + +def project_type_finder(description: str) -> str: + """ + function to match keywords to project description col to identify projects that only have bus procurement. + used to identify projects into diffferent categories: bus only, bus + others, no bus procurement. + use with .assign() to get a new col. + """ + bus_list =[ + "bus", + "transit vehicles",# for fta list + "cutaway vehicles",# for fta list + "zero-emission vehicles", # for tircp list + "zero emission vehicles", + "zero‐emissions vans", + "hybrid-electric vehicles", + "battery-electric vehicles", + "buy new replacement vehicles", # specific string for fta list + ] + + exclude_list =[ + "facility", + #"station", + "stops", + "installation", + "depot", + "construct", + "infrastructure", + "signal priority", + "improvements", + "build", + "chargers", + "charging equipment", + "install", + "rail", + "garage", + "facilities", + "bus washing system", + "build a regional transit hub" # specific string needed for fta list + #"associated infrastructure" may need to look at what is associated infrastructure is for ZEB + + ] + proj_description = description.lower().strip() + + if any(word in proj_description for word in bus_list) and not any( + word in proj_description for word in exclude_list + ): + return "bus only" + + elif any(word in proj_description for word in exclude_list) and not any( + word in proj_description for word in bus_list + ): + return "non-bus components" + + elif any(word in proj_description for word in exclude_list) and any( + word in proj_description for word in bus_list + ): + return "includes bus and non-bus components" + + else: + return "needs review" + +def col_row_updater(df: pd.DataFrame, col1: str, val1, col2: str, new_val): + """ + function used to update values at specificed columns and row value. + """ + df.loc[df[col1] == val1, col2] = new_val + + return \ No newline at end of file diff --git a/bus_procurement_cost/cost_per_bus_utils.py b/bus_procurement_cost/cost_per_bus_nb_scripts.py similarity index 100% rename from bus_procurement_cost/cost_per_bus_utils.py rename to bus_procurement_cost/cost_per_bus_nb_scripts.py diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index 96d3ab656..5ccec4f49 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -2,11 +2,911 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "c2bb63c6-6457-4433-aa9d-0136b2690464", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import pandas as pd\n", + "pd.set_option(\"display.max_rows\", False)\n", + "pd.set_option(\"display.max_columns\", False)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "910ee0fa-38ce-44f3-8e18-4cdf740e1fd0", + "metadata": {}, + "outputs": [], + "source": [ + "# Old script imports\n", + "from fta_data_cleaner import *\n", + "from dgs_data_cleaner import *\n", + "from tircp_data_cleaner import *" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c3552c45-8b28-4bbe-ae82-f2d726a45937", + "metadata": {}, + "outputs": [], + "source": [ + "GCS_PATH = \"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e1b1f367-1dac-463f-8790-2e5134b7e79b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.9/site-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Data Validation extension is not supported and will be removed\n", + " warn(msg)\n" + ] + } + ], + "source": [ + "# All Raw Data\n", + "fta_raw = pd.read_csv(f\"{GCS_PATH}raw_data-analyses_bus_procurement_cost_fta_press_release_data_csv.csv\")\n", + "tirp_raw = pd.read_excel(f\"{GCS_PATH}raw_TIRCP Tracking Sheets 2_1-10-2024.xlsx\", sheet_name=\"Project Tracking\")\n", + "dgs17b_raw = pd.read_excel(f\"{GCS_PATH}raw_17b compiled.xlsx\", sheet_name = \"Usage Report Template\")\n", + "dgs17c_raw = pd.read_excel(f\"{GCS_PATH}raw_17c compiled-Proterra Compiled Contract Usage Report .xlsx\", sheet_name = \"Proterra \")" + ] + }, + { + "cell_type": "markdown", + "id": "d04911c1-e839-41fe-87b3-5065586f2223", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "# Scripts to Save\n", + "for new `bus_cost_utils.py` script" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5513b941-ecdb-405e-bfd6-952df6b8f8b4", + "metadata": {}, + "outputs": [], + "source": [ + "# NEW PROP FINDER\n", + "def new_prop_finder(description: str) -> str:\n", + " \"\"\"\n", + " function that matches keywords from each propulsion type list against the item description col, returns a standardized prop type\n", + " now includes variable that make description input lowercase.\n", + " to be used with .assign()\n", + " \"\"\"\n", + "\n", + " BEB_list = [\n", + " \"battery electric\",\n", + " \"BEBs paratransit buses\"\n", + " ]\n", + "\n", + " cng_list = [\n", + " \"cng\",\n", + " \"compressed natural gas\" \n", + " ]\n", + "\n", + " electric_list = [\n", + " \"electric buses\",\n", + " \"electric commuter\",\n", + " \"electric\",\n", + " ]\n", + "\n", + " FCEB_list = [\n", + " \"fuel cell\",\n", + " \"hydrogen\",\n", + " #\"fuel cell electric\",\n", + " #\"hydrogen fuel cell\",\n", + " #\"fuel cell electric bus\",\n", + " #\"hydrogen electric bus\",\n", + " ]\n", + "\n", + " # low emission (hybrid)\n", + " hybrid_list = [\n", + " #\"diesel electric hybrids\",\n", + " #\"diesel-electric hybrids\",\n", + " #\"hybrid electric\",\n", + " #\"hybrid electric buses\",\n", + " #\"hybrid electrics\",\n", + " \"hybrids\",\n", + " \"hybrid\",\n", + " ]\n", + "\n", + " # low emission (propane)\n", + " propane_list = [\n", + " #\"propane buses\",\n", + " #\"propaned powered vehicles\",\n", + " \"propane\",\n", + " ]\n", + "\n", + " mix_beb_list = [\n", + " \"2 BEBs and 4 hydrogen fuel cell buses\",\n", + " ]\n", + "\n", + " mix_lowe_list = [\n", + " \"diesel and gas\",\n", + " ]\n", + "\n", + " mix_zero_low_list = [\n", + " \"15 electic, 16 hybrid\",\n", + " \"4 fuel cell / 3 CNG\",\n", + " \"estimated-cutaway vans (PM- award will not fund 68 buses\",\n", + " \"1:CNGbus ;2 cutaway CNG buses\",\n", + " ]\n", + "\n", + " zero_e_list = [\n", + " #\"zero emission buses\",\n", + " #\"zero emission electric\",\n", + " #\"zero emission vehicles\",\n", + " \"zero-emission\",\n", + " \"zero emission\",\n", + " ]\n", + "\n", + " item_description = description.lower().replace(\"‐\", \" \").strip()\n", + "\n", + " if any(word in item_description for word in BEB_list) and not any(\n", + " word in item_description for word in [\"diesel\", \"hybrid\", \"fuel cell\"]\n", + " ):\n", + " return \"BEB\"\n", + "\n", + " elif any(word in item_description for word in FCEB_list):\n", + " return \"FCEB\"\n", + "\n", + " elif any(word in item_description for word in hybrid_list):\n", + " return \"low emission (hybrid)\"\n", + "\n", + " elif any(word in item_description for word in mix_beb_list):\n", + " return \"mix (BEB and FCEB)\"\n", + "\n", + " elif any(word in item_description for word in mix_lowe_list):\n", + " return \"mix (low emission)\"\n", + "\n", + " elif any(word in item_description for word in mix_zero_low_list):\n", + " return \"mix (zero and low emission)\"\n", + "\n", + " elif any(word in item_description for word in zero_e_list):\n", + " return \"zero-emission bus (not specified)\"\n", + "\n", + " elif any(word in item_description for word in propane_list):\n", + " return \"low emission (propane)\"\n", + "\n", + " elif any(word in item_description for word in electric_list):\n", + " return \"electric (not specified)\"\n", + " \n", + " elif any(word in item_description for word in cng_list):\n", + " return \"CNG\"\n", + "\n", + " else:\n", + " return \"not specified\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8e18cbe6-bde7-4c30-8a8a-aefd8d619821", + "metadata": {}, + "outputs": [], + "source": [ + "def new_bus_size_finder(description: str) -> str:\n", + " \"\"\"\n", + " Similar to prop_type_find, matches keywords to item description col and return standardized bus size type.\n", + " now includes variable that make description input lowercase.\n", + " To be used with .assign()\n", + " \"\"\"\n", + "\n", + " articulated_list = [\n", + " \"60 foot\",\n", + " \"articulated\",\n", + " ]\n", + "\n", + " standard_bus_list = [\n", + " \"30 foot\",\n", + " \"35 foot\",\n", + " \"40 foot\",\n", + " \"40ft\",\n", + " \"45 foot\",\n", + " \"standard\",\n", + " ]\n", + "\n", + " cutaway_list = [\n", + " \"cutaway\",\n", + " ]\n", + "\n", + " other_bus_size_list = [\"feeder bus\"]\n", + "\n", + " otr_bus_list = [\n", + " \"coach style\",\n", + " \"over the road\",\n", + " ]\n", + "\n", + " item_description = description.lower().replace(\"-\", \" \").strip()\n", + "\n", + " if any(word in item_description for word in articulated_list):\n", + " return \"articulated\"\n", + "\n", + " elif any(word in item_description for word in standard_bus_list):\n", + " return \"standard/conventional (30ft-45ft)\"\n", + "\n", + " elif any(word in item_description for word in cutaway_list):\n", + " return \"cutaway\"\n", + "\n", + " elif any(word in item_description for word in otr_bus_list):\n", + " return \"over-the-road\"\n", + "\n", + " elif any(word in item_description for word in other_bus_size_list):\n", + " return \"other\"\n", + "\n", + " else:\n", + " return \"not specified\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8004cc2d-957f-4e9a-9ca8-2a6f9aba9ffb", + "metadata": {}, + "outputs": [], + "source": [ + "def project_type_finder(description: str) -> str:\n", + " \"\"\"\n", + " function to match keywords to project description col to identify projects that only have bus procurement.\n", + " used to identify projects into diffferent categories: bus only, bus + others, no bus procurement.\n", + " use with .assign() to get a new col.\n", + " \"\"\"\n", + " bus_list =[\n", + " \"bus\",\n", + " \"transit vehicles\",# for fta list\n", + " \"cutaway vehicles\",# for fta list\n", + " \"zero-emission vehicles\", # for tircp list\n", + " \"zero emission vehicles\",\n", + " \"zero‐emissions vans\",\n", + " \"hybrid-electric vehicles\",\n", + " \"battery-electric vehicles\",\n", + " \"buy new replacement vehicles\", # specific string for fta list\n", + " ]\n", + " \n", + " exclude_list =[\n", + " \"facility\",\n", + " #\"station\",\n", + " \"stops\",\n", + " \"installation\",\n", + " \"depot\",\n", + " \"construct\",\n", + " \"infrastructure\",\n", + " \"signal priority\",\n", + " \"improvements\",\n", + " \"build\",\n", + " \"chargers\",\n", + " \"charging equipment\",\n", + " \"install\",\n", + " \"rail\",\n", + " \"garage\",\n", + " \"facilities\",\n", + " \"bus washing system\",\n", + " \"build a regional transit hub\" # specific string needed for fta list\n", + " #\"associated infrastructure\" may need to look at what is associated infrastructure is for ZEB \n", + " \n", + " ]\n", + " proj_description = description.lower().strip()\n", + "\n", + " if any(word in proj_description for word in bus_list) and not any(\n", + " word in proj_description for word in exclude_list\n", + " ):\n", + " return \"bus only\"\n", + " \n", + " elif any(word in proj_description for word in exclude_list) and not any(\n", + " word in proj_description for word in bus_list\n", + " ):\n", + " return \"non-bus components\"\n", + " \n", + " elif any(word in proj_description for word in exclude_list) and any(\n", + " word in proj_description for word in bus_list\n", + " ):\n", + " return \"includes bus and non-bus components\"\n", + " \n", + " else:\n", + " return \"needs review\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0ca9254-2983-4cab-845c-f9bfb0229417", + "metadata": {}, + "outputs": [], + "source": [ + "def col_row_updater(df: pd.DataFrame, col1: str, val1, col2: str, new_val):\n", + " \"\"\"\n", + " function used to update values at specificed columns and row value.\n", + " \"\"\"\n", + " df.loc[df[col1] == val1, col2] = new_val\n", + " \n", + " return" + ] + }, + { + "cell_type": "markdown", + "id": "97bdb85b-ecaa-4634-8ea1-02ebc630567f", + "metadata": {}, + "source": [ + "# Chagnes to current scripts\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea9c3269-d53d-4d94-bc22-c6768cb63d91", + "metadata": {}, + "outputs": [], + "source": [ + "# FTA\n", + "import numpy as np\n", + "import pandas as pd\n", + "import shared_utils\n", + "from calitp_data_analysis.sql import to_snakecase\n", + "from bus_cost_utils import *\n", + "# from dgs_data_cleaner import new_bus_size_finder, new_prop_finder, project_type_checker\n", + "#from tircp_data_cleaner import col_row_updater\n", + "\n", + "def col_splitter(\n", + " df: pd.DataFrame, \n", + " col_to_split: str, \n", + " new_col1: str, \n", + " new_col2: str, \n", + " split_char: str\n", + ")-> pd.DataFrame:\n", + " \"\"\"\n", + " function to split a column into 2 columns by specific character.\n", + " ex. split 100(beb) to \"100\" & \"(beb)\"\n", + " \"\"\"\n", + " df[[new_col1, new_col2]] = df[col_to_split].str.split(\n", + " pat=split_char, n=1, expand=True\n", + " )\n", + "\n", + " df[new_col2] = df[new_col2].str.replace(\")\", \"\")\n", + "\n", + " return df\n", + "\n", + "def fta_agg_bus_only(df: pd.DataFrame) -> pd.DataFrame:\n", + " \"\"\"\n", + " filters FTA data to only show projects with bus procurement (bus count > 0).\n", + " then filters projects for new_project_type = bus only\n", + " then aggregates\n", + " \"\"\"\n", + " df1 = df[(df[\"bus_count\"] > 0) & (df[\"new_project_type\"] == \"bus only\")]\n", + "\n", + " df2 = (\n", + " df1.groupby(\n", + " [\n", + " \"project_sponsor\",\n", + " \"project_title\",\n", + " \"new_prop_type_finder\",\n", + " \"new_bus_size_type\",\n", + " \"description\",\n", + " \"new_project_type\"\n", + " ]\n", + " )\n", + " .agg(\n", + " {\n", + " \"funding\": \"sum\",\n", + " \"bus_count\": \"sum\",\n", + " }\n", + " )\n", + " .reset_index()\n", + " )\n", + "\n", + " return df2\n", + "\n", + "def clean_fta_columns() -> pd.DataFrame:\n", + " \"\"\"\n", + " Main function to clean FTA data. Reads in data, changes datatypes, change specific values.\n", + " \"\"\"\n", + " # params\n", + " \n", + " file = \"data-analyses_bus_procurement_cost_fta_press_release_data_csv.csv\"\n", + "\n", + " # read in data\n", + " df = pd.read_csv(f\"{gcs_path}{file}\")\n", + "\n", + " # snakecase df\n", + " df = to_snakecase(df)\n", + "\n", + " # clean funding values\n", + " df[\"funding\"] = (\n", + " df[\"funding\"]\n", + " .str.replace(\"$\", \"\")\n", + " .str.replace(\",\", \"\")\n", + " .str.strip()\n", + " )\n", + "\n", + " # rename initial propulsion type col to propulsion category\n", + " df = df.rename(columns={\"propulsion_type\": \"prosulsion_category\"})\n", + "\n", + " # splittign `approx_#_of_buses col to get bus count\n", + " df1 = col_splitter(df, \"approx_#_of_buses\", \"bus_count\", \"extract_prop_type\", \"(\")\n", + "\n", + " # assign new columns via new_prop_finder and new_bus_size_finder\n", + " df2 = df1.assign(\n", + " new_prop_type_finder=df1[\"description\"].apply(new_prop_finder),\n", + " new_bus_size_type=df1[\"description\"].apply(new_bus_size_finder),\n", + " new_project_type=df1[\"description\"].apply(project_type_checker)\n", + " )\n", + "\n", + " # cleaning specific values\n", + " col_row_updater(df2, \"funding\", \"7443765\", \"bus_count\", 56)\n", + " col_row_updater(df2, \"funding\", \"17532900\", \"bus_count\", 12)\n", + " col_row_updater(df2, \"funding\", \"40402548\", \"new_prop_type_finder\", \"CNG\")\n", + " col_row_updater(df2, \"funding\", \"30890413\", \"new_prop_type_finder\", \"mix (zero and low emission)\")\n", + " col_row_updater(df2, \"funding\", \"29331665\", \"new_prop_type_finder\", \"mix (zero and low emission)\")\n", + " col_row_updater(df2, \"funding\", \"7598425\", \"new_prop_type_finder\", \"mix (zero and low emission)\")\n", + " col_row_updater(df2, \"funding\", \"7443765\", \"new_prop_type_finder\", \"mix (zero and low emission)\")\n", + " col_row_updater(df2, \"funding\", \"3303600\", \"new_prop_type_finder\", \"mix (diesel and gas)\")\n", + " col_row_updater(df2, \"funding\", \"2063160\", \"new_prop_type_finder\", \"low emission (hybrid)\")\n", + " col_row_updater(df2, \"funding\", \"1760000\", \"new_prop_type_finder\", \"low emission (propane)\")\n", + " col_row_updater(df2, \"funding\", \"1006750\", \"new_prop_type_finder\", \"ethanol\")\n", + " col_row_updater(df2, \"funding\", \"723171\", \"new_prop_type_finder\", \"low emission (propane)\")\n", + " col_row_updater(df2, \"funding\", \"23280546\", \"new_prop_type_finder\", \"BEB\")\n", + "\n", + " # update data types\n", + " update_cols = [\"funding\", \"bus_count\"]\n", + "\n", + " df2[update_cols] = df2[update_cols].astype(\"int64\")\n", + "\n", + " return df2\n", + "\n", + "if __name__ == \"__main__\":\n", + "\n", + " # initial df (all projects)\n", + " all_projects = clean_fta_columns()\n", + "\n", + " # projects with bus count > 0 only.\n", + " just_bus = fta_agg_bus_only(all_projects)\n", + "\n", + " # export both DFs\n", + " all_projects.to_parquet(f\"{gcs_path}clean_fta_all_projects.parquet\")\n", + " just_bus.to_parquet(f\"{gcs_path}clean_fta_bus_only.parquet\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e16119d-f6f3-478b-a419-7c4989557910", + "metadata": {}, + "outputs": [], + "source": [ + "# TIRCP\n", + "import numpy as np\n", + "import pandas as pd\n", + "import shared_utils\n", + "from calitp_data_analysis.sql import to_snakecase\n", + "from bus_cost_utils import *\n", + "\n", + "def clean_tircp_columns() -> pd.DataFrame:\n", + " \"\"\"\n", + " main function that reads in and cleans TIRCP data.\n", + " \"\"\"\n", + " from fta_data_cleaner import gcs_path\n", + " file_name = \"TIRCP Tracking Sheets 2_1-10-2024.xlsx\"\n", + " tircp_name = \"Project Tracking\"\n", + "\n", + " # read in data\n", + " df = pd.read_excel(f\"{gcs_path}{file_name}\", sheet_name=tircp_name)\n", + "\n", + " # keep specific columns\n", + " keep_col = [\n", + " \"Award Year\",\n", + " \"Project #\",\n", + " \"Grant Recipient\",\n", + " \"Project Title\",\n", + " \"PPNO\",\n", + " \"District\",\n", + " \"County\",\n", + " \"Project Description\",\n", + " \"bus_count\",\n", + " \"Master Agreement Number\",\n", + " \"Total Project Cost\",\n", + " \"TIRCP Award Amount ($)\",\n", + " ]\n", + "\n", + " df1 = df[keep_col]\n", + "\n", + " # snakecase\n", + " df2 = to_snakecase(df1)\n", + "\n", + " # dict of replacement values\n", + " value_replace_dict = {\n", + " \"Antelope Valley Transit Authority \": \"Antelope Valley Transit Authority (AVTA)\",\n", + " \"Humboldt Transit Authority\": \"Humboldt Transit Authority (HTA)\",\n", + " \"Orange County Transportation Authority\": \"Orange County Transportation Authority (OCTA)\",\n", + " \"Capitol Corridor Joint Powers Authority\": \"Capitol Corridor Joint Powers Authority (CCJPA)\",\n", + " \"Los Angeles County Metropolitan Transportation Authority\": \"Los Angeles County Metropolitan Transportation Authority (LA Metro)\",\n", + " \"Monterey-Salinas Transit\": \"Monterey-Salinas Transit District (MST)\",\n", + " \"Sacramento Regional Transit (SacRT)\": \"Sacramento Regional Transit District (SacRT)\",\n", + " \"Sacramento Regional Transit District\": \"Sacramento Regional Transit District (SacRT)\",\n", + " \"Sacramento Regional Transit District (SacRT) \": \"Sacramento Regional Transit District (SacRT)\",\n", + " \"San Diego Association of Governments\": \"San Diego Association of Governments (SANDAG)\",\n", + " \"Santa Clara Valley Transportation Authority (SCVTA)\": \"Santa Clara Valley Transportation Authority (VTA)\",\n", + " \"Southern California Regional Rail Authority (SCRRA)\": \"Southern California Regional Rail Authority (SCRRA - Metrolink)\",\n", + " \"Southern California Regional Rail Authority\": \"Southern California Regional Rail Authority (SCRRA - Metrolink)\",\n", + " \"3, 4\": \"VAR\",\n", + " }\n", + " \n", + " # replacing values in agency & county col\n", + " df3 = df2.replace(\n", + " {\"grant_recipient\": value_replace_dict}\n", + " ).replace(\n", + " {\"county\": value_replace_dict}\n", + " )\n", + " \n", + " # using update function to update values at specific columns and rows\n", + " col_row_updater(df3, 'ppno', 'CP106', 'bus_count', 42)\n", + " col_row_updater(df3, 'ppno', 'CP005', 'bus_count', 29)\n", + " col_row_updater(df3, 'ppno', 'CP028', 'bus_count', 12)\n", + " col_row_updater(df3, 'ppno', 'CP048', 'bus_count', 5)\n", + " col_row_updater(df3, 'ppno', 'CP096', 'bus_count', 6)\n", + " col_row_updater(df3, 'ppno', 'CP111', 'bus_count', 5)\n", + " col_row_updater(df3, 'ppno', 'CP130', 'bus_count', 7)\n", + " col_row_updater(df3, 'total_project_cost', 203651000, 'bus_count', 8)\n", + " \n", + " # columns to change dtype to str\n", + " dtype_update = [\n", + " 'ppno',\n", + " 'district'\n", + " ]\n", + " \n", + " df3[dtype_update] = df3[dtype_update].astype('str')\n", + " \n", + " # assigning new columns using imported functions.\n", + " df4 = df3.assign(\n", + " prop_type = df3['project_description'].apply(new_prop_finder),\n", + " bus_size_type = df3['project_description'].apply(new_bus_size_finder),\n", + " new_project_type = df3['project_description'].apply(project_type_checker)\n", + " )\n", + "\n", + " return df4\n", + "\n", + "def tircp_agg_bus_only(df: pd.DataFrame) -> pd.DataFrame:\n", + " \"\"\"\n", + " filters df to only include projects with bus procurement and for project type = bus only \n", + " does not include engineering, planning or construction only projects.\n", + " then, aggregates the df by agency name and ppno. Agencies may have multiple projects that procure different types of buses\n", + " \"\"\"\n", + " df2 = df[\n", + " (df[\"bus_count\"] > 0) & (df[\"new_project_type\"] == \"bus only\")\n", + " ]\n", + " \n", + " df3 = (\n", + " df2.groupby(\n", + " [\n", + " \"grant_recipient\",\n", + " \"ppno\",\n", + " \"prop_type\",\n", + " \"bus_size_type\",\n", + " \"project_description\",\n", + " \"new_project_type\"\n", + " ]\n", + " )\n", + " .agg({\"total_project_cost\": \"sum\", \"bus_count\": \"sum\"})\n", + " .reset_index()\n", + " )\n", + " return df3\n", + "\n", + "if __name__ == \"__main__\":\n", + " \n", + " \n", + " \n", + " # initial df\n", + " df1 = clean_tircp_columns()\n", + " \n", + " # aggregate \n", + " df2 = tircp_agg_bus_only(df1)\n", + " \n", + " # export both df's as parquets to GCS\n", + " df1.to_parquet(f'{gcs_path}clean_tircp_all_project.parquet')\n", + " df2.to_parquet(f'{gcs_path}clean_tircp_bus_only_clean.parquet')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "359f3b7a-d691-446f-9a14-424c47fc0929", + "metadata": {}, + "outputs": [], + "source": [ + "# DGS\n", + "import numpy as np\n", + "import pandas as pd\n", + "import shared_utils\n", + "from calitp_data_analysis.sql import to_snakecase\n", + "from bus_cost_utils import *\n", + "\n", + "def calculate_total_cost(row):\n", + " \"\"\"\n", + " Calculate new column for total cost by checking if total_with_options_per_unit is present or not.\n", + " if not, then calculate using contract_unit_price.\n", + " to be used with .assign()\n", + " \"\"\"\n", + " if row[\"total_with_options_per_unit\"] > 0:\n", + " return row[\"total_with_options_per_unit\"] * row[\"quantity\"]\n", + " else:\n", + " return row[\"contract_unit_price\"] * row[\"quantity\"]\n", + " \n", + "def clean_dgs_columns() -> pd.DataFrame:\n", + " \"\"\"\n", + " reads in 2 dgs sheets, adds source column, merges both DFs, snakecase columns, update dtypes for monetary columns.\n", + " merged first becaues the snakecase function messes with the dtypes for some reason\n", + " \"\"\"\n", + " \n", + " from fta_data_cleaner import gcs_path\n", + " \n", + " # params\n", + " file_17c = \"17c compiled-Proterra Compiled Contract Usage Report .xlsx\"\n", + " file_17b = \"17b compiled.xlsx\"\n", + " sheet_17c = \"Proterra \"\n", + " sheet_17b = \"Usage Report Template\"\n", + "\n", + " # merge columns for dataframes\n", + " merge_col = [\n", + " \"Agency Billing Code\",\n", + " \"Contract Line Item Number (CLIN) (RFP ID)\",\n", + " \"Contract Unit Price\",\n", + " \"Delivery Date\",\n", + " \"Extended Contract Price Paid\",\n", + " \"Index Date / Catalog Version\",\n", + " \"Item Description\",\n", + " \"List Price/MSRP\",\n", + " \"Manufacturer (OEM)\",\n", + " \"Manufacturer Part Number (OEM #)\",\n", + " \"Ordering Agency Name\",\n", + " \"Purchase Order Date\",\n", + " \"Purchase Order Number\",\n", + " \"Purchasing Authority Number (for State departments)\",\n", + " \"Quantity in \\nUnit of Measure\\n\",\n", + " \"Quantity\",\n", + " \"source\",\n", + " \"State (S) or Local (L) agency\",\n", + " \"Unit of Measure\",\n", + " \"UNSPSC Code\\n(Version 10)\",\n", + " \"Supplier Contract Usage ID\",\n", + " ]\n", + "\n", + " # columns to change dtype\n", + " to_int64 = [\n", + " \"contract_unit_price\",\n", + " \"extended_contract_price_paid\",\n", + " \"total_with_options_per_unit\",\n", + " \"grand_total\",\n", + " ]\n", + " \n", + " # read in data\n", + " dgs_17c = pd.read_excel(f\"{gcs_path}{file_17c}\", sheet_name=sheet_17c)\n", + " dgs_17b = pd.read_excel(f\"{gcs_path}{file_17b}\", sheet_name=sheet_17b)\n", + "\n", + " # add new column to identify source\n", + " dgs_17c[\"source\"] = \"17c\"\n", + " dgs_17b[\"source\"] = \"17b\"\n", + "\n", + " # merge\n", + " dgs_17bc = pd.merge(dgs_17b, dgs_17c, how=\"outer\", on=merge_col).fillna(0)\n", + "\n", + " # snakecase\n", + " dgs_17bc = to_snakecase(dgs_17bc)\n", + "\n", + " # takes list of columns and updates to int64\n", + " dgs_17bc[to_int64] = dgs_17bc[to_int64].astype(\"int64\")\n", + "\n", + " # change purchase_order_number col to str\n", + " dgs_17bc[\"purchase_order_number\"] = dgs_17bc[\"purchase_order_number\"].astype(\"str\")\n", + "\n", + " # adds 3 new columns from functions\n", + " dgs_17bc2 = dgs_17bc.assign(\n", + " total_cost=dgs_17bc.apply(calculate_total_cost, axis=1),\n", + " new_prop_type=dgs_17bc[\"item_description\"].apply(new_prop_finder),\n", + " new_bus_size=dgs_17bc[\"item_description\"].apply(new_bus_size_finder),\n", + " )\n", + "\n", + " return dgs_17bc2\n", + "\n", + "def dgs_agg_by_agency(df: pd.DataFrame) -> pd.DataFrame:\n", + " \"\"\"\n", + " function that aggregates the DGS data frame by transit agency and purchase order number (PPNO) to get total cost of just buses without options.\n", + " first, dataframe is filtered for rows containing buses (does not include rows with 'not specified').\n", + " then, group by agency, PPNO, prop type and bus size. and aggregate the quanity and total cost of just buses.\n", + " Possible for agencies to have multiple PPNOs for different bus types and sizes.\n", + " \"\"\"\n", + " # filter for rows containing bus, does not include accessories/warranties/parts/etc.\n", + " agg_agency_bus_count = df[~df[\"new_prop_type\"].str.contains(\"not specified\")]\n", + "\n", + " agg_agency_bus_count2 = agg_agency_bus_count[\n", + " [\n", + " \"ordering_agency_name\",\n", + " \"purchase_order_number\",\n", + " \"item_description\",\n", + " \"quantity\",\n", + " \"source\",\n", + " \"total_cost\",\n", + " \"new_prop_type\",\n", + " \"new_bus_size\",\n", + " ]\n", + " ]\n", + "\n", + " agg_agency_bus_count3 = (\n", + " agg_agency_bus_count2.groupby(\n", + " [\n", + " \"ordering_agency_name\",\n", + " \"purchase_order_number\",\n", + " \"new_prop_type\",\n", + " \"new_bus_size\",\n", + " ]\n", + " )\n", + " .agg(\n", + " {\n", + " \"quantity\": \"sum\",\n", + " \"total_cost\": \"sum\",\n", + " \"source\": \"max\",\n", + " }\n", + " )\n", + " .reset_index()\n", + " )\n", + "\n", + " return agg_agency_bus_count3\n", + "\n", + "def dgs_agg_by_agency_w_options(df: pd.DataFrame) -> pd.DataFrame:\n", + " \"\"\"\n", + " similar to the previous function, aggregates the DGS dataframe by transit agency to get total cost of buses with options.\n", + " agencies may order buses with different configurations, resulting in different total cost.\n", + " function creates 1 df of only buses to retain initial proulsion type, size type and quanity of buses.\n", + " then, creates 2nd df of aggregated total cost of buses+options, by transit agency.\n", + " lastly, both df's are merged together.\n", + " \"\"\"\n", + " # filter df for rows NOT containing 'not specified'. only returns rows with buses\n", + " dfa = df[~df[\"new_prop_type\"].str.contains(\"not specified\")]\n", + "\n", + " # keep specific columns\n", + " df2 = dfa[\n", + " [\n", + " \"ordering_agency_name\",\n", + " \"purchase_order_number\",\n", + " \"quantity\",\n", + " \"new_prop_type\",\n", + " \"new_bus_size\",\n", + " \"source\",\n", + " ]\n", + " ]\n", + "\n", + " # aggregate by agency and PPNO, get total cost of buses with options\n", + " df3 = (\n", + " df.groupby([\"ordering_agency_name\", \"purchase_order_number\"])\n", + " .agg({\"total_cost\": \"sum\"})\n", + " .reset_index()\n", + " )\n", + "\n", + " # merge both dataframes on agency and PPNO to get bus only rows & total cost with options.\n", + " merge = pd.merge(\n", + " df2, df3, on=[\"ordering_agency_name\", \"purchase_order_number\"], how=\"left\"\n", + " )\n", + "\n", + " return merge\n", + "\n", + "if __name__ == \"__main__\":\n", + " \n", + "\n", + " # initial df\n", + " df1 = clean_dgs_columns()\n", + " \n", + " #df of just bus cost (no options)\n", + " just_bus = dgs_agg_by_agency(df1)\n", + " \n", + " #df of bus cost+options\n", + " bus_w_options = dgs_agg_by_agency_w_options(df1)\n", + " \n", + " #export serperate df's as parquet to GCS\n", + " just_bus.to_parquet(f'{gcs_path}clean_dgs_all_projects.parquet')\n", + " bus_w_options.to_parquet(f'{gcs_path}clean_dgs_bus_only_options.parquet')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19f4bd75-f614-4937-880a-1e1a6ff2eb7f", + "metadata": {}, + "outputs": [], + "source": [ + "# cost per bus cleaner\n", + "# rename to all_bus_cost_cleaner?\n", + "\n", + "import pandas as pd\n", + "from bus_cost_utils import *\n", + "\n", + "def prepare_all_data() ->pd.DataFrame:\n", + " \"\"\"\n", + " primary function to read-in, merge data across FTA, TIRCP and DGS data.\n", + " standardizes columns names, then exports as parquet.\n", + " \"\"\"\n", + " # variables for file names\n", + "t\n", + "\n", + " \n", + " # dictionary to update columns names \n", + " col_dict = {\n", + " \"funding\": \"total_cost\",\n", + " \"grant_recipient\": \"transit_agency\",\n", + " \"new_bus_size\": \"bus_size_type\",\n", + " \"new_bus_size_type\": \"bus_size_type\",\n", + " \"new_prop_type\": \"prop_type\",\n", + " \"new_prop_type_finder\": \"prop_type\",\n", + " \"ordering_agency_name\": \"transit_agency\",\n", + " \"purchase_order_number\": \"ppno\",\n", + " \"quantity\": \"bus_count\",\n", + " \"total_project_cost\": \"total_cost\",\n", + " \"project_sponsor\": \"transit_agency\",\n", + " }\n", + "\n", + " # reading in data\n", + " # bus only projects for each datase\n", + " fta = pd.read_parquet(f\"{gcs_path}clean_fta_bus_only.parquet\")\n", + " tircp = pd.read_parquet(f\"{gcs_path}clean_tircp_bus_only_clean.parquet\")\n", + " dgs = pd.read_parquet(f\"{gcs_path}clean_dgs_bus_only_options.parquet\")\n", + " \n", + " # adding new column to identify source\n", + " fta[\"source\"] = \"fta\"\n", + " tircp[\"source\"] = \"tircp\"\n", + " dgs[\"source\"] = \"dgs\"\n", + "\n", + " # using .replace() with dictionary to update column names\n", + " fta2 = fta.rename(columns=col_dict)\n", + " tircp2 = tircp.rename(columns=col_dict)\n", + " dgs2 = dgs.rename(columns=col_dict)\n", + " \n", + " # merging fta2 and tircp 2\n", + " merge1 = pd.merge(fta2,\n", + " tircp2,\n", + " on=[\n", + " \"transit_agency\",\n", + " \"prop_type\",\n", + " \"bus_size_type\",\n", + " \"total_cost\",\n", + " \"bus_count\",\n", + " \"source\",\n", + " \"new_project_type\"\n", + " ],\n", + " how=\"outer\",\n", + " )\n", + " \n", + " # mergeing merge1 and dgs2\n", + " merge2 = pd.merge(merge1,\n", + " dgs2,\n", + " on=[\n", + " \"transit_agency\",\n", + " \"prop_type\",\n", + " \"bus_size_type\",\n", + " \"total_cost\",\n", + " \"bus_count\",\n", + " \"source\",\n", + " \"ppno\",\n", + " ],\n", + " how=\"outer\",\n", + " )\n", + " \n", + " return merge2\n", + "\n", + "if __name__ == \"__main__\":\n", + " \n", + " # initial df\n", + " df1 = prepare_data()\n", + " \n", + " # export to gcs\n", + " df1.to_parquet(f'{gcs_path}cpb_analysis_data_merge.parquet')\n" + ] } ], "metadata": { From de61eaea8e080d326410a210e7460cb0ca3a8b02 Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Thu, 13 Jun 2024 23:33:17 +0000 Subject: [PATCH 05/36] testing new function to flag if cpb is outlier --- bus_procurement_cost/refactor_bus_cost.ipynb | 351 ++++++++++++++++++- 1 file changed, 339 insertions(+), 12 deletions(-) diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index 5ccec4f49..08c8bc93b 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -2,19 +2,19 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 7, "id": "c2bb63c6-6457-4433-aa9d-0136b2690464", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", - "pd.set_option(\"display.max_rows\", False)\n", - "pd.set_option(\"display.max_columns\", False)" + "pd.set_option(\"display.max_rows\", 300)\n", + "pd.set_option(\"display.max_columns\", 100)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "id": "910ee0fa-38ce-44f3-8e18-4cdf740e1fd0", "metadata": {}, "outputs": [], @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "id": "c3552c45-8b28-4bbe-ae82-f2d726a45937", "metadata": {}, "outputs": [], @@ -37,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "id": "e1b1f367-1dac-463f-8790-2e5134b7e79b", "metadata": {}, "outputs": [ @@ -58,6 +58,17 @@ "dgs17c_raw = pd.read_excel(f\"{GCS_PATH}raw_17c compiled-Proterra Compiled Contract Usage Report .xlsx\", sheet_name = \"Proterra \")" ] }, + { + "cell_type": "code", + "execution_count": 18, + "id": "e1e3bad8-62d3-4ca1-b4dd-b4d5b8d9a86b", + "metadata": {}, + "outputs": [], + "source": [ + "# what does the final table look like again?\n", + "final = pd.read_parquet(f'{gcs_path}old/cpb_analysis_data_merge.parquet')" + ] + }, { "cell_type": "markdown", "id": "d04911c1-e839-41fe-87b3-5065586f2223", @@ -336,7 +347,9 @@ { "cell_type": "markdown", "id": "97bdb85b-ecaa-4634-8ea1-02ebc630567f", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "# Chagnes to current scripts\n" ] @@ -816,7 +829,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "19f4bd75-f614-4937-880a-1e1a6ff2eb7f", "metadata": {}, "outputs": [], @@ -826,6 +839,7 @@ "\n", "import pandas as pd\n", "from bus_cost_utils import *\n", + "from scipy.stats import zscore\n", "\n", "def prepare_all_data() ->pd.DataFrame:\n", " \"\"\"\n", @@ -833,7 +847,7 @@ " standardizes columns names, then exports as parquet.\n", " \"\"\"\n", " # variables for file names\n", - "t\n", + "\n", "\n", " \n", " # dictionary to update columns names \n", @@ -899,14 +913,327 @@ " \n", " return merge2\n", "\n", - "if __name__ == \"__main__\":\n", + "def cpb_zscore_outliers(df: pd.DataFrame, zscore_col: str) -> pd.DataFrame:\n", + " \"\"\"\n", + " function that calculated cost per bus col, z-score col, then flags outliers\n", + " \"\"\"\n", + " #calculate cost per bus (aka unit cost per bus)\n", + " df['cpb'] = (df['total_cost'] / df['bus_count']).astype(\"int64\")\n", + " \n", + " #calculate zscore\n", + " df[\"zscore_cost_per_bus\"] = zscore(df[\"cpb\"])\n", + " \n", + " #flag outliers\n", + "\n", + "\n", + " \n", + " return df\n", + "\n", + "\n", + "#if __name__ == \"__main__\":\n", " \n", " # initial df\n", - " df1 = prepare_data()\n", + " #df1 = prepare_all_data()\n", " \n", " # export to gcs\n", - " df1.to_parquet(f'{gcs_path}cpb_analysis_data_merge.parquet')\n" + " #df1.to_parquet(f'{gcs_path}cpb_analysis_data_merge.parquet')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "15d4aca0-5dbd-40a1-8861-2501665c617a", + "metadata": {}, + "outputs": [], + "source": [ + "# new function to tag rows with an outlier flag\n", + "def outlier_flag(col):\n", + " \"\"\"\n", + " function to flag rows \n", + " \"\"\"\n", + " return col <= -3 or col >= 3\n", + "\n", + "#df[\"is cpb outlier?\"] = df[\"zscore_cost_per_bus\"].apply(outlier_flag)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "f84aaa3c-7bab-46c9-b739-021fdd6b60a2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(89, 13)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "13624fc6-86ea-4271-9a6f-6a725009ef6e", + "metadata": {}, + "outputs": [], + "source": [ + "test = cpb_zscore_outliers(final,\"cpb\")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "42c43416-981f-43d7-a642-6a22dc6619f2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(89, 13)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test.shape" ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "96a7a76d-3df0-4da0-89b5-1e0961bf16a4", + "metadata": {}, + "outputs": [], + "source": [ + "test[\"is cpb outlier?\"] = test[\"zscore_cost_per_bus\"].apply(outlier_flag)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "637032e4-d855-4190-a6f5-ff695f77143f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(89, 14)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencyproject_titleprop_typebus_size_typedescriptionnew_project_typetotal_costbus_countsourceppnoproject_descriptioncpbzscore_cost_per_busis cpb outlier?
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)Puerto Rico Initiative Minimizing Emissions Pl...electric (not specified)not specifiedThe Metropolitan Bus Authority will receive fu...bus only100000008.0ftaNoneNone12500000.917956False
1Cape Fear Public Transportation AuthorityWave Transit Low Emissions Replacement VehiclesCNGnot specifiedWave Transit will receive funding to buy compr...bus only28602505.0ftaNoneNone572050-0.529139False
2Central Oklahoma Transportation and Parking Au...COTPA, dba EMBARK Elimination of Fixed Route D...CNGnot specifiedThe Central Oklahoma Transportation and Parkin...bus only42787729.0ftaNoneNone475419-0.735399False
3Champaign-Urbana Mass Transit DistrictMTD 40-Foot Hybrid Replacement Buseslow emission (hybrid)not specifiedThe Champaign-Urbana Mass Transit District wil...bus only663539410.0ftaNoneNone663539-0.333854False
4City of BeaumontBeaumont Municipal Transit Zips to Improve Low...CNGnot specifiedBeaumont Municipal Transit will receive fundin...bus only28194605.0ftaNoneNone563892-0.546552False
\n", + "
" + ], + "text/plain": [ + " transit_agency \\\n", + "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) \n", + "1 Cape Fear Public Transportation Authority \n", + "2 Central Oklahoma Transportation and Parking Au... \n", + "3 Champaign-Urbana Mass Transit District \n", + "4 City of Beaumont \n", + "\n", + " project_title \\\n", + "0 Puerto Rico Initiative Minimizing Emissions Pl... \n", + "1 Wave Transit Low Emissions Replacement Vehicles \n", + "2 COTPA, dba EMBARK Elimination of Fixed Route D... \n", + "3 MTD 40-Foot Hybrid Replacement Buses \n", + "4 Beaumont Municipal Transit Zips to Improve Low... \n", + "\n", + " prop_type bus_size_type \\\n", + "0 electric (not specified) not specified \n", + "1 CNG not specified \n", + "2 CNG not specified \n", + "3 low emission (hybrid) not specified \n", + "4 CNG not specified \n", + "\n", + " description new_project_type \\\n", + "0 The Metropolitan Bus Authority will receive fu... bus only \n", + "1 Wave Transit will receive funding to buy compr... bus only \n", + "2 The Central Oklahoma Transportation and Parkin... bus only \n", + "3 The Champaign-Urbana Mass Transit District wil... bus only \n", + "4 Beaumont Municipal Transit will receive fundin... bus only \n", + "\n", + " total_cost bus_count source ppno project_description cpb \\\n", + "0 10000000 8.0 fta None None 1250000 \n", + "1 2860250 5.0 fta None None 572050 \n", + "2 4278772 9.0 fta None None 475419 \n", + "3 6635394 10.0 fta None None 663539 \n", + "4 2819460 5.0 fta None None 563892 \n", + "\n", + " zscore_cost_per_bus is cpb outlier? \n", + "0 0.917956 False \n", + "1 -0.529139 False \n", + "2 -0.735399 False \n", + "3 -0.333854 False \n", + "4 -0.546552 False " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "False 88\n", + "True 1\n", + "Name: is cpb outlier?, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(\n", + " test.shape,\n", + " test.head(),\n", + " test[\"is cpb outlier?\"].value_counts()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2abb9b0a-0196-452c-a83e-e7856857efc9", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 16fc0df99344178d8ddcda336202cc829e9f3068 Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Mon, 17 Jun 2024 20:30:35 +0000 Subject: [PATCH 06/36] comparing outliers in new and old DFs --- bus_procurement_cost/bus_cost_utils.py | 2 + bus_procurement_cost/refactor_bus_cost.ipynb | 482 +++++++++++++++++-- 2 files changed, 434 insertions(+), 50 deletions(-) diff --git a/bus_procurement_cost/bus_cost_utils.py b/bus_procurement_cost/bus_cost_utils.py index 7f29a26a0..2ed4ca501 100644 --- a/bus_procurement_cost/bus_cost_utils.py +++ b/bus_procurement_cost/bus_cost_utils.py @@ -1,3 +1,5 @@ +#script with shared functions used throughout the bus cost analysis. + import pandas as pd GCS_PATH = "gs://calitp-analytics-data/data-analyses/bus_procurement_cost/" diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index 08c8bc93b..ada217d74 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "id": "c2bb63c6-6457-4433-aa9d-0136b2690464", "metadata": {}, "outputs": [], @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "id": "910ee0fa-38ce-44f3-8e18-4cdf740e1fd0", "metadata": {}, "outputs": [], @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "id": "c3552c45-8b28-4bbe-ae82-f2d726a45937", "metadata": {}, "outputs": [], @@ -37,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "id": "e1b1f367-1dac-463f-8790-2e5134b7e79b", "metadata": {}, "outputs": [ @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 66, "id": "e1e3bad8-62d3-4ca1-b4dd-b4d5b8d9a86b", "metadata": {}, "outputs": [], @@ -69,11 +69,31 @@ "final = pd.read_parquet(f'{gcs_path}old/cpb_analysis_data_merge.parquet')" ] }, + { + "cell_type": "code", + "execution_count": 58, + "id": "c2865cb5-0adb-4529-b057-d2595f9f8ab6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(89, 11)" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final.shape" + ] + }, { "cell_type": "markdown", "id": "d04911c1-e839-41fe-87b3-5065586f2223", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -83,9 +103,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "5513b941-ecdb-405e-bfd6-952df6b8f8b4", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "# NEW PROP FINDER\n", @@ -202,9 +224,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "8e18cbe6-bde7-4c30-8a8a-aefd8d619821", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "def new_bus_size_finder(description: str) -> str:\n", @@ -262,9 +286,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "8004cc2d-957f-4e9a-9ca8-2a6f9aba9ffb", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "def project_type_finder(description: str) -> str:\n", @@ -330,9 +356,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "c0ca9254-2983-4cab-845c-f9bfb0229417", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "def col_row_updater(df: pd.DataFrame, col1: str, val1, col2: str, new_val):\n", @@ -356,9 +384,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "ea9c3269-d53d-4d94-bc22-c6768cb63d91", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "# FTA\n", @@ -476,24 +506,26 @@ "\n", " return df2\n", "\n", - "if __name__ == \"__main__\":\n", + "#if __name__ == \"__main__\":\n", "\n", " # initial df (all projects)\n", - " all_projects = clean_fta_columns()\n", + "# all_projects = clean_fta_columns()\n", "\n", " # projects with bus count > 0 only.\n", - " just_bus = fta_agg_bus_only(all_projects)\n", + "# just_bus = fta_agg_bus_only(all_projects)\n", "\n", " # export both DFs\n", - " all_projects.to_parquet(f\"{gcs_path}clean_fta_all_projects.parquet\")\n", - " just_bus.to_parquet(f\"{gcs_path}clean_fta_bus_only.parquet\")" + "# all_projects.to_parquet(f\"{gcs_path}clean_fta_all_projects.parquet\")\n", + "# just_bus.to_parquet(f\"{gcs_path}clean_fta_bus_only.parquet\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "4e16119d-f6f3-478b-a419-7c4989557910", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "# TIRCP\n", @@ -613,24 +645,24 @@ " )\n", " return df3\n", "\n", - "if __name__ == \"__main__\":\n", + "#if __name__ == \"__main__\":\n", " \n", " \n", " \n", " # initial df\n", - " df1 = clean_tircp_columns()\n", + "# df1 = clean_tircp_columns()\n", " \n", " # aggregate \n", - " df2 = tircp_agg_bus_only(df1)\n", + "# df2 = tircp_agg_bus_only(df1)\n", " \n", " # export both df's as parquets to GCS\n", - " df1.to_parquet(f'{gcs_path}clean_tircp_all_project.parquet')\n", - " df2.to_parquet(f'{gcs_path}clean_tircp_bus_only_clean.parquet')" + "# df1.to_parquet(f'{gcs_path}clean_tircp_all_project.parquet')\n", + "# df2.to_parquet(f'{gcs_path}clean_tircp_bus_only_clean.parquet')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "359f3b7a-d691-446f-9a14-424c47fc0929", "metadata": {}, "outputs": [], @@ -810,26 +842,26 @@ "\n", " return merge\n", "\n", - "if __name__ == \"__main__\":\n", + "#if __name__ == \"__main__\":\n", " \n", "\n", " # initial df\n", - " df1 = clean_dgs_columns()\n", + "# df1 = clean_dgs_columns()\n", " \n", " #df of just bus cost (no options)\n", - " just_bus = dgs_agg_by_agency(df1)\n", + "# just_bus = dgs_agg_by_agency(df1)\n", " \n", " #df of bus cost+options\n", - " bus_w_options = dgs_agg_by_agency_w_options(df1)\n", + "# bus_w_options = dgs_agg_by_agency_w_options(df1)\n", " \n", " #export serperate df's as parquet to GCS\n", - " just_bus.to_parquet(f'{gcs_path}clean_dgs_all_projects.parquet')\n", - " bus_w_options.to_parquet(f'{gcs_path}clean_dgs_bus_only_options.parquet')" + "# just_bus.to_parquet(f'{gcs_path}clean_dgs_all_projects.parquet')\n", + "# bus_w_options.to_parquet(f'{gcs_path}clean_dgs_bus_only_options.parquet')" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 13, "id": "19f4bd75-f614-4937-880a-1e1a6ff2eb7f", "metadata": {}, "outputs": [], @@ -941,7 +973,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "15d4aca0-5dbd-40a1-8861-2501665c617a", "metadata": {}, "outputs": [], @@ -958,59 +990,147 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 67, "id": "f84aaa3c-7bab-46c9-b739-021fdd6b60a2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(89, 13)" + "(89, 11)" ] }, - "execution_count": 29, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", + " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", + " 'ppno', 'project_description'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# initial final df from old code\n", + "# 89 rows and 11 columns\n", + "display(\n", + " final.shape,\n", + " final.columns\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "c9ffd5a9-b772-4509-b84c-9a96760b3112", + "metadata": {}, + "outputs": [], + "source": [ + "# making copy of final \n", + "# test = final #THIS DOES NOT WORK! this is just assigning a new name to final\n", + "test = final.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "3ac2c3f6-fdc9-455c-b320-f5a3f5018359", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(89, 11)" + ] + }, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "final.shape" + "test.shape" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 70, "id": "13624fc6-86ea-4271-9a6f-6a725009ef6e", "metadata": {}, "outputs": [], "source": [ - "test = cpb_zscore_outliers(final,\"cpb\")" + "# using function to on test df to get zscore \n", + "test = cpb_zscore_outliers(test,\"zscore_cost_per_bus\")" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 72, "id": "42c43416-981f-43d7-a642-6a22dc6619f2", "metadata": {}, "outputs": [ + { + "data": { + "text/plain": [ + "(89, 11)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/plain": [ "(89, 13)" ] }, - "execution_count": 30, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", + " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", + " 'ppno', 'project_description'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", + " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", + " 'ppno', 'project_description', 'cpb', 'zscore_cost_per_bus'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "test.shape" + "# why does the shape of `final` change after calling `cpb_zscore_outlier` on `test` df?\n", + "display(\n", + " final.shape,\n", + " test.shape,\n", + " final.columns,\n", + " test.columns\n", + ")" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 79, "id": "96a7a76d-3df0-4da0-89b5-1e0961bf16a4", "metadata": {}, "outputs": [], @@ -1020,10 +1140,42 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 78, + "id": "7a6bb385-40fd-464d-8206-92af5d24bf6b", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_420/848961059.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"is cpb outlier?\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"zscore_cost_per_bus\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"zscore_cost_per_bus\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1525\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1526\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mNoReturn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1527\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 1528\u001b[0m \u001b[0;34mf\"The truth value of a {type(self).__name__} is ambiguous. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1529\u001b[0m \u001b[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1530\u001b[0m )\n", + "\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()." + ] + } + ], + "source": [ + "test[\"is cpb outlier?\"] = ((test[\"zscore_cost_per_bus\"] <= -3) or (test[\"zscore_cost_per_bus\"] >= 3))" + ] + }, + { + "cell_type": "code", + "execution_count": 80, "id": "637032e4-d855-4190-a6f5-ff695f77143f", "metadata": {}, "outputs": [ + { + "data": { + "text/plain": [ + "(89, 11)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/plain": [ @@ -1207,6 +1359,104 @@ "metadata": {}, "output_type": "display_data" }, + { + "data": { + "text/plain": [ + " 0.917956 1\n", + "-0.652337 1\n", + " 0.227613 1\n", + " 0.684585 1\n", + " 0.268993 1\n", + " 0.293092 1\n", + " 0.308767 1\n", + " 1.586696 1\n", + " 0.579979 1\n", + " 0.202398 1\n", + " 0.688137 1\n", + " 0.239078 1\n", + " 0.183435 1\n", + " 0.174359 1\n", + " 0.175072 1\n", + " 0.706705 1\n", + " 0.879900 1\n", + " 0.445312 1\n", + "-0.299328 1\n", + "-1.595436 1\n", + " 0.019325 1\n", + " 0.229141 1\n", + " 0.557801 1\n", + " 0.257558 1\n", + " 0.420721 1\n", + " 0.217728 1\n", + " 0.237321 1\n", + " 0.807602 1\n", + " 5.130048 1\n", + " 1.689929 1\n", + " 0.083937 1\n", + " 0.464745 1\n", + " 0.355695 1\n", + " 0.806018 1\n", + " 0.421807 1\n", + " 0.168741 1\n", + " 2.661856 1\n", + " 0.455833 1\n", + " 0.469117 1\n", + " 0.150764 1\n", + " 0.713839 1\n", + " 0.393747 1\n", + " 0.058203 1\n", + "-0.235963 1\n", + " 0.208800 1\n", + "-0.529139 1\n", + " 1.155549 1\n", + "-0.597550 1\n", + " 0.341421 1\n", + "-0.547645 1\n", + " 0.281905 1\n", + "-1.549482 1\n", + "-0.522767 1\n", + " 1.359348 1\n", + " 0.692645 1\n", + " 0.651142 1\n", + "-1.332774 1\n", + "-0.573985 1\n", + "-1.473871 1\n", + "-1.318859 1\n", + "-0.612123 1\n", + "-0.064318 1\n", + "-0.355957 1\n", + "-0.546552 1\n", + "-0.333854 1\n", + "-0.735399 1\n", + "-0.923420 1\n", + "-1.305915 1\n", + "-0.282239 1\n", + "-0.208001 1\n", + "-1.364285 1\n", + " 0.121365 1\n", + "-0.293992 1\n", + "-1.466460 1\n", + " 0.064150 1\n", + "-1.332891 1\n", + "-0.363394 1\n", + "-0.441942 1\n", + "-1.447414 1\n", + " 0.971804 1\n", + "-1.511420 1\n", + "-1.544424 1\n", + "-1.296647 1\n", + "-0.349413 1\n", + " 0.434836 1\n", + "-1.323286 1\n", + "-1.672813 1\n", + "-0.842815 1\n", + " 0.231592 1\n", + "Name: zscore_cost_per_bus, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/plain": [ @@ -1221,17 +1471,149 @@ ], "source": [ "display(\n", + " final.shape,\n", " test.shape,\n", " test.head(),\n", + " test[\"zscore_cost_per_bus\"].value_counts(),\n", " test[\"is cpb outlier?\"].value_counts()\n", ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 82, + "id": "bc52eb56-74f0-48cd-a4c4-8b4394ce0821", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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transit_agencyproject_titleprop_typebus_size_typedescriptionnew_project_typetotal_costbus_countsourceppnoproject_descriptioncpbzscore_cost_per_busis cpb outlier?
84Transit Joint Powers Authority for Merced CountyNoneBEBstandard/conventional (30ft-45ft)NoneNone32233241.0dgsEBUS002None32233245.130048True
\n", + "
" + ], + "text/plain": [ + " transit_agency project_title prop_type \\\n", + "84 Transit Joint Powers Authority for Merced County None BEB \n", + "\n", + " bus_size_type description new_project_type \\\n", + "84 standard/conventional (30ft-45ft) None None \n", + "\n", + " total_cost bus_count source ppno project_description cpb \\\n", + "84 3223324 1.0 dgs EBUS002 None 3223324 \n", + "\n", + " zscore_cost_per_bus is cpb outlier? \n", + "84 5.130048 True " + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test[test[\"is cpb outlier?\"] == True]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 81, "id": "2abb9b0a-0196-452c-a83e-e7856857efc9", "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", + " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", + " 'ppno', 'project_description'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", + " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", + " 'ppno', 'project_description', 'cpb', 'zscore_cost_per_bus',\n", + " 'is cpb outlier?'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# need to compare the results from outlier_flag to initial method\n", + "# can i read in the df from the old NB with outliers identified?\n", + "\n", + "display(\n", + " final.columns,\n", + " test.columns\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce1f4c86-e85d-41d8-83f6-14aadce48d5c", + "metadata": {}, "outputs": [], "source": [] } From f6a88675a8c10a8bf5b5d4f81297b31b798f451c Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Mon, 17 Jun 2024 23:04:39 +0000 Subject: [PATCH 07/36] improved cpb aggregate function --- bus_procurement_cost/refactor_bus_cost.ipynb | 840 +++++++++++-------- 1 file changed, 490 insertions(+), 350 deletions(-) diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index ada217d74..86a28b6bc 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -60,18 +60,18 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 5, "id": "e1e3bad8-62d3-4ca1-b4dd-b4d5b8d9a86b", "metadata": {}, "outputs": [], "source": [ "# what does the final table look like again?\n", - "final = pd.read_parquet(f'{gcs_path}old/cpb_analysis_data_merge.parquet')" + "final = pd.read_parquet(f'{GCS_PATH}old/cpb_analysis_data_merge.parquet')" ] }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 6, "id": "c2865cb5-0adb-4529-b057-d2595f9f8ab6", "metadata": {}, "outputs": [ @@ -81,7 +81,7 @@ "(89, 11)" ] }, - "execution_count": 58, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -103,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "5513b941-ecdb-405e-bfd6-952df6b8f8b4", "metadata": { "tags": [] @@ -224,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "8e18cbe6-bde7-4c30-8a8a-aefd8d619821", "metadata": { "tags": [] @@ -286,7 +286,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "8004cc2d-957f-4e9a-9ca8-2a6f9aba9ffb", "metadata": { "tags": [] @@ -356,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "c0ca9254-2983-4cab-845c-f9bfb0229417", "metadata": { "tags": [] @@ -384,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "ea9c3269-d53d-4d94-bc22-c6768cb63d91", "metadata": { "tags": [] @@ -521,7 +521,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "4e16119d-f6f3-478b-a419-7c4989557910", "metadata": { "tags": [] @@ -662,9 +662,11 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "359f3b7a-d691-446f-9a14-424c47fc0929", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "# DGS\n", @@ -861,7 +863,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 45, "id": "19f4bd75-f614-4937-880a-1e1a6ff2eb7f", "metadata": {}, "outputs": [], @@ -945,21 +947,48 @@ " \n", " return merge2\n", "\n", - "def cpb_zscore_outliers(df: pd.DataFrame, zscore_col: str) -> pd.DataFrame:\n", + "def outlier_flag(col):\n", + " \"\"\"\n", + " function to flag outlier rows. to be used in `cpb_zscore_outlier`\n", + " \"\"\"\n", + " \n", + " return col <= -3 or col >= 3\n", + "\n", + "def cpb_aggregate(df: pd.DataFrame, column=\"transit_agency\") -> pd.DataFrame:\n", " \"\"\"\n", - " function that calculated cost per bus col, z-score col, then flags outliers\n", + " function to aggregate compiled data by different categories:\n", + " \"transit agency\", \n", + " \"propulsion type\", \n", + " \"bus_size_type\",\n", + " \"new_project_type\"\n", + " aggregate on columns:\n", + " \"project_title\"\n", + " \"ppno\"\n", + " \"total_cost\"\n", + " \"bus_count\"\n", + " \n", + " Then, cost per bus is calculated AFTER the aggregation.\n", " \"\"\"\n", - " #calculate cost per bus (aka unit cost per bus)\n", - " df['cpb'] = (df['total_cost'] / df['bus_count']).astype(\"int64\")\n", + " df_agg = (\n", + " df.groupby(column)\n", + " .agg(\n", + " total_project_count=(\"project_title\", \"count\"),\n", + " total_project_count_ppno=(\"ppno\", \"count\"),\n", + " total_agg_cost=(\"total_cost\", \"sum\"),\n", + " total_bus_count=(\"bus_count\", \"sum\"),\n", + " )\n", + " .reset_index()\n", + " )\n", + " df_agg[\"cpb\"] = (df_agg[\"total_agg_cost\"] / df_agg[\"total_bus_count\"]).astype(\"int64\")\n", " \n", " #calculate zscore\n", - " df[\"zscore_cost_per_bus\"] = zscore(df[\"cpb\"])\n", + " df_agg[\"zscore_cost_per_bus\"] = zscore(df_agg[\"cpb\"])\n", " \n", " #flag outliers\n", - "\n", - "\n", + " df_agg[\"is_cpb_outlier?\"] = df_agg[\"zscore_cost_per_bus\"].apply(outlier_flag)\n", " \n", - " return df\n", + " return df_agg\n", + "\n", "\n", "\n", "#if __name__ == \"__main__\":\n", @@ -972,25 +1001,16 @@ ] }, { - "cell_type": "code", - "execution_count": 14, - "id": "15d4aca0-5dbd-40a1-8861-2501665c617a", + "cell_type": "markdown", + "id": "3a718624-5c9e-463d-8de4-1a6f66c9e4d8", "metadata": {}, - "outputs": [], "source": [ - "# new function to tag rows with an outlier flag\n", - "def outlier_flag(col):\n", - " \"\"\"\n", - " function to flag rows \n", - " \"\"\"\n", - " return col <= -3 or col >= 3\n", - "\n", - "#df[\"is cpb outlier?\"] = df[\"zscore_cost_per_bus\"].apply(outlier_flag)" + "## Draft/Test cells" ] }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 15, "id": "f84aaa3c-7bab-46c9-b739-021fdd6b60a2", "metadata": {}, "outputs": [ @@ -1027,7 +1047,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 39, "id": "c9ffd5a9-b772-4509-b84c-9a96760b3112", "metadata": {}, "outputs": [], @@ -1039,7 +1059,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 32, "id": "3ac2c3f6-fdc9-455c-b320-f5a3f5018359", "metadata": {}, "outputs": [ @@ -1049,7 +1069,7 @@ "(89, 11)" ] }, - "execution_count": 69, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1060,18 +1080,19 @@ }, { "cell_type": "code", - "execution_count": 70, - "id": "13624fc6-86ea-4271-9a6f-6a725009ef6e", + "execution_count": 40, + "id": "f8aa3674-78fe-4ba9-8f5e-697d91ff4011", "metadata": {}, "outputs": [], "source": [ - "# using function to on test df to get zscore \n", - "test = cpb_zscore_outliers(test,\"zscore_cost_per_bus\")" + "# testing the improved cpb agg function\n", + "# default grouby column is `transit_agency`\n", + "agg1 = cpb_aggregate(test)" ] }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 41, "id": "42c43416-981f-43d7-a642-6a22dc6619f2", "metadata": {}, "outputs": [ @@ -1087,7 +1108,7 @@ { "data": { "text/plain": [ - "(89, 13)" + "(82, 8)" ] }, "metadata": {}, @@ -1108,9 +1129,9 @@ { "data": { "text/plain": [ - "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", - " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", - " 'ppno', 'project_description', 'cpb', 'zscore_cost_per_bus'],\n", + "Index(['transit_agency', 'total_project_count', 'total_project_count_ppno',\n", + " 'total_agg_cost', 'total_bus_count', 'cpb', 'zscore_cost_per_bus',\n", + " 'is_cpb_outlier?'],\n", " dtype='object')" ] }, @@ -1119,51 +1140,19 @@ } ], "source": [ - "# why does the shape of `final` change after calling `cpb_zscore_outlier` on `test` df?\n", + "# there are some duplicate agencies in the inial DF, these get aggregated together after using the function\n", + "# the resulting DF is shorter\n", "display(\n", " final.shape,\n", - " test.shape,\n", + " agg1.shape,\n", " final.columns,\n", - " test.columns\n", + " agg1.columns\n", ")" ] }, { "cell_type": "code", - "execution_count": 79, - "id": "96a7a76d-3df0-4da0-89b5-1e0961bf16a4", - "metadata": {}, - "outputs": [], - "source": [ - "test[\"is cpb outlier?\"] = test[\"zscore_cost_per_bus\"].apply(outlier_flag)" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "7a6bb385-40fd-464d-8206-92af5d24bf6b", - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_420/848961059.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"is cpb outlier?\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"zscore_cost_per_bus\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"zscore_cost_per_bus\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1525\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1526\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mNoReturn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1527\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 1528\u001b[0m \u001b[0;34mf\"The truth value of a {type(self).__name__} is ambiguous. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1529\u001b[0m \u001b[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1530\u001b[0m )\n", - "\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()." - ] - } - ], - "source": [ - "test[\"is cpb outlier?\"] = ((test[\"zscore_cost_per_bus\"] <= -3) or (test[\"zscore_cost_per_bus\"] >= 3))" - ] - }, - { - "cell_type": "code", - "execution_count": 80, + "execution_count": 42, "id": "637032e4-d855-4190-a6f5-ff695f77143f", "metadata": {}, "outputs": [ @@ -1179,7 +1168,7 @@ { "data": { "text/plain": [ - "(89, 14)" + "(82, 8)" ] }, "metadata": {}, @@ -1207,105 +1196,69 @@ " \n", " \n", " transit_agency\n", - " project_title\n", - " prop_type\n", - " bus_size_type\n", - " description\n", - " new_project_type\n", - " total_cost\n", - " bus_count\n", - " source\n", - " ppno\n", - " project_description\n", + " total_project_count\n", + " total_project_count_ppno\n", + " total_agg_cost\n", + " total_bus_count\n", " cpb\n", " zscore_cost_per_bus\n", - " is cpb outlier?\n", + " is_cpb_outlier?\n", " \n", " \n", " \n", " \n", " 0\n", " AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)\n", - " Puerto Rico Initiative Minimizing Emissions Pl...\n", - " electric (not specified)\n", - " not specified\n", - " The Metropolitan Bus Authority will receive fu...\n", - " bus only\n", + " 1\n", + " 0\n", " 10000000\n", " 8.0\n", - " fta\n", - " None\n", - " None\n", " 1250000\n", - " 0.917956\n", + " 1.163100\n", " False\n", " \n", " \n", " 1\n", - " Cape Fear Public Transportation Authority\n", - " Wave Transit Low Emissions Replacement Vehicles\n", - " CNG\n", - " not specified\n", - " Wave Transit will receive funding to buy compr...\n", - " bus only\n", - " 2860250\n", - " 5.0\n", - " fta\n", - " None\n", - " None\n", - " 572050\n", - " -0.529139\n", + " Alameda County Transit Authority\n", + " 0\n", + " 1\n", + " 22846640\n", + " 20.0\n", + " 1142332\n", + " 0.894336\n", " False\n", " \n", " \n", " 2\n", - " Central Oklahoma Transportation and Parking Au...\n", - " COTPA, dba EMBARK Elimination of Fixed Route D...\n", - " CNG\n", - " not specified\n", - " The Central Oklahoma Transportation and Parkin...\n", - " bus only\n", - " 4278772\n", - " 9.0\n", - " fta\n", - " None\n", - " None\n", - " 475419\n", - " -0.735399\n", + " Antelope Valley Transit Authority (AVTA)\n", + " 0\n", + " 1\n", + " 39478000\n", + " 29.0\n", + " 1361310\n", + " 1.440957\n", " False\n", " \n", " \n", " 3\n", - " Champaign-Urbana Mass Transit District\n", - " MTD 40-Foot Hybrid Replacement Buses\n", - " low emission (hybrid)\n", - " not specified\n", - " The Champaign-Urbana Mass Transit District wil...\n", - " bus only\n", - " 6635394\n", - " 10.0\n", - " fta\n", - " None\n", - " None\n", - " 663539\n", - " -0.333854\n", + " CITY OF PORTERVILLE (PORTERVILLE, CA)\n", + " 0\n", + " 1\n", + " 2781891\n", + " 3.0\n", + " 927297\n", + " 0.357558\n", " False\n", " \n", " \n", " 4\n", - " City of Beaumont\n", - " Beaumont Municipal Transit Zips to Improve Low...\n", - " CNG\n", - " not specified\n", - " Beaumont Municipal Transit will receive fundin...\n", - " bus only\n", - " 2819460\n", - " 5.0\n", - " fta\n", - " None\n", - " None\n", - " 563892\n", - " -0.546552\n", + " CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...\n", + " 0\n", + " 1\n", + " 3623536\n", + " 4.0\n", + " 905884\n", + " 0.304106\n", " False\n", " \n", " \n", @@ -1313,47 +1266,37 @@ "" ], "text/plain": [ - " transit_agency \\\n", - "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) \n", - "1 Cape Fear Public Transportation Authority \n", - "2 Central Oklahoma Transportation and Parking Au... \n", - "3 Champaign-Urbana Mass Transit District \n", - "4 City of Beaumont \n", + " transit_agency total_project_count \\\n", + "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) 1 \n", + "1 Alameda County Transit Authority 0 \n", + "2 Antelope Valley Transit Authority (AVTA) 0 \n", + "3 CITY OF PORTERVILLE (PORTERVILLE, CA) 0 \n", + "4 CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ... 0 \n", "\n", - " project_title \\\n", - "0 Puerto Rico Initiative Minimizing Emissions Pl... \n", - "1 Wave Transit Low Emissions Replacement Vehicles \n", - "2 COTPA, dba EMBARK Elimination of Fixed Route D... \n", - "3 MTD 40-Foot Hybrid Replacement Buses \n", - "4 Beaumont Municipal Transit Zips to Improve Low... \n", + " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", + "0 0 10000000 8.0 1250000 \n", + "1 1 22846640 20.0 1142332 \n", + "2 1 39478000 29.0 1361310 \n", + "3 1 2781891 3.0 927297 \n", + "4 1 3623536 4.0 905884 \n", "\n", - " prop_type bus_size_type \\\n", - "0 electric (not specified) not specified \n", - "1 CNG not specified \n", - "2 CNG not specified \n", - "3 low emission (hybrid) not specified \n", - "4 CNG not specified \n", - "\n", - " description new_project_type \\\n", - "0 The Metropolitan Bus Authority will receive fu... bus only \n", - "1 Wave Transit will receive funding to buy compr... bus only \n", - "2 The Central Oklahoma Transportation and Parkin... bus only \n", - "3 The Champaign-Urbana Mass Transit District wil... bus only \n", - "4 Beaumont Municipal Transit will receive fundin... bus only \n", - "\n", - " total_cost bus_count source ppno project_description cpb \\\n", - "0 10000000 8.0 fta None None 1250000 \n", - "1 2860250 5.0 fta None None 572050 \n", - "2 4278772 9.0 fta None None 475419 \n", - "3 6635394 10.0 fta None None 663539 \n", - "4 2819460 5.0 fta None None 563892 \n", - "\n", - " zscore_cost_per_bus is cpb outlier? \n", - "0 0.917956 False \n", - "1 -0.529139 False \n", - "2 -0.735399 False \n", - "3 -0.333854 False \n", - "4 -0.546552 False " + " zscore_cost_per_bus is_cpb_outlier? \n", + "0 1.163100 False \n", + "1 0.894336 False \n", + "2 1.440957 False \n", + "3 0.357558 False \n", + "4 0.304106 False " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "False 81\n", + "True 1\n", + "Name: is_cpb_outlier?, dtype: int64" ] }, "metadata": {}, @@ -1362,96 +1305,7 @@ { "data": { "text/plain": [ - " 0.917956 1\n", - "-0.652337 1\n", - " 0.227613 1\n", - " 0.684585 1\n", - " 0.268993 1\n", - " 0.293092 1\n", - " 0.308767 1\n", - " 1.586696 1\n", - " 0.579979 1\n", - " 0.202398 1\n", - " 0.688137 1\n", - 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"-0.333854 1\n", - "-0.735399 1\n", - "-0.923420 1\n", - "-1.305915 1\n", - "-0.282239 1\n", - "-0.208001 1\n", - "-1.364285 1\n", - " 0.121365 1\n", - "-0.293992 1\n", - "-1.466460 1\n", - " 0.064150 1\n", - "-1.332891 1\n", - "-0.363394 1\n", - "-0.441942 1\n", - "-1.447414 1\n", - " 0.971804 1\n", - "-1.511420 1\n", - "-1.544424 1\n", - "-1.296647 1\n", - "-0.349413 1\n", - " 0.434836 1\n", - "-1.323286 1\n", - "-1.672813 1\n", - "-0.842815 1\n", - " 0.231592 1\n", - "Name: zscore_cost_per_bus, dtype: int64" + "-1.8667057821355477" ] }, "metadata": {}, @@ -1460,9 +1314,7 @@ { "data": { "text/plain": [ - "False 88\n", - "True 1\n", - "Name: is cpb outlier?, dtype: int64" + "3.4069219663792882" ] }, "metadata": {}, @@ -1470,18 +1322,21 @@ } ], "source": [ + "# agg looks good\n", + "# double checked it against the old agg function, CPB numbers match between this new function and old one\n", "display(\n", " final.shape,\n", - " test.shape,\n", - " test.head(),\n", - " test[\"zscore_cost_per_bus\"].value_counts(),\n", - " test[\"is cpb outlier?\"].value_counts()\n", + " agg1.shape,\n", + " agg1.head(),\n", + " agg1[\"is_cpb_outlier?\"].value_counts(),\n", + " agg1[\"zscore_cost_per_bus\"].min(),\n", + " agg1[\"zscore_cost_per_bus\"].max()\n", ")" ] }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 43, "id": "bc52eb56-74f0-48cd-a4c4-8b4394ce0821", "metadata": {}, "outputs": [ @@ -1507,37 +1362,25 @@ " \n", " \n", " transit_agency\n", - " project_title\n", - " prop_type\n", - " bus_size_type\n", - " description\n", - " new_project_type\n", - " total_cost\n", - " bus_count\n", - " source\n", - " ppno\n", - " project_description\n", + " total_project_count\n", + " total_project_count_ppno\n", + " total_agg_cost\n", + " total_bus_count\n", " cpb\n", " zscore_cost_per_bus\n", - " is cpb outlier?\n", + " is_cpb_outlier?\n", " \n", " \n", " \n", " \n", - " 84\n", + " 71\n", " Transit Joint Powers Authority for Merced County\n", - " None\n", - " BEB\n", - " standard/conventional (30ft-45ft)\n", - " None\n", - " None\n", - " 3223324\n", - " 1.0\n", - " dgs\n", - " EBUS002\n", - " None\n", - " 3223324\n", - " 5.130048\n", + " 0\n", + " 2\n", + " 6446648\n", + " 3.0\n", + " 2148882\n", + " 3.406922\n", " True\n", " \n", " \n", @@ -1545,74 +1388,371 @@ "" ], "text/plain": [ - " transit_agency project_title prop_type \\\n", - "84 Transit Joint Powers Authority for Merced County None BEB \n", - "\n", - " bus_size_type description new_project_type \\\n", - "84 standard/conventional (30ft-45ft) None None \n", + " transit_agency total_project_count \\\n", + "71 Transit Joint Powers Authority for Merced County 0 \n", "\n", - " total_cost bus_count source ppno project_description cpb \\\n", - "84 3223324 1.0 dgs EBUS002 None 3223324 \n", + " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", + "71 2 6446648 3.0 2148882 \n", "\n", - " zscore_cost_per_bus is cpb outlier? \n", - "84 5.130048 True " + " zscore_cost_per_bus is_cpb_outlier? \n", + "71 3.406922 True " ] }, - "execution_count": 82, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "test[test[\"is cpb outlier?\"] == True]\n" + "agg1[agg1[\"is_cpb_outlier?\"] == True]\n" ] }, { "cell_type": "code", - "execution_count": 81, - "id": "2abb9b0a-0196-452c-a83e-e7856857efc9", + "execution_count": 46, + "id": "ce1f4c86-e85d-41d8-83f6-14aadce48d5c", + "metadata": {}, + "outputs": [], + "source": [ + "# test to aggregate by other columns \n", + "\n", + "agg_prop_type = cpb_aggregate(test,\"prop_type\")" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "01883fc0-4f6d-4e6a-a88f-97a5914b281b", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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prop_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpbzscore_cost_per_busis_cpb_outlier?
0BEB031170455813164.010393640.923505False
1CNG121176039140252.06985680.122141False
2FCEB26120951335102.011857971.267835False
3electric (not specified)125667800044.012881361.508480False
4ethanol1010067509.0111861-1.257470False
5low emission (hybrid)16091824361145.0633271-0.031401False
6low emission (propane)50840396944.0190999-1.071381False
7mix (zero and low emission)2036775430125.0294203-0.828702False
8not specified4141552404325.0127853-1.219866False
9zero-emission bus (not specified)05128156513143.08961990.586860False
\n", + "
" + ], "text/plain": [ - "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", - " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", - " 'ppno', 'project_description'],\n", - " dtype='object')" + " prop_type total_project_count \\\n", + "0 BEB 0 \n", + "1 CNG 12 \n", + "2 FCEB 2 \n", + "3 electric (not specified) 1 \n", + "4 ethanol 1 \n", + "5 low emission (hybrid) 16 \n", + "6 low emission (propane) 5 \n", + "7 mix (zero and low emission) 2 \n", + "8 not specified 4 \n", + "9 zero-emission bus (not specified) 0 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", + "0 31 170455813 164.0 1039364 \n", + "1 1 176039140 252.0 698568 \n", + "2 6 120951335 102.0 1185797 \n", + "3 2 56678000 44.0 1288136 \n", + "4 0 1006750 9.0 111861 \n", + "5 0 91824361 145.0 633271 \n", + "6 0 8403969 44.0 190999 \n", + "7 0 36775430 125.0 294203 \n", + "8 1 41552404 325.0 127853 \n", + "9 5 128156513 143.0 896199 \n", + "\n", + " zscore_cost_per_bus is_cpb_outlier? \n", + "0 0.923505 False \n", + "1 0.122141 False \n", + "2 1.267835 False \n", + "3 1.508480 False \n", + "4 -1.257470 False \n", + "5 -0.031401 False \n", + "6 -1.071381 False \n", + "7 -0.828702 False \n", + "8 -1.219866 False \n", + "9 0.586860 False " ] }, + "execution_count": 47, "metadata": {}, - "output_type": "display_data" - }, + "output_type": "execute_result" + } + ], + "source": [ + "# numbers seem to match when compared to the published chart.\n", + "agg_prop_type" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "52204752-3932-4ce6-98ac-de8ad3a1f8e8", + "metadata": {}, + "outputs": [ { "data": { + "text/html": [ + "
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bus_size_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpbzscore_cost_per_busis_cpb_outlier?
0articulated025823757641.014204281.598471False
1cutaway3016694500152.0109832-1.466801False
2not specified406509919038881.0578795-0.369972False
3over-the-road01951600014.0679714-0.133939False
4standard/conventional (30ft-45ft)037237476601265.08961380.372242False
\n", + "
" + ], "text/plain": [ - "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", - " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", - " 'ppno', 'project_description', 'cpb', 'zscore_cost_per_bus',\n", - " 'is cpb outlier?'],\n", - " dtype='object')" + " bus_size_type total_project_count \\\n", + "0 articulated 0 \n", + "1 cutaway 3 \n", + "2 not specified 40 \n", + "3 over-the-road 0 \n", + "4 standard/conventional (30ft-45ft) 0 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", + "0 2 58237576 41.0 1420428 \n", + "1 0 16694500 152.0 109832 \n", + "2 6 509919038 881.0 578795 \n", + "3 1 9516000 14.0 679714 \n", + "4 37 237476601 265.0 896138 \n", + "\n", + " zscore_cost_per_bus is_cpb_outlier? \n", + "0 1.598471 False \n", + "1 -1.466801 False \n", + "2 -0.369972 False \n", + "3 -0.133939 False \n", + "4 0.372242 False " ] }, + "execution_count": 48, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "# need to compare the results from outlier_flag to initial method\n", - "# can i read in the df from the old NB with outliers identified?\n", - "\n", - "display(\n", - " final.columns,\n", - " test.columns\n", - ")" + "agg_bus_size = cpb_aggregate(test, \"bus_size_type\")\n", + "agg_bus_size" ] }, { "cell_type": "code", "execution_count": null, - "id": "ce1f4c86-e85d-41d8-83f6-14aadce48d5c", + "id": "0391dd4d-23e1-49cb-8123-509954c796e8", "metadata": {}, "outputs": [], "source": [] From 359e707a7d531da2d386f5a1cc33db76971f4837 Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Tue, 18 Jun 2024 20:48:58 +0000 Subject: [PATCH 08/36] tested new cpb_aggregate function against old version. bus count, total cost and cpb number still match! also had to edit the nb script file to read in old/ folder in gcs --- .../cost_per_bus_analysis.ipynb | 71 +- .../cost_per_bus_nb_scripts.py | 17 +- bus_procurement_cost/refactor_bus_cost.ipynb | 2926 ++++++++++++++++- 3 files changed, 2769 insertions(+), 245 deletions(-) diff --git a/bus_procurement_cost/cost_per_bus_analysis.ipynb b/bus_procurement_cost/cost_per_bus_analysis.ipynb index 567f6cea7..8f6efe450 100644 --- a/bus_procurement_cost/cost_per_bus_analysis.ipynb +++ b/bus_procurement_cost/cost_per_bus_analysis.ipynb @@ -2,15 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 9, "id": "da041e43-e8e2-4d4b-a498-10a7c0afe43f", "metadata": { - "execution": { - "iopub.execute_input": "2024-03-29T21:43:20.780383Z", - "iopub.status.busy": "2024-03-29T21:43:20.778571Z", - "iopub.status.idle": "2024-03-29T21:43:44.073054Z", - "shell.execute_reply": "2024-03-29T21:43:44.071725Z" - }, "tags": [] }, "outputs": [], @@ -20,7 +14,7 @@ "import pandas as pd\n", "import seaborn as sns\n", "import shared_utils\n", - "from cost_per_bus_utils import *\n", + "from cost_per_bus_nb_scripts import *\n", "from IPython.display import Markdown, display\n", "from matplotlib.ticker import ScalarFormatter\n", "from scipy.stats import zscore" @@ -28,16 +22,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "id": "c51fe7dd-22e2-4686-b1a5-57b2f5ad8602", - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-29T21:43:44.082755Z", - "iopub.status.busy": "2024-03-29T21:43:44.080809Z", - "iopub.status.idle": "2024-03-29T21:43:44.091937Z", - "shell.execute_reply": "2024-03-29T21:43:44.091025Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -88,7 +75,7 @@ "" ] }, - "execution_count": 2, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -99,15 +86,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "id": "4e553e15-dc1d-47d3-9818-7dec893c5294", "metadata": { - "execution": { - "iopub.execute_input": "2024-03-29T21:43:44.097071Z", - "iopub.status.busy": "2024-03-29T21:43:44.096748Z", - "iopub.status.idle": "2024-03-29T21:43:44.502039Z", - "shell.execute_reply": "2024-03-29T21:43:44.500989Z" - }, "tags": [] }, "outputs": [ @@ -151,15 +132,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "id": "dda584ca-76fa-4e88-9b1c-f70cc438dce6", "metadata": { - "execution": { - "iopub.execute_input": "2024-03-29T21:43:44.506123Z", - "iopub.status.busy": "2024-03-29T21:43:44.505772Z", - "iopub.status.idle": "2024-03-29T21:43:44.773836Z", - "shell.execute_reply": "2024-03-29T21:43:44.772825Z" - }, "tags": [] }, "outputs": [ @@ -203,15 +178,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "id": "679d8261-85d6-4d68-9905-e4b048ebc61a", "metadata": { - "execution": { - "iopub.execute_input": "2024-03-29T21:43:44.777693Z", - "iopub.status.busy": "2024-03-29T21:43:44.777175Z", - "iopub.status.idle": "2024-03-29T21:43:45.032429Z", - "shell.execute_reply": "2024-03-29T21:43:45.031485Z" - }, "tags": [] }, "outputs": [ @@ -256,15 +225,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, "id": "31c592b0-e37e-4da4-8726-36b0a1d3e6f5", "metadata": { - "execution": { - "iopub.execute_input": "2024-03-29T21:43:45.036292Z", - "iopub.status.busy": "2024-03-29T21:43:45.035737Z", - "iopub.status.idle": "2024-03-29T21:43:45.530444Z", - "shell.execute_reply": "2024-03-29T21:43:45.528314Z" - }, "tags": [] }, "outputs": [ @@ -300,15 +263,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 15, "id": "7462b55c-29ef-4909-a7dd-27e1c84157d0", "metadata": { - "execution": { - "iopub.execute_input": "2024-03-29T21:43:45.534497Z", - "iopub.status.busy": "2024-03-29T21:43:45.534166Z", - "iopub.status.idle": "2024-03-29T21:43:45.716339Z", - "shell.execute_reply": "2024-03-29T21:43:45.715359Z" - }, "tags": [] }, "outputs": [ @@ -349,15 +306,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 16, "id": "4f092539-c4c6-4579-aa02-fbee65414ec3", "metadata": { - "execution": { - "iopub.execute_input": "2024-03-29T21:43:45.720708Z", - "iopub.status.busy": "2024-03-29T21:43:45.719411Z", - "iopub.status.idle": "2024-03-29T21:43:45.727923Z", - "shell.execute_reply": "2024-03-29T21:43:45.726938Z" - }, "tags": [] }, "outputs": [ diff --git a/bus_procurement_cost/cost_per_bus_nb_scripts.py b/bus_procurement_cost/cost_per_bus_nb_scripts.py index 65dcb6410..1380d2635 100644 --- a/bus_procurement_cost/cost_per_bus_nb_scripts.py +++ b/bus_procurement_cost/cost_per_bus_nb_scripts.py @@ -170,10 +170,11 @@ def make_chart(y_col: str, title: str, data: pd.DataFrame, x_col: str): ### VARIABLES #INITIAL DF AGG VARIABLES +gcs_path = "gs://calitp-analytics-data/data-analyses/bus_procurement_cost/old/" # initial, overall df all_bus = pd.read_parquet( - "gs://calitp-analytics-data/data-analyses/bus_procurement_cost/cpb_analysis_data_merge.parquet" + "gs://calitp-analytics-data/data-analyses/bus_procurement_cost/old/cpb_analysis_data_merge.parquet" ) # count of all projects from each source @@ -182,7 +183,7 @@ def all_project_counter(fta_file: str, tircp_file:str, dgs_file: str) -> int: function to count all the projects from fta, tircp and dgs files. use to find the total number of projects and the total number of bus only projects """ - gcs_path = "gs://calitp-analytics-data/data-analyses/bus_procurement_cost/" + gcs_path = "gs://calitp-analytics-data/data-analyses/bus_procurement_cost/old/" all_fta = len(pd.read_parquet(f"{gcs_path}{fta_file}")) @@ -207,16 +208,16 @@ def all_project_counter(fta_file: str, tircp_file:str, dgs_file: str) -> int: #count of all bus only projects per dataset bus_only_count_fta = len(pd.read_parquet( - "gs://calitp-analytics-data/data-analyses/bus_procurement_cost/fta_bus_cost_clean.parquet")) + f"{gcs_path}fta_bus_cost_clean.parquet")) bus_only_count_tircp = len(pd.read_parquet( - "gs://calitp-analytics-data/data-analyses/bus_procurement_cost/clean_tircp_project_bus_only.parquet")) + f"{gcs_path}clean_tircp_project_bus_only.parquet")) bus_only_count_dgs = len(pd.read_parquet( - "gs://calitp-analytics-data/data-analyses/bus_procurement_cost/dgs_agg_clean.parquet")) + f"{gcs_path}dgs_agg_clean.parquet")) #count of all projects per dataset -count_all_fta = len(pd.read_parquet("gs://calitp-analytics-data/data-analyses/bus_procurement_cost/fta_all_projects_clean.parquet")) -count_all_tircp = len(pd.read_parquet("gs://calitp-analytics-data/data-analyses/bus_procurement_cost/clean_tircp_project.parquet")) -count_all_dgs = len(pd.read_parquet("gs://calitp-analytics-data/data-analyses/bus_procurement_cost/dgs_agg_clean.parquet")) +count_all_fta = len(pd.read_parquet(f"{gcs_path}fta_all_projects_clean.parquet")) +count_all_tircp = len(pd.read_parquet(f"{gcs_path}clean_tircp_project.parquet")) +count_all_dgs = len(pd.read_parquet(f"{gcs_path}dgs_agg_clean.parquet")) # Variables all_bus_only_projects = len(all_bus) diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index 86a28b6bc..e17993cf7 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -37,19 +37,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "e1b1f367-1dac-463f-8790-2e5134b7e79b", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.9/site-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Data Validation extension is not supported and will be removed\n", - " warn(msg)\n" - ] - } - ], + "outputs": [], "source": [ "# All Raw Data\n", "fta_raw = pd.read_csv(f\"{GCS_PATH}raw_data-analyses_bus_procurement_cost_fta_press_release_data_csv.csv\")\n", @@ -60,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "e1e3bad8-62d3-4ca1-b4dd-b4d5b8d9a86b", "metadata": {}, "outputs": [], @@ -71,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "c2865cb5-0adb-4529-b057-d2595f9f8ab6", "metadata": {}, "outputs": [ @@ -81,7 +72,7 @@ "(89, 11)" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -94,6 +85,7 @@ "cell_type": "markdown", "id": "d04911c1-e839-41fe-87b3-5065586f2223", "metadata": { + "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -103,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "5513b941-ecdb-405e-bfd6-952df6b8f8b4", "metadata": { "tags": [] @@ -224,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "8e18cbe6-bde7-4c30-8a8a-aefd8d619821", "metadata": { "tags": [] @@ -286,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "8004cc2d-957f-4e9a-9ca8-2a6f9aba9ffb", "metadata": { "tags": [] @@ -356,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "c0ca9254-2983-4cab-845c-f9bfb0229417", "metadata": { "tags": [] @@ -379,12 +371,12 @@ "tags": [] }, "source": [ - "# Chagnes to current scripts\n" + "# Chagnes to current grant type scripts\n" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "ea9c3269-d53d-4d94-bc22-c6768cb63d91", "metadata": { "tags": [] @@ -521,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "4e16119d-f6f3-478b-a419-7c4989557910", "metadata": { "tags": [] @@ -662,7 +654,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "id": "359f3b7a-d691-446f-9a14-424c47fc0929", "metadata": { "tags": [] @@ -863,7 +855,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 25, "id": "19f4bd75-f614-4937-880a-1e1a6ff2eb7f", "metadata": {}, "outputs": [], @@ -954,7 +946,7 @@ " \n", " return col <= -3 or col >= 3\n", "\n", - "def cpb_aggregate(df: pd.DataFrame, column=\"transit_agency\") -> pd.DataFrame:\n", + "def new_cpb_aggregate(df: pd.DataFrame, column=\"transit_agency\") -> pd.DataFrame:\n", " \"\"\"\n", " function to aggregate compiled data by different categories:\n", " \"transit agency\", \n", @@ -1005,12 +997,12 @@ "id": "3a718624-5c9e-463d-8de4-1a6f66c9e4d8", "metadata": {}, "source": [ - "## Draft/Test cells" + "# Draft/Test cells" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "id": "f84aaa3c-7bab-46c9-b739-021fdd6b60a2", "metadata": {}, "outputs": [ @@ -1047,7 +1039,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 26, "id": "c9ffd5a9-b772-4509-b84c-9a96760b3112", "metadata": {}, "outputs": [], @@ -1059,7 +1051,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 27, "id": "3ac2c3f6-fdc9-455c-b320-f5a3f5018359", "metadata": {}, "outputs": [ @@ -1069,7 +1061,7 @@ "(89, 11)" ] }, - "execution_count": 32, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1080,19 +1072,20 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 28, "id": "f8aa3674-78fe-4ba9-8f5e-697d91ff4011", "metadata": {}, "outputs": [], "source": [ "# testing the improved cpb agg function\n", "# default grouby column is `transit_agency`\n", - "agg1 = cpb_aggregate(test)" + "\n", + "agg1 = new_cpb_aggregate(test)" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 29, "id": "42c43416-981f-43d7-a642-6a22dc6619f2", "metadata": {}, "outputs": [ @@ -1152,7 +1145,109 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 30, + "id": "d2bca15a-c12c-4cf5-a5a9-d591ee73a359", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpbzscore_cost_per_busis_cpb_outlier?
56Santa Maria Area Transit0118622582.09311290.367123False
38Napa Valley Transportation Authority0123966002.011983001.034045False
18City of Tucson, Sun Tran102149056039.0551040-0.581669False
\n", + "
" + ], + "text/plain": [ + " transit_agency total_project_count \\\n", + "56 Santa Maria Area Transit 0 \n", + "38 Napa Valley Transportation Authority 0 \n", + "18 City of Tucson, Sun Tran 1 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", + "56 1 1862258 2.0 931129 \n", + "38 1 2396600 2.0 1198300 \n", + "18 0 21490560 39.0 551040 \n", + "\n", + " zscore_cost_per_bus is_cpb_outlier? \n", + "56 0.367123 False \n", + "38 1.034045 False \n", + "18 -0.581669 False " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# confirming the default cpb_agg is working\n", + "agg1.sample(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, "id": "637032e4-d855-4190-a6f5-ff695f77143f", "metadata": {}, "outputs": [ @@ -1319,27 +1414,7 @@ }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "# agg looks good\n", - "# double checked it against the old agg function, CPB numbers match between this new function and old one\n", - "display(\n", - " final.shape,\n", - " agg1.shape,\n", - " agg1.head(),\n", - " agg1[\"is_cpb_outlier?\"].value_counts(),\n", - " agg1[\"zscore_cost_per_bus\"].min(),\n", - " agg1[\"zscore_cost_per_bus\"].max()\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "bc52eb56-74f0-48cd-a4c4-8b4394ce0821", - "metadata": {}, - "outputs": [ + }, { "data": { "text/html": [ @@ -1398,33 +1473,73 @@ "71 3.406922 True " ] }, - "execution_count": 43, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "agg1[agg1[\"is_cpb_outlier?\"] == True]\n" + "# agg looks good\n", + "# double checked it against the old agg function, CPB numbers match between this new function and old one\n", + "display(\n", + " final.shape,\n", + " agg1.shape,\n", + " agg1.head(),\n", + " agg1[\"is_cpb_outlier?\"].value_counts(),\n", + " agg1[\"zscore_cost_per_bus\"].min(),\n", + " agg1[\"zscore_cost_per_bus\"].max(),\n", + " agg1[agg1[\"is_cpb_outlier?\"] == True]\n", + ")" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 33, "id": "ce1f4c86-e85d-41d8-83f6-14aadce48d5c", "metadata": {}, "outputs": [], "source": [ "# test to aggregate by other columns \n", + "agg_prop_type = new_cpb_aggregate(test,\"prop_type\")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "59298193-fc78-4ffb-bfc3-326593c19edb", + "metadata": {}, + "outputs": [], + "source": [ + "# need to compare to the agg prob data in the report\n", + "\n", + "from cost_per_bus_nb_scripts import cpb_aggregate, no_outliers\n", "\n", - "agg_prop_type = cpb_aggregate(test,\"prop_type\")" + "old_prop_agg = cpb_aggregate(no_outliers, \"prop_type\")" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 44, "id": "01883fc0-4f6d-4e6a-a88f-97a5914b281b", "metadata": {}, "outputs": [ + { + "data": { + "text/plain": [ + "(10, 6)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(10, 8)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/html": [ @@ -1452,8 +1567,6 @@ " total_agg_cost\n", " total_bus_count\n", " cpb\n", - " zscore_cost_per_bus\n", - " is_cpb_outlier?\n", " \n", " \n", " \n", @@ -1461,12 +1574,10 @@ " 0\n", " BEB\n", " 0\n", - " 31\n", - " 170455813\n", - " 164.0\n", - " 1039364\n", - " 0.923505\n", - " False\n", + " 30\n", + " 167232489\n", + " 163.0\n", + " 1025966\n", " \n", " \n", " 1\n", @@ -1476,8 +1587,6 @@ " 176039140\n", " 252.0\n", " 698568\n", - " 0.122141\n", - " False\n", " \n", " \n", " 2\n", @@ -1487,8 +1596,6 @@ " 120951335\n", " 102.0\n", " 1185797\n", - " 1.267835\n", - " False\n", " \n", " \n", " 3\n", @@ -1498,8 +1605,6 @@ " 56678000\n", " 44.0\n", " 1288136\n", - " 1.508480\n", - " False\n", " \n", " \n", " 4\n", @@ -1509,8 +1614,6 @@ " 1006750\n", " 9.0\n", " 111861\n", - " -1.257470\n", - " False\n", " \n", " \n", " 5\n", @@ -1520,8 +1623,6 @@ " 91824361\n", " 145.0\n", " 633271\n", - " -0.031401\n", - " False\n", " \n", " \n", " 6\n", @@ -1531,8 +1632,6 @@ " 8403969\n", " 44.0\n", " 190999\n", - " -1.071381\n", - " False\n", " \n", " \n", " 7\n", @@ -1542,8 +1641,6 @@ " 36775430\n", " 125.0\n", " 294203\n", - " -0.828702\n", - " False\n", " \n", " \n", " 8\n", @@ -1553,8 +1650,6 @@ " 41552404\n", " 325.0\n", " 127853\n", - " -1.219866\n", - " False\n", " \n", " \n", " 9\n", @@ -1564,8 +1659,6 @@ " 128156513\n", " 143.0\n", " 896199\n", - " 0.586860\n", - " False\n", " \n", " \n", "\n", @@ -1584,47 +1677,22 @@ "8 not specified 4 \n", "9 zero-emission bus (not specified) 0 \n", "\n", - " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", - "0 31 170455813 164.0 1039364 \n", - "1 1 176039140 252.0 698568 \n", - "2 6 120951335 102.0 1185797 \n", - "3 2 56678000 44.0 1288136 \n", - "4 0 1006750 9.0 111861 \n", - "5 0 91824361 145.0 633271 \n", - "6 0 8403969 44.0 190999 \n", - "7 0 36775430 125.0 294203 \n", - "8 1 41552404 325.0 127853 \n", - "9 5 128156513 143.0 896199 \n", - "\n", - " zscore_cost_per_bus is_cpb_outlier? \n", - "0 0.923505 False \n", - "1 0.122141 False \n", - "2 1.267835 False \n", - "3 1.508480 False \n", - "4 -1.257470 False \n", - "5 -0.031401 False \n", - "6 -1.071381 False \n", - "7 -0.828702 False \n", - "8 -1.219866 False \n", - "9 0.586860 False " + " total_project_count_ppno total_agg_cost total_bus_count cpb \n", + "0 30 167232489 163.0 1025966 \n", + "1 1 176039140 252.0 698568 \n", + "2 6 120951335 102.0 1185797 \n", + "3 2 56678000 44.0 1288136 \n", + "4 0 1006750 9.0 111861 \n", + "5 0 91824361 145.0 633271 \n", + "6 0 8403969 44.0 190999 \n", + "7 0 36775430 125.0 294203 \n", + "8 1 41552404 325.0 127853 \n", + "9 5 128156513 143.0 896199 " ] }, - "execution_count": 47, "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# numbers seem to match when compared to the published chart.\n", - "agg_prop_type" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "52204752-3932-4ce6-98ac-de8ad3a1f8e8", - "metadata": {}, - "outputs": [ + "output_type": "display_data" + }, { "data": { "text/html": [ @@ -1646,7 +1714,7 @@ " \n", " \n", " \n", - " bus_size_type\n", + " prop_type\n", " total_project_count\n", " total_project_count_ppno\n", " total_agg_cost\n", @@ -1659,57 +1727,2323 @@ " \n", " \n", " 0\n", - " articulated\n", + " BEB\n", " 0\n", - " 2\n", - " 58237576\n", - " 41.0\n", - " 1420428\n", - " 1.598471\n", + " 31\n", + " 170455813\n", + " 164.0\n", + " 1039364\n", + " 0.923505\n", " False\n", " \n", " \n", " 1\n", - " cutaway\n", - " 3\n", - " 0\n", - " 16694500\n", - " 152.0\n", - " 109832\n", - " -1.466801\n", + " CNG\n", + " 12\n", + " 1\n", + " 176039140\n", + " 252.0\n", + " 698568\n", + " 0.122141\n", " False\n", " \n", " \n", " 2\n", - " not specified\n", - " 40\n", + " FCEB\n", + " 2\n", " 6\n", - " 509919038\n", - " 881.0\n", - " 578795\n", - " -0.369972\n", + " 120951335\n", + " 102.0\n", + " 1185797\n", + " 1.267835\n", " False\n", " \n", " \n", " 3\n", - " over-the-road\n", - " 0\n", + " electric (not specified)\n", + " 1\n", + " 2\n", + " 56678000\n", + " 44.0\n", + " 1288136\n", + " 1.508480\n", + " False\n", + " \n", + " \n", + " 4\n", + " ethanol\n", + " 1\n", + " 0\n", + " 1006750\n", + " 9.0\n", + " 111861\n", + " -1.257470\n", + " False\n", + " \n", + " \n", + " 5\n", + " low emission (hybrid)\n", + " 16\n", + " 0\n", + " 91824361\n", + " 145.0\n", + " 633271\n", + " -0.031401\n", + " False\n", + " \n", + " \n", + " 6\n", + " low emission (propane)\n", + " 5\n", + " 0\n", + " 8403969\n", + " 44.0\n", + " 190999\n", + " -1.071381\n", + " False\n", + " \n", + " \n", + " 7\n", + " mix (zero and low emission)\n", + " 2\n", + " 0\n", + " 36775430\n", + " 125.0\n", + " 294203\n", + " -0.828702\n", + " False\n", + " \n", + " \n", + " 8\n", + " not specified\n", + " 4\n", + " 1\n", + " 41552404\n", + " 325.0\n", + " 127853\n", + " -1.219866\n", + " False\n", + " \n", + " \n", + " 9\n", + " zero-emission bus (not specified)\n", + " 0\n", + " 5\n", + " 128156513\n", + " 143.0\n", + " 896199\n", + " 0.586860\n", + " False\n", + " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " prop_type total_project_count \\\n", + "0 BEB 0 \n", + "1 CNG 12 \n", + "2 FCEB 2 \n", + "3 electric (not specified) 1 \n", + "4 ethanol 1 \n", + "5 low emission (hybrid) 16 \n", + "6 low emission (propane) 5 \n", + "7 mix (zero and low emission) 2 \n", + "8 not specified 4 \n", + "9 zero-emission bus (not specified) 0 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", + "0 31 170455813 164.0 1039364 \n", + "1 1 176039140 252.0 698568 \n", + "2 6 120951335 102.0 1185797 \n", + "3 2 56678000 44.0 1288136 \n", + "4 0 1006750 9.0 111861 \n", + "5 0 91824361 145.0 633271 \n", + "6 0 8403969 44.0 190999 \n", + "7 0 36775430 125.0 294203 \n", + "8 1 41552404 325.0 127853 \n", + "9 5 128156513 143.0 896199 \n", + "\n", + " zscore_cost_per_bus is_cpb_outlier? \n", + "0 0.923505 False \n", + "1 0.122141 False \n", + "2 1.267835 False \n", + "3 1.508480 False \n", + "4 -1.257470 False \n", + "5 -0.031401 False \n", + "6 -1.071381 False \n", + "7 -0.828702 False \n", + "8 -1.219866 False \n", + "9 0.586860 False " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#EVERYTHING CHECKS OUT!\n", + "display(\n", + " old_prop_agg.shape,\n", + " agg_prop_type.shape,\n", + " old_prop_agg,\n", + " agg_prop_type\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "52204752-3932-4ce6-98ac-de8ad3a1f8e8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(5, 6)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(5, 8)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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bus_size_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpbzscore_cost_per_busis_cpb_outlier?
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" + ], + "text/plain": [ + " bus_size_type total_project_count \\\n", + "0 articulated 0 \n", + "1 cutaway 3 \n", + "2 not specified 40 \n", + "3 over-the-road 0 \n", + "4 standard/conventional (30ft-45ft) 0 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", + "0 2 58237576 41.0 1420428 \n", + "1 0 16694500 152.0 109832 \n", + "2 6 509919038 881.0 578795 \n", + "3 1 9516000 14.0 679714 \n", + "4 37 237476601 265.0 896138 \n", + "\n", + " zscore_cost_per_bus is_cpb_outlier? \n", + "0 1.598471 False \n", + "1 -1.466801 False \n", + "2 -0.369972 False \n", + "3 -0.133939 False \n", + "4 0.372242 False " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#EVERYTHING CHECKS OUT!\n", + "new_agg_bus_size = new_cpb_aggregate(test, \"bus_size_type\")\n", + "old_size_agg = cpb_aggregate(no_outliers, \"bus_size_type\")\n", + "display(\n", + " old_size_agg.shape,\n", + " new_agg_bus_size.shape,\n", + " old_size_agg,\n", + " new_agg_bus_size\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "0391dd4d-23e1-49cb-8123-509954c796e8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(82, 6)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(82, 8)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpb
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)10100000008.01250000
1Alameda County Transit Authority012284664020.01142332
2Antelope Valley Transit Authority (AVTA)013947800029.01361310
3CITY OF PORTERVILLE (PORTERVILLE, CA)0127818913.0927297
4CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...0136235364.0905884
5Cape Fear Public Transportation Authority1028602505.0572050
6Central Oklahoma Transportation and Parking Au...1042787729.0475419
7Champaign-Urbana Mass Transit District10663539410.0663539
8City of Beaumont1028194605.0563892
9City of Beloit106531841.0653184
10City of Brownsville1047388866.0789814
11City of Colorado Springs dba Mountain Metropol...1031990386.0533173
12City of Jonesboro, Arkansas1010103725.0202074
13City of Los Angeles (LA DOT)01102790000112.0917767
14City of Norman, Oklahoma107767146.0129452
15City of Roseville02965150710.0965150
16City of San Luis Obispo018592701.0859270
17City of Santa Rosa(Santa Rosa CityBus)0159877905.01197558
18City of Tucson, Sun Tran102149056039.0551040
19City of Visalia - Visalia City Coach(Visalia T...0136878034.0921950
20City of Wasco0115430003.0514333
21Coast Transit Authority dba MS Coast Transport...1017600009.0195555
22Conroe Connection Transit1045000004.01125000
23Culver City0135470005.0709400
24Dallas Area Rapid Transit (DART)1010300000090.01144444
25Delaware Transit Corporation (DTC)1087407286.01456788
26Foothill Transit011658000020.0829000
27Foothill Transit, West Covina, CA013764204433.01140668
28GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA)0154063555.01081271
29Golden Empire Transit111120865615.0747243
30Illinois Department of Transportation on behal...1012600000134.094029
31Indianapolis Public Transportation Corporation...101904033620.0952016
32Interurban Transit Partnership10619718011.0563380
33Lane Transit (Oregon)022789499930.0929833
34Lowell Regional Transit Authority1068592967.0979899
35Madison County Mass Transit District1010800002.0540000
36Mesa County1011620003.0387333
37Minnesota Department of Transportation on beha...1014569707.0208138
38Napa Valley Transportation Authority0123966002.01198300
39New Mexico Department of Transportation on beh...1020631603.0687720
40North County Transit District0157876066.0964601
41North County Transit District (NCTD)102933024323.01275227
42ORANGE COUNTY TRANSPORTATION AUTHORITY (ORANGE...01931952010.0931952
43Ohio Department of Transportation (ODOT) on be...102933166569.0425096
44Orange County Transportation Authority (OCTA)01290000040.072500
45Oregon Department of Transportation on behalf ...101812505.036250
46Rhode Island Public Transit Authority10500000025.0200000
47Rockford Mass Transit District1040946524.01023663
48Rogue Valley Transportation District1039375006.0656250
49SLO TRANSIT (SAN LUIS OBISPO, CA)018472141.0847214
50SUNLINE TRANSIT AGENCY (THOUSAND PALMS)0157551555.01151031
51SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA)0157718655.01154373
52Sacramento County Airport System0146422255.0928445
53San Antonio Metropolitan Transit Authority10318720015.0212480
54San Diego Metro011875957612.01563298
55Santa Barbara Metro0136590724.0914768
56Santa Maria Area Transit0118622582.0931129
57Santa Maria Regional Transit0151883795.01037675
58Santa Rosa City Bus0140682024.01017050
59Shasta Regional Transportation Agency (SRTA)01951600014.0679714
60Sonoma County Transit01899000010.0899000
61South Carolina Department of Transportation on...1015423904160.096399
62South Dakota Department of Transportation on b...1010067509.0111861
63South Dakota Department of Transportation on b...1012766289.0141847
64Southeastern Regional Transit Authority101156000016.0722500
65Southwest Ohio Regional Transit Authority10980642816.0612901
66State of California on behalf of Kern Regional...20618100020.0309050
67Tahoe Transportation District1034000004.0850000
68Texas Department of Transportation on behalf o...10744376556.0132924
69The Bus, City of Merced0147862855.0957257
70Torrance Transit Department0172000007.01028571
71Transit Joint Powers Authority for Merced County0132233242.01611662
72Transit Joint Powers Authority of Merced County0136965133.01232171
73UC DAVIS (UNITRANS) (DAVIS, CA)02932192610.0932192
74UCLA FLEET & TRANSIT0120088262.01004413
75University of California - San Diego0282680006.01378000
76University of California, Irvine0149329305.0986586
77Utah Transit Authority101705535325.0682214
78VACAVILLE PUBLIC TRANSPORTATION (CITY COACH) (...011017559010.01017559
79VICTOR VALLEY TRANSIT AUTHORITY (VVTA)0145081605.0901632
80Whatcom Transportation Authority (WTA)10964486511.0876805
81White Earth Reservation Business Committee107231714.0180792
\n", + "
" + ], + "text/plain": [ + " transit_agency total_project_count \\\n", + "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) 1 \n", + "1 Alameda County Transit Authority 0 \n", + "2 Antelope Valley Transit Authority (AVTA) 0 \n", + "3 CITY OF PORTERVILLE (PORTERVILLE, CA) 0 \n", + "4 CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ... 0 \n", + "5 Cape Fear Public Transportation Authority 1 \n", + "6 Central Oklahoma Transportation and Parking Au... 1 \n", + "7 Champaign-Urbana Mass Transit District 1 \n", + "8 City of Beaumont 1 \n", + "9 City of Beloit 1 \n", + "10 City of Brownsville 1 \n", + "11 City of Colorado Springs dba Mountain Metropol... 1 \n", + "12 City of Jonesboro, Arkansas 1 \n", + "13 City of Los Angeles (LA DOT) 0 \n", + "14 City of Norman, Oklahoma 1 \n", + "15 City of Roseville 0 \n", + "16 City of San Luis Obispo 0 \n", + "17 City of Santa Rosa(Santa Rosa CityBus) 0 \n", + "18 City of Tucson, Sun Tran 1 \n", + "19 City of Visalia - Visalia City Coach(Visalia T... 0 \n", + "20 City of Wasco 0 \n", + "21 Coast Transit Authority dba MS Coast Transport... 1 \n", + "22 Conroe Connection Transit 1 \n", + "23 Culver City 0 \n", + "24 Dallas Area Rapid Transit (DART) 1 \n", + "25 Delaware Transit Corporation (DTC) 1 \n", + "26 Foothill Transit 0 \n", + "27 Foothill Transit, West Covina, CA 0 \n", + "28 GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA) 0 \n", + "29 Golden Empire Transit 1 \n", + "30 Illinois Department of Transportation on behal... 1 \n", + "31 Indianapolis Public Transportation Corporation... 1 \n", + "32 Interurban Transit Partnership 1 \n", + "33 Lane Transit (Oregon) 0 \n", + "34 Lowell Regional Transit Authority 1 \n", + "35 Madison County Mass Transit District 1 \n", + "36 Mesa County 1 \n", + "37 Minnesota Department of Transportation on beha... 1 \n", + "38 Napa Valley Transportation Authority 0 \n", + "39 New Mexico Department of Transportation on beh... 1 \n", + "40 North County Transit District 0 \n", + "41 North County Transit District (NCTD) 1 \n", + "42 ORANGE COUNTY TRANSPORTATION AUTHORITY (ORANGE... 0 \n", + "43 Ohio Department of Transportation (ODOT) on be... 1 \n", + "44 Orange County Transportation Authority (OCTA) 0 \n", + "45 Oregon Department of Transportation on behalf ... 1 \n", + "46 Rhode Island Public Transit Authority 1 \n", + "47 Rockford Mass Transit District 1 \n", + "48 Rogue Valley Transportation District 1 \n", + "49 SLO TRANSIT (SAN LUIS OBISPO, CA) 0 \n", + "50 SUNLINE TRANSIT AGENCY (THOUSAND PALMS) 0 \n", + "51 SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA) 0 \n", + "52 Sacramento County Airport System 0 \n", + "53 San Antonio Metropolitan Transit Authority 1 \n", + "54 San Diego Metro 0 \n", + "55 Santa Barbara Metro 0 \n", + "56 Santa Maria Area Transit 0 \n", + "57 Santa Maria Regional Transit 0 \n", + "58 Santa Rosa City Bus 0 \n", + "59 Shasta Regional Transportation Agency (SRTA) 0 \n", + "60 Sonoma County Transit 0 \n", + "61 South Carolina Department of Transportation on... 1 \n", + "62 South Dakota Department of Transportation on b... 1 \n", + "63 South Dakota Department of Transportation on b... 1 \n", + "64 Southeastern Regional Transit Authority 1 \n", + "65 Southwest Ohio Regional Transit Authority 1 \n", + "66 State of California on behalf of Kern Regional... 2 \n", + "67 Tahoe Transportation District 1 \n", + "68 Texas Department of Transportation on behalf o... 1 \n", + "69 The Bus, City of Merced 0 \n", + "70 Torrance Transit Department 0 \n", + "71 Transit Joint Powers Authority for Merced County 0 \n", + "72 Transit Joint Powers Authority of Merced County 0 \n", + "73 UC DAVIS (UNITRANS) (DAVIS, CA) 0 \n", + "74 UCLA FLEET & TRANSIT 0 \n", + "75 University of California - San Diego 0 \n", + "76 University of California, Irvine 0 \n", + "77 Utah Transit Authority 1 \n", + "78 VACAVILLE PUBLIC TRANSPORTATION (CITY COACH) (... 0 \n", + "79 VICTOR VALLEY TRANSIT AUTHORITY (VVTA) 0 \n", + "80 Whatcom Transportation Authority (WTA) 1 \n", + "81 White Earth Reservation Business Committee 1 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count cpb \n", + "0 0 10000000 8.0 1250000 \n", + "1 1 22846640 20.0 1142332 \n", + "2 1 39478000 29.0 1361310 \n", + "3 1 2781891 3.0 927297 \n", + "4 1 3623536 4.0 905884 \n", + "5 0 2860250 5.0 572050 \n", + "6 0 4278772 9.0 475419 \n", + "7 0 6635394 10.0 663539 \n", + "8 0 2819460 5.0 563892 \n", + "9 0 653184 1.0 653184 \n", + "10 0 4738886 6.0 789814 \n", + "11 0 3199038 6.0 533173 \n", + "12 0 1010372 5.0 202074 \n", + "13 1 102790000 112.0 917767 \n", + "14 0 776714 6.0 129452 \n", + "15 2 9651507 10.0 965150 \n", + "16 1 859270 1.0 859270 \n", + "17 1 5987790 5.0 1197558 \n", + "18 0 21490560 39.0 551040 \n", + "19 1 3687803 4.0 921950 \n", + "20 1 1543000 3.0 514333 \n", + "21 0 1760000 9.0 195555 \n", + "22 0 4500000 4.0 1125000 \n", + "23 1 3547000 5.0 709400 \n", + "24 0 103000000 90.0 1144444 \n", + "25 0 8740728 6.0 1456788 \n", + "26 1 16580000 20.0 829000 \n", + "27 1 37642044 33.0 1140668 \n", + "28 1 5406355 5.0 1081271 \n", + "29 1 11208656 15.0 747243 \n", + "30 0 12600000 134.0 94029 \n", + "31 0 19040336 20.0 952016 \n", + "32 0 6197180 11.0 563380 \n", + "33 2 27894999 30.0 929833 \n", + "34 0 6859296 7.0 979899 \n", + "35 0 1080000 2.0 540000 \n", + "36 0 1162000 3.0 387333 \n", + "37 0 1456970 7.0 208138 \n", + "38 1 2396600 2.0 1198300 \n", + "39 0 2063160 3.0 687720 \n", + "40 1 5787606 6.0 964601 \n", + "41 0 29330243 23.0 1275227 \n", + "42 1 9319520 10.0 931952 \n", + "43 0 29331665 69.0 425096 \n", + "44 1 2900000 40.0 72500 \n", + "45 0 181250 5.0 36250 \n", + "46 0 5000000 25.0 200000 \n", + "47 0 4094652 4.0 1023663 \n", + "48 0 3937500 6.0 656250 \n", + "49 1 847214 1.0 847214 \n", + "50 1 5755155 5.0 1151031 \n", + "51 1 5771865 5.0 1154373 \n", + "52 1 4642225 5.0 928445 \n", + "53 0 3187200 15.0 212480 \n", + "54 1 18759576 12.0 1563298 \n", + "55 1 3659072 4.0 914768 \n", + "56 1 1862258 2.0 931129 \n", + "57 1 5188379 5.0 1037675 \n", + "58 1 4068202 4.0 1017050 \n", + "59 1 9516000 14.0 679714 \n", + "60 1 8990000 10.0 899000 \n", + "61 0 15423904 160.0 96399 \n", + "62 0 1006750 9.0 111861 \n", + "63 0 1276628 9.0 141847 \n", + "64 0 11560000 16.0 722500 \n", + "65 0 9806428 16.0 612901 \n", + "66 0 6181000 20.0 309050 \n", + "67 0 3400000 4.0 850000 \n", + "68 0 7443765 56.0 132924 \n", + "69 1 4786285 5.0 957257 \n", + "70 1 7200000 7.0 1028571 \n", + "71 1 3223324 2.0 1611662 \n", + "72 1 3696513 3.0 1232171 \n", + "73 2 9321926 10.0 932192 \n", + "74 1 2008826 2.0 1004413 \n", + "75 2 8268000 6.0 1378000 \n", + "76 1 4932930 5.0 986586 \n", + "77 0 17055353 25.0 682214 \n", + "78 1 10175590 10.0 1017559 \n", + "79 1 4508160 5.0 901632 \n", + "80 0 9644865 11.0 876805 \n", + "81 0 723171 4.0 180792 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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Champaign-Urbana Mass Transit District 1 \n", + "8 City of Beaumont 1 \n", + "9 City of Beloit 1 \n", + "10 City of Brownsville 1 \n", + "11 City of Colorado Springs dba Mountain Metropol... 1 \n", + "12 City of Jonesboro, Arkansas 1 \n", + "13 City of Los Angeles (LA DOT) 0 \n", + "14 City of Norman, Oklahoma 1 \n", + "15 City of Roseville 0 \n", + "16 City of San Luis Obispo 0 \n", + "17 City of Santa Rosa(Santa Rosa CityBus) 0 \n", + "18 City of Tucson, Sun Tran 1 \n", + "19 City of Visalia - Visalia City Coach(Visalia T... 0 \n", + "20 City of Wasco 0 \n", + "21 Coast Transit Authority dba MS Coast Transport... 1 \n", + "22 Conroe Connection Transit 1 \n", + "23 Culver City 0 \n", + "24 Dallas Area Rapid Transit (DART) 1 \n", + "25 Delaware Transit Corporation (DTC) 1 \n", + "26 Foothill Transit 0 \n", + "27 Foothill Transit, West Covina, CA 0 \n", + "28 GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA) 0 \n", + "29 Golden Empire Transit 1 \n", + "30 Illinois Department of Transportation on 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Department 0 \n", + "71 Transit Joint Powers Authority for Merced County 0 \n", + "72 Transit Joint Powers Authority of Merced County 0 \n", + "73 UC DAVIS (UNITRANS) (DAVIS, CA) 0 \n", + "74 UCLA FLEET & TRANSIT 0 \n", + "75 University of California - San Diego 0 \n", + "76 University of California, Irvine 0 \n", + "77 Utah Transit Authority 1 \n", + "78 VACAVILLE PUBLIC TRANSPORTATION (CITY COACH) (... 0 \n", + "79 VICTOR VALLEY TRANSIT AUTHORITY (VVTA) 0 \n", + "80 Whatcom Transportation Authority (WTA) 1 \n", + "81 White Earth Reservation Business Committee 1 \n", "\n", - " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", - "0 2 58237576 41.0 1420428 \n", - "1 0 16694500 152.0 109832 \n", - "2 6 509919038 881.0 578795 \n", - "3 1 9516000 14.0 679714 \n", - "4 37 237476601 265.0 896138 \n", + " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", + "0 0 10000000 8.0 1250000 \n", + "1 1 22846640 20.0 1142332 \n", + "2 1 39478000 29.0 1361310 \n", + "3 1 2781891 3.0 927297 \n", + "4 1 3623536 4.0 905884 \n", + "5 0 2860250 5.0 572050 \n", + "6 0 4278772 9.0 475419 \n", + "7 0 6635394 10.0 663539 \n", + "8 0 2819460 5.0 563892 \n", + "9 0 653184 1.0 653184 \n", + "10 0 4738886 6.0 789814 \n", + "11 0 3199038 6.0 533173 \n", + "12 0 1010372 5.0 202074 \n", + "13 1 102790000 112.0 917767 \n", + "14 0 776714 6.0 129452 \n", + "15 2 9651507 10.0 965150 \n", + "16 1 859270 1.0 859270 \n", + "17 1 5987790 5.0 1197558 \n", + "18 0 21490560 39.0 551040 \n", + "19 1 3687803 4.0 921950 \n", + "20 1 1543000 3.0 514333 \n", + "21 0 1760000 9.0 195555 \n", + "22 0 4500000 4.0 1125000 \n", + "23 1 3547000 5.0 709400 \n", + "24 0 103000000 90.0 1144444 \n", + "25 0 8740728 6.0 1456788 \n", + "26 1 16580000 20.0 829000 \n", + "27 1 37642044 33.0 1140668 \n", + "28 1 5406355 5.0 1081271 \n", + "29 1 11208656 15.0 747243 \n", + "30 0 12600000 134.0 94029 \n", + "31 0 19040336 20.0 952016 \n", + "32 0 6197180 11.0 563380 \n", + "33 2 27894999 30.0 929833 \n", + "34 0 6859296 7.0 979899 \n", + "35 0 1080000 2.0 540000 \n", + "36 0 1162000 3.0 387333 \n", + "37 0 1456970 7.0 208138 \n", + "38 1 2396600 2.0 1198300 \n", + "39 0 2063160 3.0 687720 \n", + "40 1 5787606 6.0 964601 \n", + "41 0 29330243 23.0 1275227 \n", + "42 1 9319520 10.0 931952 \n", + "43 0 29331665 69.0 425096 \n", + "44 1 2900000 40.0 72500 \n", + "45 0 181250 5.0 36250 \n", + "46 0 5000000 25.0 200000 \n", + "47 0 4094652 4.0 1023663 \n", + "48 0 3937500 6.0 656250 \n", + "49 1 847214 1.0 847214 \n", + "50 1 5755155 5.0 1151031 \n", + "51 1 5771865 5.0 1154373 \n", + "52 1 4642225 5.0 928445 \n", + "53 0 3187200 15.0 212480 \n", + "54 1 18759576 12.0 1563298 \n", + "55 1 3659072 4.0 914768 \n", + "56 1 1862258 2.0 931129 \n", + "57 1 5188379 5.0 1037675 \n", + "58 1 4068202 4.0 1017050 \n", + "59 1 9516000 14.0 679714 \n", + "60 1 8990000 10.0 899000 \n", + "61 0 15423904 160.0 96399 \n", + "62 0 1006750 9.0 111861 \n", + "63 0 1276628 9.0 141847 \n", + "64 0 11560000 16.0 722500 \n", + "65 0 9806428 16.0 612901 \n", + "66 0 6181000 20.0 309050 \n", + "67 0 3400000 4.0 850000 \n", + "68 0 7443765 56.0 132924 \n", + "69 1 4786285 5.0 957257 \n", + "70 1 7200000 7.0 1028571 \n", + "71 2 6446648 3.0 2148882 \n", + "72 1 3696513 3.0 1232171 \n", + "73 2 9321926 10.0 932192 \n", + "74 1 2008826 2.0 1004413 \n", + "75 2 8268000 6.0 1378000 \n", + "76 1 4932930 5.0 986586 \n", + "77 0 17055353 25.0 682214 \n", + "78 1 10175590 10.0 1017559 \n", + "79 1 4508160 5.0 901632 \n", + "80 0 9644865 11.0 876805 \n", + "81 0 723171 4.0 180792 \n", "\n", - " zscore_cost_per_bus is_cpb_outlier? \n", - "0 1.598471 False \n", - "1 -1.466801 False \n", - "2 -0.369972 False \n", - "3 -0.133939 False \n", - "4 0.372242 False " + " zscore_cost_per_bus is_cpb_outlier? \n", + "0 1.163100 False \n", + "1 0.894336 False \n", + "2 1.440957 False \n", + "3 0.357558 False \n", + "4 0.304106 False \n", + "5 -0.529223 False \n", + "6 -0.770436 False \n", + "7 -0.300844 False \n", + "8 -0.549587 False \n", + "9 -0.326693 False \n", + "10 0.014368 False \n", + "11 -0.626269 False \n", + "12 -1.452770 False \n", + "13 0.333769 False \n", + "14 -1.634052 False \n", + "15 0.452048 False \n", + "16 0.187746 False \n", + "17 1.032193 False \n", + "18 -0.581669 False \n", + "19 0.344210 False \n", + "20 -0.673298 False \n", + "21 -1.469043 False \n", + "22 0.851071 False \n", + "23 -0.186365 False \n", + "24 0.899608 False \n", + "25 1.679292 False \n", + "26 0.112185 False \n", + "27 0.890182 False \n", + "28 0.741913 False \n", + "29 -0.091900 False \n", + "30 -1.722476 False \n", + "31 0.419262 False \n", + "32 -0.550865 False \n", + "33 0.363888 False \n", + "34 0.488865 False \n", + "35 -0.609227 False \n", + "36 -0.990320 False \n", + "37 -1.437633 False \n", + "38 1.034045 False \n", + "39 -0.240483 False \n", + "40 0.450677 False \n", + "41 1.226073 False \n", + "42 0.369178 False \n", + "43 -0.896054 False \n", + "44 -1.776217 False \n", + "45 -1.866706 False \n", + "46 -1.457947 False \n", + "47 0.598110 False \n", + "48 -0.319040 False \n", + "49 0.157652 False \n", + "50 0.916051 False \n", + "51 0.924393 False \n", + "52 0.360423 False \n", + "53 -1.426794 False \n", + "54 1.945166 False \n", + "55 0.326282 False \n", + "56 0.367123 False \n", + "57 0.633087 False \n", + "58 0.581602 False \n", + "59 -0.260468 False \n", + "60 0.286922 False \n", + "61 -1.716560 False \n", + "62 -1.677963 False \n", + "63 -1.603111 False \n", + "64 -0.153664 False \n", + "65 -0.427249 False \n", + "66 -1.185733 False \n", + "67 0.164606 False \n", + "68 -1.625385 False \n", + "69 0.432345 False \n", + "70 0.610361 False \n", + "71 3.406922 True \n", + "72 1.118595 False \n", + "73 0.369777 False \n", + "74 0.550057 False \n", + "75 1.482619 False \n", + "76 0.505557 False \n", + "77 -0.254227 False \n", + "78 0.582873 False \n", + "79 0.293492 False \n", + "80 0.231518 False \n", + "81 -1.505895 False " ] }, - "execution_count": 48, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "agg_bus_size = cpb_aggregate(test, \"bus_size_type\")\n", - "agg_bus_size" + "#EVERYTHING CHECKS OUT!\n", + "# move forward with `new_cpb_aggregate` function\n", + "new_agg_agency = new_cpb_aggregate(test)\n", + "old_agency_agg = cpb_aggregate(no_outliers, \"transit_agency\")\n", + "display(\n", + " old_agency_agg.shape,\n", + " new_agg_agency.shape,\n", + " old_agency_agg,\n", + " new_agg_agency\n", + ")" ] }, { "cell_type": "code", "execution_count": null, - "id": "0391dd4d-23e1-49cb-8123-509954c796e8", + "id": "c917e729-476e-490e-a996-e729943b656a", "metadata": {}, "outputs": [], "source": [] From 0b239b9f93c2f26c1015538a23a52fa4cda7d3af Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Tue, 18 Jun 2024 22:43:19 +0000 Subject: [PATCH 09/36] testng new ways to reduce variables in favor of pivot tables --- bus_procurement_cost/refactor_bus_cost.ipynb | 703 ++++++++++++++++++- 1 file changed, 700 insertions(+), 3 deletions(-) diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index e17993cf7..aac4fd356 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -368,6 +368,7 @@ "cell_type": "markdown", "id": "97bdb85b-ecaa-4634-8ea1-02ebc630567f", "metadata": { + "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -857,7 +858,9 @@ "cell_type": "code", "execution_count": 25, "id": "19f4bd75-f614-4937-880a-1e1a6ff2eb7f", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "# cost per bus cleaner\n", @@ -994,12 +997,78 @@ }, { "cell_type": "markdown", - "id": "3a718624-5c9e-463d-8de4-1a6f66c9e4d8", + "id": "9c163a75-eb4b-4a09-b035-7692f9ea68f5", "metadata": {}, + "source": [ + "# NB Variables rework\n", + "time to organize, cut down, consolidate variables" + ] + }, + { + "cell_type": "markdown", + "id": "c8fc1d6c-85b5-4890-84f1-66e33eb9d97c", + "metadata": {}, + "source": [ + "## Variable Categories\n", + "- initial DF stuff (all cleaned merged data)\n", + " - all_bus\n", + " - all_projecT_counter function\n", + "\n", + "lots of total counts\n", + "**this can be solved by filtering the same df but its different grant type, or using a table of groupby grant type and count of projects**\n", + "\n", + " []count of all projects\n", + " - all_project_count\n", + " ~~- total_bus_count~~\n", + " ~~- total_funding~~\n", + " count of all projects for each grant type\n", + " - count_all_fta\n", + " - count_all_tircp\n", + " - count_all_dgs\n", + " fix: use all_project_count to create a pivot table with margins of each grant type. margins should also add a grand total col\n", + " \n", + " []count of bus only projects\n", + " - bus_only_project_count\n", + " count of bus only projects for each grant type\n", + " - bus_only_count_fta\n", + " - bus_only_count_tircp\n", + " - bus_only_count_dgs\n", + " fix: use all_project_count to create a summarized dataframe of each grant type\n", + "\n", + " \n", + " \n", + "- ZEB only\n", + " - zeb_only_df function\n", + "\n", + "- non-ZEB only\n", + " - non_zeb_only_df function\n", + "\n", + "- means and standard deviations\n", + " - for charts?\n" + ] + }, + { + "cell_type": "markdown", + "id": "3a718624-5c9e-463d-8de4-1a6f66c9e4d8", + "metadata": { + "tags": [] + }, "source": [ "# Draft/Test cells" ] }, + { + "cell_type": "markdown", + "id": "b5224cfe-6b3a-4c68-a7b5-58df0ff8f85e", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## Testing `new_cpb_aggregate` function against initial `cpb_appregate` function.\n", + "to make sure the core data matches, and expect the new function to provide zscores and outlier flags" + ] + }, { "cell_type": "code", "execution_count": 14, @@ -4321,10 +4390,638 @@ ")" ] }, + { + "cell_type": "markdown", + "id": "3428875a-6a64-41bc-8f9c-81902006d7f0", + "metadata": {}, + "source": [ + "## Testing variables rework\n", + "there are a lot of variables in the initial script. need to cut down the amount of variables or at least make it more efficient. " + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "f21298ee-0efb-4f91-ba63-55fc2645a4d2", + "metadata": {}, + "outputs": [], + "source": [ + "# read in all cleaned project data\n", + "# same as final from above\n", + "all_projects = pd.read_parquet(f'{GCS_PATH}old/cpb_analysis_data_merge.parquet')" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "id": "cf9e6d8e-6987-470a-9f4f-5294dbe259d1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpbzscore_cost_per_busis_cpb_outlier?
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)10100000008.012500001.163100False
1Alameda County Transit Authority012284664020.011423320.894336False
2Antelope Valley Transit Authority (AVTA)013947800029.013613101.440957False
3CITY OF PORTERVILLE (PORTERVILLE, CA)0127818913.09272970.357558False
4CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...0136235364.09058840.304106False
5Cape Fear Public Transportation Authority1028602505.0572050-0.529223False
6Central Oklahoma Transportation and Parking Au...1042787729.0475419-0.770436False
7Champaign-Urbana Mass Transit District10663539410.0663539-0.300844False
8City of Beaumont1028194605.0563892-0.549587False
9City of Beloit106531841.0653184-0.326693False
10City of Brownsville1047388866.07898140.014368False
11City of Colorado Springs dba Mountain Metropol...1031990386.0533173-0.626269False
12City of Jonesboro, Arkansas1010103725.0202074-1.452770False
13City of Los Angeles (LA DOT)01102790000112.09177670.333769False
14City of Norman, Oklahoma107767146.0129452-1.634052False
15City of Roseville02965150710.09651500.452048False
16City of San Luis Obispo018592701.08592700.187746False
17City of Santa Rosa(Santa Rosa CityBus)0159877905.011975581.032193False
18City of Tucson, Sun Tran102149056039.0551040-0.581669False
19City of Visalia - Visalia City Coach(Visalia T...0136878034.09219500.344210False
20City of Wasco0115430003.0514333-0.673298False
21Coast Transit Authority dba MS Coast Transport...1017600009.0195555-1.469043False
22Conroe Connection Transit1045000004.011250000.851071False
23Culver City0135470005.0709400-0.186365False
24Dallas Area Rapid Transit (DART)1010300000090.011444440.899608False
25Delaware Transit Corporation (DTC)1087407286.014567881.679292False
26Foothill Transit011658000020.08290000.112185False
27Foothill Transit, West Covina, CA013764204433.011406680.890182False
28GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA)0154063555.010812710.741913False
29Golden Empire Transit111120865615.0747243-0.091900False
30Illinois Department of Transportation on behal...1012600000134.094029-1.722476False
31Indianapolis Public Transportation Corporation...101904033620.09520160.419262False
32Interurban Transit Partnership10619718011.0563380-0.550865False
33Lane Transit (Oregon)022789499930.09298330.363888False
34Lowell Regional Transit Authority1068592967.09798990.488865False
35Madison County Mass Transit District1010800002.0540000-0.609227False
36Mesa County1011620003.0387333-0.990320False
37Minnesota Department of Transportation on beha...1014569707.0208138-1.437633False
38Napa Valley Transportation Authority0123966002.011983001.034045False
39New Mexico Department of Transportation on beh...1020631603.0687720-0.240483False
40North County Transit District0157876066.09646010.450677False
41North County Transit District (NCTD)102933024323.012752271.226073False
42ORANGE COUNTY TRANSPORTATION AUTHORITY (ORANGE...01931952010.09319520.369178False
43Ohio Department of Transportation (ODOT) on be...102933166569.0425096-0.896054False
44Orange County Transportation Authority (OCTA)01290000040.072500-1.776217False
45Oregon Department of Transportation on behalf ...101812505.036250-1.866706False
46Rhode Island Public Transit Authority10500000025.0200000-1.457947False
47Rockford Mass Transit District1040946524.010236630.598110False
48Rogue Valley Transportation District1039375006.0656250-0.319040False
49SLO TRANSIT (SAN LUIS OBISPO, CA)018472141.08472140.157652False
50SUNLINE TRANSIT AGENCY (THOUSAND PALMS)0157551555.011510310.916051False
51SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA)0157718655.011543730.924393False
52Sacramento County Airport System0146422255.09284450.360423False
53San Antonio Metropolitan Transit Authority10318720015.0212480-1.426794False
54San Diego Metro011875957612.015632981.945166False
55Santa Barbara Metro0136590724.09147680.326282False
56Santa Maria Area Transit0118622582.09311290.367123False
57Santa Maria Regional Transit0151883795.010376750.633087False
58Santa Rosa City Bus0140682024.010170500.581602False
59Shasta Regional Transportation Agency (SRTA)01951600014.0679714-0.133939-0.260468False
4standard/conventional (30ft-45ft)60Sonoma County Transit037237476601265.08961380.3722421899000010.08990000.286922False
61South Carolina Department of Transportation on...1015423904160.096399-1.716560False
62South Dakota Department of Transportation on b...1010067509.0111861-1.677963False
63South Dakota Department of Transportation on b...1012766289.0141847-1.603111False
64Southeastern Regional Transit Authority101156000016.0722500-0.153664False
65Southwest Ohio Regional Transit Authority10980642816.0612901-0.427249False
66State of California on behalf of Kern Regional...20618100020.0309050-1.185733False
67Tahoe Transportation District1034000004.08500000.164606False
68Texas Department of Transportation on behalf o...10744376556.0132924-1.625385False
69The Bus, City of Merced0147862855.09572570.432345False
70Torrance Transit Department0172000007.010285710.610361False
71Transit Joint Powers Authority for Merced County0264466483.021488823.406922True
72Transit Joint Powers Authority of Merced County0136965133.012321711.118595False
73UC DAVIS (UNITRANS) (DAVIS, CA)02932192610.09321920.369777False
74UCLA FLEET & TRANSIT0120088262.010044130.550057False
75University of California - San Diego0282680006.013780001.482619False
76University of California, Irvine0149329305.09865860.505557False
77Utah Transit Authority101705535325.0682214-0.254227False
78VACAVILLE PUBLIC TRANSPORTATION (CITY COACH) (...011017559010.010175590.582873False
79VICTOR VALLEY TRANSIT AUTHORITY (VVTA)0145081605.09016320.293492False
80Whatcom Transportation Authority (WTA)10964486511.08768050.231518False
81White Earth Reservation Business Committee107231714.0180792-1.505895False
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transit_agencyproject_titleprop_typebus_size_typedescriptionnew_project_typetotal_costbus_countsourceppnoproject_description
65UC DAVIS (UNITRANS) (DAVIS, CA)NoneBEBstandard/conventional (30ft-45ft)NoneNone55594866.0dgsASTR876345None
68UC DAVIS (UNITRANS) (DAVIS, CA)NoneBEBstandard/conventional (30ft-45ft)NoneNone37624404.0dgsUC DAVIS (UNITRANS) (DAVIS, CA)None
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" + ], + "text/plain": [ + " transit_agency project_title prop_type \\\n", + "65 UC DAVIS (UNITRANS) (DAVIS, CA) None BEB \n", + "68 UC DAVIS (UNITRANS) (DAVIS, CA) None BEB \n", + "\n", + " bus_size_type description new_project_type \\\n", + "65 standard/conventional (30ft-45ft) None None \n", + "68 standard/conventional (30ft-45ft) None None \n", + "\n", + " total_cost bus_count source ppno \\\n", + "65 5559486 6.0 dgs ASTR876345 \n", + "68 3762440 4.0 dgs UC DAVIS (UNITRANS) (DAVIS, CA) \n", + "\n", + " project_description \n", + "65 None \n", + "68 None " + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_projects[all_projects[\"transit_agency\"].str.contains(\"UC DAVIS\")]" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "id": "1696d78f-7018-417b-9847-d82edac3acdf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['bus_count', 'total_cost'], dtype='object')" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# testing pivot table on `all_projects`\n", + "\n", + "#pivot table to get totals for each prop type\n", + "pivot_prop_type = pd.pivot_table(\n", + " all_projects,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"prop_type\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ")\n", + "\n", + "pivot_prop_type.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "id": "4371ce4f-a3dd-40df-9835-ef15f8ad19e1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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bus_counttotal_cost
prop_type
BEB164.0170455813
CNG252.0176039140
FCEB102.0120951335
electric (not specified)44.056678000
ethanol9.01006750
low emission (hybrid)145.091824361
low emission (propane)44.08403969
mix (zero and low emission)125.036775430
not specified325.041552404
zero-emission bus (not specified)143.0128156513
Grand Total1353.0831843715
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" + ], + "text/plain": [ + " bus_count total_cost\n", + "prop_type \n", + "BEB 164.0 170455813\n", + "CNG 252.0 176039140\n", + "FCEB 102.0 120951335\n", + "electric (not specified) 44.0 56678000\n", + "ethanol 9.0 1006750\n", + "low emission (hybrid) 145.0 91824361\n", + "low emission (propane) 44.0 8403969\n", + "mix (zero and low emission) 125.0 36775430\n", + "not specified 325.0 41552404\n", + "zero-emission bus (not specified) 143.0 128156513\n", + "Grand Total 1353.0 831843715" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pivot_prop_type." + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "id": "01647d83-8b4f-47a9-ab57-a1db7cd501dd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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bus_counttotal_cost
prop_type
BEB164.0170455813
FCEB102.0120951335
electric (not specified)44.056678000
zero-emission bus (not specified)143.0128156513
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" + ], + "text/plain": [ + " bus_count total_cost\n", + "prop_type \n", + "BEB 164.0 170455813\n", + "FCEB 102.0 120951335\n", + "electric (not specified) 44.0 56678000\n", + "zero-emission bus (not specified) 143.0 128156513" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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bus_counttotal_cost
prop_type
CNG252.0176039140
ethanol9.01006750
low emission (hybrid)145.091824361
low emission (propane)44.08403969
mix (zero and low emission)125.036775430
\n", + "
" + ], + "text/plain": [ + " bus_count total_cost\n", + "prop_type \n", + "CNG 252.0 176039140\n", + "ethanol 9.0 1006750\n", + "low emission (hybrid) 145.0 91824361\n", + "low emission (propane) 44.0 8403969\n", + "mix (zero and low emission) 125.0 36775430" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "zeb_list =[\n", + " \"BEB\",\n", + " \"FCEB\",\n", + " \"electric (not specified)\",\n", + " \"zero-emission bus (not specified)\", \n", + "]\n", + "\n", + "non_zeb_list =[\n", + " \"CNG\",\n", + " \"ethanol\",\n", + " \"low emission (hybrid)\",\n", + " \"low emission (propane)\",\n", + " \"mix (zero and low emission)\",\n", + "]\n", + "# table to get list of zeb and non-zeb counts, but not totals.\n", + "display(\n", + " pivot_prop_type.loc[zeb_list],\n", + " pivot_prop_type.loc[non_zeb_list]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "3be3ef4f-0bf3-4770-a8b7-340d372ae1ce", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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bus_counttotal_cost
bus_size_type
articulated41.058237576
cutaway152.016694500
not specified881.0509919038
over-the-road14.09516000
standard/conventional (30ft-45ft)265.0237476601
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" + ], + "text/plain": [ + " bus_count total_cost\n", + "bus_size_type \n", + "articulated 41.0 58237576\n", + "cutaway 152.0 16694500\n", + "not specified 881.0 509919038\n", + "over-the-road 14.0 9516000\n", + "standard/conventional (30ft-45ft) 265.0 237476601" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# answers total buses and cost per grant type\n", + "pivot_size = pd.pivot_table(\n", + " all_projects,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"bus_size_type\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ")\n", + "\n", + "pivot_size.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "2c933257-bdc2-4007-9571-58475118073c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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bus_counttotal_cost
source
dgs237.0253336177
fta883.0391257025
tircp233.0187250513
Grand Total1353.0831843715
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" + ], + "text/plain": [ + " bus_count total_cost\n", + "source \n", + "dgs 237.0 253336177\n", + "fta 883.0 391257025\n", + "tircp 233.0 187250513\n", + "Grand Total 1353.0 831843715" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# answers total buses and cost per grant type\n", + "pivot_source = pd.pivot_table(\n", + " all_projects,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"source\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ")\n", + "\n", + "pivot_source.head()" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "c917e729-476e-490e-a996-e729943b656a", + "id": "ece95fb7-cbb8-46bd-a5f9-2b68a47a4817", "metadata": {}, "outputs": [], "source": [] From 82d6181ecd13f2d714f5ed767034d0d7b261706c Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Wed, 19 Jun 2024 18:47:52 +0000 Subject: [PATCH 10/36] comparing pivot tables against new cpb agg function. updated input and outfile file names for scripts. testing calculating cost per bus on merged df to identify/remove outliers first, then trying the new agg function --- bus_procurement_cost/refactor_bus_cost.ipynb | 5661 +++++++++--------- 1 file changed, 2917 insertions(+), 2744 deletions(-) diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index aac4fd356..879162ad7 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -37,10 +37,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "e1b1f367-1dac-463f-8790-2e5134b7e79b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.9/site-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Data Validation extension is not supported and will be removed\n", + " warn(msg)\n" + ] + } + ], "source": [ "# All Raw Data\n", "fta_raw = pd.read_csv(f\"{GCS_PATH}raw_data-analyses_bus_procurement_cost_fta_press_release_data_csv.csv\")\n", @@ -62,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "c2865cb5-0adb-4529-b057-d2595f9f8ab6", "metadata": {}, "outputs": [ @@ -72,13 +81,208 @@ "(89, 11)" ] }, - "execution_count": 5, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencyproject_titleprop_typebus_size_typedescriptionnew_project_typetotal_costbus_countsourceppnoproject_description
50Torrance Transit DepartmentNoneelectric (not specified)not specifiedNonebus only72000007.0tircpCP073Purchase 7 electric buses to expand services o...
13Dallas Area Rapid Transit (DART)DART CNG Bus Fleet Modernization ProjectCNGnot specifiedDallas Area Rapid Transit will receive funding...bus only10300000090.0ftaNoneNone
22Minnesota Department of Transportation on beha...Reducing Emissions in Rural Minnesota Transitlow emission (propane)not specifiedThe Minnesota Department of Transportation, on...bus only14569707.0ftaNoneNone
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" + ], + "text/plain": [ + " transit_agency \\\n", + "50 Torrance Transit Department \n", + "13 Dallas Area Rapid Transit (DART) \n", + "22 Minnesota Department of Transportation on beha... \n", + "\n", + " project_title prop_type \\\n", + "50 None electric (not specified) \n", + "13 DART CNG Bus Fleet Modernization Project CNG \n", + "22 Reducing Emissions in Rural Minnesota Transit low emission (propane) \n", + "\n", + " bus_size_type description \\\n", + "50 not specified None \n", + "13 not specified Dallas Area Rapid Transit will receive funding... \n", + "22 not specified The Minnesota Department of Transportation, on... \n", + "\n", + " new_project_type total_cost bus_count source ppno \\\n", + "50 bus only 7200000 7.0 tircp CP073 \n", + "13 bus only 103000000 90.0 fta None \n", + "22 bus only 1456970 7.0 fta None \n", + "\n", + " project_description \n", + "50 Purchase 7 electric buses to expand services o... \n", + "13 None \n", + "22 None " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(\n", + " final.shape,\n", + " final.sample(3)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "2fd82402-3995-495b-9423-f1de88f8c456", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "bus only 52\n", + "Name: new_project_type, dtype: int64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final['new_project_type'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "7bc1cfa9-188e-4b55-805b-d5b76627bf3d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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transit_agencyproject_titleprop_typebus_size_typedescriptionnew_project_typetotal_costbus_countsourceppnoproject_description
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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [transit_agency, project_title, prop_type, bus_size_type, description, new_project_type, total_cost, bus_count, source, ppno, project_description]\n", + "Index: []" + ] + }, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "final.shape" + "final[final['new_project_type'] == \"None\"]" ] }, { @@ -95,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "5513b941-ecdb-405e-bfd6-952df6b8f8b4", "metadata": { "tags": [] @@ -216,7 +420,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "8e18cbe6-bde7-4c30-8a8a-aefd8d619821", "metadata": { "tags": [] @@ -278,7 +482,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "8004cc2d-957f-4e9a-9ca8-2a6f9aba9ffb", "metadata": { "tags": [] @@ -348,7 +552,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "c0ca9254-2983-4cab-845c-f9bfb0229417", "metadata": { "tags": [] @@ -368,7 +572,6 @@ "cell_type": "markdown", "id": "97bdb85b-ecaa-4634-8ea1-02ebc630567f", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -377,7 +580,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "ea9c3269-d53d-4d94-bc22-c6768cb63d91", "metadata": { "tags": [] @@ -448,7 +651,7 @@ " \"\"\"\n", " # params\n", " \n", - " file = \"data-analyses_bus_procurement_cost_fta_press_release_data_csv.csv\"\n", + " file = \"raw_data-analyses_bus_procurement_cost_fta_press_release_data_csv.csv\"\n", "\n", " # read in data\n", " df = pd.read_csv(f\"{gcs_path}{file}\")\n", @@ -514,7 +717,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "4e16119d-f6f3-478b-a419-7c4989557910", "metadata": { "tags": [] @@ -533,7 +736,7 @@ " main function that reads in and cleans TIRCP data.\n", " \"\"\"\n", " from fta_data_cleaner import gcs_path\n", - " file_name = \"TIRCP Tracking Sheets 2_1-10-2024.xlsx\"\n", + " file_name = \"raw_TIRCP Tracking Sheets 2_1-10-2024.xlsx\"\n", " tircp_name = \"Project Tracking\"\n", "\n", " # read in data\n", @@ -655,7 +858,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "359f3b7a-d691-446f-9a14-424c47fc0929", "metadata": { "tags": [] @@ -689,8 +892,8 @@ " from fta_data_cleaner import gcs_path\n", " \n", " # params\n", - " file_17c = \"17c compiled-Proterra Compiled Contract Usage Report .xlsx\"\n", - " file_17b = \"17b compiled.xlsx\"\n", + " file_17c = \"raw_17c compiled-Proterra Compiled Contract Usage Report .xlsx\"\n", + " file_17b = \"raw_17b compiled.xlsx\"\n", " sheet_17c = \"Proterra \"\n", " sheet_17b = \"Usage Report Template\"\n", "\n", @@ -851,12 +1054,12 @@ " \n", " #export serperate df's as parquet to GCS\n", "# just_bus.to_parquet(f'{gcs_path}clean_dgs_all_projects.parquet')\n", - "# bus_w_options.to_parquet(f'{gcs_path}clean_dgs_bus_only_options.parquet')" + "# bus_w_options.to_parquet(f'{gcs_path}clean_dgs_bus_only_w_options.parquet')" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 111, "id": "19f4bd75-f614-4937-880a-1e1a6ff2eb7f", "metadata": { "tags": [] @@ -870,6 +1073,13 @@ "from bus_cost_utils import *\n", "from scipy.stats import zscore\n", "\n", + "def outlier_flag(col):\n", + " \"\"\"\n", + " function to flag outlier rows. use with .apply()\n", + " \"\"\"\n", + " \n", + " return col <= -3 or col >= 3\n", + "\n", "def prepare_all_data() ->pd.DataFrame:\n", " \"\"\"\n", " primary function to read-in, merge data across FTA, TIRCP and DGS data.\n", @@ -898,7 +1108,7 @@ " # bus only projects for each datase\n", " fta = pd.read_parquet(f\"{gcs_path}clean_fta_bus_only.parquet\")\n", " tircp = pd.read_parquet(f\"{gcs_path}clean_tircp_bus_only_clean.parquet\")\n", - " dgs = pd.read_parquet(f\"{gcs_path}clean_dgs_bus_only_options.parquet\")\n", + " dgs = pd.read_parquet(f\"{gcs_path}clean_dgs_bus_only_w_options.parquet\")\n", " \n", " # adding new column to identify source\n", " fta[\"source\"] = \"fta\"\n", @@ -939,15 +1149,17 @@ " ],\n", " how=\"outer\",\n", " )\n", + " #normalizing data with cost per bus\n", + " #calculating cost per bus here\n", + " merge2[\"cost_per_bus\"] = (merge2[\"total_cost\"] / merge2[\"bus_count\"]).astype(\"int64\")\n", " \n", + " #calculating zscore on cost per bus\n", + " merge2[\"zscore_cost_per_bus\"] = zscore(merge2[\"cost_per_bus\"])\n", + " #flag any outliers\n", + " df_agg[\"is_cpb_outlier?\"] = df_agg[\"zscore_cost_per_bus\"].apply(outlier_flag)\n", " return merge2\n", "\n", - "def outlier_flag(col):\n", - " \"\"\"\n", - " function to flag outlier rows. to be used in `cpb_zscore_outlier`\n", - " \"\"\"\n", - " \n", - " return col <= -3 or col >= 3\n", + "\n", "\n", "def new_cpb_aggregate(df: pd.DataFrame, column=\"transit_agency\") -> pd.DataFrame:\n", " \"\"\"\n", @@ -971,10 +1183,11 @@ " total_project_count_ppno=(\"ppno\", \"count\"),\n", " total_agg_cost=(\"total_cost\", \"sum\"),\n", " total_bus_count=(\"bus_count\", \"sum\"),\n", + " #new_prop_type=(\"prop_type\",\"max\")\n", " )\n", " .reset_index()\n", " )\n", - " df_agg[\"cpb\"] = (df_agg[\"total_agg_cost\"] / df_agg[\"total_bus_count\"]).astype(\"int64\")\n", + " df_agg[\"new_cost_per_bus\"] = (df_agg[\"total_agg_cost\"] / df_agg[\"total_bus_count\"]).astype(\"int64\")\n", " \n", " #calculate zscore\n", " df_agg[\"zscore_cost_per_bus\"] = zscore(df_agg[\"cpb\"])\n", @@ -986,19 +1199,22 @@ "\n", "\n", "\n", - "#if __name__ == \"__main__\":\n", + "if __name__ == \"__main__\":\n", " \n", " # initial df\n", - " #df1 = prepare_all_data()\n", + " df1 = prepare_all_data()\n", + "\n", " \n", " # export to gcs\n", - " #df1.to_parquet(f'{gcs_path}cpb_analysis_data_merge.parquet')\n" + " df1.to_parquet(f'{gcs_path}cleaned_cpb_analysis_data_merge.parquet')\n" ] }, { "cell_type": "markdown", "id": "9c163a75-eb4b-4a09-b035-7692f9ea68f5", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "# NB Variables rework\n", "time to organize, cut down, consolidate variables" @@ -1018,27 +1234,30 @@ "**this can be solved by filtering the same df but its different grant type, or using a table of groupby grant type and count of projects**\n", "\n", " []count of all projects\n", - " - all_project_count\n", - " ~~- total_bus_count~~\n", - " ~~- total_funding~~\n", + "- ~~all_project_count~~\n", + "- ~~total_bus_count~~\n", + "- ~~total_funding~~\n", + "\n", " count of all projects for each grant type\n", - " - count_all_fta\n", - " - count_all_tircp\n", - " - count_all_dgs\n", + "- count_all_fta\n", + "- count_all_tircp\n", + "- count_all_dgs\n", " fix: use all_project_count to create a pivot table with margins of each grant type. margins should also add a grand total col\n", " \n", " []count of bus only projects\n", - " - bus_only_project_count\n", + " - ~~bus_only_project_count~~ (this is in merged data)\n", + " \n", " count of bus only projects for each grant type\n", - " - bus_only_count_fta\n", - " - bus_only_count_tircp\n", - " - bus_only_count_dgs\n", + " - ~~bus_only_count_fta~~\n", + " - ~~bus_only_count_tircp~~\n", + " - ~~bus_only_count_dgs~~\n", " fix: use all_project_count to create a summarized dataframe of each grant type\n", "\n", " \n", " \n", "- ZEB only\n", " - zeb_only_df function\n", + " or just use the ZEB list to filter the dataframe\n", "\n", "- non-ZEB only\n", " - non_zeb_only_df function\n", @@ -1071,32 +1290,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "f84aaa3c-7bab-46c9-b739-021fdd6b60a2", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(89, 11)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", - " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", - " 'ppno', 'project_description'],\n", - " dtype='object')" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# initial final df from old code\n", "# 89 rows and 11 columns\n", @@ -1108,40 +1305,19 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 44, "id": "c9ffd5a9-b772-4509-b84c-9a96760b3112", "metadata": {}, "outputs": [], "source": [ "# making copy of final \n", "# test = final #THIS DOES NOT WORK! this is just assigning a new name to final\n", - "test = final.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "3ac2c3f6-fdc9-455c-b320-f5a3f5018359", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(89, 11)" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test.shape" + "test = pd.read_parquet(f'{GCS_PATH}old/cpb_analysis_data_merge.parquet')" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 46, "id": "f8aa3674-78fe-4ba9-8f5e-697d91ff4011", "metadata": {}, "outputs": [], @@ -1154,7 +1330,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 47, "id": "42c43416-981f-43d7-a642-6a22dc6619f2", "metadata": {}, "outputs": [ @@ -1214,7 +1390,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 48, "id": "d2bca15a-c12c-4cf5-a5a9-d591ee73a359", "metadata": {}, "outputs": [ @@ -1251,36 +1427,36 @@ " \n", " \n", " \n", - " 56\n", - " Santa Maria Area Transit\n", + " 16\n", + " City of San Luis Obispo\n", " 0\n", " 1\n", - " 1862258\n", - " 2.0\n", - " 931129\n", - " 0.367123\n", + " 859270\n", + " 1.0\n", + " 859270\n", + " 0.187746\n", " False\n", " \n", " \n", - " 38\n", - " Napa Valley Transportation Authority\n", - " 0\n", + " 46\n", + " Rhode Island Public Transit Authority\n", " 1\n", - " 2396600\n", - " 2.0\n", - " 1198300\n", - " 1.034045\n", - " False\n", - " \n", - " \n", - " 18\n", - " City of Tucson, Sun Tran\n", + " 0\n", + " 5000000\n", + " 25.0\n", + " 200000\n", + " -1.457947\n", + " False\n", + " \n", + " \n", + " 34\n", + " Lowell Regional Transit Authority\n", " 1\n", " 0\n", - " 21490560\n", - " 39.0\n", - " 551040\n", - " -0.581669\n", + " 6859296\n", + " 7.0\n", + " 979899\n", + " 0.488865\n", " False\n", " \n", " \n", @@ -1288,23 +1464,23 @@ "" ], "text/plain": [ - " transit_agency total_project_count \\\n", - "56 Santa Maria Area Transit 0 \n", - "38 Napa Valley Transportation Authority 0 \n", - "18 City of Tucson, Sun Tran 1 \n", + " transit_agency total_project_count \\\n", + "16 City of San Luis Obispo 0 \n", + "46 Rhode Island Public Transit Authority 1 \n", + "34 Lowell Regional Transit Authority 1 \n", "\n", - " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", - "56 1 1862258 2.0 931129 \n", - "38 1 2396600 2.0 1198300 \n", - "18 0 21490560 39.0 551040 \n", + " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", + "16 1 859270 1.0 859270 \n", + "46 0 5000000 25.0 200000 \n", + "34 0 6859296 7.0 979899 \n", "\n", " zscore_cost_per_bus is_cpb_outlier? \n", - "56 0.367123 False \n", - "38 1.034045 False \n", - "18 -0.581669 False " + "16 0.187746 False \n", + "46 -1.457947 False \n", + "34 0.488865 False " ] }, - "execution_count": 30, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -1316,7 +1492,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 49, "id": "637032e4-d855-4190-a6f5-ff695f77143f", "metadata": {}, "outputs": [ @@ -1562,7 +1738,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "ce1f4c86-e85d-41d8-83f6-14aadce48d5c", "metadata": {}, "outputs": [], @@ -1573,7 +1749,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "id": "59298193-fc78-4ffb-bfc3-326593c19edb", "metadata": {}, "outputs": [], @@ -1587,28 +1763,100 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "01883fc0-4f6d-4e6a-a88f-97a5914b281b", "metadata": {}, + "outputs": [], + "source": [ + "#EVERYTHING CHECKS OUT!\n", + "display(\n", + " old_prop_agg.shape,\n", + " agg_prop_type.shape,\n", + " old_prop_agg,\n", + " agg_prop_type\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "52204752-3932-4ce6-98ac-de8ad3a1f8e8", + "metadata": {}, + "outputs": [], + "source": [ + "#EVERYTHING CHECKS OUT!\n", + "new_agg_bus_size = new_cpb_aggregate(test, \"bus_size_type\")\n", + "old_size_agg = cpb_aggregate(no_outliers, \"bus_size_type\")\n", + "display(\n", + " old_size_agg.shape,\n", + " new_agg_bus_size.shape,\n", + " old_size_agg,\n", + " new_agg_bus_size\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0391dd4d-23e1-49cb-8123-509954c796e8", + "metadata": {}, + "outputs": [], + "source": [ + "#EVERYTHING CHECKS OUT!\n", + "# move forward with `new_cpb_aggregate` function\n", + "new_agg_agency = new_cpb_aggregate(test)\n", + "old_agency_agg = cpb_aggregate(no_outliers, \"transit_agency\")\n", + "display(\n", + " old_agency_agg.shape,\n", + " new_agg_agency.shape,\n", + " old_agency_agg,\n", + " new_agg_agency\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3428875a-6a64-41bc-8f9c-81902006d7f0", + "metadata": {}, + "source": [ + "## Testing variables rework\n", + "there are a lot of variables in the initial script. need to cut down the amount of variables or at least make it more efficient. " + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "f21298ee-0efb-4f91-ba63-55fc2645a4d2", + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(10, 6)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "(10, 8)" + "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", + " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", + " 'ppno', 'project_description'],\n", + " dtype='object')" ] }, + "execution_count": 64, "metadata": {}, - "output_type": "display_data" - }, + "output_type": "execute_result" + } + ], + "source": [ + "# read in all cleaned project data\n", + "# same as final from above\n", + "# cpb_analysis_data_merge is bus only projects. all DGS rows were Bus only projects anyways\n", + "merged_data = pd.read_parquet(f'{GCS_PATH}old/cpb_analysis_data_merge.parquet')\n", + "merged_data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "id": "99846e40-f8bc-4001-9373-3f4c25eb7819", + "metadata": {}, + "outputs": [ { "data": { "text/html": [ @@ -1630,1974 +1878,1928 @@ " \n", " \n", " \n", + " transit_agency\n", + " project_title\n", " prop_type\n", - " total_project_count\n", - " total_project_count_ppno\n", - " total_agg_cost\n", - " total_bus_count\n", - " cpb\n", + " bus_size_type\n", + " description\n", + " new_project_type\n", + " total_cost\n", + " bus_count\n", + " source\n", + " ppno\n", + " project_description\n", " \n", " \n", " \n", " \n", " 0\n", - " BEB\n", - " 0\n", - " 30\n", - " 167232489\n", - " 163.0\n", - " 1025966\n", + " AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)\n", + " Puerto Rico Initiative Minimizing Emissions Pl...\n", + " electric (not specified)\n", + " not specified\n", + " The Metropolitan Bus Authority will receive fu...\n", + " bus only\n", + " 10000000\n", + " 8.0\n", + " fta\n", + " None\n", + " None\n", " \n", " \n", " 1\n", + " Cape Fear Public Transportation Authority\n", + " Wave Transit Low Emissions Replacement Vehicles\n", " CNG\n", - " 12\n", - " 1\n", - " 176039140\n", - " 252.0\n", - " 698568\n", + " not specified\n", + " Wave Transit will receive funding to buy compr...\n", + " bus only\n", + " 2860250\n", + " 5.0\n", + " fta\n", + " None\n", + " None\n", " \n", " \n", " 2\n", - " FCEB\n", - " 2\n", - " 6\n", - " 120951335\n", - " 102.0\n", - " 1185797\n", + " Central Oklahoma Transportation and Parking Au...\n", + " COTPA, dba EMBARK Elimination of Fixed Route D...\n", + " CNG\n", + " not specified\n", + " The Central Oklahoma Transportation and Parkin...\n", + " bus only\n", + " 4278772\n", + " 9.0\n", + " fta\n", + " None\n", + " None\n", " \n", " \n", " 3\n", - " electric (not specified)\n", - " 1\n", - " 2\n", - " 56678000\n", - " 44.0\n", - " 1288136\n", + " Champaign-Urbana Mass Transit District\n", + " MTD 40-Foot Hybrid Replacement Buses\n", + " low emission (hybrid)\n", + " not specified\n", + " The Champaign-Urbana Mass Transit District wil...\n", + " bus only\n", + " 6635394\n", + " 10.0\n", + " fta\n", + " None\n", + " None\n", " \n", " \n", " 4\n", - " ethanol\n", - " 1\n", - " 0\n", - " 1006750\n", - " 9.0\n", - " 111861\n", + " City of Beaumont\n", + " Beaumont Municipal Transit Zips to Improve Low...\n", + " CNG\n", + " not specified\n", + " Beaumont Municipal Transit will receive fundin...\n", + " bus only\n", + " 2819460\n", + " 5.0\n", + " fta\n", + " None\n", + " None\n", " \n", " \n", " 5\n", + " City of Beloit\n", + " Beloit Transit Low-Emission Vehicle Purchase\n", " low emission (hybrid)\n", - " 16\n", - " 0\n", - " 91824361\n", - " 145.0\n", - " 633271\n", + " not specified\n", + " The Beloit Transit System will receive funding...\n", + " bus only\n", + " 653184\n", + " 1.0\n", + " fta\n", + " None\n", + " None\n", " \n", " \n", " 6\n", - " low emission (propane)\n", - " 5\n", - " 0\n", - " 8403969\n", - " 44.0\n", - " 190999\n", + " City of Brownsville\n", + " Brownsville Metro Hybrid-Electric Diesel\n", + " low emission (hybrid)\n", + " not specified\n", + " Brownsville Metro will receive funding to buy ...\n", + " bus only\n", + " 4738886\n", + " 6.0\n", + " fta\n", + " None\n", + " None\n", " \n", " \n", " 7\n", - " mix (zero and low emission)\n", - " 2\n", - " 0\n", - " 36775430\n", - " 125.0\n", - " 294203\n", + " City of Colorado Springs dba Mountain Metropol...\n", + " Mountain Metropolitan Transit Diesel Replaceme...\n", + " low emission (hybrid)\n", + " not specified\n", + " The city of Colorado Springs' Mountain Metropo...\n", + " bus only\n", + " 3199038\n", + " 6.0\n", + " fta\n", + " None\n", + " None\n", " \n", " \n", " 8\n", + " City of Jonesboro, Arkansas\n", + " JET Low Emission Fleet\n", + " low emission (hybrid)\n", " not specified\n", - " 4\n", - " 1\n", - " 41552404\n", - " 325.0\n", - " 127853\n", + " The City of Jonesboro will receive funding to ...\n", + " bus only\n", + " 1010372\n", + " 5.0\n", + " fta\n", + " None\n", + " None\n", " \n", " \n", " 9\n", - " zero-emission bus (not specified)\n", - " 0\n", - " 5\n", - " 128156513\n", - " 143.0\n", - " 896199\n", + " City of Norman, Oklahoma\n", + " Replacement CNG Vehicles for ADA Paratransit S...\n", + " CNG\n", + " not specified\n", + " The city of Norman will buy compressed natural...\n", + " bus only\n", + " 776714\n", + " 6.0\n", + " fta\n", + " None\n", + " None\n", " \n", - " \n", - "\n", - "" - ], - "text/plain": [ - " prop_type total_project_count \\\n", - "0 BEB 0 \n", - "1 CNG 12 \n", - "2 FCEB 2 \n", - "3 electric (not specified) 1 \n", - "4 ethanol 1 \n", - "5 low emission (hybrid) 16 \n", - "6 low emission (propane) 5 \n", - "7 mix (zero and low emission) 2 \n", - "8 not specified 4 \n", - "9 zero-emission bus (not specified) 0 \n", - "\n", - " total_project_count_ppno total_agg_cost total_bus_count cpb \n", - "0 30 167232489 163.0 1025966 \n", - "1 1 176039140 252.0 698568 \n", - "2 6 120951335 102.0 1185797 \n", - "3 2 56678000 44.0 1288136 \n", - "4 0 1006750 9.0 111861 \n", - "5 0 91824361 145.0 633271 \n", - "6 0 8403969 44.0 190999 \n", - "7 0 36775430 125.0 294203 \n", - "8 1 41552404 325.0 127853 \n", - "9 5 128156513 143.0 896199 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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prop_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpbzscore_cost_per_busis_cpb_outlier?
10City of Tucson, Sun TranA Clean Ride by 2025CNGnot specifiedThe city of Tucson's Sun Tran transit system w...bus only2149056039.0ftaNoneNone
0BEB031170455813164.010393640.923505False11Coast Transit Authority dba MS Coast Transport...Purchase of Replacement Buseslow emission (propane)not specifiedCoast Transit Authority will receive funding t...bus only17600009.0ftaNoneNone
112Conroe Connection TransitCCT Commuter Bus Fleet Replacement ProjectCNG121176039140252.06985680.122141Falsenot specifiedConroe Connection Transit will receive funding...bus only45000004.0ftaNoneNone
213Dallas Area Rapid Transit (DART)DART CNG Bus Fleet Modernization ProjectCNGnot specifiedDallas Area Rapid Transit will receive funding...bus only10300000090.0ftaNoneNone
14Delaware Transit Corporation (DTC)Continuation of Delaware Transit Corporations ...FCEB26120951335102.011857971.267835Falsenot specifiedDelaware Transit Corporation will receive fund...bus only87407286.0ftaNoneNone
3electric (not specified)125667800044.012881361.508480False15Golden Empire TransitReplacement of Ten (10) 40ft. CNG BusesCNGnot specifiedGolden Empire Transit will receive funding to ...bus only575035110.0ftaNoneNone
4ethanol1010067509.0111861-1.257470False16Illinois Department of Transportation on behal...Illinois DOT Statewide Paratransit Vehicle Rep...not specifiedcutawayThe Illinois Department of Transportation will...bus only12600000134.0ftaNoneNone
517Indianapolis Public Transportation Corporation...The purchase of 20 vehicles, which will use th...low emission (hybrid)16091824361145.0633271-0.031401Falsenot specifiedThe Indianapolis Public Transportation Corpora...bus only1904033620.0ftaNoneNone
6low emission (propane)50840396944.0190999-1.071381False18Interurban Transit PartnershipA Sustainable Future: Renewable Natural Gas Bu...CNGnot specifiedThe Interurban Transit Partnership (The Rapid)...bus only619718011.0ftaNoneNone
7mix (zero and low emission)2036775430125.0294203-0.828702False19Lowell Regional Transit AuthorityLowell Regional Transit Authority Revenue Vehi...low emission (hybrid)not specifiedThe Lowell Regional Transit Authority will rec...bus only68592967.0ftaNoneNone
820Madison County Mass Transit DistrictHeavy Duty 40-Foot Bus Replacementnot specified4141552404325.0127853-1.219866Falsenot specifiedThe Madison County Mass Transit District will ...bus only10800002.0ftaNoneNone
9zero-emission bus (not specified)05128156513143.08961990.586860False
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bus_size_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpb
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bus_size_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpbzscore_cost_per_busis_cpb_outlier?
0articulated025823757641.014204281.598471False
1cutaway3016694500152.0109832-1.466801False
2not specified406509919038881.0578795-0.369972False
3over-the-road01951600014.0679714-0.133939False
4standard/conventional (30ft-45ft)037237476601265.08961380.372242False
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpb
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)10100000008.01250000
1Alameda County Transit Authority012284664020.01142332
2Antelope Valley Transit Authority (AVTA)013947800029.01361310
3CITY OF PORTERVILLE (PORTERVILLE, CA)0127818913.0927297
4CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...0136235364.0905884
5Cape Fear Public Transportation Authority1028602505.0572050
6Central Oklahoma Transportation and Parking Au...1042787729.0475419
7Champaign-Urbana Mass Transit District10663539410.0663539
8City of Beaumont1028194605.0563892
9City of Beloit106531841.0653184
10City of Brownsville1047388866.0789814
11City of Colorado Springs dba Mountain Metropol...1031990386.0533173
12City of Jonesboro, Arkansas1010103725.0202074
13City of Los Angeles (LA DOT)01102790000112.0917767
14City of Norman, Oklahoma107767146.0129452
15City of Roseville02965150710.0965150
16City of San Luis Obispo018592701.0859270
17City of Santa Rosa(Santa Rosa CityBus)0159877905.01197558
18City of Tucson, Sun Tran102149056039.0551040
19City of Visalia - Visalia City Coach(Visalia T...0136878034.0921950
20City of Wasco0115430003.0514333
21Coast Transit Authority dba MS Coast Transport...1017600009.0195555
22Conroe Connection Transit1045000004.01125000
23Culver City0135470005.0709400
24Dallas Area Rapid Transit (DART)1010300000090.01144444
25Delaware Transit Corporation (DTC)1087407286.01456788
26Foothill Transit011658000020.0829000
27Foothill Transit, West Covina, CA013764204433.01140668
28GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA)0154063555.01081271
29Golden Empire Transit111120865615.0747243
30Illinois Department of Transportation on behal...1012600000134.094029
31Indianapolis Public Transportation Corporation...101904033620.0952016
32Interurban Transit Partnership10619718011.0563380
33Lane Transit (Oregon)022789499930.0929833
34Lowell Regional Transit Authority1068592967.0979899
35Madison County Mass Transit District1010800002.0540000
36Mesa County1011620003.038733321Mesa CountyGrand Valley Transit Bus PurchasesCNGcutawayMesa County's Grand Valley Transit will receiv...bus only11620003.0ftaNoneNone
3722Minnesota Department of Transportation on beha...10Reducing Emissions in Rural Minnesota Transitlow emission (propane)not specifiedThe Minnesota Department of Transportation, on...bus only14569707.0208138
38Napa Valley Transportation Authority0123966002.01198300ftaNoneNone
3923New Mexico Department of Transportation on beh...10Procurement of Three Replacement Diesel-Electr...low emission (hybrid)not specifiedThe North Central Regional Transit District wi...bus only20631603.0687720
40North County Transit District0157876066.0964601ftaNoneNone
4124North County Transit District (NCTD)10Accelerate Clean Transit (ACT)FCEBnot specifiedThe North County Transit District will receive...bus only2933024323.01275227
42ORANGE COUNTY TRANSPORTATION AUTHORITY (ORANGE...01931952010.0931952ftaNoneNone
4325Ohio Department of Transportation (ODOT) on be...10Ohio Zero Emission Ready Ohio (OH-ZERO)mix (zero and low emission)not specifiedThe Ohio Department of Transportation (ODOT) w...bus only2933166569.0425096
44Orange County Transportation Authority (OCTA)01290000040.072500ftaNoneNone
4526Oregon Department of Transportation on behalf ...10CET's Low Emission Vanpools and Support Vehicleslow emission (hybrid)not specifiedThe Oregon Department of Transportation on beh...bus only1812505.036250ftaNoneNone
4627Rhode Island Public Transit Authority10RIPTA Hybrid Bus Upgradelow emission (hybrid)not specifiedThe Rhode Island Public Transit Authority will...bus only500000025.0200000ftaNoneNone
4728Rockford Mass Transit District10Hybrid Bus Procurementlow emission (hybrid)not specifiedThe Rockford Mass Transit District will receiv...bus only40946524.01023663ftaNoneNone
4829Rogue Valley Transportation District10RVTD Renewable Resiliency Buseslow emission (hybrid)not specifiedThe Rogue Valley Transportation District will ...bus only39375006.0656250
49SLO TRANSIT (SAN LUIS OBISPO, CA)018472141.0847214
50SUNLINE TRANSIT AGENCY (THOUSAND PALMS)0157551555.01151031
51SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA)0157718655.01154373
52Sacramento County Airport System0146422255.0928445
53San Antonio Metropolitan Transit Authority10318720015.0212480
54San Diego Metro011875957612.01563298
55Santa Barbara Metro0136590724.0914768
56Santa Maria Area Transit0118622582.0931129
57Santa Maria Regional Transit0151883795.01037675
58Santa Rosa City Bus0140682024.01017050
59Shasta Regional Transportation Agency (SRTA)01951600014.0679714
60Sonoma County Transit01899000010.0899000ftaNoneNone
6130San Antonio Metropolitan Transit AuthorityVIA Metropolitan Transit ViaTrans: Providing e...low emission (propane)not specifiedThe San Antonio Metropolitan Transit Authority...bus only318720015.0ftaNoneNone
31South Carolina Department of Transportation on...10SCDOT Vehicle Replacement Projectnot specifiednot specifiedThe South Carolina Department of Transportatio...bus only15423904160.096399ftaNoneNone
6232South Dakota Department of Transportation on b...10Replacement of Aberdeen Ride Line fleet buses ...ethanolnot specifiedThe South Dakota Department of Transportation,...bus only10067509.0111861ftaNoneNone
6333South Dakota Department of Transportation on b...10Low-emission bus alternative fuel project to i...low emission (propane)not specifiedThe South Dakota Department of Transportation ...bus only12766289.0141847ftaNoneNone
6434Southeastern Regional Transit Authority10Southeastern Regional Transit Authority Fixed ...low emission (hybrid)not specifiedThe Southeastern Regional Transit Authority wi...bus only1156000016.0722500ftaNoneNone
6535Southwest Ohio Regional Transit Authority10SORTA Hybrid Transition Bus Replacement Projectlow emission (hybrid)not specifiedThe Southwest Ohio Regional Transit Authority ...bus only980642816.0612901ftaNoneNone
6636State of California on behalf of Kern Regional...20618100020.0309050Low Emission Transition/ReplacementCNGnot specifiedThe State of California, on behalf of Kern Reg...bus only32485005.0ftaNoneNone
6737State of California on behalf of Kern Regional...Purchase of Fifteen (15) Replacement Cutaway B...not specifiedcutawayThe State of California, on behalf of Kern Reg...bus only293250015.0ftaNoneNone
38Tahoe Transportation District10Tahoe Transportation District (TTD) Clean Tran...low emission (hybrid)not specifiedThe Tahoe Transportation District will receive...bus only34000004.0850000ftaNoneNone
6839Texas Department of Transportation on behalf o...10FY23 Rural Transit Asset Replacement & Moderni...mix (zero and low emission)not specifiedThe Texas Department of Transportation will re...bus only744376556.0132924ftaNoneNone
69The Bus, City of Merced0147862855.095725740Utah Transit AuthorityUtah Transit Authority Compressed Natural Gas ...CNGnot specifiedThe Utah Transit Authority will receive fundin...bus only1705535325.0ftaNoneNone
70Torrance Transit Department0172000007.0102857141Whatcom Transportation Authority (WTA)Purchase 11 diesel-electric hybrid buses (hybr...low emission (hybrid)not specifiedThe Whatcom Transportation Authority will rece...bus only964486511.0ftaNoneNone
71Transit Joint Powers Authority for Merced County0132233242.0161166242White Earth Reservation Business CommitteeWhite Earth Public Transit to replace 4 of the...low emission (propane)not specifiedWhite Earth Public Transit will receive fundin...bus only7231714.0ftaNoneNone
72Transit Joint Powers Authority of Merced County01369651343Antelope Valley Transit Authority (AVTA)Noneelectric (not specified)articulatedNonebus only3947800029.0tircpCP005Purchase 13 60-foot articulated BRT buses and ...
44City of Los Angeles (LA DOT)Nonezero-emission bus (not specified)not specifiedNonebus only102790000112.0tircpCP029Acquire 112 zero-emission buses to replace exi...
45City of WascoNonezero-emission bus (not specified)not specifiedNonebus only15430003.01232171tircpCP090Purchase of 3 zero-emission buses that will su...
73UC DAVIS (UNITRANS) (DAVIS, CA)02932192610.093219246Culver CityNonezero-emission bus (not specified)not specifiedNonebus only35470005.0tircpCP114The Project implements a new transit service u...
74UCLA FLEET & TRANSIT0120088262.0100441347Foothill TransitNonezero-emission bus (not specified)not specifiedNonebus only1658000020.0tircpCP076Purchase 20 zero-emission buses to extend Rout...
75University of California - San Diego0282680006.0137800048Orange County Transportation Authority (OCTA)NoneCNGstandard/conventional (30ft-45ft)Nonebus only290000040.0tircpCP004Purchase five 40-foot CNG buses for BRT Route ...
76University of California, Irvine0149329305.098658649Shasta Regional Transportation Agency (SRTA)Nonenot specifiedover-the-roadNonebus only951600014.0tircpCP045Purchase 7 new coach-style buses to support a ...
77Utah Transit Authority101705535325.068221450Torrance Transit DepartmentNoneelectric (not specified)not specifiedNonebus only72000007.0tircpCP073Purchase 7 electric buses to expand services o...
78VACAVILLE PUBLIC TRANSPORTATION (CITY COACH) (...011017559010.0101755951Transit Joint Powers Authority of Merced CountyNonezero-emission bus (not specified)not specifiedNonebus only36965133.0tircpCP074Purchases 3 zero-emission electric buses to in...
52SUNLINE TRANSIT AGENCY (THOUSAND PALMS)NoneFCEBstandard/conventional (30ft-45ft)NoneNone57551555.0dgs11819None
7953Lane Transit (Oregon)NoneBEBstandard/conventional (30ft-45ft)NoneNone992162611.0dgs2020-061None
54VICTOR VALLEY TRANSIT AUTHORITY (VVTA)01NoneBEBstandard/conventional (30ft-45ft)NoneNone45081605.0901632
80Whatcom Transportation Authority (WTA)10964486511.0876805dgs1416None
81White Earth Reservation Business Committee1072317155CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...NoneBEBstandard/conventional (30ft-45ft)NoneNone36235364.0180792
\n", - "
" - ], - "text/plain": [ - " transit_agency total_project_count \\\n", - "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) 1 \n", - "1 Alameda County Transit Authority 0 \n", - "2 Antelope Valley Transit Authority (AVTA) 0 \n", - "3 CITY OF PORTERVILLE (PORTERVILLE, CA) 0 \n", - "4 CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ... 0 \n", - "5 Cape Fear Public Transportation Authority 1 \n", - "6 Central Oklahoma Transportation and Parking Au... 1 \n", - "7 Champaign-Urbana Mass Transit District 1 \n", - "8 City of Beaumont 1 \n", - "9 City of Beloit 1 \n", - "10 City of Brownsville 1 \n", - "11 City of Colorado Springs dba Mountain Metropol... 1 \n", - "12 City of Jonesboro, Arkansas 1 \n", - "13 City of Los Angeles (LA DOT) 0 \n", - "14 City of Norman, Oklahoma 1 \n", - "15 City of Roseville 0 \n", - "16 City of San Luis Obispo 0 \n", - "17 City of Santa Rosa(Santa Rosa CityBus) 0 \n", - "18 City of Tucson, Sun Tran 1 \n", - "19 City of Visalia - Visalia City Coach(Visalia T... 0 \n", - "20 City of Wasco 0 \n", - "21 Coast Transit Authority dba MS Coast Transport... 1 \n", - "22 Conroe Connection Transit 1 \n", - "23 Culver City 0 \n", - "24 Dallas Area Rapid Transit (DART) 1 \n", - "25 Delaware Transit Corporation (DTC) 1 \n", - "26 Foothill Transit 0 \n", - "27 Foothill Transit, West Covina, CA 0 \n", - "28 GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA) 0 \n", - "29 Golden Empire Transit 1 \n", - "30 Illinois Department of Transportation on behal... 1 \n", - "31 Indianapolis Public Transportation Corporation... 1 \n", - "32 Interurban Transit Partnership 1 \n", - "33 Lane Transit (Oregon) 0 \n", - "34 Lowell Regional Transit Authority 1 \n", - "35 Madison County Mass Transit District 1 \n", - "36 Mesa County 1 \n", - "37 Minnesota Department of Transportation on beha... 1 \n", - "38 Napa Valley Transportation Authority 0 \n", - "39 New Mexico Department of Transportation on beh... 1 \n", - "40 North County Transit District 0 \n", - "41 North County Transit District (NCTD) 1 \n", - "42 ORANGE COUNTY TRANSPORTATION AUTHORITY (ORANGE... 0 \n", - "43 Ohio Department of Transportation (ODOT) on be... 1 \n", - "44 Orange County Transportation Authority (OCTA) 0 \n", - "45 Oregon Department of Transportation on behalf ... 1 \n", - "46 Rhode Island Public Transit Authority 1 \n", - "47 Rockford Mass Transit District 1 \n", - "48 Rogue Valley Transportation District 1 \n", - "49 SLO TRANSIT (SAN LUIS OBISPO, CA) 0 \n", - "50 SUNLINE TRANSIT AGENCY (THOUSAND PALMS) 0 \n", - "51 SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA) 0 \n", - "52 Sacramento County Airport System 0 \n", - "53 San Antonio Metropolitan Transit Authority 1 \n", - "54 San Diego Metro 0 \n", - "55 Santa Barbara Metro 0 \n", - "56 Santa Maria Area Transit 0 \n", - "57 Santa Maria Regional Transit 0 \n", - "58 Santa Rosa City Bus 0 \n", - "59 Shasta Regional Transportation Agency (SRTA) 0 \n", - "60 Sonoma County Transit 0 \n", - "61 South Carolina Department of Transportation on... 1 \n", - "62 South Dakota Department of Transportation on b... 1 \n", - "63 South Dakota Department of Transportation on b... 1 \n", - "64 Southeastern Regional Transit Authority 1 \n", - "65 Southwest Ohio Regional Transit Authority 1 \n", - "66 State of California on behalf of Kern Regional... 2 \n", - "67 Tahoe Transportation District 1 \n", - "68 Texas Department of Transportation on behalf o... 1 \n", - "69 The Bus, City of Merced 0 \n", - "70 Torrance Transit Department 0 \n", - "71 Transit Joint Powers Authority for Merced County 0 \n", - "72 Transit Joint Powers Authority of Merced County 0 \n", - "73 UC DAVIS (UNITRANS) (DAVIS, CA) 0 \n", - "74 UCLA FLEET & TRANSIT 0 \n", - "75 University of California - San Diego 0 \n", - "76 University of California, Irvine 0 \n", - "77 Utah Transit Authority 1 \n", - "78 VACAVILLE PUBLIC TRANSPORTATION (CITY COACH) (... 0 \n", - "79 VICTOR VALLEY TRANSIT AUTHORITY (VVTA) 0 \n", - "80 Whatcom Transportation Authority (WTA) 1 \n", - "81 White Earth Reservation Business Committee 1 \n", - "\n", - " total_project_count_ppno total_agg_cost total_bus_count cpb \n", - "0 0 10000000 8.0 1250000 \n", - "1 1 22846640 20.0 1142332 \n", - "2 1 39478000 29.0 1361310 \n", - "3 1 2781891 3.0 927297 \n", - "4 1 3623536 4.0 905884 \n", - "5 0 2860250 5.0 572050 \n", - "6 0 4278772 9.0 475419 \n", - "7 0 6635394 10.0 663539 \n", - "8 0 2819460 5.0 563892 \n", - "9 0 653184 1.0 653184 \n", - "10 0 4738886 6.0 789814 \n", - "11 0 3199038 6.0 533173 \n", - "12 0 1010372 5.0 202074 \n", - "13 1 102790000 112.0 917767 \n", - "14 0 776714 6.0 129452 \n", - "15 2 9651507 10.0 965150 \n", - "16 1 859270 1.0 859270 \n", - "17 1 5987790 5.0 1197558 \n", - "18 0 21490560 39.0 551040 \n", - "19 1 3687803 4.0 921950 \n", - "20 1 1543000 3.0 514333 \n", - "21 0 1760000 9.0 195555 \n", - "22 0 4500000 4.0 1125000 \n", - "23 1 3547000 5.0 709400 \n", - "24 0 103000000 90.0 1144444 \n", - "25 0 8740728 6.0 1456788 \n", - "26 1 16580000 20.0 829000 \n", - "27 1 37642044 33.0 1140668 \n", - "28 1 5406355 5.0 1081271 \n", - "29 1 11208656 15.0 747243 \n", - "30 0 12600000 134.0 94029 \n", - "31 0 19040336 20.0 952016 \n", - "32 0 6197180 11.0 563380 \n", - "33 2 27894999 30.0 929833 \n", - "34 0 6859296 7.0 979899 \n", - "35 0 1080000 2.0 540000 \n", - "36 0 1162000 3.0 387333 \n", - "37 0 1456970 7.0 208138 \n", - "38 1 2396600 2.0 1198300 \n", - "39 0 2063160 3.0 687720 \n", - "40 1 5787606 6.0 964601 \n", - "41 0 29330243 23.0 1275227 \n", - "42 1 9319520 10.0 931952 \n", - "43 0 29331665 69.0 425096 \n", - "44 1 2900000 40.0 72500 \n", - "45 0 181250 5.0 36250 \n", - "46 0 5000000 25.0 200000 \n", - "47 0 4094652 4.0 1023663 \n", - "48 0 3937500 6.0 656250 \n", - "49 1 847214 1.0 847214 \n", - "50 1 5755155 5.0 1151031 \n", - "51 1 5771865 5.0 1154373 \n", - "52 1 4642225 5.0 928445 \n", - "53 0 3187200 15.0 212480 \n", - "54 1 18759576 12.0 1563298 \n", - "55 1 3659072 4.0 914768 \n", - "56 1 1862258 2.0 931129 \n", - "57 1 5188379 5.0 1037675 \n", - "58 1 4068202 4.0 1017050 \n", - "59 1 9516000 14.0 679714 \n", - "60 1 8990000 10.0 899000 \n", - "61 0 15423904 160.0 96399 \n", - "62 0 1006750 9.0 111861 \n", - "63 0 1276628 9.0 141847 \n", - "64 0 11560000 16.0 722500 \n", - "65 0 9806428 16.0 612901 \n", - "66 0 6181000 20.0 309050 \n", - "67 0 3400000 4.0 850000 \n", - "68 0 7443765 56.0 132924 \n", - "69 1 4786285 5.0 957257 \n", - "70 1 7200000 7.0 1028571 \n", - "71 1 3223324 2.0 1611662 \n", - "72 1 3696513 3.0 1232171 \n", - "73 2 9321926 10.0 932192 \n", - "74 1 2008826 2.0 1004413 \n", - "75 2 8268000 6.0 1378000 \n", - "76 1 4932930 5.0 986586 \n", - "77 0 17055353 25.0 682214 \n", - "78 1 10175590 10.0 1017559 \n", - "79 1 4508160 5.0 901632 \n", - "80 0 9644865 11.0 876805 \n", - "81 0 723171 4.0 180792 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpbzscore_cost_per_busis_cpb_outlier?dgs22100367 - 00None
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)10100000008.012500001.163100False56ORANGE COUNTY TRANSPORTATION AUTHORITY (ORANGE...NoneBEBstandard/conventional (30ft-45ft)NoneNone931952010.0dgsCO2165None
157Alameda County Transit Authority01NoneFCEBstandard/conventional (30ft-45ft)NoneNone2284664020.011423320.894336False
2Antelope Valley Transit Authority (AVTA)013947800029.013613101.440957False
3CITY OF PORTERVILLE (PORTERVILLE, CA)0127818913.09272970.357558Falsedgs57071None
4CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...01362353658Santa Barbara MetroNoneBEBstandard/conventional (30ft-45ft)NoneNone36590724.09058840.304106Falsedgs10609None
5Cape Fear Public Transportation Authority10286025059Golden Empire TransitNoneFCEBstandard/conventional (30ft-45ft)NoneNone54583055.0572050-0.529223Falsedgs10076963-000None
6Central Oklahoma Transportation and Parking Au...1042787729.0475419-0.770436False60San Diego MetroNoneBEBarticulatedNoneNone1875957612.0dgs4500040166None
7Champaign-Urbana Mass Transit District10663539410.0663539-0.300844False61North County Transit DistrictNoneBEBstandard/conventional (30ft-45ft)NoneNone57876066.0dgs347750000PNone
8City of Beaumont10281946062The Bus, City of MercedNoneBEBstandard/conventional (30ft-45ft)NoneNone47862855.0563892-0.549587FalsedgsEBUS001None
9City of Beloit106531841.0653184-0.326693False63Lane Transit (Oregon)NoneBEBstandard/conventional (30ft-45ft)NoneNone1797337319.0dgsA-21587None
64Foothill Transit, West Covina, CANoneFCEBstandard/conventional (30ft-45ft)NoneNone3764204433.0dgs21-077None
10City of Brownsville10473888665UC DAVIS (UNITRANS) (DAVIS, CA)NoneBEBstandard/conventional (30ft-45ft)NoneNone55594866.07898140.014368FalsedgsASTR876345None
11City of Colorado Springs dba Mountain Metropol...1031990386.0533173-0.626269False66CITY OF PORTERVILLE (PORTERVILLE, CA)NoneBEBstandard/conventional (30ft-45ft)NoneNone27818913.0dgs20-18895None
12City of Jonesboro, Arkansas10101037267GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA)NoneFCEBstandard/conventional (30ft-45ft)NoneNone54063555.0202074-1.452770False
13City of Los Angeles (LA DOT)01102790000112.09177670.333769Falsedgs10076963-000None
14City of Norman, Oklahoma107767146.0129452-1.634052False68UC DAVIS (UNITRANS) (DAVIS, CA)NoneBEBstandard/conventional (30ft-45ft)NoneNone37624404.0dgsUC DAVIS (UNITRANS) (DAVIS, CA)None
15City of Roseville02965150769VACAVILLE PUBLIC TRANSPORTATION (CITY COACH) (...NoneBEBstandard/conventional (30ft-45ft)NoneNone1017559010.09651500.452048Falsedgs2201132None
16City of San Luis Obispo0185927070SLO TRANSIT (SAN LUIS OBISPO, CA)NoneBEBstandard/conventional (30ft-45ft)NoneNone8472141.08592700.187746Falsedgs609571None
17City of Santa Rosa(Santa Rosa CityBus)0159877905.011975581.032193False71UCLA FLEET & TRANSITNoneBEBstandard/conventional (30ft-45ft)NoneNone20088262.0dgsZC654 / ZC653None
18City of Tucson, Sun Tran102149056039.0551040-0.581669False72SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA)NoneFCEBstandard/conventional (30ft-45ft)NoneNone57718655.0dgs120706None
19City of Visalia - Visalia City Coach(Visalia T...0136878034.09219500.344210False73City of RosevilleNoneBEBstandard/conventional (30ft-45ft)NoneNone44528925.0dgs9009418None
20City of Wasco0115430003.0514333-0.673298False74City of RosevilleNoneBEBstandard/conventional (30ft-45ft)NoneNone51986155.0dgs9011071None
21Coast Transit Authority dba MS Coast Transport...1017600009.0195555-1.469043False75University of California - San DiegoNoneBEBstandard/conventional (30ft-45ft)NoneNone41340004.0dgsPUR00318127None
22Conroe Connection Transit1045000004.011250000.851071False76University of California - San DiegoNoneBEBstandard/conventional (30ft-45ft)NoneNone41340002.0dgsPUR00318127None
23Culver City0135470005.0709400-0.186365False77Sonoma County TransitNoneBEBstandard/conventional (30ft-45ft)NoneNone899000010.0dgsSC001-2300002779None
24Dallas Area Rapid Transit (DART)1010300000090.011444440.899608False78City of Santa Rosa(Santa Rosa CityBus)NoneBEBstandard/conventional (30ft-45ft)NoneNone59877905.0dgsCity of Santa Rosa(Santa Rosa CityBus)None
25Delaware Transit Corporation (DTC)1087407286.014567881.679292False79Santa Rosa City BusNoneBEBstandard/conventional (30ft-45ft)NoneNone40682024.0dgsPA-2021-001-SRCBNone
26Foothill Transit011658000020.08290000.112185False80University of California, IrvineNoneBEBstandard/conventional (30ft-45ft)NoneNone49329305.0dgsUniversity of California, IrvineNone
27Foothill Transit, West Covina, CA013764204433.011406680.890182False81Santa Maria Regional TransitNoneBEBstandard/conventional (30ft-45ft)NoneNone51883795.0dgs63759None
28GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA)0154063555.010812710.741913False82City of San Luis ObispoNoneBEBstandard/conventional (30ft-45ft)NoneNone8592701.0dgs609570None
29Golden Empire Transit111120865615.0747243-0.091900False83Transit Joint Powers Authority for Merced CountyNoneBEBstandard/conventional (30ft-45ft)NoneNone32233242.0dgsEBUS002None
30Illinois Department of Transportation on behal...1012600000134.094029-1.722476False84Transit Joint Powers Authority for Merced CountyNoneBEBstandard/conventional (30ft-45ft)NoneNone32233241.0dgsEBUS002None
31Indianapolis Public Transportation Corporation...101904033620.09520160.419262False85Napa Valley Transportation AuthorityNoneBEBstandard/conventional (30ft-45ft)NoneNone23966002.0dgs21-2002None
32Interurban Transit Partnership10619718011.0563380-0.550865False86Santa Maria Area TransitNoneBEBstandard/conventional (30ft-45ft)NoneNone18622582.0dgs57614None
33Lane Transit (Oregon)022789499930.09298330.363888False87City of Visalia - Visalia City Coach(Visalia T...NoneBEBstandard/conventional (30ft-45ft)NoneNone36878034.0dgsP02771None
34Lowell Regional Transit Authority1068592967.09798990.488865False88Sacramento County Airport SystemNoneBEBstandard/conventional (30ft-45ft)NoneNone46422255.0dgsPA81335877None
\n", + "
" + ], + "text/plain": [ + " transit_agency \\\n", + "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) \n", + "1 Cape Fear Public Transportation Authority \n", + "2 Central Oklahoma Transportation and Parking Au... \n", + "3 Champaign-Urbana Mass Transit District \n", + "4 City of Beaumont \n", + "5 City of Beloit \n", + "6 City of Brownsville \n", + "7 City of Colorado Springs dba Mountain Metropol... \n", + "8 City of Jonesboro, Arkansas \n", + "9 City of Norman, Oklahoma \n", + "10 City of Tucson, Sun Tran \n", + "11 Coast Transit Authority dba MS Coast Transport... \n", + "12 Conroe Connection Transit \n", + "13 Dallas Area Rapid Transit (DART) \n", + "14 Delaware Transit Corporation (DTC) \n", + "15 Golden Empire Transit \n", + "16 Illinois Department of Transportation on behal... \n", + "17 Indianapolis Public Transportation Corporation... \n", + "18 Interurban Transit Partnership \n", + "19 Lowell Regional Transit Authority \n", + "20 Madison County Mass Transit District \n", + "21 Mesa County \n", + "22 Minnesota Department of Transportation on beha... \n", + "23 New Mexico Department of Transportation on beh... \n", + "24 North County Transit District (NCTD) \n", + "25 Ohio Department of Transportation (ODOT) on be... \n", + "26 Oregon Department of Transportation on behalf ... \n", + "27 Rhode Island Public Transit Authority \n", + "28 Rockford Mass Transit District \n", + "29 Rogue Valley Transportation District \n", + "30 San Antonio Metropolitan Transit Authority \n", + "31 South Carolina Department of Transportation on... \n", + "32 South Dakota Department of Transportation on b... \n", + "33 South Dakota Department of Transportation on b... \n", + "34 Southeastern Regional Transit Authority \n", + "35 Southwest Ohio Regional Transit Authority \n", + "36 State of California on behalf of Kern Regional... \n", + "37 State of California on behalf of Kern Regional... \n", + "38 Tahoe Transportation District \n", + "39 Texas Department of Transportation on behalf o... \n", + "40 Utah Transit Authority \n", + "41 Whatcom Transportation Authority (WTA) \n", + "42 White Earth Reservation Business Committee \n", + "43 Antelope Valley Transit Authority (AVTA) \n", + "44 City of Los Angeles (LA DOT) \n", + "45 City of Wasco \n", + "46 Culver City \n", + "47 Foothill Transit \n", + "48 Orange County Transportation Authority (OCTA) \n", + "49 Shasta Regional Transportation Agency (SRTA) \n", + "50 Torrance Transit Department \n", + "51 Transit Joint Powers Authority of Merced County \n", + "52 SUNLINE TRANSIT AGENCY (THOUSAND PALMS) \n", + "53 Lane Transit (Oregon) \n", + "54 VICTOR VALLEY TRANSIT AUTHORITY (VVTA) \n", + "55 CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ... \n", + "56 ORANGE COUNTY TRANSPORTATION AUTHORITY (ORANGE... \n", + "57 Alameda County Transit Authority \n", + "58 Santa Barbara Metro \n", + "59 Golden Empire Transit \n", + "60 San Diego Metro \n", + "61 North County Transit District \n", + "62 The Bus, City of Merced \n", + "63 Lane Transit (Oregon) \n", + "64 Foothill Transit, West Covina, CA \n", + "65 UC DAVIS (UNITRANS) (DAVIS, CA) \n", + "66 CITY OF PORTERVILLE (PORTERVILLE, CA) \n", + "67 GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA) \n", + "68 UC DAVIS (UNITRANS) (DAVIS, CA) \n", + "69 VACAVILLE PUBLIC TRANSPORTATION (CITY COACH) (... \n", + "70 SLO TRANSIT (SAN LUIS OBISPO, CA) \n", + "71 UCLA FLEET & TRANSIT \n", + "72 SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA) \n", + "73 City of Roseville \n", + "74 City of Roseville \n", + "75 University of California - San Diego \n", + "76 University of California - San Diego \n", + "77 Sonoma County Transit \n", + "78 City of Santa Rosa(Santa Rosa CityBus) \n", + "79 Santa Rosa City Bus \n", + "80 University of California, Irvine \n", + "81 Santa Maria Regional Transit \n", + "82 City of San Luis Obispo \n", + "83 Transit Joint Powers Authority for Merced County \n", + "84 Transit Joint Powers Authority for Merced County \n", + "85 Napa Valley Transportation Authority \n", + "86 Santa Maria Area Transit \n", + "87 City of Visalia - Visalia City Coach(Visalia T... \n", + "88 Sacramento County Airport System \n", + "\n", + " project_title \\\n", + "0 Puerto Rico Initiative Minimizing Emissions Pl... \n", + "1 Wave Transit Low Emissions Replacement Vehicles \n", + "2 COTPA, dba EMBARK Elimination of Fixed Route D... \n", + "3 MTD 40-Foot Hybrid Replacement Buses \n", + "4 Beaumont Municipal Transit Zips to Improve Low... \n", + "5 Beloit Transit Low-Emission Vehicle Purchase \n", + "6 Brownsville Metro Hybrid-Electric Diesel \n", + "7 Mountain Metropolitan Transit Diesel Replaceme... \n", + "8 JET Low Emission Fleet \n", + "9 Replacement CNG Vehicles for ADA Paratransit S... \n", + "10 A Clean Ride by 2025 \n", + "11 Purchase of Replacement Buses \n", + "12 CCT Commuter Bus Fleet Replacement Project \n", + "13 DART CNG Bus Fleet Modernization Project \n", + "14 Continuation of Delaware Transit Corporations ... \n", + "15 Replacement of Ten (10) 40ft. CNG Buses \n", + "16 Illinois DOT Statewide Paratransit Vehicle Rep... \n", + "17 The purchase of 20 vehicles, which will use th... \n", + "18 A Sustainable Future: Renewable Natural Gas Bu... \n", + "19 Lowell Regional Transit Authority Revenue Vehi... \n", + "20 Heavy Duty 40-Foot Bus Replacement \n", + "21 Grand Valley Transit Bus Purchases \n", + "22 Reducing Emissions in Rural Minnesota Transit \n", + "23 Procurement of Three Replacement Diesel-Electr... \n", + "24 Accelerate Clean Transit (ACT) \n", + "25 Ohio Zero Emission Ready Ohio (OH-ZERO) \n", + "26 CET's Low Emission Vanpools and Support Vehicles \n", + "27 RIPTA Hybrid Bus Upgrade \n", + "28 Hybrid Bus Procurement \n", + "29 RVTD Renewable Resiliency Buses \n", + "30 VIA Metropolitan Transit ViaTrans: Providing e... \n", + "31 SCDOT Vehicle Replacement Project \n", + "32 Replacement of Aberdeen Ride Line fleet buses ... \n", + "33 Low-emission bus alternative fuel project to i... \n", + "34 Southeastern Regional Transit Authority Fixed ... \n", + "35 SORTA Hybrid Transition Bus Replacement Project \n", + "36 Low Emission Transition/Replacement \n", + "37 Purchase of Fifteen (15) Replacement Cutaway B... \n", + "38 Tahoe Transportation District (TTD) Clean Tran... \n", + "39 FY23 Rural Transit Asset Replacement & Moderni... \n", + "40 Utah Transit Authority Compressed Natural Gas ... \n", + "41 Purchase 11 diesel-electric hybrid buses (hybr... \n", + "42 White Earth Public Transit to replace 4 of the... \n", + "43 None \n", + "44 None \n", + "45 None \n", + "46 None \n", + "47 None \n", + "48 None \n", + "49 None \n", + "50 None \n", + "51 None \n", + "52 None \n", + "53 None \n", + "54 None \n", + "55 None \n", + "56 None \n", + "57 None \n", + "58 None \n", + "59 None \n", + "60 None \n", + "61 None \n", + "62 None \n", + "63 None \n", + "64 None \n", + "65 None \n", + "66 None \n", + "67 None \n", + "68 None \n", + "69 None \n", + "70 None \n", + "71 None \n", + "72 None \n", + "73 None \n", + "74 None \n", + "75 None \n", + "76 None \n", + "77 None \n", + "78 None \n", + "79 None \n", + "80 None \n", + "81 None \n", + "82 None \n", + "83 None \n", + "84 None \n", + "85 None \n", + "86 None \n", + "87 None \n", + "88 None \n", + "\n", + " prop_type bus_size_type \\\n", + "0 electric (not specified) not specified \n", + "1 CNG not specified \n", + "2 CNG not specified \n", + "3 low emission (hybrid) not specified \n", + "4 CNG not specified \n", + "5 low emission (hybrid) not specified \n", + "6 low emission (hybrid) not specified \n", + "7 low emission (hybrid) not specified \n", + "8 low emission (hybrid) not specified \n", + "9 CNG not specified \n", + "10 CNG not specified \n", + "11 low emission (propane) not specified \n", + "12 CNG not specified \n", + "13 CNG not specified \n", + "14 FCEB not specified \n", + "15 CNG not specified \n", + "16 not specified cutaway \n", + "17 low emission (hybrid) not specified \n", + "18 CNG not specified \n", + "19 low emission (hybrid) not specified \n", + "20 not specified not specified \n", + "21 CNG cutaway \n", + "22 low emission (propane) not specified \n", + "23 low emission (hybrid) not specified \n", + "24 FCEB not specified \n", + "25 mix (zero and low emission) not specified \n", + "26 low emission (hybrid) not specified \n", + "27 low emission (hybrid) not specified \n", + "28 low emission (hybrid) not specified \n", + "29 low emission (hybrid) not specified \n", + "30 low emission (propane) not specified \n", + "31 not specified not specified \n", + "32 ethanol not specified \n", + "33 low emission (propane) not specified \n", + "34 low emission (hybrid) not specified \n", + "35 low emission (hybrid) not specified \n", + "36 CNG not specified \n", + "37 not specified cutaway \n", + "38 low emission (hybrid) not specified \n", + "39 mix (zero and low emission) not specified \n", + "40 CNG not specified \n", + "41 low emission (hybrid) not specified \n", + "42 low emission (propane) not specified \n", + "43 electric (not specified) articulated \n", + "44 zero-emission bus (not specified) not specified \n", + "45 zero-emission bus (not specified) not specified \n", + "46 zero-emission bus (not specified) not specified \n", + "47 zero-emission bus (not specified) not specified \n", + "48 CNG standard/conventional (30ft-45ft) \n", + "49 not specified over-the-road \n", + "50 electric (not specified) not specified \n", + "51 zero-emission bus (not specified) not specified \n", + "52 FCEB standard/conventional (30ft-45ft) \n", + "53 BEB standard/conventional (30ft-45ft) \n", + "54 BEB standard/conventional (30ft-45ft) \n", + "55 BEB standard/conventional (30ft-45ft) \n", + "56 BEB standard/conventional (30ft-45ft) \n", + "57 FCEB standard/conventional (30ft-45ft) \n", + "58 BEB standard/conventional (30ft-45ft) \n", + "59 FCEB standard/conventional (30ft-45ft) \n", + "60 BEB articulated \n", + "61 BEB standard/conventional (30ft-45ft) \n", + "62 BEB standard/conventional (30ft-45ft) \n", + "63 BEB standard/conventional (30ft-45ft) \n", + "64 FCEB standard/conventional (30ft-45ft) \n", + "65 BEB standard/conventional (30ft-45ft) \n", + "66 BEB standard/conventional (30ft-45ft) \n", + "67 FCEB standard/conventional (30ft-45ft) \n", + "68 BEB standard/conventional (30ft-45ft) \n", + "69 BEB standard/conventional (30ft-45ft) \n", + "70 BEB standard/conventional (30ft-45ft) \n", + "71 BEB standard/conventional (30ft-45ft) \n", + "72 FCEB standard/conventional (30ft-45ft) \n", + "73 BEB standard/conventional (30ft-45ft) \n", + "74 BEB standard/conventional (30ft-45ft) \n", + "75 BEB standard/conventional (30ft-45ft) \n", + "76 BEB standard/conventional (30ft-45ft) \n", + "77 BEB standard/conventional (30ft-45ft) \n", + "78 BEB standard/conventional (30ft-45ft) \n", + "79 BEB standard/conventional (30ft-45ft) \n", + "80 BEB standard/conventional (30ft-45ft) \n", + "81 BEB standard/conventional (30ft-45ft) \n", + "82 BEB standard/conventional (30ft-45ft) \n", + "83 BEB standard/conventional (30ft-45ft) \n", + "84 BEB standard/conventional (30ft-45ft) \n", + "85 BEB standard/conventional (30ft-45ft) \n", + "86 BEB standard/conventional (30ft-45ft) \n", + "87 BEB standard/conventional (30ft-45ft) \n", + "88 BEB standard/conventional (30ft-45ft) \n", + "\n", + " description new_project_type \\\n", + "0 The Metropolitan Bus Authority will receive fu... bus only \n", + "1 Wave Transit will receive funding to buy compr... bus only \n", + "2 The Central Oklahoma Transportation and Parkin... bus only \n", + "3 The Champaign-Urbana Mass Transit District wil... bus only \n", + "4 Beaumont Municipal Transit will receive fundin... bus only \n", + "5 The Beloit Transit System will receive funding... bus only \n", + "6 Brownsville Metro will receive funding to buy ... bus only \n", + "7 The city of Colorado Springs' Mountain Metropo... bus only \n", + "8 The City of Jonesboro will receive funding to ... bus only \n", + "9 The city of Norman will buy compressed natural... bus only \n", + "10 The city of Tucson's Sun Tran transit system w... bus only \n", + "11 Coast Transit Authority will receive funding t... bus only \n", + "12 Conroe Connection Transit will receive funding... bus only \n", + "13 Dallas Area Rapid Transit will receive funding... bus only \n", + "14 Delaware Transit Corporation will receive fund... bus only \n", + "15 Golden Empire Transit will receive funding to ... bus only \n", + "16 The Illinois Department of Transportation will... bus only \n", + "17 The Indianapolis Public Transportation Corpora... bus only \n", + "18 The Interurban Transit Partnership (The Rapid)... bus only \n", + "19 The Lowell Regional Transit Authority will rec... bus only \n", + "20 The Madison County Mass Transit District will ... bus only \n", + "21 Mesa County's Grand Valley Transit will receiv... bus only \n", + "22 The Minnesota Department of Transportation, on... bus only \n", + "23 The North Central Regional Transit District wi... bus only \n", + "24 The North County Transit District will receive... bus only \n", + "25 The Ohio Department of Transportation (ODOT) w... bus only \n", + "26 The Oregon Department of Transportation on beh... bus only \n", + "27 The Rhode Island Public Transit Authority will... bus only \n", + "28 The Rockford Mass Transit District will receiv... bus only \n", + "29 The Rogue Valley Transportation District will ... bus only \n", + "30 The San Antonio Metropolitan Transit Authority... bus only \n", + "31 The South Carolina Department of Transportatio... bus only \n", + "32 The South Dakota Department of Transportation,... bus only \n", + "33 The South Dakota Department of Transportation ... bus only \n", + "34 The Southeastern Regional Transit Authority wi... bus only \n", + "35 The Southwest Ohio Regional Transit Authority ... bus only \n", + "36 The State of California, on behalf of Kern Reg... bus only \n", + "37 The State of California, on behalf of Kern Reg... bus only \n", + "38 The Tahoe Transportation District will receive... bus only \n", + "39 The Texas Department of Transportation will re... bus only \n", + "40 The Utah Transit Authority will receive fundin... bus only \n", + "41 The Whatcom Transportation Authority will rece... bus only \n", + "42 White Earth Public Transit will receive fundin... bus only \n", + "43 None bus only \n", + "44 None bus only \n", + "45 None bus only \n", + "46 None bus only \n", + "47 None bus only \n", + "48 None bus only \n", + "49 None bus only \n", + "50 None bus only \n", + "51 None bus only \n", + "52 None None \n", + "53 None None \n", + "54 None None \n", + "55 None None \n", + "56 None None \n", + "57 None None \n", + "58 None None \n", + "59 None None \n", + "60 None None \n", + "61 None None \n", + "62 None None \n", + "63 None None \n", + "64 None None \n", + "65 None None \n", + "66 None None \n", + "67 None None \n", + "68 None None \n", + "69 None None \n", + "70 None None \n", + "71 None None \n", + "72 None None \n", + "73 None None \n", + "74 None None \n", + "75 None None \n", + "76 None None \n", + "77 None None \n", + "78 None None \n", + "79 None None \n", + "80 None None \n", + "81 None None \n", + "82 None None \n", + "83 None None \n", + "84 None None \n", + "85 None None \n", + "86 None None \n", + "87 None None \n", + "88 None None \n", + "\n", + " total_cost bus_count source ppno \\\n", + "0 10000000 8.0 fta None \n", + "1 2860250 5.0 fta None \n", + "2 4278772 9.0 fta None \n", + "3 6635394 10.0 fta None \n", + "4 2819460 5.0 fta None \n", + "5 653184 1.0 fta None \n", + "6 4738886 6.0 fta None \n", + "7 3199038 6.0 fta None \n", + "8 1010372 5.0 fta None \n", + "9 776714 6.0 fta None \n", + "10 21490560 39.0 fta None \n", + "11 1760000 9.0 fta None \n", + "12 4500000 4.0 fta None \n", + "13 103000000 90.0 fta None \n", + "14 8740728 6.0 fta None \n", + "15 5750351 10.0 fta None \n", + "16 12600000 134.0 fta None \n", + "17 19040336 20.0 fta None \n", + "18 6197180 11.0 fta None \n", + "19 6859296 7.0 fta None \n", + "20 1080000 2.0 fta None \n", + "21 1162000 3.0 fta None \n", + "22 1456970 7.0 fta None \n", + "23 2063160 3.0 fta None \n", + "24 29330243 23.0 fta None \n", + "25 29331665 69.0 fta None \n", + "26 181250 5.0 fta None \n", + "27 5000000 25.0 fta None \n", + "28 4094652 4.0 fta None \n", + "29 3937500 6.0 fta None \n", + "30 3187200 15.0 fta None \n", + "31 15423904 160.0 fta None \n", + "32 1006750 9.0 fta None \n", + "33 1276628 9.0 fta None \n", + "34 11560000 16.0 fta None \n", + "35 9806428 16.0 fta None \n", + "36 3248500 5.0 fta None \n", + "37 2932500 15.0 fta None \n", + "38 3400000 4.0 fta None \n", + "39 7443765 56.0 fta None \n", + "40 17055353 25.0 fta None \n", + "41 9644865 11.0 fta None \n", + "42 723171 4.0 fta None \n", + "43 39478000 29.0 tircp CP005 \n", + "44 102790000 112.0 tircp CP029 \n", + "45 1543000 3.0 tircp CP090 \n", + "46 3547000 5.0 tircp CP114 \n", + "47 16580000 20.0 tircp CP076 \n", + "48 2900000 40.0 tircp CP004 \n", + "49 9516000 14.0 tircp CP045 \n", + "50 7200000 7.0 tircp CP073 \n", + "51 3696513 3.0 tircp CP074 \n", + "52 5755155 5.0 dgs 11819 \n", + "53 9921626 11.0 dgs 2020-061 \n", + "54 4508160 5.0 dgs 1416 \n", + "55 3623536 4.0 dgs 22100367 - 00 \n", + "56 9319520 10.0 dgs CO2165 \n", + "57 22846640 20.0 dgs 57071 \n", + "58 3659072 4.0 dgs 10609 \n", + "59 5458305 5.0 dgs 10076963-000 \n", + "60 18759576 12.0 dgs 4500040166 \n", + "61 5787606 6.0 dgs 347750000P \n", + "62 4786285 5.0 dgs EBUS001 \n", + "63 17973373 19.0 dgs A-21587 \n", + "64 37642044 33.0 dgs 21-077 \n", + "65 5559486 6.0 dgs ASTR876345 \n", + "66 2781891 3.0 dgs 20-18895 \n", + "67 5406355 5.0 dgs 10076963-000 \n", + "68 3762440 4.0 dgs UC DAVIS (UNITRANS) (DAVIS, CA) \n", + "69 10175590 10.0 dgs 2201132 \n", + "70 847214 1.0 dgs 609571 \n", + "71 2008826 2.0 dgs ZC654 / ZC653 \n", + "72 5771865 5.0 dgs 120706 \n", + "73 4452892 5.0 dgs 9009418 \n", + "74 5198615 5.0 dgs 9011071 \n", + "75 4134000 4.0 dgs PUR00318127 \n", + "76 4134000 2.0 dgs PUR00318127 \n", + "77 8990000 10.0 dgs SC001-2300002779 \n", + "78 5987790 5.0 dgs City of Santa Rosa(Santa Rosa CityBus) \n", + "79 4068202 4.0 dgs PA-2021-001-SRCB \n", + "80 4932930 5.0 dgs University of California, Irvine \n", + "81 5188379 5.0 dgs 63759 \n", + "82 859270 1.0 dgs 609570 \n", + "83 3223324 2.0 dgs EBUS002 \n", + "84 3223324 1.0 dgs EBUS002 \n", + "85 2396600 2.0 dgs 21-2002 \n", + "86 1862258 2.0 dgs 57614 \n", + "87 3687803 4.0 dgs P02771 \n", + "88 4642225 5.0 dgs PA81335877 \n", + "\n", + " project_description \n", + "0 None \n", + "1 None \n", + "2 None \n", + "3 None \n", + "4 None \n", + "5 None \n", + "6 None \n", + "7 None \n", + "8 None \n", + "9 None \n", + "10 None \n", + "11 None \n", + "12 None \n", + "13 None \n", + "14 None \n", + "15 None \n", + "16 None \n", + "17 None \n", + "18 None \n", + "19 None \n", + "20 None \n", + "21 None \n", + "22 None \n", + "23 None \n", + "24 None \n", + "25 None \n", + "26 None \n", + "27 None \n", + "28 None \n", + "29 None \n", + "30 None \n", + "31 None \n", + "32 None \n", + "33 None \n", + "34 None \n", + "35 None \n", + "36 None \n", + "37 None \n", + "38 None \n", + "39 None \n", + "40 None \n", + "41 None \n", + "42 None \n", + "43 Purchase 13 60-foot articulated BRT buses and ... \n", + "44 Acquire 112 zero-emission buses to replace exi... \n", + "45 Purchase of 3 zero-emission buses that will su... \n", + "46 The Project implements a new transit service u... \n", + "47 Purchase 20 zero-emission buses to extend Rout... \n", + "48 Purchase five 40-foot CNG buses for BRT Route ... \n", + "49 Purchase 7 new coach-style buses to support a ... \n", + "50 Purchase 7 electric buses to expand services o... \n", + "51 Purchases 3 zero-emission electric buses to in... \n", + "52 None \n", + "53 None \n", + "54 None \n", + "55 None \n", + "56 None \n", + "57 None \n", + "58 None \n", + "59 None \n", + "60 None \n", + "61 None \n", + "62 None \n", + "63 None \n", + "64 None \n", + "65 None \n", + "66 None \n", + "67 None \n", + "68 None \n", + "69 None \n", + "70 None \n", + "71 None \n", + "72 None \n", + "73 None \n", + "74 None \n", + "75 None \n", + "76 None \n", + "77 None \n", + "78 None \n", + "79 None \n", + "80 None \n", + "81 None \n", + "82 None \n", + "83 None \n", + "84 None \n", + "85 None \n", + "86 None \n", + "87 None \n", + "88 None " + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged_data" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "ece95fb7-cbb8-46bd-a5f9-2b68a47a4817", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['transit_agency', 'total_project_count', 'total_project_count_ppno',\n", + " 'total_agg_cost', 'total_bus_count', 'cpb', 'zscore_cost_per_bus',\n", + " 'is_cpb_outlier?'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(82, 8)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "81" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "max 1563298\n", + "min 36250\n", + "Name: cpb, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "max 160.0\n", + "min 1.0\n", + "Name: total_bus_count, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "max 103000000\n", + "min 181250\n", + "Name: total_agg_cost, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpbzscore_cost_per_busis_cpb_outlier?
35Madison County Mass Transit DistrictFalse
36Mesa County1011620003.0387333-0.990320False
37Minnesota Department of Transportation on beha...1014569707.0208138-1.437633False
38Napa Valley Transportation Authority0123966002.011983001.034045False
39New Mexico Department of Transportation on beh...1020631603.0687720-0.240483False
40North County Transit District0157876066.09646010.450677False
41North County Transit District (NCTD)102933024323.012752271.226073False
42ORANGE COUNTY TRANSPORTATION AUTHORITY (ORANGE...01931952010.09319520.369178False
43Ohio Department of Transportation (ODOT) on be...102933166569.0425096-0.896054False
44Orange County Transportation Authority (OCTA)01290000040.072500-1.776217False
45Oregon Department of Transportation on behalf ...101812505.036250-1.866706False
46Rhode Island Public Transit Authority77Utah Transit Authority1050000001705535325.0200000-1.457947False
47Rockford Mass Transit District1040946524.010236630.598110False
48Rogue Valley Transportation District1039375006.0656250-0.319040False
49SLO TRANSIT (SAN LUIS OBISPO, CA)018472141.08472140.157652False
50SUNLINE TRANSIT AGENCY (THOUSAND PALMS)0157551555.011510310.916051682214-0.254227False
0.924393False
52Sacramento County Airport System0146422255.09284450.360423False
53San Antonio Metropolitan Transit Authority10318720015.0212480-1.426794False
54San Diego Metro011875957612.015632981.945166False
55Santa Barbara Metro0136590724.09147680.326282False
56Santa Maria Area Transit0118622582.09311290.367123False
57Santa Maria Regional Transit0151883795.010376750.633087False
\n", + "
" + ], + "text/plain": [ + " transit_agency total_project_count \\\n", + "35 Madison County Mass Transit District 1 \n", + "77 Utah Transit Authority 1 \n", + "51 SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA) 0 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", + "35 0 1080000 2.0 540000 \n", + "77 0 17055353 25.0 682214 \n", + "51 1 5771865 5.0 1154373 \n", + "\n", + " zscore_cost_per_bus is_cpb_outlier? \n", + "35 -0.609227 False \n", + "77 -0.254227 False \n", + "51 0.924393 False " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# no outliers\n", + "# use new_cpb_aggregaete on merged data then filter for outliers == False\n", + "agg_agency = new_cpb_aggregate(merged_data)\n", + "agg_agency_no_outliers = agg_agency[agg_agency[\"is_cpb_outlier?\"] == False]\n", + "agg_prop = new_cpb_aggregate(merged_data, column=\"prop_type\")\n", + "agg_bus_size = new_cpb_aggregate(merged_data, column=\"bus_size_type\")\n", + "agg_source = new_cpb_aggregate(merged_data, column=\"source\")\n", + "\n", + "#overall agency info\n", + "display(\n", + " agg_agency.columns,\n", + " agg_agency.shape,\n", + " #removed outliers\n", + " len(agg_agency[agg_agency[\"is_cpb_outlier?\"] == False]),\n", + " #min max, without outlier\n", + " agg_agency_no_outliers[\"cpb\"].agg([\"max\",\"min\"]),\n", + " agg_agency_no_outliers[\"total_bus_count\"].agg([\"max\",\"min\"]),\n", + " agg_agency_no_outliers[\"total_agg_cost\"].agg([\"max\",\"min\"]),\n", + " agg_agency.sample(3)\n", + " \n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "1696d78f-7018-417b-9847-d82edac3acdf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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prop_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpbzscore_cost_per_busis_cpb_outlier?
58Santa Rosa City Bus0BEB0140682024.010170500.58160231170455813164.010393640.923505False
59Shasta Regional Transportation Agency (SRTA)01CNG121951600014.0679714-0.260468176039140252.06985680.122141False
60Sonoma County Transit01899000010.08990000.2869222FCEB26120951335102.011857971.267835False
61South Carolina Department of Transportation on...3electric (not specified)1015423904160.096399-1.71656025667800044.012881361.508480False
62South Dakota Department of Transportation on b...4ethanol1010067509.0111861-1.677963False
63South Dakota Department of Transportation on b...1012766289.0141847-1.603111-1.257470False
64Southeastern Regional Transit Authority15low emission (hybrid)1601156000016.0722500-0.15366491824361145.0633271-0.031401False
65Southwest Ohio Regional Transit Authority16low emission (propane)50980642816.0612901-0.427249840396944.0190999-1.071381False
66State of California on behalf of Kern Regional...7mix (zero and low emission)20618100020.0309050-1.18573336775430125.0294203-0.828702False
67Tahoe Transportation District8not specified41034000004.08500000.16460641552404325.0127853-1.219866False
68Texas Department of Transportation on behalf o...19zero-emission bus (not specified)0744376556.0132924-1.6253855128156513143.08961990.586860False
69The Bus, City of Merced0147862855.09572570.432345False
\n", + "
" + ], + "text/plain": [ + " prop_type total_project_count \\\n", + "0 BEB 0 \n", + "1 CNG 12 \n", + "2 FCEB 2 \n", + "3 electric (not specified) 1 \n", + "4 ethanol 1 \n", + "5 low emission (hybrid) 16 \n", + "6 low emission (propane) 5 \n", + "7 mix (zero and low emission) 2 \n", + "8 not specified 4 \n", + "9 zero-emission bus (not specified) 0 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", + "0 31 170455813 164.0 1039364 \n", + "1 1 176039140 252.0 698568 \n", + "2 6 120951335 102.0 1185797 \n", + "3 2 56678000 44.0 1288136 \n", + "4 0 1006750 9.0 111861 \n", + "5 0 91824361 145.0 633271 \n", + "6 0 8403969 44.0 190999 \n", + "7 0 36775430 125.0 294203 \n", + "8 1 41552404 325.0 127853 \n", + "9 5 128156513 143.0 896199 \n", + "\n", + " zscore_cost_per_bus is_cpb_outlier? \n", + "0 0.923505 False \n", + "1 0.122141 False \n", + "2 1.267835 False \n", + "3 1.508480 False \n", + "4 -1.257470 False \n", + "5 -0.031401 False \n", + "6 -1.071381 False \n", + "7 -0.828702 False \n", + "8 -1.219866 False \n", + "9 0.586860 False " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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bus_counttotal_cost
70Torrance Transit Department0172000007.010285710.610361Falseprop_type
71Transit Joint Powers Authority for Merced County0264466483.021488823.406922TrueBEB164.0170455813
72Transit Joint Powers Authority of Merced County0136965133.012321711.118595FalseCNG252.0176039140
73UC DAVIS (UNITRANS) (DAVIS, CA)02932192610.09321920.369777FalseFCEB102.0120951335
74UCLA FLEET & TRANSIT0120088262.010044130.550057Falseelectric (not specified)44.056678000
75University of California - San Diego0282680006.013780001.482619Falseethanol9.01006750
76University of California, Irvine0149329305.09865860.505557Falselow emission (hybrid)145.091824361
77Utah Transit Authority101705535325.0682214-0.254227Falselow emission (propane)44.08403969
78VACAVILLE PUBLIC TRANSPORTATION (CITY COACH) (...011017559010.010175590.582873Falsemix (zero and low emission)125.036775430
79VICTOR VALLEY TRANSIT AUTHORITY (VVTA)0145081605.09016320.293492Falsenot specified325.041552404
80Whatcom Transportation Authority (WTA)10964486511.08768050.231518Falsezero-emission bus (not specified)143.0128156513
81White Earth Reservation Business Committee107231714.0180792-1.505895FalseGrand Total1353.0831843715
\n", "
" ], "text/plain": [ - " transit_agency total_project_count \\\n", - "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) 1 \n", - "1 Alameda County Transit Authority 0 \n", - "2 Antelope Valley Transit Authority (AVTA) 0 \n", - "3 CITY OF PORTERVILLE (PORTERVILLE, CA) 0 \n", - "4 CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ... 0 \n", - "5 Cape Fear Public Transportation Authority 1 \n", - "6 Central Oklahoma Transportation and Parking Au... 1 \n", - "7 Champaign-Urbana Mass Transit District 1 \n", - "8 City of Beaumont 1 \n", - "9 City of Beloit 1 \n", - "10 City of Brownsville 1 \n", - "11 City of Colorado Springs dba Mountain Metropol... 1 \n", - "12 City of Jonesboro, Arkansas 1 \n", - "13 City of Los Angeles (LA DOT) 0 \n", - "14 City of Norman, Oklahoma 1 \n", - "15 City of Roseville 0 \n", - "16 City of San Luis Obispo 0 \n", - "17 City of Santa Rosa(Santa Rosa CityBus) 0 \n", - "18 City of Tucson, Sun Tran 1 \n", - "19 City of Visalia - Visalia City Coach(Visalia T... 0 \n", - "20 City of Wasco 0 \n", - "21 Coast Transit Authority dba MS Coast Transport... 1 \n", - "22 Conroe Connection Transit 1 \n", - "23 Culver City 0 \n", - "24 Dallas Area Rapid Transit (DART) 1 \n", - "25 Delaware Transit Corporation (DTC) 1 \n", - "26 Foothill Transit 0 \n", - "27 Foothill Transit, West Covina, CA 0 \n", - "28 GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA) 0 \n", - "29 Golden Empire Transit 1 \n", - "30 Illinois Department of Transportation on behal... 1 \n", - "31 Indianapolis Public Transportation Corporation... 1 \n", - "32 Interurban Transit Partnership 1 \n", - "33 Lane Transit (Oregon) 0 \n", - "34 Lowell Regional Transit Authority 1 \n", - "35 Madison County Mass Transit District 1 \n", - "36 Mesa County 1 \n", - "37 Minnesota Department of Transportation on beha... 1 \n", - "38 Napa Valley Transportation Authority 0 \n", - "39 New Mexico Department of Transportation on beh... 1 \n", - "40 North County Transit District 0 \n", - "41 North County Transit District (NCTD) 1 \n", - "42 ORANGE COUNTY TRANSPORTATION AUTHORITY (ORANGE... 0 \n", - "43 Ohio Department of Transportation (ODOT) on be... 1 \n", - "44 Orange County Transportation Authority (OCTA) 0 \n", - "45 Oregon Department of Transportation on behalf ... 1 \n", - "46 Rhode Island Public Transit Authority 1 \n", - "47 Rockford Mass Transit District 1 \n", - "48 Rogue Valley Transportation District 1 \n", - "49 SLO TRANSIT (SAN LUIS OBISPO, CA) 0 \n", - "50 SUNLINE TRANSIT AGENCY (THOUSAND PALMS) 0 \n", - "51 SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA) 0 \n", - "52 Sacramento County Airport System 0 \n", - "53 San Antonio Metropolitan Transit Authority 1 \n", - "54 San Diego Metro 0 \n", - "55 Santa Barbara Metro 0 \n", - "56 Santa Maria Area Transit 0 \n", - "57 Santa Maria Regional Transit 0 \n", - "58 Santa Rosa City Bus 0 \n", - "59 Shasta Regional Transportation Agency (SRTA) 0 \n", - "60 Sonoma County Transit 0 \n", - "61 South Carolina Department of Transportation on... 1 \n", - "62 South Dakota Department of Transportation on b... 1 \n", - "63 South Dakota Department of Transportation on b... 1 \n", - "64 Southeastern Regional Transit Authority 1 \n", - "65 Southwest Ohio Regional Transit Authority 1 \n", - "66 State of California on behalf of Kern Regional... 2 \n", - "67 Tahoe Transportation District 1 \n", - "68 Texas Department of Transportation on behalf o... 1 \n", - "69 The Bus, City of Merced 0 \n", - "70 Torrance Transit Department 0 \n", - "71 Transit Joint Powers Authority for Merced County 0 \n", - "72 Transit Joint Powers Authority of Merced County 0 \n", - "73 UC DAVIS (UNITRANS) (DAVIS, CA) 0 \n", - "74 UCLA FLEET & TRANSIT 0 \n", - "75 University of California - San Diego 0 \n", - "76 University of California, Irvine 0 \n", - "77 Utah Transit Authority 1 \n", - "78 VACAVILLE PUBLIC TRANSPORTATION (CITY COACH) (... 0 \n", - "79 VICTOR VALLEY TRANSIT AUTHORITY (VVTA) 0 \n", - "80 Whatcom Transportation Authority (WTA) 1 \n", - "81 White Earth Reservation Business Committee 1 \n", - "\n", - " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", - "0 0 10000000 8.0 1250000 \n", - "1 1 22846640 20.0 1142332 \n", - "2 1 39478000 29.0 1361310 \n", - "3 1 2781891 3.0 927297 \n", - "4 1 3623536 4.0 905884 \n", - "5 0 2860250 5.0 572050 \n", - "6 0 4278772 9.0 475419 \n", - "7 0 6635394 10.0 663539 \n", - "8 0 2819460 5.0 563892 \n", - "9 0 653184 1.0 653184 \n", - "10 0 4738886 6.0 789814 \n", - "11 0 3199038 6.0 533173 \n", - "12 0 1010372 5.0 202074 \n", - "13 1 102790000 112.0 917767 \n", - "14 0 776714 6.0 129452 \n", - "15 2 9651507 10.0 965150 \n", - "16 1 859270 1.0 859270 \n", - "17 1 5987790 5.0 1197558 \n", - "18 0 21490560 39.0 551040 \n", - "19 1 3687803 4.0 921950 \n", - "20 1 1543000 3.0 514333 \n", - "21 0 1760000 9.0 195555 \n", - "22 0 4500000 4.0 1125000 \n", - "23 1 3547000 5.0 709400 \n", - "24 0 103000000 90.0 1144444 \n", - "25 0 8740728 6.0 1456788 \n", - "26 1 16580000 20.0 829000 \n", - "27 1 37642044 33.0 1140668 \n", - "28 1 5406355 5.0 1081271 \n", - "29 1 11208656 15.0 747243 \n", - "30 0 12600000 134.0 94029 \n", - "31 0 19040336 20.0 952016 \n", - "32 0 6197180 11.0 563380 \n", - "33 2 27894999 30.0 929833 \n", - "34 0 6859296 7.0 979899 \n", - "35 0 1080000 2.0 540000 \n", - "36 0 1162000 3.0 387333 \n", - "37 0 1456970 7.0 208138 \n", - "38 1 2396600 2.0 1198300 \n", - "39 0 2063160 3.0 687720 \n", - "40 1 5787606 6.0 964601 \n", - "41 0 29330243 23.0 1275227 \n", - "42 1 9319520 10.0 931952 \n", - "43 0 29331665 69.0 425096 \n", - "44 1 2900000 40.0 72500 \n", - "45 0 181250 5.0 36250 \n", - "46 0 5000000 25.0 200000 \n", - "47 0 4094652 4.0 1023663 \n", - "48 0 3937500 6.0 656250 \n", - "49 1 847214 1.0 847214 \n", - "50 1 5755155 5.0 1151031 \n", - "51 1 5771865 5.0 1154373 \n", - "52 1 4642225 5.0 928445 \n", - "53 0 3187200 15.0 212480 \n", - "54 1 18759576 12.0 1563298 \n", - "55 1 3659072 4.0 914768 \n", - "56 1 1862258 2.0 931129 \n", - "57 1 5188379 5.0 1037675 \n", - "58 1 4068202 4.0 1017050 \n", - "59 1 9516000 14.0 679714 \n", - "60 1 8990000 10.0 899000 \n", - "61 0 15423904 160.0 96399 \n", - "62 0 1006750 9.0 111861 \n", - "63 0 1276628 9.0 141847 \n", - "64 0 11560000 16.0 722500 \n", - "65 0 9806428 16.0 612901 \n", - "66 0 6181000 20.0 309050 \n", - "67 0 3400000 4.0 850000 \n", - "68 0 7443765 56.0 132924 \n", - "69 1 4786285 5.0 957257 \n", - "70 1 7200000 7.0 1028571 \n", - "71 2 6446648 3.0 2148882 \n", - "72 1 3696513 3.0 1232171 \n", - "73 2 9321926 10.0 932192 \n", - "74 1 2008826 2.0 1004413 \n", - "75 2 8268000 6.0 1378000 \n", - "76 1 4932930 5.0 986586 \n", - "77 0 17055353 25.0 682214 \n", - "78 1 10175590 10.0 1017559 \n", - "79 1 4508160 5.0 901632 \n", - "80 0 9644865 11.0 876805 \n", - "81 0 723171 4.0 180792 \n", - "\n", - " zscore_cost_per_bus is_cpb_outlier? \n", - "0 1.163100 False \n", - "1 0.894336 False \n", - "2 1.440957 False \n", - "3 0.357558 False \n", - "4 0.304106 False \n", - "5 -0.529223 False \n", - "6 -0.770436 False \n", - "7 -0.300844 False \n", - "8 -0.549587 False \n", - "9 -0.326693 False \n", - "10 0.014368 False \n", - "11 -0.626269 False \n", - "12 -1.452770 False \n", - "13 0.333769 False \n", - "14 -1.634052 False \n", - "15 0.452048 False \n", - "16 0.187746 False \n", - "17 1.032193 False \n", - "18 -0.581669 False \n", - "19 0.344210 False \n", - "20 -0.673298 False \n", - "21 -1.469043 False \n", - "22 0.851071 False \n", - "23 -0.186365 False \n", - "24 0.899608 False \n", - "25 1.679292 False \n", - "26 0.112185 False \n", - "27 0.890182 False \n", - "28 0.741913 False \n", - "29 -0.091900 False \n", - "30 -1.722476 False \n", - "31 0.419262 False \n", - "32 -0.550865 False \n", - "33 0.363888 False \n", - "34 0.488865 False \n", - "35 -0.609227 False \n", - "36 -0.990320 False \n", - "37 -1.437633 False \n", - "38 1.034045 False \n", - "39 -0.240483 False \n", - "40 0.450677 False \n", - "41 1.226073 False \n", - "42 0.369178 False \n", - "43 -0.896054 False \n", - "44 -1.776217 False \n", - "45 -1.866706 False \n", - "46 -1.457947 False \n", - "47 0.598110 False \n", - "48 -0.319040 False \n", - "49 0.157652 False \n", - "50 0.916051 False \n", - "51 0.924393 False \n", - "52 0.360423 False \n", - "53 -1.426794 False \n", - "54 1.945166 False \n", - "55 0.326282 False \n", - "56 0.367123 False \n", - "57 0.633087 False \n", - "58 0.581602 False \n", - "59 -0.260468 False \n", - "60 0.286922 False \n", - "61 -1.716560 False \n", - "62 -1.677963 False \n", - "63 -1.603111 False \n", - "64 -0.153664 False \n", - "65 -0.427249 False \n", - "66 -1.185733 False \n", - "67 0.164606 False \n", - "68 -1.625385 False \n", - "69 0.432345 False \n", - "70 0.610361 False \n", - "71 3.406922 True \n", - "72 1.118595 False \n", - "73 0.369777 False \n", - "74 0.550057 False \n", - "75 1.482619 False \n", - "76 0.505557 False \n", - "77 -0.254227 False \n", - "78 0.582873 False \n", - "79 0.293492 False \n", - "80 0.231518 False \n", - "81 -1.505895 False " + " bus_count total_cost\n", + "prop_type \n", + "BEB 164.0 170455813\n", + "CNG 252.0 176039140\n", + "FCEB 102.0 120951335\n", + "electric (not specified) 44.0 56678000\n", + "ethanol 9.0 1006750\n", + "low emission (hybrid) 145.0 91824361\n", + "low emission (propane) 44.0 8403969\n", + "mix (zero and low emission) 125.0 36775430\n", + "not specified 325.0 41552404\n", + "zero-emission bus (not specified) 143.0 128156513\n", + "Grand Total 1353.0 831843715" ] }, "metadata": {}, @@ -4378,43 +4186,61 @@ } ], "source": [ - "#EVERYTHING CHECKS OUT!\n", - "# move forward with `new_cpb_aggregate` function\n", - "new_agg_agency = new_cpb_aggregate(test)\n", - "old_agency_agg = cpb_aggregate(no_outliers, \"transit_agency\")\n", + "# testing pivot table on `merged_data`\n", + "\n", + "#pivot table to get totals for each prop type\n", + "pivot_prop_type = pd.pivot_table(\n", + " merged_data,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"prop_type\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ")\n", "display(\n", - " old_agency_agg.shape,\n", - " new_agg_agency.shape,\n", - " old_agency_agg,\n", - " new_agg_agency\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "3428875a-6a64-41bc-8f9c-81902006d7f0", - "metadata": {}, - "source": [ - "## Testing variables rework\n", - "there are a lot of variables in the initial script. need to cut down the amount of variables or at least make it more efficient. " + " #from new_cpb_agg\n", + " agg_prop,\n", + " #piot\n", + " pivot_prop_type\n", + ")\n", + "# same data, might not need the pivot table anymore" ] }, { "cell_type": "code", - "execution_count": 47, - "id": "f21298ee-0efb-4f91-ba63-55fc2645a4d2", + "execution_count": 95, + "id": "ca709e43-4947-4a34-970f-216d4b6ab7cc", "metadata": {}, "outputs": [], "source": [ - "# read in all cleaned project data\n", - "# same as final from above\n", - "all_projects = pd.read_parquet(f'{GCS_PATH}old/cpb_analysis_data_merge.parquet')" + "#pivot table to get grand total for zeb only data\n", + "#keep this\n", + "pivot_zeb_prop = pd.pivot_table(\n", + " #filted incoming DF for zeb prop types\n", + " merged_data[merged_data[\"prop_type\"].isin(zeb_list)],\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"prop_type\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ") \n", + "\n", + "#keep this\n", + "pivot_non_zeb_prop = pd.pivot_table(\n", + " #filted incoming DF for non-zeb prop types\n", + " merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)],\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"prop_type\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ")" ] }, { "cell_type": "code", - "execution_count": 115, - "id": "cf9e6d8e-6987-470a-9f4f-5294dbe259d1", + "execution_count": 97, + "id": "01647d83-8b4f-47a9-ab57-a1db7cd501dd", "metadata": {}, "outputs": [ { @@ -4438,118 +4264,155 @@ " \n", " \n", " \n", - " transit_agency\n", - " project_title\n", - " prop_type\n", - " bus_size_type\n", - " description\n", - " new_project_type\n", - " total_cost\n", + " prop_type\n", + " total_project_count\n", + " total_project_count_ppno\n", + " total_agg_cost\n", + " total_bus_count\n", + " cpb\n", + " zscore_cost_per_bus\n", + " is_cpb_outlier?\n", + " \n", + " \n", + " \n", + " \n", + " 0\n", + " BEB\n", + " 0\n", + " 31\n", + " 170455813\n", + " 164.0\n", + " 1039364\n", + " 0.923505\n", + " False\n", + " \n", + " \n", + " 2\n", + " FCEB\n", + " 2\n", + " 6\n", + " 120951335\n", + " 102.0\n", + " 1185797\n", + " 1.267835\n", + " False\n", + " \n", + " \n", + " 3\n", + " electric (not specified)\n", + " 1\n", + " 2\n", + " 56678000\n", + " 44.0\n", + " 1288136\n", + " 1.508480\n", + " False\n", + " \n", + " \n", + " 9\n", + " zero-emission bus (not specified)\n", + " 0\n", + " 5\n", + " 128156513\n", + " 143.0\n", + " 896199\n", + " 0.586860\n", + " False\n", + " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " prop_type total_project_count \\\n", + "0 BEB 0 \n", + "2 FCEB 2 \n", + "3 electric (not specified) 1 \n", + "9 zero-emission bus (not specified) 0 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", + "0 31 170455813 164.0 1039364 \n", + "2 6 120951335 102.0 1185797 \n", + "3 2 56678000 44.0 1288136 \n", + "9 5 128156513 143.0 896199 \n", + "\n", + " zscore_cost_per_bus is_cpb_outlier? \n", + "0 0.923505 False \n", + "2 1.267835 False \n", + "3 1.508480 False \n", + "9 0.586860 False " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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bus_countsourceppnoproject_descriptiontotal_cost
prop_type
65UC DAVIS (UNITRANS) (DAVIS, CA)NoneBEBstandard/conventional (30ft-45ft)NoneNone55594866.0dgsASTR876345NoneBEB164.0170455813
68UC DAVIS (UNITRANS) (DAVIS, CA)NoneBEBstandard/conventional (30ft-45ft)NoneNone37624404.0dgsUC DAVIS (UNITRANS) (DAVIS, CA)NoneFCEB102.0120951335
electric (not specified)44.056678000
zero-emission bus (not specified)143.0128156513
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" ], "text/plain": [ - " transit_agency project_title prop_type \\\n", - "65 UC DAVIS (UNITRANS) (DAVIS, CA) None BEB \n", - "68 UC DAVIS (UNITRANS) (DAVIS, CA) None BEB \n", - "\n", - " bus_size_type description new_project_type \\\n", - "65 standard/conventional (30ft-45ft) None None \n", - "68 standard/conventional (30ft-45ft) None None \n", - "\n", - " total_cost bus_count source ppno \\\n", - "65 5559486 6.0 dgs ASTR876345 \n", - "68 3762440 4.0 dgs UC DAVIS (UNITRANS) (DAVIS, CA) \n", - "\n", - " project_description \n", - "65 None \n", - "68 None " - ] - }, - "execution_count": 115, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "all_projects[all_projects[\"transit_agency\"].str.contains(\"UC DAVIS\")]" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "id": "1696d78f-7018-417b-9847-d82edac3acdf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['bus_count', 'total_cost'], dtype='object')" + " bus_count total_cost\n", + "prop_type \n", + "BEB 164.0 170455813\n", + "FCEB 102.0 120951335\n", + "electric (not specified) 44.0 56678000\n", + "zero-emission bus (not specified) 143.0 128156513" ] }, - "execution_count": 124, "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# testing pivot table on `all_projects`\n", - "\n", - "#pivot table to get totals for each prop type\n", - "pivot_prop_type = pd.pivot_table(\n", - " all_projects,\n", - " values = [\"bus_count\", \"total_cost\"],\n", - " index = \"prop_type\",\n", - " aggfunc = \"sum\",\n", - " margins = True,\n", - " margins_name = \"Grand Total\"\n", - ")\n", - "\n", - "pivot_prop_type.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "id": "4371ce4f-a3dd-40df-9835-ef15f8ad19e1", - "metadata": {}, - "outputs": [ + "output_type": "display_data" + }, { "data": { "text/html": [ @@ -4587,11 +4450,6 @@ " 170455813\n", " \n", " \n", - " CNG\n", - " 252.0\n", - " 176039140\n", - " \n", - " \n", " FCEB\n", " 102.0\n", " 120951335\n", @@ -4602,39 +4460,14 @@ " 56678000\n", " \n", " \n", - " ethanol\n", - " 9.0\n", - " 1006750\n", - " \n", - " \n", - " low emission (hybrid)\n", - " 145.0\n", - " 91824361\n", - " \n", - " \n", - " low emission (propane)\n", - " 44.0\n", - " 8403969\n", - " \n", - " \n", - " mix (zero and low emission)\n", - " 125.0\n", - " 36775430\n", - " \n", - " \n", - " not specified\n", - " 325.0\n", - " 41552404\n", - " \n", - " \n", " zero-emission bus (not specified)\n", " 143.0\n", " 128156513\n", " \n", " \n", " Grand Total\n", - " 1353.0\n", - " 831843715\n", + " 453.0\n", + " 476241661\n", " \n", " \n", "\n", @@ -4644,33 +4477,132 @@ " bus_count total_cost\n", "prop_type \n", "BEB 164.0 170455813\n", - "CNG 252.0 176039140\n", "FCEB 102.0 120951335\n", "electric (not specified) 44.0 56678000\n", - "ethanol 9.0 1006750\n", - "low emission (hybrid) 145.0 91824361\n", - "low emission (propane) 44.0 8403969\n", - "mix (zero and low emission) 125.0 36775430\n", - "not specified 325.0 41552404\n", "zero-emission bus (not specified) 143.0 128156513\n", - "Grand Total 1353.0 831843715" + "Grand Total 453.0 476241661" ] }, - "execution_count": 129, "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pivot_prop_type." - ] - }, - { - "cell_type": "code", - "execution_count": 136, - "id": "01647d83-8b4f-47a9-ab57-a1db7cd501dd", - "metadata": {}, - "outputs": [ + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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prop_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpbzscore_cost_per_busis_cpb_outlier?
1CNG121176039140252.06985680.122141False
4ethanol1010067509.0111861-1.257470False
5low emission (hybrid)16091824361145.0633271-0.031401False
6low emission (propane)50840396944.0190999-1.071381False
7mix (zero and low emission)2036775430125.0294203-0.828702False
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" + ], + "text/plain": [ + " prop_type total_project_count total_project_count_ppno \\\n", + "1 CNG 12 1 \n", + "4 ethanol 1 0 \n", + "5 low emission (hybrid) 16 0 \n", + "6 low emission (propane) 5 0 \n", + "7 mix (zero and low emission) 2 0 \n", + "\n", + " total_agg_cost total_bus_count cpb zscore_cost_per_bus \\\n", + "1 176039140 252.0 698568 0.122141 \n", + "4 1006750 9.0 111861 -1.257470 \n", + "5 91824361 145.0 633271 -0.031401 \n", + "6 8403969 44.0 190999 -1.071381 \n", + "7 36775430 125.0 294203 -0.828702 \n", + "\n", + " is_cpb_outlier? \n", + "1 False \n", + "4 False \n", + "5 False \n", + "6 False \n", + "7 False " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/html": [ @@ -4703,36 +4635,42 @@ " \n", " \n", " \n", - " BEB\n", - " 164.0\n", - " 170455813\n", + " CNG\n", + " 252.0\n", + " 176039140\n", " \n", " \n", - " FCEB\n", - " 102.0\n", - " 120951335\n", + " ethanol\n", + " 9.0\n", + " 1006750\n", " \n", " \n", - " electric (not specified)\n", + " low emission (hybrid)\n", + " 145.0\n", + " 91824361\n", + " \n", + " \n", + " low emission (propane)\n", " 44.0\n", - " 56678000\n", + " 8403969\n", " \n", " \n", - " zero-emission bus (not specified)\n", - " 143.0\n", - " 128156513\n", + " mix (zero and low emission)\n", + " 125.0\n", + " 36775430\n", " \n", " \n", "\n", "" ], "text/plain": [ - " bus_count total_cost\n", - "prop_type \n", - "BEB 164.0 170455813\n", - "FCEB 102.0 120951335\n", - "electric (not specified) 44.0 56678000\n", - "zero-emission bus (not specified) 143.0 128156513" + " bus_count total_cost\n", + "prop_type \n", + "CNG 252.0 176039140\n", + "ethanol 9.0 1006750\n", + "low emission (hybrid) 145.0 91824361\n", + "low emission (propane) 44.0 8403969\n", + "mix (zero and low emission) 125.0 36775430" ] }, "metadata": {}, @@ -4794,6 +4732,11 @@ " 125.0\n", " 36775430\n", " \n", + " \n", + " Grand Total\n", + " 575.0\n", + " 314049650\n", + " \n", " \n", "\n", "" @@ -4805,7 +4748,8 @@ "ethanol 9.0 1006750\n", "low emission (hybrid) 145.0 91824361\n", "low emission (propane) 44.0 8403969\n", - "mix (zero and low emission) 125.0 36775430" + "mix (zero and low emission) 125.0 36775430\n", + "Grand Total 575.0 314049650" ] }, "metadata": {}, @@ -4813,13 +4757,16 @@ } ], "source": [ + "# keep this\n", "zeb_list =[\n", " \"BEB\",\n", " \"FCEB\",\n", " \"electric (not specified)\",\n", - " \"zero-emission bus (not specified)\", \n", + " \"zero-emission bus (not specified)\",\n", + "\n", "]\n", "\n", + "#keep this\n", "non_zeb_list =[\n", " \"CNG\",\n", " \"ethanol\",\n", @@ -4827,19 +4774,149 @@ " \"low emission (propane)\",\n", " \"mix (zero and low emission)\",\n", "]\n", - "# table to get list of zeb and non-zeb counts, but not totals.\n", + "\n", + "\n", + "\n", "display(\n", + " #zeb data 3 different methods\n", + " #1. filtering agg_prop by zeb list, no grand totas\n", + " #2. filtering pivot talbe by zeb list, without grand totals\n", + " #3. dedicated pivot table for zeb, with grand totals\n", + " agg_prop[agg_prop[\"prop_type\"].isin(zeb_list)],\n", " pivot_prop_type.loc[zeb_list],\n", - " pivot_prop_type.loc[non_zeb_list]\n", - ")" + " pivot_zeb_prop,\n", + " \n", + " #non-zeb same 3 methods\n", + " agg_prop[agg_prop[\"prop_type\"].isin(non_zeb_list)],\n", + " pivot_prop_type.loc[non_zeb_list],\n", + " pivot_non_zeb_prop\n", + ")\n", + "# confirmed all data is the same, but need pivot for grand total rows" ] }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 84, "id": "3be3ef4f-0bf3-4770-a8b7-340d372ae1ce", "metadata": {}, "outputs": [ + { + "data": { + "text/html": [ + "
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bus_size_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpbzscore_cost_per_busis_cpb_outlier?
0articulated025823757641.014204281.598471False
1cutaway3016694500152.0109832-1.466801False
2not specified406509919038881.0578795-0.369972False
3over-the-road01951600014.0679714-0.133939False
4standard/conventional (30ft-45ft)037237476601265.08961380.372242False
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" + ], + "text/plain": [ + " bus_size_type total_project_count \\\n", + "0 articulated 0 \n", + "1 cutaway 3 \n", + "2 not specified 40 \n", + "3 over-the-road 0 \n", + "4 standard/conventional (30ft-45ft) 0 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", + "0 2 58237576 41.0 1420428 \n", + "1 0 16694500 152.0 109832 \n", + "2 6 509919038 881.0 578795 \n", + "3 1 9516000 14.0 679714 \n", + "4 37 237476601 265.0 896138 \n", + "\n", + " zscore_cost_per_bus is_cpb_outlier? \n", + "0 1.598471 False \n", + "1 -1.466801 False \n", + "2 -0.369972 False \n", + "3 -0.133939 False \n", + "4 0.372242 False " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/html": [ @@ -4896,6 +4973,11 @@ " 265.0\n", " 237476601\n", " \n", + " \n", + " Grand Total\n", + " 1353.0\n", + " 831843715\n", + " \n", " \n", "\n", "" @@ -4907,34 +4989,122 @@ "cutaway 152.0 16694500\n", "not specified 881.0 509919038\n", "over-the-road 14.0 9516000\n", - "standard/conventional (30ft-45ft) 265.0 237476601" + "standard/conventional (30ft-45ft) 265.0 237476601\n", + "Grand Total 1353.0 831843715" ] }, - "execution_count": 123, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ "# answers total buses and cost per grant type\n", "pivot_size = pd.pivot_table(\n", - " all_projects,\n", + " merged_data,\n", " values = [\"bus_count\", \"total_cost\"],\n", " index = \"bus_size_type\",\n", " aggfunc = \"sum\",\n", " margins = True,\n", " margins_name = \"Grand Total\"\n", ")\n", + "display(\n", + " agg_bus_size,\n", + " pivot_size\n", + ")\n", "\n", - "pivot_size.head()" + "#same data, dont need pivot for this one because the grand totals will be the same. and the pivot tables dont have cpb" ] }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 85, "id": "2c933257-bdc2-4007-9571-58475118073c", "metadata": {}, "outputs": [ + { + "data": { + "text/html": [ + "
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sourcetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpbzscore_cost_per_busis_cpb_outlier?
0dgs037253336177237.010689291.158131False
1fta430391257025883.0443099-1.281952False
2tircp09187250513233.08036500.123820False
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" + ], + "text/plain": [ + " source total_project_count total_project_count_ppno total_agg_cost \\\n", + "0 dgs 0 37 253336177 \n", + "1 fta 43 0 391257025 \n", + "2 tircp 0 9 187250513 \n", + "\n", + " total_bus_count cpb zscore_cost_per_bus is_cpb_outlier? \n", + "0 237.0 1068929 1.158131 False \n", + "1 883.0 443099 -1.281952 False \n", + "2 233.0 803650 0.123820 False " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/html": [ @@ -4999,15 +5169,14 @@ "Grand Total 1353.0 831843715" ] }, - "execution_count": 101, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ "# answers total buses and cost per grant type\n", "pivot_source = pd.pivot_table(\n", - " all_projects,\n", + " merged_data,\n", " values = [\"bus_count\", \"total_cost\"],\n", " index = \"source\",\n", " aggfunc = \"sum\",\n", @@ -5015,13 +5184,17 @@ " margins_name = \"Grand Total\"\n", ")\n", "\n", - "pivot_source.head()" + "display(\n", + " agg_source,\n", + " pivot_source\n", + ")\n", + "# dont need pivot, keep agg_source to retain cpb" ] }, { "cell_type": "code", "execution_count": null, - "id": "ece95fb7-cbb8-46bd-a5f9-2b68a47a4817", + "id": "4753f3ea-00b6-4d5e-a3f0-73b3d3593acb", "metadata": {}, "outputs": [], "source": [] From 173694d35053135992c80695bfba361101f5503b Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Wed, 19 Jun 2024 19:07:00 +0000 Subject: [PATCH 11/36] ran updated scripts, everything exported to gcs with no errors. new method of finding outliers first is working. reran all the agg by __ functions and they all work and match --- bus_procurement_cost/refactor_bus_cost.ipynb | 2604 ++++-------------- 1 file changed, 543 insertions(+), 2061 deletions(-) diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index 879162ad7..af4a41d25 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -580,12 +580,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 113, "id": "ea9c3269-d53d-4d94-bc22-c6768cb63d91", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_290/2582954682.py:75: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n", + " df[\"funding\"]\n", + "/tmp/ipykernel_290/2582954682.py:25: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n", + " df[new_col2] = df[new_col2].str.replace(\")\", \"\")\n" + ] + } + ], "source": [ "# FTA\n", "import numpy as np\n", @@ -717,12 +728,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 115, "id": "4e16119d-f6f3-478b-a419-7c4989557910", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.9/site-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Data Validation extension is not supported and will be removed\n", + " warn(msg)\n" + ] + } + ], "source": [ "# TIRCP\n", "import numpy as np\n", @@ -858,7 +878,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 116, "id": "359f3b7a-d691-446f-9a14-424c47fc0929", "metadata": { "tags": [] @@ -1059,7 +1079,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 137, "id": "19f4bd75-f614-4937-880a-1e1a6ff2eb7f", "metadata": { "tags": [] @@ -1156,7 +1176,7 @@ " #calculating zscore on cost per bus\n", " merge2[\"zscore_cost_per_bus\"] = zscore(merge2[\"cost_per_bus\"])\n", " #flag any outliers\n", - " df_agg[\"is_cpb_outlier?\"] = df_agg[\"zscore_cost_per_bus\"].apply(outlier_flag)\n", + " merge2[\"is_cpb_outlier?\"] = merge2[\"zscore_cost_per_bus\"].apply(outlier_flag)\n", " return merge2\n", "\n", "\n", @@ -1190,23 +1210,269 @@ " df_agg[\"new_cost_per_bus\"] = (df_agg[\"total_agg_cost\"] / df_agg[\"total_bus_count\"]).astype(\"int64\")\n", " \n", " #calculate zscore\n", - " df_agg[\"zscore_cost_per_bus\"] = zscore(df_agg[\"cpb\"])\n", + " df_agg[\"new_zscore_cost_per_bus\"] = zscore(df_agg[\"new_cost_per_bus\"])\n", " \n", " #flag outliers\n", - " df_agg[\"is_cpb_outlier?\"] = df_agg[\"zscore_cost_per_bus\"].apply(outlier_flag)\n", + " df_agg[\"new_is_cpb_outlier?\"] = df_agg[\"new_zscore_cost_per_bus\"].apply(outlier_flag)\n", " \n", " return df_agg\n", "\n", "\n", "\n", - "if __name__ == \"__main__\":\n", + "#if __name__ == \"__main__\":\n", " \n", " # initial df\n", - " df1 = prepare_all_data()\n", - "\n", + "# df1 = prepare_all_data()\n", + " #remove outliers based on cost per bus zscore\n", + "# df2 = df1[df1[\"is_cpb_outlier?\"]==False]\n", " \n", " # export to gcs\n", - " df1.to_parquet(f'{gcs_path}cleaned_cpb_analysis_data_merge.parquet')\n" + "# df1.to_parquet(f'{gcs_path}cleaned_cpb_analysis_data_merge.parquet')\n", + "# df2.to_parquet(f'{gcs_path}cleaned_no_outliers_cpb_analysis_data_merge.parquet')" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "id": "8340e510-a934-4730-b340-74a5e1eeb377", + "metadata": {}, + "outputs": [], + "source": [ + "# find outliers \n", + "find_outliers = pd.read_parquet(f'{GCS_PATH}cleaned_cpb_analysis_data_merge.parquet')" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "id": "3c2a88c2-3a07-4336-aee2-237d949bdd94", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "89" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "1" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencyproject_titleprop_typebus_size_typedescriptionnew_project_typetotal_costbus_countsourceppnoproject_descriptioncost_per_buszscore_cost_per_busis_cpb_outlier?
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transit_agencyproject_titleprop_typebus_size_typedescriptionnew_project_typetotal_costbus_countsourceppnoproject_descriptioncost_per_buszscore_cost_per_busis_cpb_outlier?
51Transit Joint Powers Authority of Merced CountyNonezero-emission bus (not specified)not specifiedNonebus only36965133.0tircpCP074Purchases 3 zero-emission electric buses to in...12321710.879900False
83Transit Joint Powers Authority for Merced CountyNoneBEBstandard/conventional (30ft-45ft)NoneNone32233242.0dgsEBUS002None16116621.689929False
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" + ], + "text/plain": [ + " transit_agency project_title \\\n", + "51 Transit Joint Powers Authority of Merced County None \n", + "83 Transit Joint Powers Authority for Merced County None \n", + "84 Transit Joint Powers Authority for Merced County None \n", + "\n", + " prop_type bus_size_type \\\n", + "51 zero-emission bus (not specified) not specified \n", + "83 BEB standard/conventional (30ft-45ft) \n", + "84 BEB standard/conventional (30ft-45ft) \n", + "\n", + " description new_project_type total_cost bus_count source ppno \\\n", + "51 None bus only 3696513 3.0 tircp CP074 \n", + "83 None None 3223324 2.0 dgs EBUS002 \n", + "84 None None 3223324 1.0 dgs EBUS002 \n", + "\n", + " project_description cost_per_bus \\\n", + "51 Purchases 3 zero-emission electric buses to in... 1232171 \n", + "83 None 1611662 \n", + "84 None 3223324 \n", + "\n", + " zscore_cost_per_bus is_cpb_outlier? \n", + "51 0.879900 False \n", + "83 1.689929 False \n", + "84 5.130048 True " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(\n", + " len(find_outliers),\n", + " len(find_outliers[find_outliers[\"is_cpb_outlier?\"]==True]),\n", + " find_outliers[find_outliers[\"is_cpb_outlier?\"]==True],\n", + " find_outliers[find_outliers[\"transit_agency\"].str.contains(\"Merced County\")]\n", + ")" ] }, { @@ -1825,7 +2091,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 138, "id": "f21298ee-0efb-4f91-ba63-55fc2645a4d2", "metadata": {}, "outputs": [ @@ -1834,11 +2100,12 @@ "text/plain": [ "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", - " 'ppno', 'project_description'],\n", + " 'ppno', 'project_description', 'cost_per_bus', 'zscore_cost_per_bus',\n", + " 'is_cpb_outlier?'],\n", " dtype='object')" ] }, - "execution_count": 64, + "execution_count": 138, "metadata": {}, "output_type": "execute_result" } @@ -1847,1871 +2114,32 @@ "# read in all cleaned project data\n", "# same as final from above\n", "# cpb_analysis_data_merge is bus only projects. all DGS rows were Bus only projects anyways\n", - "merged_data = pd.read_parquet(f'{GCS_PATH}old/cpb_analysis_data_merge.parquet')\n", + "merged_data = pd.read_parquet(f'{GCS_PATH}cleaned_no_outliers_cpb_analysis_data_merge.parquet')\n", "merged_data.columns" ] }, { "cell_type": "code", - "execution_count": 110, - "id": "99846e40-f8bc-4001-9373-3f4c25eb7819", + "execution_count": 140, + "id": "ece95fb7-cbb8-46bd-a5f9-2b68a47a4817", "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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transit_agencyproject_titleprop_typebus_size_typedescriptionnew_project_typetotal_costbus_countsourceppnoproject_description
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)Puerto Rico Initiative Minimizing Emissions Pl...electric (not specified)not specifiedThe Metropolitan Bus Authority will receive fu...bus only100000008.0ftaNoneNone
1Cape Fear Public Transportation AuthorityWave Transit Low Emissions Replacement VehiclesCNGnot specifiedWave Transit will receive funding to buy compr...bus only28602505.0ftaNoneNone
2Central Oklahoma Transportation and Parking Au...COTPA, dba EMBARK Elimination of Fixed Route D...CNGnot specifiedThe Central Oklahoma Transportation and Parkin...bus only42787729.0ftaNoneNone
3Champaign-Urbana Mass Transit DistrictMTD 40-Foot Hybrid Replacement Buseslow emission (hybrid)not specifiedThe Champaign-Urbana Mass Transit District wil...bus only663539410.0ftaNoneNone
4City of BeaumontBeaumont Municipal Transit Zips to Improve Low...CNGnot specifiedBeaumont Municipal Transit will receive fundin...bus only28194605.0ftaNoneNone
5City of BeloitBeloit Transit Low-Emission Vehicle Purchaselow emission (hybrid)not specifiedThe Beloit Transit System will receive funding...bus only6531841.0ftaNoneNone
6City of BrownsvilleBrownsville Metro Hybrid-Electric Diesellow emission (hybrid)not specifiedBrownsville Metro will receive funding to buy ...bus only47388866.0ftaNoneNone
7City of Colorado Springs dba Mountain Metropol...Mountain Metropolitan Transit Diesel Replaceme...low emission (hybrid)not specifiedThe city of Colorado Springs' Mountain Metropo...bus only31990386.0ftaNoneNone
8City of Jonesboro, ArkansasJET Low Emission Fleetlow emission (hybrid)not specifiedThe City of Jonesboro will receive funding to ...bus only10103725.0ftaNoneNone
9City of Norman, OklahomaReplacement CNG Vehicles for ADA Paratransit S...CNGnot specifiedThe city of Norman will buy compressed natural...bus only7767146.0ftaNoneNone
10City of Tucson, Sun TranA Clean Ride by 2025CNGnot specifiedThe city of Tucson's Sun Tran transit system w...bus only2149056039.0ftaNoneNone
11Coast Transit Authority dba MS Coast Transport...Purchase of Replacement Buseslow emission (propane)not specifiedCoast Transit Authority will receive funding t...bus only17600009.0ftaNoneNone
12Conroe Connection TransitCCT Commuter Bus Fleet Replacement ProjectCNGnot specifiedConroe Connection Transit will receive funding...bus only45000004.0ftaNoneNone
13Dallas Area Rapid Transit (DART)DART CNG Bus Fleet Modernization ProjectCNGnot specifiedDallas Area Rapid Transit will receive funding...bus only10300000090.0ftaNoneNone
14Delaware Transit Corporation (DTC)Continuation of Delaware Transit Corporations ...FCEBnot specifiedDelaware Transit Corporation will receive fund...bus only87407286.0ftaNoneNone
15Golden Empire TransitReplacement of Ten (10) 40ft. CNG BusesCNGnot specifiedGolden Empire Transit will receive funding to ...bus only575035110.0ftaNoneNone
16Illinois Department of Transportation on behal...Illinois DOT Statewide Paratransit Vehicle Rep...not specifiedcutawayThe Illinois Department of Transportation will...bus only12600000134.0ftaNoneNone
17Indianapolis Public Transportation Corporation...The purchase of 20 vehicles, which will use th...low emission (hybrid)not specifiedThe Indianapolis Public Transportation Corpora...bus only1904033620.0ftaNoneNone
18Interurban Transit PartnershipA Sustainable Future: Renewable Natural Gas Bu...CNGnot specifiedThe Interurban Transit Partnership (The Rapid)...bus only619718011.0ftaNoneNone
19Lowell Regional Transit AuthorityLowell Regional Transit Authority Revenue Vehi...low emission (hybrid)not specifiedThe Lowell Regional Transit Authority will rec...bus only68592967.0ftaNoneNone
20Madison County Mass Transit DistrictHeavy Duty 40-Foot Bus Replacementnot specifiednot specifiedThe Madison County Mass Transit District will ...bus only10800002.0ftaNoneNone
21Mesa CountyGrand Valley Transit Bus PurchasesCNGcutawayMesa County's Grand Valley Transit will receiv...bus only11620003.0ftaNoneNone
22Minnesota Department of Transportation on beha...Reducing Emissions in Rural Minnesota Transitlow emission (propane)not specifiedThe Minnesota Department of Transportation, on...bus only14569707.0ftaNoneNone
23New Mexico Department of Transportation on beh...Procurement of Three Replacement Diesel-Electr...low emission (hybrid)not specifiedThe North Central Regional Transit District wi...bus only20631603.0ftaNoneNone
24North County Transit District (NCTD)Accelerate Clean Transit (ACT)FCEBnot specifiedThe North County Transit District will receive...bus only2933024323.0ftaNoneNone
25Ohio Department of Transportation (ODOT) on be...Ohio Zero Emission Ready Ohio (OH-ZERO)mix (zero and low emission)not specifiedThe Ohio Department of Transportation (ODOT) w...bus only2933166569.0ftaNoneNone
26Oregon Department of Transportation on behalf ...CET's Low Emission Vanpools and Support Vehicleslow emission (hybrid)not specifiedThe Oregon Department of Transportation on beh...bus only1812505.0ftaNoneNone
27Rhode Island Public Transit AuthorityRIPTA Hybrid Bus Upgradelow emission (hybrid)not specifiedThe Rhode Island Public Transit Authority will...bus only500000025.0ftaNoneNone
28Rockford Mass Transit DistrictHybrid Bus Procurementlow emission (hybrid)not specifiedThe Rockford Mass Transit District will receiv...bus only40946524.0ftaNoneNone
29Rogue Valley Transportation DistrictRVTD Renewable Resiliency Buseslow emission (hybrid)not specifiedThe Rogue Valley Transportation District will ...bus only39375006.0ftaNoneNone
30San Antonio Metropolitan Transit AuthorityVIA Metropolitan Transit ViaTrans: Providing e...low emission (propane)not specifiedThe San Antonio Metropolitan Transit Authority...bus only318720015.0ftaNoneNone
31South Carolina Department of Transportation on...SCDOT Vehicle Replacement Projectnot specifiednot specifiedThe South Carolina Department of Transportatio...bus only15423904160.0ftaNoneNone
32South Dakota Department of Transportation on b...Replacement of Aberdeen Ride Line fleet buses ...ethanolnot specifiedThe South Dakota Department of Transportation,...bus only10067509.0ftaNoneNone
33South Dakota Department of Transportation on b...Low-emission bus alternative fuel project to i...low emission (propane)not specifiedThe South Dakota Department of Transportation ...bus only12766289.0ftaNoneNone
34Southeastern Regional Transit AuthoritySoutheastern Regional Transit Authority Fixed ...low emission (hybrid)not specifiedThe Southeastern Regional Transit Authority wi...bus only1156000016.0ftaNoneNone
35Southwest Ohio Regional Transit AuthoritySORTA Hybrid Transition Bus Replacement Projectlow emission (hybrid)not specifiedThe Southwest Ohio Regional Transit Authority ...bus only980642816.0ftaNoneNone
36State of California on behalf of Kern Regional...Low Emission Transition/ReplacementCNGnot specifiedThe State of California, on behalf of Kern Reg...bus only32485005.0ftaNoneNone
37State of California on behalf of Kern Regional...Purchase of Fifteen (15) Replacement Cutaway B...not specifiedcutawayThe State of California, on behalf of Kern Reg...bus only293250015.0ftaNoneNone
38Tahoe Transportation DistrictTahoe Transportation District (TTD) Clean Tran...low emission (hybrid)not specifiedThe Tahoe Transportation District will receive...bus only34000004.0ftaNoneNone
39Texas Department of Transportation on behalf o...FY23 Rural Transit Asset Replacement & Moderni...mix (zero and low emission)not specifiedThe Texas Department of Transportation will re...bus only744376556.0ftaNoneNone
40Utah Transit AuthorityUtah Transit Authority Compressed Natural Gas ...CNGnot specifiedThe Utah Transit Authority will receive fundin...bus only1705535325.0ftaNoneNone
41Whatcom Transportation Authority (WTA)Purchase 11 diesel-electric hybrid buses (hybr...low emission (hybrid)not specifiedThe Whatcom Transportation Authority will rece...bus only964486511.0ftaNoneNone
42White Earth Reservation Business CommitteeWhite Earth Public Transit to replace 4 of the...low emission (propane)not specifiedWhite Earth Public Transit will receive fundin...bus only7231714.0ftaNoneNone
43Antelope Valley Transit Authority (AVTA)Noneelectric (not specified)articulatedNonebus only3947800029.0tircpCP005Purchase 13 60-foot articulated BRT buses and ...
44City of Los Angeles (LA DOT)Nonezero-emission bus (not specified)not specifiedNonebus only102790000112.0tircpCP029Acquire 112 zero-emission buses to replace exi...
45City of WascoNonezero-emission bus (not specified)not specifiedNonebus only15430003.0tircpCP090Purchase of 3 zero-emission buses that will su...
46Culver CityNonezero-emission bus (not specified)not specifiedNonebus only35470005.0tircpCP114The Project implements a new transit service u...
47Foothill TransitNonezero-emission bus (not specified)not specifiedNonebus only1658000020.0tircpCP076Purchase 20 zero-emission buses to extend Rout...
48Orange County Transportation Authority (OCTA)NoneCNGstandard/conventional (30ft-45ft)Nonebus only290000040.0tircpCP004Purchase five 40-foot CNG buses for BRT Route ...
49Shasta Regional Transportation Agency (SRTA)Nonenot specifiedover-the-roadNonebus only951600014.0tircpCP045Purchase 7 new coach-style buses to support a ...
50Torrance Transit DepartmentNoneelectric (not specified)not specifiedNonebus only72000007.0tircpCP073Purchase 7 electric buses to expand services o...
51Transit Joint Powers Authority of Merced CountyNonezero-emission bus (not specified)not specifiedNonebus only36965133.0tircpCP074Purchases 3 zero-emission electric buses to in...
52SUNLINE TRANSIT AGENCY (THOUSAND PALMS)NoneFCEBstandard/conventional (30ft-45ft)NoneNone57551555.0dgs11819None
53Lane Transit (Oregon)NoneBEBstandard/conventional (30ft-45ft)NoneNone992162611.0dgs2020-061None
54VICTOR VALLEY TRANSIT AUTHORITY (VVTA)NoneBEBstandard/conventional (30ft-45ft)NoneNone45081605.0dgs1416None
55CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...NoneBEBstandard/conventional (30ft-45ft)NoneNone36235364.0dgs22100367 - 00None
56ORANGE COUNTY TRANSPORTATION AUTHORITY (ORANGE...NoneBEBstandard/conventional (30ft-45ft)NoneNone931952010.0dgsCO2165None
57Alameda County Transit AuthorityNoneFCEBstandard/conventional (30ft-45ft)NoneNone2284664020.0dgs57071None
58Santa Barbara MetroNoneBEBstandard/conventional (30ft-45ft)NoneNone36590724.0dgs10609None
59Golden Empire TransitNoneFCEBstandard/conventional (30ft-45ft)NoneNone54583055.0dgs10076963-000None
60San Diego MetroNoneBEBarticulatedNoneNone1875957612.0dgs4500040166None
61North County Transit DistrictNoneBEBstandard/conventional (30ft-45ft)NoneNone57876066.0dgs347750000PNone
62The Bus, City of MercedNoneBEBstandard/conventional (30ft-45ft)NoneNone47862855.0dgsEBUS001None
63Lane Transit (Oregon)NoneBEBstandard/conventional (30ft-45ft)NoneNone1797337319.0dgsA-21587None
64Foothill Transit, West Covina, CANoneFCEBstandard/conventional (30ft-45ft)NoneNone3764204433.0dgs21-077None
65UC DAVIS (UNITRANS) (DAVIS, CA)NoneBEBstandard/conventional (30ft-45ft)NoneNone55594866.0dgsASTR876345None
66CITY OF PORTERVILLE (PORTERVILLE, CA)NoneBEBstandard/conventional (30ft-45ft)NoneNone27818913.0dgs20-18895None
67GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA)NoneFCEBstandard/conventional (30ft-45ft)NoneNone54063555.0dgs10076963-000None
68UC DAVIS (UNITRANS) (DAVIS, CA)NoneBEBstandard/conventional (30ft-45ft)NoneNone37624404.0dgsUC DAVIS (UNITRANS) (DAVIS, CA)None
69VACAVILLE PUBLIC TRANSPORTATION (CITY COACH) (...NoneBEBstandard/conventional (30ft-45ft)NoneNone1017559010.0dgs2201132None
70SLO TRANSIT (SAN LUIS OBISPO, CA)NoneBEBstandard/conventional (30ft-45ft)NoneNone8472141.0dgs609571None
71UCLA FLEET & TRANSITNoneBEBstandard/conventional (30ft-45ft)NoneNone20088262.0dgsZC654 / ZC653None
72SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA)NoneFCEBstandard/conventional (30ft-45ft)NoneNone57718655.0dgs120706None
73City of RosevilleNoneBEBstandard/conventional (30ft-45ft)NoneNone44528925.0dgs9009418None
74City of RosevilleNoneBEBstandard/conventional (30ft-45ft)NoneNone51986155.0dgs9011071None
75University of California - San DiegoNoneBEBstandard/conventional (30ft-45ft)NoneNone41340004.0dgsPUR00318127None
76University of California - San DiegoNoneBEBstandard/conventional (30ft-45ft)NoneNone41340002.0dgsPUR00318127None
77Sonoma County TransitNoneBEBstandard/conventional (30ft-45ft)NoneNone899000010.0dgsSC001-2300002779None
78City of Santa Rosa(Santa Rosa CityBus)NoneBEBstandard/conventional (30ft-45ft)NoneNone59877905.0dgsCity of Santa Rosa(Santa Rosa CityBus)None
79Santa Rosa City BusNoneBEBstandard/conventional (30ft-45ft)NoneNone40682024.0dgsPA-2021-001-SRCBNone
80University of California, IrvineNoneBEBstandard/conventional (30ft-45ft)NoneNone49329305.0dgsUniversity of California, IrvineNone
81Santa Maria Regional TransitNoneBEBstandard/conventional (30ft-45ft)NoneNone51883795.0dgs63759None
82City of San Luis ObispoNoneBEBstandard/conventional (30ft-45ft)NoneNone8592701.0dgs609570None
83Transit Joint Powers Authority for Merced CountyNoneBEBstandard/conventional (30ft-45ft)NoneNone32233242.0dgsEBUS002None
84Transit Joint Powers Authority for Merced CountyNoneBEBstandard/conventional (30ft-45ft)NoneNone32233241.0dgsEBUS002None
85Napa Valley Transportation AuthorityNoneBEBstandard/conventional (30ft-45ft)NoneNone23966002.0dgs21-2002None
86Santa Maria Area TransitNoneBEBstandard/conventional (30ft-45ft)NoneNone18622582.0dgs57614None
87City of Visalia - Visalia City Coach(Visalia T...NoneBEBstandard/conventional (30ft-45ft)NoneNone36878034.0dgsP02771None
88Sacramento County Airport SystemNoneBEBstandard/conventional (30ft-45ft)NoneNone46422255.0dgsPA81335877None
\n", - "
" - ], "text/plain": [ - " transit_agency \\\n", - "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) \n", - "1 Cape Fear Public Transportation Authority \n", - "2 Central Oklahoma Transportation and Parking Au... \n", - "3 Champaign-Urbana Mass Transit District \n", - "4 City of Beaumont \n", - "5 City of Beloit \n", - "6 City of Brownsville \n", - "7 City of Colorado Springs dba Mountain Metropol... \n", - "8 City of Jonesboro, Arkansas \n", - "9 City of Norman, Oklahoma \n", - "10 City of Tucson, Sun Tran \n", - "11 Coast Transit Authority dba MS Coast Transport... \n", - "12 Conroe Connection Transit \n", - "13 Dallas Area Rapid Transit (DART) \n", - "14 Delaware Transit Corporation (DTC) \n", - "15 Golden Empire Transit \n", - "16 Illinois Department of Transportation on behal... \n", - "17 Indianapolis Public Transportation Corporation... \n", - "18 Interurban Transit Partnership \n", - "19 Lowell Regional Transit Authority \n", - "20 Madison County Mass Transit District \n", - "21 Mesa County \n", - "22 Minnesota Department of Transportation on beha... \n", - "23 New Mexico Department of Transportation on beh... \n", - "24 North County Transit District (NCTD) \n", - "25 Ohio Department of Transportation (ODOT) on be... \n", - "26 Oregon Department of Transportation on behalf ... \n", - "27 Rhode Island Public Transit Authority \n", - "28 Rockford Mass Transit District \n", - "29 Rogue Valley Transportation District \n", - "30 San Antonio Metropolitan Transit Authority \n", - "31 South Carolina Department of Transportation on... \n", - "32 South Dakota Department of Transportation on b... \n", - "33 South Dakota Department of Transportation on b... \n", - "34 Southeastern Regional Transit Authority \n", - "35 Southwest Ohio Regional Transit Authority \n", - "36 State of California on behalf of Kern Regional... \n", - "37 State of California on behalf of Kern Regional... \n", - "38 Tahoe Transportation District \n", - "39 Texas Department of Transportation on behalf o... \n", - "40 Utah Transit Authority \n", - "41 Whatcom Transportation Authority (WTA) \n", - "42 White Earth Reservation Business Committee \n", - "43 Antelope Valley Transit Authority (AVTA) \n", - "44 City of Los Angeles (LA DOT) \n", - "45 City of Wasco \n", - "46 Culver City \n", - "47 Foothill Transit \n", - "48 Orange County Transportation Authority (OCTA) \n", - "49 Shasta Regional Transportation Agency (SRTA) \n", - "50 Torrance Transit Department \n", - "51 Transit Joint Powers Authority of Merced County \n", - "52 SUNLINE TRANSIT AGENCY (THOUSAND PALMS) \n", - "53 Lane Transit (Oregon) \n", - "54 VICTOR VALLEY TRANSIT AUTHORITY (VVTA) \n", - "55 CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ... \n", - "56 ORANGE COUNTY TRANSPORTATION AUTHORITY (ORANGE... \n", - "57 Alameda County Transit Authority \n", - "58 Santa Barbara Metro \n", - "59 Golden Empire Transit \n", - "60 San Diego Metro \n", - "61 North County Transit District \n", - "62 The Bus, City of Merced \n", - "63 Lane Transit (Oregon) \n", - "64 Foothill Transit, West Covina, CA \n", - "65 UC DAVIS (UNITRANS) (DAVIS, CA) \n", - "66 CITY OF PORTERVILLE (PORTERVILLE, CA) \n", - "67 GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA) \n", - "68 UC DAVIS (UNITRANS) (DAVIS, CA) \n", - "69 VACAVILLE PUBLIC TRANSPORTATION (CITY COACH) (... \n", - "70 SLO TRANSIT (SAN LUIS OBISPO, CA) \n", - "71 UCLA FLEET & TRANSIT \n", - "72 SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA) \n", - "73 City of Roseville \n", - "74 City of Roseville \n", - "75 University of California - San Diego \n", - "76 University of California - San Diego \n", - "77 Sonoma County Transit \n", - "78 City of Santa Rosa(Santa Rosa CityBus) \n", - "79 Santa Rosa City Bus \n", - "80 University of California, Irvine \n", - "81 Santa Maria Regional Transit \n", - "82 City of San Luis Obispo \n", - "83 Transit Joint Powers Authority for Merced County \n", - "84 Transit Joint Powers Authority for Merced County \n", - "85 Napa Valley Transportation Authority \n", - "86 Santa Maria Area Transit \n", - "87 City of Visalia - Visalia City Coach(Visalia T... \n", - "88 Sacramento County Airport System \n", - "\n", - " project_title \\\n", - "0 Puerto Rico Initiative Minimizing Emissions Pl... \n", - "1 Wave Transit Low Emissions Replacement Vehicles \n", - "2 COTPA, dba EMBARK Elimination of Fixed Route D... \n", - "3 MTD 40-Foot Hybrid Replacement Buses \n", - "4 Beaumont Municipal Transit Zips to Improve Low... \n", - "5 Beloit Transit Low-Emission Vehicle Purchase \n", - "6 Brownsville Metro Hybrid-Electric Diesel \n", - "7 Mountain Metropolitan Transit Diesel Replaceme... \n", - "8 JET Low Emission Fleet \n", - "9 Replacement CNG Vehicles for ADA Paratransit S... \n", - "10 A Clean Ride by 2025 \n", - "11 Purchase of Replacement Buses \n", - "12 CCT Commuter Bus Fleet Replacement Project \n", - "13 DART CNG Bus Fleet Modernization Project \n", - "14 Continuation of Delaware Transit Corporations ... \n", - "15 Replacement of Ten (10) 40ft. CNG Buses \n", - "16 Illinois DOT Statewide Paratransit Vehicle Rep... \n", - "17 The purchase of 20 vehicles, which will use th... \n", - "18 A Sustainable Future: Renewable Natural Gas Bu... \n", - "19 Lowell Regional Transit Authority Revenue Vehi... \n", - "20 Heavy Duty 40-Foot Bus Replacement \n", - "21 Grand Valley Transit Bus Purchases \n", - "22 Reducing Emissions in Rural Minnesota Transit \n", - "23 Procurement of Three Replacement Diesel-Electr... \n", - "24 Accelerate Clean Transit (ACT) \n", - "25 Ohio Zero Emission Ready Ohio (OH-ZERO) \n", - "26 CET's Low Emission Vanpools and Support Vehicles \n", - "27 RIPTA Hybrid Bus Upgrade \n", - "28 Hybrid Bus Procurement \n", - "29 RVTD Renewable Resiliency Buses \n", - "30 VIA Metropolitan Transit ViaTrans: Providing e... \n", - "31 SCDOT Vehicle Replacement Project \n", - "32 Replacement of Aberdeen Ride Line fleet buses ... \n", - "33 Low-emission bus alternative fuel project to i... \n", - "34 Southeastern Regional Transit Authority Fixed ... \n", - "35 SORTA Hybrid Transition Bus Replacement Project \n", - "36 Low Emission Transition/Replacement \n", - "37 Purchase of Fifteen (15) Replacement Cutaway B... \n", - "38 Tahoe Transportation District (TTD) Clean Tran... \n", - "39 FY23 Rural Transit Asset Replacement & Moderni... \n", - "40 Utah Transit Authority Compressed Natural Gas ... \n", - "41 Purchase 11 diesel-electric hybrid buses (hybr... \n", - "42 White Earth Public Transit to replace 4 of the... \n", - "43 None \n", - "44 None \n", - "45 None \n", - "46 None \n", - "47 None \n", - "48 None \n", - "49 None \n", - "50 None \n", - "51 None \n", - "52 None \n", - "53 None \n", - "54 None \n", - "55 None \n", - "56 None \n", - "57 None \n", - "58 None \n", - "59 None \n", - "60 None \n", - "61 None \n", - "62 None \n", - "63 None \n", - "64 None \n", - "65 None \n", - "66 None \n", - "67 None \n", - "68 None \n", - "69 None \n", - "70 None \n", - "71 None \n", - "72 None \n", - "73 None \n", - "74 None \n", - "75 None \n", - "76 None \n", - "77 None \n", - "78 None \n", - "79 None \n", - "80 None \n", - "81 None \n", - "82 None \n", - "83 None \n", - "84 None \n", - "85 None \n", - "86 None \n", - "87 None \n", - "88 None \n", - "\n", - " prop_type bus_size_type \\\n", - "0 electric (not specified) not specified \n", - "1 CNG not specified \n", - "2 CNG not specified \n", - "3 low emission (hybrid) not specified \n", - "4 CNG not specified \n", - "5 low emission (hybrid) not specified \n", - "6 low emission (hybrid) not specified \n", - "7 low emission (hybrid) not specified \n", - "8 low emission (hybrid) not specified \n", - "9 CNG not specified \n", - "10 CNG not specified \n", - "11 low emission (propane) not specified \n", - "12 CNG not specified \n", - "13 CNG not specified \n", - "14 FCEB not specified \n", - "15 CNG not specified \n", - "16 not specified cutaway \n", - "17 low emission (hybrid) not specified \n", - "18 CNG not specified \n", - "19 low emission (hybrid) not specified \n", - "20 not specified not specified \n", - "21 CNG cutaway \n", - "22 low emission (propane) not specified \n", - "23 low emission (hybrid) not specified \n", - "24 FCEB not specified \n", - "25 mix (zero and low emission) not specified \n", - "26 low emission (hybrid) not specified \n", - "27 low emission (hybrid) not specified \n", - "28 low emission (hybrid) not specified \n", - "29 low emission (hybrid) not specified \n", - "30 low emission (propane) not specified \n", - "31 not specified not specified \n", - "32 ethanol not specified \n", - "33 low emission (propane) not specified \n", - "34 low emission (hybrid) not specified \n", - "35 low emission (hybrid) not specified \n", - "36 CNG not specified \n", - "37 not specified cutaway \n", - "38 low emission (hybrid) not specified \n", - "39 mix (zero and low emission) not specified \n", - "40 CNG not specified \n", - "41 low emission (hybrid) not specified \n", - "42 low emission (propane) not specified \n", - "43 electric (not specified) articulated \n", - "44 zero-emission bus (not specified) not specified \n", - "45 zero-emission bus (not specified) not specified \n", - "46 zero-emission bus (not specified) not specified \n", - "47 zero-emission bus (not specified) not specified \n", - "48 CNG standard/conventional (30ft-45ft) \n", - "49 not specified over-the-road \n", - "50 electric (not specified) not specified \n", - "51 zero-emission bus (not specified) not specified \n", - "52 FCEB standard/conventional (30ft-45ft) \n", - "53 BEB standard/conventional (30ft-45ft) \n", - "54 BEB standard/conventional (30ft-45ft) \n", - "55 BEB standard/conventional (30ft-45ft) \n", - "56 BEB standard/conventional (30ft-45ft) \n", - "57 FCEB standard/conventional (30ft-45ft) \n", - "58 BEB standard/conventional (30ft-45ft) \n", - "59 FCEB standard/conventional (30ft-45ft) \n", - "60 BEB articulated \n", - "61 BEB standard/conventional (30ft-45ft) \n", - "62 BEB standard/conventional (30ft-45ft) \n", - "63 BEB standard/conventional (30ft-45ft) \n", - "64 FCEB standard/conventional (30ft-45ft) \n", - "65 BEB standard/conventional (30ft-45ft) \n", - "66 BEB standard/conventional (30ft-45ft) \n", - "67 FCEB standard/conventional (30ft-45ft) \n", - "68 BEB standard/conventional (30ft-45ft) \n", - "69 BEB standard/conventional (30ft-45ft) \n", - "70 BEB standard/conventional (30ft-45ft) \n", - "71 BEB standard/conventional (30ft-45ft) \n", - "72 FCEB standard/conventional (30ft-45ft) \n", - "73 BEB standard/conventional (30ft-45ft) \n", - "74 BEB standard/conventional (30ft-45ft) \n", - "75 BEB standard/conventional (30ft-45ft) \n", - "76 BEB standard/conventional (30ft-45ft) \n", - "77 BEB standard/conventional (30ft-45ft) \n", - "78 BEB standard/conventional (30ft-45ft) \n", - "79 BEB standard/conventional (30ft-45ft) \n", - "80 BEB standard/conventional (30ft-45ft) \n", - "81 BEB standard/conventional (30ft-45ft) \n", - "82 BEB standard/conventional (30ft-45ft) \n", - "83 BEB standard/conventional (30ft-45ft) \n", - "84 BEB standard/conventional (30ft-45ft) \n", - "85 BEB standard/conventional (30ft-45ft) \n", - "86 BEB standard/conventional (30ft-45ft) \n", - "87 BEB standard/conventional (30ft-45ft) \n", - "88 BEB standard/conventional (30ft-45ft) \n", - "\n", - " description new_project_type \\\n", - "0 The Metropolitan Bus Authority will receive fu... bus only \n", - "1 Wave Transit will receive funding to buy compr... bus only \n", - "2 The Central Oklahoma Transportation and Parkin... bus only \n", - "3 The Champaign-Urbana Mass Transit District wil... bus only \n", - "4 Beaumont Municipal Transit will receive fundin... bus only \n", - "5 The Beloit Transit System will receive funding... bus only \n", - "6 Brownsville Metro will receive funding to buy ... bus only \n", - "7 The city of Colorado Springs' Mountain Metropo... bus only \n", - "8 The City of Jonesboro will receive funding to ... bus only \n", - "9 The city of Norman will buy compressed natural... bus only \n", - "10 The city of Tucson's Sun Tran transit system w... bus only \n", - "11 Coast Transit Authority will receive funding t... bus only \n", - "12 Conroe Connection Transit will receive funding... bus only \n", - "13 Dallas Area Rapid Transit will receive funding... bus only \n", - "14 Delaware Transit Corporation will receive fund... bus only \n", - "15 Golden Empire Transit will receive funding to ... bus only \n", - "16 The Illinois Department of Transportation will... bus only \n", - "17 The Indianapolis Public Transportation Corpora... bus only \n", - "18 The Interurban Transit Partnership (The Rapid)... bus only \n", - "19 The Lowell Regional Transit Authority will rec... bus only \n", - "20 The Madison County Mass Transit District will ... bus only \n", - "21 Mesa County's Grand Valley Transit will receiv... bus only \n", - "22 The Minnesota Department of Transportation, on... bus only \n", - "23 The North Central Regional Transit District wi... bus only \n", - "24 The North County Transit District will receive... bus only \n", - "25 The Ohio Department of Transportation (ODOT) w... bus only \n", - "26 The Oregon Department of Transportation on beh... bus only \n", - "27 The Rhode Island Public Transit Authority will... bus only \n", - "28 The Rockford Mass Transit District will receiv... bus only \n", - "29 The Rogue Valley Transportation District will ... bus only \n", - "30 The San Antonio Metropolitan Transit Authority... bus only \n", - "31 The South Carolina Department of Transportatio... bus only \n", - "32 The South Dakota Department of Transportation,... bus only \n", - "33 The South Dakota Department of Transportation ... bus only \n", - "34 The Southeastern Regional Transit Authority wi... bus only \n", - "35 The Southwest Ohio Regional Transit Authority ... bus only \n", - "36 The State of California, on behalf of Kern Reg... bus only \n", - "37 The State of California, on behalf of Kern Reg... bus only \n", - "38 The Tahoe Transportation District will receive... bus only \n", - "39 The Texas Department of Transportation will re... bus only \n", - "40 The Utah Transit Authority will receive fundin... bus only \n", - "41 The Whatcom Transportation Authority will rece... bus only \n", - "42 White Earth Public Transit will receive fundin... bus only \n", - "43 None bus only \n", - "44 None bus only \n", - "45 None bus only \n", - "46 None bus only \n", - "47 None bus only \n", - "48 None bus only \n", - "49 None bus only \n", - "50 None bus only \n", - "51 None bus only \n", - "52 None None \n", - "53 None None \n", - "54 None None \n", - "55 None None \n", - "56 None None \n", - "57 None None \n", - "58 None None \n", - "59 None None \n", - "60 None None \n", - "61 None None \n", - "62 None None \n", - "63 None None \n", - "64 None None \n", - "65 None None \n", - "66 None None \n", - "67 None None \n", - "68 None None \n", - "69 None None \n", - "70 None None \n", - "71 None None \n", - "72 None None \n", - "73 None None \n", - "74 None None \n", - "75 None None \n", - "76 None None \n", - "77 None None \n", - "78 None None \n", - "79 None None \n", - "80 None None \n", - "81 None None \n", - "82 None None \n", - "83 None None \n", - "84 None None \n", - "85 None None \n", - "86 None None \n", - "87 None None \n", - "88 None None \n", - "\n", - " total_cost bus_count source ppno \\\n", - "0 10000000 8.0 fta None \n", - "1 2860250 5.0 fta None \n", - "2 4278772 9.0 fta None \n", - "3 6635394 10.0 fta None \n", - "4 2819460 5.0 fta None \n", - "5 653184 1.0 fta None \n", - "6 4738886 6.0 fta None \n", - "7 3199038 6.0 fta None \n", - "8 1010372 5.0 fta None \n", - "9 776714 6.0 fta None \n", - "10 21490560 39.0 fta None \n", - "11 1760000 9.0 fta None \n", - "12 4500000 4.0 fta None \n", - "13 103000000 90.0 fta None \n", - "14 8740728 6.0 fta None \n", - "15 5750351 10.0 fta None \n", - "16 12600000 134.0 fta None \n", - "17 19040336 20.0 fta None \n", - "18 6197180 11.0 fta None \n", - "19 6859296 7.0 fta None \n", - "20 1080000 2.0 fta None \n", - "21 1162000 3.0 fta None \n", - "22 1456970 7.0 fta None \n", - "23 2063160 3.0 fta None \n", - "24 29330243 23.0 fta None \n", - "25 29331665 69.0 fta None \n", - "26 181250 5.0 fta None \n", - "27 5000000 25.0 fta None \n", - "28 4094652 4.0 fta None \n", - "29 3937500 6.0 fta None \n", - "30 3187200 15.0 fta None \n", - "31 15423904 160.0 fta None \n", - "32 1006750 9.0 fta None \n", - "33 1276628 9.0 fta None \n", - "34 11560000 16.0 fta None \n", - "35 9806428 16.0 fta None \n", - "36 3248500 5.0 fta None \n", - "37 2932500 15.0 fta None \n", - "38 3400000 4.0 fta None \n", - "39 7443765 56.0 fta None \n", - "40 17055353 25.0 fta None \n", - "41 9644865 11.0 fta None \n", - "42 723171 4.0 fta None \n", - "43 39478000 29.0 tircp CP005 \n", - "44 102790000 112.0 tircp CP029 \n", - "45 1543000 3.0 tircp CP090 \n", - "46 3547000 5.0 tircp CP114 \n", - "47 16580000 20.0 tircp CP076 \n", - "48 2900000 40.0 tircp CP004 \n", - "49 9516000 14.0 tircp CP045 \n", - "50 7200000 7.0 tircp CP073 \n", - "51 3696513 3.0 tircp CP074 \n", - "52 5755155 5.0 dgs 11819 \n", - "53 9921626 11.0 dgs 2020-061 \n", - "54 4508160 5.0 dgs 1416 \n", - "55 3623536 4.0 dgs 22100367 - 00 \n", - "56 9319520 10.0 dgs CO2165 \n", - "57 22846640 20.0 dgs 57071 \n", - "58 3659072 4.0 dgs 10609 \n", - "59 5458305 5.0 dgs 10076963-000 \n", - "60 18759576 12.0 dgs 4500040166 \n", - "61 5787606 6.0 dgs 347750000P \n", - "62 4786285 5.0 dgs EBUS001 \n", - "63 17973373 19.0 dgs A-21587 \n", - "64 37642044 33.0 dgs 21-077 \n", - "65 5559486 6.0 dgs ASTR876345 \n", - "66 2781891 3.0 dgs 20-18895 \n", - "67 5406355 5.0 dgs 10076963-000 \n", - "68 3762440 4.0 dgs UC DAVIS (UNITRANS) (DAVIS, CA) \n", - "69 10175590 10.0 dgs 2201132 \n", - "70 847214 1.0 dgs 609571 \n", - "71 2008826 2.0 dgs ZC654 / ZC653 \n", - "72 5771865 5.0 dgs 120706 \n", - "73 4452892 5.0 dgs 9009418 \n", - "74 5198615 5.0 dgs 9011071 \n", - "75 4134000 4.0 dgs PUR00318127 \n", - "76 4134000 2.0 dgs PUR00318127 \n", - "77 8990000 10.0 dgs SC001-2300002779 \n", - "78 5987790 5.0 dgs City of Santa Rosa(Santa Rosa CityBus) \n", - "79 4068202 4.0 dgs PA-2021-001-SRCB \n", - "80 4932930 5.0 dgs University of California, Irvine \n", - "81 5188379 5.0 dgs 63759 \n", - "82 859270 1.0 dgs 609570 \n", - "83 3223324 2.0 dgs EBUS002 \n", - "84 3223324 1.0 dgs EBUS002 \n", - "85 2396600 2.0 dgs 21-2002 \n", - "86 1862258 2.0 dgs 57614 \n", - "87 3687803 4.0 dgs P02771 \n", - "88 4642225 5.0 dgs PA81335877 \n", - "\n", - " project_description \n", - "0 None \n", - "1 None \n", - "2 None \n", - "3 None \n", - "4 None \n", - "5 None \n", - "6 None \n", - "7 None \n", - "8 None \n", - "9 None \n", - "10 None \n", - "11 None \n", - "12 None \n", - "13 None \n", - "14 None \n", - "15 None \n", - "16 None \n", - "17 None \n", - "18 None \n", - "19 None \n", - "20 None \n", - "21 None \n", - "22 None \n", - "23 None \n", - "24 None \n", - "25 None \n", - "26 None \n", - "27 None \n", - "28 None \n", - "29 None \n", - "30 None \n", - "31 None \n", - "32 None \n", - "33 None \n", - "34 None \n", - "35 None \n", - "36 None \n", - "37 None \n", - "38 None \n", - "39 None \n", - "40 None \n", - "41 None \n", - "42 None \n", - "43 Purchase 13 60-foot articulated BRT buses and ... \n", - "44 Acquire 112 zero-emission buses to replace exi... \n", - "45 Purchase of 3 zero-emission buses that will su... \n", - "46 The Project implements a new transit service u... \n", - "47 Purchase 20 zero-emission buses to extend Rout... \n", - "48 Purchase five 40-foot CNG buses for BRT Route ... \n", - "49 Purchase 7 new coach-style buses to support a ... \n", - "50 Purchase 7 electric buses to expand services o... \n", - "51 Purchases 3 zero-emission electric buses to in... \n", - "52 None \n", - "53 None \n", - "54 None \n", - "55 None \n", - "56 None \n", - "57 None \n", - "58 None \n", - "59 None \n", - "60 None \n", - "61 None \n", - "62 None \n", - "63 None \n", - "64 None \n", - "65 None \n", - "66 None \n", - "67 None \n", - "68 None \n", - "69 None \n", - "70 None \n", - "71 None \n", - "72 None \n", - "73 None \n", - "74 None \n", - "75 None \n", - "76 None \n", - "77 None \n", - "78 None \n", - "79 None \n", - "80 None \n", - "81 None \n", - "82 None \n", - "83 None \n", - "84 None \n", - "85 None \n", - "86 None \n", - "87 None \n", - "88 None " + "Index(['transit_agency', 'total_project_count', 'total_project_count_ppno',\n", + " 'total_agg_cost', 'total_bus_count', 'new_cost_per_bus',\n", + " 'new_zscore_cost_per_bus', 'new_is_cpb_outlier?'],\n", + " dtype='object')" ] }, - "execution_count": 110, "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged_data" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "id": "ece95fb7-cbb8-46bd-a5f9-2b68a47a4817", - "metadata": {}, - "outputs": [ + "output_type": "display_data" + }, { "data": { "text/plain": [ - "Index(['transit_agency', 'total_project_count', 'total_project_count_ppno',\n", - " 'total_agg_cost', 'total_bus_count', 'cpb', 'zscore_cost_per_bus',\n", - " 'is_cpb_outlier?'],\n", - " dtype='object')" + "(82, 8)" ] }, "metadata": {}, @@ -3720,7 +2148,7 @@ { "data": { "text/plain": [ - "(82, 8)" + "0" ] }, "metadata": {}, @@ -3728,8 +2156,44 @@ }, { "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
\n", + "
" + ], "text/plain": [ - "81" + "Empty DataFrame\n", + "Columns: [transit_agency, total_project_count, total_project_count_ppno, total_agg_cost, total_bus_count, new_cost_per_bus, new_zscore_cost_per_bus, new_is_cpb_outlier?]\n", + "Index: []" ] }, "metadata": {}, @@ -3738,9 +2202,9 @@ { "data": { "text/plain": [ - "max 1563298\n", + "max 1611662\n", "min 36250\n", - "Name: cpb, dtype: int64" + "Name: new_cost_per_bus, dtype: int64" ] }, "metadata": {}, @@ -3768,6 +2232,17 @@ "metadata": {}, "output_type": "display_data" }, + { + "data": { + "text/plain": [ + "max 2.182513\n", + "min -1.939451\n", + "Name: new_zscore_cost_per_bus, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/html": [ @@ -3794,43 +2269,43 @@ " total_project_count_ppno\n", " total_agg_cost\n", " total_bus_count\n", - " cpb\n", - " zscore_cost_per_bus\n", - " is_cpb_outlier?\n", + " new_cost_per_bus\n", + " new_zscore_cost_per_bus\n", + " new_is_cpb_outlier?\n", " \n", " \n", " \n", " \n", - " 35\n", - " Madison County Mass Transit District\n", + " 5\n", + " Cape Fear Public Transportation Authority\n", " 1\n", " 0\n", - " 1080000\n", - " 2.0\n", - " 540000\n", - " -0.609227\n", + " 2860250\n", + " 5.0\n", + " 572050\n", + " -0.537564\n", " False\n", " \n", " \n", - " 77\n", - " Utah Transit Authority\n", + " 46\n", + " Rhode Island Public Transit Authority\n", " 1\n", " 0\n", - " 17055353\n", + " 5000000\n", " 25.0\n", - " 682214\n", - " -0.254227\n", + " 200000\n", + " -1.511009\n", " False\n", " \n", " \n", - " 51\n", - " SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA)\n", + " 2\n", + " Antelope Valley Transit Authority (AVTA)\n", " 0\n", " 1\n", - " 5771865\n", - " 5.0\n", - " 1154373\n", - " 0.924393\n", + " 39478000\n", + " 29.0\n", + " 1361310\n", + " 1.527483\n", " False\n", " \n", " \n", @@ -3838,20 +2313,20 @@ "" ], "text/plain": [ - " transit_agency total_project_count \\\n", - "35 Madison County Mass Transit District 1 \n", - "77 Utah Transit Authority 1 \n", - "51 SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA) 0 \n", - "\n", - " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", - "35 0 1080000 2.0 540000 \n", - "77 0 17055353 25.0 682214 \n", - "51 1 5771865 5.0 1154373 \n", - "\n", - " zscore_cost_per_bus is_cpb_outlier? \n", - "35 -0.609227 False \n", - "77 -0.254227 False \n", - "51 0.924393 False " + " transit_agency total_project_count \\\n", + "5 Cape Fear Public Transportation Authority 1 \n", + "46 Rhode Island Public Transit Authority 1 \n", + "2 Antelope Valley Transit Authority (AVTA) 0 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count \\\n", + "5 0 2860250 5.0 \n", + "46 0 5000000 25.0 \n", + "2 1 39478000 29.0 \n", + "\n", + " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", + "5 572050 -0.537564 False \n", + "46 200000 -1.511009 False \n", + "2 1361310 1.527483 False " ] }, "metadata": {}, @@ -3862,7 +2337,7 @@ "# no outliers\n", "# use new_cpb_aggregaete on merged data then filter for outliers == False\n", "agg_agency = new_cpb_aggregate(merged_data)\n", - "agg_agency_no_outliers = agg_agency[agg_agency[\"is_cpb_outlier?\"] == False]\n", + "agg_agency_no_outliers = agg_agency[agg_agency[\"new_is_cpb_outlier?\"] == False]\n", "agg_prop = new_cpb_aggregate(merged_data, column=\"prop_type\")\n", "agg_bus_size = new_cpb_aggregate(merged_data, column=\"bus_size_type\")\n", "agg_source = new_cpb_aggregate(merged_data, column=\"source\")\n", @@ -3872,11 +2347,13 @@ " agg_agency.columns,\n", " agg_agency.shape,\n", " #removed outliers\n", - " len(agg_agency[agg_agency[\"is_cpb_outlier?\"] == False]),\n", + " len(agg_agency[agg_agency[\"new_is_cpb_outlier?\"] == True]),\n", + " agg_agency[agg_agency[\"new_is_cpb_outlier?\"] == True],\n", " #min max, without outlier\n", - " agg_agency_no_outliers[\"cpb\"].agg([\"max\",\"min\"]),\n", + " agg_agency_no_outliers[\"new_cost_per_bus\"].agg([\"max\",\"min\"]),\n", " agg_agency_no_outliers[\"total_bus_count\"].agg([\"max\",\"min\"]),\n", " agg_agency_no_outliers[\"total_agg_cost\"].agg([\"max\",\"min\"]),\n", + " agg_agency_no_outliers[\"new_zscore_cost_per_bus\"].agg([\"max\",\"min\"]),\n", " agg_agency.sample(3)\n", " \n", ")" @@ -3884,7 +2361,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 141, "id": "1696d78f-7018-417b-9847-d82edac3acdf", "metadata": {}, "outputs": [ @@ -3914,9 +2391,9 @@ " total_project_count_ppno\n", " total_agg_cost\n", " total_bus_count\n", - " cpb\n", - " zscore_cost_per_bus\n", - " is_cpb_outlier?\n", + " new_cost_per_bus\n", + " new_zscore_cost_per_bus\n", + " new_is_cpb_outlier?\n", " \n", " \n", " \n", @@ -3924,11 +2401,11 @@ " 0\n", " BEB\n", " 0\n", - " 31\n", - " 170455813\n", - " 164.0\n", - " 1039364\n", - " 0.923505\n", + " 30\n", + " 167232489\n", + " 163.0\n", + " 1025966\n", + " 0.897727\n", " False\n", " \n", " \n", @@ -3939,7 +2416,7 @@ " 176039140\n", " 252.0\n", " 698568\n", - " 0.122141\n", + " 0.125652\n", " False\n", " \n", " \n", @@ -3950,7 +2427,7 @@ " 120951335\n", " 102.0\n", " 1185797\n", - " 1.267835\n", + " 1.274642\n", " False\n", " \n", " \n", @@ -3961,7 +2438,7 @@ " 56678000\n", " 44.0\n", " 1288136\n", - " 1.508480\n", + " 1.515980\n", " False\n", " \n", " \n", @@ -3972,7 +2449,7 @@ " 1006750\n", " 9.0\n", " 111861\n", - " -1.257470\n", + " -1.257929\n", " False\n", " \n", " \n", @@ -3983,7 +2460,7 @@ " 91824361\n", " 145.0\n", " 633271\n", - " -0.031401\n", + " -0.028332\n", " False\n", " \n", " \n", @@ -3994,7 +2471,7 @@ " 8403969\n", " 44.0\n", " 190999\n", - " -1.071381\n", + " -1.071304\n", " False\n", " \n", " \n", @@ -4005,7 +2482,7 @@ " 36775430\n", " 125.0\n", " 294203\n", - " -0.828702\n", + " -0.827927\n", " False\n", " \n", " \n", @@ -4016,7 +2493,7 @@ " 41552404\n", " 325.0\n", " 127853\n", - " -1.219866\n", + " -1.220216\n", " False\n", " \n", " \n", @@ -4027,7 +2504,7 @@ " 128156513\n", " 143.0\n", " 896199\n", - " 0.586860\n", + " 0.591708\n", " False\n", " \n", " \n", @@ -4047,29 +2524,29 @@ "8 not specified 4 \n", "9 zero-emission bus (not specified) 0 \n", "\n", - " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", - "0 31 170455813 164.0 1039364 \n", - "1 1 176039140 252.0 698568 \n", - "2 6 120951335 102.0 1185797 \n", - "3 2 56678000 44.0 1288136 \n", - "4 0 1006750 9.0 111861 \n", - "5 0 91824361 145.0 633271 \n", - "6 0 8403969 44.0 190999 \n", - "7 0 36775430 125.0 294203 \n", - "8 1 41552404 325.0 127853 \n", - "9 5 128156513 143.0 896199 \n", - "\n", - " zscore_cost_per_bus is_cpb_outlier? \n", - "0 0.923505 False \n", - "1 0.122141 False \n", - "2 1.267835 False \n", - "3 1.508480 False \n", - "4 -1.257470 False \n", - "5 -0.031401 False \n", - "6 -1.071381 False \n", - "7 -0.828702 False \n", - "8 -1.219866 False \n", - "9 0.586860 False " + " total_project_count_ppno total_agg_cost total_bus_count \\\n", + "0 30 167232489 163.0 \n", + "1 1 176039140 252.0 \n", + "2 6 120951335 102.0 \n", + "3 2 56678000 44.0 \n", + "4 0 1006750 9.0 \n", + "5 0 91824361 145.0 \n", + "6 0 8403969 44.0 \n", + "7 0 36775430 125.0 \n", + "8 1 41552404 325.0 \n", + "9 5 128156513 143.0 \n", + "\n", + " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", + "0 1025966 0.897727 False \n", + "1 698568 0.125652 False \n", + "2 1185797 1.274642 False \n", + "3 1288136 1.515980 False \n", + "4 111861 -1.257929 False \n", + "5 633271 -0.028332 False \n", + "6 190999 -1.071304 False \n", + "7 294203 -0.827927 False \n", + "8 127853 -1.220216 False \n", + "9 896199 0.591708 False " ] }, "metadata": {}, @@ -4108,8 +2585,8 @@ " \n", " \n", " BEB\n", - " 164.0\n", - " 170455813\n", + " 163.0\n", + " 167232489\n", " \n", " \n", " CNG\n", @@ -4158,8 +2635,8 @@ " \n", " \n", " Grand Total\n", - " 1353.0\n", - " 831843715\n", + " 1352.0\n", + " 828620391\n", " \n", " \n", "\n", @@ -4168,7 +2645,7 @@ "text/plain": [ " bus_count total_cost\n", "prop_type \n", - "BEB 164.0 170455813\n", + "BEB 163.0 167232489\n", "CNG 252.0 176039140\n", "FCEB 102.0 120951335\n", "electric (not specified) 44.0 56678000\n", @@ -4178,7 +2655,7 @@ "mix (zero and low emission) 125.0 36775430\n", "not specified 325.0 41552404\n", "zero-emission bus (not specified) 143.0 128156513\n", - "Grand Total 1353.0 831843715" + "Grand Total 1352.0 828620391" ] }, "metadata": {}, @@ -4208,7 +2685,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 142, "id": "ca709e43-4947-4a34-970f-216d4b6ab7cc", "metadata": {}, "outputs": [], @@ -4239,7 +2716,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 143, "id": "01647d83-8b4f-47a9-ab57-a1db7cd501dd", "metadata": {}, "outputs": [ @@ -4269,9 +2746,9 @@ " total_project_count_ppno\n", " total_agg_cost\n", " total_bus_count\n", - " cpb\n", - " zscore_cost_per_bus\n", - " is_cpb_outlier?\n", + " new_cost_per_bus\n", + " new_zscore_cost_per_bus\n", + " new_is_cpb_outlier?\n", " \n", " \n", " \n", @@ -4279,11 +2756,11 @@ " 0\n", " BEB\n", " 0\n", - " 31\n", - " 170455813\n", - " 164.0\n", - " 1039364\n", - " 0.923505\n", + " 30\n", + " 167232489\n", + " 163.0\n", + " 1025966\n", + " 0.897727\n", " False\n", " \n", " \n", @@ -4294,7 +2771,7 @@ " 120951335\n", " 102.0\n", " 1185797\n", - " 1.267835\n", + " 1.274642\n", " False\n", " \n", " \n", @@ -4305,7 +2782,7 @@ " 56678000\n", " 44.0\n", " 1288136\n", - " 1.508480\n", + " 1.515980\n", " False\n", " \n", " \n", @@ -4316,7 +2793,7 @@ " 128156513\n", " 143.0\n", " 896199\n", - " 0.586860\n", + " 0.591708\n", " False\n", " \n", " \n", @@ -4330,17 +2807,17 @@ "3 electric (not specified) 1 \n", "9 zero-emission bus (not specified) 0 \n", "\n", - " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", - "0 31 170455813 164.0 1039364 \n", - "2 6 120951335 102.0 1185797 \n", - "3 2 56678000 44.0 1288136 \n", - "9 5 128156513 143.0 896199 \n", - "\n", - " zscore_cost_per_bus is_cpb_outlier? \n", - "0 0.923505 False \n", - "2 1.267835 False \n", - "3 1.508480 False \n", - "9 0.586860 False " + " total_project_count_ppno total_agg_cost total_bus_count \\\n", + "0 30 167232489 163.0 \n", + "2 6 120951335 102.0 \n", + "3 2 56678000 44.0 \n", + "9 5 128156513 143.0 \n", + "\n", + " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", + "0 1025966 0.897727 False \n", + "2 1185797 1.274642 False \n", + "3 1288136 1.515980 False \n", + "9 896199 0.591708 False " ] }, "metadata": {}, @@ -4379,8 +2856,8 @@ " \n", " \n", " BEB\n", - " 164.0\n", - " 170455813\n", + " 163.0\n", + " 167232489\n", " \n", " \n", " FCEB\n", @@ -4404,7 +2881,7 @@ "text/plain": [ " bus_count total_cost\n", "prop_type \n", - "BEB 164.0 170455813\n", + "BEB 163.0 167232489\n", "FCEB 102.0 120951335\n", "electric (not specified) 44.0 56678000\n", "zero-emission bus (not specified) 143.0 128156513" @@ -4446,8 +2923,8 @@ " \n", " \n", " BEB\n", - " 164.0\n", - " 170455813\n", + " 163.0\n", + " 167232489\n", " \n", " \n", " FCEB\n", @@ -4466,8 +2943,8 @@ " \n", " \n", " Grand Total\n", - " 453.0\n", - " 476241661\n", + " 452.0\n", + " 473018337\n", " \n", " \n", "\n", @@ -4476,11 +2953,11 @@ "text/plain": [ " bus_count total_cost\n", "prop_type \n", - "BEB 164.0 170455813\n", + "BEB 163.0 167232489\n", "FCEB 102.0 120951335\n", "electric (not specified) 44.0 56678000\n", "zero-emission bus (not specified) 143.0 128156513\n", - "Grand Total 453.0 476241661" + "Grand Total 452.0 473018337" ] }, "metadata": {}, @@ -4512,9 +2989,9 @@ " total_project_count_ppno\n", " total_agg_cost\n", " total_bus_count\n", - " cpb\n", - " zscore_cost_per_bus\n", - " is_cpb_outlier?\n", + " new_cost_per_bus\n", + " new_zscore_cost_per_bus\n", + " new_is_cpb_outlier?\n", " \n", " \n", " \n", @@ -4526,7 +3003,7 @@ " 176039140\n", " 252.0\n", " 698568\n", - " 0.122141\n", + " 0.125652\n", " False\n", " \n", " \n", @@ -4537,7 +3014,7 @@ " 1006750\n", " 9.0\n", " 111861\n", - " -1.257470\n", + " -1.257929\n", " False\n", " \n", " \n", @@ -4548,7 +3025,7 @@ " 91824361\n", " 145.0\n", " 633271\n", - " -0.031401\n", + " -0.028332\n", " False\n", " \n", " \n", @@ -4559,7 +3036,7 @@ " 8403969\n", " 44.0\n", " 190999\n", - " -1.071381\n", + " -1.071304\n", " False\n", " \n", " \n", @@ -4570,7 +3047,7 @@ " 36775430\n", " 125.0\n", " 294203\n", - " -0.828702\n", + " -0.827927\n", " False\n", " \n", " \n", @@ -4585,19 +3062,19 @@ "6 low emission (propane) 5 0 \n", "7 mix (zero and low emission) 2 0 \n", "\n", - " total_agg_cost total_bus_count cpb zscore_cost_per_bus \\\n", - "1 176039140 252.0 698568 0.122141 \n", - "4 1006750 9.0 111861 -1.257470 \n", - "5 91824361 145.0 633271 -0.031401 \n", - "6 8403969 44.0 190999 -1.071381 \n", - "7 36775430 125.0 294203 -0.828702 \n", - "\n", - " is_cpb_outlier? \n", - "1 False \n", - "4 False \n", - "5 False \n", - "6 False \n", - "7 False " + " total_agg_cost total_bus_count new_cost_per_bus new_zscore_cost_per_bus \\\n", + "1 176039140 252.0 698568 0.125652 \n", + "4 1006750 9.0 111861 -1.257929 \n", + "5 91824361 145.0 633271 -0.028332 \n", + "6 8403969 44.0 190999 -1.071304 \n", + "7 36775430 125.0 294203 -0.827927 \n", + "\n", + " new_is_cpb_outlier? \n", + "1 False \n", + "4 False \n", + "5 False \n", + "6 False \n", + "7 False " ] }, "metadata": {}, @@ -4796,7 +3273,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 144, "id": "3be3ef4f-0bf3-4770-a8b7-340d372ae1ce", "metadata": {}, "outputs": [ @@ -4826,9 +3303,9 @@ " total_project_count_ppno\n", " total_agg_cost\n", " total_bus_count\n", - " cpb\n", - " zscore_cost_per_bus\n", - " is_cpb_outlier?\n", + " new_cost_per_bus\n", + " new_zscore_cost_per_bus\n", + " new_is_cpb_outlier?\n", " \n", " \n", " \n", @@ -4840,7 +3317,7 @@ " 58237576\n", " 41.0\n", " 1420428\n", - " 1.598471\n", + " 1.605005\n", " False\n", " \n", " \n", @@ -4851,7 +3328,7 @@ " 16694500\n", " 152.0\n", " 109832\n", - " -1.466801\n", + " -1.464878\n", " False\n", " \n", " \n", @@ -4862,7 +3339,7 @@ " 509919038\n", " 881.0\n", " 578795\n", - " -0.369972\n", + " -0.366399\n", " False\n", " \n", " \n", @@ -4873,18 +3350,18 @@ " 9516000\n", " 14.0\n", " 679714\n", - " -0.133939\n", + " -0.130011\n", " False\n", " \n", " \n", " 4\n", " standard/conventional (30ft-45ft)\n", " 0\n", - " 37\n", - " 237476601\n", - " 265.0\n", - " 896138\n", - " 0.372242\n", + " 36\n", + " 234253277\n", + " 264.0\n", + " 887323\n", + " 0.356283\n", " False\n", " \n", " \n", @@ -4899,19 +3376,19 @@ "3 over-the-road 0 \n", "4 standard/conventional (30ft-45ft) 0 \n", "\n", - " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", - "0 2 58237576 41.0 1420428 \n", - "1 0 16694500 152.0 109832 \n", - "2 6 509919038 881.0 578795 \n", - "3 1 9516000 14.0 679714 \n", - "4 37 237476601 265.0 896138 \n", - "\n", - " zscore_cost_per_bus is_cpb_outlier? \n", - "0 1.598471 False \n", - "1 -1.466801 False \n", - "2 -0.369972 False \n", - "3 -0.133939 False \n", - "4 0.372242 False " + " total_project_count_ppno total_agg_cost total_bus_count \\\n", + "0 2 58237576 41.0 \n", + "1 0 16694500 152.0 \n", + "2 6 509919038 881.0 \n", + "3 1 9516000 14.0 \n", + "4 36 234253277 264.0 \n", + "\n", + " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", + "0 1420428 1.605005 False \n", + "1 109832 -1.464878 False \n", + "2 578795 -0.366399 False \n", + "3 679714 -0.130011 False \n", + "4 887323 0.356283 False " ] }, "metadata": {}, @@ -4970,13 +3447,13 @@ " \n", " \n", " standard/conventional (30ft-45ft)\n", - " 265.0\n", - " 237476601\n", + " 264.0\n", + " 234253277\n", " \n", " \n", " Grand Total\n", - " 1353.0\n", - " 831843715\n", + " 1352.0\n", + " 828620391\n", " \n", " \n", "\n", @@ -4989,8 +3466,8 @@ "cutaway 152.0 16694500\n", "not specified 881.0 509919038\n", "over-the-road 14.0 9516000\n", - "standard/conventional (30ft-45ft) 265.0 237476601\n", - "Grand Total 1353.0 831843715" + "standard/conventional (30ft-45ft) 264.0 234253277\n", + "Grand Total 1352.0 828620391" ] }, "metadata": {}, @@ -5017,7 +3494,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 145, "id": "2c933257-bdc2-4007-9571-58475118073c", "metadata": {}, "outputs": [ @@ -5047,9 +3524,9 @@ " total_project_count_ppno\n", " total_agg_cost\n", " total_bus_count\n", - " cpb\n", - " zscore_cost_per_bus\n", - " is_cpb_outlier?\n", + " new_cost_per_bus\n", + " new_zscore_cost_per_bus\n", + " new_is_cpb_outlier?\n", " \n", " \n", " \n", @@ -5057,11 +3534,11 @@ " 0\n", " dgs\n", " 0\n", - " 37\n", - " 253336177\n", - " 237.0\n", - " 1068929\n", - " 1.158131\n", + " 36\n", + " 250112853\n", + " 236.0\n", + " 1059800\n", + " 1.150152\n", " False\n", " \n", " \n", @@ -5072,7 +3549,7 @@ " 391257025\n", " 883.0\n", " 443099\n", - " -1.281952\n", + " -1.287721\n", " False\n", " \n", " \n", @@ -5083,7 +3560,7 @@ " 187250513\n", " 233.0\n", " 803650\n", - " 0.123820\n", + " 0.137569\n", " False\n", " \n", " \n", @@ -5092,14 +3569,19 @@ ], "text/plain": [ " source total_project_count total_project_count_ppno total_agg_cost \\\n", - "0 dgs 0 37 253336177 \n", + "0 dgs 0 36 250112853 \n", "1 fta 43 0 391257025 \n", "2 tircp 0 9 187250513 \n", "\n", - " total_bus_count cpb zscore_cost_per_bus is_cpb_outlier? \n", - "0 237.0 1068929 1.158131 False \n", - "1 883.0 443099 -1.281952 False \n", - "2 233.0 803650 0.123820 False " + " total_bus_count new_cost_per_bus new_zscore_cost_per_bus \\\n", + "0 236.0 1059800 1.150152 \n", + "1 883.0 443099 -1.287721 \n", + "2 233.0 803650 0.137569 \n", + "\n", + " new_is_cpb_outlier? \n", + "0 False \n", + "1 False \n", + "2 False " ] }, "metadata": {}, @@ -5138,8 +3620,8 @@ " \n", " \n", " dgs\n", - " 237.0\n", - " 253336177\n", + " 236.0\n", + " 250112853\n", " \n", " \n", " fta\n", @@ -5153,8 +3635,8 @@ " \n", " \n", " Grand Total\n", - " 1353.0\n", - " 831843715\n", + " 1352.0\n", + " 828620391\n", " \n", " \n", "\n", @@ -5163,10 +3645,10 @@ "text/plain": [ " bus_count total_cost\n", "source \n", - "dgs 237.0 253336177\n", + "dgs 236.0 250112853\n", "fta 883.0 391257025\n", "tircp 233.0 187250513\n", - "Grand Total 1353.0 831843715" + "Grand Total 1352.0 828620391" ] }, "metadata": {}, From cd2c247294812e25b4c237103c75f362de5534bd Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Wed, 19 Jun 2024 23:25:31 +0000 Subject: [PATCH 12/36] made sure charts and graphs are still workinh. started work on trimming down the summary section --- bus_procurement_cost/refactor_bus_cost.ipynb | 1978 +++++++++--------- 1 file changed, 933 insertions(+), 1045 deletions(-) diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index af4a41d25..fc47bfeb6 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -37,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 4, "id": "e1b1f367-1dac-463f-8790-2e5134b7e79b", "metadata": {}, "outputs": [ @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "e1e3bad8-62d3-4ca1-b4dd-b4d5b8d9a86b", "metadata": {}, "outputs": [], @@ -120,45 +120,45 @@ " \n", " \n", " \n", - " 50\n", - " Torrance Transit Department\n", + " 54\n", + " VICTOR VALLEY TRANSIT AUTHORITY (VVTA)\n", " None\n", - " electric (not specified)\n", - " not specified\n", + " BEB\n", + " standard/conventional (30ft-45ft)\n", + " None\n", + " None\n", + " 4508160\n", + " 5.0\n", + " dgs\n", + " 1416\n", " None\n", - " bus only\n", - " 7200000\n", - " 7.0\n", - " tircp\n", - " CP073\n", - " Purchase 7 electric buses to expand services o...\n", " \n", " \n", - " 13\n", - " Dallas Area Rapid Transit (DART)\n", - " DART CNG Bus Fleet Modernization Project\n", - " CNG\n", - " not specified\n", - " Dallas Area Rapid Transit will receive funding...\n", - " bus only\n", - " 103000000\n", - " 90.0\n", - " fta\n", + " 57\n", + " Alameda County Transit Authority\n", + " None\n", + " FCEB\n", + " standard/conventional (30ft-45ft)\n", " None\n", " None\n", + " 22846640\n", + " 20.0\n", + " dgs\n", + " 57071\n", + " None\n", " \n", " \n", - " 22\n", - " Minnesota Department of Transportation on beha...\n", - " Reducing Emissions in Rural Minnesota Transit\n", - " low emission (propane)\n", - " not specified\n", - " The Minnesota Department of Transportation, on...\n", - " bus only\n", - " 1456970\n", - " 7.0\n", - " fta\n", + " 81\n", + " Santa Maria Regional Transit\n", + " None\n", + " BEB\n", + " standard/conventional (30ft-45ft)\n", + " None\n", " None\n", + " 5188379\n", + " 5.0\n", + " dgs\n", + " 63759\n", " None\n", " \n", " \n", @@ -166,30 +166,20 @@ "" ], "text/plain": [ - " transit_agency \\\n", - "50 Torrance Transit Department \n", - "13 Dallas Area Rapid Transit (DART) \n", - "22 Minnesota Department of Transportation on beha... \n", - "\n", - " project_title prop_type \\\n", - "50 None electric (not specified) \n", - "13 DART CNG Bus Fleet Modernization Project CNG \n", - "22 Reducing Emissions in Rural Minnesota Transit low emission (propane) \n", - "\n", - " bus_size_type description \\\n", - "50 not specified None \n", - "13 not specified Dallas Area Rapid Transit will receive funding... \n", - "22 not specified The Minnesota Department of Transportation, on... \n", - "\n", - " new_project_type total_cost bus_count source ppno \\\n", - "50 bus only 7200000 7.0 tircp CP073 \n", - "13 bus only 103000000 90.0 fta None \n", - "22 bus only 1456970 7.0 fta None \n", - "\n", - " project_description \n", - "50 Purchase 7 electric buses to expand services o... \n", - "13 None \n", - "22 None " + " transit_agency project_title prop_type \\\n", + "54 VICTOR VALLEY TRANSIT AUTHORITY (VVTA) None BEB \n", + "57 Alameda County Transit Authority None FCEB \n", + "81 Santa Maria Regional Transit None BEB \n", + "\n", + " bus_size_type description new_project_type \\\n", + "54 standard/conventional (30ft-45ft) None None \n", + "57 standard/conventional (30ft-45ft) None None \n", + "81 standard/conventional (30ft-45ft) None None \n", + "\n", + " total_cost bus_count source ppno project_description \n", + "54 4508160 5.0 dgs 1416 None \n", + "57 22846640 20.0 dgs 57071 None \n", + "81 5188379 5.0 dgs 63759 None " ] }, "metadata": {}, @@ -205,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 7, "id": "2fd82402-3995-495b-9423-f1de88f8c456", "metadata": {}, "outputs": [ @@ -216,7 +206,7 @@ "Name: new_project_type, dtype: int64" ] }, - "execution_count": 24, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -227,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 8, "id": "7bc1cfa9-188e-4b55-805b-d5b76627bf3d", "metadata": {}, "outputs": [ @@ -276,7 +266,7 @@ "Index: []" ] }, - "execution_count": 23, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -289,7 +279,6 @@ "cell_type": "markdown", "id": "d04911c1-e839-41fe-87b3-5065586f2223", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -299,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "5513b941-ecdb-405e-bfd6-952df6b8f8b4", "metadata": { "tags": [] @@ -420,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "8e18cbe6-bde7-4c30-8a8a-aefd8d619821", "metadata": { "tags": [] @@ -482,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "8004cc2d-957f-4e9a-9ca8-2a6f9aba9ffb", "metadata": { "tags": [] @@ -552,7 +541,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "c0ca9254-2983-4cab-845c-f9bfb0229417", "metadata": { "tags": [] @@ -568,6 +557,71 @@ " return" ] }, + { + "cell_type": "code", + "execution_count": 42, + "id": "4a5bc209-a660-4c18-86b0-574640391a7d", + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.ticker import ScalarFormatter\n", + "\n", + "def dist_curve(\n", + " df: pd.DataFrame,\n", + " mean: str,\n", + " std: str,\n", + " title: str,\n", + " xlabel: str,\n", + "):\n", + " \"\"\"\n", + " function to make distribution curve. uses the \"cpb\" column of the df.\n", + " \"\"\"\n", + " sns.histplot(df[\"cost_per_bus\"], kde=True, color=\"skyblue\", bins=20)\n", + " # mean line\n", + " plt.axvline(\n", + " mean, color=\"red\", linestyle=\"dashed\", linewidth=2, label=f\"Mean: ${mean:,.2f}\"\n", + " )\n", + " # mean+1std\n", + " plt.axvline(\n", + " mean + std,\n", + " color=\"green\",\n", + " linestyle=\"dashed\",\n", + " linewidth=2,\n", + " label=f\"Standard Deviation: ${std:,.2f}\",\n", + " )\n", + " plt.axvline(mean - std, color=\"green\", linestyle=\"dashed\", linewidth=2)\n", + " plt.axvline(mean + (std * 2), color=\"green\", linestyle=\"dashed\", linewidth=2)\n", + " plt.axvline(mean + (std * 3), color=\"green\", linestyle=\"dashed\", linewidth=2)\n", + "\n", + " plt.title(title + \" with Mean and Standard Deviation\")\n", + " plt.xlabel(xlabel)\n", + " plt.ylabel(\"Frequency\")\n", + "\n", + " # Turn off scientific notation on x-axis?\n", + " plt.gca().xaxis.set_major_formatter(ScalarFormatter(useMathText=False))\n", + "\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + " return\n", + "\n", + "def make_chart(y_col: str, title: str, data: pd.DataFrame, x_col: str):\n", + " \"\"\"\n", + " function to create chart. sorts values by y_col ascending.\"\"\"\n", + " \n", + " data.sort_values(by=y_col, ascending=False).head(10).plot(\n", + " x=x_col, y=y_col, kind=\"bar\", color=\"skyblue\"\n", + " )\n", + " plt.title(title)\n", + " plt.xlabel(x_col)\n", + " plt.ylabel(y_col)\n", + "\n", + " plt.ticklabel_format(style=\"plain\", axis=\"y\")\n", + " plt.show()" + ] + }, { "cell_type": "markdown", "id": "97bdb85b-ecaa-4634-8ea1-02ebc630567f", @@ -580,23 +634,12 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 13, "id": "ea9c3269-d53d-4d94-bc22-c6768cb63d91", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_290/2582954682.py:75: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n", - " df[\"funding\"]\n", - "/tmp/ipykernel_290/2582954682.py:25: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n", - " df[new_col2] = df[new_col2].str.replace(\")\", \"\")\n" - ] - } - ], + "outputs": [], "source": [ "# FTA\n", "import numpy as np\n", @@ -728,21 +771,12 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 14, "id": "4e16119d-f6f3-478b-a419-7c4989557910", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.9/site-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Data Validation extension is not supported and will be removed\n", - " warn(msg)\n" - ] - } - ], + "outputs": [], "source": [ "# TIRCP\n", "import numpy as np\n", @@ -878,7 +912,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 15, "id": "359f3b7a-d691-446f-9a14-424c47fc0929", "metadata": { "tags": [] @@ -1079,7 +1113,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 16, "id": "19f4bd75-f614-4937-880a-1e1a6ff2eb7f", "metadata": { "tags": [] @@ -1233,7 +1267,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 17, "id": "8340e510-a934-4730-b340-74a5e1eeb377", "metadata": {}, "outputs": [], @@ -1244,7 +1278,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 18, "id": "3c2a88c2-3a07-4336-aee2-237d949bdd94", "metadata": {}, "outputs": [ @@ -1522,12 +1556,13 @@ " \n", " \n", "- ZEB only\n", - " - zeb_only_df function\n", - " or just use the ZEB list to filter the dataframe\n", + " ~~- zeb_only_df function~~\n", + " switched to filtering the dataframe to get ZEB answers\n", "\n", "- non-ZEB only\n", - " - non_zeb_only_df function\n", - "\n", + " ~~- non_zeb_only_df function~~\n", + " switched to filtering the dataframe\n", + " \n", "- means and standard deviations\n", " - for charts?\n" ] @@ -1556,10 +1591,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "f84aaa3c-7bab-46c9-b739-021fdd6b60a2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(89, 11)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", + " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", + " 'ppno', 'project_description'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# initial final df from old code\n", "# 89 rows and 11 columns\n", @@ -1571,7 +1628,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 20, "id": "c9ffd5a9-b772-4509-b84c-9a96760b3112", "metadata": {}, "outputs": [], @@ -1583,7 +1640,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 21, "id": "f8aa3674-78fe-4ba9-8f5e-697d91ff4011", "metadata": {}, "outputs": [], @@ -1596,7 +1653,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 22, "id": "42c43416-981f-43d7-a642-6a22dc6619f2", "metadata": {}, "outputs": [ @@ -1634,8 +1691,8 @@ "data": { "text/plain": [ "Index(['transit_agency', 'total_project_count', 'total_project_count_ppno',\n", - " 'total_agg_cost', 'total_bus_count', 'cpb', 'zscore_cost_per_bus',\n", - " 'is_cpb_outlier?'],\n", + " 'total_agg_cost', 'total_bus_count', 'new_cost_per_bus',\n", + " 'new_zscore_cost_per_bus', 'new_is_cpb_outlier?'],\n", " dtype='object')" ] }, @@ -1656,7 +1713,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 23, "id": "d2bca15a-c12c-4cf5-a5a9-d591ee73a359", "metadata": {}, "outputs": [ @@ -1686,43 +1743,43 @@ " total_project_count_ppno\n", " total_agg_cost\n", " total_bus_count\n", - " cpb\n", - " zscore_cost_per_bus\n", - " is_cpb_outlier?\n", + " new_cost_per_bus\n", + " new_zscore_cost_per_bus\n", + " new_is_cpb_outlier?\n", " \n", " \n", " \n", " \n", - " 16\n", - " City of San Luis Obispo\n", - " 0\n", + " 45\n", + " Oregon Department of Transportation on behalf ...\n", " 1\n", - " 859270\n", - " 1.0\n", - " 859270\n", - " 0.187746\n", + " 0\n", + " 181250\n", + " 5.0\n", + " 36250\n", + " -1.866706\n", " False\n", " \n", " \n", - " 46\n", - " Rhode Island Public Transit Authority\n", + " 63\n", + " South Dakota Department of Transportation on b...\n", " 1\n", " 0\n", - " 5000000\n", - " 25.0\n", - " 200000\n", - " -1.457947\n", + " 1276628\n", + " 9.0\n", + " 141847\n", + " -1.603111\n", " False\n", " \n", " \n", - " 34\n", - " Lowell Regional Transit Authority\n", - " 1\n", + " 17\n", + " City of Santa Rosa(Santa Rosa CityBus)\n", " 0\n", - " 6859296\n", - " 7.0\n", - " 979899\n", - " 0.488865\n", + " 1\n", + " 5987790\n", + " 5.0\n", + " 1197558\n", + " 1.032193\n", " False\n", " \n", " \n", @@ -1730,23 +1787,23 @@ "" ], "text/plain": [ - " transit_agency total_project_count \\\n", - "16 City of San Luis Obispo 0 \n", - "46 Rhode Island Public Transit Authority 1 \n", - "34 Lowell Regional Transit Authority 1 \n", + " transit_agency total_project_count \\\n", + "45 Oregon Department of Transportation on behalf ... 1 \n", + "63 South Dakota Department of Transportation on b... 1 \n", + "17 City of Santa Rosa(Santa Rosa CityBus) 0 \n", "\n", - " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", - "16 1 859270 1.0 859270 \n", - "46 0 5000000 25.0 200000 \n", - "34 0 6859296 7.0 979899 \n", + " total_project_count_ppno total_agg_cost total_bus_count \\\n", + "45 0 181250 5.0 \n", + "63 0 1276628 9.0 \n", + "17 1 5987790 5.0 \n", "\n", - " zscore_cost_per_bus is_cpb_outlier? \n", - "16 0.187746 False \n", - "46 -1.457947 False \n", - "34 0.488865 False " + " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", + "45 36250 -1.866706 False \n", + "63 141847 -1.603111 False \n", + "17 1197558 1.032193 False " ] }, - "execution_count": 48, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1758,7 +1815,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 79, "id": "637032e4-d855-4190-a6f5-ff695f77143f", "metadata": {}, "outputs": [ @@ -1806,9 +1863,9 @@ " total_project_count_ppno\n", " total_agg_cost\n", " total_bus_count\n", - " cpb\n", - " zscore_cost_per_bus\n", - " is_cpb_outlier?\n", + " new_cost_per_bus\n", + " new_zscore_cost_per_bus\n", + " new_is_cpb_outlier?\n", " \n", " \n", " \n", @@ -1879,19 +1936,19 @@ "3 CITY OF PORTERVILLE (PORTERVILLE, CA) 0 \n", "4 CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ... 0 \n", "\n", - " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", - "0 0 10000000 8.0 1250000 \n", - "1 1 22846640 20.0 1142332 \n", - "2 1 39478000 29.0 1361310 \n", - "3 1 2781891 3.0 927297 \n", - "4 1 3623536 4.0 905884 \n", - "\n", - " zscore_cost_per_bus is_cpb_outlier? \n", - "0 1.163100 False \n", - "1 0.894336 False \n", - "2 1.440957 False \n", - "3 0.357558 False \n", - "4 0.304106 False " + " total_project_count_ppno total_agg_cost total_bus_count \\\n", + "0 0 10000000 8.0 \n", + "1 1 22846640 20.0 \n", + "2 1 39478000 29.0 \n", + "3 1 2781891 3.0 \n", + "4 1 3623536 4.0 \n", + "\n", + " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", + "0 1250000 1.163100 False \n", + "1 1142332 0.894336 False \n", + "2 1361310 1.440957 False \n", + "3 927297 0.357558 False \n", + "4 905884 0.304106 False " ] }, "metadata": {}, @@ -1902,7 +1959,7 @@ "text/plain": [ "False 81\n", "True 1\n", - "Name: is_cpb_outlier?, dtype: int64" + "Name: new_is_cpb_outlier?, dtype: int64" ] }, "metadata": {}, @@ -1952,9 +2009,9 @@ " total_project_count_ppno\n", " total_agg_cost\n", " total_bus_count\n", - " cpb\n", - " zscore_cost_per_bus\n", - " is_cpb_outlier?\n", + " new_cost_per_bus\n", + " new_zscore_cost_per_bus\n", + " new_is_cpb_outlier?\n", " \n", " \n", " \n", @@ -1977,11 +2034,11 @@ " transit_agency total_project_count \\\n", "71 Transit Joint Powers Authority for Merced County 0 \n", "\n", - " total_project_count_ppno total_agg_cost total_bus_count cpb \\\n", - "71 2 6446648 3.0 2148882 \n", + " total_project_count_ppno total_agg_cost total_bus_count \\\n", + "71 2 6446648 3.0 \n", "\n", - " zscore_cost_per_bus is_cpb_outlier? \n", - "71 3.406922 True " + " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", + "71 2148882 3.406922 True " ] }, "metadata": {}, @@ -1995,10 +2052,10 @@ " final.shape,\n", " agg1.shape,\n", " agg1.head(),\n", - " agg1[\"is_cpb_outlier?\"].value_counts(),\n", - " agg1[\"zscore_cost_per_bus\"].min(),\n", - " agg1[\"zscore_cost_per_bus\"].max(),\n", - " agg1[agg1[\"is_cpb_outlier?\"] == True]\n", + " agg1[\"new_is_cpb_outlier?\"].value_counts(),\n", + " agg1[\"new_zscore_cost_per_bus\"].min(),\n", + " agg1[\"new_zscore_cost_per_bus\"].max(),\n", + " agg1[agg1[\"new_is_cpb_outlier?\"] == True]\n", ")" ] }, @@ -2083,7 +2140,9 @@ { "cell_type": "markdown", "id": "3428875a-6a64-41bc-8f9c-81902006d7f0", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "## Testing variables rework\n", "there are a lot of variables in the initial script. need to cut down the amount of variables or at least make it more efficient. " @@ -2091,7 +2150,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 25, "id": "f21298ee-0efb-4f91-ba63-55fc2645a4d2", "metadata": {}, "outputs": [ @@ -2105,7 +2164,7 @@ " dtype='object')" ] }, - "execution_count": 138, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -2120,85 +2179,10 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 73, "id": "ece95fb7-cbb8-46bd-a5f9-2b68a47a4817", "metadata": {}, "outputs": [ - { - "data": { - "text/plain": [ - "Index(['transit_agency', 'total_project_count', 'total_project_count_ppno',\n", - " 'total_agg_cost', 'total_bus_count', 'new_cost_per_bus',\n", - " 'new_zscore_cost_per_bus', 'new_is_cpb_outlier?'],\n", - " dtype='object')" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "(82, 8)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
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" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [transit_agency, total_project_count, total_project_count_ppno, total_agg_cost, total_bus_count, new_cost_per_bus, new_zscore_cost_per_bus, new_is_cpb_outlier?]\n", - "Index: []" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "text/plain": [ @@ -2242,7 +2226,35 @@ }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "# no outliers\n", + "# use new_cpb_aggregaete on merged data then filter for outliers == False\n", + "agg_agency = new_cpb_aggregate(merged_data)\n", + "\n", + "agg_prop = new_cpb_aggregate(merged_data, column=\"prop_type\")\n", + "agg_bus_size = new_cpb_aggregate(merged_data, column=\"bus_size_type\")\n", + "agg_source = new_cpb_aggregate(merged_data, column=\"source\")\n", + "\n", + "#overall agency info\n", + "display(\n", + "\n", + " #min max, without outlier\n", + " agg_agency[\"new_cost_per_bus\"].agg([\"max\",\"min\"]),\n", + " agg_agency[\"total_bus_count\"].agg([\"max\",\"min\"]),\n", + " agg_agency[\"total_agg_cost\"].agg([\"max\",\"min\"]),\n", + " agg_agency[\"new_zscore_cost_per_bus\"].agg([\"max\",\"min\"]),\n", + " \n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "1696d78f-7018-417b-9847-d82edac3acdf", + "metadata": {}, + "outputs": [ { "data": { "text/html": [ @@ -2264,129 +2276,7 @@ " \n", " \n", " \n", - " transit_agency\n", - " total_project_count\n", - " total_project_count_ppno\n", - " total_agg_cost\n", - " total_bus_count\n", - " new_cost_per_bus\n", - " new_zscore_cost_per_bus\n", - " new_is_cpb_outlier?\n", - " \n", - " \n", - " \n", - " \n", - " 5\n", - " Cape Fear Public Transportation Authority\n", - " 1\n", - " 0\n", - " 2860250\n", - " 5.0\n", - " 572050\n", - " -0.537564\n", - " False\n", - " \n", - " \n", - " 46\n", - " Rhode Island Public Transit Authority\n", - " 1\n", - " 0\n", - " 5000000\n", - " 25.0\n", - " 200000\n", - " -1.511009\n", - " False\n", - " \n", - " \n", - " 2\n", - " Antelope Valley Transit Authority (AVTA)\n", - " 0\n", - " 1\n", - " 39478000\n", - " 29.0\n", - " 1361310\n", - " 1.527483\n", - " False\n", - " \n", - " \n", - "\n", - "" - ], - "text/plain": [ - " transit_agency total_project_count \\\n", - "5 Cape Fear Public Transportation Authority 1 \n", - "46 Rhode Island Public Transit Authority 1 \n", - "2 Antelope Valley Transit Authority (AVTA) 0 \n", - "\n", - " total_project_count_ppno total_agg_cost total_bus_count \\\n", - "5 0 2860250 5.0 \n", - "46 0 5000000 25.0 \n", - "2 1 39478000 29.0 \n", - "\n", - " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", - "5 572050 -0.537564 False \n", - "46 200000 -1.511009 False \n", - "2 1361310 1.527483 False " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# no outliers\n", - "# use new_cpb_aggregaete on merged data then filter for outliers == False\n", - "agg_agency = new_cpb_aggregate(merged_data)\n", - "agg_agency_no_outliers = agg_agency[agg_agency[\"new_is_cpb_outlier?\"] == False]\n", - "agg_prop = new_cpb_aggregate(merged_data, column=\"prop_type\")\n", - "agg_bus_size = new_cpb_aggregate(merged_data, column=\"bus_size_type\")\n", - "agg_source = new_cpb_aggregate(merged_data, column=\"source\")\n", - "\n", - "#overall agency info\n", - "display(\n", - " agg_agency.columns,\n", - " agg_agency.shape,\n", - " #removed outliers\n", - " len(agg_agency[agg_agency[\"new_is_cpb_outlier?\"] == True]),\n", - " agg_agency[agg_agency[\"new_is_cpb_outlier?\"] == True],\n", - " #min max, without outlier\n", - " agg_agency_no_outliers[\"new_cost_per_bus\"].agg([\"max\",\"min\"]),\n", - " agg_agency_no_outliers[\"total_bus_count\"].agg([\"max\",\"min\"]),\n", - " agg_agency_no_outliers[\"total_agg_cost\"].agg([\"max\",\"min\"]),\n", - " agg_agency_no_outliers[\"new_zscore_cost_per_bus\"].agg([\"max\",\"min\"]),\n", - " agg_agency.sample(3)\n", - " \n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "id": "1696d78f-7018-417b-9847-d82edac3acdf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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prop_typeprop_typetotal_project_counttotal_project_count_ppnototal_agg_cost
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bus_counttotal_cost
prop_type
BEB163.0167232489
CNG252.0176039140
FCEB102.0120951335
electric (not specified)44.056678000
ethanol9.01006750
low emission (hybrid)145.091824361
low emission (propane)44.08403969
mix (zero and low emission)125.036775430
not specified325.041552404
zero-emission bus (not specified)143.0128156513
Grand Total1352.0828620391
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" - ], - "text/plain": [ - " bus_count total_cost\n", - "prop_type \n", - "BEB 163.0 167232489\n", - "CNG 252.0 176039140\n", - "FCEB 102.0 120951335\n", - "electric (not specified) 44.0 56678000\n", - "ethanol 9.0 1006750\n", - "low emission (hybrid) 145.0 91824361\n", - "low emission (propane) 44.0 8403969\n", - "mix (zero and low emission) 125.0 36775430\n", - "not specified 325.0 41552404\n", - "zero-emission bus (not specified) 143.0 128156513\n", - "Grand Total 1352.0 828620391" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -2677,20 +2458,41 @@ "display(\n", " #from new_cpb_agg\n", " agg_prop,\n", - " #piot\n", - " pivot_prop_type\n", + " #pivot\n", + " #pivot_prop_type\n", ")\n", - "# same data, might not need the pivot table anymore" + "# same data, dont need the pivot table anymore" ] }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 116, "id": "ca709e43-4947-4a34-970f-216d4b6ab7cc", "metadata": {}, "outputs": [], "source": [ "#pivot table to get grand total for zeb only data\n", + "\n", + "# keep this\n", + "zeb_list =[\n", + " \"BEB\",\n", + " \"FCEB\",\n", + " \"electric (not specified)\",\n", + " \"zero-emission bus (not specified)\",\n", + "\n", + "]\n", + "\n", + "#keep this\n", + "non_zeb_list =[\n", + " \"CNG\",\n", + " \"ethanol\",\n", + " \"low emission (hybrid)\",\n", + " \"low emission (propane)\",\n", + " \"mix (zero and low emission)\",\n", + "]\n", + "\n", + "\n", + "\n", "#keep this\n", "pivot_zeb_prop = pd.pivot_table(\n", " #filted incoming DF for zeb prop types\n", @@ -2700,7 +2502,7 @@ " aggfunc = \"sum\",\n", " margins = True,\n", " margins_name = \"Grand Total\"\n", - ") \n", + ").reset_index() \n", "\n", "#keep this\n", "pivot_non_zeb_prop = pd.pivot_table(\n", @@ -2711,12 +2513,12 @@ " aggfunc = \"sum\",\n", " margins = True,\n", " margins_name = \"Grand Total\"\n", - ")" + ").reset_index()" ] }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 117, "id": "01647d83-8b4f-47a9-ab57-a1db7cd501dd", "metadata": {}, "outputs": [ @@ -2742,82 +2544,52 @@ " \n", " \n", " prop_type\n", - " total_project_count\n", - " total_project_count_ppno\n", - " total_agg_cost\n", - " total_bus_count\n", - " new_cost_per_bus\n", - " new_zscore_cost_per_bus\n", - " new_is_cpb_outlier?\n", + " bus_count\n", + " total_cost\n", " \n", " \n", " \n", " \n", " 0\n", " BEB\n", - " 0\n", - " 30\n", - " 167232489\n", " 163.0\n", - " 1025966\n", - " 0.897727\n", - " False\n", + " 167232489\n", " \n", " \n", - " 2\n", + " 1\n", " FCEB\n", - " 2\n", - " 6\n", - " 120951335\n", " 102.0\n", - " 1185797\n", - " 1.274642\n", - " False\n", + " 120951335\n", " \n", " \n", - " 3\n", + " 2\n", " electric (not specified)\n", - " 1\n", - " 2\n", - " 56678000\n", " 44.0\n", - " 1288136\n", - " 1.515980\n", - " False\n", + " 56678000\n", " \n", " \n", - " 9\n", + " 3\n", " zero-emission bus (not specified)\n", - " 0\n", - " 5\n", - " 128156513\n", " 143.0\n", - " 896199\n", - " 0.591708\n", - " False\n", + " 128156513\n", + " \n", + " \n", + " 4\n", + " Grand Total\n", + " 452.0\n", + " 473018337\n", " \n", " \n", "\n", "" ], "text/plain": [ - " prop_type total_project_count \\\n", - "0 BEB 0 \n", - "2 FCEB 2 \n", - "3 electric (not specified) 1 \n", - "9 zero-emission bus (not specified) 0 \n", - "\n", - " total_project_count_ppno total_agg_cost total_bus_count \\\n", - "0 30 167232489 163.0 \n", - "2 6 120951335 102.0 \n", - "3 2 56678000 44.0 \n", - "9 5 128156513 143.0 \n", - "\n", - " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", - "0 1025966 0.897727 False \n", - "2 1185797 1.274642 False \n", - "3 1288136 1.515980 False \n", - "9 896199 0.591708 False " + " prop_type bus_count total_cost\n", + "0 BEB 163.0 167232489\n", + "1 FCEB 102.0 120951335\n", + "2 electric (not specified) 44.0 56678000\n", + "3 zero-emission bus (not specified) 143.0 128156513\n", + "4 Grand Total 452.0 473018337" ] }, "metadata": {}, @@ -2844,125 +2616,90 @@ " \n", " \n", " \n", + " prop_type\n", " bus_count\n", " total_cost\n", " \n", - " \n", - " prop_type\n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " BEB\n", - " 163.0\n", - " 167232489\n", - " \n", - " \n", - " FCEB\n", - " 102.0\n", - " 120951335\n", - " \n", - " \n", - " electric (not specified)\n", - " 44.0\n", - " 56678000\n", - " \n", - " \n", - " zero-emission bus (not specified)\n", - " 143.0\n", - " 128156513\n", - " \n", - " \n", - "\n", - "" - ], - "text/plain": [ - " bus_count total_cost\n", - "prop_type \n", - "BEB 163.0 167232489\n", - "FCEB 102.0 120951335\n", - "electric (not specified) 44.0 56678000\n", - "zero-emission bus (not specified) 143.0 128156513" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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zero-emission bus (not specified)143.01281565134mix (zero and low emission)125.036775430
Grand Total452.04730183375Grand Total575.0314049650
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" ], "text/plain": [ - " bus_count total_cost\n", - "prop_type \n", - "BEB 163.0 167232489\n", - "FCEB 102.0 120951335\n", - "electric (not specified) 44.0 56678000\n", - "zero-emission bus (not specified) 143.0 128156513\n", - "Grand Total 452.0 473018337" + " prop_type bus_count total_cost\n", + "0 CNG 252.0 176039140\n", + "1 ethanol 9.0 1006750\n", + "2 low emission (hybrid) 145.0 91824361\n", + "3 low emission (propane) 44.0 8403969\n", + "4 mix (zero and low emission) 125.0 36775430\n", + "5 Grand Total 575.0 314049650" ] }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "display(\n", + " #zeb data 3 different methods\n", + " #1. filtering agg_prop by zeb list, no grand totas\n", + " #2. filtering pivot talbe by zeb list, without grand totals\n", + " #3. dedicated pivot table for zeb, with grand totals\n", + " #agg_prop[agg_prop[\"prop_type\"].isin(zeb_list)],\n", + " #pivot_prop_type.loc[zeb_list],\n", + " pivot_zeb_prop,\n", + " \n", + " #non-zeb same 3 methods\n", + " #agg_prop[agg_prop[\"prop_type\"].isin(non_zeb_list)],\n", + " #pivot_prop_type.loc[non_zeb_list],\n", + " pivot_non_zeb_prop\n", + ")\n", + "# confirmed all data is the same, but need pivot for grand total rows" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "3be3ef4f-0bf3-4770-a8b7-340d372ae1ce", + "metadata": {}, + "outputs": [ { "data": { "text/html": [ @@ -2984,7 +2721,7 @@ " \n", " \n", " \n", - " prop_type\n", + " bus_size_type\n", " total_project_count\n", " total_project_count_ppno\n", " total_agg_cost\n", @@ -2996,58 +2733,58 @@ " \n", " \n", " \n", - " 1\n", - " CNG\n", - " 12\n", - " 1\n", - " 176039140\n", - " 252.0\n", - " 698568\n", - " 0.125652\n", + " 0\n", + " articulated\n", + " 0\n", + " 2\n", + " 58237576\n", + " 41.0\n", + " 1420428\n", + " 1.605005\n", " False\n", " \n", " \n", - " 4\n", - " ethanol\n", - " 1\n", + " 1\n", + " cutaway\n", + " 3\n", " 0\n", - " 1006750\n", - " 9.0\n", - " 111861\n", - " -1.257929\n", + " 16694500\n", + " 152.0\n", + " 109832\n", + " -1.464878\n", " False\n", " \n", " \n", - " 5\n", - " low emission (hybrid)\n", - " 16\n", - " 0\n", - " 91824361\n", - " 145.0\n", - " 633271\n", - " -0.028332\n", + " 2\n", + " not specified\n", + " 40\n", + " 6\n", + " 509919038\n", + " 881.0\n", + " 578795\n", + " -0.366399\n", " False\n", " \n", " \n", - " 6\n", - " low emission (propane)\n", - " 5\n", + " 3\n", + " over-the-road\n", " 0\n", - " 8403969\n", - " 44.0\n", - " 190999\n", - " -1.071304\n", + " 1\n", + " 9516000\n", + " 14.0\n", + " 679714\n", + " -0.130011\n", " False\n", " \n", " \n", - " 7\n", - " mix (zero and low emission)\n", - " 2\n", + " 4\n", + " standard/conventional (30ft-45ft)\n", " 0\n", - " 36775430\n", - " 125.0\n", - " 294203\n", - " -0.827927\n", + " 36\n", + " 234253277\n", + " 264.0\n", + " 887323\n", + " 0.356283\n", " False\n", " \n", " \n", @@ -3055,26 +2792,26 @@ "" ], "text/plain": [ - " prop_type total_project_count total_project_count_ppno \\\n", - "1 CNG 12 1 \n", - "4 ethanol 1 0 \n", - "5 low emission (hybrid) 16 0 \n", - "6 low emission (propane) 5 0 \n", - "7 mix (zero and low emission) 2 0 \n", - "\n", - " total_agg_cost total_bus_count new_cost_per_bus new_zscore_cost_per_bus \\\n", - "1 176039140 252.0 698568 0.125652 \n", - "4 1006750 9.0 111861 -1.257929 \n", - "5 91824361 145.0 633271 -0.028332 \n", - "6 8403969 44.0 190999 -1.071304 \n", - "7 36775430 125.0 294203 -0.827927 \n", + " bus_size_type total_project_count \\\n", + "0 articulated 0 \n", + "1 cutaway 3 \n", + "2 not specified 40 \n", + "3 over-the-road 0 \n", + "4 standard/conventional (30ft-45ft) 0 \n", "\n", - " new_is_cpb_outlier? \n", - "1 False \n", - "4 False \n", - "5 False \n", - "6 False \n", - "7 False " + " total_project_count_ppno total_agg_cost total_bus_count \\\n", + "0 2 58237576 41.0 \n", + "1 0 16694500 152.0 \n", + "2 6 509919038 881.0 \n", + "3 1 9516000 14.0 \n", + "4 36 234253277 264.0 \n", + "\n", + " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", + "0 1420428 1.605005 False \n", + "1 109832 -1.464878 False \n", + "2 578795 -0.366399 False \n", + "3 679714 -0.130011 False \n", + "4 887323 0.356283 False " ] }, "metadata": {}, @@ -3105,128 +2842,55 @@ " total_cost\n", " \n", " \n", - " prop_type\n", + " bus_size_type\n", " \n", " \n", " \n", " \n", " \n", " \n", - " CNG\n", - " 252.0\n", - " 176039140\n", + " articulated\n", + " 41.0\n", + " 58237576\n", " \n", " \n", - " ethanol\n", - " 9.0\n", - " 1006750\n", + " cutaway\n", + " 152.0\n", + " 16694500\n", " \n", " \n", - " low emission (hybrid)\n", - " 145.0\n", - " 91824361\n", + " not specified\n", + " 881.0\n", + " 509919038\n", " \n", " \n", - " low emission (propane)\n", - " 44.0\n", - " 8403969\n", + " over-the-road\n", + " 14.0\n", + " 9516000\n", " \n", " \n", - " mix (zero and low emission)\n", - " 125.0\n", - " 36775430\n", + " standard/conventional (30ft-45ft)\n", + " 264.0\n", + " 234253277\n", + " \n", + " \n", + " Grand Total\n", + " 1352.0\n", + " 828620391\n", " \n", " \n", "\n", "" ], "text/plain": [ - " bus_count total_cost\n", - "prop_type \n", - "CNG 252.0 176039140\n", - "ethanol 9.0 1006750\n", - "low emission (hybrid) 145.0 91824361\n", - "low emission (propane) 44.0 8403969\n", - "mix (zero and low emission) 125.0 36775430" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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prop_type
CNG252.0176039140
ethanol9.01006750
low emission (hybrid)145.091824361
low emission (propane)44.08403969
mix (zero and low emission)125.036775430
Grand Total575.0314049650
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" - ], - "text/plain": [ - " bus_count total_cost\n", - "prop_type \n", - "CNG 252.0 176039140\n", - "ethanol 9.0 1006750\n", - "low emission (hybrid) 145.0 91824361\n", - "low emission (propane) 44.0 8403969\n", - "mix (zero and low emission) 125.0 36775430\n", - "Grand Total 575.0 314049650" + " bus_count total_cost\n", + "bus_size_type \n", + "articulated 41.0 58237576\n", + "cutaway 152.0 16694500\n", + "not specified 881.0 509919038\n", + "over-the-road 14.0 9516000\n", + "standard/conventional (30ft-45ft) 264.0 234253277\n", + "Grand Total 1352.0 828620391" ] }, "metadata": {}, @@ -3234,47 +2898,27 @@ } ], "source": [ - "# keep this\n", - "zeb_list =[\n", - " \"BEB\",\n", - " \"FCEB\",\n", - " \"electric (not specified)\",\n", - " \"zero-emission bus (not specified)\",\n", - "\n", - "]\n", - "\n", - "#keep this\n", - "non_zeb_list =[\n", - " \"CNG\",\n", - " \"ethanol\",\n", - " \"low emission (hybrid)\",\n", - " \"low emission (propane)\",\n", - " \"mix (zero and low emission)\",\n", - "]\n", - "\n", - "\n", - "\n", + "# answers total buses and cost per grant type\n", + "pivot_size = pd.pivot_table(\n", + " merged_data,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"bus_size_type\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ")\n", "display(\n", - " #zeb data 3 different methods\n", - " #1. filtering agg_prop by zeb list, no grand totas\n", - " #2. filtering pivot talbe by zeb list, without grand totals\n", - " #3. dedicated pivot table for zeb, with grand totals\n", - " agg_prop[agg_prop[\"prop_type\"].isin(zeb_list)],\n", - " pivot_prop_type.loc[zeb_list],\n", - " pivot_zeb_prop,\n", - " \n", - " #non-zeb same 3 methods\n", - " agg_prop[agg_prop[\"prop_type\"].isin(non_zeb_list)],\n", - " pivot_prop_type.loc[non_zeb_list],\n", - " pivot_non_zeb_prop\n", + " agg_bus_size,\n", + " #pivot_size\n", ")\n", - "# confirmed all data is the same, but need pivot for grand total rows" + "\n", + "#same data, dont need pivot for this one because the grand totals will be the same. and the pivot tables dont have cpb" ] }, { "cell_type": "code", - "execution_count": 144, - "id": "3be3ef4f-0bf3-4770-a8b7-340d372ae1ce", + "execution_count": 123, + "id": "2c933257-bdc2-4007-9571-58475118073c", "metadata": {}, "outputs": [ { @@ -3298,7 +2942,7 @@ " \n", " \n", " \n", - " bus_size_type\n", + " source\n", " total_project_count\n", " total_project_count_ppno\n", " total_agg_cost\n", @@ -3311,57 +2955,35 @@ " \n", " \n", " 0\n", - " articulated\n", + " dgs\n", " 0\n", - " 2\n", - " 58237576\n", - " 41.0\n", - " 1420428\n", - " 1.605005\n", + " 36\n", + " 250112853\n", + " 236.0\n", + " 1059800\n", + " 1.150152\n", " False\n", " \n", " \n", " 1\n", - " cutaway\n", - " 3\n", + " fta\n", + " 43\n", " 0\n", - " 16694500\n", - " 152.0\n", - " 109832\n", - " -1.464878\n", + " 391257025\n", + " 883.0\n", + " 443099\n", + " -1.287721\n", " False\n", " \n", " \n", " 2\n", - " not specified\n", - " 40\n", - " 6\n", - " 509919038\n", - " 881.0\n", - " 578795\n", - " -0.366399\n", - " False\n", - " \n", - " \n", - " 3\n", - " over-the-road\n", - " 0\n", - " 1\n", - " 9516000\n", - " 14.0\n", - " 679714\n", - " -0.130011\n", - " False\n", - " \n", - " \n", - " 4\n", - " standard/conventional (30ft-45ft)\n", + " tircp\n", " 0\n", - " 36\n", - " 234253277\n", - " 264.0\n", - " 887323\n", - " 0.356283\n", + " 9\n", + " 187250513\n", + " 233.0\n", + " 803650\n", + " 0.137569\n", " False\n", " \n", " \n", @@ -3369,26 +2991,20 @@ "" ], "text/plain": [ - " bus_size_type total_project_count \\\n", - "0 articulated 0 \n", - "1 cutaway 3 \n", - "2 not specified 40 \n", - "3 over-the-road 0 \n", - "4 standard/conventional (30ft-45ft) 0 \n", + " source total_project_count total_project_count_ppno total_agg_cost \\\n", + "0 dgs 0 36 250112853 \n", + "1 fta 43 0 391257025 \n", + "2 tircp 0 9 187250513 \n", "\n", - " total_project_count_ppno total_agg_cost total_bus_count \\\n", - "0 2 58237576 41.0 \n", - "1 0 16694500 152.0 \n", - "2 6 509919038 881.0 \n", - "3 1 9516000 14.0 \n", - "4 36 234253277 264.0 \n", + " total_bus_count new_cost_per_bus new_zscore_cost_per_bus \\\n", + "0 236.0 1059800 1.150152 \n", + "1 883.0 443099 -1.287721 \n", + "2 233.0 803650 0.137569 \n", "\n", - " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", - "0 1420428 1.605005 False \n", - "1 109832 -1.464878 False \n", - "2 578795 -0.366399 False \n", - "3 679714 -0.130011 False \n", - "4 887323 0.356283 False " + " new_is_cpb_outlier? \n", + "0 False \n", + "1 False \n", + "2 False " ] }, "metadata": {}, @@ -3415,43 +3031,33 @@ " \n", " \n", " \n", + " source\n", " bus_count\n", " total_cost\n", " \n", - " \n", - " bus_size_type\n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " articulated\n", - " 41.0\n", - " 58237576\n", - " \n", - " \n", - " cutaway\n", - " 152.0\n", - " 16694500\n", - " \n", - " \n", - " not specified\n", - " 881.0\n", - " 509919038\n", + " 0\n", + " dgs\n", + " 236.0\n", + " 250112853\n", " \n", " \n", - " over-the-road\n", - " 14.0\n", - " 9516000\n", + " 1\n", + " fta\n", + " 883.0\n", + " 391257025\n", " \n", " \n", - " standard/conventional (30ft-45ft)\n", - " 264.0\n", - " 234253277\n", + " 2\n", + " tircp\n", + " 233.0\n", + " 187250513\n", " \n", " \n", - " Grand Total\n", + " 3\n", + " Grand Total\n", " 1352.0\n", " 828620391\n", " \n", @@ -3460,14 +3066,11 @@ "" ], "text/plain": [ - " bus_count total_cost\n", - "bus_size_type \n", - "articulated 41.0 58237576\n", - "cutaway 152.0 16694500\n", - "not specified 881.0 509919038\n", - "over-the-road 14.0 9516000\n", - "standard/conventional (30ft-45ft) 264.0 234253277\n", - "Grand Total 1352.0 828620391" + " source bus_count total_cost\n", + "0 dgs 236.0 250112853\n", + "1 fta 883.0 391257025\n", + "2 tircp 233.0 187250513\n", + "3 Grand Total 1352.0 828620391" ] }, "metadata": {}, @@ -3476,28 +3079,222 @@ ], "source": [ "# answers total buses and cost per grant type\n", - "pivot_size = pd.pivot_table(\n", + "pivot_source = pd.pivot_table(\n", " merged_data,\n", " values = [\"bus_count\", \"total_cost\"],\n", - " index = \"bus_size_type\",\n", + " index = \"source\",\n", " aggfunc = \"sum\",\n", " margins = True,\n", " margins_name = \"Grand Total\"\n", - ")\n", + ").reset_index()\n", + "\n", "display(\n", - " agg_bus_size,\n", - " pivot_size\n", + " agg_source,\n", + " pivot_source\n", ")\n", + "# dont need pivot, keep agg_source to retain cpb" + ] + }, + { + "cell_type": "markdown", + "id": "11547020-dd35-4745-98f8-bbd02fccaa23", + "metadata": { + "tags": [] + }, + "source": [ + "## Testing Charts\n", "\n", - "#same data, dont need pivot for this one because the grand totals will be the same. and the pivot tables dont have cpb" + "using `merged_data`, now without outliers.\n", + "charts looking good, similar results to initial charts" ] }, { "cell_type": "code", - "execution_count": 145, - "id": "2c933257-bdc2-4007-9571-58475118073c", + "execution_count": 75, + "id": "4753f3ea-00b6-4d5e-a3f0-73b3d3593acb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "792635.3409090909" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "396712.6067531972" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# means and standard deviations\n", + "# for graphs\n", + "cpb_mean = merged_data[\"cost_per_bus\"].mean()\n", + "cpb_std = merged_data[\"cost_per_bus\"].std()\n", + "\n", + "display(\n", + " cpb_mean,\n", + " cpb_std\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "8645cf77-b30a-4c45-b943-ac81e8b5a613", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dist_curve(\n", + " df=merged_data,\n", + " mean=cpb_mean,\n", + " std=cpb_std,\n", + " title=\"all buses, cost per bus distribution\",\n", + " xlabel=\"cost per bus, $ million(s)\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "cefa6800-df50-4eda-95f8-74363ef942d0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dist_curve(\n", + " df=merged_data[merged_data[\"prop_type\"].isin(zeb_list)],\n", + " mean=merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"cost_per_bus\"].mean(),\n", + " std=merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"cost_per_bus\"].std(),\n", + " title=\"ZEB buses, cost per bus distribution\",\n", + " xlabel=\"cost per bus, $ million(s)\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "563304d2-2d98-44e6-b3a4-fd54f63fc0d8", "metadata": {}, "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "dist_curve(\n", + " df=merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)],\n", + " mean=merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"cost_per_bus\"].mean(),\n", + " std=merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"cost_per_bus\"].std(),\n", + " title=\"non-ZEB costper bus Distribution\",\n", + " xlabel='\"cost per bus, $ million(s)\"',\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "85beee26-6316-43df-8f91-71b1e699ab65", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['prop_type', 'total_project_count', 'total_project_count_ppno',\n", + " 'total_agg_cost', 'total_bus_count', 'new_cost_per_bus',\n", + " 'new_zscore_cost_per_bus', 'new_is_cpb_outlier?'],\n", + " dtype='object')" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "agg_prop.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "5f11c857-ddbe-4871-aeca-e27fa00fbde8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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bus_counttotal_cost
9zero-emission bus (not specified)896199143.0
source1CNG698568252.0
dgs236.02501128535low emission (hybrid)633271145.0
fta883.03912570257mix (zero and low emission)294203125.0
tircp233.01872505136low emission (propane)19099944.0
Grand Total1352.08286203918not specified127853325.0
4ethanol1118619.0
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" ], "text/plain": [ - " bus_count total_cost\n", - "source \n", - "dgs 236.0 250112853\n", - "fta 883.0 391257025\n", - "tircp 233.0 187250513\n", - "Grand Total 1352.0 828620391" + " prop_type new_cost_per_bus total_bus_count\n", + "3 electric (not specified) 1288136 44.0\n", + "2 FCEB 1185797 102.0\n", + "0 BEB 1025966 163.0\n", + "9 zero-emission bus (not specified) 896199 143.0\n", + "1 CNG 698568 252.0\n", + "5 low emission (hybrid) 633271 145.0\n", + "7 mix (zero and low emission) 294203 125.0\n", + "6 low emission (propane) 190999 44.0\n", + "8 not specified 127853 325.0\n", + "4 ethanol 111861 9.0" ] }, "metadata": {}, @@ -3656,27 +3405,166 @@ } ], "source": [ - "# answers total buses and cost per grant type\n", - "pivot_source = pd.pivot_table(\n", - " merged_data,\n", - " values = [\"bus_count\", \"total_cost\"],\n", - " index = \"source\",\n", - " aggfunc = \"sum\",\n", - " margins = True,\n", - " margins_name = \"Grand Total\"\n", - ")\n", - "\n", "display(\n", - " agg_source,\n", - " pivot_source\n", - ")\n", - "# dont need pivot, keep agg_source to retain cpb" + " make_chart(\"new_cost_per_bus\", \"Cost per bus by propulsion type\", x_col=\"prop_type\", data=agg_prop),\n", + " make_chart(\"total_bus_count\", \"Bus count by propulsion type\", x_col=\"prop_type\", data=agg_prop),\n", + " agg_prop[[\"prop_type\",\"new_cost_per_bus\",\"total_bus_count\"]].sort_values(by=\"new_cost_per_bus\", ascending=False)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9270ab8f-25ff-4de3-aca5-7ef4637a4f9c", + "metadata": {}, + "source": [ + "## Testing summary\n", + "time to rework the summary section.\n", + "\n", + "no more long expositions and variables. try to get the same point across using tables instead." + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "id": "2472461d-7663-4b66-9bde-4c2a199707a5", + "metadata": {}, + "outputs": [], + "source": [ + "summary = f\"\"\"\n", + "\n", + "# Bus Procurement Cost Analysis\n", + "\n", + "## Summary\n", + "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", + "\n", + "Data was compiled from three data sources:\n", + " 1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", + " 2. TIRCP project data (state-funded, California only)\n", + " 3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", + "\n", + "Breakdown of each data souce:\n", + "{pivot_source.to_markdown(index=False)}\n", + "\n", + "The initial dataset was contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries.\n", + "The resulting dataset only contained projects that were solely used to procure buses. 87 projects remained. These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", + "\n", + "ZEB buses include: \n", + " - zero-emission (not specified) \n", + " - electric (not specified)\n", + " - battery electric \n", + " - fuel cell electric\n", + "\n", + "Non-ZEB buses include: \n", + " - CNG \n", + " - ethanol \n", + " - ow emission (hybrid, propane) \n", + " - diesel \n", + " - gas\n", + "\n", + "Below are charts and tables that summarize the findings.\n", + "\n", + "\n", + "ZEB Summary\n", + "\n", + "{pivot_zeb_prop.to_markdown(index=False)}\n", + "\n", + "Non-ZEB Summary\n", + "\n", + "{pivot_non_zeb_prop.to_markdown(index=False)}\n", + "\n", + "the remaining buses did not specify a propulsion type\n", + "\n", + "\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "id": "1441f3d5-9630-420c-836b-4b7251e4c310", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "\n", + "\n", + "# Bus Procurement Cost Analysis\n", + "\n", + "## Summary\n", + "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", + "\n", + "Data was compiled from three data sources:\n", + " 1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", + " 2. TIRCP project data (state-funded, California only)\n", + " 3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", + "\n", + "Breakdown of each data souce:\n", + "| source | bus_count | total_cost |\n", + "|:------------|------------:|-------------:|\n", + "| dgs | 236 | 250112853 |\n", + "| fta | 883 | 391257025 |\n", + "| tircp | 233 | 187250513 |\n", + "| Grand Total | 1352 | 828620391 |\n", + "\n", + "The initial dataset was contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries.\n", + "The resulting dataset only contained projects that were solely used to procure buses. 87 projects remained. These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", + "\n", + "ZEB buses include: \n", + " * zero-emission (not specified) \n", + " - electric (not specified)\n", + " - battery electric \n", + " - fuel cell electric\n", + "\n", + "Non-ZEB buses include: \n", + " - CNG \n", + " - ethanol \n", + " - ow emission (hybrid, propane) \n", + " - diesel \n", + " - gas\n", + "\n", + "Below are charts and tables that summarize the findings.\n", + "\n", + "\n", + "ZEB Summary\n", + "\n", + "| prop_type | bus_count | total_cost |\n", + "|:----------------------------------|------------:|-------------:|\n", + "| BEB | 163 | 167232489 |\n", + "| FCEB | 102 | 120951335 |\n", + "| electric (not specified) | 44 | 56678000 |\n", + "| zero-emission bus (not specified) | 143 | 128156513 |\n", + "| Grand Total | 452 | 473018337 |\n", + "\n", + "Non-ZEB Summary\n", + "\n", + "| prop_type | bus_count | total_cost |\n", + "|:----------------------------|------------:|-------------:|\n", + "| CNG | 252 | 176039140 |\n", + "| ethanol | 9 | 1006750 |\n", + "| low emission (hybrid) | 145 | 91824361 |\n", + "| low emission (propane) | 44 | 8403969 |\n", + "| mix (zero and low emission) | 125 | 36775430 |\n", + "| Grand Total | 575 | 314049650 |\n", + "the remaining buses did not specify a propulsion type\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Markdown, display\n", + "display(Markdown(summary))" ] }, { "cell_type": "code", "execution_count": null, - "id": "4753f3ea-00b6-4d5e-a3f0-73b3d3593acb", + "id": "a3f71064-b7d2-47b2-bae7-db27ab3c09ee", "metadata": {}, "outputs": [], "source": [] From 519b07b7de16a4eeae16ea2909dcb84726a47af8 Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Thu, 20 Jun 2024 17:25:56 +0000 Subject: [PATCH 13/36] more changes --- bus_procurement_cost/refactor_bus_cost.ipynb | 3938 ++++++++++++++---- 1 file changed, 3142 insertions(+), 796 deletions(-) diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index fc47bfeb6..0f5ac5cab 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -120,66 +120,76 @@ " \n", " \n", " \n", - " 54\n", - " VICTOR VALLEY TRANSIT AUTHORITY (VVTA)\n", + " 80\n", + " University of California, Irvine\n", " None\n", " BEB\n", " standard/conventional (30ft-45ft)\n", " None\n", " None\n", - " 4508160\n", + " 4932930\n", " 5.0\n", " dgs\n", - " 1416\n", + " University of California, Irvine\n", " None\n", " \n", " \n", - " 57\n", - " Alameda County Transit Authority\n", - " None\n", - " FCEB\n", - " standard/conventional (30ft-45ft)\n", - " None\n", + " 45\n", + " City of Wasco\n", " None\n", - " 22846640\n", - " 20.0\n", - " dgs\n", - " 57071\n", + " zero-emission bus (not specified)\n", + " not specified\n", " None\n", + " bus only\n", + " 1543000\n", + " 3.0\n", + " tircp\n", + " CP090\n", + " Purchase of 3 zero-emission buses that will su...\n", " \n", " \n", - " 81\n", - " Santa Maria Regional Transit\n", - " None\n", - " BEB\n", - " standard/conventional (30ft-45ft)\n", - " None\n", + " 51\n", + " Transit Joint Powers Authority of Merced County\n", " None\n", - " 5188379\n", - " 5.0\n", - " dgs\n", - " 63759\n", + " zero-emission bus (not specified)\n", + " not specified\n", " None\n", + " bus only\n", + " 3696513\n", + " 3.0\n", + " tircp\n", + " CP074\n", + " Purchases 3 zero-emission electric buses to in...\n", " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency project_title prop_type \\\n", - "54 VICTOR VALLEY TRANSIT AUTHORITY (VVTA) None BEB \n", - "57 Alameda County Transit Authority None FCEB \n", - "81 Santa Maria Regional Transit None BEB \n", - "\n", - " bus_size_type description new_project_type \\\n", - "54 standard/conventional (30ft-45ft) None None \n", - "57 standard/conventional (30ft-45ft) None None \n", - "81 standard/conventional (30ft-45ft) None None \n", - "\n", - " total_cost bus_count source ppno project_description \n", - "54 4508160 5.0 dgs 1416 None \n", - "57 22846640 20.0 dgs 57071 None \n", - "81 5188379 5.0 dgs 63759 None " + " transit_agency project_title \\\n", + "80 University of California, Irvine None \n", + "45 City of Wasco None \n", + "51 Transit Joint Powers Authority of Merced County None \n", + "\n", + " prop_type bus_size_type \\\n", + "80 BEB standard/conventional (30ft-45ft) \n", + "45 zero-emission bus (not specified) not specified \n", + "51 zero-emission bus (not specified) not specified \n", + "\n", + " description new_project_type total_cost bus_count source \\\n", + "80 None None 4932930 5.0 dgs \n", + "45 None bus only 1543000 3.0 tircp \n", + "51 None bus only 3696513 3.0 tircp \n", + "\n", + " ppno \\\n", + "80 University of California, Irvine \n", + "45 CP090 \n", + "51 CP074 \n", + "\n", + " project_description \n", + "80 None \n", + "45 Purchase of 3 zero-emission buses that will su... \n", + "51 Purchases 3 zero-emission electric buses to in... " ] }, "metadata": {}, @@ -193,88 +203,6 @@ ")" ] }, - { - "cell_type": "code", - "execution_count": 7, - "id": "2fd82402-3995-495b-9423-f1de88f8c456", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "bus only 52\n", - "Name: new_project_type, dtype: int64" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "final['new_project_type'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7bc1cfa9-188e-4b55-805b-d5b76627bf3d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
transit_agencyproject_titleprop_typebus_size_typedescriptionnew_project_typetotal_costbus_countsourceppnoproject_description
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" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [transit_agency, project_title, prop_type, bus_size_type, description, new_project_type, total_cost, bus_count, source, ppno, project_description]\n", - "Index: []" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "final[final['new_project_type'] == \"None\"]" - ] - }, { "cell_type": "markdown", "id": "d04911c1-e839-41fe-87b3-5065586f2223", @@ -288,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "id": "5513b941-ecdb-405e-bfd6-952df6b8f8b4", "metadata": { "tags": [] @@ -409,7 +337,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "id": "8e18cbe6-bde7-4c30-8a8a-aefd8d619821", "metadata": { "tags": [] @@ -471,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "id": "8004cc2d-957f-4e9a-9ca8-2a6f9aba9ffb", "metadata": { "tags": [] @@ -541,7 +469,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "id": "c0ca9254-2983-4cab-845c-f9bfb0229417", "metadata": { "tags": [] @@ -559,9 +487,35 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 11, + "id": "fb1ae513-a8bf-4eb1-9e7b-71f828ebb9ea", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def make_chart(y_col: str, title: str, data: pd.DataFrame, x_col: str):\n", + " \"\"\"\n", + " function to create chart. sorts values by y_col ascending.\"\"\"\n", + " \n", + " data.sort_values(by=y_col, ascending=False).head(10).plot(\n", + " x=x_col, y=y_col, kind=\"bar\", color=\"skyblue\"\n", + " )\n", + " plt.title(title)\n", + " plt.xlabel(x_col)\n", + " plt.ylabel(y_col)\n", + "\n", + " plt.ticklabel_format(style=\"plain\", axis=\"y\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "id": "4a5bc209-a660-4c18-86b0-574640391a7d", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "import seaborn as sns\n", @@ -622,6 +576,60 @@ " plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 13, + "id": "fa387f41-c9b3-455a-9829-cfabb3f98c9b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def outlier_flag(col):\n", + " \"\"\"\n", + " function to flag outlier rows. use with .apply()\n", + " \"\"\"\n", + " \n", + " return col <= -3 or col >= 3\n", + "\n", + "from scipy.stats import zscore\n", + "def new_cpb_aggregate(df: pd.DataFrame, column=\"transit_agency\") -> pd.DataFrame:\n", + " \"\"\"\n", + " function to aggregate compiled data by different categories:\n", + " \"transit agency\", \n", + " \"propulsion type\", \n", + " \"bus_size_type\",\n", + " \"new_project_type\"\n", + " aggregate on columns:\n", + " \"project_title\"\n", + " \"ppno\"\n", + " \"total_cost\"\n", + " \"bus_count\"\n", + " \n", + " Then, cost per bus is calculated AFTER the aggregation.\n", + " \"\"\"\n", + " df_agg = (\n", + " df.groupby(column)\n", + " .agg(\n", + " total_project_count=(\"project_title\", \"count\"),\n", + " total_project_count_ppno=(\"ppno\", \"count\"),\n", + " total_agg_cost=(\"total_cost\", \"sum\"),\n", + " total_bus_count=(\"bus_count\", \"sum\"),\n", + " #new_prop_type=(\"prop_type\",\"max\")\n", + " )\n", + " .reset_index()\n", + " )\n", + " df_agg[\"new_cost_per_bus\"] = (df_agg[\"total_agg_cost\"] / df_agg[\"total_bus_count\"]).astype(\"int64\")\n", + " \n", + " #calculate zscore\n", + " df_agg[\"new_zscore_cost_per_bus\"] = zscore(df_agg[\"new_cost_per_bus\"])\n", + " \n", + " #flag outliers\n", + " df_agg[\"new_is_cpb_outlier?\"] = df_agg[\"new_zscore_cost_per_bus\"].apply(outlier_flag)\n", + " \n", + " return df_agg" + ] + }, { "cell_type": "markdown", "id": "97bdb85b-ecaa-4634-8ea1-02ebc630567f", @@ -634,7 +642,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "ea9c3269-d53d-4d94-bc22-c6768cb63d91", "metadata": { "tags": [] @@ -771,7 +779,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "4e16119d-f6f3-478b-a419-7c4989557910", "metadata": { "tags": [] @@ -912,7 +920,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "359f3b7a-d691-446f-9a14-424c47fc0929", "metadata": { "tags": [] @@ -1113,7 +1121,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "19f4bd75-f614-4937-880a-1e1a6ff2eb7f", "metadata": { "tags": [] @@ -1127,12 +1135,7 @@ "from bus_cost_utils import *\n", "from scipy.stats import zscore\n", "\n", - "def outlier_flag(col):\n", - " \"\"\"\n", - " function to flag outlier rows. use with .apply()\n", - " \"\"\"\n", - " \n", - " return col <= -3 or col >= 3\n", + "\n", "\n", "def prepare_all_data() ->pd.DataFrame:\n", " \"\"\"\n", @@ -1215,43 +1218,6 @@ "\n", "\n", "\n", - "def new_cpb_aggregate(df: pd.DataFrame, column=\"transit_agency\") -> pd.DataFrame:\n", - " \"\"\"\n", - " function to aggregate compiled data by different categories:\n", - " \"transit agency\", \n", - " \"propulsion type\", \n", - " \"bus_size_type\",\n", - " \"new_project_type\"\n", - " aggregate on columns:\n", - " \"project_title\"\n", - " \"ppno\"\n", - " \"total_cost\"\n", - " \"bus_count\"\n", - " \n", - " Then, cost per bus is calculated AFTER the aggregation.\n", - " \"\"\"\n", - " df_agg = (\n", - " df.groupby(column)\n", - " .agg(\n", - " total_project_count=(\"project_title\", \"count\"),\n", - " total_project_count_ppno=(\"ppno\", \"count\"),\n", - " total_agg_cost=(\"total_cost\", \"sum\"),\n", - " total_bus_count=(\"bus_count\", \"sum\"),\n", - " #new_prop_type=(\"prop_type\",\"max\")\n", - " )\n", - " .reset_index()\n", - " )\n", - " df_agg[\"new_cost_per_bus\"] = (df_agg[\"total_agg_cost\"] / df_agg[\"total_bus_count\"]).astype(\"int64\")\n", - " \n", - " #calculate zscore\n", - " df_agg[\"new_zscore_cost_per_bus\"] = zscore(df_agg[\"new_cost_per_bus\"])\n", - " \n", - " #flag outliers\n", - " df_agg[\"new_is_cpb_outlier?\"] = df_agg[\"new_zscore_cost_per_bus\"].apply(outlier_flag)\n", - " \n", - " return df_agg\n", - "\n", - "\n", "\n", "#if __name__ == \"__main__\":\n", " \n", @@ -1265,250 +1231,6 @@ "# df2.to_parquet(f'{gcs_path}cleaned_no_outliers_cpb_analysis_data_merge.parquet')" ] }, - { - "cell_type": "code", - "execution_count": 17, - "id": "8340e510-a934-4730-b340-74a5e1eeb377", - "metadata": {}, - "outputs": [], - "source": [ - "# find outliers \n", - "find_outliers = pd.read_parquet(f'{GCS_PATH}cleaned_cpb_analysis_data_merge.parquet')" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "3c2a88c2-3a07-4336-aee2-237d949bdd94", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "89" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "1" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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51Transit Joint Powers Authority of Merced CountyNonezero-emission bus (not specified)not specifiedNonebus only36965133.0tircpCP074Purchases 3 zero-emission electric buses to in...12321710.879900False
83Transit Joint Powers Authority for Merced CountyNoneBEBstandard/conventional (30ft-45ft)NoneNone32233242.0dgsEBUS002None16116621.689929False
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" - ], - "text/plain": [ - " transit_agency project_title \\\n", - "51 Transit Joint Powers Authority of Merced County None \n", - "83 Transit Joint Powers Authority for Merced County None \n", - "84 Transit Joint Powers Authority for Merced County None \n", - "\n", - " prop_type bus_size_type \\\n", - "51 zero-emission bus (not specified) not specified \n", - "83 BEB standard/conventional (30ft-45ft) \n", - "84 BEB standard/conventional (30ft-45ft) \n", - "\n", - " description new_project_type total_cost bus_count source ppno \\\n", - "51 None bus only 3696513 3.0 tircp CP074 \n", - "83 None None 3223324 2.0 dgs EBUS002 \n", - "84 None None 3223324 1.0 dgs EBUS002 \n", - "\n", - " project_description cost_per_bus \\\n", - "51 Purchases 3 zero-emission electric buses to in... 1232171 \n", - "83 None 1611662 \n", - "84 None 3223324 \n", - "\n", - " zscore_cost_per_bus is_cpb_outlier? \n", - "51 0.879900 False \n", - "83 1.689929 False \n", - "84 5.130048 True " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(\n", - " len(find_outliers),\n", - " len(find_outliers[find_outliers[\"is_cpb_outlier?\"]==True]),\n", - " find_outliers[find_outliers[\"is_cpb_outlier?\"]==True],\n", - " find_outliers[find_outliers[\"transit_agency\"].str.contains(\"Merced County\")]\n", - ")" - ] - }, { "cell_type": "markdown", "id": "9c163a75-eb4b-4a09-b035-7692f9ea68f5", @@ -1591,7 +1313,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "id": "f84aaa3c-7bab-46c9-b739-021fdd6b60a2", "metadata": {}, "outputs": [ @@ -1628,7 +1350,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "id": "c9ffd5a9-b772-4509-b84c-9a96760b3112", "metadata": {}, "outputs": [], @@ -1640,7 +1362,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "id": "f8aa3674-78fe-4ba9-8f5e-697d91ff4011", "metadata": {}, "outputs": [], @@ -1653,7 +1375,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "id": "42c43416-981f-43d7-a642-6a22dc6619f2", "metadata": {}, "outputs": [ @@ -1713,7 +1435,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "id": "d2bca15a-c12c-4cf5-a5a9-d591ee73a359", "metadata": {}, "outputs": [ @@ -1750,36 +1472,36 @@ " \n", " \n", " \n", - " 45\n", - " Oregon Department of Transportation on behalf ...\n", + " 47\n", + " Rockford Mass Transit District\n", " 1\n", " 0\n", - " 181250\n", - " 5.0\n", - " 36250\n", - " -1.866706\n", + " 4094652\n", + " 4.0\n", + " 1023663\n", + " 0.598110\n", " False\n", " \n", " \n", - " 63\n", - " South Dakota Department of Transportation on b...\n", - " 1\n", + " 13\n", + " City of Los Angeles (LA DOT)\n", " 0\n", - " 1276628\n", - " 9.0\n", - " 141847\n", - " -1.603111\n", + " 1\n", + " 102790000\n", + " 112.0\n", + " 917767\n", + " 0.333769\n", " False\n", " \n", " \n", - " 17\n", - " City of Santa Rosa(Santa Rosa CityBus)\n", + " 19\n", + " City of Visalia - Visalia City Coach(Visalia T...\n", " 0\n", " 1\n", - " 5987790\n", - " 5.0\n", - " 1197558\n", - " 1.032193\n", + " 3687803\n", + " 4.0\n", + " 921950\n", + " 0.344210\n", " False\n", " \n", " \n", @@ -1788,22 +1510,22 @@ ], "text/plain": [ " transit_agency total_project_count \\\n", - "45 Oregon Department of Transportation on behalf ... 1 \n", - "63 South Dakota Department of Transportation on b... 1 \n", - "17 City of Santa Rosa(Santa Rosa CityBus) 0 \n", + "47 Rockford Mass Transit District 1 \n", + "13 City of Los Angeles (LA DOT) 0 \n", + "19 City of Visalia - Visalia City Coach(Visalia T... 0 \n", "\n", " total_project_count_ppno total_agg_cost total_bus_count \\\n", - "45 0 181250 5.0 \n", - "63 0 1276628 9.0 \n", - "17 1 5987790 5.0 \n", + "47 0 4094652 4.0 \n", + "13 1 102790000 112.0 \n", + "19 1 3687803 4.0 \n", "\n", " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", - "45 36250 -1.866706 False \n", - "63 141847 -1.603111 False \n", - "17 1197558 1.032193 False " + "47 1023663 0.598110 False \n", + "13 917767 0.333769 False \n", + "19 921950 0.344210 False " ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1815,7 +1537,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 23, "id": "637032e4-d855-4190-a6f5-ff695f77143f", "metadata": {}, "outputs": [ @@ -2061,7 +1783,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "ce1f4c86-e85d-41d8-83f6-14aadce48d5c", "metadata": {}, "outputs": [], @@ -2072,7 +1794,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "59298193-fc78-4ffb-bfc3-326593c19edb", "metadata": {}, "outputs": [], @@ -2086,33 +1808,626 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "01883fc0-4f6d-4e6a-a88f-97a5914b281b", "metadata": {}, - "outputs": [], - "source": [ - "#EVERYTHING CHECKS OUT!\n", - "display(\n", - " old_prop_agg.shape,\n", - " agg_prop_type.shape,\n", - " old_prop_agg,\n", - " agg_prop_type\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "52204752-3932-4ce6-98ac-de8ad3a1f8e8", - "metadata": {}, - "outputs": [], - "source": [ - "#EVERYTHING CHECKS OUT!\n", - "new_agg_bus_size = new_cpb_aggregate(test, \"bus_size_type\")\n", - "old_size_agg = cpb_aggregate(no_outliers, \"bus_size_type\")\n", - "display(\n", - " old_size_agg.shape,\n", - " new_agg_bus_size.shape,\n", + "outputs": [ + { + "data": { + "text/plain": [ + "(10, 6)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(10, 8)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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prop_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpb
0BEB030167232489163.01025966
1CNG121176039140252.0698568
2FCEB26120951335102.01185797
3electric (not specified)125667800044.01288136
4ethanol1010067509.0111861
5low emission (hybrid)16091824361145.0633271
6low emission (propane)50840396944.0190999
7mix (zero and low emission)2036775430125.0294203
8not specified4141552404325.0127853
9zero-emission bus (not specified)05128156513143.0896199
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prop_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
0BEB031170455813164.010393640.923505False
1CNG121176039140252.06985680.122141False
2FCEB26120951335102.011857971.267835False
3electric (not specified)125667800044.012881361.508480False
4ethanol1010067509.0111861-1.257470False
5low emission (hybrid)16091824361145.0633271-0.031401False
6low emission (propane)50840396944.0190999-1.071381False
7mix (zero and low emission)2036775430125.0294203-0.828702False
8not specified4141552404325.0127853-1.219866False
9zero-emission bus (not specified)05128156513143.08961990.586860False
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bus_size_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpb
0articulated025823757641.01420428
1cutaway3016694500152.0109832
2not specified406509919038881.0578795
3over-the-road01951600014.0679714
4standard/conventional (30ft-45ft)036234253277264.0887323
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bus_size_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
0articulated025823757641.014204281.598471False
1cutaway3016694500152.0109832-1.466801False
2not specified406509919038881.0578795-0.369972False
3over-the-road01951600014.0679714-0.133939False
4standard/conventional (30ft-45ft)037237476601265.08961380.372242False
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" + ], + "text/plain": [ + " bus_size_type total_project_count \\\n", + "0 articulated 0 \n", + "1 cutaway 3 \n", + "2 not specified 40 \n", + "3 over-the-road 0 \n", + "4 standard/conventional (30ft-45ft) 0 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count \\\n", + "0 2 58237576 41.0 \n", + "1 0 16694500 152.0 \n", + "2 6 509919038 881.0 \n", + "3 1 9516000 14.0 \n", + "4 37 237476601 265.0 \n", + "\n", + " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", + "0 1420428 1.598471 False \n", + "1 109832 -1.466801 False \n", + "2 578795 -0.369972 False \n", + "3 679714 -0.133939 False \n", + "4 896138 0.372242 False " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#EVERYTHING CHECKS OUT!\n", + "new_agg_bus_size = new_cpb_aggregate(test, \"bus_size_type\")\n", + "old_size_agg = cpb_aggregate(no_outliers, \"bus_size_type\")\n", + "display(\n", + " old_size_agg.shape,\n", + " new_agg_bus_size.shape,\n", " old_size_agg,\n", " new_agg_bus_size\n", ")" @@ -2120,10 +2435,2169 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "0391dd4d-23e1-49cb-8123-509954c796e8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(82, 6)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(82, 8)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpb
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)10100000008.01250000
1Alameda County Transit Authority012284664020.01142332
2Antelope Valley Transit Authority (AVTA)013947800029.01361310
3CITY OF PORTERVILLE (PORTERVILLE, CA)0127818913.0927297
4CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...0136235364.0905884
5Cape Fear Public Transportation Authority1028602505.0572050
6Central Oklahoma Transportation and Parking Au...1042787729.0475419
7Champaign-Urbana Mass Transit District10663539410.0663539
8City of Beaumont1028194605.0563892
9City of Beloit106531841.0653184
10City of Brownsville1047388866.0789814
11City of Colorado Springs dba Mountain Metropol...1031990386.0533173
12City of Jonesboro, Arkansas1010103725.0202074
13City of Los Angeles (LA DOT)01102790000112.0917767
14City of Norman, Oklahoma107767146.0129452
15City of Roseville02965150710.0965150
16City of San Luis Obispo018592701.0859270
17City of Santa Rosa(Santa Rosa CityBus)0159877905.01197558
18City of Tucson, Sun Tran102149056039.0551040
19City of Visalia - Visalia City Coach(Visalia T...0136878034.0921950
20City of Wasco0115430003.0514333
21Coast Transit Authority dba MS Coast Transport...1017600009.0195555
22Conroe Connection Transit1045000004.01125000
23Culver City0135470005.0709400
24Dallas Area Rapid Transit (DART)1010300000090.01144444
25Delaware Transit Corporation (DTC)1087407286.01456788
26Foothill Transit011658000020.0829000
27Foothill Transit, West Covina, CA013764204433.01140668
28GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA)0154063555.01081271
29Golden Empire Transit111120865615.0747243
30Illinois Department of Transportation on behal...1012600000134.094029
31Indianapolis Public Transportation Corporation...101904033620.0952016
32Interurban Transit Partnership10619718011.0563380
33Lane Transit (Oregon)022789499930.0929833
34Lowell Regional Transit Authority1068592967.0979899
35Madison County Mass Transit District1010800002.0540000
36Mesa County1011620003.0387333
37Minnesota Department of Transportation on beha...1014569707.0208138
38Napa Valley Transportation Authority0123966002.01198300
39New Mexico Department of Transportation on beh...1020631603.0687720
40North County Transit District0157876066.0964601
41North County Transit District (NCTD)102933024323.01275227
42ORANGE COUNTY TRANSPORTATION AUTHORITY (ORANGE...01931952010.0931952
43Ohio Department of Transportation (ODOT) on be...102933166569.0425096
44Orange County Transportation Authority (OCTA)01290000040.072500
45Oregon Department of Transportation on behalf ...101812505.036250
46Rhode Island Public Transit Authority10500000025.0200000
47Rockford Mass Transit District1040946524.01023663
48Rogue Valley Transportation District1039375006.0656250
49SLO TRANSIT (SAN LUIS OBISPO, CA)018472141.0847214
50SUNLINE TRANSIT AGENCY (THOUSAND PALMS)0157551555.01151031
51SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA)0157718655.01154373
52Sacramento County Airport System0146422255.0928445
53San Antonio Metropolitan Transit Authority10318720015.0212480
54San Diego Metro011875957612.01563298
55Santa Barbara Metro0136590724.0914768
56Santa Maria Area Transit0118622582.0931129
57Santa Maria Regional Transit0151883795.01037675
58Santa Rosa City Bus0140682024.01017050
59Shasta Regional Transportation Agency (SRTA)01951600014.0679714
60Sonoma County Transit01899000010.0899000
61South Carolina Department of Transportation on...1015423904160.096399
62South Dakota Department of Transportation on b...1010067509.0111861
63South Dakota Department of Transportation on b...1012766289.0141847
64Southeastern Regional Transit Authority101156000016.0722500
65Southwest Ohio Regional Transit Authority10980642816.0612901
66State of California on behalf of Kern Regional...20618100020.0309050
67Tahoe Transportation District1034000004.0850000
68Texas Department of Transportation on behalf o...10744376556.0132924
69The Bus, City of Merced0147862855.0957257
70Torrance Transit Department0172000007.01028571
71Transit Joint Powers Authority for Merced County0132233242.01611662
72Transit Joint Powers Authority of Merced County0136965133.01232171
73UC DAVIS (UNITRANS) (DAVIS, CA)02932192610.0932192
74UCLA FLEET & TRANSIT0120088262.01004413
75University of California - San Diego0282680006.01378000
76University of California, Irvine0149329305.0986586
77Utah Transit Authority101705535325.0682214
78VACAVILLE PUBLIC TRANSPORTATION (CITY COACH) (...011017559010.01017559
79VICTOR VALLEY TRANSIT AUTHORITY (VVTA)0145081605.0901632
80Whatcom Transportation Authority (WTA)10964486511.0876805
81White Earth Reservation Business Committee107231714.0180792
\n", + "
" + ], + "text/plain": [ + " transit_agency total_project_count \\\n", + "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) 1 \n", + "1 Alameda County Transit Authority 0 \n", + "2 Antelope Valley Transit Authority (AVTA) 0 \n", + "3 CITY OF PORTERVILLE (PORTERVILLE, CA) 0 \n", + "4 CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ... 0 \n", + "5 Cape Fear Public Transportation Authority 1 \n", + "6 Central Oklahoma Transportation and Parking Au... 1 \n", + "7 Champaign-Urbana Mass Transit District 1 \n", + "8 City of Beaumont 1 \n", + "9 City of Beloit 1 \n", + "10 City of Brownsville 1 \n", + "11 City of Colorado Springs dba Mountain Metropol... 1 \n", + "12 City of Jonesboro, Arkansas 1 \n", + "13 City of Los Angeles (LA DOT) 0 \n", + "14 City of Norman, Oklahoma 1 \n", + "15 City of Roseville 0 \n", + "16 City of San Luis Obispo 0 \n", + "17 City of Santa Rosa(Santa Rosa CityBus) 0 \n", + "18 City of Tucson, Sun Tran 1 \n", + "19 City of Visalia - Visalia City Coach(Visalia T... 0 \n", + "20 City of Wasco 0 \n", + "21 Coast Transit Authority dba MS Coast Transport... 1 \n", + "22 Conroe Connection Transit 1 \n", + "23 Culver City 0 \n", + "24 Dallas Area Rapid Transit (DART) 1 \n", + "25 Delaware Transit Corporation (DTC) 1 \n", + "26 Foothill Transit 0 \n", + "27 Foothill Transit, West Covina, CA 0 \n", + "28 GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA) 0 \n", + "29 Golden Empire Transit 1 \n", + "30 Illinois Department of Transportation on behal... 1 \n", + "31 Indianapolis Public Transportation Corporation... 1 \n", + "32 Interurban Transit Partnership 1 \n", + "33 Lane Transit (Oregon) 0 \n", + "34 Lowell Regional Transit Authority 1 \n", + "35 Madison County Mass Transit District 1 \n", + "36 Mesa County 1 \n", + "37 Minnesota Department of Transportation on beha... 1 \n", + "38 Napa Valley Transportation Authority 0 \n", + "39 New Mexico Department of Transportation on beh... 1 \n", + "40 North County Transit District 0 \n", + "41 North County Transit District (NCTD) 1 \n", + "42 ORANGE COUNTY TRANSPORTATION AUTHORITY (ORANGE... 0 \n", + "43 Ohio Department of Transportation (ODOT) on be... 1 \n", + "44 Orange County Transportation Authority (OCTA) 0 \n", + "45 Oregon Department of Transportation on behalf ... 1 \n", + "46 Rhode Island Public Transit Authority 1 \n", + "47 Rockford Mass Transit District 1 \n", + "48 Rogue Valley Transportation District 1 \n", + "49 SLO TRANSIT (SAN LUIS OBISPO, CA) 0 \n", + "50 SUNLINE TRANSIT AGENCY (THOUSAND PALMS) 0 \n", + "51 SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA) 0 \n", + "52 Sacramento County Airport System 0 \n", + "53 San Antonio Metropolitan Transit Authority 1 \n", + "54 San Diego Metro 0 \n", + "55 Santa Barbara Metro 0 \n", + "56 Santa Maria Area Transit 0 \n", + "57 Santa Maria Regional Transit 0 \n", + "58 Santa Rosa City Bus 0 \n", + "59 Shasta Regional Transportation Agency (SRTA) 0 \n", + "60 Sonoma County Transit 0 \n", + "61 South Carolina Department of Transportation on... 1 \n", + "62 South Dakota Department of Transportation on b... 1 \n", + "63 South Dakota Department of Transportation on b... 1 \n", + "64 Southeastern Regional Transit Authority 1 \n", + "65 Southwest Ohio Regional Transit Authority 1 \n", + "66 State of California on behalf of Kern Regional... 2 \n", + "67 Tahoe Transportation District 1 \n", + "68 Texas Department of Transportation on behalf o... 1 \n", + "69 The Bus, City of Merced 0 \n", + "70 Torrance Transit Department 0 \n", + "71 Transit Joint Powers Authority for Merced County 0 \n", + "72 Transit Joint Powers Authority of Merced County 0 \n", + "73 UC DAVIS (UNITRANS) (DAVIS, CA) 0 \n", + "74 UCLA FLEET & TRANSIT 0 \n", + "75 University of California - San Diego 0 \n", + "76 University of California, Irvine 0 \n", + "77 Utah Transit Authority 1 \n", + "78 VACAVILLE PUBLIC TRANSPORTATION (CITY COACH) (... 0 \n", + "79 VICTOR VALLEY TRANSIT AUTHORITY (VVTA) 0 \n", + "80 Whatcom Transportation Authority (WTA) 1 \n", + "81 White Earth Reservation Business Committee 1 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count cpb \n", + "0 0 10000000 8.0 1250000 \n", + "1 1 22846640 20.0 1142332 \n", + "2 1 39478000 29.0 1361310 \n", + "3 1 2781891 3.0 927297 \n", + "4 1 3623536 4.0 905884 \n", + "5 0 2860250 5.0 572050 \n", + "6 0 4278772 9.0 475419 \n", + "7 0 6635394 10.0 663539 \n", + "8 0 2819460 5.0 563892 \n", + "9 0 653184 1.0 653184 \n", + "10 0 4738886 6.0 789814 \n", + "11 0 3199038 6.0 533173 \n", + "12 0 1010372 5.0 202074 \n", + "13 1 102790000 112.0 917767 \n", + "14 0 776714 6.0 129452 \n", + "15 2 9651507 10.0 965150 \n", + "16 1 859270 1.0 859270 \n", + "17 1 5987790 5.0 1197558 \n", + "18 0 21490560 39.0 551040 \n", + "19 1 3687803 4.0 921950 \n", + "20 1 1543000 3.0 514333 \n", + "21 0 1760000 9.0 195555 \n", + "22 0 4500000 4.0 1125000 \n", + "23 1 3547000 5.0 709400 \n", + "24 0 103000000 90.0 1144444 \n", + "25 0 8740728 6.0 1456788 \n", + "26 1 16580000 20.0 829000 \n", + "27 1 37642044 33.0 1140668 \n", + "28 1 5406355 5.0 1081271 \n", + "29 1 11208656 15.0 747243 \n", + "30 0 12600000 134.0 94029 \n", + "31 0 19040336 20.0 952016 \n", + "32 0 6197180 11.0 563380 \n", + "33 2 27894999 30.0 929833 \n", + "34 0 6859296 7.0 979899 \n", + "35 0 1080000 2.0 540000 \n", + "36 0 1162000 3.0 387333 \n", + "37 0 1456970 7.0 208138 \n", + "38 1 2396600 2.0 1198300 \n", + "39 0 2063160 3.0 687720 \n", + "40 1 5787606 6.0 964601 \n", + "41 0 29330243 23.0 1275227 \n", + "42 1 9319520 10.0 931952 \n", + "43 0 29331665 69.0 425096 \n", + "44 1 2900000 40.0 72500 \n", + "45 0 181250 5.0 36250 \n", + "46 0 5000000 25.0 200000 \n", + "47 0 4094652 4.0 1023663 \n", + "48 0 3937500 6.0 656250 \n", + "49 1 847214 1.0 847214 \n", + "50 1 5755155 5.0 1151031 \n", + "51 1 5771865 5.0 1154373 \n", + "52 1 4642225 5.0 928445 \n", + "53 0 3187200 15.0 212480 \n", + "54 1 18759576 12.0 1563298 \n", + "55 1 3659072 4.0 914768 \n", + "56 1 1862258 2.0 931129 \n", + "57 1 5188379 5.0 1037675 \n", + "58 1 4068202 4.0 1017050 \n", + "59 1 9516000 14.0 679714 \n", + "60 1 8990000 10.0 899000 \n", + "61 0 15423904 160.0 96399 \n", + "62 0 1006750 9.0 111861 \n", + "63 0 1276628 9.0 141847 \n", + "64 0 11560000 16.0 722500 \n", + "65 0 9806428 16.0 612901 \n", + "66 0 6181000 20.0 309050 \n", + "67 0 3400000 4.0 850000 \n", + "68 0 7443765 56.0 132924 \n", + "69 1 4786285 5.0 957257 \n", + "70 1 7200000 7.0 1028571 \n", + "71 1 3223324 2.0 1611662 \n", + "72 1 3696513 3.0 1232171 \n", + "73 2 9321926 10.0 932192 \n", + "74 1 2008826 2.0 1004413 \n", + "75 2 8268000 6.0 1378000 \n", + "76 1 4932930 5.0 986586 \n", + "77 0 17055353 25.0 682214 \n", + "78 1 10175590 10.0 1017559 \n", + "79 1 4508160 5.0 901632 \n", + "80 0 9644865 11.0 876805 \n", + "81 0 723171 4.0 180792 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)10100000008.012500001.163100False
1Alameda County Transit Authority012284664020.011423320.894336False
2Antelope Valley Transit Authority (AVTA)013947800029.013613101.440957False
3CITY OF PORTERVILLE (PORTERVILLE, CA)0127818913.09272970.357558False
4CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...0136235364.09058840.304106False
5Cape Fear Public Transportation Authority1028602505.0572050-0.529223False
6Central Oklahoma Transportation and Parking Au...1042787729.0475419-0.770436False
7Champaign-Urbana Mass Transit District10663539410.0663539-0.300844False
8City of Beaumont1028194605.0563892-0.549587False
9City of Beloit106531841.0653184-0.326693False
10City of Brownsville1047388866.07898140.014368False
11City of Colorado Springs dba Mountain Metropol...1031990386.0533173-0.626269False
12City of Jonesboro, Arkansas1010103725.0202074-1.452770False
13City of Los Angeles (LA DOT)01102790000112.09177670.333769False
14City of Norman, Oklahoma107767146.0129452-1.634052False
15City of Roseville02965150710.09651500.452048False
16City of San Luis Obispo018592701.08592700.187746False
17City of Santa Rosa(Santa Rosa CityBus)0159877905.011975581.032193False
18City of Tucson, Sun Tran102149056039.0551040-0.581669False
19City of Visalia - Visalia City Coach(Visalia T...0136878034.09219500.344210False
20City of Wasco0115430003.0514333-0.673298False
21Coast Transit Authority dba MS Coast Transport...1017600009.0195555-1.469043False
22Conroe Connection Transit1045000004.011250000.851071False
23Culver City0135470005.0709400-0.186365False
24Dallas Area Rapid Transit (DART)1010300000090.011444440.899608False
25Delaware Transit Corporation (DTC)1087407286.014567881.679292False
26Foothill Transit011658000020.08290000.112185False
27Foothill Transit, West Covina, CA013764204433.011406680.890182False
28GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA)0154063555.010812710.741913False
29Golden Empire Transit111120865615.0747243-0.091900False
30Illinois Department of Transportation on behal...1012600000134.094029-1.722476False
31Indianapolis Public Transportation Corporation...101904033620.09520160.419262False
32Interurban Transit Partnership10619718011.0563380-0.550865False
33Lane Transit (Oregon)022789499930.09298330.363888False
34Lowell Regional Transit Authority1068592967.09798990.488865False
35Madison County Mass Transit District1010800002.0540000-0.609227False
36Mesa County1011620003.0387333-0.990320False
37Minnesota Department of Transportation on beha...1014569707.0208138-1.437633False
38Napa Valley Transportation Authority0123966002.011983001.034045False
39New Mexico Department of Transportation on beh...1020631603.0687720-0.240483False
40North County Transit District0157876066.09646010.450677False
41North County Transit District (NCTD)102933024323.012752271.226073False
42ORANGE COUNTY TRANSPORTATION AUTHORITY (ORANGE...01931952010.09319520.369178False
43Ohio Department of Transportation (ODOT) on be...102933166569.0425096-0.896054False
44Orange County Transportation Authority (OCTA)01290000040.072500-1.776217False
45Oregon Department of Transportation on behalf ...101812505.036250-1.866706False
46Rhode Island Public Transit Authority10500000025.0200000-1.457947False
47Rockford Mass Transit District1040946524.010236630.598110False
48Rogue Valley Transportation District1039375006.0656250-0.319040False
49SLO TRANSIT (SAN LUIS OBISPO, CA)018472141.08472140.157652False
50SUNLINE TRANSIT AGENCY (THOUSAND PALMS)0157551555.011510310.916051False
51SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA)0157718655.011543730.924393False
52Sacramento County Airport System0146422255.09284450.360423False
53San Antonio Metropolitan Transit Authority10318720015.0212480-1.426794False
54San Diego Metro011875957612.015632981.945166False
55Santa Barbara Metro0136590724.09147680.326282False
56Santa Maria Area Transit0118622582.09311290.367123False
57Santa Maria Regional Transit0151883795.010376750.633087False
58Santa Rosa City Bus0140682024.010170500.581602False
59Shasta Regional Transportation Agency (SRTA)01951600014.0679714-0.260468False
60Sonoma County Transit01899000010.08990000.286922False
61South Carolina Department of Transportation on...1015423904160.096399-1.716560False
62South Dakota Department of Transportation on b...1010067509.0111861-1.677963False
63South Dakota Department of Transportation on b...1012766289.0141847-1.603111False
64Southeastern Regional Transit Authority101156000016.0722500-0.153664False
65Southwest Ohio Regional Transit Authority10980642816.0612901-0.427249False
66State of California on behalf of Kern Regional...20618100020.0309050-1.185733False
67Tahoe Transportation District1034000004.08500000.164606False
68Texas Department of Transportation on behalf o...10744376556.0132924-1.625385False
69The Bus, City of Merced0147862855.09572570.432345False
70Torrance Transit Department0172000007.010285710.610361False
71Transit Joint Powers Authority for Merced County0264466483.021488823.406922True
72Transit Joint Powers Authority of Merced County0136965133.012321711.118595False
73UC DAVIS (UNITRANS) (DAVIS, CA)02932192610.09321920.369777False
74UCLA FLEET & TRANSIT0120088262.010044130.550057False
75University of California - San Diego0282680006.013780001.482619False
76University of California, Irvine0149329305.09865860.505557False
77Utah Transit Authority101705535325.0682214-0.254227False
78VACAVILLE PUBLIC TRANSPORTATION (CITY COACH) (...011017559010.010175590.582873False
79VICTOR VALLEY TRANSIT AUTHORITY (VVTA)0145081605.09016320.293492False
80Whatcom Transportation Authority (WTA)10964486511.08768050.231518False
81White Earth Reservation Business Committee107231714.0180792-1.505895False
\n", + "
" + ], + "text/plain": [ + " transit_agency total_project_count \\\n", + "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) 1 \n", + "1 Alameda County Transit Authority 0 \n", + "2 Antelope Valley Transit Authority (AVTA) 0 \n", + "3 CITY OF PORTERVILLE (PORTERVILLE, CA) 0 \n", + "4 CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ... 0 \n", + "5 Cape Fear Public Transportation Authority 1 \n", + "6 Central Oklahoma Transportation and Parking Au... 1 \n", + "7 Champaign-Urbana Mass Transit District 1 \n", + "8 City of Beaumont 1 \n", + "9 City of Beloit 1 \n", + "10 City of Brownsville 1 \n", + "11 City of Colorado Springs dba Mountain Metropol... 1 \n", + "12 City of Jonesboro, Arkansas 1 \n", + "13 City of Los Angeles (LA DOT) 0 \n", + "14 City of Norman, Oklahoma 1 \n", + "15 City of Roseville 0 \n", + "16 City of San Luis Obispo 0 \n", + "17 City of Santa Rosa(Santa Rosa CityBus) 0 \n", + "18 City of Tucson, Sun Tran 1 \n", + "19 City of Visalia - Visalia City Coach(Visalia T... 0 \n", + "20 City of Wasco 0 \n", + "21 Coast Transit Authority dba MS Coast Transport... 1 \n", + "22 Conroe Connection Transit 1 \n", + "23 Culver City 0 \n", + "24 Dallas Area Rapid Transit (DART) 1 \n", + "25 Delaware Transit Corporation (DTC) 1 \n", + "26 Foothill Transit 0 \n", + "27 Foothill Transit, West Covina, CA 0 \n", + "28 GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA) 0 \n", + "29 Golden Empire Transit 1 \n", + "30 Illinois Department of Transportation on behal... 1 \n", + "31 Indianapolis Public Transportation Corporation... 1 \n", + "32 Interurban Transit Partnership 1 \n", + "33 Lane Transit (Oregon) 0 \n", + "34 Lowell Regional Transit Authority 1 \n", + "35 Madison County Mass Transit District 1 \n", + "36 Mesa County 1 \n", + "37 Minnesota Department of Transportation on beha... 1 \n", + "38 Napa Valley Transportation Authority 0 \n", + "39 New Mexico Department of Transportation on beh... 1 \n", + "40 North County Transit District 0 \n", + "41 North County Transit District (NCTD) 1 \n", + "42 ORANGE COUNTY TRANSPORTATION AUTHORITY (ORANGE... 0 \n", + "43 Ohio Department of Transportation (ODOT) on be... 1 \n", + "44 Orange County Transportation Authority (OCTA) 0 \n", + "45 Oregon Department of Transportation on behalf ... 1 \n", + "46 Rhode Island Public Transit Authority 1 \n", + "47 Rockford Mass Transit District 1 \n", + "48 Rogue Valley Transportation District 1 \n", + "49 SLO TRANSIT (SAN LUIS OBISPO, CA) 0 \n", + "50 SUNLINE TRANSIT AGENCY (THOUSAND PALMS) 0 \n", + "51 SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA) 0 \n", + "52 Sacramento County Airport System 0 \n", + "53 San Antonio Metropolitan Transit Authority 1 \n", + "54 San Diego Metro 0 \n", + "55 Santa Barbara Metro 0 \n", + "56 Santa Maria Area Transit 0 \n", + "57 Santa Maria Regional Transit 0 \n", + "58 Santa Rosa City Bus 0 \n", + "59 Shasta Regional Transportation Agency (SRTA) 0 \n", + "60 Sonoma County Transit 0 \n", + "61 South Carolina Department of Transportation on... 1 \n", + "62 South Dakota Department of Transportation on b... 1 \n", + "63 South Dakota Department of Transportation on b... 1 \n", + "64 Southeastern Regional Transit Authority 1 \n", + "65 Southwest Ohio Regional Transit Authority 1 \n", + "66 State of California on behalf of Kern Regional... 2 \n", + "67 Tahoe Transportation District 1 \n", + "68 Texas Department of Transportation on behalf o... 1 \n", + "69 The Bus, City of Merced 0 \n", + "70 Torrance Transit Department 0 \n", + "71 Transit Joint Powers Authority for Merced County 0 \n", + "72 Transit Joint Powers Authority of Merced County 0 \n", + "73 UC DAVIS (UNITRANS) (DAVIS, CA) 0 \n", + "74 UCLA FLEET & TRANSIT 0 \n", + "75 University of California - San Diego 0 \n", + "76 University of California, Irvine 0 \n", + "77 Utah Transit Authority 1 \n", + "78 VACAVILLE PUBLIC TRANSPORTATION (CITY COACH) (... 0 \n", + "79 VICTOR VALLEY TRANSIT AUTHORITY (VVTA) 0 \n", + "80 Whatcom Transportation Authority (WTA) 1 \n", + "81 White Earth Reservation Business Committee 1 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count \\\n", + "0 0 10000000 8.0 \n", + "1 1 22846640 20.0 \n", + "2 1 39478000 29.0 \n", + "3 1 2781891 3.0 \n", + "4 1 3623536 4.0 \n", + "5 0 2860250 5.0 \n", + "6 0 4278772 9.0 \n", + "7 0 6635394 10.0 \n", + "8 0 2819460 5.0 \n", + "9 0 653184 1.0 \n", + "10 0 4738886 6.0 \n", + "11 0 3199038 6.0 \n", + "12 0 1010372 5.0 \n", + "13 1 102790000 112.0 \n", + "14 0 776714 6.0 \n", + "15 2 9651507 10.0 \n", + "16 1 859270 1.0 \n", + "17 1 5987790 5.0 \n", + "18 0 21490560 39.0 \n", + "19 1 3687803 4.0 \n", + "20 1 1543000 3.0 \n", + "21 0 1760000 9.0 \n", + "22 0 4500000 4.0 \n", + "23 1 3547000 5.0 \n", + "24 0 103000000 90.0 \n", + "25 0 8740728 6.0 \n", + "26 1 16580000 20.0 \n", + "27 1 37642044 33.0 \n", + "28 1 5406355 5.0 \n", + "29 1 11208656 15.0 \n", + "30 0 12600000 134.0 \n", + "31 0 19040336 20.0 \n", + "32 0 6197180 11.0 \n", + "33 2 27894999 30.0 \n", + "34 0 6859296 7.0 \n", + "35 0 1080000 2.0 \n", + "36 0 1162000 3.0 \n", + "37 0 1456970 7.0 \n", + "38 1 2396600 2.0 \n", + "39 0 2063160 3.0 \n", + "40 1 5787606 6.0 \n", + "41 0 29330243 23.0 \n", + "42 1 9319520 10.0 \n", + "43 0 29331665 69.0 \n", + "44 1 2900000 40.0 \n", + "45 0 181250 5.0 \n", + "46 0 5000000 25.0 \n", + "47 0 4094652 4.0 \n", + "48 0 3937500 6.0 \n", + "49 1 847214 1.0 \n", + "50 1 5755155 5.0 \n", + "51 1 5771865 5.0 \n", + "52 1 4642225 5.0 \n", + "53 0 3187200 15.0 \n", + "54 1 18759576 12.0 \n", + "55 1 3659072 4.0 \n", + "56 1 1862258 2.0 \n", + "57 1 5188379 5.0 \n", + "58 1 4068202 4.0 \n", + "59 1 9516000 14.0 \n", + "60 1 8990000 10.0 \n", + "61 0 15423904 160.0 \n", + "62 0 1006750 9.0 \n", + "63 0 1276628 9.0 \n", + "64 0 11560000 16.0 \n", + "65 0 9806428 16.0 \n", + "66 0 6181000 20.0 \n", + "67 0 3400000 4.0 \n", + "68 0 7443765 56.0 \n", + "69 1 4786285 5.0 \n", + "70 1 7200000 7.0 \n", + "71 2 6446648 3.0 \n", + "72 1 3696513 3.0 \n", + "73 2 9321926 10.0 \n", + "74 1 2008826 2.0 \n", + "75 2 8268000 6.0 \n", + "76 1 4932930 5.0 \n", + "77 0 17055353 25.0 \n", + "78 1 10175590 10.0 \n", + "79 1 4508160 5.0 \n", + "80 0 9644865 11.0 \n", + "81 0 723171 4.0 \n", + "\n", + " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", + "0 1250000 1.163100 False \n", + "1 1142332 0.894336 False \n", + "2 1361310 1.440957 False \n", + "3 927297 0.357558 False \n", + "4 905884 0.304106 False \n", + "5 572050 -0.529223 False \n", + "6 475419 -0.770436 False \n", + "7 663539 -0.300844 False \n", + "8 563892 -0.549587 False \n", + "9 653184 -0.326693 False \n", + "10 789814 0.014368 False \n", + "11 533173 -0.626269 False \n", + "12 202074 -1.452770 False \n", + "13 917767 0.333769 False \n", + "14 129452 -1.634052 False \n", + "15 965150 0.452048 False \n", + "16 859270 0.187746 False \n", + "17 1197558 1.032193 False \n", + "18 551040 -0.581669 False \n", + "19 921950 0.344210 False \n", + "20 514333 -0.673298 False \n", + "21 195555 -1.469043 False \n", + "22 1125000 0.851071 False \n", + "23 709400 -0.186365 False \n", + "24 1144444 0.899608 False \n", + "25 1456788 1.679292 False \n", + "26 829000 0.112185 False \n", + "27 1140668 0.890182 False \n", + "28 1081271 0.741913 False \n", + "29 747243 -0.091900 False \n", + "30 94029 -1.722476 False \n", + "31 952016 0.419262 False \n", + "32 563380 -0.550865 False \n", + "33 929833 0.363888 False \n", + "34 979899 0.488865 False \n", + "35 540000 -0.609227 False \n", + "36 387333 -0.990320 False \n", + "37 208138 -1.437633 False \n", + "38 1198300 1.034045 False \n", + "39 687720 -0.240483 False \n", + "40 964601 0.450677 False \n", + "41 1275227 1.226073 False \n", + "42 931952 0.369178 False \n", + "43 425096 -0.896054 False \n", + "44 72500 -1.776217 False \n", + "45 36250 -1.866706 False \n", + "46 200000 -1.457947 False \n", + "47 1023663 0.598110 False \n", + "48 656250 -0.319040 False \n", + "49 847214 0.157652 False \n", + "50 1151031 0.916051 False \n", + "51 1154373 0.924393 False \n", + "52 928445 0.360423 False \n", + "53 212480 -1.426794 False \n", + "54 1563298 1.945166 False \n", + "55 914768 0.326282 False \n", + "56 931129 0.367123 False \n", + "57 1037675 0.633087 False \n", + "58 1017050 0.581602 False \n", + "59 679714 -0.260468 False \n", + "60 899000 0.286922 False \n", + "61 96399 -1.716560 False \n", + "62 111861 -1.677963 False \n", + "63 141847 -1.603111 False \n", + "64 722500 -0.153664 False \n", + "65 612901 -0.427249 False \n", + "66 309050 -1.185733 False \n", + "67 850000 0.164606 False \n", + "68 132924 -1.625385 False \n", + "69 957257 0.432345 False \n", + "70 1028571 0.610361 False \n", + "71 2148882 3.406922 True \n", + "72 1232171 1.118595 False \n", + "73 932192 0.369777 False \n", + "74 1004413 0.550057 False \n", + "75 1378000 1.482619 False \n", + "76 986586 0.505557 False \n", + "77 682214 -0.254227 False \n", + "78 1017559 0.582873 False \n", + "79 901632 0.293492 False \n", + "80 876805 0.231518 False \n", + "81 180792 -1.505895 False " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#EVERYTHING CHECKS OUT!\n", "# move forward with `new_cpb_aggregate` function\n", @@ -2150,7 +4624,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 29, "id": "f21298ee-0efb-4f91-ba63-55fc2645a4d2", "metadata": {}, "outputs": [ @@ -2164,7 +4638,7 @@ " dtype='object')" ] }, - "execution_count": 25, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -2179,15 +4653,15 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 30, "id": "ece95fb7-cbb8-46bd-a5f9-2b68a47a4817", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "max 1611662\n", "min 36250\n", + "max 1611662\n", "Name: new_cost_per_bus, dtype: int64" ] }, @@ -2197,8 +4671,8 @@ { "data": { "text/plain": [ - "max 160.0\n", "min 1.0\n", + "max 160.0\n", "Name: total_bus_count, dtype: float64" ] }, @@ -2208,8 +4682,8 @@ { "data": { "text/plain": [ - "max 103000000\n", "min 181250\n", + "max 103000000\n", "Name: total_agg_cost, dtype: int64" ] }, @@ -2219,8 +4693,8 @@ { "data": { "text/plain": [ - "max 2.182513\n", "min -1.939451\n", + "max 2.182513\n", "Name: new_zscore_cost_per_bus, dtype: float64" ] }, @@ -2240,18 +4714,18 @@ "#overall agency info\n", "display(\n", "\n", - " #min max, without outlier\n", - " agg_agency[\"new_cost_per_bus\"].agg([\"max\",\"min\"]),\n", - " agg_agency[\"total_bus_count\"].agg([\"max\",\"min\"]),\n", - " agg_agency[\"total_agg_cost\"].agg([\"max\",\"min\"]),\n", - " agg_agency[\"new_zscore_cost_per_bus\"].agg([\"max\",\"min\"]),\n", + " #min max,\n", + " agg_agency[\"new_cost_per_bus\"].agg([\"min\",\"max\"]),\n", + " agg_agency[\"total_bus_count\"].agg([\"min\",\"max\"]),\n", + " agg_agency[\"total_agg_cost\"].agg([\"min\",\"max\"]),\n", + " agg_agency[\"new_zscore_cost_per_bus\"].agg([\"min\",\"max\"]),\n", " \n", ")" ] }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 31, "id": "1696d78f-7018-417b-9847-d82edac3acdf", "metadata": {}, "outputs": [ @@ -2277,166 +4751,110 @@ " \n", " \n", " prop_type\n", - " total_project_count\n", - " total_project_count_ppno\n", " total_agg_cost\n", " total_bus_count\n", " new_cost_per_bus\n", - " new_zscore_cost_per_bus\n", - " new_is_cpb_outlier?\n", " \n", " \n", " \n", " \n", " 0\n", " BEB\n", - " 0\n", - " 30\n", " 167232489\n", " 163.0\n", " 1025966\n", - " 0.897727\n", - " False\n", " \n", " \n", " 1\n", " CNG\n", - " 12\n", - " 1\n", " 176039140\n", " 252.0\n", " 698568\n", - " 0.125652\n", - " False\n", " \n", " \n", " 2\n", " FCEB\n", - " 2\n", - " 6\n", " 120951335\n", " 102.0\n", " 1185797\n", - " 1.274642\n", - " False\n", " \n", " \n", " 3\n", " electric (not specified)\n", - " 1\n", - " 2\n", " 56678000\n", " 44.0\n", " 1288136\n", - " 1.515980\n", - " False\n", " \n", " \n", " 4\n", " ethanol\n", - " 1\n", - " 0\n", " 1006750\n", " 9.0\n", " 111861\n", - " -1.257929\n", - " False\n", " \n", " \n", " 5\n", " low emission (hybrid)\n", - " 16\n", - " 0\n", " 91824361\n", " 145.0\n", " 633271\n", - " -0.028332\n", - " False\n", " \n", " \n", " 6\n", " low emission (propane)\n", - " 5\n", - " 0\n", " 8403969\n", " 44.0\n", " 190999\n", - " -1.071304\n", - " False\n", " \n", " \n", " 7\n", " mix (zero and low emission)\n", - " 2\n", - " 0\n", " 36775430\n", " 125.0\n", " 294203\n", - " -0.827927\n", - " False\n", " \n", " \n", " 8\n", " not specified\n", - " 4\n", - " 1\n", " 41552404\n", " 325.0\n", " 127853\n", - " -1.220216\n", - " False\n", " \n", " \n", " 9\n", " zero-emission bus (not specified)\n", - " 0\n", - " 5\n", " 128156513\n", " 143.0\n", " 896199\n", - " 0.591708\n", - " False\n", " \n", " \n", "\n", "" ], "text/plain": [ - " prop_type total_project_count \\\n", - "0 BEB 0 \n", - "1 CNG 12 \n", - "2 FCEB 2 \n", - "3 electric (not specified) 1 \n", - "4 ethanol 1 \n", - "5 low emission (hybrid) 16 \n", - "6 low emission (propane) 5 \n", - "7 mix (zero and low emission) 2 \n", - "8 not specified 4 \n", - "9 zero-emission bus (not specified) 0 \n", - "\n", - " total_project_count_ppno total_agg_cost total_bus_count \\\n", - "0 30 167232489 163.0 \n", - "1 1 176039140 252.0 \n", - "2 6 120951335 102.0 \n", - "3 2 56678000 44.0 \n", - "4 0 1006750 9.0 \n", - "5 0 91824361 145.0 \n", - "6 0 8403969 44.0 \n", - "7 0 36775430 125.0 \n", - "8 1 41552404 325.0 \n", - "9 5 128156513 143.0 \n", + " prop_type total_agg_cost total_bus_count \\\n", + "0 BEB 167232489 163.0 \n", + "1 CNG 176039140 252.0 \n", + "2 FCEB 120951335 102.0 \n", + "3 electric (not specified) 56678000 44.0 \n", + "4 ethanol 1006750 9.0 \n", + "5 low emission (hybrid) 91824361 145.0 \n", + "6 low emission (propane) 8403969 44.0 \n", + "7 mix (zero and low emission) 36775430 125.0 \n", + "8 not specified 41552404 325.0 \n", + "9 zero-emission bus (not specified) 128156513 143.0 \n", "\n", - " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", - "0 1025966 0.897727 False \n", - "1 698568 0.125652 False \n", - "2 1185797 1.274642 False \n", - "3 1288136 1.515980 False \n", - "4 111861 -1.257929 False \n", - "5 633271 -0.028332 False \n", - "6 190999 -1.071304 False \n", - "7 294203 -0.827927 False \n", - "8 127853 -1.220216 False \n", - "9 896199 0.591708 False " + " new_cost_per_bus \n", + "0 1025966 \n", + "1 698568 \n", + "2 1185797 \n", + "3 1288136 \n", + "4 111861 \n", + "5 633271 \n", + "6 190999 \n", + "7 294203 \n", + "8 127853 \n", + "9 896199 " ] }, "metadata": {}, @@ -2457,7 +4875,7 @@ ")\n", "display(\n", " #from new_cpb_agg\n", - " agg_prop,\n", + " agg_prop[[\"prop_type\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]]\n", " #pivot\n", " #pivot_prop_type\n", ")\n", @@ -2466,7 +4884,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 32, "id": "ca709e43-4947-4a34-970f-216d4b6ab7cc", "metadata": {}, "outputs": [], @@ -2518,7 +4936,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 33, "id": "01647d83-8b4f-47a9-ab57-a1db7cd501dd", "metadata": {}, "outputs": [ @@ -2696,7 +5114,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 34, "id": "3be3ef4f-0bf3-4770-a8b7-340d372ae1ce", "metadata": {}, "outputs": [ @@ -2722,175 +5140,65 @@ " \n", " \n", " bus_size_type\n", - " total_project_count\n", - " total_project_count_ppno\n", " total_agg_cost\n", " total_bus_count\n", " new_cost_per_bus\n", - " new_zscore_cost_per_bus\n", - " new_is_cpb_outlier?\n", " \n", " \n", " \n", " \n", " 0\n", " articulated\n", - " 0\n", - " 2\n", " 58237576\n", " 41.0\n", " 1420428\n", - " 1.605005\n", - " False\n", " \n", " \n", " 1\n", " cutaway\n", - " 3\n", - " 0\n", " 16694500\n", " 152.0\n", " 109832\n", - " -1.464878\n", - " False\n", " \n", " \n", " 2\n", " not specified\n", - " 40\n", - " 6\n", " 509919038\n", " 881.0\n", " 578795\n", - " -0.366399\n", - " False\n", " \n", " \n", " 3\n", " over-the-road\n", - " 0\n", - " 1\n", " 9516000\n", " 14.0\n", " 679714\n", - " -0.130011\n", - " False\n", " \n", " \n", " 4\n", " standard/conventional (30ft-45ft)\n", - " 0\n", - " 36\n", " 234253277\n", " 264.0\n", " 887323\n", - " 0.356283\n", - " False\n", " \n", " \n", "\n", "" ], "text/plain": [ - " bus_size_type total_project_count \\\n", - "0 articulated 0 \n", - "1 cutaway 3 \n", - "2 not specified 40 \n", - "3 over-the-road 0 \n", - "4 standard/conventional (30ft-45ft) 0 \n", - "\n", - " total_project_count_ppno total_agg_cost total_bus_count \\\n", - "0 2 58237576 41.0 \n", - "1 0 16694500 152.0 \n", - "2 6 509919038 881.0 \n", - "3 1 9516000 14.0 \n", - "4 36 234253277 264.0 \n", + " bus_size_type total_agg_cost total_bus_count \\\n", + "0 articulated 58237576 41.0 \n", + "1 cutaway 16694500 152.0 \n", + "2 not specified 509919038 881.0 \n", + "3 over-the-road 9516000 14.0 \n", + "4 standard/conventional (30ft-45ft) 234253277 264.0 \n", "\n", - " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", - "0 1420428 1.605005 False \n", - "1 109832 -1.464878 False \n", - "2 578795 -0.366399 False \n", - "3 679714 -0.130011 False \n", - "4 887323 0.356283 False " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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bus_counttotal_cost
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articulated41.058237576
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over-the-road14.09516000
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" - ], - "text/plain": [ - " bus_count total_cost\n", - "bus_size_type \n", - "articulated 41.0 58237576\n", - "cutaway 152.0 16694500\n", - "not specified 881.0 509919038\n", - "over-the-road 14.0 9516000\n", - "standard/conventional (30ft-45ft) 264.0 234253277\n", - "Grand Total 1352.0 828620391" + " new_cost_per_bus \n", + "0 1420428 \n", + "1 109832 \n", + "2 578795 \n", + "3 679714 \n", + "4 887323 " ] }, "metadata": {}, @@ -2908,7 +5216,7 @@ " margins_name = \"Grand Total\"\n", ")\n", "display(\n", - " agg_bus_size,\n", + " agg_bus_size[[\"bus_size_type\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]]\n", " #pivot_size\n", ")\n", "\n", @@ -2917,7 +5225,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 55, "id": "2c933257-bdc2-4007-9571-58475118073c", "metadata": {}, "outputs": [ @@ -2943,68 +5251,42 @@ " \n", " \n", " source\n", - " total_project_count\n", - " total_project_count_ppno\n", " total_agg_cost\n", " total_bus_count\n", " new_cost_per_bus\n", - " new_zscore_cost_per_bus\n", - " new_is_cpb_outlier?\n", " \n", " \n", " \n", " \n", " 0\n", " dgs\n", - " 0\n", - " 36\n", " 250112853\n", " 236.0\n", " 1059800\n", - " 1.150152\n", - " False\n", " \n", " \n", " 1\n", " fta\n", - " 43\n", - " 0\n", " 391257025\n", " 883.0\n", " 443099\n", - " -1.287721\n", - " False\n", " \n", " \n", " 2\n", " tircp\n", - " 0\n", - " 9\n", " 187250513\n", " 233.0\n", " 803650\n", - " 0.137569\n", - " False\n", " \n", " \n", "\n", "" ], "text/plain": [ - " source total_project_count total_project_count_ppno total_agg_cost \\\n", - "0 dgs 0 36 250112853 \n", - "1 fta 43 0 391257025 \n", - "2 tircp 0 9 187250513 \n", - "\n", - " total_bus_count new_cost_per_bus new_zscore_cost_per_bus \\\n", - "0 236.0 1059800 1.150152 \n", - "1 883.0 443099 -1.287721 \n", - "2 233.0 803650 0.137569 \n", - "\n", - " new_is_cpb_outlier? \n", - "0 False \n", - "1 False \n", - "2 False " + " source total_agg_cost total_bus_count new_cost_per_bus\n", + "0 dgs 250112853 236.0 1059800\n", + "1 fta 391257025 883.0 443099\n", + "2 tircp 187250513 233.0 803650" ] }, "metadata": {}, @@ -3088,17 +5370,44 @@ " margins_name = \"Grand Total\"\n", ").reset_index()\n", "\n", + "\n", + "\n", "display(\n", - " agg_source,\n", + " agg_source[[\"source\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]],\n", " pivot_source\n", ")\n", "# dont need pivot, keep agg_source to retain cpb" ] }, + { + "cell_type": "code", + "execution_count": 52, + "id": "31156225-644d-4709-9ebc-0a7f0fe7b78d", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Cannot set a DataFrame with multiple columns to the single column cost_per_bus", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_392/3719685169.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpivot_source\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"cost_per_bus\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpivot_source\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"total_cost\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpivot_source\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"bus_count\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"int64\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 3966\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setitem_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3967\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3968\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setitem_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3969\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3970\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item_frame_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3971\u001b[0m elif (\n\u001b[1;32m 3972\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3973\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 4121\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marraylike\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4122\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4124\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4125\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 4126\u001b[0m \u001b[0;34m\"Cannot set a DataFrame with multiple columns to the single \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4127\u001b[0m \u001b[0;34mf\"column {key}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4128\u001b[0m )\n", + "\u001b[0;31mValueError\u001b[0m: Cannot set a DataFrame with multiple columns to the single column cost_per_bus" + ] + } + ], + "source": [ + "pivot_source[\"cost_per_bus\"] = ((pivot_source[\"total_cost\"]) / (pivot_source[\"bus_count\"])).astype(\"int64\").reset_index()" + ] + }, { "cell_type": "markdown", "id": "11547020-dd35-4745-98f8-bbd02fccaa23", "metadata": { + "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -3110,7 +5419,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 36, "id": "4753f3ea-00b6-4d5e-a3f0-73b3d3593acb", "metadata": {}, "outputs": [ @@ -3147,7 +5456,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 37, "id": "8645cf77-b30a-4c45-b943-ac81e8b5a613", "metadata": {}, "outputs": [ @@ -3174,7 +5483,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 38, "id": "cefa6800-df50-4eda-95f8-74363ef942d0", "metadata": {}, "outputs": [ @@ -3201,13 +5510,13 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 39, "id": "563304d2-2d98-44e6-b3a4-fd54f63fc0d8", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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t7c0GDhxYYnyM5X82vXr1Ylqtlnl7e7OFCxcyxhi7c+cOA8D++usvw3f4+f21U6dOrGHDhiwvL8+wTK/Xs1atWrHatWsbluXl5Rk+zwIPHz5kEomELViwwLCsrJ+1UqkstOzChQuFPpuCbencubPhuMUYY++//z4TCoUsMzOTMZZ/zBaLxaxXr15G5T755BMGwOj7VFw8YWFhDAALDAxkY8aMYevWrWNPnjwpVHbq1KmsuD9hL26XWq1mDRo0YB07djRaDoCJxWL24MEDw7IbN24wAOynn34yLOvfvz+TSqUsPj7esOzOnTtMKBQWiqGoz7Rbt24sODjYaFlxx84ffviBAWA7d+40LMvJyWG1atUyOvYVp6jv3YtcXFzYK6+8Ynhu6vdyzpw5TCQSsWfPnhmWqVQq5urqapQPFHX8LupzmTRpEnNwcDBab69evVhgYGChskUd1wr+bqSnpxuW3bhxgwkEAjZq1CjDMlNzFlOUaZTn81eZAUCbNm2Qnp4OuVwOIH+gHgDMnDnTqNwHH3wAAIW69evVq2f4RQjk/+INCwtDbGxsqeLaunUrfv31V4wcObLIgd4F+vXrh2PHjhV6dOjQwaicVCo1vHbkyBGsXr0aTk5O6Nmzp9Gpm6Ls2bMHHMchIiKi0GsFv6JM/ZwKBur+8ccf0Gg0Ja63JNOmTTOKYdq0aVCr1Th+/DgAYNeuXXBxcUGXLl2QlpZmeISHh8PJyQmnTp0yqq9mzZol/sJ4kZ2dHSZNmmR4LhaLMWnSJKSmpuLq1aul2paCnqQjR46Uupv1ZQp+4ZR0tYmrqysSEhJw+fJls9fTrl071KtXz+TyL/5afffddwH8//eovBw6dAjNmzfH66+/bljm5OSEiRMnIi4urtDVpGPHjjXqsS3Yt1+2P7dp0waRkZHIyckBkN8T0LNnTzRp0gRnz54FkN/7xHGcUSyl5eTkZDSeTSwWo3nz5qU63giFQgwePBjbtm0DkD8APCAgwOg4VuDZs2c4efIkBg8ejOzsbMN+lZ6ejm7duiE6OhqJiYkA8seTFQzC1+l0SE9Ph5OTE8LCwhAZGVmobnM/6+fHtGg0GqSnp6NWrVpwdXUtcj0TJ0406v1p06YNdDod4uPjAeT/8ler1Xj33XeNyr04qLekeC5dumQ4bbxhwwaMHz8ePj4+ePfdd00+RfX8dmVkZCArK8vwvXpR586dDb2EQH7PvLOzs+Gz0+l0OHLkCPr3748aNWoYytWtW7fI497z687KykJaWhratWuH2NjYQkMGijp2Hjp0CD4+Phg0aJBhmYODQ7G9quZwcnIyHNdK870cMmQINBqN4ewGkH8BRmZmJoYMGVLiOp//XArW06ZNGyiVSty9e7fU25CcnIzr169jzJgxqFatmmF5o0aN0KVLlyKPhy/LWUxRpqTp+S8QALi5uQGAYdxLfHw8BAIBatWqZVTO29sbrq6uhh2tuPoK6iyoT6fTISUlxejxYpdvdHQ0Jk+ejNDQUPz8888lxu/v74/OnTsXerzY5SoUCg2vde3aFRMnTsTx48eRlZWFOXPmlLiOmJgY+Pr6GjXqi0z9nNq1a4eBAwdi/vz58PDwQL9+/bB+/fpSnesWCAQIDg42WlZw6qng/HN0dDSysrLg5eUFT09Po4dCoUBqaqrR+2vWrGny+gHA19cXjo6OJcZgqpo1a2LmzJn45Zdf4OHhgW7dumHFihVlGs9UQKFQAABkMlmxZT7++GM4OTmhefPmqF27NqZOnYpz586Vaj2l/fxq165t9DwkJAQCgcCic6IUJT4+vshTr3Xr1jW8/ryXHR+K06ZNG2i1Wly4cAH37t1Damoq2rRpg7Zt2xolTfXq1Stxv3oZf3//Qqd/nj/emGr48OG4c+cObty4ga1bt2Lo0KFFnlZ68OABGGP47LPPCu1XBT+qCvYtvV6P77//HrVr14ZEIoGHhwc8PT1x8+bNIr/b5n7Wubm5+PzzzxEQEGC0nszMTLPWU/AdePE76unpaSj7Mi4uLvjmm28QFxeHuLg4rFu3DmFhYVi+fDkWLlxoUh1//PEHXnvtNUilUlSrVs0wdMGUbSrYroJtevr0KXJzcwttE4Ai94dz586hc+fOhjE2np6e+OSTTwCgyKTpRfHx8ahVq1ah79DLhj2UhkKhMBzXSvO9bNy4MerUqWN0OnbHjh3w8PBAx44dS1zn7du3MWDAALi4uMDZ2Rmenp6GHy3mHK8LvmvFHZPS0tIMP7wKmLufPK9MY5qKGx3PXhhUbOrkXy+r7/Hjx4W+ZKdOnTIM/lSpVBgyZIjhHHZJ50LLyt/fH2FhYThz5ozF6nzZ58RxHHbv3o2LFy/if//7H44cOYJx48ZhyZIluHjxosW2V6/Xw8vLC1u2bCny9RfHvJTHFRjFfRbPDxossGTJEowZMwa///47jh49iunTpxvG+/j7+5sdw61btwCgUDL7vLp16+LevXv4448/cPjwYezZswc///wzPv/8c8PUDy9T1s/vxc+qNJ9deTL1+PCiZs2aQSqV4syZM6hRowa8vLwQGhqKNm3a4Oeff4ZKpTJcncZHfC9q0aIFQkJC8N577+Hhw4eFrvoqUDDv16xZs4rtmS34rn311Vf47LPPMG7cOCxcuBDVqlWDQCDAe++9V+T8YeZuy7vvvov169fjvffeQ8uWLeHi4gKO4zB06FCLrsdcgYGBGDduHAYMGIDg4GBs2bIFX3zxRYnvOXv2LPr27Yu2bdvi559/ho+PD0QiEdavX19o0D5g2W2KiYlBp06dUKdOHSxduhQBAQEQi8U4dOgQvv/++0KfKR9XGSckJCArK8vwXSvN9xLI72368ssvkZaWBplMhgMHDmDYsGElXhSVmZmJdu3awdnZGQsWLEBISAikUikiIyPx8ccfW2xOvJexRFuX65QDgYGB0Ov1iI6ONvwaBfIHlGZmZhoG/ZnK29sbx44dM1rWuHFjw/9nzZqFa9euYdmyZXjllVfKFrwJtFqtoTeiOCEhIThy5AiePXtW7K/i0n5Or732Gl577TV8+eWX2Lp1K0aMGIHt27fj7bfffmnipdfrERsbazSwueAUY8GguZCQEBw/fhytW7cul506KSkJOTk5Rr1NL8ZQ8AvgxUkXX+zNKNCwYUM0bNgQc+fOxfnz59G6dWusWrXqpQfYkmzatAkcx6FLly4llnN0dMSQIUMMCfsbb7yBL7/8EnPmzIFUKrX4jMHR0dFGPx4ePHgAvV5v1mdXmtgCAwNx7969QssLutZLuz8Xp+A02dmzZ1GjRg3DqaY2bdpApVJhy5YtePLkCdq2bVtiPRU1UzMADBs2DF988QXq1q1b7JxQBT28IpGo2EkxC+zevRsdOnTAunXrjJZnZmYaBuxbwu7duzF69GgsWbLEsCwvL8/syU4LvgPR0dFGPdpPnz4tdQ/e89zc3BASEmL4IQMU37579uyBVCrFkSNHjC4AWr9+vVnr9vT0hL29PaKjowu99uL+8L///Q8qlQoHDhww6tV4cUhDSQIDA3Hr1i0wxoy2sah9zxwFA+cLEqTSfC+B/KRp/vz52LNnD6pXrw65XI6hQ4eW+J7Tp08jPT0de/fuNdpvHz58WKisqfttwXetuGOSh4dHoTMallCuM9f17NkTAArN7rl06VIAKHQVw8tIpdJCp9IK/kDs27cPy5cvR9++fTF9+vSyB/8S9+/fx71794yStqIMHDgQjLEiex0KsltTP6eMjIxCGXHBAbrgFF3B1S4lHfSWL19uFMPy5cshEonQqVMnAPlXGOh0uiK7wrVabZlnj9ZqtUaX66rVaqxevRqenp4IDw8HAMMYg+d78nQ6HdasWWNUl1wuh1arNVrWsGFDCASCMl2iu3jxYhw9ehRDhgwpslu+QHp6utFzsViMevXqgTFmGHdWsONaatbtgkutC/z0008AYLjiy9nZGR4eHoV6QYs6XV2a2Hr27Il//vkHFy5cMCzLycnBmjVrEBQUVKpxWS/Tpk0bXLp0CadOnTIkTR4eHqhbt67hSpeixg09z9Kfe0nefvttREREGCUfL/Ly8kL79u2xevVqJCcnF3r9+Uv3hUJhoX19165dhrElllLUen766SezeyU7d+4MkUiEn376yaheU2d4vnHjRpFXNMfHx+POnTtGp2KKa1+hUAiO44y2IS4uDvv37zd9Q16or1u3bti/fz8ePXpkWB4VFYUjR44UKgsY91xkZWWVKmHr2bMnkpKSjKZIUCqVhY595jh58iQWLlyImjVrYsSIEQBK970E8nvXGzZsiB07dmDHjh3w8fF56Q+Yoj4XtVpd7DHJlNN1Pj4+aNKkCTZu3Gj0Hbh16xaOHj1q+LtqaeXa09S4cWOMHj0aa9asMXTP/fPPP9i4cSP69+9faMC1uZKTkzF+/HgIhUJ06tSp0PwTBUJCQtCyZUvD8/v37xdZtnr16ka9C1qt1lBOr9cjLi4Oq1atgl6vL3KA9/M6dOiAkSNH4scff0R0dDS6d+8OvV6Ps2fPokOHDpg2bZrJn9PGjRvx888/Y8CAAQgJCUF2djbWrl0LZ2dnwxfE3t4e9erVw44dOxAaGopq1aqhQYMGhvtsSaVSHD58GKNHj0aLFi3w559/4uDBg/jkk08Mp93atWuHSZMmYdGiRbh+/Tq6du0KkUiE6Oho7Nq1C8uWLTMapFhavr6++PrrrxEXF4fQ0FDs2LED169fx5o1awyXadevXx+vvfYa5syZY+il2759e6EE6eTJk5g2bRrefPNNhIaGQqvVYtOmTRAKhRg4cOBLY3m+bfPy8hAfH48DBw7g5s2b6NChw0sPVF27doW3tzdat26N6tWrIyoqCsuXL0evXr0MYwYKEsFPP/0UQ4cOhUgkQp8+fcz+FfTw4UP07dsX3bt3x4ULF7B582YMHz7cKIF/++23sXjxYrz99tto1qwZzpw5U+RFC6WJbfbs2di2bRt69OiB6dOno1q1ati4cSMePnyIPXv2WHT28DZt2uDLL7/E48ePjZKjtm3bYvXq1QgKCnrpqdeQkBC4urpi1apVkMlkcHR0RIsWLUo9hswUgYGBJs1MvWLFCrz++uto2LAhJkyYgODgYDx58gQXLlxAQkKCYR6m3r17Y8GCBRg7dixatWqFf//9F1u2bCk0HrGsevfujU2bNsHFxQX16tXDhQsXcPz4ccOl9qXl6emJWbNmYdGiRejduzd69uyJa9eu4c8//zSph+zYsWOIiIhA37598dprrxnmlfv111+hUqmMPuOC7+706dPRrVs3CIVCDB06FL169cLSpUvRvXt3DB8+HKmpqVixYgVq1aqFmzdvmrVd8+fPx+HDh9GmTRtMmTIFWq3WMD/b83V27doVYrEYffr0waRJk6BQKLB27Vp4eXkVmZAUZcKECVi+fDlGjRqFq1evwsfHB5s2bSo0/cPL/Pnnn7h79y60Wi2ePHmCkydP4tixYwgMDMSBAweMJoo09XtZYMiQIfj8888hlUoxfvz4l+77rVq1gpubG0aPHo3p06eD4zhs2rSpyNNi4eHh2LFjB2bOnIlXX30VTk5O6NOnT5H1fvvtt+jRowdatmyJ8ePHG6YccHFxKb+Z4kt1rd0Ll+8VXKJeoKjLDDUaDZs/fz6rWbMmE4lELCAggM2ZM8foEkPG/v8S3he9eBlxUQouu33Z4/nLXUsq9/z6ippywNnZmXXq1IkdP37cpM9Lq9Wyb7/9ltWpU4eJxWLm6enJevToYTRlgSmfU2RkJBs2bBirUaMGk0gkzMvLi/Xu3ZtduXLFaH3nz59n4eHhTCwWG11iXnCJfUxMDOvatStzcHBg1atXZxEREYUub2aMsTVr1rDw8HBmb2/PZDIZa9iwIfvoo49YUlKSoUxx7Vacdu3asfr167MrV66wli1bMqlUygIDA9ny5csLlY2JiWGdO3dmEomEVa9enX3yySfs2LFjRpfdxsbGsnHjxrGQkBAmlUpZtWrVWIcOHUxqmxfb1sHBgQUFBbGBAwey3bt3F/mZvPh9XL16NWvbti1zd3dnEomEhYSEsA8//JBlZWUZvW/hwoXMz8+PCQQCo30EAJs6dWqR8T3fdoz9/353584dNmjQICaTyZibmxubNm0ay83NNXqvUqlk48ePZy4uLkwmk7HBgwez1NTUIqejKC62F6ccYCy/TQYNGsRcXV2ZVCplzZs3Z3/88YdRmYL98cVpIEqaCuFFcrmcCYVCJpPJmFarNSzfvHkzA8BGjhxZ6D1FHSt+//13Vq9ePWZnZ2e07oLv4YtGjx5d5OXOLzLle1/cpd8xMTFs1KhRzNvbm4lEIubn58d69+7Ndu/ebSiTl5fHPvjgA+bj48Ps7e1Z69at2YULFwptY1k/64yMDDZ27Fjm4eHBnJycWLdu3djdu3cLtX1x2/LiFCCMMabT6dj8+fMNsbdv357dunWryO/Ti2JjY9nnn3/OXnvtNebl5cXs7OyYp6cn69Wrl9EUNYzlH1ffffdd5unpyTiOM7r0f926dax27dpMIpGwOnXqsPXr1xv2n+cVt/8VFetff/1lOK4GBwezVatWFVnngQMHWKNGjZhUKmVBQUHs66+/Zr/++muhv40lfYfi4+NZ3759mYODA/Pw8GAzZswwTPli6pQDBQ+xWMy8vb1Zly5d2LJly5hcLi/yfaZ8LwtER0cb6v/777+LjeH57T137hx77bXXmL29PfP19WUfffQRO3LkSKFtUigUbPjw4czV1dUw9QRjxX+njx8/zlq3bs3s7e2Zs7Mz69OnD7tz545RmdLkLC/DMVZOI/iI1RkzZgx279790nFYhBBCCCms8tyNkxBCCCGER5Q0EUIIIYSYgJImQgghhBAT0JgmQgghhBATUE8TIYQQQogJKGkihBBCCDFBuU5uWd70ej2SkpIgk8kq9JYJhBBCCDEfYwzZ2dnw9fW16MS45a1SJ01JSUkICAjgOwxCCCGEmOHx48dlurF6RavUSVPBbSoeP34MZ2dnnqOpPOosr4Pk7GT4yHxwd9pdvsOxbXXqAMnJgI8PcJfagk+0X1gPagvrUV5tIZfLERAQYPg7XllU6qSp4JScs7MzJU2lMK/bPCjUCjiJnehz49u8eYBCATg5AdQWvKL9wnpQW1iP8m6Lyja0plJPOSCXy+Hi4oKsrCzasQghhJBKorL+/a48o68IIYQQQnhESRMhhBBCiAkq9ZgmYp7k7GTomA5CTggfmQ/f4di25GRApwOEwvzB4DzR6XTQaDS8rd8apOakQs/0EHACeDl68R2OTaO2sB7mtoVIJIJQKCzHyPhBSZMNenXtq0jMToSfzA8JMxP4Dse2vfoqkJgI+PkBCRXfFowxpKSkIDMzs8LXbW0S5AnQ6XUQCoTIcc7hOxybRm1hPcrSFq6urvD29q50g71LQkkTITasIGHy8vKCg4NDlTq4lZbqqQpapoUdZ4eanjX5DsemUVtYD3PagjEGpVKJ1NRUAIAPj73olkZJEyE2SqfTGRImd3d3vsPhHSfiAD3ACThIpVK+w7Fp1BbWw9y2sLe3BwCkpqbCy8urypyqo4HghNiogjFMDg4OPEdCCKmKCo4tVWm8JCVNhNg4Wz4lRwgpP1Xx2EJJEyGEEEKICXhPmhITE/HWW2/B3d0d9vb2aNiwIa5cucJ3WIQQUm7i4uIwb948vsMghJQSr0lTRkYGWrduDZFIhD///BN37tzBkiVL4ObmxmdYhBArNmbMGHAch8mTJxd6berUqeA4DmPGjKn4wCykffv24DjO6PH8tt64cQPDhg1DQEAA7O3tUbduXSxbtqxQPVu2bEHjxo3h4OAAHx8fjBs3Dunp6SWue/r06QgPD4dEIkGTJk2KLHPz5k20adMGUqkUAQEB+OabbwqVyczMxNSpU+Hj4wOJRILQ0FAcOnTIpO1/8OABWtVqhQ51Oxgt12g0WLBgAUJCQiCVStG4cWMcPnzYpDoJsRRer577+uuvERAQgPXr1xuW1axJl5cSQkoWEBCA7du34/vvvzdcpZOXl4etW7eiRo0aPEdXvIcPH2LmzJm4cOEC5HI5tm/fjvbt22PVqlVG5SZMmIAFCxYYnj8/WP/q1avw8vLC5s2bERAQgPPnz2PixIkQCoWYNm0aAODcuXMYNWoUvv/+e/Tp0weJiYmYPHkyJkyYgL1795YY47hx43Dp0iXcvHmz0GtyuRxdu3ZF586dsWrVKvz7778YN24cXF1dMXHiRACAWq1Gly5d4OXlhd27d8PPzw/x8fFwdXV96eej0WgwbNgwvNLiFdy4csPotblz52Lz5s1Yu3Yt6tSpgyNHjmDAgAE4f/48XnnllZfWTYgl8Jo0HThwAN26dcObb76Jv/76C35+fpgyZQomTJjAZ1iEECvXtGlTxMTEYO/evRgxYgQAYO/evahRo0ahH156vR5ff/011qxZg5SUFISGhuKzzz7DoEGDAORPvTBx4kQcPnYY6U/T4e3njffffR8zZsww1DFmzBhkZmbi9ddfx5IlS6BWqzF06FD88MMPEIlEJsc9atQo6HQ6rFy5EseOHUO/fv1w5syZQuUcHBzg7e1dZB3jxo0zeh4cHIwLFy5g7969hqTpwoULCAoKwvTp0wHk/xidNGkSvv766xLj+/HHHwEAT58+LTJp2rJlC9RqNX799VeIxWLUr18f169fx9KlSw1J06+//opnz57h/Pnzhs8mKCioxPUWmDt3LurUqYPazWoXSpo2bdqETz/9FD179gQAvPPOOzh+/DiWLFmCzZs3m1Q/IWXF6+m52NhYrFy5ErVr18aRI0fwzjvvYPr06di4cWOR5VUqFeRyudGDlN6JUSdw651bODHqBN+hkBMngFu38v+1IiqVCjk5OWV6qFSqco1x3LhxRr3Uv/76K8aOHVuo3KJFi/Dbb79h1apVuH37Nt5//3289dZb+OuvvwDkJ1X+/v7YuXMnrt24hojPIvDJJ59g586dRvWcOnUKMTExOHXqFDZu3IgNGzZgw4YNhtfnzZv30uTg2rVrmDp1Kl555RV4eXmhW7du+PLLLwuV27JlCzw8PNCgQQPMmTMHSqWyxHqzsrJQrVo1w/OWLVvi8ePHOHToEBhjePLkCXbv3m1IOMx14cIFtG3bFmKx2LCsW7duuHfvHjIyMgDk/xhu2bIlpk6diurVq6NBgwb46quvoNPpSqz75MmT2LVrF1asWAEfJx8IOSHC3MMMr6tUqkLzBNnb2+Pvv/8u0zaRkoW5h6G+Z32jtrBlvPY06fV6NGvWDF999RUA4JVXXsGtW7ewatUqjB49ulD5RYsWYf78+RUdZpUT5kFffqsRZn1tof3mG3BLluBl/SfKsDA8WLrUaFmtmTPhcO+e4TkTiVDkRcczZ+Y/yuCtt97CnDlzEB8fDyD/lNT27dtx+vRpQxmVSoWvvvoKx48fR8uWLQHk98z8/fffWL16Ndq1aweRSGR0XKkbWhdXL1/Fzp07MXjwYMNyNzc3LF++HEKhEHXq1EGvXr1w4sQJQ8+4h4cHQkJCSoy5devW+OGHH6DX64stM3z4cAQGBsLX1xc3b97Exx9/jHv37hV7Wu38+fPYsWMHDh48aLSeLVu2YMiQIcjLy4NWq0WfPn2wYsWKEuN7mZSUlEI9edWrVze85ubmhtjYWJw8eRIjRozAoUOH8ODBA0yZMgUajQYRERFF1pueno4xY8Zg8+bNcHZ2hkiY/+2Tiv4/SerWrRuWLl2Ktm3bIiQkBCdOnMDevXtfmoyRsnm+DQjPSZOPjw/q1atntKxu3brYs2dPkeXnzJmDmc8daOVyOQICAso1RkJsjT4zE+L/bn9QEi4wEIG16xgtc8zLg8iE98ICvcSenp7o1asXNmzYAMYYevXqBQ8PD6MyDx48gFKpRJcuXYyWq9Vqo3EwK1aswK+//opHjx4hNzcXarW60EDo+vXrG81q7OPjg3///dfwfNq0aYbTY8XZsmUL5s+fj08++QQpKSk4cuQIPvjgA8OpQgCG01wA0LBhQ/j4+KBTp06IiYkplJTdunUL/fr1Q0REBLp27WpYfufOHcyYMQOff/45unXrhuTkZHz44YeYPHky1q1bV2KMZaXX6+Hl5YU1a9ZAKBQiPDwciYmJ+Pbbb4tNmiZMmIDhw4ejbdu2xda7bNkyTJgwAXXq1AHHcQgJCcHYsWPx66+/ltemEFIIr0lT69atce+5X6UAcP/+fQQGBhZZXiKRQCKRVERohNgs5uwMtZcXhHZ2JU5Ox3lVh/S/QdjPL9P7+YExBp1WCzs7OwiKqsPZ2SKxjhs3zpCoFNWLolAoAAAHDx6En5+f0WsFx5Lt27dj1qxZWLJkCVq2bAmZTIZvv/0Wly5dMir/4tgljuNK7DEqioeHB3766Sd88MEHWLx4MYKCgjBkyBD8+eefRknP81q0aAEgPwF8Pmm6c+cOOnXqhIkTJ2Lu3LlG71m0aBFat26NDz/8EADQqFEjODo6ok2bNvjiiy/MvheYt7c3njx5YrSs4HnBGCwfH59Cd7ivW7cuUlJSoFarjU7tFTh58iQOHDiA7777DkD+vcv0ej3s7OywZs0ajBs3Dp6enti/fz/y8vKQnp4OX19fzJ49G8HBwWZtCyHm4DVpev/999GqVSt89dVXGDx4MP755x+sWbMGa9as4TOsKm/rv1uh1CjhIHLA8IbD+Q7Htm3dCiiVgIMDMNw62kL77ruI6twZgbXrFEqKXiZ3734AQF5uLuKj76Ju3bpwdHQshyjzde/eHWq1GhzHoVu3boVer1evHiQSCR49eoR27doVWce5c+fQqlUrDBkzBHqmh4ATICYmptxiLuDt7Y3Zs2dj165dOHv2bLFJ0/Xr1wEY3/T09u3b6NixI0aPHl3kmCilUgk7O+PDe0ESwxgzO+aWLVvi008/hUajMSSRx44dQ1hYmGGqmNatW2Pr1q3Q6/UQCPKHzd6/fx8+Pj5FJkxA/lip50+zbdu9DT8u/RF/nvgT9WvVNyorlUrh5+cHjUaDPXv2GJ1CJZaXrkw37BfuDnSPSl4Hgr/66qvYt28ftm3bhgYNGmDhwoX44YcfDFfDkPLx0bGPMOF/E/DRsY/4DoV89BEwYUL+v6TUhEIhoqKicOfOnSJvCCqTyTBr1iy8//772LhxI2JiYhAZGYmffvrJcMFJ7dq1ceXKFez8fSfORJ7BnE/n4PLly6WOZfny5ejUqVOJZcaPH49//vnHMFB+7969uH37NsLDwwEAMTExWLhwIa5evYq4uDgcOHAAo0aNQtu2bdGoUSMA+afkOnTogK5du2LmzJlISUlBSkoKnj59alhPnz59sHfvXqxcuRKxsbE4d+4cpk+fjubNm8PX17fY+B48eIDr168jJSUFubm5uH79Oq5fvw61Wg0gf7yVWCzG+PHjcfv2bezYsQPLli0zGjbxzjvv4NmzZ5gxYwbu37+PgwcP4quvvsLUqVOL/azq1q2LBg0aGB4SNwnAAbIAmSEZu3TpEvbu3YvY2FicPXsW3bt3h16vx0e075SrBHkC4rPikSBP4DsUq8BrTxMA9O7dG7179+Y7DEJIJeX8klN9CxcuhKenJxYtWoTY2Fi4urqiadOm+OSTTwAAkyZNwrVr1/DR5I/AcRy69++OKVOm4M8//yxVHGlpaS/tofLy8sK4cePw8OFDqFQq1KhRAwsXLkT//v0BAGKxGMePH8cPP/yAnJwcBAQEYODAgUan33bv3o2nT59i8+bNRpfaBwYGIi4uDkD+FAnZ2dlYvnw5PvjgA7i6uqJjx45GUw6cPn0aHTp0wMOHDw1X/b399tuGqwoBGMZ9FZRxcXHB0aNHMXXqVISHh8PDwwOff/650TisgIAAHDlyBO+//z4aNWoEPz8/zJgxAx9//HGpPqsX5eXlYe7cuYiNjYWTkxN69uyJTZs2mTT/EyGWwrGy9NXyTC6Xw8XFBVlZWS89cJL/57/UH4nZifCT+SFhJv164JW/P5CYCPj5AQkV2xZ5eXl4+PAhatasaXQpd05ODqKiosw6PWeou4JOz1nSjZQb0Og1EAlEaOzduFzXFRcXhw0bNvB6K5X169fjq6++wp07d0o111RFqMi2ICUrS1sUd4wBKu/fb97vPUcIIaTiHTp0CF999ZXVJUyEWDPeT88RQoitCQoK4v2Gvbt27eJ1/YRURtTTRAghhBBiAkqaCCGEEEJMQEkTIYQQQogJKGkihBBCCDEBDQS3Qd5O3kb/Eh79d+sJw7+ENwU3iS34l/CH2sJ6UFsYo6TJBl2ZeIXvEEiBK9QW1qKeZ72XFyIVgtrCelBbGKPTc4QQQgghJqCkiRBCyiAuLg4cxxlurFtZ6jbHhg0bLHLbEkvVUxbz5s0z3HaGEFNR0kQIqVSePn2Kd955BzVq1IBEIoG3tze6deuGc+fOGcpwHIf9+/fzF2QFat++PTiOA8dxkEgk8PPzM9yw19KGDBmC+/fvl+o9QUFB+OGHH8pcT0W5d+8eOnTogOrVq0MqlSI4OBhz586FRqMxlLl9+zYGDhyIoKAgcBxXaPuA/KSsoF0KHnXq1Clx3RqNBgsWLEBISAikUikaN26Mw4cPG5VZuXIlGjVqBGdnZzg7O6Nly5ZG90l89uwZ3n33XYSFhcHe3h41atTA9OnTkZWVVeJ6P/74YzRs2BCOjo7w9fXFqFGjkJSUZFTu2bNnGDFiBJydneHq6orx48dDoVCUuE1VDY1pskGT/jcJz/KeoZq0Glb3Wc13OLZt0iTg2TOgWjVgNbWFKQYOHAi1Wo2NGzciODgYT548wYkTJ5Cenl6meuMz46HVa2EnsEOga6CFojWNWq2GWCw2+/0TJkzAggULoNVqkZCQgH379mHo0KEYM2YM1qxZY7E47e3tYW/m/QhLU095tsWuXbuwePFi3L17FytWrEBISAg+/PBDDBw4EAAgEokwatQoNG3aFK6urrhx4wYmTJgAvV6Pr776CgCgVCoRHByMN998E++//36x66pfvz6OHz9ueG5nV/Kf3Llz52Lz5s1Yu3Yt6tSpgyNHjmDAgAE4f/684ebJ/v7+WLx4MWrXrg3GGDZu3Ih+/frh2rVrqF+/PpKSkpCUlITvvvsO9erVQ3x8PCZPnoykpCTs3r27yPUqlUpERkbis88+Q+PGjZGRkYEZM2agb9++2HN8j6EtJo+YjOTkZBw7dgwajQZjx47FxIkTsXXr1lK1QaXGKrGsrCwGgGVlZfEdSqXit8SPYR6Y3xI/vkMhfn6MAfn/VrDc3Fx2584dlpuba7RcoVCwy5cvs9TMbCZXac16pGZms8uXLzOFQmHRmDMyMhgAdvr06WLLBAYGMgCGR2BgIGOMsQcPHrC+ffsyLy8v5ujoyJo1a8aOHTtmeN/15OvMx9+HTZs9jY0dO5Y5OTmxgIAAtnr1aqP6L126xJo0acIkEgkLDw9ne/fuZQDYtWvXGGOMabVaNm7cOBYUFMSkUikLDQ1lP/zwg1Edo0ePZv369WNffPEF8/HxYUFBQSbVXZR27dqxGTNmFFr+66+/MgBG2/jo0SP25ptvMhcXF+bm5sb69u3LHj58yBhj7MiRI0wikbCMjAyjeqZPn846dOjAGGNs/fr1zMXFxfDayz7Tdu3aGbVFwZ+cF+thjLGff/6ZBQcHM5FIxAJDAtn8ZfPZ9eTrhtcBsLVr17L+/fsze3t7VqtWLfb7778X+7kU5d69e0woFLLPPvuMTZkyhf3vf/9jv/32G9u2bVuJ73v//ffZ66+/XuRrgYGB7Pvvvy+0PCIigjVu3LhU8fn4+LDly5cbLXvjjTfYiBEjSnyfm5sb++WXX4p9fefOnUwsFjONRmNyLP/88w8DwP68/Ce7nHiZ7f0r/7t4+fJlQ5k///yTcRzHEhMTi6yjuGMMY5X37zedniOEVBpOTk5wcnLC/v37oVKpiixz+fJlAMD69euRnJxseK5QKNCzZ0+cOHEC165dQ/fu3dGnTx88evTI6P2bVm9Cs2bNcO3aNUyZMgXvvPMO7t27Z6ijd+/eqFevHq5evYp58+Zh1qxZRu/X6/Xw9/fHrl27cOfOHXz++ef45JNPsHPnTqNyJ06cwL1793Ds2DH88ccfJtVdGqNHj4abm5vhNJ1Go0G3bt0gk8lw9uxZnDt3Dk5OTujevTvUajU6deoEV1dX7Nmzx1CHTqfDjh07MGLEiCLX8bLPdO/evfD398eCBQuQnJyM5OTkIuvZt28fZsyYgQ8++AC3bt3CoLcGYcHMBbh87rJRufnz52Pw4MG4efMmevbsiREjRuDZs2eG1192T7+bN29CIBBg/vz58PT0RIMGDTBy5EgMHTq02Pc8ePAAhw8fRrt27YotU5zo6Gj4+voiODgYI0aMKPRde5FKpYJUKjVaZm9vj7///rvI8jqdDtu3b0dOTg5atmxZbL1ZWVlwdnZ+aU/Xi+/hOA4yFxkA4ObVm3B1dUWzZs0MZTp37gyBQIBLly6ZXG+lx3fWVhaVNVPlG/U0WREr7GladHoR81rsxXy+82W+S/yKffTY3LtQD1OPzb2Z7xI/5vOdL/Na7MV8v/Nlfkv8Cj2WnF9idty7d+9mbm5uTCqVslatWrE5c+awGzduGJUBwPbt2/fSuurXr89++uknxtj/9zT1GtjL8Lper2deXl5s5cqVjDHGVq9ezdzd3Y0+s5UrV760N2jq1Kls4MCBhuejR49m1atXZyqVyrDM3LqL62lijLEWLVqwHj16MMYY27RpEwsLC2N6vd7wukqlYvb29uzIkSOMMcZmzJjBOnbsaHj9xd6nonqIXvT8Z8pY0T0xL9bTqlUrNmHCBMPz68nXWefendnrnf6/dwcAmzt3ruG5QqHI7wn580/Dso4dOxqt+0WxsbFMIpGwDz74gI0fP97Qy1aUli1bMolEwgCwiRMnMp1OV2S54nqaDh06xHbu3Mlu3LjBDh8+zFq2bMlq1KjB5HJ5sescNmwYq1evHrt//z7T6XTs6NGjzN7enonFYqNyN2/eZI6OjkwoFDIXFxd28ODBYut8+vQpq1GjBvvkk0+KLfOi3Nxc1rRpUzZ8+HB2Pfk6u5x4mU2bPY2FhoYWKuvp6cl+/vnnYuuhniZCSJUmV8mRmpeKZEUSkrITi32kKZ8Wem+a8imSshORrEhCal4qkhRJSMxOLPSQq+Rmxzdw4EAkJSXhwIED6N69O06fPo2mTZtiw4YNJb5PoVBg1qxZqFu3LlxdXeHk5ISoqKhCv/5r16tt+D/HcfD29kZqaioAICoqCo0aNTLqDSjqF/6KFSsQHh4OT09PODk5Yc2aNYXW07BhQ6NxTKbWXRqMMXAcBwC4ceMGHjx4AJlMZuixq1atGvLy8hATEwMAGDFiBE6fPm0YALxlyxb06tWr2CvdTP1MXyYqKgqtW7c2Wtbo1UZ4GP3QeFmjRob/Ozo6wtnZ2dA2QH7v3bRp04pdT82aNXHs2DHcunUL27ZtQ9OmTTF8+HDD9j9vx44diIyMxNatW3Hw4EF89913pdqmHj164M0330SjRo3QrVs3HDp0CJmZmYV6HJ+3bNky1K5dG3Xq1IFYLMa0adMwduxYCATGf6rDwsJw/fp1XLp0Ce+88w5Gjx6NO3fuFKpPLpejV69eqFevXok9cM/TaDQYPHgwGGNYuXJlqbbZFtBAcEKIEWeJM7ykXhDa2Rn+4BbFw8GzyGW+Mj8wxqDTamFXTB3OEucyxSiVStGlSxd06dIFn332Gd5++21ERERgzJgxxb5n1qxZOHbsGL777jvUqlUL9vb2GDRoENRqtVG5F09hcBwHvV5vcmzbt2/HrFmzsGTJErRs2RIymQzffvttoVMYjo6OJtdpDp1Oh+joaLz66qsA8hOc8PBwbNmypVBZT8/8tnz11VcREhKC7du345133sG+fftKTEZN/UwtRSQynpW6tG0DAG3atMHhw4cxb9481K9fH+vWrUPHjh0RExNj1PYBAQEAgHr16kGn02HixIn44IMPIBQKzYrd1dUVoaGhePDgQbFlPD09sX//fuTl5SE9PR2+vr6YPXs2goODjcqJxWLUqlULABAeHo7Lly9j2bJlWP3cxSTZ2dno3r07ZDIZ9u3bV+izK0pBwhQfH4+TJ0/C2dkZUOa/5uHlYZSgAoBWq8WzZ8/gbUN3NKCkiRBi5N1m76KzY2cE1q4DaSmvlNrx5n4AQF5uLuKj76Ju3brlnhwA+X/Ynp9iQCQSQafTGZU5d+4cxowZgwEDBgDITyJKO09P3bp1sWnTJuTl5Rl6hC5evFhoPa1atcKUKVMMy4rqyTCn7tLYuHEjMjIyDFeFNW3aFDt27ICXl1f+H8NijBgxAlu2bIG/vz8EAgF69epVbFlTPlOxWFyoLV5Ut25dnDt3DqNHjzYsu3n5JoJDg0t4V9m9+uqrqFOnDho1aoT4+HiEhIQUWU6v10Oj0UCv15udNCkUCsTExGDkyJEvLSuVSuHn5weNRoM9e/Zg8ODBJZbX6/VGY/zkcjm6desGiUSCAwcOFBonVZSChCk6OhqnTp2Cu7u70euNwhshMzMTV69eRXh4OADg5MmT0Ov1aNGixUvrryro9BwhpNJIT09Hx44dsXnzZty8eRMPHz7Erl278M0336Bfv36GckFBQThx4gRSUlKQkZEBAKhduzb27t2L69ev48aNGxg+fHipeymGDx8OjuMwYcIE3LlzB4cOHSp02qZ27dq4cuUKjhw5gvv37+Ozzz4zDEYva93FUSqVSElJQUJCAi5evIiPP/4YkydPxjvvvIMOHToAyE+GPDw80K9fP5w9exYPHz7E6dOnMX36dCQkJBjqGjFiBCIjI/Hll19i0KBBkEgkxa7XlM80KCgIZ86cQWJiItLS0oqs58MPP8SGDRuwcuVKREdHY9OqTTj15ymMemeUSdtfoFOnTli+fHmxrx8+fBjff/89YmNjodfrkZqaih9//BEeHh6oUaMGgPxTkjt37kRUVBRiY2Oxc+dOzJkzB0OGDDH01qjValy/fh3Xr1+HWq1GYmIirl+/btSLNGvWLPz111+Ii4vD+fPnMWDAAAiFQgwbNqzY+C5duoS9e/ciNjYWZ8+eRffu3aHX6/HRRx8ZysyZMwdnzpxBXFwc/v33X8yZMwenT582DNaXy+Xo2rUrcnJysG7dOsjlcqSkpCAlJcUoea1Tpw727dsHID9hGjRoEK5cuYItW7ZAp9MZ3qNR589PFRwajO7du2PChAn4559/cO7cOUybNg1Dhw6Fr69vqdqpUuN5TFWZVNaBZHyjgeBWxAoHglvzlAN5eXls9uzZrGnTpszFxYU5ODiwsLAwNnfuXKZUKg3lDhw4wGrVqsXs7OwMUw48fPiQdejQgdnb27OAgAC2fPlyo0HUBQPBZ82fZbTOxo0bs4iICMPzCxcusMaNGzOxWMyaNGnC9uzZYzRYOy8vj40ZM4a5uLgwV1dX9s4777DZs2cbXX5eMOXAi15Wd1Gev6xfLBYzHx8f1rt3b7Z3795CZZOTk9moUaOYh4cHk0gkLDg4mE2YMKHQMbR58+YMADt58qTR8hcHcL/sMy3YpkaNGhkGVRdVD2OmTTnw4uB+FxcXtn79esPzwMBAo7Z6UVRUFBsyZAjz8/NjQqGQOTk5sdatW7OLFy8aymzfvp01bdqUOTk5MUdHR1avXj321VdfGe0nDx8+LDSVAgDWrl07Q5khQ4YwHx8fJhaLmZ+fHxsyZAh78OCBUTyjR482es/p06dZ3bp1mUQiYe7u7mzkyJGFLucfN24cCwwMZGKxmHl6erJOnTqxo0ePGl4/depUkbEBMBr4DsDw2RW3PQDY2j1r2eXEy+x68nWWnp7Ohg0bxpycnJizszMbO3Ysy87OLvbzrooDwTnGGKug/Mzi5HI5XFxcDJdTEtP4L/VHYnYi/GR+SJiZ8PI3kPLj7w8kJgJ+fkBCxbZFXl4eHj58iJo1axp13+fk5CAqKsqs03OGuiv49Jwl3Ei5AY1eA5FAhMbejfkOx6ZVRFvMmzcPY8aMQVBQULnUb4p27dqhQ4cOJg/S5kNZ2qK4YwxQef9+05gmGzSswTBk5GXATerGdyhk2DAgIwNwo7bgWzX7atAxHYSceWNWiOXYQltkZWUhJiYGBw8e5DuUEtlCW5QGJU026Nuu3/IdAinwLbWFtQhwCeA7BPKfimgLvnt3XFxcjMaSWSvaL4zRQHBCCCGEEBNQ0kQIIYQQYgJKmgixcZX4WhBCiBWriscWGtNkg+osr4Ok7CT4ynxxd9pdvsOxbXXqAElJgK8vcLdi26JgzhmlUgl7M6+Sq0pupd6CWqeGWChGA68GfIdj06gtrEdZ2kKpzJ9O3JTZyCsLSppskEKtQLY6Gwq1gu9QiEIBZGfn/1vBhEIhXF1dDbdGcHBwAMdxhpmFNWoVBCXcRqUkGnV+HSqVyuwZlCuaVqWFnumh1WqRl5fHdzg2jdrCepjTFowxKJVKpKamwtXVtdIcA0xBSRMhNqzgnlHP31NKrVYjLS0NeoEdRGLzfiFq1Bo8S0uDSCQyuimtNXsqfwqdXgehQAiJovhZsEn5o7awHmVpC1dX1yp3XzpKmgixYRzHwcfHB15eXtBo8m+XcPv2bUyePBkLf9mKmqF1zKr34f27+GzyZOzZswdhYWGWDLncjFk/Bk9ynqC6Y3X8NfYvvsOxadQW1sPcthCJRFWqh6kAJU2EEAiFQsMBjuM4xMfHQ6VjYCLzfuWrdAzx8fHgOM6km4Vag8TcRCTmJEIr0FaamKsqagvrQW1hjK6eI4QQQggxASVNhBBCCCEmoKSJEEIIIcQElDQRQgghhJiAkiZCCCGEEBPQ1XM2aFXvVcjV5MJeRLNA827VKiA3F6AZuXlH+4X1oLawHtQWxihpskG9Q3vzHQIp0JvawlrQfmE9qC2sB7WFMTo9RwghhBBiAkqaCCGEEEJMQKfnbNDVpKuGu1aH+4bzHY5tu3oVUKsBsRgIp7bgE+0X1oPawnpQWxijpMkG9dveD4nZifCT+SFhZgLf4di2fv2AxETAzw9IoLbgE+0X1oPawnpQWxij03OEEEIIISagpIkQQgghxASUNBFCCCGEmICSJkIIIYQQE1DSRAghhBBiAl6Tpnnz5oHjOKNHnTp1+AyJEEIIIaRIvE85UL9+fRw/ftzw3M6O95AIIYQQQgrhPUOxs7ODt7c332EQQgghhJSI96QpOjoavr6+kEqlaNmyJRYtWoQaNWoUWValUkGlUhmey+XyigqTkHL36NEjpKWllakODw+PYvcfQgghZcNr0tSiRQts2LABYWFhSE5Oxvz589GmTRvcunULMpmsUPlFixZh/vz5PERatURNjQIDAweO71BIVBTAGB4nJKBu3bpQKpVlqs7BwQFRUVGUOJmB9gvrQW1hPagtjPGaNPXo0cPw/0aNGqFFixYIDAzEzp07MX78+ELl58yZg5kzZxqey+VyBAQEVEisVYlMUjghJTz578fB07w8KJVKzF2+DoG1wsyqKv7BPXwxbTzS0tIoaTID7RfWg9rCelBbGOP99NzzXF1dERoaigcPHhT5ukQigUQiqeCoCKk4gbXCENaoCd9hEEIIKYJVzdOkUCgQExMDHx8fvkMhhBBCCDHCa0/TrFmz0KdPHwQGBiIpKQkREREQCoUYNmwYn2FVeUsvLIVcJYezxBkzW858+RtI+Vm6FJDL4UUXNfCO9gvrQW1hPagtjPGaNCUkJGDYsGFIT0+Hp6cnXn/9dVy8eBGenp58hlXlLb2wFInZifCT+dFOwLelS4HERHh5efEdic2j/cJ6UFtYD2oLY7wmTdu3b+dz9YQQQgghJrOqMU2EEEIIIdaKkiZCCCGEEBNQ0kQIIYQQYgJKmgghhBBCTEBJEyGEEEKICShpIoQQQggxASVNhBBCCCEmsKp7z5GK0dSnKQJcAuDpQJOI8q5pUyAgAEqxGEhN5Tsam0b7hfWgtrAe1BbGKGmyQQeGHeA7BFLgQH5bxEZGAuHhPAdj22i/sB7UFtaD2sIYnZ4jhBBCCDEBJU2EEEIIISagpIkQQgghxAQ0pskG9d3WF0+VT+Hp4Ennq/nWty/w9CmCxWK+I7F5tF9YD2oL60FtYYySJhsUmRyJxOxE+Mn8+A6FREYCiYlw8PLiOxKbR/uF9aC2sB7UFsbo9BwhhBBCiAkoaSKEEEIIMQElTYQQQgghJqCkiRBCCCHEBJQ0EUIIIYSYgJImQgghhBATUNJECCGEEGICSpoIIYQQQkxAk1vaoJktZ0KuksNZ4sx3KGTmTEAuR6pcDnz/Pd/R2DTaL6wHtYX1oLYwRkmTDZrZcibfIZACM/PbIjUykpImntF+YT2oLawHtYUxOj1HCCGEEGICSpoIIYQQQkxAp+dsULYqGwwMHDjIJDK+w7Ft2dkAYxDk5PAdic2j/cJ6UFtYD2oLY5Q02aC6K+oa7lqdMDOB73BsW926QGIi6nl58R2JzaP9wnpQW1gPagtjdHqOEEIIIcQElDQRQgghhJiAkiZCCCGEEBNQ0kQIIYQQYgJKmgghhBBCTEBJEyGEEEKICShpIoQQQggxASVNhBBCCCEmoKSJEEIIIcQENCO4Dfp96O9Q69QQC8V8h0J+/x1QqxETGwu89Rbf0dg02i+sB7WF9aC2MEZJkw0K9w3nOwRSIDy/LXIlEp4DIbRfWA9qC+tBbWGMTs8RQgghhJiAkiZCCCGEEBPQ6Tkb9Mf9P5CryYW9yB69Q3vzHY5t++MPIDcXzomJfEdi82i/sB7UFtaD2sIYJU02aPIfk5GYnQg/mR8SZibwHY5tmzwZSExEDS8vviOxebRfWA9qC+tBbWGMTs8RQgghhJiAkiZCCCGEEBNQ0kQIIYQQYgJKmgghhBBCTEBJEyGEEEKICawmaVq8eDE4jsN7773HdyiEEEIIIYVYRdJ0+fJlrF69Go0aNeI7FEIIIYSQIvGeNCkUCowYMQJr166Fm5sb3+EQQgghhBSJ98ktp06dil69eqFz58744osvSiyrUqmgUqkMz+VyeXmHV2aPHj1CWlpamepQqVSQWOCGrh4eHqhRowacxE6QiWVwEjuVuU5SRk5OgEwGvb0935HYPNovrAe1hfWgtjDGa9K0fft2REZG4vLlyyaVX7RoEebPn1/OUVnOo0ePULduXSiVyjLVw3EcGGNljsfBwQFRUVG4O+1umesiFnI3vy3uREYC4XQ3cT7RfmE9qC2sB7WFMd6SpsePH2PGjBk4duwYpFKpSe+ZM2cOZs6caXgul8sREBBQXiGWWVpaGpRKJeYuX4fAWmFm1XHx1FGs+3oBpi1cgsavtjA7lvgH9/DFtPFIS0tDjRo1zK6HEEIIsVW8JU1Xr15FamoqmjZtalim0+lw5swZLF++HCqVCkKh0Og9EonEIqepKlpgrTCENWpi1nvjo+8BAPxqhphdByGEEELKjrekqVOnTvj333+Nlo0dOxZ16tTBxx9/XChhIoQQQgjhE29Jk0wmQ4MGDYyWOTo6wt3dvdByYlkfHv0QGXkZcJO64duu3/Idjm378EMgIwN+ajXfkdg82i+sB7WF9aC2MMb71XOk4m27tQ2J2Ynwk/nRTsC3bduAxES4eXnxHYnNo/3CelBbWA9qC2NWlTSdPn2a7xAIIYQQQorE++SWhBBCCCGVASVNhBBCCCEmoKSJEEIIIcQElDQRQgghhJiAkiZCCCGEEBNQ0kQIIYQQYgJKmgghhBBCTGBV8zSRitGrdi88y3uGatJqfIdCevUCnj1Dll4P7N3LdzQ2jfYL60FtYT2oLYxR0mSDVvdZzXcIpMDq/LZ4HBlJSRPPaL+wHtQW1oPawhidniOEEEIIMYFZSVNsbKyl4yCEEEIIsWpmJU21atVChw4dsHnzZuTl5Vk6JkIIIYQQq2NW0hQZGYlGjRph5syZ8Pb2xqRJk/DPP/9YOjZSTpqtaQb/pf5otqYZ36GQZs0Af3+EvfUW35HYPNovrAe1hfWgtjBmVtLUpEkTLFu2DElJSfj111+RnJyM119/HQ0aNMDSpUvx9OlTS8dJLChFkYLE7ESkKFL4DoWkpACJiRClp/Mdic2j/cJ6UFtYD2oLY2UaCG5nZ4c33ngDu3btwtdff40HDx5g1qxZCAgIwKhRo5CcnGypOAkhhBBCeFWmpOnKlSuYMmUKfHx8sHTpUsyaNQsxMTE4duwYkpKS0K9fP0vFSQghhBDCK7PmaVq6dCnWr1+Pe/fuoWfPnvjtt9/Qs2dPCAT5OVjNmjWxYcMGBAUFWTJWQgghhBDemJU0rVy5EuPGjcOYMWPg4+NTZBkvLy+sW7euTMERQgghhFgLs5Km6Ojol5YRi8UYPXq0OdUTQgghhFgds8Y0rV+/Hrt27Sq0fNeuXdi4cWOZgyKEEEIIsTZmJU2LFi2Ch4dHoeVeXl746quvyhwUIYQQQoi1MStpevToEWrWrFloeWBgIB49elTmoAghhBBCrI1ZY5q8vLxw8+bNQlfH3bhxA+7u7paIi5Sjb7p8A6VGCQeRA9+hkG++AZRKJD55Asydy3c0No32C+tBbWE9qC2MmZU0DRs2DNOnT4dMJkPbtm0BAH/99RdmzJiBoUOHWjRAYnnDGw7nOwRSYHh+W2RERlLSxDPaL6wHtYX1oLYwZlbStHDhQsTFxaFTp06ws8uvQq/XY9SoUTSmiRBCCCFVkllJk1gsxo4dO7Bw4ULcuHED9vb2aNiwIQIDAy0dHyGEEEKIVTAraSoQGhqK0NBQS8VCKsi9tHvQ6rWwE9ghzCOM73Bs2717gFYLSVwc35HYPNovrAe1hfWgtjBmVtKk0+mwYcMGnDhxAqmpqdDr9Uavnzx50iLBkfLR6bdOSMxOhJ/MDwkzE/gOx7Z16gQkJqK2lxffkdg82i+sB7WF9aC2MGZW0jRjxgxs2LABvXr1QoMGDcBxnKXjIoQQQgixKmYlTdu3b8fOnTvRs2dPS8dDCCGEEGKVzJrcUiwWo1atWpaOhRBCCCHEapmVNH3wwQdYtmwZGGOWjocQQgghxCqZdXru77//xqlTp/Dnn3+ifv36EIlERq/v3bvXIsERQgghhFgLs5ImV1dXDBgwwNKxEEIIIYRYLbOSpvXr11s6DkIIIYQQq2bWmCYA0Gq1OH78OFavXo3s7GwAQFJSEhQKhcWCI4QQQgixFmb1NMXHx6N79+549OgRVCoVunTpAplMhq+//hoqlQqrVq2ydJyEEEIIIbwye3LLZs2a4caNG3B3dzcsHzBgACZMmGCx4Ej5uDzhMnRMByEn5DsUcvkyoNPh7u3bQPfufEdj02i/sB7UFtaD2sKYWUnT2bNncf78eYjFYqPlQUFBSExMtEhgpPz4yHz4DoEU8MlvC21qKs+BENovrAe1hfWgtjBm1pgmvV4PnU5XaHlCQgJkMlmZgyKEEEIIsTZmJU1du3bFDz/8YHjOcRwUCgUiIiLo1iqEEEIIqZLMOj23ZMkSdOvWDfXq1UNeXh6GDx+O6OhoeHh4YNu2bZaOkVjYmqtroFAr4CR2wsTwiXyHY9vWrAEUCrg/e8Z3JDaP9gvrQW1hPagtjJmVNPn7++PGjRvYvn07bt68CYVCgfHjx2PEiBGwt7e3dIzEwhb8tQCJ2Ynwk/nRTsC3BQuAxET4eHnxHYnNo/3CelBbWA9qC2NmJU0AYGdnh7feesuSsRBCCCGEWC2zkqbffvutxNdHjRplVjCEEEIIIdbK7HmanqfRaKBUKiEWi+Hg4EBJEyGEEEKqHLOunsvIyDB6KBQK3Lt3D6+//joNBCeEEEJIlWT2vedeVLt2bSxevLhQL1RJVq5ciUaNGsHZ2RnOzs5o2bIl/vzzT0uFRAghhBBiMRZLmoD8weFJSUkml/f398fixYtx9epVXLlyBR07dkS/fv1w+/ZtS4ZFCCGEEFJmZo1pOnDggNFzxhiSk5OxfPlytG7d2uR6+vTpY/T8yy+/xMqVK3Hx4kXUr1/fnNAIIYQQQsqFWUlT//79jZ5zHAdPT0907NgRS5YsMSsQnU6HXbt2IScnBy1btjSrDkIIIYSQ8mJW0qTX6y0WwL///ouWLVsiLy8PTk5O2LdvH+rVq1dkWZVKBZVKZXgul8stFoctCXUPhYvUBdUdq/MdCgkNBVxcoHJwAOimvbyi/cJ6UFtYD2oLY2ZPbmkpYWFhuH79OrKysrB7926MHj0af/31V5GJ06JFizB//nweoqxaTo4+yXcIpMDJ/LaIjowEwsN5Dsa20X5hPagtrAe1hTGzkqaZM2eaXHbp0qUlvi4Wi1GrVi0AQHh4OC5fvoxly5Zh9erVhcrOmTPHaN1yuRwBAQEmx0IIIYQQYi6zkqZr167h2rVr0Gg0CAsLAwDcv38fQqEQTZs2NZTjOK7Udev1eqNTcM+TSCSQSCTmhEwIIYQQUiZmJU19+vSBTCbDxo0b4ebmBiB/wsuxY8eiTZs2+OCDD0yqZ86cOejRowdq1KiB7OxsbN26FadPn8aRI0fMCYsQQgghpNyYlTQtWbIER48eNSRMAODm5oYvvvgCXbt2NTlpSk1NxahRo5CcnAwXFxc0atQIR44cQZcuXcwJi5hoxN4RSFOmwcPBA1ve2MJ3OLZtxAggLQ1BAotOmUbMQPuF9aC2sB7UFsbMSprkcjmePn1aaPnTp0+RnZ1tcj3r1q0zZ/WkjP6K+wuJ2Ynwk/nxHQr56y8gMRFOXl58R2LzaL+wHtQW1oPawphZP28HDBiAsWPHYu/evUhISEBCQgL27NmD8ePH44033rB0jIQQQgghvDOrp2nVqlWYNWsWhg8fDo1Gk1+RnR3Gjx+Pb7/91qIBEkIIIYRYA7OSJgcHB/z888/49ttvERMTAwAICQmBo6OjRYMjhBBCCLEWZRp9mpycjOTkZNSuXRuOjo5gjFkqLkIIIYQQq2JW0pSeno5OnTohNDQUPXv2RHJyMgBg/PjxJl85RwghhBBSmZiVNL3//vsQiUR49OgRHBwcDMuHDBmCw4cPWyw4QgghhBBrYdaYpqNHj+LIkSPw9/c3Wl67dm3Ex8dbJDBCCCGEEGtiVk9TTk6OUQ9TgWfPntFtTgghhBBSJZnV09SmTRv89ttvWLhwIYD8e8zp9Xp888036NChg0UDJJY3oekEZKmy4CJx4TsUMmECkJWFNKUSKOIm1aTi0H5hPagtrAe1hTGzkqZvvvkGnTp1wpUrV6BWq/HRRx/h9u3bePbsGc6dO2fpGImFRbSP4DsEUiAivy1SIiMpaeIZ7RfWg9rCelBbGDPr9FyDBg1w//59vP766+jXrx9ycnLwxhtv4Nq1awgJCbF0jIQQQgghvCt1T5NGo0H37t2xatUqfPrpp+UREyGEEEKI1Sl1T5NIJMLNmzfLIxZCCCGEEKtl1um5t956C+vWrbN0LKSC+C/1Bzefg/9S/5cXJuXL3x/gODTo0YPvSGwe7RfWg9rCelBbGDNrILhWq8Wvv/6K48ePIzw8vNA955YuXWqR4AghhBBCrEWpkqbY2FgEBQXh1q1baNq0KQDg/v37RmU4jrNcdIQQQgghVqJUSVPt2rWRnJyMU6dOAci/bcqPP/6I6tWrl0twhBBCCCHWolRjmhhjRs///PNP5OTkWDQgQgghhBBrZNZA8AIvJlGEEEIIIVVVqZImjuMKjVmiMUyEEEIIsQWlGtPEGMOYMWMMN+XNy8vD5MmTC109t3fvXstFSAghhBBiBUqVNI0ePdro+VtvvWXRYAghhBBCrFWpkqb169eXVxyEEEIIIVbNrMktSeW2+Y3NUGlVkNhJ+A6FbN4MqFSIi48HJk3iOxqbRvuF9aC2sB7UFsYoabJB7YPa8x0CKdC+PQBAERnJbxyE9gsrQm1hPagtjJVpygFCCCGEEFtBSRMhhBBCiAno9JwNOh132nCOmrpeeXb6NKBSwSk+nu9IbB7tF9aD2sJ6UFsYo6TJBr219y0kZifCT+aHhJkJfIdj2956C0hMRJCXF9+R2DzaL6wHtYX1oLYwRqfnCCGEEEJMQEkTIYQQQogJKGkihBBCCDEBJU2EEEIIISagpIkQG6HVM6h0eugZ4zsUQgiplOjqOUKqCAbAu3Z9JMIR8sQcpKl0yFDpkKfVQ6Vj0D6XKwkAiIQcZCIBXMVCuEgE8JAK4esgAqVUhBBSNEqaCKnE8nR6PMvTI0utQ4ZHLczYcRq3ASA1t8T36QGodAwqnQ5peTqj14Twx7gVO5Fr74Y8nR5SIXVIE0IIQEkTIZWOSseQlqdFWp4OCs3z3UdCqJQ58LEXIsjTFe5SIapJhHAUCSARcpAIOQg5Dlo9g1bPoNYzyNV6ZKp1yFTp8SRXi6QcLdR6AWq37IAcAFefquBox8FDKoSXvR3EQo637SaEEL5R0kRIJcAYQ5ZajxSlFukqvdFrziIB3CQCZD6KwbTur+HypUtoGuBfbF0iwf8nPu5S49f0jOHstVv4dMkK9J/+CbRiB+RoGXIUWjxSaOEuFcLbQQhnkQAcRwkUIcS2UNJkg2hWVyuSkN8WtyIjgfDwQi8zxvA0T4eEHC1ynxuUJBMJ4CkVwl0qNPT+5GjzoNdqyxSOgOMggwbntq7GmFEjUbN+YzxT6fAkVwuFhiEtL/90npMdhwAnEdwkVSd5ov3CelBbWA9qC2OUNBFihfSMITU3P1lS6fKTJQEHeEmF8Hawg6OoYsYZiYUcvB3s4O1gB4Umv6fraa4OCi1DVKYajv8lT9WqUPJECCHFoaSJECvCAKTlahGn+P9kSSQAfP9LXOwE/CUmTiIBarmIUcOJIUmpRbJSixwtw91MNZxFAtR0FsGpgpI5QgjhAyVNhFiJwCYtkOVWA+lZGgD5yZKfowje9kIIeUyWXiQWcgiSieDnaIeknPzB43KNHjfSVfCyFyLQScR3iIQQUi4oabJB80/PR5YqCy4SF0S0j+A7HNs2fz7UGZng8oDJv/4BLfJPw/k72sHXwc6qkqUXiQQcAmUieDsIEZedfzVfaq4O6Xk62Etd+A6v1Gi/sB7UFtaD2sIYJU02aG3kWiRmJ8JP5kc7AY/0jEG7eg3EyUkI8fKBfsIc2KvkaFTDu1Jd2i8RChDmKoavWo8YuRo5WgaFszfeXr0POZXoEEP7hfWgtrAe1BbGaAACITxIz9Pit/tZyCsY5A2Gn0d2hSz7SaVKmJ4nEwvQ2F2CIJkdwPQIefV1XIA3rj7NBaNbtxBCqgBKmgipQIwxXE7Nxfq7mUhRalGQHomhR2LUDV5jswSO4+DnKIJbehyiL56GnhPgWEIOdsbIka3RvbwCQgixYpQ0EVJBstQ6bH8gx4nEHGgZUFMmqrCpAyqaUK/B+qmDUYc9gx0HPMzWYF1UJu5nqvgOjRBCzFY1j9iEWBHGGP5Nz8OvUZmIV2ggEgBd/R0xOMS5Su+AjDHUgAJj6riiur0QeTqGvQ+zcTIxBzo6XUcIqYR4PWYvWrQIr776KmQyGby8vNC/f3/cu3ePz5AIsSilRo99D7Nx8JECKj2Dr4Mdxoa5oamnvc1MBukhtcOoUFe86pl/z5Z/UnOxLToLcjWdriOEVC68Jk1//fUXpk6diosXL+LYsWPQaDTo2rUrcnJy+AyLEIuIzlLhl7sZuJ+lhgBAWx8HvBXqgmpSId+hVTihgEMnfycMqCmDRMAhIUeL9fcy8VCu5js0QggxGa/XAx8+fNjo+YYNG+Dl5YWrV6+ibdu2PEVFSNmodHqcSMzBzfT88TseUiF6B8rg7VB5Lr8vL2GuEnjZ22H/Qzme5OqwI0aO170d0NrbdnreCCGVl1UdxbOysgAA1apVK/J1lUoFler/B5LK5fJyjefRo0dIS0sz+/1RUVEWjMYyoqKioNHkzzit0WgQGRlZqvd7eHigRo0a5REaL8raxgUKPpdHCg0OxmcjS60HADT3skdbHwdeb39ibdwkQowMdcXxhBxcT8/D3ylKPM3TolcNWaWdbqGiWeJ7a4l92dL7DyHWzmqSJr1ej/feew+tW7dGgwYNiiyzaNEizJ8/v0LiefToEerWrQulUlnmuhQKhQUiKpv01BSA4/DWW28BbwBwAFKVqQifHV6qehwcHBAVFVUlDnCWbGOZqxs2nb+NqLz8W4i4iAXoFShDjZfdUqRdOyAtDQqBAHih57UqsxNw6F7DCT6OdjjyWIF7mWpkqDIxMNgZLmJ+Tl+2C2qHNGUaPBw8eFm/qSz1vS3rvmzJ/efFWCpLW9gCagtjVpM0TZ06Fbdu3cLff/9dbJk5c+Zg5syZhudyuRwBAQHlEk9aWhqUSiXmLl+HwFphZtVx8dRRrPt6AfLy8iwcXekpsrIAxjBt4RI0frXF/78w0fQ64h/cwxfTxiMtLa1KJE2WaGMAeJycjCzH6oaEqZG7BJ38HCERmjBkcMsWAEBcZKRNJU0FGrtL4S4RYu9DOVJzddhwLxMDajq/PNksB1ve2FLh6zSHJb63ltiXLbX/FBVLZWkLW0BtYcwqkqZp06bhjz/+wJkzZ+Dv719sOYlEAolEUoGRAYG1whDWqIlZ742Ptr4rAf1qhpi9PVWVuW3MGENCjhb2nqGw5ziImQ59QlxR26Viv6OVnb+TCGPCXLEnNn+c0/boLHQNcEITDynfoVm1shybqmIchFQEXq+eY4xh2rRp2LdvH06ePImaNWvyGQ4hJsvV6vHvMzUeKbQAx+HWiT/QCsmUMJnJWSzEW6GuqOsqhh7A4ccKnEzModuvEEKsCq9J09SpU7F582Zs3boVMpkMKSkpSElJQW5uLp9hEVIsxhiSlVpcT1chW6OHkAOc5MnY8uFY5P+5J+YSCTj0DZKhjY8DgPz5nPY9zIZGT4kTIcQ68Hp6buXKlQCA9u3bGy1fv349xowZU/EB2YgZf/RERm4q3Oy9sKz3Ib7DqTRUOj0eZGmQ+d+VcS5iAWq7iBD3pAxXcXbsCDx5gtoODhaKsnLjOA6tvR3gJhbi4KNs3M9SY2t0FgYFO5f7LWc6buyIJzlPUN2xOk6OPlmu6yIlo7awHtQWxnhNmqjrnR8JWQ/wNCcJOerynbKhqmCM4UmuDnHZGuhYfvdsoEwEHwdh2ecWun8fSEyExMvLIrFWFfWqSSATC7AnVo5kpRYb72dicLAzPOzL75B1P/0+ErMTkZWXVW7rIKahtrAe1BbGqvKtrwgpM5VOjzsZasTI8xMmmUiAxh4S+Dra0WSM5SzASYRRoa5wkwggV+uxKToLcdk0gzghhD+UNBFSBMYYUpRaRKapkKnWQwAgSGaHhtXEcLCj3aaiVJMKMSrUFf6OdlDpGHY+kONGOv9TeBBCbBMd/Ql5gUKTf2VcjFwD/X+9S008JPBzFFHvEg/s7QQYWssF9dwk0AP485ECZ5LoyjpCSMWzinmaCLEGWj3DI4UGyUodAEDAAYFOFhq7RMrETsChT6ATXMUCnH+Si/NPcpGl1qNnDScI6RY1hJAKQkkTsXkMQGquFnHZGmj+mzXAQypEkEwECd0LzWpwHIe2vo5wkQhx+JECtzPyp314o6YMUjplSgipAJQ0VXIaPUOuVg+VjiFPx6DRM+gYoNMz6BkALv8crCS0Kfp/8i3gGQBdUv579QzQMQahDfei1AxvhSy3GkjPyr+Jsb2QQ7CzCK4Sfu5/Rl6usbsUziIB9j3MxiOFBpuis/BmsDO1GSGk3FHSVIkwxpCjZchS65Gt0UOh1kNl4sR/dp7+aDFoDID801BAfsJ18UkeJAIO9nYcnESC/x4cxAKuSp+SeqLUIhKemLj2d2iRfyouwNEOvo52EFTh7a4qajqLMaK2C3bHypGep8Om+5kYFOIMH4eKv2cdIcR2UNJUCYQ0bwt4B+PKUxXURSRJEgEHiZCD1C4/2RFygJDjIODyTz0xAFE3ruHs0UPo/OYoFAwBKUgNVHoGlZoZJm0EAJEAcBIJ4CwSwEUshKOoaiQSiTkanE9RIkauATh76DQaOGoUaBjoAzGdiqtUqjvYYWSoC3bFyPE0T4et0VnoF+SMWi5ivkMjhFRRlDRZKZWOITVXC/tmnfF2674AALWeQcDlz0TtLBJA9l/PkCkDYf9NisWJ1d+ic5vWeLvZHORqciC1c0BzLylytXrkaBkUGj1yNPn/1+iBDJUeGSo9AC0EHCB08Uf7ce8hA2Lo9KzSDMDVM4YHWWpceZqHRwqNYbk3y8GsQR2weO0miIW+/AT3+eeAQoHkZ8+AL7/kJ4ZKLP+edS7Y/zAbD7M12BMrRxd/RzT1tC91XZ+3+xwKtQJOYqdyiJSUBrWF9aC2MEZJk5XJ0eiRkKNFWt5/V3BJHKDMfAYHpkbdmjXgKhGU+fRR37rjjJ6LxEI4P/fjXMcYlBoGuUYPuVoHuVoPLQP0Ekd0m/YpLgO4djMdfo4iBDiJEOBkBz9HEeysLInK1uhwM12F62l5yP5vhLcAQINqErxW3QFxdx4h/fFDfoOcOBEAkB4ZSUmTmSRCAQaFOOPIYwVupqtwNCEHmWo9Ovg6lOoU88TwieUYJSkNagvrQW1hjJImK5Gj0eOxQoN01f+fInMWCZB6+woWjeqLhb/uQLW6NSskFiHHQSbmIBML4OdoB8YYlFqG6LhHuHjpHzTt1BMaCBGv0CD+v54bIQf4ONihhpMINZxE8HUU8XK6K0ejx71MFaIyVXis0BqW2ws5NHKXoqmnFC7i/AHDcRUeHSkvQo5DjwAnuIqFOJOsxD+puZCrdegdKLO6ZJ4QUnlR0sQztY4hXqFBaq7OsMxdIkCAkwiOIgGOPk2AVq3iMcL8S70dRRzsczOx9aNxmHn1KgLrNMIjhQaPFRo8UmiQo2VIyNEiIUeL809yIQDg5WAHb3s7eDvkPzykQov/AVPrGBJzNIjP1iAuW4OUXK3R6/6OdmjiIUUdVwn98aziOI5DK28HuIgFOPhIgbuZamRrsjAw2JlmcSeEWAQlTTzRM4YUpQ6PFPn3NAMAd6kQAY525X439zRlCvR6HQQCITwcvEv9fg6Ah70dPOzt0NTTHowxZKj0hgTqsUIDuUaPFKUWKUotkJ7/PgHyt7GaVAg3sRBuUiFkIgEc7ARwtOMgtRPAjoPRKRUdy59GIU/LIFfrka3RIUutR2quFqm5OjxT6QrF5+NghzquYtRxkxh6laxWcjKg08Hu6VO+I6ky6leTwkkkwN6H2UjM0WLT/UwMDnGB20umJEjOToaO6SDkhPCR+VRQtKQo1BbWg9rCGCVNPFBo9IjOUkOpzc+WHO3y5wZyrqA/8JP2tcXTnCR4Ovpiz4j7Za6P4zhU+y8ZauwhBWP50yIkK7V4otQiJTc/ecrTMTzN0+FpXuFE53kCLv90n04P6Essmc9ZJECgTGR4yERWnig979VXgcRE1PHy4juSKiVQJjZcWZeh0uO3+5kYFOwMP8fipyR4de2rSMxOhJ/MDwkzEyowWvIiagvrQW1hjJKmCsRY/imsxwotGAA7DgiUiVDdvmrdpoPjOLhKhHCVCFHXTQIAhkQqLU+HDNX/P3K0+VfsKbUMBZMp6Fn+43l2HCATCyATCeEsFsBDKkR1ezt42Zd/zxypnDykdhgZ6ordsXKkKLXYFp2F3kEy1HGV8B0aIaSSoqSpguRq9bifpYZCk58NVJMIEOIstpm5gZ5PpIrCGINaz6DTA1qWP6u5kAPEAg52/809VZUSS1IxnEQCDK/lggNx2XggV2P/w2x08NWhuZc9fZ8IIaVGSVNFkFXDjXSVIREIdhbBU1q1epfKiuPyJ+hEJTqzRioHsZDDG8EyHE/IQWRaHk4lKfE0T4fuAU50cQAhpFTovEZ54jj0mrkACKgLHcsfe/OKhxRe9naUMBFSgQQchy7+juji7wgOwK1nKmyNzoJCY8qoOUIIyUc9TeVEo2eQ1m+F11u5AwD8HO1Qw4nua0YIXziOQ7inPdylQux/mI0kpRYb72ViYLAzvB3oUEgIeTnqaSoHSo0eN9JVELq4Iy9bDjyOQpBMRAkTIVYgSCbGqFBXuEuEyNbosfl+JqIy+J0LjRBSOVDSZGEZKh1uPlNBpWPQ5+bg59HdgOxnfIdFCHlONakQI8NcEOIsgpYBv8dlF3kzbEIIeR4lTRaUotTiTobaMH4p9+ZZPI17wHdYhJAiSIUCDAx2RnOv/Jv7qv6bZZZSJ0JIcShpsgDGGBIUGsTI8+/D5iUVon41MaBV8xwZIaQkAo5DRz9H9KrhhIKT50qtHk+U2hLfRwixTTT6sYwYY3iYrUGyMn+Wa///Bnxb89Vx3/f6Azq9FkIBNT/vTpwAtFpE37sHDBzIdzQ2q6G7FH+MOIbDj7KQqxNg0/1MdK/hhAbVpHyHZpNOjDoBrV4LOzpG8Y7awhh9CmXAGEN0lsZwW5AgmQh+jtb/kdZwDeU7BFIgLAwAoFLRQGS+ve5fH+HeevwvLhux2Rr8Ea9AYo4WnfwcaT6nChbmEcZ3COQ/1BbG6PScmRhjuP9fwsQBqO1SORImQkjx7O0EGBTijNbe+eOcrqXlYUt0FjKLuDE0IcT2UNJkhoKEKe2/hCnMVQwve0qYCKkKBByHNj6OeDPYGVIhh2SlFuvv0rQEhBA6PVdqjDHcy9Ig/bmEyV1aue79cezBTqi0SkjsHNCl1mC+w7FtW7cCSiXcnjzhOxKbt/XfrVBqlHAQOWB4w+EIcRFjbB1X/C8uGwk5Wvwel434bA06+TtCRKfrytWLbUH4Q21hjJKmUtD/18NUmRMmAFh1aS6e5iTB09GXkia+ffQRkJgIPy8vviOxeR8d+wiJ2Ynwk/kZ/ji4iIUYXtsFfycrcf5JLq6n5yEhR4N+QTJ4Uu9yuSmqLQg/qC2M0ek5EzHGcD9TbUiY6lTShIkQUjoCjkNbX0cMDXGGox2HtDwdNt7LxJXUXDBGszoRYksoaTJBwVVy6Sq9IWGqRgkTITYlyFmMcXXcUFOWP4v48cQcbH8gh1xNg8QJsRWUNL0EA/Aw+/+nFaCEiRDb5SgSYHCIM7r6O8KOA+IVGqy7m4nbz/Ko14kQG0BJ00soHd0NE1eGuogoYSLExnEch6ae9hhbxxU+DnZQ6Rj+F6/A73HZUGr0fIdHCClHlDSVoM3IKch19AAABDuLaOAnIcTAXWqHkaEueN3bARyAu5lqrI3KoF4nQqowSpqKkQBH9Hx/PgAg0MkOPg6UMBFCjAk4Dq/7OGBUmAs8pULk/tfrtDuWxjoRUhVR0lSEqAwV7qAaAMA+Jx3+TiKeIyKEWDMfBxHGhLmijY8DhBwQI9fgl6hMXEujK+wIqUooaSqCvZCDEAwXd62HQ04a3+EQQioBoYBDa28HjA1zhZ+jHdR6hiOPc7A5OgtPlFq+wyOEWAAlTUUIchbjNaTgwOKPURXn/a1mXx2ejr6oZl+d71CItzfg5weNuzvfkdg8bydv+Mn84O3kXaZ6POztMKK2Czr7OUIs4JCYo8WGe5k4nqCASkcDxU1hqbYgZUdtYYwG6hTDEdoq262+9o2zfIdACly5AgC4FxkJhIfzHIxtuzLxisXqEnAcmnnZI9RVjJOJObibqcaVp3mIylChk58T6rqJwXFV8SeZZViyLUjZUFsYo54mQggpJ85iIfrXdMaQEGe4SQTI0TIciM/G1gdZSKFTdoRUOpQ0EUJIOavpLMb4Om5o4+MAOw54rMg/ZXcwPhsKmtuJkEqDTs8RQkgFsPtvoHiDahL8laTEnQwV/n2mwt1MFV6r7oDmXvYQCeiUHSHWjJImG/TtmXeRrcqATOKGD9v+xHc4tm3SJODZMwToqbeBb5P+NwnP8p6hmrQaVvdZXW7rcREL0TdIhnBPKU4k5CBJqcXZZCVupOWhna8D6rlJbH68U0W1BXk5agtjlDTZoIuPj+BpThI8HX35DoUcPAgkJsLFy4vvSGzeweiDSMxOhJ/Mr0LW5+cowshQF0RlqHE6KQdyjR7/i1fgytP85ClIJq6QOKxRRbcFKR61hTFKmgghhCccx6FeNQlqu4pxOTUXF54okazUYvsDOQKdRGjv6wAfR5pclxBrQQPBCSGEZyIBh1beDphcrxrCPaUQcEC8QoON97OwN1aOtDy60o4Qa0A9TYQQYiUcRQJ08XfCq572+DtFidvPVLifpUZ0lhoNqknwuo8D3yESYtMoaSKEECvjKhGid6AMLbzscSZZiegsNf59psLtDBV84QZXH3++QyTEJvF6eu7MmTPo06cPfH19wXEc9u/fz2c4hBBiVTzt7TAw2BkjQ10Q6CSCngEJnAwf7L8Ehaw68ui2LIRUKF6TppycHDRu3BgrVqzgMwxCCLFqfo4iDKvtghG1XVCN5cFOJEaevSsin6rwIEtNyRMhFYTX03M9evRAjx49+AyBEEIqjQAnEZohFQPHT8W0lduhETviSa4Oqbk6eNkL4e9kB6mQru8hpLxUqjFNKpUKKpXK8Fwul/MYDeHbo0ePkJaWZvb7o6KiLBiNefU10GggBqDVaHiNw5Lvt2RdKpUKEomkzHGYUo/mvzbQaDSIjIwsl1gsUUdUVBTirl2ES2YCvOs0xGOFFllqPSVPhFSASpU0LVq0CPPnz+c7jEqvU8ibyFZlQiZx5TsUsz169Ah169aFUqksc10KhaJM709PTQE4Dm+99Vap3/sNADcAGRkZZY6lLHEUxRpi4TgOjLEy1WFyPV0A2AOpuakInx1eLrFYanuA/PZxEQvhUk0IuVqHR0UlT452kNpVvuRpWINhyMjLgJvUje9QbB61hbFKlTTNmTMHM2fONDyXy+UICAjgMaLKacprX/IdQpmlpaVBqVRi7vJ1CKwVZlYdF08dxbqvFyAvL69MsSiysgDGMG3hEjR+tYVZddw7dRQoYyyWiAOwzOdiiVgK4rDU9pSqng8sH4ult+f59nEWC9GgiOTpSa4OXtL8nif7SpQ8fdv1W75DIP+htjBWqZImiURika56UnUE1gpDWKMmZr03PvqeRWPxqxliFbGUJQ5riqUgDkttD9+xWHp7ivJ88vRYoUWmWo/UPB1S83TwkAoR4GQHh0qUPBFibSpV0kQIIeTlnMVC1K8mRLZaj8c5GmSo9EjL0yEtTwd3qRA6oe3e146QsuA1aVIoFHjw4IHh+cOHD3H9+nVUq1YNNWrU4DEyQgip/GRiAeqJJVBo9His0OCZSo/0PB3gXhMjvlsPOei+doSUBq9J05UrV9ChQwfD84LxSqNHj8aGDRt4iqrqe2vHK0hTpsDDwRubh1zjOxybFt7mFYifpKCOgyO+4DsYG1eV9wsnkQB13STI0ejxOEeL9FwtGnTsjYsAnsZkobW3A3yt6MbAdZbXQVJ2Enxlvrg77S7f4dg0agtjvCZN7du3t9iVJMR0udocKDXZyNXK+A7F5gmVObBTZENsR2fK+WYL+4WjSIA6rmLcTnyAU5eu4JUegxAj1yBGnoVgmQitvB3g78R/8qRQK5CtzoZCXbYrW0nZUVsYoxGBhBBiY+x0auycOwWtkYwG1STgAMRma7A5OgvborPwKNty84YRUpXQz1tCCLFRjtCiTaAMr3s74MITJf5NVyFeoUH8gywEONmhtbcDAp1E4DiO71AJsQqUNBFCiI1zlQjRo4YMrbwdcPFJLm6m5+GxQovtD+Twc8xPnmrKKHkihJImQgghAAAXsRDdApzQsro9LqXm4kZaHhJztNgZI4ePQ37yFOJMyROxXZQ0EUIIMeIsFqKLvxNaVnfApSdKXEvLQ7JSi92xclS3F6KVtwPoEh5iiyhpIoQQUiQnkQCd/J3wWnUH/JOai8i0XDzJ1WHfw2w4wRsNu/Sj5InYFEqaCCGElMhRJEAHP0e0qG6PK6m5uPI0Dwq9GMO//gWZWhWe5mrhIRXSaTtS5dGUA4QQQkziYCdAW19HTKnvhhCWiVx5JnR2EtzP0iAyTYXUXC3NvUeqNOppskEfvL4MKl0uJEJ7vkOxeQ8WL4MgLxdXIq8Aq3/kOxybRvuF6aR2AoRAjpG92+ObQ5eglnkiT8cQnaVBgkKLGk52cC9Dz9Oq3quQq8mFvYjagm/UFsYoabJBrQJ78B0C+c+zLvltcV9NkwnyjfaL0lMpsuGgfIaGwQFIUWqRmKNFro7hXpYGDjla1HASoZpEUOrkqXdo73KKmJQWtYUxSpoIIYSUiZ2Ag7+TCN4OdkhSapGUo4VSy3A3Uw0nEYcaTiK4ikufPBFibShpIoQQYhF2gvwEycfBDkk5WiQptVBoGO5kqOEsEqCGzA4uYiHfYRJiNkqabNC9p9eg0ashEogR5vkK3+HYNKeb18Cp1fB9FMd3KDaP9gvLEQk4BMryk6fEHA2SlTrINXrceqaGi1iAQCcRZOLir0O6mnQVap0aYqEY4b7hFRg5eRG1hTFKmmzQJ0eH4GlOEjwdfbFnxH2+w7Fp9cYOgSQ5CTVd3TCF72BsHO0XlicWcqjpLIavI0NCjgZPlDpkqfW4+UwFN4kANZxERb6v3/Z+SMxOhJ/MDwkzEyo4avI8agtjlDQRQggpVxIhhxBnMfwc9Hico0Vqrg4ZKj0yVCqInX3hFRzGd4iEmITmaSKEEFIhpHYC1HYRo6mHBJ7S/LFNaqkM72z4E1rQIHFi/ShpIoQQUqHs7QQIdRXjFXcJxHnZuLBzHezohiykEqCkiRBCCC8cRAI4y5NwdPmXfIdCiEkoaSKEEEIIMQElTYQQQgghJqCkiRBCCCHEBJQ0EUIIIYSYgJImQgghhBAT0OSWNmjTm1fBwMDRvCi8u/rXVYAxnDiwF/hwGt/h2DTaL6xH1NQoagsrQW1hjJImG+QglvEdAvmPzim/LdRSe54jIbRfWA+ZhNrCWlBbGKPTc4QQQgghJqCkiRBCCCHEBHR6zgbtuPkTctRyOIqdMaTRu3yHY9P8Vv8EYbYcrR7G8B2KzaP9wnosvbAUcpUczhJnzGw5k+9wbBq1hTFKmmzQzn9/wtOcJHg6+tIfB575rfkJkuQkuLq68R2KzaP9wnosvbAUidmJ8JP50R9qnlFbGKPTc4QQQgghJqCkiRBCCCHEBJQ0EUIIIYSYgJImQgghhBATUNJECCGEEGICSpoIIYQQQkxASRMhhBBCiAkoaSKEEEIIMQFNbmmDars3gZejP1ykHnyHYvMUDZpA5euPZJUKyMzgOxybRvuF9Wjq0xQBLgHwdPDkOxSbR21hjJImG7S4+06+QyD/ubMxvy2O7tkBvDue52hsG+0X1uPAsAN8h0D+Q21hjE7PEUIIIYSYgJImQgghhBATUNJECCGEEGICGtNkg2YfHoysvDS4SD1oHAfP6o0eDNGzNHiqVPiC72BsHO0X1qPvtr54qnwKTwdPGlPDM2oLY5Q02aDo9Ot4mpMET0dfvkOxeU63rkOSnATm6sZ3KDaP9gvrEZkcicTsRPjJ/PgOxeZRWxij03OEEEIIISagpIkQQgghxASUNBFCCCGEmMAqkqYVK1YgKCgIUqkULVq0wD///MN3SIQQQgghRnhPmnbs2IGZM2ciIiICkZGRaNy4Mbp164bU1FS+QyOEEEIIMeA9aVq6dCkmTJiAsWPHol69eli1ahUcHBzw66+/8h0aIYQQQogBr0mTWq3G1atX0blzZ8MygUCAzp0748KFCzxGRgghhBBijNd5mtLS0qDT6VC9enWj5dWrV8fdu3cLlVepVFCpVIbnWVlZAAC5XG7x2BQKBQDg/r/XkZuTY1Yd8TH3AQAPo27D0d6etzperEctVAFqQK1X4fqFcybX8Tg2GgBw9epVw+djDoFAAL1eb/b7AeDevXsA+G+fstZTS6WCI4AMjabMsVjD9lhbHaWpR51d/H5RGbenJJbYly2xDz4fi0KhMBzL9Xl6IA/Qi/TlcnwnpiuvtiioizFmsTorBONRYmIiA8DOnz9vtPzDDz9kzZs3L1Q+IiKCAaAHPehBD3rQgx5V4PH48eOKSjksgteeJg8PDwiFQjx58sRo+ZMnT+Dt7V2o/Jw5czBz5kzDc71ej2fPnsHd3R0cx5W4LrlcjoCAADx+/BjOzs6W2YBKgrbd9rbdVrcboG23xW231e0GKu+2M8aQnZ0NX9/KNQM/r0mTWCxGeHg4Tpw4gf79+wPIT4ROnDiBadOmFSovkUggkUiMlrm6upZqnc7OzpXqi2VJtO22t+22ut0AbbstbrutbjdQObfdxcWF7xBKjfd7z82cOROjR49Gs2bN0Lx5c/zwww/IycnB2LFj+Q6NEEIIIcSA96RpyJAhePr0KT7//HOkpKSgSZMmOHz4cKHB4YQQQgghfOI9aQKAadOmFXk6zpIkEgkiIiIKnd6zBbTttrfttrrdAG27LW67rW43YNvbzgeOscp2vR8hhBBCSMXjfUZwQgghhJDKgJImQgghhBATUNJECCGEEGICSpoIIYQQQkxQpZKmFStWICgoCFKpFC1atMA///xTYvldu3ahTp06kEqlaNiwIQ4dOlRBkVpeabZ97dq1aNOmDdzc3ODm5obOnTu/9LOyZqVt9wLbt28Hx3GGiVUrm9Jud2ZmJqZOnQofHx9IJBKEhoZW2u98abf9hx9+QFhYGOzt7REQEID3338feXl5FRStZZw5cwZ9+vSBr68vOI7D/v37X/qe06dPo2nTppBIJKhVqxY2bNhQ7nGWh9Ju+969e9GlSxd4enrC2dkZLVu2xJEjRyomWAsyp80LnDt3DnZ2dmjSpEm5xWeLqkzStGPHDsycORMRERGIjIxE48aN0a1bN6SmphZZ/vz58xg2bBjGjx+Pa9euoX///ujfvz9u3bpVwZGXXWm3/fTp0xg2bBhOnTqFCxcuICAgAF27dkViYmIFR152pd32AnFxcZg1axbatGlTQZFaVmm3W61Wo0uXLoiLi8Pu3btx7949rF27Fn5+fhUcedmVdtu3bt2K2bNnIyIiAlFRUVi3bh127NiBTz75pIIjL5ucnBw0btwYK1asMKn8w4cP0atXL3To0AHXr1/He++9h7fffrtSJg+l3fYzZ86gS5cuOHToEK5evYoOHTqgT58+uHbtWjlHalml3e4CmZmZGDVqFDp16lROkdkwnu99ZzHNmzdnU6dONTzX6XTM19eXLVq0qMjygwcPZr169TJa1qJFCzZp0qRyjbM8lHbbX6TVaplMJmMbN24srxDLjTnbrtVqWatWrdgvv/zCRo8ezfr161cBkVpWabd75cqVLDg4mKnV6ooKsdyUdtunTp3KOnbsaLRs5syZrHXr1uUaZ3kCwPbt21dimY8++ojVr1/faNmQIUNYt27dyjGy8mfKthelXr16bP78+ZYPqIKUZruHDBnC5s6dyyIiIljjxo3LNS5bUyV6mtRqNa5evYrOnTsblgkEAnTu3BkXLlwo8j0XLlwwKg8A3bp1K7a8tTJn21+kVCqh0WhQrVq18gqzXJi77QsWLICXlxfGjx9fEWFanDnbfeDAAbRs2RJTp05F9erV0aBBA3z11VfQ6XQVFbZFmLPtrVq1wtWrVw2n8GJjY3Ho0CH07NmzQmLmS1U5xlmCXq9HdnZ2pTvGmWP9+vWIjY1FREQE36FUSVYxI3hZpaWlQafTFbr1SvXq1XH37t0i35OSklJk+ZSUlHKLszyYs+0v+vjjj+Hr61voAGvtzNn2v//+G+vWrcP169crIMLyYc52x8bG4uTJkxgxYgQOHTqEBw8eYMqUKdBoNJXq4GrOtg8fPhxpaWl4/fXXwRiDVqvF5MmTK93pudIq7hgnl8uRm5sLe3t7niKreN999x0UCgUGDx7MdyjlKjo6GrNnz8bZs2dhZ1cl/rxbnSrR00TMt3jxYmzfvh379u2DVCrlO5xylZ2djZEjR2Lt2rXw8PDgO5wKpdfr4eXlhTVr1iA8PBxDhgzBp59+ilWrVvEdWrk7ffo0vvrqK/z888+IjIzE3r17cfDgQSxcuJDv0EgF2Lp1K+bPn4+dO3fCy8uL73DKjU6nw/DhwzF//nyEhobyHU6VVSVSUQ8PDwiFQjx58sRo+ZMnT+Dt7V3ke7y9vUtV3lqZs+0FvvvuOyxevBjHjx9Ho0aNyjPMclHabY+JiUFcXBz69OljWKbX6wEAdnZ2uHfvHkJCQso3aAswp819fHwgEokgFAoNy+rWrYuUlBSo1WqIxeJyjdlSzNn2zz77DCNHjsTbb78NAGjYsCFycnIwceJEfPrppxAIquZvx+KOcc7OzjbTy7R9+3a8/fbb2LVrV6XrSS+t7OxsXLlyBdeuXTPcy1Wv14MxBjs7Oxw9ehQdO3bkOcrKr0ocLcRiMcLDw3HixAnDMr1ejxMnTqBly5ZFvqdly5ZG5QHg2LFjxZa3VuZsOwB88803WLhwIQ4fPoxmzZpVRKgWV9ptr1OnDv79919cv37d8Ojbt6/h6qKAgICKDN9s5rR569at8eDBA0OSCAD379+Hj49PpUmYAPO2XalUFkqMCpJHVoVvvVlVjnHm2rZtG8aOHYtt27ahV69efIdT7pydnQsd3yZPnoywsDBcv34dLVq04DvEqoHngegWs337diaRSNiGDRvYnTt32MSJE5mrqytLSUlhjDE2cuRINnv2bEP5c+fOMTs7O/bdd9+xqKgoFhERwUQiEfv333/52gSzlXbbFy9ezMRiMdu9ezdLTk42PLKzs/naBLOVdttfVFmvnivtdj969IjJZDI2bdo0du/ePfbHH38wLy8v9sUXX/C1CWYr7bZHREQwmUzGtm3bxmJjY9nRo0dZSEgIGzx4MF+bYJbs7Gx27do1du3aNQaALV26lF27do3Fx8czxhibPXs2GzlypKF8bGwsc3BwYB9++CGLiopiK1asYEKhkB0+fJivTTBbabd9y5YtzM7Ojq1YscLoGJeZmcnXJpiltNv9Irp6zvKqTNLEGGM//fQTq1GjBhOLxax58+bs4sWLhtfatWvHRo8ebVR+586dLDQ0lInFYla/fn128ODBCo7Yckqz7YGBgQxAoUdERETFB24BpW3351XWpImx0m/3+fPnWYsWLZhEImHBwcHsyy+/ZFqttoKjtozSbLtGo2Hz5s1jISEhTCqVsoCAADZlyhSWkZFR8YGXwalTp4rcbwu2dfTo0axdu3aF3tOkSRMmFotZcHAwW79+fYXHbQml3fZ27dqVWL6yMKfNn0dJk+VxjFXh/mlCCCGEEAupEmOaCCGEEELKGyVNhBBCCCEmoKSJEEIIIcQElDQRQgghhJiAkiZCCCGEEBNQ0kQIIYQQYgJKmgghhBBCTEBJEyHEbGPGjEH//v35DsNgw4YNOH36dLnW7+rqang+b948NGnSxPD8xc+jffv2eO+99yweh1qtRq1atXD+/HmTygYFBeHKlSsWj4NUXWfOnEGfPn3g6+sLjuOwf//+UtfBGMN3332H0NBQSCQS+Pn54csvv7R8sBWIkiZCSmnMmDGYN28eAIDjOMTFxVXIes09cBHLGTJkCO7fv29y+b1792LhwoUWj2PVqlWoWbMmWrVq9dKyYrEYs2bNwscff2y0fN68eRgzZgwAICgoqFyTTVL55OTkoHHjxlixYoXZdcyYMQO//PILvvvuO9y9excHDhxA8+bNLRhlxbPjOwBCiPVTq9VWfWPfU6dOYe7cubh16xYEAgFq1qyJCRMm4J133rHoeuzt7WFvb29y+WrVqll0/UD+r/fly5djwYIFJr9nxIgR+OCDD3D79m3Ur1/f4jGRqqdHjx7o0aNHsa+rVCp8+umn2LZtGzIzM9GgQQN8/fXXaN++PQAgKioKK1euxK1btxAWFgYAqFmzZkWEXq6op4kQC7p9+zZ69+4NZ2dnyGQytGnTBjExMQAAvV6PBQsWwN/fHxKJBE2aNMHhw4cN71Wr1Zg2bRp8fHwglUoRGBiIRYsWAcjvCQCAAQMGgOM4w/MXxcXFgeM4bN++Ha1atYJUKkWDBg3w119/GZW7desWevToAScnJ1SvXh0jR45EWlqa4fX27dtj2rRpeO+99+Dh4YFu3bqVuN3z58+Hp6cnnJ2dMXnyZKjVasNrQUFB+OGHH4zKN2nSxNBbx/6vvbuPqaqMAzj+RSAFr5GBUzDkorwEDehS1hyJSUkUIrUF5iIoirWpyQJc4VboqAUpEW5YzTUoc74wpIGxbBNZyKjgygUzuCLeghmFCUIEE8GnP1hnXV/gmphpv892tnuec57zPOfw7PA7z3nOOUqxceNG5s2bx9SpU/Hw8GDdunXjlvd3Z8+eJTY2lnvuuYeMjAw2b95MZmbmhPn0ej1vvfUWiYmJ6HQ6vLy8KC8v5/Tp08TGxqLT6QgODra6rXXx7bmJXHx7rre3l8TERGbOnImzszOPP/44bW1tl2z/wIEDBAQEoNPpiIqKoqurS1vHaDTS3t5OdHS0ljZe2wGYOXMmYWFh7N692+a6CzGetWvXUldXx+7du2lubiYuLo6oqCitPVdUVDB//nz279+Pt7c3er2el156iZ6enhtc82sjQZMQk+TUqVOEh4czdepUqqqqMBqNJCcnMzIyAkBBQQF5eXls2bKF5uZmHnvsMVasWKGdZLZu3Up5eTl79+7FbDazc+dOLTiqr68HoKioiK6uLm3+StavX096ejqNjY0sWrSImJgYzpw5A4wFGRERERgMBhoaGvjyyy/59ddfiY+Pt9rGJ598wm233UZtbS0ffvjhFcs6ePAgLS0tVFdXs2vXLvbt28emTZtsPm6lpaXk5+fz0Ucf0dbWxueff05QUJDN+U+cOMHvv/9OVlYWnp6e+Pj4EBcXZ1MvU35+PmFhYTQ2NhIdHc1zzz1HYmIiCQkJHDlyhAULFpCYmMhkfaLz+eefp6GhgfLycurq6lBK8cQTT3D+/HltncHBQbZs2cKOHTv4+uuv6ejoICMjQ1teU1ODn58fM2bM0NLGazt/eeCBB6ipqZmU/RD/bx0dHRQVFVFSUsLixYtZsGABGRkZPPTQQxQVFQFw8uRJfvrpJ0pKSvj0008pLi7GaDTy9NNP3+DaX6Mb+LFgIW4pmZmZytvbWw0PD192uYeHh3r77bet0hYuXKhWr16tlFLqlVdeUREREerChQuXzQ+osrKycetgsVgUoHJycrS08+fPq7vuukvl5uYqpZTKzs5WkZGRVvk6OzsVoMxms1Jq7CvxBoNh3LKUGvvK+p133qn++OMPLe2DDz5QOp1OjY6OKqWU8vLyUvn5+Vb5QkJCVFZWllJKqby8POXn53fF4zaR/v5+5ebmphISEtSGDRvUoUOHbMrn5eWlEhIStPmuri4FqDfeeENLq6urU4Dq6upSSilVVFSkXFxctOUXf0U+KSlJxcbGavNLlixRqampSimljh8/rgBVW1urLf/tt9+Uk5OT2rt3r7Z9QJ04cUJbp7CwUM2ePVubT01NVREREVb7MlHbUUqpgoICpdfrxzkiQlzexeee/fv3K0BNnz7danJwcFDx8fFKKaVSUlKszilKKWU0GhWgWltb/+1dmDTS0yTEJDGZTCxevBhHR8dLlvX39/Pzzz8TFhZmlR4WFkZLSwsw1gthMpnw9/dn3bp1fPXVV/+4LosWLdJ+Ozg4cP/992vlNDU1cejQIXQ6nTbdfffdANqtRID77rvPprJCQkJwdna2KntgYIDOzk6b8sfFxTE0NMT8+fNJSUmhrKxM652zxYwZM6iqqmJwcJDCwkJiYmJYsWIFjY2NE+YNDg7Wfs+ePRvAqpfrr7Tu7m6b63MlLS0tODg48OCDD2pprq6u+Pv7a38bAGdnZxYsWKDNu7u7W5U/NDTEtGnTrLZtS9txcnJicHDwmvdDiIGBAezt7TEajZhMJm1qaWmhoKAAGGu3Dg4O+Pn5afkCAgKAsZ6qm5UETUJMkqsZIHw5oaGhWCwWsrOzGRoaIj4+/rp0ZQ8MDBATE2N1sjOZTLS1tREeHq6tN3369Ekpb8qUKZfc3vr77ShPT0/MZjPbtm3DycmJ1atXEx4ebrXORIKCgigtLeX9998nNzcXFxcXli5dyunTp8fN9/cA187O7oppFy5csLku1+rioNvOzs7q+Lm5udHb22u1ji1tp6enh1mzZl2/iov/DYPBwOjoKN3d3fj4+FhNc+bMAcYuCEdGRqwuxP568tTLy+uG1HsySNAkxCQJDg6mpqbmsv/sb7/9djw8PKitrbVKr62tJTAw0Gq9lStXsn37dvbs2UNpaak2cNLR0ZHR0VGb6vLNN99ov0dGRjAajdpVXmhoKMeOHUOv119ywvsngVJTUxNDQ0NWZet0Ojw9PQGYNWuW1UDm/v5+LBaL1TacnJyIiYlh69atVFdXU1dXx9GjR6+6LgCBgYFs27aNvr4+mpub/9E2roeAgABGRkb49ttvtbQzZ85gNput2sBEDAYDra2tlwSi47UdGBv8bzAYrn1HxP/CwMCAdkEFYLFYMJlMdHR04Ofnx7PPPktiYiL79u3DYrHw3Xff8c477/DFF18A8OijjxIaGkpycjKNjY0YjUZefvllli1bZtX7dLORoEmISbJ27Vr6+/t55plnaGhooK2tjR07dmA2m4Gxwdm5ubns2bMHs9nM66+/jslkIjU1FYD33nuPXbt20drayvHjxykpKWHOnDna01p6vZ6DBw/yyy+/XNLTcLHCwkLKyspobW1lzZo19Pb2kpycDMCaNWvo6elh1apV1NfX097ezoEDB3jhhRdsDsr+bnh4mBdffJEffviByspKsrKyWLt2LVOmjJ1eIiIi2LFjBzU1NRw9epSkpCTs7e21/MXFxXz88cd8//33nDx5ks8++wwnJyebr0aPHDnCxo0bMZvNjIyMcPbsWTZv3sy0adOuKhi53nx9fYmNjSUlJYXDhw/T1NREQkICc+fOJTY21ubtLF26lIGBAY4dO6alTdR2YGwAeWRk5GTukriFNTQ0YDAYtEA7LS0Ng8HAm2++CYw9lJKYmEh6ejr+/v48+eST1NfXM2/ePGCsh7miogI3NzfCw8OJjo4mICDgpn+CU97TJMQkcXV1paqqivXr17NkyRLs7e259957tXFM69ato6+vj/T0dLq7uwkMDKS8vBxfX19gbGzOu+++S1tbG/b29ixcuJDKykot+MjLyyMtLY3t27czd+7ccV+qmZOTQ05ODiaTCR8fH8rLy3FzcwPQerxee+01IiMjOXfuHF5eXkRFRWllXY1HHnkEX19fwsPDOXfuHKtWrdJeJwCQmZmJxWJh+fLluLi4kJ2dbdXTdMcdd5CTk0NaWhqjo6MEBQVRUVGBq6srMPYSxuLi4ivur7u7O52dnURFRXHq1Cns7e0JCAigtLQUd3f3q96f66moqIjU1FSWL1/O8PAw4eHhVFZWXnYc3JW4urry1FNPsXPnTu21AhO1nbq6Ovr6+m7+J5fEv+bhhx8e96lRR0dHNm3aNO6Tsh4eHpSWll6P6t0wdmq8oyKEuKn8+OOPeHt709jYaPV5j5tZUlISdnZ2FBcXT7hucXExer1ee8Heraq5uZlly5bR3t6OTqebcP2VK1cSEhLChg0b/oXaCXHrkp4mIcR/llKK6upqDh8+fKOr8p8SHBxMbm4uFotlwndaDQ8PExQUxKuvvvov1U6IW5f0NAlxC7kVe5qEEOK/QoImIYQQQggbyNNzQgghhBA2kKBJCCGEEMIGEjQJIYQQQthAgiYhhBBCCBtI0CSEEEIIYQMJmoQQQgghbCBBkxBCCCGEDSRoEkIIIYSwgQRNQgghhBA2+BPPm71Fujc/PgAAAABJRU5ErkJggg==", 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" ] @@ -3217,7 +5526,6 @@ } ], "source": [ - "\n", "dist_curve(\n", " df=merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)],\n", " mean=merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"cost_per_bus\"].mean(),\n", @@ -3229,31 +5537,7 @@ }, { "cell_type": "code", - "execution_count": 63, - "id": "85beee26-6316-43df-8f91-71b1e699ab65", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['prop_type', 'total_project_count', 'total_project_count_ppno',\n", - " 'total_agg_cost', 'total_bus_count', 'new_cost_per_bus',\n", - " 'new_zscore_cost_per_bus', 'new_is_cpb_outlier?'],\n", - " dtype='object')" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "agg_prop.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 66, + "execution_count": 40, "id": "5f11c857-ddbe-4871-aeca-e27fa00fbde8", "metadata": {}, "outputs": [ @@ -3425,11 +5709,12 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 41, "id": "2472461d-7663-4b66-9bde-4c2a199707a5", "metadata": {}, "outputs": [], "source": [ + "\n", "summary = f\"\"\"\n", "\n", "# Bus Procurement Cost Analysis\n", @@ -3438,41 +5723,65 @@ "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", "\n", "Data was compiled from three data sources:\n", - " 1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", - " 2. TIRCP project data (state-funded, California only)\n", - " 3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", + "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", + "2. TIRCP project data (state-funded, California only)\n", + "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", "\n", "Breakdown of each data souce:\n", "{pivot_source.to_markdown(index=False)}\n", "\n", - "The initial dataset was contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries.\n", + "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries.\n", "The resulting dataset only contained projects that were solely used to procure buses. 87 projects remained. These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", "\n", "ZEB buses include: \n", - " - zero-emission (not specified) \n", - " - electric (not specified)\n", - " - battery electric \n", - " - fuel cell electric\n", + "- zero-emission (not specified) \n", + "- electric (not specified)\n", + "- battery electric \n", + "- fuel cell electric\n", "\n", "Non-ZEB buses include: \n", - " - CNG \n", - " - ethanol \n", - " - ow emission (hybrid, propane) \n", - " - diesel \n", - " - gas\n", + "- CNG \n", + "- ethanol \n", + "- ow emission (hybrid, propane) \n", + "- diesel \n", + "- gas\n", "\n", "Below are charts and tables that summarize the findings.\n", + "
\n", + "
\n", "\n", + "**Lowest and highest cost per bus of all projects**\n", + "{agg_agency[\"new_cost_per_bus\"].agg([\"min\",\"max\"]).to_markdown()},\n", "\n", - "ZEB Summary\n", + "
\n", + "
\n", "\n", + "**Least and Most buses order of all projects**\n", + "{agg_agency[\"total_bus_count\"].agg([\"min\",\"max\"]).to_markdown()},\n", + "\n", + "
\n", + "
\n", + "\n", + "**Lowest and highest total aggregate bus cost**\n", + "{agg_agency[\"total_agg_cost\"].agg([\"min\",\"max\"]).to_markdown()},\n", + "\n", + "
\n", + "
\n", + "\n", + "\n", + "**ZEB Summary**\n", "{pivot_zeb_prop.to_markdown(index=False)}\n", "\n", - "Non-ZEB Summary\n", + "
\n", + "
\n", "\n", + "**Non-ZEB Summary**\n", "{pivot_non_zeb_prop.to_markdown(index=False)}\n", "\n", - "the remaining buses did not specify a propulsion type\n", + "
\n", + "
\n", + "\n", + "The remaining buses did not specify a propulsion type\n", "\n", "\n", "\"\"\"" @@ -3480,7 +5789,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 42, "id": "1441f3d5-9630-420c-836b-4b7251e4c310", "metadata": {}, "outputs": [ @@ -3495,9 +5804,9 @@ "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", "\n", "Data was compiled from three data sources:\n", - " 1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", - " 2. TIRCP project data (state-funded, California only)\n", - " 3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", + "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", + "2. TIRCP project data (state-funded, California only)\n", + "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", "\n", "Breakdown of each data souce:\n", "| source | bus_count | total_cost |\n", @@ -3507,27 +5816,55 @@ "| tircp | 233 | 187250513 |\n", "| Grand Total | 1352 | 828620391 |\n", "\n", - "The initial dataset was contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries.\n", + "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries.\n", "The resulting dataset only contained projects that were solely used to procure buses. 87 projects remained. These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", "\n", "ZEB buses include: \n", - " * zero-emission (not specified) \n", - " - electric (not specified)\n", - " - battery electric \n", - " - fuel cell electric\n", + "- zero-emission (not specified) \n", + "- electric (not specified)\n", + "- battery electric \n", + "- fuel cell electric\n", "\n", "Non-ZEB buses include: \n", - " - CNG \n", - " - ethanol \n", - " - ow emission (hybrid, propane) \n", - " - diesel \n", - " - gas\n", + "- CNG \n", + "- ethanol \n", + "- ow emission (hybrid, propane) \n", + "- diesel \n", + "- gas\n", "\n", "Below are charts and tables that summarize the findings.\n", + "
\n", + "
\n", + "\n", + "**Lowest and highest cost per bus of all projects**\n", + "| | new_cost_per_bus |\n", + "|:----|-------------------:|\n", + "| min | 36250 |\n", + "| max | 1.61166e+06 |,\n", "\n", + "
\n", + "
\n", "\n", - "ZEB Summary\n", + "**Least and Most buses order of all projects**\n", + "| | total_bus_count |\n", + "|:----|------------------:|\n", + "| min | 1 |\n", + "| max | 160 |,\n", "\n", + "
\n", + "
\n", + "\n", + "**Lowest and highest total aggregate bus cost**\n", + "| | total_agg_cost |\n", + "|:----|-----------------:|\n", + "| min | 181250 |\n", + "| max | 1.03e+08 |,\n", + "\n", + "
\n", + "
\n", + "\n", + "\n", + "**ZEB Summary**\n", "| prop_type | bus_count | total_cost |\n", "|:----------------------------------|------------:|-------------:|\n", "| BEB | 163 | 167232489 |\n", @@ -3536,8 +5873,10 @@ "| zero-emission bus (not specified) | 143 | 128156513 |\n", "| Grand Total | 452 | 473018337 |\n", "\n", - "Non-ZEB Summary\n", + "
\n", + "
\n", "\n", + "**Non-ZEB Summary**\n", "| prop_type | bus_count | total_cost |\n", "|:----------------------------|------------:|-------------:|\n", "| CNG | 252 | 176039140 |\n", @@ -3546,7 +5885,13 @@ "| low emission (propane) | 44 | 8403969 |\n", "| mix (zero and low emission) | 125 | 36775430 |\n", "| Grand Total | 575 | 314049650 |\n", - "the remaining buses did not specify a propulsion type\n" + "\n", + "
\n", + "
\n", + "\n", + "The remaining buses did not specify a propulsion type\n", + "\n", + "\n" ], "text/plain": [ "" @@ -3558,6 +5903,7 @@ ], "source": [ "from IPython.display import Markdown, display\n", + "\n", "display(Markdown(summary))" ] }, From 1887b144a451ca61912ecf845cd98c195a80eb3e Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Thu, 20 Jun 2024 21:44:56 +0000 Subject: [PATCH 14/36] switching the weighted average caclulation for average cost per bus and for charts --- bus_procurement_cost/refactor_bus_cost.ipynb | 918 +++++++++++++++---- 1 file changed, 758 insertions(+), 160 deletions(-) diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index 0f5ac5cab..e1d34a65a 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -207,6 +207,7 @@ "cell_type": "markdown", "id": "d04911c1-e839-41fe-87b3-5065586f2223", "metadata": { + "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -634,6 +635,7 @@ "cell_type": "markdown", "id": "97bdb85b-ecaa-4634-8ea1-02ebc630567f", "metadata": { + "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -1286,7 +1288,10 @@ " switched to filtering the dataframe\n", " \n", "- means and standard deviations\n", - " - for charts?\n" + " - for charts?\n", + "\n", + "- other things from the initial analysis to include/re-work?\n", + "\n" ] }, { @@ -4725,7 +4730,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 96, "id": "1696d78f-7018-417b-9847-d82edac3acdf", "metadata": {}, "outputs": [ @@ -4859,6 +4864,115 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
bus_counttotal_cost
prop_type
BEB163.0167232489
CNG252.0176039140
FCEB102.0120951335
electric (not specified)44.056678000
ethanol9.01006750
low emission (hybrid)145.091824361
low emission (propane)44.08403969
mix (zero and low emission)125.036775430
not specified325.041552404
zero-emission bus (not specified)143.0128156513
Grand Total1352.0828620391
\n", + "
" + ], + "text/plain": [ + " bus_count total_cost\n", + "prop_type \n", + "BEB 163.0 167232489\n", + "CNG 252.0 176039140\n", + "FCEB 102.0 120951335\n", + "electric (not specified) 44.0 56678000\n", + "ethanol 9.0 1006750\n", + "low emission (hybrid) 145.0 91824361\n", + "low emission (propane) 44.0 8403969\n", + "mix (zero and low emission) 125.0 36775430\n", + "not specified 325.0 41552404\n", + "zero-emission bus (not specified) 143.0 128156513\n", + "Grand Total 1352.0 828620391" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -4875,7 +4989,7 @@ ")\n", "display(\n", " #from new_cpb_agg\n", - " agg_prop[[\"prop_type\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]]\n", + " agg_prop[[\"prop_type\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]],\n", " #pivot\n", " #pivot_prop_type\n", ")\n", @@ -4884,7 +4998,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 93, "id": "ca709e43-4947-4a34-970f-216d4b6ab7cc", "metadata": {}, "outputs": [], @@ -4922,6 +5036,8 @@ " margins_name = \"Grand Total\"\n", ").reset_index() \n", "\n", + "pivot_zeb_prop[\"cost_per_bus\"] = (pivot_zeb_prop[\"total_cost\"] / pivot_zeb_prop[\"bus_count\"]).astype(\"int64\")\n", + "\n", "#keep this\n", "pivot_non_zeb_prop = pd.pivot_table(\n", " #filted incoming DF for non-zeb prop types\n", @@ -4931,12 +5047,14 @@ " aggfunc = \"sum\",\n", " margins = True,\n", " margins_name = \"Grand Total\"\n", - ").reset_index()" + ").reset_index()\n", + "\n", + "pivot_non_zeb_prop[\"cost_per_bus\"] = (pivot_non_zeb_prop[\"total_cost\"] / pivot_non_zeb_prop[\"bus_count\"]).astype(\"int64\")" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 94, "id": "01647d83-8b4f-47a9-ab57-a1db7cd501dd", "metadata": {}, "outputs": [ @@ -4964,6 +5082,7 @@ " prop_type\n", " bus_count\n", " total_cost\n", + " cost_per_bus\n", " \n", " \n", " \n", @@ -4972,42 +5091,47 @@ " BEB\n", " 163.0\n", " 167232489\n", + " 1025966\n", " \n", " \n", " 1\n", " FCEB\n", " 102.0\n", " 120951335\n", + " 1185797\n", " \n", " \n", " 2\n", " electric (not specified)\n", " 44.0\n", " 56678000\n", + " 1288136\n", " \n", " \n", " 3\n", " zero-emission bus (not specified)\n", " 143.0\n", " 128156513\n", + " 896199\n", " \n", " \n", " 4\n", " Grand Total\n", " 452.0\n", " 473018337\n", + " 1046500\n", " \n", " \n", "\n", "" ], "text/plain": [ - " prop_type bus_count total_cost\n", - "0 BEB 163.0 167232489\n", - "1 FCEB 102.0 120951335\n", - "2 electric (not specified) 44.0 56678000\n", - "3 zero-emission bus (not specified) 143.0 128156513\n", - "4 Grand Total 452.0 473018337" + " prop_type bus_count total_cost cost_per_bus\n", + "0 BEB 163.0 167232489 1025966\n", + "1 FCEB 102.0 120951335 1185797\n", + "2 electric (not specified) 44.0 56678000 1288136\n", + "3 zero-emission bus (not specified) 143.0 128156513 896199\n", + "4 Grand Total 452.0 473018337 1046500" ] }, "metadata": {}, @@ -5037,6 +5161,7 @@ " prop_type\n", " bus_count\n", " total_cost\n", + " cost_per_bus\n", " \n", " \n", " \n", @@ -5045,49 +5170,55 @@ " CNG\n", " 252.0\n", " 176039140\n", + " 698568\n", " \n", " \n", " 1\n", " ethanol\n", " 9.0\n", " 1006750\n", + " 111861\n", " \n", " \n", " 2\n", " low emission (hybrid)\n", " 145.0\n", " 91824361\n", + " 633271\n", " \n", " \n", " 3\n", " low emission (propane)\n", " 44.0\n", " 8403969\n", + " 190999\n", " \n", " \n", " 4\n", " mix (zero and low emission)\n", " 125.0\n", " 36775430\n", + " 294203\n", " \n", " \n", " 5\n", " Grand Total\n", " 575.0\n", " 314049650\n", + " 546173\n", " \n", " \n", "\n", "" ], "text/plain": [ - " prop_type bus_count total_cost\n", - "0 CNG 252.0 176039140\n", - "1 ethanol 9.0 1006750\n", - "2 low emission (hybrid) 145.0 91824361\n", - "3 low emission (propane) 44.0 8403969\n", - "4 mix (zero and low emission) 125.0 36775430\n", - "5 Grand Total 575.0 314049650" + " prop_type bus_count total_cost cost_per_bus\n", + "0 CNG 252.0 176039140 698568\n", + "1 ethanol 9.0 1006750 111861\n", + "2 low emission (hybrid) 145.0 91824361 633271\n", + "3 low emission (propane) 44.0 8403969 190999\n", + "4 mix (zero and low emission) 125.0 36775430 294203\n", + "5 Grand Total 575.0 314049650 546173" ] }, "metadata": {}, @@ -5225,7 +5356,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 99, "id": "2c933257-bdc2-4007-9571-58475118073c", "metadata": {}, "outputs": [ @@ -5316,6 +5447,7 @@ " source\n", " bus_count\n", " total_cost\n", + " cost_per_bus\n", " \n", " \n", " \n", @@ -5324,35 +5456,39 @@ " dgs\n", " 236.0\n", " 250112853\n", + " 1059800\n", " \n", " \n", " 1\n", " fta\n", " 883.0\n", " 391257025\n", + " 443099\n", " \n", " \n", " 2\n", " tircp\n", " 233.0\n", " 187250513\n", + " 803650\n", " \n", " \n", " 3\n", " Grand Total\n", " 1352.0\n", " 828620391\n", + " 612884\n", " \n", " \n", "\n", "" ], "text/plain": [ - " source bus_count total_cost\n", - "0 dgs 236.0 250112853\n", - "1 fta 883.0 391257025\n", - "2 tircp 233.0 187250513\n", - "3 Grand Total 1352.0 828620391" + " source bus_count total_cost cost_per_bus\n", + "0 dgs 236.0 250112853 1059800\n", + "1 fta 883.0 391257025 443099\n", + "2 tircp 233.0 187250513 803650\n", + "3 Grand Total 1352.0 828620391 612884" ] }, "metadata": {}, @@ -5370,44 +5506,18 @@ " margins_name = \"Grand Total\"\n", ").reset_index()\n", "\n", - "\n", + "pivot_source[\"cost_per_bus\"] = (pivot_source[\"total_cost\"] / pivot_source[\"bus_count\"]).astype(\"int64\")\n", "\n", "display(\n", " agg_source[[\"source\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]],\n", " pivot_source\n", - ")\n", - "# dont need pivot, keep agg_source to retain cpb" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "31156225-644d-4709-9ebc-0a7f0fe7b78d", - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Cannot set a DataFrame with multiple columns to the single column cost_per_bus", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_392/3719685169.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpivot_source\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"cost_per_bus\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpivot_source\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"total_cost\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpivot_source\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"bus_count\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"int64\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 3966\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setitem_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3967\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3968\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setitem_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3969\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3970\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item_frame_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3971\u001b[0m elif (\n\u001b[1;32m 3972\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3973\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 4121\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marraylike\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4122\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4124\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4125\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 4126\u001b[0m \u001b[0;34m\"Cannot set a DataFrame with multiple columns to the single \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4127\u001b[0m \u001b[0;34mf\"column {key}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4128\u001b[0m )\n", - "\u001b[0;31mValueError\u001b[0m: Cannot set a DataFrame with multiple columns to the single column cost_per_bus" - ] - } - ], - "source": [ - "pivot_source[\"cost_per_bus\"] = ((pivot_source[\"total_cost\"]) / (pivot_source[\"bus_count\"])).astype(\"int64\").reset_index()" + ")\n" ] }, { "cell_type": "markdown", "id": "11547020-dd35-4745-98f8-bbd02fccaa23", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -5417,6 +5527,16 @@ "charts looking good, similar results to initial charts" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "aace38a4-3f2d-460d-a258-59efa659f852", + "metadata": {}, + "outputs": [], + "source": [ + "merged_data" + ] + }, { "cell_type": "code", "execution_count": 36, @@ -5483,13 +5603,129 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 127, + "id": "2007be9d-13ec-4d0d-a642-d9a42448b924", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1056659.3043478262" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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prop_typebus_counttotal_costcost_per_bus
0BEB163.01672324891025966
1FCEB102.01209513351185797
2electric (not specified)44.0566780001288136
3zero-emission bus (not specified)143.0128156513896199
4Grand Total452.04730183371046500
\n", + "
" + ], + "text/plain": [ + " prop_type bus_count total_cost cost_per_bus\n", + "0 BEB 163.0 167232489 1025966\n", + "1 FCEB 102.0 120951335 1185797\n", + "2 electric (not specified) 44.0 56678000 1288136\n", + "3 zero-emission bus (not specified) 143.0 128156513 896199\n", + "4 Grand Total 452.0 473018337 1046500" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "1046500.7455752213" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#why is the average different when i use .mean() vs. total cost / bus cout\n", + "display(\n", + " #this is the arithmatic mean, sums all the `cost_per_bus` rows, the divide by the number of rows. (row-wise)\n", + " merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"cost_per_bus\"].mean(),\n", + " #this is like the accounting method of calculating average (Total Cost and Total Quantity Approach (Weighted Average))\n", + " pivot_zeb_prop,\n", + " # calculating mean by weighted average aproach\n", + " (merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"bus_count\"].sum())\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 129, "id": "cefa6800-df50-4eda-95f8-74363ef942d0", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -5501,7 +5737,8 @@ "source": [ "dist_curve(\n", " df=merged_data[merged_data[\"prop_type\"].isin(zeb_list)],\n", - " mean=merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"cost_per_bus\"].mean(),\n", + " #using the accounting, weighted average approach to mean\n", + " mean=(merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"bus_count\"].sum()),\n", " std=merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"cost_per_bus\"].std(),\n", " title=\"ZEB buses, cost per bus distribution\",\n", " xlabel=\"cost per bus, $ million(s)\",\n", @@ -5510,13 +5747,13 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 130, "id": "563304d2-2d98-44e6-b3a4-fd54f63fc0d8", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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" ] @@ -5528,7 +5765,7 @@ "source": [ "dist_curve(\n", " df=merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)],\n", - " mean=merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"cost_per_bus\"].mean(),\n", + " mean=(merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"bus_count\"].sum()),\n", " std=merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"cost_per_bus\"].std(),\n", " title=\"non-ZEB costper bus Distribution\",\n", " xlabel='\"cost per bus, $ million(s)\"',\n", @@ -5699,7 +5936,9 @@ { "cell_type": "markdown", "id": "9270ab8f-25ff-4de3-aca5-7ef4637a4f9c", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "## Testing summary\n", "time to rework the summary section.\n", @@ -5709,12 +5948,11 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 100, "id": "2472461d-7663-4b66-9bde-4c2a199707a5", "metadata": {}, "outputs": [], "source": [ - "\n", "summary = f\"\"\"\n", "\n", "# Bus Procurement Cost Analysis\n", @@ -5730,16 +5968,16 @@ "Breakdown of each data souce:\n", "{pivot_source.to_markdown(index=False)}\n", "\n", - "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries.\n", + "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", "The resulting dataset only contained projects that were solely used to procure buses. 87 projects remained. These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", "\n", - "ZEB buses include: \n", + "**ZEB buses include:**\n", "- zero-emission (not specified) \n", "- electric (not specified)\n", "- battery electric \n", "- fuel cell electric\n", "\n", - "Non-ZEB buses include: \n", + "**Non-ZEB buses include:**\n", "- CNG \n", "- ethanol \n", "- ow emission (hybrid, propane) \n", @@ -5747,41 +5985,10 @@ "- gas\n", "\n", "Below are charts and tables that summarize the findings.\n", - "
\n", - "
\n", - "\n", - "**Lowest and highest cost per bus of all projects**\n", - "{agg_agency[\"new_cost_per_bus\"].agg([\"min\",\"max\"]).to_markdown()},\n", - "\n", - "
\n", - "
\n", - "\n", - "**Least and Most buses order of all projects**\n", - "{agg_agency[\"total_bus_count\"].agg([\"min\",\"max\"]).to_markdown()},\n", - "\n", - "
\n", - "
\n", - "\n", - "**Lowest and highest total aggregate bus cost**\n", - "{agg_agency[\"total_agg_cost\"].agg([\"min\",\"max\"]).to_markdown()},\n", "\n", - "
\n", - "
\n", "\n", "\n", - "**ZEB Summary**\n", - "{pivot_zeb_prop.to_markdown(index=False)}\n", "\n", - "
\n", - "
\n", - "\n", - "**Non-ZEB Summary**\n", - "{pivot_non_zeb_prop.to_markdown(index=False)}\n", - "\n", - "
\n", - "
\n", - "\n", - "The remaining buses did not specify a propulsion type\n", "\n", "\n", "\"\"\"" @@ -5789,7 +5996,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 101, "id": "1441f3d5-9630-420c-836b-4b7251e4c310", "metadata": {}, "outputs": [ @@ -5809,23 +6016,23 @@ "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", "\n", "Breakdown of each data souce:\n", - "| source | bus_count | total_cost |\n", - "|:------------|------------:|-------------:|\n", - "| dgs | 236 | 250112853 |\n", - "| fta | 883 | 391257025 |\n", - "| tircp | 233 | 187250513 |\n", - "| Grand Total | 1352 | 828620391 |\n", + "| source | bus_count | total_cost | cost_per_bus |\n", + "|:------------|------------:|-------------:|---------------:|\n", + "| dgs | 236 | 250112853 | 1059800 |\n", + "| fta | 883 | 391257025 | 443099 |\n", + "| tircp | 233 | 187250513 | 803650 |\n", + "| Grand Total | 1352 | 828620391 | 612884 |\n", "\n", - "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries.\n", + "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", "The resulting dataset only contained projects that were solely used to procure buses. 87 projects remained. These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", "\n", - "ZEB buses include: \n", + "**ZEB buses include:**\n", "- zero-emission (not specified) \n", "- electric (not specified)\n", "- battery electric \n", "- fuel cell electric\n", "\n", - "Non-ZEB buses include: \n", + "**Non-ZEB buses include:**\n", "- CNG \n", "- ethanol \n", "- ow emission (hybrid, propane) \n", @@ -5833,62 +6040,9 @@ "- gas\n", "\n", "Below are charts and tables that summarize the findings.\n", - "
\n", - "
\n", - "\n", - "**Lowest and highest cost per bus of all projects**\n", - "| | new_cost_per_bus |\n", - "|:----|-------------------:|\n", - "| min | 36250 |\n", - "| max | 1.61166e+06 |,\n", - "\n", - "
\n", - "
\n", - "\n", - "**Least and Most buses order of all projects**\n", - "| | total_bus_count |\n", - "|:----|------------------:|\n", - "| min | 1 |\n", - "| max | 160 |,\n", - "\n", - "
\n", - "
\n", - "\n", - "**Lowest and highest total aggregate bus cost**\n", - "| | total_agg_cost |\n", - "|:----|-----------------:|\n", - "| min | 181250 |\n", - "| max | 1.03e+08 |,\n", "\n", - "
\n", - "
\n", "\n", "\n", - "**ZEB Summary**\n", - "| prop_type | bus_count | total_cost |\n", - "|:----------------------------------|------------:|-------------:|\n", - "| BEB | 163 | 167232489 |\n", - "| FCEB | 102 | 120951335 |\n", - "| electric (not specified) | 44 | 56678000 |\n", - "| zero-emission bus (not specified) | 143 | 128156513 |\n", - "| Grand Total | 452 | 473018337 |\n", - "\n", - "
\n", - "
\n", - "\n", - "**Non-ZEB Summary**\n", - "| prop_type | bus_count | total_cost |\n", - "|:----------------------------|------------:|-------------:|\n", - "| CNG | 252 | 176039140 |\n", - "| ethanol | 9 | 1006750 |\n", - "| low emission (hybrid) | 145 | 91824361 |\n", - "| low emission (propane) | 44 | 8403969 |\n", - "| mix (zero and low emission) | 125 | 36775430 |\n", - "| Grand Total | 575 | 314049650 |\n", - "\n", - "
\n", - "
\n", - "\n", "The remaining buses did not specify a propulsion type\n", "\n", "\n" @@ -5907,10 +6061,454 @@ "display(Markdown(summary))" ] }, + { + "cell_type": "code", + "execution_count": 105, + "id": "45727e49-ed56-4571-b60b-41f5933d5b96", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "**Lowest and highest cost per bus of all projects**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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5Grand Total575.0314049650546173
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" + ], + "text/plain": [ + " prop_type bus_count total_cost cost_per_bus\n", + "0 CNG 252.0 176039140 698568\n", + "1 ethanol 9.0 1006750 111861\n", + "2 low emission (hybrid) 145.0 91824361 633271\n", + "3 low emission (propane) 44.0 8403969 190999\n", + "4 mix (zero and low emission) 125.0 36775430 294203\n", + "5 Grand Total 575.0 314049650 546173" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "The remaining buses did not specify a propulsion type" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Markdown(\"**Non-ZEB Summary**\"),\n", + "pivot_non_zeb_prop,\n", + "Markdown(\"The remaining buses did not specify a propulsion type\")\n", + " )" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "a3f71064-b7d2-47b2-bae7-db27ab3c09ee", + "id": "e39c89a1-a726-44f9-808b-bcf936c77254", "metadata": {}, "outputs": [], "source": [] From 731285071914f26cc4adf314cf85411e5a8272cb Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Thu, 20 Jun 2024 23:02:07 +0000 Subject: [PATCH 15/36] more organization --- bus_procurement_cost/refactor_bus_cost.ipynb | 88 ++++++++++---------- 1 file changed, 42 insertions(+), 46 deletions(-) diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index e1d34a65a..814302875 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -560,21 +560,7 @@ " plt.legend()\n", " plt.show()\n", "\n", - " return\n", - "\n", - "def make_chart(y_col: str, title: str, data: pd.DataFrame, x_col: str):\n", - " \"\"\"\n", - " function to create chart. sorts values by y_col ascending.\"\"\"\n", - " \n", - " data.sort_values(by=y_col, ascending=False).head(10).plot(\n", - " x=x_col, y=y_col, kind=\"bar\", color=\"skyblue\"\n", - " )\n", - " plt.title(title)\n", - " plt.xlabel(x_col)\n", - " plt.ylabel(y_col)\n", - "\n", - " plt.ticklabel_format(style=\"plain\", axis=\"y\")\n", - " plt.show()" + " return\n" ] }, { @@ -635,7 +621,6 @@ "cell_type": "markdown", "id": "97bdb85b-ecaa-4634-8ea1-02ebc630567f", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -647,6 +632,9 @@ "execution_count": 14, "id": "ea9c3269-d53d-4d94-bc22-c6768cb63d91", "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [] }, "outputs": [], @@ -784,6 +772,9 @@ "execution_count": 15, "id": "4e16119d-f6f3-478b-a419-7c4989557910", "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [] }, "outputs": [], @@ -925,6 +916,9 @@ "execution_count": 16, "id": "359f3b7a-d691-446f-9a14-424c47fc0929", "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [] }, "outputs": [], @@ -5574,33 +5568,6 @@ ")" ] }, - { - "cell_type": "code", - "execution_count": 37, - "id": "8645cf77-b30a-4c45-b943-ac81e8b5a613", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dist_curve(\n", - " df=merged_data,\n", - " mean=cpb_mean,\n", - " std=cpb_std,\n", - " title=\"all buses, cost per bus distribution\",\n", - " xlabel=\"cost per bus, $ million(s)\",\n", - ")" - ] - }, { "cell_type": "code", "execution_count": 127, @@ -5706,17 +5673,45 @@ } ], "source": [ - "#why is the average different when i use .mean() vs. total cost / bus cout\n", + "# why is the average different when i use .mean() vs. total cost / bus cout\n", + "\n", "display(\n", " #this is the arithmatic mean, sums all the `cost_per_bus` rows, the divide by the number of rows. (row-wise)\n", " merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"cost_per_bus\"].mean(),\n", " #this is like the accounting method of calculating average (Total Cost and Total Quantity Approach (Weighted Average))\n", " pivot_zeb_prop,\n", - " # calculating mean by weighted average aproach\n", + " # calculating mean by weighted average aproach (total cost / total bus count, similar to pivot table)\n", " (merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"bus_count\"].sum())\n", ")\n" ] }, + { + "cell_type": "code", + "execution_count": 37, + "id": "8645cf77-b30a-4c45-b943-ac81e8b5a613", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dist_curve(\n", + " df=merged_data,\n", + " mean=cpb_mean,\n", + " std=cpb_std,\n", + " title=\"all buses, cost per bus distribution\",\n", + " xlabel=\"cost per bus, $ million(s)\",\n", + ")" + ] + }, { "cell_type": "code", "execution_count": 129, @@ -5737,8 +5732,9 @@ "source": [ "dist_curve(\n", " df=merged_data[merged_data[\"prop_type\"].isin(zeb_list)],\n", - " #using the accounting, weighted average approach to mean\n", + " #using the accounting, weighted average approach to mean (total cost/total number of buses)\n", " mean=(merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"bus_count\"].sum()),\n", + " # need to investigate if std needs to be weighted as well?\n", " std=merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"cost_per_bus\"].std(),\n", " title=\"ZEB buses, cost per bus distribution\",\n", " xlabel=\"cost per bus, $ million(s)\",\n", From 877d41f6047d2d32d6fe91dddbfa972e700a74ac Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Fri, 21 Jun 2024 22:09:06 +0000 Subject: [PATCH 16/36] started writing conclusion. created new function to min/max values of a column in a df --- bus_procurement_cost/refactor_bus_cost.ipynb | 923 +++++++++++++------ 1 file changed, 629 insertions(+), 294 deletions(-) diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index 814302875..4e899d9c9 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -120,76 +120,76 @@ " \n", " \n", " \n", - " 80\n", - " University of California, Irvine\n", + " 11\n", + " Coast Transit Authority dba MS Coast Transport...\n", + " Purchase of Replacement Buses\n", + " low emission (propane)\n", + " not specified\n", + " Coast Transit Authority will receive funding t...\n", + " bus only\n", + " 1760000\n", + " 9.0\n", + " fta\n", + " None\n", + " None\n", + " \n", + " \n", + " 66\n", + " CITY OF PORTERVILLE (PORTERVILLE, CA)\n", " None\n", " BEB\n", " standard/conventional (30ft-45ft)\n", " None\n", " None\n", - " 4932930\n", - " 5.0\n", + " 2781891\n", + " 3.0\n", " dgs\n", - " University of California, Irvine\n", + " 20-18895\n", " None\n", " \n", " \n", - " 45\n", - " City of Wasco\n", + " 73\n", + " City of Roseville\n", " None\n", - " zero-emission bus (not specified)\n", - " not specified\n", + " BEB\n", + " standard/conventional (30ft-45ft)\n", " None\n", - " bus only\n", - " 1543000\n", - " 3.0\n", - " tircp\n", - " CP090\n", - " Purchase of 3 zero-emission buses that will su...\n", - " \n", - " \n", - " 51\n", - " Transit Joint Powers Authority of Merced County\n", " None\n", - " zero-emission bus (not specified)\n", - " not specified\n", + " 4452892\n", + " 5.0\n", + " dgs\n", + " 9009418\n", " None\n", - " bus only\n", - " 3696513\n", - " 3.0\n", - " tircp\n", - " CP074\n", - " Purchases 3 zero-emission electric buses to in...\n", " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency project_title \\\n", - "80 University of California, Irvine None \n", - "45 City of Wasco None \n", - "51 Transit Joint Powers Authority of Merced County None \n", + " transit_agency \\\n", + "11 Coast Transit Authority dba MS Coast Transport... \n", + "66 CITY OF PORTERVILLE (PORTERVILLE, CA) \n", + "73 City of Roseville \n", "\n", - " prop_type bus_size_type \\\n", - "80 BEB standard/conventional (30ft-45ft) \n", - "45 zero-emission bus (not specified) not specified \n", - "51 zero-emission bus (not specified) not specified \n", + " project_title prop_type \\\n", + "11 Purchase of Replacement Buses low emission (propane) \n", + "66 None BEB \n", + "73 None BEB \n", "\n", - " description new_project_type total_cost bus_count source \\\n", - "80 None None 4932930 5.0 dgs \n", - "45 None bus only 1543000 3.0 tircp \n", - "51 None bus only 3696513 3.0 tircp \n", + " bus_size_type \\\n", + "11 not specified \n", + "66 standard/conventional (30ft-45ft) \n", + "73 standard/conventional (30ft-45ft) \n", "\n", - " ppno \\\n", - "80 University of California, Irvine \n", - "45 CP090 \n", - "51 CP074 \n", + " description new_project_type \\\n", + "11 Coast Transit Authority will receive funding t... bus only \n", + "66 None None \n", + "73 None None \n", "\n", - " project_description \n", - "80 None \n", - "45 Purchase of 3 zero-emission buses that will su... \n", - "51 Purchases 3 zero-emission electric buses to in... " + " total_cost bus_count source ppno project_description \n", + "11 1760000 9.0 fta None None \n", + "66 2781891 3.0 dgs 20-18895 None \n", + "73 4452892 5.0 dgs 9009418 None " ] }, "metadata": {}, @@ -207,7 +207,6 @@ "cell_type": "markdown", "id": "d04911c1-e839-41fe-87b3-5065586f2223", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -215,6 +214,34 @@ "for new `bus_cost_utils.py` script" ] }, + { + "cell_type": "code", + "execution_count": 134, + "id": "c6dacaba-c6f7-4cb0-afef-a84f77de25fc", + "metadata": {}, + "outputs": [], + "source": [ + "# NEW FUNCTION\n", + "# move this to bus_cost_utils\n", + "def bus_min_max_summary(data:pd.DataFrame, col1:str):\n", + " \"\"\"\n", + " function to display min/max of specific column in aggregated bus df.\n", + " \n", + " \"\"\"\n", + " \n", + " return display(Markdown(f\"**Max {col1}**\"),\n", + " data[data[col1] == data[col1].max()][[\"transit_agency\",\n", + " \"total_agg_cost\",\n", + " \"total_bus_count\",\n", + " \"new_cost_per_bus\"]],\n", + " Markdown(f\"**Min {col1}**\"),\n", + " data[data[col1] == data[col1].min()][[\"transit_agency\",\n", + " \"total_agg_cost\",\n", + " \"total_bus_count\",\n", + " \"new_cost_per_bus\"]])\n", + " \n" + ] + }, { "cell_type": "code", "execution_count": 7, @@ -621,6 +648,7 @@ "cell_type": "markdown", "id": "97bdb85b-ecaa-4634-8ea1-02ebc630567f", "metadata": { + "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -632,9 +660,6 @@ "execution_count": 14, "id": "ea9c3269-d53d-4d94-bc22-c6768cb63d91", "metadata": { - "jupyter": { - "source_hidden": true - }, "tags": [] }, "outputs": [], @@ -772,9 +797,6 @@ "execution_count": 15, "id": "4e16119d-f6f3-478b-a419-7c4989557910", "metadata": { - "jupyter": { - "source_hidden": true - }, "tags": [] }, "outputs": [], @@ -916,9 +938,6 @@ "execution_count": 16, "id": "359f3b7a-d691-446f-9a14-424c47fc0929", "metadata": { - "jupyter": { - "source_hidden": true - }, "tags": [] }, "outputs": [], @@ -1282,6 +1301,7 @@ " switched to filtering the dataframe\n", " \n", "- means and standard deviations\n", + " - switched to using weighted average for chart calculation\n", " - for charts?\n", "\n", "- other things from the initial analysis to include/re-work?\n", @@ -1471,25 +1491,14 @@ " \n", " \n", " \n", - " 47\n", - " Rockford Mass Transit District\n", + " 81\n", + " White Earth Reservation Business Committee\n", " 1\n", " 0\n", - " 4094652\n", + " 723171\n", " 4.0\n", - " 1023663\n", - " 0.598110\n", - " False\n", - " \n", - " \n", - " 13\n", - " City of Los Angeles (LA DOT)\n", - " 0\n", - " 1\n", - " 102790000\n", - " 112.0\n", - " 917767\n", - " 0.333769\n", + " 180792\n", + " -1.505895\n", " False\n", " \n", " \n", @@ -1503,25 +1512,36 @@ " 0.344210\n", " False\n", " \n", + " \n", + " 48\n", + " Rogue Valley Transportation District\n", + " 1\n", + " 0\n", + " 3937500\n", + " 6.0\n", + " 656250\n", + " -0.319040\n", + " False\n", + " \n", " \n", "\n", "" ], "text/plain": [ " transit_agency total_project_count \\\n", - "47 Rockford Mass Transit District 1 \n", - "13 City of Los Angeles (LA DOT) 0 \n", + "81 White Earth Reservation Business Committee 1 \n", "19 City of Visalia - Visalia City Coach(Visalia T... 0 \n", + "48 Rogue Valley Transportation District 1 \n", "\n", " total_project_count_ppno total_agg_cost total_bus_count \\\n", - "47 0 4094652 4.0 \n", - "13 1 102790000 112.0 \n", + "81 0 723171 4.0 \n", "19 1 3687803 4.0 \n", + "48 0 3937500 6.0 \n", "\n", " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", - "47 1023663 0.598110 False \n", - "13 917767 0.333769 False \n", - "19 921950 0.344210 False " + "81 180792 -1.505895 False \n", + "19 921950 0.344210 False \n", + "48 656250 -0.319040 False " ] }, "execution_count": 22, @@ -4724,7 +4744,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 31, "id": "1696d78f-7018-417b-9847-d82edac3acdf", "metadata": {}, "outputs": [ @@ -4858,115 +4878,6 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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low emission (propane)44.08403969
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" - ], - "text/plain": [ - " bus_count total_cost\n", - "prop_type \n", - "BEB 163.0 167232489\n", - "CNG 252.0 176039140\n", - "FCEB 102.0 120951335\n", - "electric (not specified) 44.0 56678000\n", - "ethanol 9.0 1006750\n", - "low emission (hybrid) 145.0 91824361\n", - "low emission (propane) 44.0 8403969\n", - "mix (zero and low emission) 125.0 36775430\n", - "not specified 325.0 41552404\n", - "zero-emission bus (not specified) 143.0 128156513\n", - "Grand Total 1352.0 828620391" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -4992,7 +4903,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 32, "id": "ca709e43-4947-4a34-970f-216d4b6ab7cc", "metadata": {}, "outputs": [], @@ -5048,7 +4959,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 33, "id": "01647d83-8b4f-47a9-ab57-a1db7cd501dd", "metadata": {}, "outputs": [ @@ -5350,7 +5261,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 35, "id": "2c933257-bdc2-4007-9571-58475118073c", "metadata": {}, "outputs": [ @@ -5523,66 +5434,10 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 140, "id": "aace38a4-3f2d-460d-a258-59efa659f852", "metadata": {}, - "outputs": [], - "source": [ - "merged_data" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "4753f3ea-00b6-4d5e-a3f0-73b3d3593acb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "792635.3409090909" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "396712.6067531972" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# means and standard deviations\n", - "# for graphs\n", - "cpb_mean = merged_data[\"cost_per_bus\"].mean()\n", - "cpb_std = merged_data[\"cost_per_bus\"].std()\n", - "\n", - "display(\n", - " cpb_mean,\n", - " cpb_std\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "id": "2007be9d-13ec-4d0d-a642-d9a42448b924", - "metadata": {}, "outputs": [ - { - "data": { - "text/plain": [ - "1056659.3043478262" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "text/html": [ @@ -5604,18 +5459,181 @@ " \n", " \n", " \n", + " transit_agency\n", + " project_title\n", " prop_type\n", - " bus_count\n", + " bus_size_type\n", + " description\n", + " new_project_type\n", " total_cost\n", + " bus_count\n", + " source\n", + " ppno\n", + " project_description\n", " cost_per_bus\n", + " zscore_cost_per_bus\n", + " is_cpb_outlier?\n", " \n", " \n", " \n", " \n", " 0\n", - " BEB\n", - " 163.0\n", - " 167232489\n", + " AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)\n", + " Puerto Rico Initiative Minimizing Emissions Pl...\n", + " electric (not specified)\n", + " not specified\n", + " The Metropolitan Bus Authority will receive fu...\n", + " bus only\n", + " 10000000\n", + " 8.0\n", + " fta\n", + " None\n", + " None\n", + " 1250000\n", + " 0.917956\n", + " False\n", + " \n", + " \n", + " 1\n", + " Cape Fear Public Transportation Authority\n", + " Wave Transit Low Emissions Replacement Vehicles\n", + " CNG\n", + " not specified\n", + " Wave Transit will receive funding to buy compr...\n", + " bus only\n", + " 2860250\n", + " 5.0\n", + " fta\n", + " None\n", + " None\n", + " 572050\n", + " -0.529139\n", + " False\n", + " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " transit_agency \\\n", + "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) \n", + "1 Cape Fear Public Transportation Authority \n", + "\n", + " project_title \\\n", + "0 Puerto Rico Initiative Minimizing Emissions Pl... \n", + "1 Wave Transit Low Emissions Replacement Vehicles \n", + "\n", + " prop_type bus_size_type \\\n", + "0 electric (not specified) not specified \n", + "1 CNG not specified \n", + "\n", + " description new_project_type \\\n", + "0 The Metropolitan Bus Authority will receive fu... bus only \n", + "1 Wave Transit will receive funding to buy compr... bus only \n", + "\n", + " total_cost bus_count source ppno project_description cost_per_bus \\\n", + "0 10000000 8.0 fta None None 1250000 \n", + "1 2860250 5.0 fta None None 572050 \n", + "\n", + " zscore_cost_per_bus is_cpb_outlier? \n", + "0 0.917956 False \n", + "1 -0.529139 False " + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged_data.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "4753f3ea-00b6-4d5e-a3f0-73b3d3593acb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "792635.3409090909" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "396712.6067531972" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# means and standard deviations\n", + "# for graphs\n", + "cpb_mean = merged_data[\"cost_per_bus\"].mean()\n", + "cpb_std = merged_data[\"cost_per_bus\"].std()\n", + "\n", + "display(\n", + " cpb_mean,\n", + " cpb_std\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "2007be9d-13ec-4d0d-a642-d9a42448b924", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1056659.3043478262" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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9City of Beloit6531841.0653184
max160.016City of San Luis Obispo8592701.0859270
49SLO TRANSIT (SAN LUIS OBISPO, CA)8472141.0847214
\n", "
" ], "text/plain": [ - " total_bus_count\n", - "min 1.0\n", - "max 160.0" + " transit_agency total_agg_cost total_bus_count \\\n", + "9 City of Beloit 653184 1.0 \n", + "16 City of San Luis Obispo 859270 1.0 \n", + "49 SLO TRANSIT (SAN LUIS OBISPO, CA) 847214 1.0 \n", + "\n", + " new_cost_per_bus \n", + "9 653184 \n", + "16 859270 \n", + "49 847214 " ] }, "metadata": {}, @@ -6194,21 +6432,19 @@ } ], "source": [ - "display(Markdown(\"**Least and Most buses order of all projects**\"),\n", - "agg_agency[\"total_bus_count\"].agg([\"min\",\"max\"]).to_frame()\n", - " )" + "bus_min_max_summary(data=agg_agency,col1=\"total_bus_count\")" ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 139, "id": "bd846af8-1c48-4b35-a410-c751f14f8a9a", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "**Lowest and highest total aggregate bus cost**" + "**Max total_agg_cost**" ], "text/plain": [ "" @@ -6238,42 +6474,105 @@ " \n", " \n", " \n", + " transit_agency\n", " total_agg_cost\n", + " total_bus_count\n", + " new_cost_per_bus\n", " \n", " \n", " \n", " \n", - " min\n", - " 181250\n", + " 24\n", + " Dallas Area Rapid Transit (DART)\n", + " 103000000\n", + " 90.0\n", + " 1144444\n", " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " transit_agency total_agg_cost total_bus_count \\\n", + "24 Dallas Area Rapid Transit (DART) 103000000 90.0 \n", + "\n", + " new_cost_per_bus \n", + "24 1144444 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Min total_agg_cost**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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" ], "text/plain": [ - " total_agg_cost\n", - "min 181250\n", - "max 103000000" + " transit_agency total_agg_cost \\\n", + "45 Oregon Department of Transportation on behalf ... 181250 \n", + "\n", + " total_bus_count new_cost_per_bus \n", + "45 5.0 36250 " ] }, - "execution_count": 76, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "display(Markdown(\"**Lowest and highest total aggregate bus cost**\"),\n", - "agg_agency[\"total_agg_cost\"].agg([\"min\",\"max\"]).to_frame()\n", - " )" + "bus_min_max_summary(data=agg_agency,col1=\"total_agg_cost\")" ] }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 49, "id": "a78e45d1-6e38-4190-ab6c-3634f807ae6b", "metadata": {}, "outputs": [ @@ -6365,20 +6664,19 @@ "4 Grand Total 452.0 473018337 1046500" ] }, - "execution_count": 97, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "display(Markdown(\"**ZEB Summary**\")\n", + "display(Markdown(\"**ZEB Summary**\"),\n", "pivot_zeb_prop\n", " )" ] }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 50, "id": "9fccde69-37c4-4cfc-b2e1-1ed4b838b1b9", "metadata": {}, "outputs": [ @@ -6503,9 +6801,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "id": "e39c89a1-a726-44f9-808b-bcf936c77254", "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "\n", + "**Conclusion**\n", + "\n", + "Based on these findings, The average cost of a ZEB, throughout the US, is ~$1,000,000, roughly twice the price of a convention, non-ZEB.\n", + "Reasons for variance in cost depends on the options the Trasnit\n", + "Agencies chose for their bus. Unfortunately, analyzing the cost of configuable options is outside the scope of this analysis. \n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "conclusion = f\"\"\"\n", + "**Conclusion**\n", + "\n", + "Based on these findings, The average cost of a ZEB, throughout the US, is ~$1,000,000, roughly twice the price of a convention, non-ZEB.\n", + "Reasons for variance in cost depends on the options the Trasnit\n", + "Agencies chose for their bus. Unfortunately, analyzing the cost of configuable options is outside the scope of this analysis. \n", + "\"\"\"\n", + "display(\n", + " Markdown(conclusion)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "414b0e56-4b8f-41ae-9e07-9223abf95c45", + "metadata": {}, "outputs": [], "source": [] } From 8ece2d75e1db00da98e9fdc768fef9bf5386f800 Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Fri, 21 Jun 2024 22:21:47 +0000 Subject: [PATCH 17/36] consolidated some of the summary cells down to 1 cell per section --- bus_procurement_cost/refactor_bus_cost.ipynb | 222 +++++-------------- 1 file changed, 54 insertions(+), 168 deletions(-) diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index 4e899d9c9..18cea5d09 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -5962,57 +5962,9 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 147, "id": "2472461d-7663-4b66-9bde-4c2a199707a5", "metadata": {}, - "outputs": [], - "source": [ - "summary = f\"\"\"\n", - "\n", - "# Bus Procurement Cost Analysis\n", - "\n", - "## Summary\n", - "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", - "\n", - "Data was compiled from three data sources:\n", - "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", - "2. TIRCP project data (state-funded, California only)\n", - "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", - "\n", - "Breakdown of each data souce:\n", - "{pivot_source.to_markdown(index=False)}\n", - "\n", - "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", - "The resulting dataset only contained projects that were solely used to procure buses. 87 projects remained. These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", - "\n", - "**ZEB buses include:**\n", - "- zero-emission (not specified) \n", - "- electric (not specified)\n", - "- battery electric \n", - "- fuel cell electric\n", - "\n", - "**Non-ZEB buses include:**\n", - "- CNG \n", - "- ethanol \n", - "- ow emission (hybrid, propane) \n", - "- diesel \n", - "- gas\n", - "\n", - "Below are charts and tables that summarize the findings.\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "1441f3d5-9630-420c-836b-4b7251e4c310", - "metadata": {}, "outputs": [ { "data": { @@ -6057,8 +6009,6 @@ "\n", "\n", "\n", - "\n", - "\n", "\n" ], "text/plain": [ @@ -6070,6 +6020,43 @@ } ], "source": [ + "summary = f\"\"\"\n", + "\n", + "# Bus Procurement Cost Analysis\n", + "\n", + "## Summary\n", + "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", + "\n", + "Data was compiled from three data sources:\n", + "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", + "2. TIRCP project data (state-funded, California only)\n", + "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", + "\n", + "Breakdown of each data souce:\n", + "{pivot_source.to_markdown(index=False)}\n", + "\n", + "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", + "The resulting dataset only contained projects that were solely used to procure buses. 87 projects remained. These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", + "\n", + "**ZEB buses include:**\n", + "- zero-emission (not specified) \n", + "- electric (not specified)\n", + "- battery electric \n", + "- fuel cell electric\n", + "\n", + "**Non-ZEB buses include:**\n", + "- CNG \n", + "- ethanol \n", + "- ow emission (hybrid, propane) \n", + "- diesel \n", + "- gas\n", + "\n", + "Below are charts and tables that summarize the findings.\n", + "\n", + "\n", + "\n", + "\n", + "\"\"\"\n", "from IPython.display import Markdown, display\n", "\n", "display(Markdown(summary))" @@ -6077,79 +6064,7 @@ }, { "cell_type": "code", - "execution_count": 60, - "id": "45727e49-ed56-4571-b60b-41f5933d5b96", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "**Lowest and highest cost per bus of all projects**" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " new_cost_per_bus\n", - "min 36250\n", - "max 1611662" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(Markdown(\"**Lowest and highest cost per bus of all projects**\"),\n", - "agg_agency[\"new_cost_per_bus\"].agg([\"min\",\"max\"]).to_frame()\n", - " )\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 137, + "execution_count": 144, "id": "91d0361d-b165-4607-b22e-66ae4234863d", "metadata": {}, "outputs": [ @@ -6276,18 +6191,7 @@ }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "bus_min_max_summary(data=agg_agency,col1=\"new_cost_per_bus\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "id": "85b3e7a8-6e82-4d67-aa2e-811f5b314974", - "metadata": {}, - "outputs": [ + }, { "data": { "text/markdown": [ @@ -6429,18 +6333,7 @@ }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "bus_min_max_summary(data=agg_agency,col1=\"total_bus_count\")" - ] - }, - { - "cell_type": "code", - "execution_count": 139, - "id": "bd846af8-1c48-4b35-a410-c751f14f8a9a", - "metadata": {}, - "outputs": [ + }, { "data": { "text/markdown": [ @@ -6567,12 +6460,14 @@ } ], "source": [ - "bus_min_max_summary(data=agg_agency,col1=\"total_agg_cost\")" + "bus_min_max_summary(data=agg_agency,col1=\"new_cost_per_bus\")\n", + "bus_min_max_summary(data=agg_agency,col1=\"total_bus_count\")\n", + "bus_min_max_summary(data=agg_agency,col1=\"total_agg_cost\")\n" ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 146, "id": "a78e45d1-6e38-4190-ab6c-3634f807ae6b", "metadata": {}, "outputs": [ @@ -6666,20 +6561,7 @@ }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "display(Markdown(\"**ZEB Summary**\"),\n", - "pivot_zeb_prop\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "9fccde69-37c4-4cfc-b2e1-1ed4b838b1b9", - "metadata": {}, - "outputs": [ + }, { "data": { "text/markdown": [ @@ -6793,10 +6675,14 @@ } ], "source": [ - "display(Markdown(\"**Non-ZEB Summary**\"),\n", - "pivot_non_zeb_prop,\n", - "Markdown(\"The remaining buses did not specify a propulsion type\")\n", - " )" + "display(\n", + " Markdown(\"**ZEB Summary**\"),\n", + " pivot_zeb_prop,\n", + " \n", + " Markdown(\"**Non-ZEB Summary**\"),\n", + " pivot_non_zeb_prop,\n", + " Markdown(\"The remaining buses did not specify a propulsion type\")\n", + ")" ] }, { From 167d356ecd003d4e982dc2b532bbf57505dcefc1 Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Fri, 21 Jun 2024 23:10:21 +0000 Subject: [PATCH 18/36] small edits --- bus_procurement_cost/refactor_bus_cost.ipynb | 66 ++++++++++---------- 1 file changed, 34 insertions(+), 32 deletions(-) diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index 18cea5d09..a6d428ae0 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -5551,7 +5551,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 156, "id": "4753f3ea-00b6-4d5e-a3f0-73b3d3593acb", "metadata": {}, "outputs": [ @@ -5572,6 +5572,24 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "1046500.7455752213" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "546173.304347826" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -5579,10 +5597,13 @@ "# for graphs\n", "cpb_mean = merged_data[\"cost_per_bus\"].mean()\n", "cpb_std = merged_data[\"cost_per_bus\"].std()\n", - "\n", + "zeb_cpb_wt_avg = (merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"bus_count\"].sum())\n", + "non_zeb_cpb_wt_avg = (merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"bus_count\"].sum())\n", "display(\n", " cpb_mean,\n", - " cpb_std\n", + " cpb_std,\n", + " wt_avg_mean,\n", + " non_zeb_cpb_wt_avg\n", ")" ] }, @@ -5732,7 +5753,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 153, "id": "cefa6800-df50-4eda-95f8-74363ef942d0", "metadata": {}, "outputs": [ @@ -5751,7 +5772,7 @@ "dist_curve(\n", " df=merged_data[merged_data[\"prop_type\"].isin(zeb_list)],\n", " #using the accounting, weighted average approach to mean (total cost/total number of buses)\n", - " mean=(merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"bus_count\"].sum()),\n", + " mean=zeb_cpb_wt_avg,\n", " # need to investigate if std needs to be weighted as well?\n", " std=merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"cost_per_bus\"].std(),\n", " title=\"ZEB buses, cost per bus distribution\",\n", @@ -5761,7 +5782,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 157, "id": "563304d2-2d98-44e6-b3a4-fd54f63fc0d8", "metadata": {}, "outputs": [ @@ -5779,7 +5800,7 @@ "source": [ "dist_curve(\n", " df=merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)],\n", - " mean=(merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"bus_count\"].sum()),\n", + " mean=non_zeb_cpb_wt_avg,\n", " std=merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"cost_per_bus\"].std(),\n", " title=\"non-ZEB costper bus Distribution\",\n", " xlabel='\"cost per bus, $ million(s)\"',\n", @@ -5788,7 +5809,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 149, "id": "5f11c857-ddbe-4871-aeca-e27fa00fbde8", "metadata": {}, "outputs": [ @@ -5812,24 +5833,6 @@ "metadata": {}, "output_type": "display_data" }, - { - "data": { - "text/plain": [ - "None" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "None" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "text/html": [ @@ -5935,16 +5938,15 @@ "4 ethanol 111861 9.0" ] }, + "execution_count": 149, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "display(\n", - " make_chart(\"new_cost_per_bus\", \"Cost per bus by propulsion type\", x_col=\"prop_type\", data=agg_prop),\n", - " make_chart(\"total_bus_count\", \"Bus count by propulsion type\", x_col=\"prop_type\", data=agg_prop),\n", - " agg_prop[[\"prop_type\",\"new_cost_per_bus\",\"total_bus_count\"]].sort_values(by=\"new_cost_per_bus\", ascending=False)\n", - ")" + "make_chart(\"new_cost_per_bus\", \"Cost per bus by propulsion type\", x_col=\"prop_type\", data=agg_prop)\n", + "make_chart(\"total_bus_count\", \"Bus count by propulsion type\", x_col=\"prop_type\", data=agg_prop)\n", + "agg_prop[[\"prop_type\",\"new_cost_per_bus\",\"total_bus_count\"]].sort_values(by=\"new_cost_per_bus\", ascending=False)\n" ] }, { From dc485c43f7dd5f40874fc4619c648eb8fd9400f1 Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Mon, 24 Jun 2024 19:00:38 +0000 Subject: [PATCH 19/36] more organizing of cells and creating headings for better navigation --- bus_procurement_cost/refactor_bus_cost.ipynb | 5586 ++++++------------ 1 file changed, 1679 insertions(+), 3907 deletions(-) diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index a6d428ae0..8707d00f8 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -1,5 +1,14 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "e50ef668-5812-4b3a-a257-ef0a424b46e3", + "metadata": {}, + "source": [ + "# Bus Cost Refactor\n", + "## overall imports and data sources" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -28,16 +37,28 @@ { "cell_type": "code", "execution_count": 3, + "id": "8a1fd0b4-14a6-4cad-bb15-0ce0437ed125", + "metadata": {}, + "outputs": [], + "source": [ + "# updated Script imports for bus cost utils\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "id": "c3552c45-8b28-4bbe-ae82-f2d726a45937", "metadata": {}, "outputs": [], "source": [ + "#immutable GCS path\n", + "# save to bus cost utils\n", "GCS_PATH = \"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/\"" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "e1b1f367-1dac-463f-8790-2e5134b7e79b", "metadata": {}, "outputs": [ @@ -51,158 +72,13 @@ } ], "source": [ - "# All Raw Data\n", + "# links to all Raw Data\n", "fta_raw = pd.read_csv(f\"{GCS_PATH}raw_data-analyses_bus_procurement_cost_fta_press_release_data_csv.csv\")\n", "tirp_raw = pd.read_excel(f\"{GCS_PATH}raw_TIRCP Tracking Sheets 2_1-10-2024.xlsx\", sheet_name=\"Project Tracking\")\n", "dgs17b_raw = pd.read_excel(f\"{GCS_PATH}raw_17b compiled.xlsx\", sheet_name = \"Usage Report Template\")\n", "dgs17c_raw = pd.read_excel(f\"{GCS_PATH}raw_17c compiled-Proterra Compiled Contract Usage Report .xlsx\", sheet_name = \"Proterra \")" ] }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e1e3bad8-62d3-4ca1-b4dd-b4d5b8d9a86b", - "metadata": {}, - "outputs": [], - "source": [ - "# what does the final table look like again?\n", - "final = pd.read_parquet(f'{GCS_PATH}old/cpb_analysis_data_merge.parquet')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "c2865cb5-0adb-4529-b057-d2595f9f8ab6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(89, 11)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencyproject_titleprop_typebus_size_typedescriptionnew_project_typetotal_costbus_countsourceppnoproject_description
11Coast Transit Authority dba MS Coast Transport...Purchase of Replacement Buseslow emission (propane)not specifiedCoast Transit Authority will receive funding t...bus only17600009.0ftaNoneNone
66CITY OF PORTERVILLE (PORTERVILLE, CA)NoneBEBstandard/conventional (30ft-45ft)NoneNone27818913.0dgs20-18895None
73City of RosevilleNoneBEBstandard/conventional (30ft-45ft)NoneNone44528925.0dgs9009418None
\n", - "
" - ], - "text/plain": [ - " transit_agency \\\n", - "11 Coast Transit Authority dba MS Coast Transport... \n", - "66 CITY OF PORTERVILLE (PORTERVILLE, CA) \n", - "73 City of Roseville \n", - "\n", - " project_title prop_type \\\n", - "11 Purchase of Replacement Buses low emission (propane) \n", - "66 None BEB \n", - "73 None BEB \n", - "\n", - " bus_size_type \\\n", - "11 not specified \n", - "66 standard/conventional (30ft-45ft) \n", - "73 standard/conventional (30ft-45ft) \n", - "\n", - " description new_project_type \\\n", - "11 Coast Transit Authority will receive funding t... bus only \n", - "66 None None \n", - "73 None None \n", - "\n", - " total_cost bus_count source ppno project_description \n", - "11 1760000 9.0 fta None None \n", - "66 2781891 3.0 dgs 20-18895 None \n", - "73 4452892 5.0 dgs 9009418 None " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(\n", - " final.shape,\n", - " final.sample(3)\n", - ")" - ] - }, { "cell_type": "markdown", "id": "d04911c1-e839-41fe-87b3-5065586f2223", @@ -210,13 +86,22 @@ "tags": [] }, "source": [ - "# Scripts to Save\n", + "# Functions to Save\n", "for new `bus_cost_utils.py` script" ] }, + { + "cell_type": "markdown", + "id": "29f25932-dddd-409f-a57f-eef29570a178", + "metadata": {}, + "source": [ + "## save to bus_cost_utils.py\n", + "for everything to use from" + ] + }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 6, "id": "c6dacaba-c6f7-4cb0-afef-a84f77de25fc", "metadata": {}, "outputs": [], @@ -229,16 +114,18 @@ " \n", " \"\"\"\n", " \n", - " return display(Markdown(f\"**Max {col1}**\"),\n", + " return display(\n", + " Markdown(f\"**Max {col1}**\"),\n", " data[data[col1] == data[col1].max()][[\"transit_agency\",\n", " \"total_agg_cost\",\n", " \"total_bus_count\",\n", " \"new_cost_per_bus\"]],\n", - " Markdown(f\"**Min {col1}**\"),\n", - " data[data[col1] == data[col1].min()][[\"transit_agency\",\n", + " Markdown(f\"**Min {col1}**\"),\n", + " data[data[col1] == data[col1].min()][[\"transit_agency\",\n", " \"total_agg_cost\",\n", " \"total_bus_count\",\n", - " \"new_cost_per_bus\"]])\n", + " \"new_cost_per_bus\"]]\n", + " )\n", " \n" ] }, @@ -513,6 +400,70 @@ " return" ] }, + { + "cell_type": "code", + "execution_count": 13, + "id": "fa387f41-c9b3-455a-9829-cfabb3f98c9b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# to new bus_cost_util\n", + "def outlier_flag(col):\n", + " \"\"\"\n", + " function to flag outlier rows. use with .apply()\n", + " \"\"\"\n", + " \n", + " return col <= -3 or col >= 3\n", + "\n", + "from scipy.stats import zscore\n", + "def new_cpb_aggregate(df: pd.DataFrame, column=\"transit_agency\") -> pd.DataFrame:\n", + " \"\"\"\n", + " function to aggregate compiled data by different categories:\n", + " \"transit agency\", \n", + " \"propulsion type\", \n", + " \"bus_size_type\",\n", + " \"new_project_type\"\n", + " aggregate on columns:\n", + " \"project_title\"\n", + " \"ppno\"\n", + " \"total_cost\"\n", + " \"bus_count\"\n", + " \n", + " Then, cost per bus is calculated AFTER the aggregation.\n", + " \"\"\"\n", + " df_agg = (\n", + " df.groupby(column)\n", + " .agg(\n", + " total_project_count=(\"project_title\", \"count\"),\n", + " total_project_count_ppno=(\"ppno\", \"count\"),\n", + " total_agg_cost=(\"total_cost\", \"sum\"),\n", + " total_bus_count=(\"bus_count\", \"sum\"),\n", + " #new_prop_type=(\"prop_type\",\"max\")\n", + " )\n", + " .reset_index()\n", + " )\n", + " df_agg[\"new_cost_per_bus\"] = (df_agg[\"total_agg_cost\"] / df_agg[\"total_bus_count\"]).astype(\"int64\")\n", + " \n", + " #calculate zscore\n", + " df_agg[\"new_zscore_cost_per_bus\"] = zscore(df_agg[\"new_cost_per_bus\"])\n", + " \n", + " #flag outliers\n", + " df_agg[\"new_is_cpb_outlier?\"] = df_agg[\"new_zscore_cost_per_bus\"].apply(outlier_flag)\n", + " \n", + " return df_agg" + ] + }, + { + "cell_type": "markdown", + "id": "d46d8747-5d4e-418d-a362-c80a093de4dd", + "metadata": {}, + "source": [ + "## save to analysis notebook\n", + "chart functions should stay in the analysis notebook since the charts only exist in the analysis notebook\n" + ] + }, { "cell_type": "code", "execution_count": 11, @@ -591,68 +542,24 @@ ] }, { - "cell_type": "code", - "execution_count": 13, - "id": "fa387f41-c9b3-455a-9829-cfabb3f98c9b", + "cell_type": "markdown", + "id": "97bdb85b-ecaa-4634-8ea1-02ebc630567f", "metadata": { "tags": [] }, - "outputs": [], "source": [ - "def outlier_flag(col):\n", - " \"\"\"\n", - " function to flag outlier rows. use with .apply()\n", - " \"\"\"\n", - " \n", - " return col <= -3 or col >= 3\n", - "\n", - "from scipy.stats import zscore\n", - "def new_cpb_aggregate(df: pd.DataFrame, column=\"transit_agency\") -> pd.DataFrame:\n", - " \"\"\"\n", - " function to aggregate compiled data by different categories:\n", - " \"transit agency\", \n", - " \"propulsion type\", \n", - " \"bus_size_type\",\n", - " \"new_project_type\"\n", - " aggregate on columns:\n", - " \"project_title\"\n", - " \"ppno\"\n", - " \"total_cost\"\n", - " \"bus_count\"\n", - " \n", - " Then, cost per bus is calculated AFTER the aggregation.\n", - " \"\"\"\n", - " df_agg = (\n", - " df.groupby(column)\n", - " .agg(\n", - " total_project_count=(\"project_title\", \"count\"),\n", - " total_project_count_ppno=(\"ppno\", \"count\"),\n", - " total_agg_cost=(\"total_cost\", \"sum\"),\n", - " total_bus_count=(\"bus_count\", \"sum\"),\n", - " #new_prop_type=(\"prop_type\",\"max\")\n", - " )\n", - " .reset_index()\n", - " )\n", - " df_agg[\"new_cost_per_bus\"] = (df_agg[\"total_agg_cost\"] / df_agg[\"total_bus_count\"]).astype(\"int64\")\n", - " \n", - " #calculate zscore\n", - " df_agg[\"new_zscore_cost_per_bus\"] = zscore(df_agg[\"new_cost_per_bus\"])\n", - " \n", - " #flag outliers\n", - " df_agg[\"new_is_cpb_outlier?\"] = df_agg[\"new_zscore_cost_per_bus\"].apply(outlier_flag)\n", - " \n", - " return df_agg" + "# Chagnes to current grant type scripts\n" ] }, { "cell_type": "markdown", - "id": "97bdb85b-ecaa-4634-8ea1-02ebc630567f", + "id": "84e4d8ed-131b-4a03-a2af-55c2bd4efc66", "metadata": { "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ - "# Chagnes to current grant type scripts\n" + "## FTA Script" ] }, { @@ -792,6 +699,17 @@ "# just_bus.to_parquet(f\"{gcs_path}clean_fta_bus_only.parquet\")" ] }, + { + "cell_type": "markdown", + "id": "0a60d451-7532-4053-b0de-3fc7c5a55792", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## TIRCP script" + ] + }, { "cell_type": "code", "execution_count": 15, @@ -934,9 +852,20 @@ ] }, { - "cell_type": "code", - "execution_count": 16, - "id": "359f3b7a-d691-446f-9a14-424c47fc0929", + "cell_type": "markdown", + "id": "0a57f455-8b86-47c4-9cda-2114cac504db", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## DGS Script" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "359f3b7a-d691-446f-9a14-424c47fc0929", "metadata": { "tags": [] }, @@ -1134,6 +1063,16 @@ "# bus_w_options.to_parquet(f'{gcs_path}clean_dgs_bus_only_w_options.parquet')" ] }, + { + "cell_type": "markdown", + "id": "24ab982a-afff-4c07-a19a-703ab82d27b1", + "metadata": { + "tags": [] + }, + "source": [ + "## cost_per_bus_cleaner / all_bus_cost_cleaner" + ] + }, { "cell_type": "code", "execution_count": 17, @@ -1227,6 +1166,7 @@ " \n", " #calculating zscore on cost per bus\n", " merge2[\"zscore_cost_per_bus\"] = zscore(merge2[\"cost_per_bus\"])\n", + " \n", " #flag any outliers\n", " merge2[\"is_cpb_outlier?\"] = merge2[\"zscore_cost_per_bus\"].apply(outlier_flag)\n", " return merge2\n", @@ -1242,7 +1182,9 @@ "# df2 = df1[df1[\"is_cpb_outlier?\"]==False]\n", " \n", " # export to gcs\n", + " #full data, with outliers\n", "# df1.to_parquet(f'{gcs_path}cleaned_cpb_analysis_data_merge.parquet')\n", + " # no outliers\n", "# df2.to_parquet(f'{gcs_path}cleaned_no_outliers_cpb_analysis_data_merge.parquet')" ] }, @@ -1262,7 +1204,7 @@ "id": "c8fc1d6c-85b5-4890-84f1-66e33eb9d97c", "metadata": {}, "source": [ - "## Variable Categories\n", + "## Notes Variable Categories\n", "- initial DF stuff (all cleaned merged data)\n", " - all_bus\n", " - all_projecT_counter function\n", @@ -1270,10 +1212,11 @@ "lots of total counts\n", "**this can be solved by filtering the same df but its different grant type, or using a table of groupby grant type and count of projects**\n", "\n", - " []count of all projects\n", + " COMPLETE count of all projects\n", "- ~~all_project_count~~\n", "- ~~total_bus_count~~\n", "- ~~total_funding~~\n", + "- determined that all projects count isnt needed, total bus and funding is caclulated in multiple pivot tables.\n", "\n", " count of all projects for each grant type\n", "- count_all_fta\n", @@ -1305,7 +1248,7 @@ " - for charts?\n", "\n", "- other things from the initial analysis to include/re-work?\n", - "\n" + " - \n" ] }, { @@ -1330,43 +1273,6 @@ "to make sure the core data matches, and expect the new function to provide zscores and outlier flags" ] }, - { - "cell_type": "code", - "execution_count": 18, - "id": "f84aaa3c-7bab-46c9-b739-021fdd6b60a2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(89, 11)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", - " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", - " 'ppno', 'project_description'],\n", - " dtype='object')" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# initial final df from old code\n", - "# 89 rows and 11 columns\n", - "display(\n", - " final.shape,\n", - " final.columns\n", - ")" - ] - }, { "cell_type": "code", "execution_count": 19, @@ -1381,32 +1287,10 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "id": "f8aa3674-78fe-4ba9-8f5e-697d91ff4011", "metadata": {}, - "outputs": [], - "source": [ - "# testing the improved cpb agg function\n", - "# default grouby column is `transit_agency`\n", - "\n", - "agg1 = new_cpb_aggregate(test)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "42c43416-981f-43d7-a642-6a22dc6619f2", - "metadata": {}, "outputs": [ - { - "data": { - "text/plain": [ - "(89, 11)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "text/plain": [ @@ -1416,18 +1300,6 @@ "metadata": {}, "output_type": "display_data" }, - { - "data": { - "text/plain": [ - "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", - " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", - " 'ppno', 'project_description'],\n", - " dtype='object')" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "text/plain": [ @@ -1442,19 +1314,22 @@ } ], "source": [ + "# testing the improved cpb agg function\n", + "# default grouby column is `transit_agency`\n", + "\n", + "agg1 = new_cpb_aggregate(test)\n", + "\n", "# there are some duplicate agencies in the inial DF, these get aggregated together after using the function\n", "# the resulting DF is shorter\n", "display(\n", - " final.shape,\n", " agg1.shape,\n", - " final.columns,\n", " agg1.columns\n", ")" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "id": "d2bca15a-c12c-4cf5-a5a9-d591ee73a359", "metadata": {}, "outputs": [ @@ -1491,36 +1366,36 @@ " \n", " \n", " \n", - " 81\n", - " White Earth Reservation Business Committee\n", - " 1\n", + " 27\n", + " Foothill Transit, West Covina, CA\n", " 0\n", - " 723171\n", - " 4.0\n", - " 180792\n", - " -1.505895\n", + " 1\n", + " 37642044\n", + " 33.0\n", + " 1140668\n", + " 0.890182\n", " False\n", " \n", " \n", - " 19\n", - " City of Visalia - Visalia City Coach(Visalia T...\n", - " 0\n", + " 24\n", + " Dallas Area Rapid Transit (DART)\n", " 1\n", - " 3687803\n", - " 4.0\n", - " 921950\n", - " 0.344210\n", + " 0\n", + " 103000000\n", + " 90.0\n", + " 1144444\n", + " 0.899608\n", " False\n", " \n", " \n", - " 48\n", - " Rogue Valley Transportation District\n", - " 1\n", + " 28\n", + " GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA)\n", " 0\n", - " 3937500\n", - " 6.0\n", - " 656250\n", - " -0.319040\n", + " 1\n", + " 5406355\n", + " 5.0\n", + " 1081271\n", + " 0.741913\n", " False\n", " \n", " \n", @@ -1528,23 +1403,23 @@ "" ], "text/plain": [ - " transit_agency total_project_count \\\n", - "81 White Earth Reservation Business Committee 1 \n", - "19 City of Visalia - Visalia City Coach(Visalia T... 0 \n", - "48 Rogue Valley Transportation District 1 \n", + " transit_agency total_project_count \\\n", + "27 Foothill Transit, West Covina, CA 0 \n", + "24 Dallas Area Rapid Transit (DART) 1 \n", + "28 GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA) 0 \n", "\n", " total_project_count_ppno total_agg_cost total_bus_count \\\n", - "81 0 723171 4.0 \n", - "19 1 3687803 4.0 \n", - "48 0 3937500 6.0 \n", + "27 1 37642044 33.0 \n", + "24 0 103000000 90.0 \n", + "28 1 5406355 5.0 \n", "\n", " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", - "81 180792 -1.505895 False \n", - "19 921950 0.344210 False \n", - "48 656250 -0.319040 False " + "27 1140668 0.890182 False \n", + "24 1144444 0.899608 False \n", + "28 1081271 0.741913 False " ] }, - "execution_count": 22, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1556,19 +1431,10 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "id": "637032e4-d855-4190-a6f5-ff695f77143f", "metadata": {}, "outputs": [ - { - "data": { - "text/plain": [ - "(89, 11)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "text/plain": [ @@ -1790,9 +1656,7 @@ "# agg looks good\n", "# double checked it against the old agg function, CPB numbers match between this new function and old one\n", "display(\n", - " final.shape,\n", - " agg1.shape,\n", - " agg1.head(),\n", + "\n", " agg1[\"new_is_cpb_outlier?\"].value_counts(),\n", " agg1[\"new_zscore_cost_per_bus\"].min(),\n", " agg1[\"new_zscore_cost_per_bus\"].max(),\n", @@ -1802,7 +1666,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "id": "ce1f4c86-e85d-41d8-83f6-14aadce48d5c", "metadata": {}, "outputs": [], @@ -1813,12 +1677,12 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 27, "id": "59298193-fc78-4ffb-bfc3-326593c19edb", "metadata": {}, "outputs": [], "source": [ - "# need to compare to the agg prob data in the report\n", + "# need to compare to the agg prop data in the report\n", "\n", "from cost_per_bus_nb_scripts import cpb_aggregate, no_outliers\n", "\n", @@ -1827,7 +1691,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 29, "id": "01883fc0-4f6d-4e6a-a88f-97a5914b281b", "metadata": {}, "outputs": [ @@ -1924,79 +1788,24 @@ " 9.0\n", " 111861\n", " \n", - " \n", - " 5\n", - " low emission (hybrid)\n", - " 16\n", - " 0\n", - " 91824361\n", - " 145.0\n", - " 633271\n", - " \n", - " \n", - " 6\n", - " low emission (propane)\n", - " 5\n", - " 0\n", - " 8403969\n", - " 44.0\n", - " 190999\n", - " \n", - " \n", - " 7\n", - " mix (zero and low emission)\n", - " 2\n", - " 0\n", - " 36775430\n", - " 125.0\n", - " 294203\n", - " \n", - " \n", - " 8\n", - " not specified\n", - " 4\n", - " 1\n", - " 41552404\n", - " 325.0\n", - " 127853\n", - " \n", - " \n", - " 9\n", - " zero-emission bus (not specified)\n", - " 0\n", - " 5\n", - " 128156513\n", - " 143.0\n", - " 896199\n", - " \n", " \n", "\n", "" ], "text/plain": [ - " prop_type total_project_count \\\n", - "0 BEB 0 \n", - "1 CNG 12 \n", - "2 FCEB 2 \n", - "3 electric (not specified) 1 \n", - "4 ethanol 1 \n", - "5 low emission (hybrid) 16 \n", - "6 low emission (propane) 5 \n", - "7 mix (zero and low emission) 2 \n", - "8 not specified 4 \n", - "9 zero-emission bus (not specified) 0 \n", + " prop_type total_project_count total_project_count_ppno \\\n", + "0 BEB 0 30 \n", + "1 CNG 12 1 \n", + "2 FCEB 2 6 \n", + "3 electric (not specified) 1 2 \n", + "4 ethanol 1 0 \n", "\n", - " total_project_count_ppno total_agg_cost total_bus_count cpb \n", - "0 30 167232489 163.0 1025966 \n", - "1 1 176039140 252.0 698568 \n", - "2 6 120951335 102.0 1185797 \n", - "3 2 56678000 44.0 1288136 \n", - "4 0 1006750 9.0 111861 \n", - "5 0 91824361 145.0 633271 \n", - "6 0 8403969 44.0 190999 \n", - "7 0 36775430 125.0 294203 \n", - "8 1 41552404 325.0 127853 \n", - "9 5 128156513 143.0 896199 " + " total_agg_cost total_bus_count cpb \n", + "0 167232489 163.0 1025966 \n", + "1 176039140 252.0 698568 \n", + "2 120951335 102.0 1185797 \n", + "3 56678000 44.0 1288136 \n", + "4 1006750 9.0 111861 " ] }, "metadata": {}, @@ -2089,101 +1898,31 @@ " -1.257470\n", " False\n", " \n", - " \n", - " 5\n", - " low emission (hybrid)\n", - " 16\n", - " 0\n", - " 91824361\n", - " 145.0\n", - " 633271\n", - " -0.031401\n", - " False\n", - " \n", - " \n", - " 6\n", - " low emission (propane)\n", - " 5\n", - " 0\n", - " 8403969\n", - " 44.0\n", - " 190999\n", - " -1.071381\n", - " False\n", - " \n", - " \n", - " 7\n", - " mix (zero and low emission)\n", - " 2\n", - " 0\n", - " 36775430\n", - " 125.0\n", - " 294203\n", - " -0.828702\n", - " False\n", - " \n", - " \n", - " 8\n", - " not specified\n", - " 4\n", - " 1\n", - " 41552404\n", - " 325.0\n", - " 127853\n", - " -1.219866\n", - " False\n", - " \n", - " \n", - " 9\n", - " zero-emission bus (not specified)\n", - " 0\n", - " 5\n", - " 128156513\n", - " 143.0\n", - " 896199\n", - " 0.586860\n", - " False\n", - " \n", " \n", "\n", "" ], "text/plain": [ - " prop_type total_project_count \\\n", - "0 BEB 0 \n", - "1 CNG 12 \n", - "2 FCEB 2 \n", - "3 electric (not specified) 1 \n", - "4 ethanol 1 \n", - "5 low emission (hybrid) 16 \n", - "6 low emission (propane) 5 \n", - "7 mix (zero and low emission) 2 \n", - "8 not specified 4 \n", - "9 zero-emission bus (not specified) 0 \n", + " prop_type total_project_count total_project_count_ppno \\\n", + "0 BEB 0 31 \n", + "1 CNG 12 1 \n", + "2 FCEB 2 6 \n", + "3 electric (not specified) 1 2 \n", + "4 ethanol 1 0 \n", "\n", - " total_project_count_ppno total_agg_cost total_bus_count \\\n", - "0 31 170455813 164.0 \n", - "1 1 176039140 252.0 \n", - "2 6 120951335 102.0 \n", - "3 2 56678000 44.0 \n", - "4 0 1006750 9.0 \n", - "5 0 91824361 145.0 \n", - "6 0 8403969 44.0 \n", - "7 0 36775430 125.0 \n", - "8 1 41552404 325.0 \n", - "9 5 128156513 143.0 \n", + " total_agg_cost total_bus_count new_cost_per_bus new_zscore_cost_per_bus \\\n", + "0 170455813 164.0 1039364 0.923505 \n", + "1 176039140 252.0 698568 0.122141 \n", + "2 120951335 102.0 1185797 1.267835 \n", + "3 56678000 44.0 1288136 1.508480 \n", + "4 1006750 9.0 111861 -1.257470 \n", "\n", - " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", - "0 1039364 0.923505 False \n", - "1 698568 0.122141 False \n", - "2 1185797 1.267835 False \n", - "3 1288136 1.508480 False \n", - "4 111861 -1.257470 False \n", - "5 633271 -0.031401 False \n", - "6 190999 -1.071381 False \n", - "7 294203 -0.828702 False \n", - "8 127853 -1.219866 False \n", - "9 896199 0.586860 False " + " new_is_cpb_outlier? \n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False " ] }, "metadata": {}, @@ -2195,14 +1934,14 @@ "display(\n", " old_prop_agg.shape,\n", " agg_prop_type.shape,\n", - " old_prop_agg,\n", - " agg_prop_type\n", + " old_prop_agg.head(),\n", + " agg_prop_type.head()\n", ")" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 30, "id": "52204752-3932-4ce6-98ac-de8ad3a1f8e8", "metadata": {}, "outputs": [ @@ -2441,6 +2180,7 @@ } ], "source": [ + "# double checking the bus size agg new vs old\n", "#EVERYTHING CHECKS OUT!\n", "new_agg_bus_size = new_cpb_aggregate(test, \"bus_size_type\")\n", "old_size_agg = cpb_aggregate(no_outliers, \"bus_size_type\")\n", @@ -2454,7 +2194,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 32, "id": "0391dd4d-23e1-49cb-8123-509954c796e8", "metadata": {}, "outputs": [ @@ -2551,2609 +2291,29 @@ " 4.0\n", " 905884\n", " \n", - " \n", - " 5\n", - " Cape Fear Public Transportation Authority\n", - " 1\n", - " 0\n", - " 2860250\n", - " 5.0\n", - " 572050\n", - " \n", - " \n", - " 6\n", - " Central Oklahoma Transportation and Parking Au...\n", - " 1\n", - " 0\n", - " 4278772\n", - " 9.0\n", - " 475419\n", - " \n", - " \n", - " 7\n", - " Champaign-Urbana Mass Transit District\n", - " 1\n", - " 0\n", - " 6635394\n", - " 10.0\n", - " 663539\n", - " \n", - " \n", - " 8\n", - " City of Beaumont\n", - " 1\n", - " 0\n", - " 2819460\n", - " 5.0\n", - " 563892\n", - " \n", - " \n", - " 9\n", - " City of Beloit\n", - " 1\n", - " 0\n", - " 653184\n", - " 1.0\n", - " 653184\n", - " \n", - " \n", - " 10\n", - " City of Brownsville\n", - " 1\n", - " 0\n", - " 4738886\n", - " 6.0\n", - " 789814\n", - " \n", - " \n", - " 11\n", - " City of Colorado Springs dba Mountain Metropol...\n", - " 1\n", - " 0\n", - " 3199038\n", - " 6.0\n", - " 533173\n", - " \n", - " \n", - " 12\n", - " City of Jonesboro, Arkansas\n", - " 1\n", - " 0\n", - " 1010372\n", - " 5.0\n", - " 202074\n", - " \n", - " \n", - " 13\n", - " City of Los Angeles (LA DOT)\n", - " 0\n", - " 1\n", - " 102790000\n", - " 112.0\n", - " 917767\n", - " \n", - " \n", - " 14\n", - " City of Norman, Oklahoma\n", - " 1\n", - " 0\n", - " 776714\n", - " 6.0\n", - " 129452\n", - " \n", - " \n", - " 15\n", - " City of Roseville\n", - " 0\n", - " 2\n", - " 9651507\n", - " 10.0\n", - " 965150\n", - " \n", - " \n", - " 16\n", - " City of San Luis Obispo\n", - " 0\n", - " 1\n", - " 859270\n", - " 1.0\n", - " 859270\n", - " \n", - " \n", - " 17\n", - " City of Santa Rosa(Santa Rosa CityBus)\n", - " 0\n", - " 1\n", - " 5987790\n", - " 5.0\n", - " 1197558\n", - " \n", - " \n", - " 18\n", - " City of Tucson, Sun Tran\n", - " 1\n", - " 0\n", - " 21490560\n", - " 39.0\n", - " 551040\n", - " \n", - " \n", - " 19\n", - " City of Visalia - Visalia City Coach(Visalia T...\n", - " 0\n", - " 1\n", - " 3687803\n", - " 4.0\n", - " 921950\n", - " \n", - " \n", - " 20\n", - " City of Wasco\n", - " 0\n", - " 1\n", - " 1543000\n", - " 3.0\n", - " 514333\n", - " \n", - " \n", - " 21\n", - " Coast Transit Authority dba MS Coast Transport...\n", - " 1\n", - " 0\n", - " 1760000\n", - " 9.0\n", - " 195555\n", - " \n", - " \n", - " 22\n", - " Conroe Connection Transit\n", - " 1\n", - " 0\n", - " 4500000\n", - " 4.0\n", - " 1125000\n", - " \n", - " \n", - " 23\n", - " Culver City\n", - " 0\n", - " 1\n", - " 3547000\n", - " 5.0\n", - " 709400\n", - " \n", - " \n", - " 24\n", - " Dallas Area Rapid Transit (DART)\n", - " 1\n", - " 0\n", - " 103000000\n", - " 90.0\n", - " 1144444\n", - " \n", - " \n", - " 25\n", - " Delaware Transit Corporation (DTC)\n", - " 1\n", - " 0\n", - " 8740728\n", - " 6.0\n", - " 1456788\n", - " \n", - " \n", - " 26\n", - " Foothill Transit\n", - " 0\n", - " 1\n", - " 16580000\n", - " 20.0\n", - " 829000\n", - " \n", - " \n", - " 27\n", - " Foothill Transit, West Covina, CA\n", - " 0\n", - " 1\n", - " 37642044\n", - " 33.0\n", - " 1140668\n", - " \n", - " \n", - " 28\n", - " GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA)\n", - " 0\n", - " 1\n", - " 5406355\n", - " 5.0\n", - " 1081271\n", - " \n", - " \n", - " 29\n", - " Golden Empire Transit\n", - " 1\n", - " 1\n", - " 11208656\n", - " 15.0\n", - " 747243\n", - " \n", - " \n", - " 30\n", - " Illinois Department of Transportation on behal...\n", - " 1\n", - " 0\n", - " 12600000\n", - " 134.0\n", - " 94029\n", - " \n", - " \n", - " 31\n", - " Indianapolis Public Transportation Corporation...\n", - " 1\n", - " 0\n", - " 19040336\n", - " 20.0\n", - " 952016\n", - " \n", - " \n", - " 32\n", - " Interurban Transit Partnership\n", - " 1\n", - " 0\n", - " 6197180\n", - " 11.0\n", - " 563380\n", - " \n", - " \n", - " 33\n", - " Lane Transit (Oregon)\n", - " 0\n", - " 2\n", - " 27894999\n", - " 30.0\n", - " 929833\n", - " \n", - " \n", - " 34\n", - " Lowell Regional Transit Authority\n", - " 1\n", - " 0\n", - " 6859296\n", - " 7.0\n", - " 979899\n", - " \n", - " \n", - " 35\n", - " Madison County Mass Transit District\n", - " 1\n", - " 0\n", - " 1080000\n", - " 2.0\n", - " 540000\n", - " \n", - " \n", - " 36\n", - " Mesa County\n", - " 1\n", - " 0\n", - " 1162000\n", - " 3.0\n", - " 387333\n", - " \n", - " \n", - " 37\n", - " Minnesota Department of Transportation on beha...\n", - " 1\n", - " 0\n", - " 1456970\n", - " 7.0\n", - " 208138\n", - " \n", - " \n", - " 38\n", - " Napa Valley Transportation Authority\n", - " 0\n", - " 1\n", - " 2396600\n", - " 2.0\n", - " 1198300\n", - " \n", - " \n", - " 39\n", - " New Mexico Department of Transportation on beh...\n", - " 1\n", - " 0\n", - " 2063160\n", - " 3.0\n", - " 687720\n", - " \n", - " \n", - " 40\n", - " North County Transit District\n", - " 0\n", - " 1\n", - " 5787606\n", - " 6.0\n", - " 964601\n", - " \n", - " \n", - " 41\n", - " North County Transit District (NCTD)\n", - " 1\n", - " 0\n", - " 29330243\n", - " 23.0\n", - " 1275227\n", - " \n", - " \n", - " 42\n", - " ORANGE COUNTY TRANSPORTATION AUTHORITY (ORANGE...\n", - " 0\n", - " 1\n", - " 9319520\n", - " 10.0\n", - " 931952\n", - " \n", - " \n", - " 43\n", - " Ohio Department of Transportation (ODOT) on be...\n", - " 1\n", - " 0\n", - " 29331665\n", - " 69.0\n", - " 425096\n", - " \n", - " \n", - " 44\n", - " Orange County Transportation Authority (OCTA)\n", - " 0\n", - " 1\n", - " 2900000\n", - " 40.0\n", - " 72500\n", - " \n", - " \n", - " 45\n", - " Oregon Department of Transportation on behalf ...\n", - " 1\n", - " 0\n", - " 181250\n", - " 5.0\n", - " 36250\n", - " \n", - " \n", - " 46\n", - " Rhode Island Public Transit Authority\n", - " 1\n", - " 0\n", - " 5000000\n", - " 25.0\n", - " 200000\n", - " \n", - " \n", - " 47\n", - " Rockford Mass Transit District\n", - " 1\n", - " 0\n", - " 4094652\n", - " 4.0\n", - " 1023663\n", - " \n", - " \n", - " 48\n", - " Rogue Valley Transportation District\n", - " 1\n", - " 0\n", - " 3937500\n", - " 6.0\n", - " 656250\n", - " \n", - " \n", - " 49\n", - " SLO TRANSIT (SAN LUIS OBISPO, CA)\n", - " 0\n", - " 1\n", - " 847214\n", - " 1.0\n", - " 847214\n", - " \n", - " \n", - " 50\n", - " SUNLINE TRANSIT AGENCY (THOUSAND PALMS)\n", - " 0\n", - " 1\n", - " 5755155\n", - " 5.0\n", - " 1151031\n", - " \n", - " \n", - " 51\n", - " SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA)\n", - " 0\n", - " 1\n", - " 5771865\n", - " 5.0\n", - " 1154373\n", - " \n", - " \n", - " 52\n", - " Sacramento County Airport System\n", - " 0\n", - " 1\n", - " 4642225\n", - " 5.0\n", - " 928445\n", - " \n", - " \n", - " 53\n", - " San Antonio Metropolitan Transit Authority\n", - " 1\n", - " 0\n", - " 3187200\n", - " 15.0\n", - " 212480\n", - " \n", - " \n", - " 54\n", - " San Diego Metro\n", - " 0\n", - " 1\n", - " 18759576\n", - " 12.0\n", - " 1563298\n", - " \n", - " \n", - " 55\n", - " Santa Barbara Metro\n", - " 0\n", - " 1\n", - " 3659072\n", - " 4.0\n", - " 914768\n", - " \n", - " \n", - " 56\n", - " Santa Maria Area Transit\n", - " 0\n", - " 1\n", - " 1862258\n", - " 2.0\n", - " 931129\n", - " \n", - " \n", - " 57\n", - " Santa Maria Regional Transit\n", - " 0\n", - " 1\n", - " 5188379\n", - " 5.0\n", - " 1037675\n", - " \n", - " \n", - " 58\n", - " Santa Rosa City Bus\n", - " 0\n", - " 1\n", - " 4068202\n", - " 4.0\n", - " 1017050\n", - " \n", - " \n", - " 59\n", - " Shasta Regional Transportation Agency (SRTA)\n", - " 0\n", - " 1\n", - " 9516000\n", - " 14.0\n", - " 679714\n", - " \n", - " \n", - " 60\n", - " Sonoma County Transit\n", - " 0\n", - " 1\n", - " 8990000\n", - " 10.0\n", - " 899000\n", - " \n", - " \n", - " 61\n", - " South Carolina Department of Transportation on...\n", - " 1\n", - " 0\n", - " 15423904\n", - " 160.0\n", - " 96399\n", - " \n", - " \n", - " 62\n", - " South Dakota Department of Transportation on b...\n", - " 1\n", - " 0\n", - " 1006750\n", - " 9.0\n", - " 111861\n", - " \n", - " \n", - " 63\n", - " South Dakota Department of Transportation on b...\n", - " 1\n", - " 0\n", - " 1276628\n", - " 9.0\n", - " 141847\n", - " \n", - " \n", - " 64\n", - " Southeastern Regional Transit Authority\n", - " 1\n", - " 0\n", - " 11560000\n", - " 16.0\n", - " 722500\n", - " \n", - " \n", - " 65\n", - " Southwest Ohio Regional Transit Authority\n", - " 1\n", - " 0\n", - " 9806428\n", - " 16.0\n", - " 612901\n", - " \n", - " \n", - " 66\n", - " State of California on behalf of Kern Regional...\n", - " 2\n", - " 0\n", - " 6181000\n", - " 20.0\n", - " 309050\n", - " \n", - " \n", - " 67\n", - " Tahoe Transportation District\n", - " 1\n", - " 0\n", - " 3400000\n", - " 4.0\n", - " 850000\n", - " \n", - " \n", - " 68\n", - " Texas Department of Transportation on behalf o...\n", - " 1\n", - " 0\n", - " 7443765\n", - " 56.0\n", - " 132924\n", - " \n", - " \n", - " 69\n", - " The Bus, City of Merced\n", - " 0\n", - " 1\n", - " 4786285\n", - " 5.0\n", - " 957257\n", - " \n", - " \n", - " 70\n", - " Torrance Transit Department\n", - " 0\n", - " 1\n", - " 7200000\n", - " 7.0\n", - " 1028571\n", - " \n", - " \n", - " 71\n", - " Transit Joint Powers Authority for Merced County\n", - " 0\n", - " 1\n", - " 3223324\n", - " 2.0\n", - " 1611662\n", - " \n", - " \n", - " 72\n", - " Transit Joint Powers Authority of Merced County\n", - " 0\n", - " 1\n", - " 3696513\n", - " 3.0\n", - " 1232171\n", - " \n", - " \n", - " 73\n", - " UC DAVIS (UNITRANS) (DAVIS, CA)\n", - " 0\n", - " 2\n", - " 9321926\n", - " 10.0\n", - " 932192\n", - " \n", - " \n", - " 74\n", - " UCLA FLEET & TRANSIT\n", - " 0\n", - " 1\n", - " 2008826\n", - " 2.0\n", - " 1004413\n", - " \n", - " \n", - " 75\n", - " University of California - San Diego\n", - " 0\n", - " 2\n", - " 8268000\n", - " 6.0\n", - " 1378000\n", - " \n", - " \n", - " 76\n", - " University of California, Irvine\n", - " 0\n", - " 1\n", - " 4932930\n", - " 5.0\n", - " 986586\n", - " \n", - " \n", - " 77\n", - " Utah Transit Authority\n", - " 1\n", - " 0\n", - " 17055353\n", - " 25.0\n", - " 682214\n", - " \n", - " \n", - " 78\n", - " VACAVILLE PUBLIC TRANSPORTATION (CITY COACH) (...\n", - " 0\n", - " 1\n", - " 10175590\n", - " 10.0\n", - " 1017559\n", - " \n", - " \n", - " 79\n", - " VICTOR VALLEY TRANSIT AUTHORITY (VVTA)\n", - " 0\n", - " 1\n", - " 4508160\n", - " 5.0\n", - " 901632\n", - " \n", - " \n", - " 80\n", - " Whatcom Transportation Authority (WTA)\n", - " 1\n", - " 0\n", - " 9644865\n", - " 11.0\n", - " 876805\n", - " \n", - " \n", - " 81\n", - " White Earth Reservation Business Committee\n", - " 1\n", - " 0\n", - " 723171\n", - " 4.0\n", - " 180792\n", - " \n", - " \n", - "\n", - "" - ], - "text/plain": [ - " transit_agency total_project_count \\\n", - "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) 1 \n", - "1 Alameda County Transit Authority 0 \n", - "2 Antelope Valley Transit Authority (AVTA) 0 \n", - "3 CITY OF PORTERVILLE (PORTERVILLE, CA) 0 \n", - "4 CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ... 0 \n", - "5 Cape Fear Public Transportation Authority 1 \n", - "6 Central Oklahoma Transportation and Parking Au... 1 \n", - "7 Champaign-Urbana Mass Transit District 1 \n", - "8 City of Beaumont 1 \n", - "9 City of Beloit 1 \n", - "10 City of Brownsville 1 \n", - "11 City of Colorado Springs dba Mountain Metropol... 1 \n", - "12 City of Jonesboro, Arkansas 1 \n", - "13 City of Los Angeles (LA DOT) 0 \n", - "14 City of Norman, Oklahoma 1 \n", - "15 City of Roseville 0 \n", - "16 City of San Luis Obispo 0 \n", - "17 City of Santa Rosa(Santa Rosa CityBus) 0 \n", - "18 City of Tucson, Sun Tran 1 \n", - "19 City of Visalia - Visalia City Coach(Visalia T... 0 \n", - "20 City of Wasco 0 \n", - "21 Coast Transit Authority dba MS Coast Transport... 1 \n", - "22 Conroe Connection Transit 1 \n", - "23 Culver City 0 \n", - "24 Dallas Area Rapid Transit (DART) 1 \n", - "25 Delaware Transit Corporation (DTC) 1 \n", - "26 Foothill Transit 0 \n", - "27 Foothill Transit, West Covina, CA 0 \n", - "28 GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA) 0 \n", - "29 Golden Empire Transit 1 \n", - "30 Illinois Department of Transportation on behal... 1 \n", - "31 Indianapolis Public Transportation Corporation... 1 \n", - "32 Interurban Transit Partnership 1 \n", - "33 Lane Transit (Oregon) 0 \n", - "34 Lowell Regional Transit Authority 1 \n", - "35 Madison County Mass Transit District 1 \n", - "36 Mesa County 1 \n", - "37 Minnesota Department of Transportation on beha... 1 \n", - "38 Napa Valley Transportation Authority 0 \n", - "39 New Mexico Department of Transportation on beh... 1 \n", - "40 North County Transit District 0 \n", - "41 North County Transit District (NCTD) 1 \n", - "42 ORANGE COUNTY TRANSPORTATION AUTHORITY (ORANGE... 0 \n", - "43 Ohio Department of Transportation (ODOT) on be... 1 \n", - "44 Orange County Transportation Authority (OCTA) 0 \n", - "45 Oregon Department of Transportation on behalf ... 1 \n", - "46 Rhode Island Public Transit Authority 1 \n", - "47 Rockford Mass Transit District 1 \n", - "48 Rogue Valley Transportation District 1 \n", - "49 SLO TRANSIT (SAN LUIS OBISPO, CA) 0 \n", - "50 SUNLINE TRANSIT AGENCY (THOUSAND PALMS) 0 \n", - "51 SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA) 0 \n", - "52 Sacramento County Airport System 0 \n", - "53 San Antonio Metropolitan Transit Authority 1 \n", - "54 San Diego Metro 0 \n", - "55 Santa Barbara Metro 0 \n", - "56 Santa Maria Area Transit 0 \n", - "57 Santa Maria Regional Transit 0 \n", - "58 Santa Rosa City Bus 0 \n", - "59 Shasta Regional Transportation Agency (SRTA) 0 \n", - "60 Sonoma County Transit 0 \n", - "61 South Carolina Department of Transportation on... 1 \n", - "62 South Dakota Department of Transportation on b... 1 \n", - "63 South Dakota Department of Transportation on b... 1 \n", - "64 Southeastern Regional Transit Authority 1 \n", - "65 Southwest Ohio Regional Transit Authority 1 \n", - "66 State of California on behalf of Kern Regional... 2 \n", - "67 Tahoe Transportation District 1 \n", - "68 Texas Department of Transportation on behalf o... 1 \n", - "69 The Bus, City of Merced 0 \n", - "70 Torrance Transit Department 0 \n", - "71 Transit Joint Powers Authority for Merced County 0 \n", - "72 Transit Joint Powers Authority of Merced County 0 \n", - "73 UC DAVIS (UNITRANS) (DAVIS, CA) 0 \n", - "74 UCLA FLEET & TRANSIT 0 \n", - "75 University of California - San Diego 0 \n", - "76 University of California, Irvine 0 \n", - "77 Utah Transit Authority 1 \n", - "78 VACAVILLE PUBLIC TRANSPORTATION (CITY COACH) (... 0 \n", - "79 VICTOR VALLEY TRANSIT AUTHORITY (VVTA) 0 \n", - "80 Whatcom Transportation Authority (WTA) 1 \n", - "81 White Earth Reservation Business Committee 1 \n", - "\n", - " total_project_count_ppno total_agg_cost total_bus_count cpb \n", - "0 0 10000000 8.0 1250000 \n", - "1 1 22846640 20.0 1142332 \n", - "2 1 39478000 29.0 1361310 \n", - "3 1 2781891 3.0 927297 \n", - "4 1 3623536 4.0 905884 \n", - "5 0 2860250 5.0 572050 \n", - "6 0 4278772 9.0 475419 \n", - "7 0 6635394 10.0 663539 \n", - "8 0 2819460 5.0 563892 \n", - "9 0 653184 1.0 653184 \n", - "10 0 4738886 6.0 789814 \n", - "11 0 3199038 6.0 533173 \n", - "12 0 1010372 5.0 202074 \n", - "13 1 102790000 112.0 917767 \n", - "14 0 776714 6.0 129452 \n", - "15 2 9651507 10.0 965150 \n", - "16 1 859270 1.0 859270 \n", - "17 1 5987790 5.0 1197558 \n", - "18 0 21490560 39.0 551040 \n", - "19 1 3687803 4.0 921950 \n", - "20 1 1543000 3.0 514333 \n", - "21 0 1760000 9.0 195555 \n", - "22 0 4500000 4.0 1125000 \n", - "23 1 3547000 5.0 709400 \n", - "24 0 103000000 90.0 1144444 \n", - "25 0 8740728 6.0 1456788 \n", - "26 1 16580000 20.0 829000 \n", - "27 1 37642044 33.0 1140668 \n", - "28 1 5406355 5.0 1081271 \n", - "29 1 11208656 15.0 747243 \n", - "30 0 12600000 134.0 94029 \n", - "31 0 19040336 20.0 952016 \n", - "32 0 6197180 11.0 563380 \n", - "33 2 27894999 30.0 929833 \n", - "34 0 6859296 7.0 979899 \n", - "35 0 1080000 2.0 540000 \n", - "36 0 1162000 3.0 387333 \n", - "37 0 1456970 7.0 208138 \n", - "38 1 2396600 2.0 1198300 \n", - "39 0 2063160 3.0 687720 \n", - "40 1 5787606 6.0 964601 \n", - "41 0 29330243 23.0 1275227 \n", - "42 1 9319520 10.0 931952 \n", - "43 0 29331665 69.0 425096 \n", - "44 1 2900000 40.0 72500 \n", - "45 0 181250 5.0 36250 \n", - "46 0 5000000 25.0 200000 \n", - "47 0 4094652 4.0 1023663 \n", - "48 0 3937500 6.0 656250 \n", - "49 1 847214 1.0 847214 \n", - "50 1 5755155 5.0 1151031 \n", - "51 1 5771865 5.0 1154373 \n", - "52 1 4642225 5.0 928445 \n", - "53 0 3187200 15.0 212480 \n", - "54 1 18759576 12.0 1563298 \n", - "55 1 3659072 4.0 914768 \n", - "56 1 1862258 2.0 931129 \n", - "57 1 5188379 5.0 1037675 \n", - "58 1 4068202 4.0 1017050 \n", - "59 1 9516000 14.0 679714 \n", - 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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)10100000008.012500001.163100False
1Alameda County Transit Authority012284664020.011423320.894336False
2Antelope Valley Transit Authority (AVTA)013947800029.013613101.440957False
3CITY OF PORTERVILLE (PORTERVILLE, CA)0127818913.09272970.357558False
4CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...0136235364.09058840.304106False
5Cape Fear Public Transportation Authority1028602505.0572050-0.529223False
6Central Oklahoma Transportation and Parking Au...1042787729.0475419-0.770436False
7Champaign-Urbana Mass Transit District10663539410.0663539-0.300844False
8City of Beaumont1028194605.0563892-0.549587False
9City of Beloit106531841.0653184-0.326693False
10City of Brownsville1047388866.07898140.014368False
11City of Colorado Springs dba Mountain Metropol...1031990386.0533173-0.626269False
12City of Jonesboro, Arkansas1010103725.0202074-1.452770False
13City of Los Angeles (LA DOT)01102790000112.09177670.333769False
14City of Norman, Oklahoma107767146.0129452-1.634052False
15City of Roseville02965150710.09651500.452048False
16City of San Luis Obispo018592701.08592700.187746False
17City of Santa Rosa(Santa Rosa CityBus)0159877905.011975581.032193False
18City of Tucson, Sun Tran102149056039.0551040-0.581669False
19City of Visalia - Visalia City Coach(Visalia T...0136878034.09219500.344210False
20City of Wasco0115430003.0514333-0.673298False
21Coast Transit Authority dba MS Coast Transport...1017600009.0195555-1.469043False
22Conroe Connection Transit1045000004.011250000.851071False
23Culver City0135470005.0709400-0.186365False
24Dallas Area Rapid Transit (DART)1010300000090.011444440.899608False
25Delaware Transit Corporation (DTC)1087407286.014567881.679292False
26Foothill Transit011658000020.08290000.112185False
27Foothill Transit, West Covina, CA013764204433.011406680.890182False
28GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA)0154063555.010812710.741913False
29Golden Empire Transit111120865615.0747243-0.091900False
30Illinois Department of Transportation on behal...1012600000134.094029-1.722476False
31Indianapolis Public Transportation Corporation...101904033620.09520160.419262False
32Interurban Transit Partnership10619718011.0563380-0.550865False
33Lane Transit (Oregon)022789499930.09298330.363888False
34Lowell Regional Transit Authority1068592967.09798990.488865False
35Madison County Mass Transit District1010800002.0540000-0.609227False
36Mesa County1011620003.0387333-0.990320False
37Minnesota Department of Transportation on beha...1014569707.0208138-1.437633False
38Napa Valley Transportation Authority0123966002.011983001.034045False
39New Mexico Department of Transportation on beh...1020631603.0687720-0.240483False
40North County Transit District0157876066.09646010.450677False
41North County Transit District (NCTD)102933024323.012752271.226073False
42ORANGE COUNTY TRANSPORTATION AUTHORITY (ORANGE...01931952010.09319520.369178False
43Ohio Department of Transportation (ODOT) on be...102933166569.0425096-0.896054False
44Orange County Transportation Authority (OCTA)01290000040.072500-1.776217False
45Oregon Department of Transportation on behalf ...101812505.036250-1.866706False
46Rhode Island Public Transit Authority10500000025.0200000-1.457947False
47Rockford Mass Transit District1040946524.010236630.598110False
48Rogue Valley Transportation District1039375006.0656250-0.319040False
49SLO TRANSIT (SAN LUIS OBISPO, CA)018472141.08472140.157652False
50SUNLINE TRANSIT AGENCY (THOUSAND PALMS)0157551555.011510310.916051False
51SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA)0157718655.011543730.924393False
52Sacramento County Airport System0146422255.09284450.360423False
53San Antonio Metropolitan Transit Authority10318720015.0212480-1.426794False
54San Diego Metro011875957612.015632981.945166False
55Santa Barbara Metro0136590724.09147680.326282False
56Santa Maria Area Transit0118622582.09311290.367123False
57Santa Maria Regional Transit0151883795.010376750.633087False
58Santa Rosa City Bus0140682024.010170500.581602False
59Shasta Regional Transportation Agency (SRTA)01951600014.0679714-0.260468False
60Sonoma County Transit01899000010.08990000.286922False
61South Carolina Department of Transportation on...1015423904160.096399-1.716560False
62South Dakota Department of Transportation on b...1010067509.0111861-1.677963False
63South Dakota Department of Transportation on b...1012766289.0141847-1.603111False
64Southeastern Regional Transit Authority101156000016.0722500-0.153664False
65Southwest Ohio Regional Transit Authority10980642816.0612901-0.427249False
66State of California on behalf of Kern Regional...20618100020.0309050-1.185733False
67Tahoe Transportation District1034000004.08500000.164606False
68Texas Department of Transportation on behalf o...10744376556.0132924-1.625385False
69The Bus, City of Merced0147862855.09572570.432345False
70Torrance Transit Department0172000007.010285710.610361False
71Transit Joint Powers Authority for Merced County0264466483.021488823.406922True
72Transit Joint Powers Authority of Merced County0136965133.012321711.118595False
73UC DAVIS (UNITRANS) (DAVIS, CA)02932192610.09321920.369777False
74UCLA FLEET & TRANSIT0120088262.010044130.550057False
75University of California - San Diego0282680006.013780001.482619False
76University of California, Irvine0149329305.09865860.505557False
77Utah Transit Authority101705535325.0682214-0.254227False
78VACAVILLE PUBLIC TRANSPORTATION (CITY COACH) (...011017559010.010175590.582873False
79VICTOR VALLEY TRANSIT AUTHORITY (VVTA)0145081605.09016320.293492False
80Whatcom Transportation Authority (WTA)10964486511.08768050.231518False
81White Earth Reservation Business Committee107231714.0180792-1.505895False
\n", - "
" - ], - "text/plain": [ - " transit_agency total_project_count \\\n", - "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) 1 \n", - "1 Alameda County Transit Authority 0 \n", - "2 Antelope Valley Transit Authority (AVTA) 0 \n", - "3 CITY OF PORTERVILLE (PORTERVILLE, CA) 0 \n", - "4 CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ... 0 \n", - "5 Cape Fear Public Transportation Authority 1 \n", - "6 Central Oklahoma Transportation and Parking Au... 1 \n", - "7 Champaign-Urbana Mass Transit District 1 \n", - "8 City of Beaumont 1 \n", - "9 City of Beloit 1 \n", - "10 City of Brownsville 1 \n", - "11 City of Colorado Springs dba Mountain Metropol... 1 \n", - "12 City of Jonesboro, Arkansas 1 \n", - "13 City of Los Angeles (LA DOT) 0 \n", - "14 City of Norman, Oklahoma 1 \n", - "15 City of Roseville 0 \n", - "16 City of San Luis Obispo 0 \n", - "17 City of Santa Rosa(Santa Rosa CityBus) 0 \n", - "18 City of Tucson, Sun Tran 1 \n", - "19 City of Visalia - Visalia City Coach(Visalia T... 0 \n", - "20 City of Wasco 0 \n", - "21 Coast Transit Authority dba MS Coast Transport... 1 \n", - "22 Conroe Connection Transit 1 \n", - "23 Culver City 0 \n", - "24 Dallas Area Rapid Transit (DART) 1 \n", - "25 Delaware Transit Corporation (DTC) 1 \n", - "26 Foothill Transit 0 \n", - "27 Foothill Transit, West Covina, CA 0 \n", - "28 GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA) 0 \n", - "29 Golden Empire Transit 1 \n", - "30 Illinois Department of Transportation on behal... 1 \n", - "31 Indianapolis Public Transportation Corporation... 1 \n", - "32 Interurban Transit Partnership 1 \n", - "33 Lane Transit (Oregon) 0 \n", - "34 Lowell Regional Transit Authority 1 \n", - "35 Madison County Mass Transit District 1 \n", - "36 Mesa County 1 \n", - "37 Minnesota Department of Transportation on beha... 1 \n", - "38 Napa Valley Transportation Authority 0 \n", - "39 New Mexico Department of Transportation on beh... 1 \n", - "40 North County Transit District 0 \n", - "41 North County Transit District (NCTD) 1 \n", - "42 ORANGE COUNTY TRANSPORTATION AUTHORITY (ORANGE... 0 \n", - "43 Ohio Department of Transportation (ODOT) on be... 1 \n", - "44 Orange County Transportation Authority (OCTA) 0 \n", - "45 Oregon Department of Transportation on behalf ... 1 \n", - "46 Rhode Island Public Transit Authority 1 \n", - "47 Rockford Mass Transit District 1 \n", - "48 Rogue Valley Transportation District 1 \n", - "49 SLO TRANSIT (SAN LUIS OBISPO, CA) 0 \n", - "50 SUNLINE TRANSIT AGENCY (THOUSAND PALMS) 0 \n", - "51 SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA) 0 \n", - "52 Sacramento County Airport System 0 \n", - "53 San Antonio Metropolitan Transit Authority 1 \n", - "54 San Diego Metro 0 \n", - "55 Santa Barbara Metro 0 \n", - "56 Santa Maria Area Transit 0 \n", - "57 Santa Maria Regional Transit 0 \n", - "58 Santa Rosa City Bus 0 \n", - "59 Shasta Regional Transportation Agency (SRTA) 0 \n", - "60 Sonoma County Transit 0 \n", - "61 South Carolina Department of Transportation on... 1 \n", - "62 South Dakota Department of Transportation on b... 1 \n", - "63 South Dakota Department of Transportation on b... 1 \n", - "64 Southeastern Regional Transit Authority 1 \n", - "65 Southwest Ohio Regional Transit Authority 1 \n", - "66 State of California on behalf of Kern Regional... 2 \n", - "67 Tahoe Transportation District 1 \n", - "68 Texas Department of Transportation on behalf o... 1 \n", - "69 The Bus, City of Merced 0 \n", - "70 Torrance Transit Department 0 \n", - "71 Transit Joint Powers Authority for Merced County 0 \n", - "72 Transit Joint Powers Authority of Merced County 0 \n", - "73 UC DAVIS (UNITRANS) (DAVIS, CA) 0 \n", - "74 UCLA FLEET & TRANSIT 0 \n", - "75 University of California - San Diego 0 \n", - "76 University of California, Irvine 0 \n", - "77 Utah Transit Authority 1 \n", - "78 VACAVILLE PUBLIC TRANSPORTATION (CITY COACH) (... 0 \n", - "79 VICTOR VALLEY TRANSIT AUTHORITY (VVTA) 0 \n", - "80 Whatcom Transportation Authority (WTA) 1 \n", - "81 White Earth Reservation Business Committee 1 \n", - "\n", - " total_project_count_ppno total_agg_cost total_bus_count \\\n", - "0 0 10000000 8.0 \n", - "1 1 22846640 20.0 \n", - "2 1 39478000 29.0 \n", - "3 1 2781891 3.0 \n", - "4 1 3623536 4.0 \n", - "5 0 2860250 5.0 \n", - "6 0 4278772 9.0 \n", - "7 0 6635394 10.0 \n", - "8 0 2819460 5.0 \n", - "9 0 653184 1.0 \n", - "10 0 4738886 6.0 \n", - "11 0 3199038 6.0 \n", - "12 0 1010372 5.0 \n", - "13 1 102790000 112.0 \n", - "14 0 776714 6.0 \n", - "15 2 9651507 10.0 \n", - "16 1 859270 1.0 \n", - "17 1 5987790 5.0 \n", - "18 0 21490560 39.0 \n", - "19 1 3687803 4.0 \n", - "20 1 1543000 3.0 \n", - "21 0 1760000 9.0 \n", - "22 0 4500000 4.0 \n", - "23 1 3547000 5.0 \n", - "24 0 103000000 90.0 \n", - "25 0 8740728 6.0 \n", - "26 1 16580000 20.0 \n", - "27 1 37642044 33.0 \n", - "28 1 5406355 5.0 \n", - "29 1 11208656 15.0 \n", - "30 0 12600000 134.0 \n", - "31 0 19040336 20.0 \n", - "32 0 6197180 11.0 \n", - "33 2 27894999 30.0 \n", - "34 0 6859296 7.0 \n", - "35 0 1080000 2.0 \n", - "36 0 1162000 3.0 \n", - "37 0 1456970 7.0 \n", - "38 1 2396600 2.0 \n", - "39 0 2063160 3.0 \n", - "40 1 5787606 6.0 \n", - "41 0 29330243 23.0 \n", - "42 1 9319520 10.0 \n", - "43 0 29331665 69.0 \n", - "44 1 2900000 40.0 \n", - "45 0 181250 5.0 \n", - "46 0 5000000 25.0 \n", - "47 0 4094652 4.0 \n", - "48 0 3937500 6.0 \n", - "49 1 847214 1.0 \n", - "50 1 5755155 5.0 \n", - "51 1 5771865 5.0 \n", - "52 1 4642225 5.0 \n", - "53 0 3187200 15.0 \n", - "54 1 18759576 12.0 \n", - "55 1 3659072 4.0 \n", - "56 1 1862258 2.0 \n", - "57 1 5188379 5.0 \n", - "58 1 4068202 4.0 \n", - "59 1 9516000 14.0 \n", - "60 1 8990000 10.0 \n", - "61 0 15423904 160.0 \n", - "62 0 1006750 9.0 \n", - "63 0 1276628 9.0 \n", - "64 0 11560000 16.0 \n", - "65 0 9806428 16.0 \n", - "66 0 6181000 20.0 \n", - "67 0 3400000 4.0 \n", - "68 0 7443765 56.0 \n", - "69 1 4786285 5.0 \n", - "70 1 7200000 7.0 \n", - "71 2 6446648 3.0 \n", - "72 1 3696513 3.0 \n", - "73 2 9321926 10.0 \n", - "74 1 2008826 2.0 \n", - "75 2 8268000 6.0 \n", - "76 1 4932930 5.0 \n", - "77 0 17055353 25.0 \n", - "78 1 10175590 10.0 \n", - "79 1 4508160 5.0 \n", - "80 0 9644865 11.0 \n", - "81 0 723171 4.0 \n", - "\n", - " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", - "0 1250000 1.163100 False \n", - "1 1142332 0.894336 False \n", - "2 1361310 1.440957 False \n", - "3 927297 0.357558 False \n", - "4 905884 0.304106 False \n", - "5 572050 -0.529223 False \n", - "6 475419 -0.770436 False \n", - "7 663539 -0.300844 False \n", - "8 563892 -0.549587 False \n", - "9 653184 -0.326693 False \n", - "10 789814 0.014368 False \n", - "11 533173 -0.626269 False \n", - "12 202074 -1.452770 False \n", - "13 917767 0.333769 False \n", - "14 129452 -1.634052 False \n", - "15 965150 0.452048 False \n", - "16 859270 0.187746 False \n", - "17 1197558 1.032193 False \n", - "18 551040 -0.581669 False \n", - "19 921950 0.344210 False \n", - "20 514333 -0.673298 False \n", - "21 195555 -1.469043 False \n", - "22 1125000 0.851071 False \n", - "23 709400 -0.186365 False \n", - "24 1144444 0.899608 False \n", - "25 1456788 1.679292 False \n", - "26 829000 0.112185 False \n", - "27 1140668 0.890182 False \n", - "28 1081271 0.741913 False \n", - "29 747243 -0.091900 False \n", - "30 94029 -1.722476 False \n", - "31 952016 0.419262 False \n", - "32 563380 -0.550865 False \n", - "33 929833 0.363888 False \n", - "34 979899 0.488865 False \n", - "35 540000 -0.609227 False \n", - "36 387333 -0.990320 False \n", - "37 208138 -1.437633 False \n", - "38 1198300 1.034045 False \n", - "39 687720 -0.240483 False \n", - "40 964601 0.450677 False \n", - "41 1275227 1.226073 False \n", - "42 931952 0.369178 False \n", - "43 425096 -0.896054 False \n", - "44 72500 -1.776217 False \n", - "45 36250 -1.866706 False \n", - "46 200000 -1.457947 False \n", - "47 1023663 0.598110 False \n", - "48 656250 -0.319040 False \n", - "49 847214 0.157652 False \n", - "50 1151031 0.916051 False \n", - "51 1154373 0.924393 False \n", - "52 928445 0.360423 False \n", - "53 212480 -1.426794 False \n", - "54 1563298 1.945166 False \n", - "55 914768 0.326282 False \n", - "56 931129 0.367123 False \n", - "57 1037675 0.633087 False \n", - "58 1017050 0.581602 False \n", - "59 679714 -0.260468 False \n", - "60 899000 0.286922 False \n", - "61 96399 -1.716560 False \n", - "62 111861 -1.677963 False \n", - "63 141847 -1.603111 False \n", - "64 722500 -0.153664 False \n", - "65 612901 -0.427249 False \n", - "66 309050 -1.185733 False \n", - "67 850000 0.164606 False \n", - "68 132924 -1.625385 False \n", - "69 957257 0.432345 False \n", - "70 1028571 0.610361 False \n", - "71 2148882 3.406922 True \n", - "72 1232171 1.118595 False \n", - "73 932192 0.369777 False \n", - "74 1004413 0.550057 False \n", - "75 1378000 1.482619 False \n", - "76 986586 0.505557 False \n", - "77 682214 -0.254227 False \n", - "78 1017559 0.582873 False \n", - "79 901632 0.293492 False \n", - "80 876805 0.231518 False \n", - "81 180792 -1.505895 False " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#EVERYTHING CHECKS OUT!\n", - "# move forward with `new_cpb_aggregate` function\n", - "new_agg_agency = new_cpb_aggregate(test)\n", - "old_agency_agg = cpb_aggregate(no_outliers, \"transit_agency\")\n", - "display(\n", - " old_agency_agg.shape,\n", - " new_agg_agency.shape,\n", - " old_agency_agg,\n", - " new_agg_agency\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "3428875a-6a64-41bc-8f9c-81902006d7f0", - "metadata": { - "tags": [] - }, - "source": [ - "## Testing variables rework\n", - "there are a lot of variables in the initial script. need to cut down the amount of variables or at least make it more efficient. " - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "f21298ee-0efb-4f91-ba63-55fc2645a4d2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", - " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", - " 'ppno', 'project_description', 'cost_per_bus', 'zscore_cost_per_bus',\n", - " 'is_cpb_outlier?'],\n", - " dtype='object')" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# read in all cleaned project data\n", - "# same as final from above\n", - "# cpb_analysis_data_merge is bus only projects. all DGS rows were Bus only projects anyways\n", - "merged_data = pd.read_parquet(f'{GCS_PATH}cleaned_no_outliers_cpb_analysis_data_merge.parquet')\n", - "merged_data.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "ece95fb7-cbb8-46bd-a5f9-2b68a47a4817", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "min 36250\n", - "max 1611662\n", - "Name: new_cost_per_bus, dtype: int64" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "min 1.0\n", - "max 160.0\n", - "Name: total_bus_count, dtype: float64" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "min 181250\n", - "max 103000000\n", - "Name: total_agg_cost, dtype: int64" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "min -1.939451\n", - "max 2.182513\n", - "Name: new_zscore_cost_per_bus, dtype: float64" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# no outliers\n", - "# use new_cpb_aggregaete on merged data then filter for outliers == False\n", - "agg_agency = new_cpb_aggregate(merged_data)\n", - "\n", - "agg_prop = new_cpb_aggregate(merged_data, column=\"prop_type\")\n", - "agg_bus_size = new_cpb_aggregate(merged_data, column=\"bus_size_type\")\n", - "agg_source = new_cpb_aggregate(merged_data, column=\"source\")\n", - "\n", - "#overall agency info\n", - "display(\n", - "\n", - " #min max,\n", - " agg_agency[\"new_cost_per_bus\"].agg([\"min\",\"max\"]),\n", - " agg_agency[\"total_bus_count\"].agg([\"min\",\"max\"]),\n", - " agg_agency[\"total_agg_cost\"].agg([\"min\",\"max\"]),\n", - " agg_agency[\"new_zscore_cost_per_bus\"].agg([\"min\",\"max\"]),\n", - " \n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "1696d78f-7018-417b-9847-d82edac3acdf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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prop_typetotal_agg_costtotal_bus_countnew_cost_per_bus
0BEB167232489163.01025966
1CNG176039140252.0698568
2FCEB120951335102.01185797
3electric (not specified)5667800044.01288136
4ethanol10067509.0111861
5low emission (hybrid)91824361145.0633271
6low emission (propane)840396944.0190999
7mix (zero and low emission)36775430125.0294203
8not specified41552404325.0127853
9zero-emission bus (not specified)128156513143.0896199
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" - ], - "text/plain": [ - " prop_type total_agg_cost total_bus_count \\\n", - "0 BEB 167232489 163.0 \n", - "1 CNG 176039140 252.0 \n", - "2 FCEB 120951335 102.0 \n", - "3 electric (not specified) 56678000 44.0 \n", - "4 ethanol 1006750 9.0 \n", - "5 low emission (hybrid) 91824361 145.0 \n", - "6 low emission (propane) 8403969 44.0 \n", - "7 mix (zero and low emission) 36775430 125.0 \n", - "8 not specified 41552404 325.0 \n", - "9 zero-emission bus (not specified) 128156513 143.0 \n", - "\n", - " new_cost_per_bus \n", - "0 1025966 \n", - "1 698568 \n", - "2 1185797 \n", - "3 1288136 \n", - "4 111861 \n", - "5 633271 \n", - "6 190999 \n", - "7 294203 \n", - "8 127853 \n", - "9 896199 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# testing pivot table on `merged_data`\n", - "\n", - "#pivot table to get totals for each prop type\n", - "pivot_prop_type = pd.pivot_table(\n", - " merged_data,\n", - " values = [\"bus_count\", \"total_cost\"],\n", - " index = \"prop_type\",\n", - " aggfunc = \"sum\",\n", - " margins = True,\n", - " margins_name = \"Grand Total\"\n", - ")\n", - "display(\n", - " #from new_cpb_agg\n", - " agg_prop[[\"prop_type\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]],\n", - " #pivot\n", - " #pivot_prop_type\n", - ")\n", - "# same data, dont need the pivot table anymore" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "ca709e43-4947-4a34-970f-216d4b6ab7cc", - "metadata": {}, - "outputs": [], - "source": [ - "#pivot table to get grand total for zeb only data\n", - "\n", - "# keep this\n", - "zeb_list =[\n", - " \"BEB\",\n", - " \"FCEB\",\n", - " \"electric (not specified)\",\n", - " \"zero-emission bus (not specified)\",\n", - "\n", - "]\n", - "\n", - "#keep this\n", - "non_zeb_list =[\n", - " \"CNG\",\n", - " \"ethanol\",\n", - " \"low emission (hybrid)\",\n", - " \"low emission (propane)\",\n", - " \"mix (zero and low emission)\",\n", - "]\n", - "\n", - "\n", - "\n", - "#keep this\n", - "pivot_zeb_prop = pd.pivot_table(\n", - " #filted incoming DF for zeb prop types\n", - " merged_data[merged_data[\"prop_type\"].isin(zeb_list)],\n", - " values = [\"bus_count\", \"total_cost\"],\n", - " index = \"prop_type\",\n", - " aggfunc = \"sum\",\n", - " margins = True,\n", - " margins_name = \"Grand Total\"\n", - ").reset_index() \n", - "\n", - "pivot_zeb_prop[\"cost_per_bus\"] = (pivot_zeb_prop[\"total_cost\"] / pivot_zeb_prop[\"bus_count\"]).astype(\"int64\")\n", - "\n", - "#keep this\n", - "pivot_non_zeb_prop = pd.pivot_table(\n", - " #filted incoming DF for non-zeb prop types\n", - " merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)],\n", - " values = [\"bus_count\", \"total_cost\"],\n", - " index = \"prop_type\",\n", - " aggfunc = \"sum\",\n", - " margins = True,\n", - " margins_name = \"Grand Total\"\n", - ").reset_index()\n", - "\n", - "pivot_non_zeb_prop[\"cost_per_bus\"] = (pivot_non_zeb_prop[\"total_cost\"] / pivot_non_zeb_prop[\"bus_count\"]).astype(\"int64\")" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "01647d83-8b4f-47a9-ab57-a1db7cd501dd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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prop_typebus_counttotal_costcost_per_bus
0BEB163.01672324891025966
1FCEB102.01209513351185797
2electric (not specified)44.0566780001288136
3zero-emission bus (not specified)143.0128156513896199
4Grand Total452.04730183371046500
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" - ], - "text/plain": [ - " prop_type bus_count total_cost cost_per_bus\n", - "0 BEB 163.0 167232489 1025966\n", - "1 FCEB 102.0 120951335 1185797\n", - "2 electric (not specified) 44.0 56678000 1288136\n", - "3 zero-emission bus (not specified) 143.0 128156513 896199\n", - "4 Grand Total 452.0 473018337 1046500" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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prop_typebus_counttotal_costcost_per_bus
0CNG252.0176039140698568
1ethanol9.01006750111861
2low emission (hybrid)145.091824361633271
3low emission (propane)44.08403969190999
4mix (zero and low emission)125.036775430294203
5Grand Total575.0314049650546173
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" ], "text/plain": [ - " prop_type bus_count total_cost cost_per_bus\n", - "0 CNG 252.0 176039140 698568\n", - "1 ethanol 9.0 1006750 111861\n", - "2 low emission (hybrid) 145.0 91824361 633271\n", - "3 low emission (propane) 44.0 8403969 190999\n", - "4 mix (zero and low emission) 125.0 36775430 294203\n", - "5 Grand Total 575.0 314049650 546173" + " transit_agency total_project_count \\\n", + "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) 1 \n", + "1 Alameda County Transit Authority 0 \n", + "2 Antelope Valley Transit Authority (AVTA) 0 \n", + "3 CITY OF PORTERVILLE (PORTERVILLE, CA) 0 \n", + "4 CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ... 0 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count cpb \n", + "0 0 10000000 8.0 1250000 \n", + "1 1 22846640 20.0 1142332 \n", + "2 1 39478000 29.0 1361310 \n", + "3 1 2781891 3.0 927297 \n", + "4 1 3623536 4.0 905884 " ] }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "display(\n", - " #zeb data 3 different methods\n", - " #1. filtering agg_prop by zeb list, no grand totas\n", - " #2. filtering pivot talbe by zeb list, without grand totals\n", - " #3. dedicated pivot table for zeb, with grand totals\n", - " #agg_prop[agg_prop[\"prop_type\"].isin(zeb_list)],\n", - " #pivot_prop_type.loc[zeb_list],\n", - " pivot_zeb_prop,\n", - " \n", - " #non-zeb same 3 methods\n", - " #agg_prop[agg_prop[\"prop_type\"].isin(non_zeb_list)],\n", - " #pivot_prop_type.loc[non_zeb_list],\n", - " pivot_non_zeb_prop\n", - ")\n", - "# confirmed all data is the same, but need pivot for grand total rows" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "3be3ef4f-0bf3-4770-a8b7-340d372ae1ce", - "metadata": {}, - "outputs": [ + }, { "data": { "text/html": [ @@ -5175,154 +2335,150 @@ " \n", " \n", " \n", - " bus_size_type\n", + " transit_agency\n", + " total_project_count\n", + " total_project_count_ppno\n", " total_agg_cost\n", " total_bus_count\n", " new_cost_per_bus\n", + " new_zscore_cost_per_bus\n", + " new_is_cpb_outlier?\n", " \n", " \n", " \n", " \n", " 0\n", - " articulated\n", - " 58237576\n", - " 41.0\n", - " 1420428\n", + " AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)\n", + " 1\n", + " 0\n", + " 10000000\n", + " 8.0\n", + " 1250000\n", + " 1.163100\n", + " False\n", " \n", " \n", " 1\n", - " cutaway\n", - " 16694500\n", - " 152.0\n", - " 109832\n", + " Alameda County Transit Authority\n", + " 0\n", + " 1\n", + " 22846640\n", + " 20.0\n", + " 1142332\n", + " 0.894336\n", + " False\n", " \n", " \n", " 2\n", - " not specified\n", - " 509919038\n", - " 881.0\n", - " 578795\n", + " Antelope Valley Transit Authority (AVTA)\n", + " 0\n", + " 1\n", + " 39478000\n", + " 29.0\n", + " 1361310\n", + " 1.440957\n", + " False\n", " \n", " \n", " 3\n", - " over-the-road\n", - " 9516000\n", - " 14.0\n", - " 679714\n", + " CITY OF PORTERVILLE (PORTERVILLE, CA)\n", + " 0\n", + " 1\n", + " 2781891\n", + " 3.0\n", + " 927297\n", + " 0.357558\n", + " False\n", " \n", " \n", " 4\n", - " standard/conventional (30ft-45ft)\n", - " 234253277\n", - " 264.0\n", - " 887323\n", + " CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...\n", + " 0\n", + " 1\n", + " 3623536\n", + " 4.0\n", + " 905884\n", + " 0.304106\n", + " False\n", " \n", " \n", "\n", "" ], "text/plain": [ - " bus_size_type total_agg_cost total_bus_count \\\n", - "0 articulated 58237576 41.0 \n", - "1 cutaway 16694500 152.0 \n", - "2 not specified 509919038 881.0 \n", - "3 over-the-road 9516000 14.0 \n", - "4 standard/conventional (30ft-45ft) 234253277 264.0 \n", + " transit_agency total_project_count \\\n", + "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) 1 \n", + "1 Alameda County Transit Authority 0 \n", + "2 Antelope Valley Transit Authority (AVTA) 0 \n", + "3 CITY OF PORTERVILLE (PORTERVILLE, CA) 0 \n", + "4 CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ... 0 \n", "\n", - " new_cost_per_bus \n", - "0 1420428 \n", - "1 109832 \n", - "2 578795 \n", - "3 679714 \n", - "4 887323 " + " total_project_count_ppno total_agg_cost total_bus_count \\\n", + "0 0 10000000 8.0 \n", + "1 1 22846640 20.0 \n", + "2 1 39478000 29.0 \n", + "3 1 2781891 3.0 \n", + "4 1 3623536 4.0 \n", + "\n", + " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", + "0 1250000 1.163100 False \n", + "1 1142332 0.894336 False \n", + "2 1361310 1.440957 False \n", + "3 927297 0.357558 False \n", + "4 905884 0.304106 False " ] }, "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# answers total buses and cost per grant type\n", - "pivot_size = pd.pivot_table(\n", - " merged_data,\n", - " values = [\"bus_count\", \"total_cost\"],\n", - " index = \"bus_size_type\",\n", - " aggfunc = \"sum\",\n", - " margins = True,\n", - " margins_name = \"Grand Total\"\n", - ")\n", - "display(\n", - " agg_bus_size[[\"bus_size_type\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]]\n", - " #pivot_size\n", - ")\n", - "\n", - "#same data, dont need pivot for this one because the grand totals will be the same. and the pivot tables dont have cpb" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "2c933257-bdc2-4007-9571-58475118073c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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sourcetotal_agg_costtotal_bus_countnew_cost_per_bus
0dgs250112853236.01059800
1fta391257025883.0443099
2tircp187250513233.0803650
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" - ], + "output_type": "display_data" + } + ], + "source": [ + "#EVERYTHING CHECKS OUT!\n", + "# move forward with `new_cpb_aggregate` function\n", + "new_agg_agency = new_cpb_aggregate(test)\n", + "old_agency_agg = cpb_aggregate(no_outliers, \"transit_agency\")\n", + "display(\n", + " old_agency_agg.shape,\n", + " new_agg_agency.shape,\n", + " old_agency_agg.head(),\n", + " new_agg_agency.head()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3428875a-6a64-41bc-8f9c-81902006d7f0", + "metadata": { + "tags": [] + }, + "source": [ + "## Testing variables rework\n", + "there are a lot of variables in the initial script. need to cut down the amount of variables or at least make it more efficient. " + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "f21298ee-0efb-4f91-ba63-55fc2645a4d2", + "metadata": {}, + "outputs": [ + { + "data": { "text/plain": [ - " source total_agg_cost total_bus_count new_cost_per_bus\n", - "0 dgs 250112853 236.0 1059800\n", - "1 fta 391257025 883.0 443099\n", - "2 tircp 187250513 233.0 803650" + "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", + " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", + " 'ppno', 'project_description', 'cost_per_bus', 'zscore_cost_per_bus',\n", + " 'is_cpb_outlier?'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(88, 14)" ] }, "metadata": {}, @@ -5349,51 +2505,165 @@ " \n", " \n", " \n", - " source\n", - " bus_count\n", + " transit_agency\n", + " project_title\n", + " prop_type\n", + " bus_size_type\n", + " description\n", + " new_project_type\n", " total_cost\n", + " bus_count\n", + " source\n", + " ppno\n", + " project_description\n", " cost_per_bus\n", + " zscore_cost_per_bus\n", + " is_cpb_outlier?\n", " \n", " \n", " \n", " \n", " 0\n", - " dgs\n", - " 236.0\n", - " 250112853\n", - " 1059800\n", + " AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)\n", + " Puerto Rico Initiative Minimizing Emissions Pl...\n", + " electric (not specified)\n", + " not specified\n", + " The Metropolitan Bus Authority will receive fu...\n", + " bus only\n", + " 10000000\n", + " 8.0\n", + " fta\n", + " None\n", + " None\n", + " 1250000\n", + " 0.917956\n", + " False\n", " \n", " \n", " 1\n", + " Cape Fear Public Transportation Authority\n", + " Wave Transit Low Emissions Replacement Vehicles\n", + " CNG\n", + " not specified\n", + " Wave Transit will receive funding to buy compr...\n", + " bus only\n", + " 2860250\n", + " 5.0\n", " fta\n", - " 883.0\n", - " 391257025\n", - " 443099\n", + " None\n", + " None\n", + " 572050\n", + " -0.529139\n", + " False\n", " \n", " \n", " 2\n", - " tircp\n", - " 233.0\n", - " 187250513\n", - " 803650\n", + " Central Oklahoma Transportation and Parking Au...\n", + " COTPA, dba EMBARK Elimination of Fixed Route D...\n", + " CNG\n", + " not specified\n", + " The Central Oklahoma Transportation and Parkin...\n", + " bus only\n", + " 4278772\n", + " 9.0\n", + " fta\n", + " None\n", + " None\n", + " 475419\n", + " -0.735399\n", + " False\n", " \n", " \n", " 3\n", - " Grand Total\n", - " 1352.0\n", - " 828620391\n", - " 612884\n", + " Champaign-Urbana Mass Transit District\n", + " MTD 40-Foot Hybrid Replacement Buses\n", + " low emission (hybrid)\n", + " not specified\n", + " The Champaign-Urbana Mass Transit District wil...\n", + " bus only\n", + " 6635394\n", + " 10.0\n", + " fta\n", + " None\n", + " None\n", + " 663539\n", + " -0.333854\n", + " False\n", + " \n", + " \n", + " 4\n", + " City of Beaumont\n", + " Beaumont Municipal Transit Zips to Improve Low...\n", + " CNG\n", + " not specified\n", + " Beaumont Municipal Transit will receive fundin...\n", + " bus only\n", + " 2819460\n", + " 5.0\n", + " fta\n", + " None\n", + " None\n", + " 563892\n", + " -0.546552\n", + " False\n", " \n", " \n", "\n", "" ], "text/plain": [ - " source bus_count total_cost cost_per_bus\n", - "0 dgs 236.0 250112853 1059800\n", - "1 fta 883.0 391257025 443099\n", - "2 tircp 233.0 187250513 803650\n", - "3 Grand Total 1352.0 828620391 612884" + " transit_agency \\\n", + "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) \n", + "1 Cape Fear Public Transportation Authority \n", + "2 Central Oklahoma Transportation and Parking Au... \n", + "3 Champaign-Urbana Mass Transit District \n", + "4 City of Beaumont \n", + "\n", + " project_title \\\n", + "0 Puerto Rico Initiative Minimizing Emissions Pl... \n", + "1 Wave Transit Low Emissions Replacement Vehicles \n", + "2 COTPA, dba EMBARK Elimination of Fixed Route D... \n", + "3 MTD 40-Foot Hybrid Replacement Buses \n", + "4 Beaumont Municipal Transit Zips to Improve Low... \n", + "\n", + " prop_type bus_size_type \\\n", + "0 electric (not specified) not specified \n", + "1 CNG not specified \n", + "2 CNG not specified \n", + "3 low emission (hybrid) not specified \n", + "4 CNG not specified \n", + "\n", + " description new_project_type \\\n", + "0 The Metropolitan Bus Authority will receive fu... bus only \n", + "1 Wave Transit will receive funding to buy compr... bus only \n", + "2 The Central Oklahoma Transportation and Parkin... bus only \n", + "3 The Champaign-Urbana Mass Transit District wil... bus only \n", + "4 Beaumont Municipal Transit will receive fundin... bus only \n", + "\n", + " total_cost bus_count source ppno project_description cost_per_bus \\\n", + "0 10000000 8.0 fta None None 1250000 \n", + "1 2860250 5.0 fta None None 572050 \n", + "2 4278772 9.0 fta None None 475419 \n", + "3 6635394 10.0 fta None None 663539 \n", + "4 2819460 5.0 fta None None 563892 \n", + "\n", + " zscore_cost_per_bus is_cpb_outlier? \n", + "0 0.917956 False \n", + "1 -0.529139 False \n", + "2 -0.735399 False \n", + "3 -0.333854 False \n", + "4 -0.546552 False " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "min -1.672813\n", + "max 2.661856\n", + "Name: zscore_cost_per_bus, dtype: float64" ] }, "metadata": {}, @@ -5401,43 +2671,111 @@ } ], "source": [ - "# answers total buses and cost per grant type\n", - "pivot_source = pd.pivot_table(\n", - " merged_data,\n", - " values = [\"bus_count\", \"total_cost\"],\n", - " index = \"source\",\n", - " aggfunc = \"sum\",\n", - " margins = True,\n", - " margins_name = \"Grand Total\"\n", - ").reset_index()\n", - "\n", - "pivot_source[\"cost_per_bus\"] = (pivot_source[\"total_cost\"] / pivot_source[\"bus_count\"]).astype(\"int64\")\n", + "# read in all cleaned project data without outliers\n", + "# cpb_analysis_data_merge is bus only projects. all DGS rows were Bus only projects anyways\n", "\n", + "merged_data = pd.read_parquet(f'{GCS_PATH}cleaned_no_outliers_cpb_analysis_data_merge.parquet')\n", "display(\n", - " agg_source[[\"source\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]],\n", - " pivot_source\n", - ")\n" + " merged_data.columns,\n", + " merged_data.shape,\n", + " merged_data.head(),\n", + " merged_data[\"zscore_cost_per_bus\"].agg([\"min\",\"max\"])\n", + ")" ] }, { - "cell_type": "markdown", - "id": "11547020-dd35-4745-98f8-bbd02fccaa23", - "metadata": { - "tags": [] - }, + "cell_type": "code", + "execution_count": 36, + "id": "ece95fb7-cbb8-46bd-a5f9-2b68a47a4817", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "min 36250\n", + "max 1611662\n", + "Name: new_cost_per_bus, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "min 1.0\n", + "max 160.0\n", + "Name: total_bus_count, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "min 181250\n", + "max 103000000\n", + "Name: total_agg_cost, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "min -1.939451\n", + "max 2.182513\n", + "Name: new_zscore_cost_per_bus, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "## Testing Charts\n", + "# aggregating by big categories\n", + "agg_agency = new_cpb_aggregate(merged_data)\n", + "agg_prop = new_cpb_aggregate(merged_data, column=\"prop_type\")\n", + "agg_bus_size = new_cpb_aggregate(merged_data, column=\"bus_size_type\")\n", + "agg_source = new_cpb_aggregate(merged_data, column=\"source\")\n", "\n", - "using `merged_data`, now without outliers.\n", - "charts looking good, similar results to initial charts" + "#overall agency info\n", + "display(\n", + " #min max,\n", + " agg_agency[\"new_cost_per_bus\"].agg([\"min\",\"max\"]),\n", + " agg_agency[\"total_bus_count\"].agg([\"min\",\"max\"]),\n", + " agg_agency[\"total_agg_cost\"].agg([\"min\",\"max\"]),\n", + " agg_agency[\"new_zscore_cost_per_bus\"].agg([\"min\",\"max\"]),\n", + " \n", + ")" ] }, { "cell_type": "code", - "execution_count": 140, - "id": "aace38a4-3f2d-460d-a258-59efa659f852", + "execution_count": 42, + "id": "03940ccb-d1e6-439d-a930-13dae17537b2", "metadata": {}, "outputs": [ + { + "data": { + "text/plain": [ + "(88, 14)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(82, 8)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/html": [ @@ -5510,73 +2848,103 @@ " -0.529139\n", " False\n", " \n", + " \n", + " 2\n", + " Central Oklahoma Transportation and Parking Au...\n", + " COTPA, dba EMBARK Elimination of Fixed Route D...\n", + " CNG\n", + " not specified\n", + " The Central Oklahoma Transportation and Parkin...\n", + " bus only\n", + " 4278772\n", + " 9.0\n", + " fta\n", + " None\n", + " None\n", + " 475419\n", + " -0.735399\n", + " False\n", + " \n", + " \n", + " 3\n", + " Champaign-Urbana Mass Transit District\n", + " MTD 40-Foot Hybrid Replacement Buses\n", + " low emission (hybrid)\n", + " not specified\n", + " The Champaign-Urbana Mass Transit District wil...\n", + " bus only\n", + " 6635394\n", + " 10.0\n", + " fta\n", + " None\n", + " None\n", + " 663539\n", + " -0.333854\n", + " False\n", + " \n", + " \n", + " 4\n", + " City of Beaumont\n", + " Beaumont Municipal Transit Zips to Improve Low...\n", + " CNG\n", + " not specified\n", + " Beaumont Municipal Transit will receive fundin...\n", + " bus only\n", + " 2819460\n", + " 5.0\n", + " fta\n", + " None\n", + " None\n", + " 563892\n", + " -0.546552\n", + " False\n", + " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency \\\n", - "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) \n", - "1 Cape Fear Public Transportation Authority \n", + " transit_agency \\\n", + "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) \n", + "1 Cape Fear Public Transportation Authority \n", + "2 Central Oklahoma Transportation and Parking Au... \n", + "3 Champaign-Urbana Mass Transit District \n", + "4 City of Beaumont \n", "\n", " project_title \\\n", "0 Puerto Rico Initiative Minimizing Emissions Pl... \n", "1 Wave Transit Low Emissions Replacement Vehicles \n", + "2 COTPA, dba EMBARK Elimination of Fixed Route D... \n", + "3 MTD 40-Foot Hybrid Replacement Buses \n", + "4 Beaumont Municipal Transit Zips to Improve Low... \n", "\n", " prop_type bus_size_type \\\n", "0 electric (not specified) not specified \n", "1 CNG not specified \n", + "2 CNG not specified \n", + "3 low emission (hybrid) not specified \n", + "4 CNG not specified \n", "\n", " description new_project_type \\\n", "0 The Metropolitan Bus Authority will receive fu... bus only \n", "1 Wave Transit will receive funding to buy compr... bus only \n", + "2 The Central Oklahoma Transportation and Parkin... bus only \n", + "3 The Champaign-Urbana Mass Transit District wil... bus only \n", + "4 Beaumont Municipal Transit will receive fundin... bus only \n", "\n", " total_cost bus_count source ppno project_description cost_per_bus \\\n", "0 10000000 8.0 fta None None 1250000 \n", "1 2860250 5.0 fta None None 572050 \n", + "2 4278772 9.0 fta None None 475419 \n", + "3 6635394 10.0 fta None None 663539 \n", + "4 2819460 5.0 fta None None 563892 \n", "\n", " zscore_cost_per_bus is_cpb_outlier? \n", "0 0.917956 False \n", - "1 -0.529139 False " - ] - }, - "execution_count": 140, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged_data.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "id": "4753f3ea-00b6-4d5e-a3f0-73b3d3593acb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "792635.3409090909" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "396712.6067531972" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "1046500.7455752213" + "1 -0.529139 False \n", + "2 -0.735399 False \n", + "3 -0.333854 False \n", + "4 -0.546552 False " ] }, "metadata": {}, @@ -5584,8 +2952,116 @@ }, { "data": { + "text/html": [ + "
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)10100000008.012500001.236248False
1Alameda County Transit Authority012284664020.011423320.954542False
2Antelope Valley Transit Authority (AVTA)013947800029.013613101.527483False
3CITY OF PORTERVILLE (PORTERVILLE, CA)0127818913.09272970.391916False
4CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...0136235364.09058840.335891False
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" + ], "text/plain": [ - "546173.304347826" + " transit_agency total_project_count \\\n", + "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) 1 \n", + "1 Alameda County Transit Authority 0 \n", + "2 Antelope Valley Transit Authority (AVTA) 0 \n", + "3 CITY OF PORTERVILLE (PORTERVILLE, CA) 0 \n", + "4 CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ... 0 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count \\\n", + "0 0 10000000 8.0 \n", + "1 1 22846640 20.0 \n", + "2 1 39478000 29.0 \n", + "3 1 2781891 3.0 \n", + "4 1 3623536 4.0 \n", + "\n", + " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", + "0 1250000 1.236248 False \n", + "1 1142332 0.954542 False \n", + "2 1361310 1.527483 False \n", + "3 927297 0.391916 False \n", + "4 905884 0.335891 False " ] }, "metadata": {}, @@ -5593,30 +3069,146 @@ } ], "source": [ - "# means and standard deviations\n", - "# for graphs\n", - "cpb_mean = merged_data[\"cost_per_bus\"].mean()\n", - "cpb_std = merged_data[\"cost_per_bus\"].std()\n", - "zeb_cpb_wt_avg = (merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"bus_count\"].sum())\n", - "non_zeb_cpb_wt_avg = (merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"bus_count\"].sum())\n", "display(\n", - " cpb_mean,\n", - " cpb_std,\n", - " wt_avg_mean,\n", - " non_zeb_cpb_wt_avg\n", + " merged_data.shape,\n", + " agg_agency.shape,\n", + " merged_data.head(),\n", + " agg_agency.head()\n", ")" ] }, { "cell_type": "code", - "execution_count": 38, - "id": "2007be9d-13ec-4d0d-a642-d9a42448b924", + "execution_count": 64, + "id": "1696d78f-7018-417b-9847-d82edac3acdf", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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prop_typetotal_agg_costtotal_bus_countnew_cost_per_bus
0BEB167232489163.01025966
1CNG176039140252.0698568
2FCEB120951335102.01185797
3electric (not specified)5667800044.01288136
4ethanol10067509.0111861
5low emission (hybrid)91824361145.0633271
6low emission (propane)840396944.0190999
7mix (zero and low emission)36775430125.0294203
8not specified41552404325.0127853
9zero-emission bus (not specified)128156513143.0896199
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" + ], "text/plain": [ - "1056659.3043478262" + " prop_type total_agg_cost total_bus_count \\\n", + "0 BEB 167232489 163.0 \n", + "1 CNG 176039140 252.0 \n", + "2 FCEB 120951335 102.0 \n", + "3 electric (not specified) 56678000 44.0 \n", + "4 ethanol 1006750 9.0 \n", + "5 low emission (hybrid) 91824361 145.0 \n", + "6 low emission (propane) 8403969 44.0 \n", + "7 mix (zero and low emission) 36775430 125.0 \n", + "8 not specified 41552404 325.0 \n", + "9 zero-emission bus (not specified) 128156513 143.0 \n", + "\n", + " new_cost_per_bus \n", + "0 1025966 \n", + "1 698568 \n", + "2 1185797 \n", + "3 1288136 \n", + "4 111861 \n", + "5 633271 \n", + "6 190999 \n", + "7 294203 \n", + "8 127853 \n", + "9 896199 " ] }, "metadata": {}, @@ -5659,52 +3251,91 @@ " \n", " \n", " 1\n", + " CNG\n", + " 252.0\n", + " 176039140\n", + " 698568\n", + " \n", + " \n", + " 2\n", " FCEB\n", " 102.0\n", " 120951335\n", " 1185797\n", " \n", " \n", - " 2\n", + " 3\n", " electric (not specified)\n", " 44.0\n", " 56678000\n", " 1288136\n", " \n", " \n", - " 3\n", + " 4\n", + " ethanol\n", + " 9.0\n", + " 1006750\n", + " 111861\n", + " \n", + " \n", + " 5\n", + " low emission (hybrid)\n", + " 145.0\n", + " 91824361\n", + " 633271\n", + " \n", + " \n", + " 6\n", + " low emission (propane)\n", + " 44.0\n", + " 8403969\n", + " 190999\n", + " \n", + " \n", + " 7\n", + " mix (zero and low emission)\n", + " 125.0\n", + " 36775430\n", + " 294203\n", + " \n", + " \n", + " 8\n", + " not specified\n", + " 325.0\n", + " 41552404\n", + " 127853\n", + " \n", + " \n", + " 9\n", " zero-emission bus (not specified)\n", " 143.0\n", " 128156513\n", " 896199\n", " \n", " \n", - " 4\n", + " 10\n", " Grand Total\n", - " 452.0\n", - " 473018337\n", - " 1046500\n", + " 1352.0\n", + " 828620391\n", + " 612884\n", " \n", " \n", "\n", "" ], "text/plain": [ - " prop_type bus_count total_cost cost_per_bus\n", - "0 BEB 163.0 167232489 1025966\n", - "1 FCEB 102.0 120951335 1185797\n", - "2 electric (not specified) 44.0 56678000 1288136\n", - "3 zero-emission bus (not specified) 143.0 128156513 896199\n", - "4 Grand Total 452.0 473018337 1046500" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "1046500.7455752213" + " prop_type bus_count total_cost cost_per_bus\n", + "0 BEB 163.0 167232489 1025966\n", + "1 CNG 252.0 176039140 698568\n", + "2 FCEB 102.0 120951335 1185797\n", + "3 electric (not specified) 44.0 56678000 1288136\n", + "4 ethanol 9.0 1006750 111861\n", + "5 low emission (hybrid) 145.0 91824361 633271\n", + "6 low emission (propane) 44.0 8403969 190999\n", + "7 mix (zero and low emission) 125.0 36775430 294203\n", + "8 not specified 325.0 41552404 127853\n", + "9 zero-emission bus (not specified) 143.0 128156513 896199\n", + "10 Grand Total 1352.0 828620391 612884" ] }, "metadata": {}, @@ -5712,127 +3343,88 @@ } ], "source": [ - "# why is the average different when i use .mean() vs. total cost / bus cout\n", + "# testing pivot table on `merged_data`\n", + "\n", + "#pivot table to get totals for each prop type\n", + "pivot_prop_type = pd.pivot_table(\n", + " merged_data,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"prop_type\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ").reset_index()\n", + "pivot_prop_type[\"cost_per_bus\"] = (pivot_prop_type[\"total_cost\"] / pivot_prop_type[\"bus_count\"]).astype(\"int64\")\n", "\n", "display(\n", - " #this is the arithmatic mean, sums all the `cost_per_bus` rows, the divide by the number of rows. (row-wise)\n", - " merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"cost_per_bus\"].mean(),\n", - " #this is like the accounting method of calculating average (Total Cost and Total Quantity Approach (Weighted Average))\n", - " pivot_zeb_prop,\n", - " # calculating mean by weighted average aproach (total cost / total bus count, similar to pivot table)\n", - " (merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"bus_count\"].sum())\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "8645cf77-b30a-4c45-b943-ac81e8b5a613", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dist_curve(\n", - " df=merged_data,\n", - " mean=cpb_mean,\n", - " std=cpb_std,\n", - " title=\"all buses, cost per bus distribution\",\n", - " xlabel=\"cost per bus, $ million(s)\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "id": "cefa6800-df50-4eda-95f8-74363ef942d0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dist_curve(\n", - " df=merged_data[merged_data[\"prop_type\"].isin(zeb_list)],\n", - " #using the accounting, weighted average approach to mean (total cost/total number of buses)\n", - " mean=zeb_cpb_wt_avg,\n", - " # need to investigate if std needs to be weighted as well?\n", - " std=merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"cost_per_bus\"].std(),\n", - " title=\"ZEB buses, cost per bus distribution\",\n", - " xlabel=\"cost per bus, $ million(s)\",\n", - ")" + " #from new_cpb_agg\n", + " agg_prop[[\"prop_type\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]],\n", + " #pivot\n", + " pivot_prop_type\n", + ")\n", + "# same data, dont need the pivot table anymore, but the pivot table does have grand total" ] }, { "cell_type": "code", - "execution_count": 157, - "id": "563304d2-2d98-44e6-b3a4-fd54f63fc0d8", + "execution_count": 54, + "id": "ca709e43-4947-4a34-970f-216d4b6ab7cc", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "dist_curve(\n", - " df=merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)],\n", - " mean=non_zeb_cpb_wt_avg,\n", - " std=merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"cost_per_bus\"].std(),\n", - " title=\"non-ZEB costper bus Distribution\",\n", - " xlabel='\"cost per bus, $ million(s)\"',\n", - ")" + "#pivot table to get grand total for zeb only data\n", + "\n", + "# keep this\n", + "zeb_list =[\n", + " \"BEB\",\n", + " \"FCEB\",\n", + " \"electric (not specified)\",\n", + " \"zero-emission bus (not specified)\",\n", + "]\n", + "\n", + "#keep this\n", + "non_zeb_list =[\n", + " \"CNG\",\n", + " \"ethanol\",\n", + " \"low emission (hybrid)\",\n", + " \"low emission (propane)\",\n", + " \"mix (zero and low emission)\",\n", + "]\n", + "\n", + "\n", + "#keep this\n", + "pivot_zeb_prop = pd.pivot_table(\n", + " #filted incoming DF for zeb prop types\n", + " merged_data[merged_data[\"prop_type\"].isin(zeb_list)],\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"prop_type\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ").reset_index() \n", + "\n", + "pivot_zeb_prop[\"cost_per_bus\"] = (pivot_zeb_prop[\"total_cost\"] / pivot_zeb_prop[\"bus_count\"]).astype(\"int64\")\n", + "\n", + "#keep this\n", + "pivot_non_zeb_prop = pd.pivot_table(\n", + " #filted incoming DF for non-zeb prop types\n", + " merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)],\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"prop_type\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ").reset_index()\n", + "\n", + "pivot_non_zeb_prop[\"cost_per_bus\"] = (pivot_non_zeb_prop[\"total_cost\"] / pivot_non_zeb_prop[\"bus_count\"]).astype(\"int64\")" ] }, { "cell_type": "code", - "execution_count": 149, - "id": "5f11c857-ddbe-4871-aeca-e27fa00fbde8", + "execution_count": 55, + "id": "01647d83-8b4f-47a9-ab57-a1db7cd501dd", "metadata": {}, "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "text/html": [ @@ -5855,228 +3447,58 @@ " \n", " \n", " prop_type\n", - " new_cost_per_bus\n", - " total_bus_count\n", + " bus_count\n", + " total_cost\n", + " cost_per_bus\n", " \n", " \n", " \n", " \n", - " 3\n", - " electric (not specified)\n", - " 1288136\n", - " 44.0\n", - " \n", - " \n", - " 2\n", - " FCEB\n", - " 1185797\n", - " 102.0\n", - " \n", - " \n", " 0\n", " BEB\n", - " 1025966\n", " 163.0\n", - " \n", - " \n", - " 9\n", - " zero-emission bus (not specified)\n", - " 896199\n", - " 143.0\n", + " 167232489\n", + " 1025966\n", " \n", " \n", " 1\n", - " CNG\n", - " 698568\n", - " 252.0\n", - " \n", - " \n", - " 5\n", - " low emission (hybrid)\n", - " 633271\n", - " 145.0\n", - " \n", - " \n", - " 7\n", - " mix (zero and low emission)\n", - " 294203\n", - " 125.0\n", + " FCEB\n", + " 102.0\n", + " 120951335\n", + " 1185797\n", " \n", " \n", - " 6\n", - " low emission (propane)\n", - " 190999\n", + " 2\n", + " electric (not specified)\n", " 44.0\n", + " 56678000\n", + " 1288136\n", " \n", " \n", - " 8\n", - " not specified\n", - " 127853\n", - " 325.0\n", + " 3\n", + " zero-emission bus (not specified)\n", + " 143.0\n", + " 128156513\n", + " 896199\n", " \n", " \n", " 4\n", - " ethanol\n", - " 111861\n", - " 9.0\n", + " Grand Total\n", + " 452.0\n", + " 473018337\n", + " 1046500\n", " \n", " \n", "\n", "" ], "text/plain": [ - " prop_type new_cost_per_bus total_bus_count\n", - "3 electric (not specified) 1288136 44.0\n", - "2 FCEB 1185797 102.0\n", - "0 BEB 1025966 163.0\n", - "9 zero-emission bus (not specified) 896199 143.0\n", - "1 CNG 698568 252.0\n", - "5 low emission (hybrid) 633271 145.0\n", - "7 mix (zero and low emission) 294203 125.0\n", - "6 low emission (propane) 190999 44.0\n", - "8 not specified 127853 325.0\n", - "4 ethanol 111861 9.0" - ] - }, - "execution_count": 149, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "make_chart(\"new_cost_per_bus\", \"Cost per bus by propulsion type\", x_col=\"prop_type\", data=agg_prop)\n", - "make_chart(\"total_bus_count\", \"Bus count by propulsion type\", x_col=\"prop_type\", data=agg_prop)\n", - "agg_prop[[\"prop_type\",\"new_cost_per_bus\",\"total_bus_count\"]].sort_values(by=\"new_cost_per_bus\", ascending=False)\n" - ] - }, - { - "cell_type": "markdown", - "id": "9270ab8f-25ff-4de3-aca5-7ef4637a4f9c", - "metadata": { - "tags": [] - }, - "source": [ - "## Testing summary\n", - "time to rework the summary section.\n", - "\n", - "no more long expositions and variables. try to get the same point across using tables instead." - ] - }, - { - "cell_type": "code", - "execution_count": 147, - "id": "2472461d-7663-4b66-9bde-4c2a199707a5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "\n", - "\n", - "# Bus Procurement Cost Analysis\n", - "\n", - "## Summary\n", - "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", - "\n", - "Data was compiled from three data sources:\n", - "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", - "2. TIRCP project data (state-funded, California only)\n", - "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", - "\n", - "Breakdown of each data souce:\n", - "| source | bus_count | total_cost | cost_per_bus |\n", - "|:------------|------------:|-------------:|---------------:|\n", - "| dgs | 236 | 250112853 | 1059800 |\n", - "| fta | 883 | 391257025 | 443099 |\n", - "| tircp | 233 | 187250513 | 803650 |\n", - "| Grand Total | 1352 | 828620391 | 612884 |\n", - "\n", - "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", - "The resulting dataset only contained projects that were solely used to procure buses. 87 projects remained. These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", - "\n", - "**ZEB buses include:**\n", - "- zero-emission (not specified) \n", - "- electric (not specified)\n", - "- battery electric \n", - "- fuel cell electric\n", - "\n", - "**Non-ZEB buses include:**\n", - "- CNG \n", - "- ethanol \n", - "- ow emission (hybrid, propane) \n", - "- diesel \n", - "- gas\n", - "\n", - "Below are charts and tables that summarize the findings.\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "summary = f\"\"\"\n", - "\n", - "# Bus Procurement Cost Analysis\n", - "\n", - "## Summary\n", - "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", - "\n", - "Data was compiled from three data sources:\n", - "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", - "2. TIRCP project data (state-funded, California only)\n", - "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", - "\n", - "Breakdown of each data souce:\n", - "{pivot_source.to_markdown(index=False)}\n", - "\n", - "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", - "The resulting dataset only contained projects that were solely used to procure buses. 87 projects remained. These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", - "\n", - "**ZEB buses include:**\n", - "- zero-emission (not specified) \n", - "- electric (not specified)\n", - "- battery electric \n", - "- fuel cell electric\n", - "\n", - "**Non-ZEB buses include:**\n", - "- CNG \n", - "- ethanol \n", - "- ow emission (hybrid, propane) \n", - "- diesel \n", - "- gas\n", - "\n", - "Below are charts and tables that summarize the findings.\n", - "\n", - "\n", - "\n", - "\n", - "\"\"\"\n", - "from IPython.display import Markdown, display\n", - "\n", - "display(Markdown(summary))" - ] - }, - { - "cell_type": "code", - "execution_count": 144, - "id": "91d0361d-b165-4607-b22e-66ae4234863d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "**Max new_cost_per_bus**" - ], - "text/plain": [ - "" + " prop_type bus_count total_cost cost_per_bus\n", + "0 BEB 163.0 167232489 1025966\n", + "1 FCEB 102.0 120951335 1185797\n", + "2 electric (not specified) 44.0 56678000 1288136\n", + "3 zero-emission bus (not specified) 143.0 128156513 896199\n", + "4 Grand Total 452.0 473018337 1046500" ] }, "metadata": {}, @@ -6103,47 +3525,97 @@ " \n", " \n", " \n", - " transit_agency\n", - " total_agg_cost\n", - " total_bus_count\n", - " new_cost_per_bus\n", + " prop_type\n", + " bus_count\n", + " total_cost\n", + " cost_per_bus\n", + " \n", + " \n", + " \n", + " \n", + " 0\n", + " CNG\n", + " 252.0\n", + " 176039140\n", + " 698568\n", + " \n", + " \n", + " 1\n", + " ethanol\n", + " 9.0\n", + " 1006750\n", + " 111861\n", " \n", - " \n", - " \n", " \n", - " 71\n", - " Transit Joint Powers Authority for Merced County\n", - " 3223324\n", - " 2.0\n", - " 1611662\n", + " 2\n", + " low emission (hybrid)\n", + " 145.0\n", + " 91824361\n", + " 633271\n", + " \n", + " \n", + " 3\n", + " low emission (propane)\n", + " 44.0\n", + " 8403969\n", + " 190999\n", + " \n", + " \n", + " 4\n", + " mix (zero and low emission)\n", + " 125.0\n", + " 36775430\n", + " 294203\n", + " \n", + " \n", + " 5\n", + " Grand Total\n", + " 575.0\n", + " 314049650\n", + " 546173\n", " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency total_agg_cost \\\n", - "71 Transit Joint Powers Authority for Merced County 3223324 \n", - "\n", - " total_bus_count new_cost_per_bus \n", - "71 2.0 1611662 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "**Min new_cost_per_bus**" - ], - "text/plain": [ - "" + " prop_type bus_count total_cost cost_per_bus\n", + "0 CNG 252.0 176039140 698568\n", + "1 ethanol 9.0 1006750 111861\n", + "2 low emission (hybrid) 145.0 91824361 633271\n", + "3 low emission (propane) 44.0 8403969 190999\n", + "4 mix (zero and low emission) 125.0 36775430 294203\n", + "5 Grand Total 575.0 314049650 546173" ] }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "display(\n", + " #zeb data 3 different methods\n", + " #1. filtering agg_prop by zeb list, no grand totas\n", + " #2. filtering pivot talbe by zeb list, without grand totals\n", + " #3. dedicated pivot table for zeb, with grand totals\n", + " #agg_prop[agg_prop[\"prop_type\"].isin(zeb_list)],\n", + " #pivot_prop_type.loc[zeb_list],\n", + " pivot_zeb_prop,\n", + " \n", + " #non-zeb same 3 methods\n", + " #agg_prop[agg_prop[\"prop_type\"].isin(non_zeb_list)],\n", + " #pivot_prop_type.loc[non_zeb_list],\n", + " pivot_non_zeb_prop\n", + ")\n", + "# confirmed all data is the same, but need pivot for grand total rows" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "3be3ef4f-0bf3-4770-a8b7-340d372ae1ce", + "metadata": {}, + "outputs": [ { "data": { "text/html": [ @@ -6165,7 +3637,7 @@ " \n", " \n", " \n", - " transit_agency\n", + " bus_size_type\n", " total_agg_cost\n", " total_bus_count\n", " new_cost_per_bus\n", @@ -6173,34 +3645,58 @@ " \n", " \n", " \n", - " 45\n", - " Oregon Department of Transportation on behalf ...\n", - " 181250\n", - " 5.0\n", - " 36250\n", + " 0\n", + " articulated\n", + " 58237576\n", + " 41.0\n", + " 1420428\n", + " \n", + " \n", + " 1\n", + " cutaway\n", + " 16694500\n", + " 152.0\n", + " 109832\n", + " \n", + " \n", + " 2\n", + " not specified\n", + " 509919038\n", + " 881.0\n", + " 578795\n", + " \n", + " \n", + " 3\n", + " over-the-road\n", + " 9516000\n", + " 14.0\n", + " 679714\n", + " \n", + " \n", + " 4\n", + " standard/conventional (30ft-45ft)\n", + " 234253277\n", + " 264.0\n", + " 887323\n", " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency total_agg_cost \\\n", - "45 Oregon Department of Transportation on behalf ... 181250 \n", + " bus_size_type total_agg_cost total_bus_count \\\n", + "0 articulated 58237576 41.0 \n", + "1 cutaway 16694500 152.0 \n", + "2 not specified 509919038 881.0 \n", + "3 over-the-road 9516000 14.0 \n", + "4 standard/conventional (30ft-45ft) 234253277 264.0 \n", "\n", - " total_bus_count new_cost_per_bus \n", - "45 5.0 36250 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "**Max total_bus_count**" - ], - "text/plain": [ - "" + " new_cost_per_bus \n", + "0 1420428 \n", + "1 109832 \n", + "2 578795 \n", + "3 679714 \n", + "4 887323 " ] }, "metadata": {}, @@ -6227,42 +3723,67 @@ " \n", " \n", " \n", - " transit_agency\n", - " total_agg_cost\n", - " total_bus_count\n", - " new_cost_per_bus\n", + " bus_size_type\n", + " bus_count\n", + " total_cost\n", + " cost_per_bus\n", " \n", " \n", " \n", " \n", - " 61\n", - " South Carolina Department of Transportation on...\n", - " 15423904\n", - " 160.0\n", - " 96399\n", + " 0\n", + " articulated\n", + " 41.0\n", + " 58237576\n", + " 1420428\n", + " \n", + " \n", + " 1\n", + " cutaway\n", + " 152.0\n", + " 16694500\n", + " 109832\n", + " \n", + " \n", + " 2\n", + " not specified\n", + " 881.0\n", + " 509919038\n", + " 578795\n", + " \n", + " \n", + " 3\n", + " over-the-road\n", + " 14.0\n", + " 9516000\n", + " 679714\n", + " \n", + " \n", + " 4\n", + " standard/conventional (30ft-45ft)\n", + " 264.0\n", + " 234253277\n", + " 887323\n", + " \n", + " \n", + " 5\n", + " Grand Total\n", + " 1352.0\n", + " 828620391\n", + " 612884\n", " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency total_agg_cost \\\n", - "61 South Carolina Department of Transportation on... 15423904 \n", - "\n", - " total_bus_count new_cost_per_bus \n", - "61 160.0 96399 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "**Min total_bus_count**" - ], - "text/plain": [ - "" + " bus_size_type bus_count total_cost cost_per_bus\n", + "0 articulated 41.0 58237576 1420428\n", + "1 cutaway 152.0 16694500 109832\n", + "2 not specified 881.0 509919038 578795\n", + "3 over-the-road 14.0 9516000 679714\n", + "4 standard/conventional (30ft-45ft) 264.0 234253277 887323\n", + "5 Grand Total 1352.0 828620391 612884" ] }, "metadata": {}, @@ -6289,65 +3810,61 @@ " \n", " \n", " \n", - " transit_agency\n", - " total_agg_cost\n", - " total_bus_count\n", - " new_cost_per_bus\n", + " prop_type\n", + " bus_count\n", + " total_cost\n", + " cost_per_bus\n", " \n", " \n", " \n", " \n", - " 9\n", - " City of Beloit\n", - " 653184\n", - " 1.0\n", - " 653184\n", - " \n", - " \n", - " 16\n", - " City of San Luis Obispo\n", - " 859270\n", - " 1.0\n", - " 859270\n", - " \n", - " \n", - " 49\n", - " SLO TRANSIT (SAN LUIS OBISPO, CA)\n", - " 847214\n", - " 1.0\n", - " 847214\n", + " 10\n", + " Grand Total\n", + " 1352.0\n", + " 828620391\n", + " 612884\n", " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency total_agg_cost total_bus_count \\\n", - "9 City of Beloit 653184 1.0 \n", - "16 City of San Luis Obispo 859270 1.0 \n", - "49 SLO TRANSIT (SAN LUIS OBISPO, CA) 847214 1.0 \n", - "\n", - " new_cost_per_bus \n", - "9 653184 \n", - "16 859270 \n", - "49 847214 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "**Max total_agg_cost**" - ], - "text/plain": [ - "" + " prop_type bus_count total_cost cost_per_bus\n", + "10 Grand Total 1352.0 828620391 612884" ] }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "# answers total buses and cost per grant type\n", + "pivot_size = pd.pivot_table(\n", + " merged_data,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"bus_size_type\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ").reset_index()\n", + "\n", + "pivot_size[\"cost_per_bus\"] = (pivot_size[\"total_cost\"] / pivot_size[\"bus_count\"]).astype(\"int64\")\n", + "\n", + "display(\n", + " agg_bus_size[[\"bus_size_type\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]],\n", + " pivot_size,\n", + " pivot_prop_type[pivot_prop_type[\"prop_type\"] == \"Grand Total\"]\n", + ")\n", + "\n", + "#same data, dont need pivot for this one because the grand totals will be the same as pivot_prop_type. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "2c933257-bdc2-4007-9571-58475118073c", + "metadata": {}, + "outputs": [ { "data": { "text/html": [ @@ -6369,42 +3886,43 @@ " \n", " \n", " \n", - " transit_agency\n", + " source\n", " total_agg_cost\n", " total_bus_count\n", " new_cost_per_bus\n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " 0\n", + " dgs\n", + " 250112853\n", + " 236.0\n", + " 1059800\n", + " \n", + " \n", + " 1\n", + " fta\n", + " 391257025\n", + " 883.0\n", + " 443099\n", + " \n", " \n", - " 24\n", - " Dallas Area Rapid Transit (DART)\n", - " 103000000\n", - " 90.0\n", - " 1144444\n", + " 2\n", + " tircp\n", + " 187250513\n", + " 233.0\n", + " 803650\n", " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency total_agg_cost total_bus_count \\\n", - "24 Dallas Area Rapid Transit (DART) 103000000 90.0 \n", - "\n", - " new_cost_per_bus \n", - "24 1144444 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "**Min total_agg_cost**" - ], - "text/plain": [ - "" + " source total_agg_cost total_bus_count new_cost_per_bus\n", + "0 dgs 250112853 236.0 1059800\n", + "1 fta 391257025 883.0 443099\n", + "2 tircp 187250513 233.0 803650" ] }, "metadata": {}, @@ -6431,30 +3949,51 @@ " \n", " \n", " \n", - " transit_agency\n", - " total_agg_cost\n", - " total_bus_count\n", - " new_cost_per_bus\n", + " source\n", + " bus_count\n", + " total_cost\n", + " cost_per_bus\n", " \n", " \n", " \n", " \n", - " 45\n", - " Oregon Department of Transportation on behalf ...\n", - " 181250\n", - " 5.0\n", - " 36250\n", + " 0\n", + " dgs\n", + " 236.0\n", + " 250112853\n", + " 1059800\n", + " \n", + " \n", + " 1\n", + " fta\n", + " 883.0\n", + " 391257025\n", + " 443099\n", + " \n", + " \n", + " 2\n", + " tircp\n", + " 233.0\n", + " 187250513\n", + " 803650\n", + " \n", + " \n", + " 3\n", + " Grand Total\n", + " 1352.0\n", + " 828620391\n", + " 612884\n", " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency total_agg_cost \\\n", - "45 Oregon Department of Transportation on behalf ... 181250 \n", - "\n", - " total_bus_count new_cost_per_bus \n", - "45 5.0 36250 " + " source bus_count total_cost cost_per_bus\n", + "0 dgs 236.0 250112853 1059800\n", + "1 fta 883.0 391257025 443099\n", + "2 tircp 233.0 187250513 803650\n", + "3 Grand Total 1352.0 828620391 612884" ] }, "metadata": {}, @@ -6462,29 +4001,43 @@ } ], "source": [ - "bus_min_max_summary(data=agg_agency,col1=\"new_cost_per_bus\")\n", - "bus_min_max_summary(data=agg_agency,col1=\"total_bus_count\")\n", - "bus_min_max_summary(data=agg_agency,col1=\"total_agg_cost\")\n" + "# answers total buses and cost per grant type\n", + "pivot_source = pd.pivot_table(\n", + " merged_data,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"source\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ").reset_index()\n", + "\n", + "pivot_source[\"cost_per_bus\"] = (pivot_source[\"total_cost\"] / pivot_source[\"bus_count\"]).astype(\"int64\")\n", + "\n", + "display(\n", + " agg_source[[\"source\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]],\n", + " pivot_source\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "11547020-dd35-4745-98f8-bbd02fccaa23", + "metadata": { + "tags": [] + }, + "source": [ + "## Testing Charts\n", + "\n", + "using `merged_data`, now without outliers.\n", + "charts looking good, similar results to initial charts" ] }, { "cell_type": "code", - "execution_count": 146, - "id": "a78e45d1-6e38-4190-ab6c-3634f807ae6b", + "execution_count": 48, + "id": "aace38a4-3f2d-460d-a258-59efa659f852", "metadata": {}, "outputs": [ - { - "data": { - "text/markdown": [ - "**ZEB Summary**" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "text/html": [ @@ -6506,71 +4059,164 @@ " \n", " \n", " \n", + " transit_agency\n", + " project_title\n", " prop_type\n", - " bus_count\n", + " bus_size_type\n", + " description\n", + " new_project_type\n", " total_cost\n", + " bus_count\n", + " source\n", + " ppno\n", + " project_description\n", " cost_per_bus\n", + " zscore_cost_per_bus\n", + " is_cpb_outlier?\n", " \n", " \n", " \n", " \n", " 0\n", - " BEB\n", - " 163.0\n", - " 167232489\n", - " 1025966\n", - " \n", - " \n", - " 1\n", - " FCEB\n", - " 102.0\n", - " 120951335\n", - " 1185797\n", - " \n", - " \n", - " 2\n", + " AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)\n", + " Puerto Rico Initiative Minimizing Emissions Pl...\n", " electric (not specified)\n", - " 44.0\n", - " 56678000\n", - " 1288136\n", - " \n", - " \n", - " 3\n", - " zero-emission bus (not specified)\n", - " 143.0\n", - " 128156513\n", - " 896199\n", + " not specified\n", + " The Metropolitan Bus Authority will receive fu...\n", + " bus only\n", + " 10000000\n", + " 8.0\n", + " fta\n", + " None\n", + " None\n", + " 1250000\n", + " 0.917956\n", + " False\n", " \n", " \n", - " 4\n", - " Grand Total\n", - " 452.0\n", - " 473018337\n", - " 1046500\n", + " 1\n", + " Cape Fear Public Transportation Authority\n", + " Wave Transit Low Emissions Replacement Vehicles\n", + " CNG\n", + " not specified\n", + " Wave Transit will receive funding to buy compr...\n", + " bus only\n", + " 2860250\n", + " 5.0\n", + " fta\n", + " None\n", + " None\n", + " 572050\n", + " -0.529139\n", + " False\n", " \n", " \n", "\n", "" ], "text/plain": [ - " prop_type bus_count total_cost cost_per_bus\n", - "0 BEB 163.0 167232489 1025966\n", - "1 FCEB 102.0 120951335 1185797\n", - "2 electric (not specified) 44.0 56678000 1288136\n", - "3 zero-emission bus (not specified) 143.0 128156513 896199\n", - "4 Grand Total 452.0 473018337 1046500" + " transit_agency \\\n", + "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) \n", + "1 Cape Fear Public Transportation Authority \n", + "\n", + " project_title \\\n", + "0 Puerto Rico Initiative Minimizing Emissions Pl... \n", + "1 Wave Transit Low Emissions Replacement Vehicles \n", + "\n", + " prop_type bus_size_type \\\n", + "0 electric (not specified) not specified \n", + "1 CNG not specified \n", + "\n", + " description new_project_type \\\n", + "0 The Metropolitan Bus Authority will receive fu... bus only \n", + "1 Wave Transit will receive funding to buy compr... bus only \n", + "\n", + " total_cost bus_count source ppno project_description cost_per_bus \\\n", + "0 10000000 8.0 fta None None 1250000 \n", + "1 2860250 5.0 fta None None 572050 \n", + "\n", + " zscore_cost_per_bus is_cpb_outlier? \n", + "0 0.917956 False \n", + "1 -0.529139 False " + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged_data.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "4753f3ea-00b6-4d5e-a3f0-73b3d3593acb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "792635.3409090909" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "396712.6067531972" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "1046500.7455752213" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "546173.304347826" ] }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "# means and standard deviations\n", + "# for graphs\n", + "cpb_mean = merged_data[\"cost_per_bus\"].mean()\n", + "cpb_std = merged_data[\"cost_per_bus\"].std()\n", + "zeb_cpb_wt_avg = (merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"bus_count\"].sum())\n", + "non_zeb_cpb_wt_avg = (merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"bus_count\"].sum())\n", + "display(\n", + " cpb_mean,\n", + " cpb_std,\n", + " zeb_cpb_wt_avg,\n", + " non_zeb_cpb_wt_avg\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "2007be9d-13ec-4d0d-a642-d9a42448b924", + "metadata": {}, + "outputs": [ { "data": { - "text/markdown": [ - "**Non-ZEB Summary**" - ], "text/plain": [ - "" + "1056659.3043478262" ] }, "metadata": {}, @@ -6606,58 +4252,50 @@ " \n", " \n", " 0\n", - " CNG\n", - " 252.0\n", - " 176039140\n", - " 698568\n", + " BEB\n", + " 163.0\n", + " 167232489\n", + " 1025966\n", " \n", " \n", " 1\n", - " ethanol\n", - " 9.0\n", - " 1006750\n", - " 111861\n", + " FCEB\n", + " 102.0\n", + " 120951335\n", + " 1185797\n", " \n", " \n", " 2\n", - " low emission (hybrid)\n", - " 145.0\n", - " 91824361\n", - " 633271\n", + " electric (not specified)\n", + " 44.0\n", + " 56678000\n", + " 1288136\n", " \n", " \n", " 3\n", - " low emission (propane)\n", - " 44.0\n", - " 8403969\n", - " 190999\n", + " zero-emission bus (not specified)\n", + " 143.0\n", + " 128156513\n", + " 896199\n", " \n", " \n", " 4\n", - " mix (zero and low emission)\n", - " 125.0\n", - " 36775430\n", - " 294203\n", - " \n", - " \n", - " 5\n", " Grand Total\n", - " 575.0\n", - " 314049650\n", - " 546173\n", + " 452.0\n", + " 473018337\n", + " 1046500\n", " \n", " \n", "\n", "" ], "text/plain": [ - " prop_type bus_count total_cost cost_per_bus\n", - "0 CNG 252.0 176039140 698568\n", - "1 ethanol 9.0 1006750 111861\n", - "2 low emission (hybrid) 145.0 91824361 633271\n", - "3 low emission (propane) 44.0 8403969 190999\n", - "4 mix (zero and low emission) 125.0 36775430 294203\n", - "5 Grand Total 575.0 314049650 546173" + " prop_type bus_count total_cost cost_per_bus\n", + "0 BEB 163.0 167232489 1025966\n", + "1 FCEB 102.0 120951335 1185797\n", + "2 electric (not specified) 44.0 56678000 1288136\n", + "3 zero-emission bus (not specified) 143.0 128156513 896199\n", + "4 Grand Total 452.0 473018337 1046500" ] }, "metadata": {}, @@ -6665,17 +4303,169 @@ }, { "data": { - "text/markdown": [ - "The remaining buses did not specify a propulsion type" - ], "text/plain": [ - "" + "1046500.7455752213" ] }, "metadata": {}, "output_type": "display_data" } ], + "source": [ + "# why is the average different when i use .mean() vs. total cost / bus cout\n", + "\n", + "display(\n", + " #this is the arithmatic mean, sums all the `cost_per_bus` rows, the divide by the number of rows. (row-wise)\n", + " merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"cost_per_bus\"].mean(),\n", + " #this is like the accounting method of calculating average (Total Cost and Total Quantity Approach (Weighted Average))\n", + " pivot_zeb_prop,\n", + " # calculating mean by weighted average aproach (total cost / total bus count, similar to pivot table)\n", + " (merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"bus_count\"].sum())\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8645cf77-b30a-4c45-b943-ac81e8b5a613", + "metadata": {}, + "outputs": [], + "source": [ + "dist_curve(\n", + " df=merged_data,\n", + " mean=cpb_mean,\n", + " std=cpb_std,\n", + " title=\"all buses, cost per bus distribution\",\n", + " xlabel=\"cost per bus, $ million(s)\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cefa6800-df50-4eda-95f8-74363ef942d0", + "metadata": {}, + "outputs": [], + "source": [ + "dist_curve(\n", + " df=merged_data[merged_data[\"prop_type\"].isin(zeb_list)],\n", + " #using the accounting, weighted average approach to mean (total cost/total number of buses)\n", + " mean=zeb_cpb_wt_avg,\n", + " # need to investigate if std needs to be weighted as well?\n", + " std=merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"cost_per_bus\"].std(),\n", + " title=\"ZEB buses, cost per bus distribution\",\n", + " xlabel=\"cost per bus, $ million(s)\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "563304d2-2d98-44e6-b3a4-fd54f63fc0d8", + "metadata": {}, + "outputs": [], + "source": [ + "dist_curve(\n", + " df=merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)],\n", + " mean=non_zeb_cpb_wt_avg,\n", + " std=merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"cost_per_bus\"].std(),\n", + " title=\"non-ZEB costper bus Distribution\",\n", + " xlabel='\"cost per bus, $ million(s)\"',\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f11c857-ddbe-4871-aeca-e27fa00fbde8", + "metadata": {}, + "outputs": [], + "source": [ + "make_chart(\"new_cost_per_bus\", \"Cost per bus by propulsion type\", x_col=\"prop_type\", data=agg_prop)\n", + "make_chart(\"total_bus_count\", \"Bus count by propulsion type\", x_col=\"prop_type\", data=agg_prop)\n", + "agg_prop[[\"prop_type\",\"new_cost_per_bus\",\"total_bus_count\"]].sort_values(by=\"new_cost_per_bus\", ascending=False)\n" + ] + }, + { + "cell_type": "markdown", + "id": "9270ab8f-25ff-4de3-aca5-7ef4637a4f9c", + "metadata": { + "tags": [] + }, + "source": [ + "## Testing summary\n", + "time to rework the summary section.\n", + "\n", + "no more long expositions and variables. try to get the same point across using tables instead." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2472461d-7663-4b66-9bde-4c2a199707a5", + "metadata": {}, + "outputs": [], + "source": [ + "summary = f\"\"\"\n", + "\n", + "# Bus Procurement Cost Analysis\n", + "\n", + "## Summary\n", + "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", + "\n", + "Data was compiled from three data sources:\n", + "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", + "2. TIRCP project data (state-funded, California only)\n", + "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", + "\n", + "Breakdown of each data souce:\n", + "{pivot_source.to_markdown(index=False)}\n", + "\n", + "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", + "The resulting dataset only contained projects that were solely used to procure buses. 87 projects remained. These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", + "\n", + "**ZEB buses include:**\n", + "- zero-emission (not specified) \n", + "- electric (not specified)\n", + "- battery electric \n", + "- fuel cell electric\n", + "\n", + "**Non-ZEB buses include:**\n", + "- CNG \n", + "- ethanol \n", + "- ow emission (hybrid, propane) \n", + "- diesel \n", + "- gas\n", + "\n", + "Below are charts and tables that summarize the findings.\n", + "\n", + "\n", + "\n", + "\n", + "\"\"\"\n", + "from IPython.display import Markdown, display\n", + "\n", + "display(Markdown(summary))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91d0361d-b165-4607-b22e-66ae4234863d", + "metadata": {}, + "outputs": [], + "source": [ + "bus_min_max_summary(data=agg_agency,col1=\"new_cost_per_bus\")\n", + "bus_min_max_summary(data=agg_agency,col1=\"total_bus_count\")\n", + "bus_min_max_summary(data=agg_agency,col1=\"total_agg_cost\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a78e45d1-6e38-4190-ab6c-3634f807ae6b", + "metadata": {}, + "outputs": [], "source": [ "display(\n", " Markdown(\"**ZEB Summary**\"),\n", @@ -6689,28 +4479,10 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "id": "e39c89a1-a726-44f9-808b-bcf936c77254", "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "\n", - "**Conclusion**\n", - "\n", - "Based on these findings, The average cost of a ZEB, throughout the US, is ~$1,000,000, roughly twice the price of a convention, non-ZEB.\n", - "Reasons for variance in cost depends on the options the Trasnit\n", - "Agencies chose for their bus. Unfortunately, analyzing the cost of configuable options is outside the scope of this analysis. \n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "conclusion = f\"\"\"\n", "**Conclusion**\n", From b9bc6c12720f467ab4163412be4e2c4f9dc047d0 Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Mon, 24 Jun 2024 22:00:58 +0000 Subject: [PATCH 20/36] turned zeb projects list to a variable, updated all the proceeding variables, graphs and functions to the zeb projects variable. also more organizing --- bus_procurement_cost/refactor_bus_cost.ipynb | 1466 +++++++++++++++++- 1 file changed, 1397 insertions(+), 69 deletions(-) diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index 8707d00f8..be9f0406f 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -101,14 +101,17 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 88, "id": "c6dacaba-c6f7-4cb0-afef-a84f77de25fc", "metadata": {}, "outputs": [], "source": [ "# NEW FUNCTION\n", "# move this to bus_cost_utils\n", - "def bus_min_max_summary(data:pd.DataFrame, col1:str):\n", + "def bus_min_max_summary(data:pd.DataFrame, col1:str, col_list=[\"transit_agency\",\n", + " \"total_agg_cost\",\n", + " \"total_bus_count\",\n", + " \"new_cost_per_bus\"]):\n", " \"\"\"\n", " function to display min/max of specific column in aggregated bus df.\n", " \n", @@ -116,15 +119,9 @@ " \n", " return display(\n", " Markdown(f\"**Max {col1}**\"),\n", - " data[data[col1] == data[col1].max()][[\"transit_agency\",\n", - " \"total_agg_cost\",\n", - " \"total_bus_count\",\n", - " \"new_cost_per_bus\"]],\n", + " data[data[col1] == data[col1].max()][col_list],\n", " Markdown(f\"**Min {col1}**\"),\n", - " data[data[col1] == data[col1].min()][[\"transit_agency\",\n", - " \"total_agg_cost\",\n", - " \"total_bus_count\",\n", - " \"new_cost_per_bus\"]]\n", + " data[data[col1] == data[col1].min()][col_list]\n", " )\n", " \n" ] @@ -3367,12 +3364,12 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 107, "id": "ca709e43-4947-4a34-970f-216d4b6ab7cc", "metadata": {}, "outputs": [], "source": [ - "#pivot table to get grand total for zeb only data\n", + "#pivot table to get grand total for zeb/non-zeb only data\n", "\n", "# keep this\n", "zeb_list =[\n", @@ -3382,6 +3379,8 @@ " \"zero-emission bus (not specified)\",\n", "]\n", "\n", + "zeb_projects = merged_data[merged_data[\"prop_type\"].isin(zeb_list)]\n", + "\n", "#keep this\n", "non_zeb_list =[\n", " \"CNG\",\n", @@ -3391,11 +3390,12 @@ " \"mix (zero and low emission)\",\n", "]\n", "\n", + "non_zeb_projects = merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)]\n", "\n", "#keep this\n", "pivot_zeb_prop = pd.pivot_table(\n", " #filted incoming DF for zeb prop types\n", - " merged_data[merged_data[\"prop_type\"].isin(zeb_list)],\n", + " zeb_projects,\n", " values = [\"bus_count\", \"total_cost\"],\n", " index = \"prop_type\",\n", " aggfunc = \"sum\",\n", @@ -3408,7 +3408,7 @@ "#keep this\n", "pivot_non_zeb_prop = pd.pivot_table(\n", " #filted incoming DF for non-zeb prop types\n", - " merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)],\n", + " non_zeb_projects,\n", " values = [\"bus_count\", \"total_cost\"],\n", " index = \"prop_type\",\n", " aggfunc = \"sum\",\n", @@ -3838,7 +3838,7 @@ } ], "source": [ - "# answers total buses and cost per grant type\n", + "# answers total buses sizes\n", "pivot_size = pd.pivot_table(\n", " merged_data,\n", " values = [\"bus_count\", \"total_cost\"],\n", @@ -4197,6 +4197,8 @@ "# for graphs\n", "cpb_mean = merged_data[\"cost_per_bus\"].mean()\n", "cpb_std = merged_data[\"cost_per_bus\"].std()\n", + "\n", + "#testing weighted average calculation for sub-set non-zeb and zeb\n", "zeb_cpb_wt_avg = (merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"bus_count\"].sum())\n", "non_zeb_cpb_wt_avg = (merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"bus_count\"].sum())\n", "display(\n", @@ -4209,7 +4211,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 108, "id": "2007be9d-13ec-4d0d-a642-d9a42448b924", "metadata": {}, "outputs": [ @@ -4316,21 +4318,38 @@ "\n", "display(\n", " #this is the arithmatic mean, sums all the `cost_per_bus` rows, the divide by the number of rows. (row-wise)\n", - " merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"cost_per_bus\"].mean(),\n", + " zeb_projects[\"cost_per_bus\"].mean(),\n", + " \n", " #this is like the accounting method of calculating average (Total Cost and Total Quantity Approach (Weighted Average))\n", " pivot_zeb_prop,\n", - " # calculating mean by weighted average aproach (total cost / total bus count, similar to pivot table)\n", - " (merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"bus_count\"].sum())\n", - ")\n" + " \n", + " # calculating mean by weighted average the long way (total cost / total bus count, similar to pivot table)\n", + " (zeb_projects[\"total_cost\"].sum() / zeb_projects[\"bus_count\"].sum())\n", + ")\n", + "\n", + "# so the calculated grand total cost_per_bus is equivilent to the weighted average cost per bus\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "id": "8645cf77-b30a-4c45-b943-ac81e8b5a613", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "# chart of all cost per bus in the analysis.\n", + "\n", "dist_curve(\n", " df=merged_data,\n", " mean=cpb_mean,\n", @@ -4342,17 +4361,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 109, "id": "cefa6800-df50-4eda-95f8-74363ef942d0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "# ZEB cost per bus \n", "dist_curve(\n", - " df=merged_data[merged_data[\"prop_type\"].isin(zeb_list)],\n", + " df=zeb_projects,\n", " #using the accounting, weighted average approach to mean (total cost/total number of buses)\n", " mean=zeb_cpb_wt_avg,\n", " # need to investigate if std needs to be weighted as well?\n", - " std=merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"cost_per_bus\"].std(),\n", + " std=zeb_projects[\"cost_per_bus\"].std(),\n", " title=\"ZEB buses, cost per bus distribution\",\n", " xlabel=\"cost per bus, $ million(s)\",\n", ")" @@ -4360,15 +4391,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 110, "id": "563304d2-2d98-44e6-b3a4-fd54f63fc0d8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "# non-zeb cost per bus\n", "dist_curve(\n", - " df=merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)],\n", + " df=non_zeb_projects,\n", " mean=non_zeb_cpb_wt_avg,\n", - " std=merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"cost_per_bus\"].std(),\n", + " std=non_zeb_projects[\"cost_per_bus\"].std(),\n", " title=\"non-ZEB costper bus Distribution\",\n", " xlabel='\"cost per bus, $ million(s)\"',\n", ")" @@ -4376,13 +4419,150 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "id": "5f11c857-ddbe-4871-aeca-e27fa00fbde8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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prop_typenew_cost_per_bustotal_bus_count
3electric (not specified)128813644.0
2FCEB1185797102.0
0BEB1025966163.0
9zero-emission bus (not specified)896199143.0
1CNG698568252.0
5low emission (hybrid)633271145.0
7mix (zero and low emission)294203125.0
6low emission (propane)19099944.0
8not specified127853325.0
4ethanol1118619.0
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" + ], + "text/plain": [ + " prop_type new_cost_per_bus total_bus_count\n", + "3 electric (not specified) 1288136 44.0\n", + "2 FCEB 1185797 102.0\n", + "0 BEB 1025966 163.0\n", + "9 zero-emission bus (not specified) 896199 143.0\n", + "1 CNG 698568 252.0\n", + "5 low emission (hybrid) 633271 145.0\n", + "7 mix (zero and low emission) 294203 125.0\n", + "6 low emission (propane) 190999 44.0\n", + "8 not specified 127853 325.0\n", + "4 ethanol 111861 9.0" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ + "# multiple bar charts in one cell\n", + "\n", + "# cpb by prop type\n", "make_chart(\"new_cost_per_bus\", \"Cost per bus by propulsion type\", x_col=\"prop_type\", data=agg_prop)\n", + "\n", + "# bus count by prop type\n", "make_chart(\"total_bus_count\", \"Bus count by propulsion type\", x_col=\"prop_type\", data=agg_prop)\n", + "\n", + "# pivot table to\n", "agg_prop[[\"prop_type\",\"new_cost_per_bus\",\"total_bus_count\"]].sort_values(by=\"new_cost_per_bus\", ascending=False)\n" ] }, @@ -4393,7 +4573,7 @@ "tags": [] }, "source": [ - "## Testing summary\n", + "## Testing summary and conclusion\n", "time to rework the summary section.\n", "\n", "no more long expositions and variables. try to get the same point across using tables instead." @@ -4401,10 +4581,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "id": "2472461d-7663-4b66-9bde-4c2a199707a5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/markdown": [ + "\n", + "\n", + "# Bus Procurement Cost Analysis\n", + "\n", + "## Summary\n", + "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", + "\n", + "Data was compiled from three data sources:\n", + "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", + "2. TIRCP project data (state-funded, California only)\n", + "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", + "\n", + "Breakdown of each data souce:\n", + "| source | bus_count | total_cost | cost_per_bus |\n", + "|:------------|------------:|-------------:|---------------:|\n", + "| dgs | 236 | 250112853 | 1059800 |\n", + "| fta | 883 | 391257025 | 443099 |\n", + "| tircp | 233 | 187250513 | 803650 |\n", + "| Grand Total | 1352 | 828620391 | 612884 |\n", + "\n", + "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", + "The resulting dataset only contained projects that were solely used to procure buses. 87 projects remained. These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", + "\n", + "**ZEB buses include:**\n", + "- zero-emission (not specified) \n", + "- electric (not specified)\n", + "- battery electric \n", + "- fuel cell electric\n", + "\n", + "**Non-ZEB buses include:**\n", + "- CNG \n", + "- ethanol \n", + "- ow emission (hybrid, propane) \n", + "- diesel \n", + "- gas\n", + "\n", + "Below are charts and tables that summarize the findings.\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "summary = f\"\"\"\n", "\n", @@ -4450,46 +4683,1141 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 90, "id": "91d0361d-b165-4607-b22e-66ae4234863d", "metadata": {}, - "outputs": [], - "source": [ - "bus_min_max_summary(data=agg_agency,col1=\"new_cost_per_bus\")\n", - "bus_min_max_summary(data=agg_agency,col1=\"total_bus_count\")\n", - "bus_min_max_summary(data=agg_agency,col1=\"total_agg_cost\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a78e45d1-6e38-4190-ab6c-3634f807ae6b", - "metadata": {}, - "outputs": [], - "source": [ - "display(\n", - " Markdown(\"**ZEB Summary**\"),\n", - " pivot_zeb_prop,\n", - " \n", - " Markdown(\"**Non-ZEB Summary**\"),\n", - " pivot_non_zeb_prop,\n", - " Markdown(\"The remaining buses did not specify a propulsion type\")\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e39c89a1-a726-44f9-808b-bcf936c77254", - "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/markdown": [ + "**Max new_cost_per_bus**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
71Transit Joint Powers Authority for Merced County32233242.01611662
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
45Oregon Department of Transportation on behalf ...1812505.036250
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
61South Carolina Department of Transportation on...15423904160.096399
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
9City of Beloit6531841.0653184
16City of San Luis Obispo8592701.0859270
49SLO TRANSIT (SAN LUIS OBISPO, CA)8472141.0847214
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" + ], + "text/plain": [ + " transit_agency total_agg_cost total_bus_count \\\n", + "9 City of Beloit 653184 1.0 \n", + "16 City of San Luis Obispo 859270 1.0 \n", + "49 SLO TRANSIT (SAN LUIS OBISPO, CA) 847214 1.0 \n", + "\n", + " new_cost_per_bus \n", + "9 653184 \n", + "16 859270 \n", + "49 847214 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Max total_agg_cost**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
24Dallas Area Rapid Transit (DART)10300000090.01144444
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" + ], + "text/plain": [ + " transit_agency total_agg_cost total_bus_count \\\n", + "24 Dallas Area Rapid Transit (DART) 103000000 90.0 \n", + "\n", + " new_cost_per_bus \n", + "24 1144444 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Min total_agg_cost**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
45Oregon Department of Transportation on behalf ...1812505.036250
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" + ], + "text/plain": [ + " transit_agency total_agg_cost \\\n", + "45 Oregon Department of Transportation on behalf ... 181250 \n", + "\n", + " total_bus_count new_cost_per_bus \n", + "45 5.0 36250 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#min max values for all projects\n", + "bus_min_max_summary(data=agg_agency,col1=\"new_cost_per_bus\")\n", + "bus_min_max_summary(data=agg_agency,col1=\"total_bus_count\")\n", + "bus_min_max_summary(data=agg_agency,col1=\"total_agg_cost\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "87f0521f-3bd7-441a-9ec6-0c9d29bdd1f8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", + " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", + " 'ppno', 'project_description', 'cost_per_bus', 'zscore_cost_per_bus',\n", + " 'is_cpb_outlier?'],\n", + " dtype='object')" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged_data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "id": "6896d09d-a8e8-4351-bf69-6538d031bf93", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "**Which Agneices had the highest and lowest cost per bus?**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Max cost_per_bus**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_costbus_countcost_per_bus
76University of California - San Diego41340002.02067000
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" + ], + "text/plain": [ + " transit_agency total_cost bus_count cost_per_bus\n", + "76 University of California - San Diego 4134000 2.0 2067000" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Min cost_per_bus**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_costbus_countcost_per_bus
45City of Wasco15430003.0514333
\n", + "
" + ], + "text/plain": [ + " transit_agency total_cost bus_count cost_per_bus\n", + "45 City of Wasco 1543000 3.0 514333" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## min max values of just ZEB projects\n", + "# YES I CAN!!\n", + "new_cols =[\n", + " \"transit_agency\",\n", + " \"total_cost\",\n", + " \"bus_count\",\n", + " \"cost_per_bus\"]\n", + "\n", + "display(Markdown(\"**Which Agneices had the highest and lowest cost per bus?**\")),\n", + "bus_min_max_summary(data=zeb_projects, col1=\"cost_per_bus\", col_list=new_cols)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "id": "743b25a2-8693-44f7-98fe-384e910620a7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "**Which agency procured the most and least amount of buses?**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Max bus_count**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_costbus_countcost_per_bus
44City of Los Angeles (LA DOT)102790000112.0917767
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" + ], + "text/plain": [ + " transit_agency total_cost bus_count cost_per_bus\n", + "44 City of Los Angeles (LA DOT) 102790000 112.0 917767" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Min bus_count**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_costbus_countcost_per_bus
70SLO TRANSIT (SAN LUIS OBISPO, CA)8472141.0847214
82City of San Luis Obispo8592701.0859270
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" + ], + "text/plain": [ + " transit_agency total_cost bus_count cost_per_bus\n", + "70 SLO TRANSIT (SAN LUIS OBISPO, CA) 847214 1.0 847214\n", + "82 City of San Luis Obispo 859270 1.0 859270" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Markdown(\n", + " \"**Which agency procured the most and least amount of ZEBs?**\"\n", + "))\n", + "bus_min_max_summary(data=zeb_projects, col1=\"bus_count\", col_list=new_cols)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "id": "45a95018-0ac8-450d-97d2-aa394e94779a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "**Which Agency had the most and least total cost?**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Max total_cost**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_costbus_countcost_per_bus
44City of Los Angeles (LA DOT)102790000112.0917767
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" + ], + "text/plain": [ + " transit_agency total_cost bus_count cost_per_bus\n", + "44 City of Los Angeles (LA DOT) 102790000 112.0 917767" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Min total_cost**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_costbus_countcost_per_bus
70SLO TRANSIT (SAN LUIS OBISPO, CA)8472141.0847214
\n", + "
" + ], + "text/plain": [ + " transit_agency total_cost bus_count cost_per_bus\n", + "70 SLO TRANSIT (SAN LUIS OBISPO, CA) 847214 1.0 847214" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Markdown(\n", + " \"**Which Agency had the most and least total ZEB cost?**\"\n", + "))\n", + "bus_min_max_summary(data=zeb_projects, col1=\"total_cost\", col_list=new_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "id": "7472ba04-7def-46ef-814b-bf63c1016f3b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "**ZEB Summary**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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prop_typebus_counttotal_costcost_per_bus
0BEB163.01672324891025966
1FCEB102.01209513351185797
2electric (not specified)44.0566780001288136
3zero-emission bus (not specified)143.0128156513896199
4Grand Total452.04730183371046500
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" + ], + "text/plain": [ + " prop_type bus_count total_cost cost_per_bus\n", + "0 BEB 163.0 167232489 1025966\n", + "1 FCEB 102.0 120951335 1185797\n", + "2 electric (not specified) 44.0 56678000 1288136\n", + "3 zero-emission bus (not specified) 143.0 128156513 896199\n", + "4 Grand Total 452.0 473018337 1046500" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Non-ZEB Summary**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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prop_typebus_counttotal_costcost_per_bus
0CNG252.0176039140698568
1ethanol9.01006750111861
2low emission (hybrid)145.091824361633271
3low emission (propane)44.08403969190999
4mix (zero and low emission)125.036775430294203
5Grand Total575.0314049650546173
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" + ], + "text/plain": [ + " prop_type bus_count total_cost cost_per_bus\n", + "0 CNG 252.0 176039140 698568\n", + "1 ethanol 9.0 1006750 111861\n", + "2 low emission (hybrid) 145.0 91824361 633271\n", + "3 low emission (propane) 44.0 8403969 190999\n", + "4 mix (zero and low emission) 125.0 36775430 294203\n", + "5 Grand Total 575.0 314049650 546173" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "The remaining buses did not specify a propulsion type" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(\n", + " Markdown(\"**ZEB Summary**\"),\n", + " pivot_zeb_prop,\n", + " \n", + " Markdown(\"**Non-ZEB Summary**\"),\n", + " pivot_non_zeb_prop,\n", + " \n", + " Markdown(\"The remaining buses did not specify a propulsion type\")\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ee2f5572-0683-4909-8e2a-deea22c006fb", + "metadata": {}, + "source": [ + "## conslusion" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "e39c89a1-a726-44f9-808b-bcf936c77254", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "\n", + "**Conclusion**\n", + "\n", + "Based on these findings, The average cost of a ZEB, throughout the US, is ~$1,000,000, roughly twice the price of a convention, non-ZEB.\n", + "Reasons for variance in cost depends on the options the Trasnit\n", + "Agencies chose for their bus. Unfortunately, analyzing the cost of configuable options is outside the scope of this analysis. \n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "conclusion = f\"\"\"\n", "**Conclusion**\n", "\n", - "Based on these findings, The average cost of a ZEB, throughout the US, is ~$1,000,000, roughly twice the price of a convention, non-ZEB.\n", - "Reasons for variance in cost depends on the options the Trasnit\n", - "Agencies chose for their bus. Unfortunately, analyzing the cost of configuable options is outside the scope of this analysis. \n", + "Based on these findings, The average cost of a ZEB, throughout the US, is ~$1,000,000, roughly twice the price of a conventional, non-ZEB.\n", + "Reasons for the variance in cost depends mainly on the options the Trasnit\n", + "Agencies chose for their bus. Unfortunately, analyzing the cost of configuable options is outside the scope of data provided. \n", "\"\"\"\n", "display(\n", " Markdown(conclusion)\n", From 8b58b8e9d17262a0aa2cb47fb43e68b36eb765f3 Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Mon, 24 Jun 2024 23:15:43 +0000 Subject: [PATCH 21/36] added bus size chart that excluded the not-specified responses --- bus_procurement_cost/refactor_bus_cost.ipynb | 252 +++++++++++++++---- 1 file changed, 202 insertions(+), 50 deletions(-) diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index be9f0406f..fade5a2da 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 125, "id": "e1b1f367-1dac-463f-8790-2e5134b7e79b", "metadata": {}, "outputs": [ @@ -74,7 +74,7 @@ "source": [ "# links to all Raw Data\n", "fta_raw = pd.read_csv(f\"{GCS_PATH}raw_data-analyses_bus_procurement_cost_fta_press_release_data_csv.csv\")\n", - "tirp_raw = pd.read_excel(f\"{GCS_PATH}raw_TIRCP Tracking Sheets 2_1-10-2024.xlsx\", sheet_name=\"Project Tracking\")\n", + "tircp_raw = pd.read_excel(f\"{GCS_PATH}raw_TIRCP Tracking Sheets 2_1-10-2024.xlsx\", sheet_name=\"Project Tracking\")\n", "dgs17b_raw = pd.read_excel(f\"{GCS_PATH}raw_17b compiled.xlsx\", sheet_name = \"Usage Report Template\")\n", "dgs17c_raw = pd.read_excel(f\"{GCS_PATH}raw_17c compiled-Proterra Compiled Contract Usage Report .xlsx\", sheet_name = \"Proterra \")" ] @@ -4419,7 +4419,136 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 137, + "id": "aa916127-57d9-4c1c-b5eb-8b7b7e4ac672", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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bus_size_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
0articulated025823757641.014204281.605005False
1cutaway3016694500152.0109832-1.464878False
2not specified406509919038881.0578795-0.366399False
3over-the-road01951600014.0679714-0.130011False
4standard/conventional (30ft-45ft)036234253277264.08873230.356283False
\n", + "
" + ], + "text/plain": [ + " bus_size_type total_project_count \\\n", + "0 articulated 0 \n", + "1 cutaway 3 \n", + "2 not specified 40 \n", + "3 over-the-road 0 \n", + "4 standard/conventional (30ft-45ft) 0 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count \\\n", + "0 2 58237576 41.0 \n", + "1 0 16694500 152.0 \n", + "2 6 509919038 881.0 \n", + "3 1 9516000 14.0 \n", + "4 36 234253277 264.0 \n", + "\n", + " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", + "0 1420428 1.605005 False \n", + "1 109832 -1.464878 False \n", + "2 578795 -0.366399 False \n", + "3 679714 -0.130011 False \n", + "4 887323 0.356283 False " + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "agg_bus_size" + ] + }, + { + "cell_type": "code", + "execution_count": 142, "id": "5f11c857-ddbe-4871-aeca-e27fa00fbde8", "metadata": {}, "outputs": [ @@ -4443,6 +4572,16 @@ "metadata": {}, "output_type": "display_data" }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/html": [ @@ -4548,7 +4687,7 @@ "4 ethanol 111861 9.0" ] }, - "execution_count": 71, + "execution_count": 142, "metadata": {}, "output_type": "execute_result" } @@ -4562,6 +4701,10 @@ "# bus count by prop type\n", "make_chart(\"total_bus_count\", \"Bus count by propulsion type\", x_col=\"prop_type\", data=agg_prop)\n", "\n", + "#bus size bar chart\n", + "make_chart(\"total_bus_count\", \"\"\"Amount of buses procured by bus size.\n", + "excluding 'not specified' responses.\"\"\", x_col=\"bus_size_type\",data=agg_bus_size[agg_bus_size[\"bus_size_type\"]!=\"not specified\"])\n", + "\n", "# pivot table to\n", "agg_prop[[\"prop_type\",\"new_cost_per_bus\",\"total_bus_count\"]].sort_values(by=\"new_cost_per_bus\", ascending=False)\n" ] @@ -5087,32 +5230,7 @@ }, { "cell_type": "code", - "execution_count": 78, - "id": "87f0521f-3bd7-441a-9ec6-0c9d29bdd1f8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", - " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", - " 'ppno', 'project_description', 'cost_per_bus', 'zscore_cost_per_bus',\n", - " 'is_cpb_outlier?'],\n", - " dtype='object')" - ] - }, - "execution_count": 78, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged_data.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 111, + "execution_count": 129, "id": "6896d09d-a8e8-4351-bf69-6538d031bf93", "metadata": {}, "outputs": [ @@ -5162,6 +5280,7 @@ " \n", " \n", " transit_agency\n", + " prop_type\n", " total_cost\n", " bus_count\n", " cost_per_bus\n", @@ -5171,6 +5290,7 @@ " \n", " 76\n", " University of California - San Diego\n", + " BEB\n", " 4134000\n", " 2.0\n", " 2067000\n", @@ -5180,8 +5300,11 @@ "" ], "text/plain": [ - " transit_agency total_cost bus_count cost_per_bus\n", - "76 University of California - San Diego 4134000 2.0 2067000" + " transit_agency prop_type total_cost bus_count \\\n", + "76 University of California - San Diego BEB 4134000 2.0 \n", + "\n", + " cost_per_bus \n", + "76 2067000 " ] }, "metadata": {}, @@ -5221,6 +5344,7 @@ " \n", " \n", " transit_agency\n", + " prop_type\n", " total_cost\n", " bus_count\n", " cost_per_bus\n", @@ -5230,6 +5354,7 @@ " \n", " 45\n", " City of Wasco\n", + " zero-emission bus (not specified)\n", " 1543000\n", " 3.0\n", " 514333\n", @@ -5239,8 +5364,11 @@ "" ], "text/plain": [ - " transit_agency total_cost bus_count cost_per_bus\n", - "45 City of Wasco 1543000 3.0 514333" + " transit_agency prop_type total_cost bus_count \\\n", + "45 City of Wasco zero-emission bus (not specified) 1543000 3.0 \n", + "\n", + " cost_per_bus \n", + "45 514333 " ] }, "metadata": {}, @@ -5252,6 +5380,7 @@ "# YES I CAN!!\n", "new_cols =[\n", " \"transit_agency\",\n", + " \"prop_type\",\n", " \"total_cost\",\n", " \"bus_count\",\n", " \"cost_per_bus\"]\n", @@ -5263,14 +5392,14 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 130, "id": "743b25a2-8693-44f7-98fe-384e910620a7", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "**Which agency procured the most and least amount of buses?**" + "**Which agency procured the most and least amount of ZEBs?**" ], "text/plain": [ "" @@ -5313,6 +5442,7 @@ " \n", " \n", " transit_agency\n", + " prop_type\n", " total_cost\n", " bus_count\n", " cost_per_bus\n", @@ -5322,6 +5452,7 @@ " \n", " 44\n", " City of Los Angeles (LA DOT)\n", + " zero-emission bus (not specified)\n", " 102790000\n", " 112.0\n", " 917767\n", @@ -5331,8 +5462,11 @@ "" ], "text/plain": [ - " transit_agency total_cost bus_count cost_per_bus\n", - "44 City of Los Angeles (LA DOT) 102790000 112.0 917767" + " transit_agency prop_type \\\n", + "44 City of Los Angeles (LA DOT) zero-emission bus (not specified) \n", + "\n", + " total_cost bus_count cost_per_bus \n", + "44 102790000 112.0 917767 " ] }, "metadata": {}, @@ -5372,6 +5506,7 @@ " \n", " \n", " transit_agency\n", + " prop_type\n", " total_cost\n", " bus_count\n", " cost_per_bus\n", @@ -5381,6 +5516,7 @@ " \n", " 70\n", " SLO TRANSIT (SAN LUIS OBISPO, CA)\n", + " BEB\n", " 847214\n", " 1.0\n", " 847214\n", @@ -5388,6 +5524,7 @@ " \n", " 82\n", " City of San Luis Obispo\n", + " BEB\n", " 859270\n", " 1.0\n", " 859270\n", @@ -5397,9 +5534,13 @@ "" ], "text/plain": [ - " transit_agency total_cost bus_count cost_per_bus\n", - "70 SLO TRANSIT (SAN LUIS OBISPO, CA) 847214 1.0 847214\n", - "82 City of San Luis Obispo 859270 1.0 859270" + " transit_agency prop_type total_cost bus_count \\\n", + "70 SLO TRANSIT (SAN LUIS OBISPO, CA) BEB 847214 1.0 \n", + "82 City of San Luis Obispo BEB 859270 1.0 \n", + "\n", + " cost_per_bus \n", + "70 847214 \n", + "82 859270 " ] }, "metadata": {}, @@ -5416,14 +5557,14 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 131, "id": "45a95018-0ac8-450d-97d2-aa394e94779a", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "**Which Agency had the most and least total cost?**" + "**Which Agency had the most and least total ZEB cost?**" ], "text/plain": [ "" @@ -5466,6 +5607,7 @@ " \n", " \n", " transit_agency\n", + " prop_type\n", " total_cost\n", " bus_count\n", " cost_per_bus\n", @@ -5475,6 +5617,7 @@ " \n", " 44\n", " City of Los Angeles (LA DOT)\n", + " zero-emission bus (not specified)\n", " 102790000\n", " 112.0\n", " 917767\n", @@ -5484,8 +5627,11 @@ "" ], "text/plain": [ - " transit_agency total_cost bus_count cost_per_bus\n", - "44 City of Los Angeles (LA DOT) 102790000 112.0 917767" + " transit_agency prop_type \\\n", + "44 City of Los Angeles (LA DOT) zero-emission bus (not specified) \n", + "\n", + " total_cost bus_count cost_per_bus \n", + "44 102790000 112.0 917767 " ] }, "metadata": {}, @@ -5525,6 +5671,7 @@ " \n", " \n", " transit_agency\n", + " prop_type\n", " total_cost\n", " bus_count\n", " cost_per_bus\n", @@ -5534,6 +5681,7 @@ " \n", " 70\n", " SLO TRANSIT (SAN LUIS OBISPO, CA)\n", + " BEB\n", " 847214\n", " 1.0\n", " 847214\n", @@ -5543,8 +5691,11 @@ "" ], "text/plain": [ - " transit_agency total_cost bus_count cost_per_bus\n", - "70 SLO TRANSIT (SAN LUIS OBISPO, CA) 847214 1.0 847214" + " transit_agency prop_type total_cost bus_count \\\n", + "70 SLO TRANSIT (SAN LUIS OBISPO, CA) BEB 847214 1.0 \n", + "\n", + " cost_per_bus \n", + "70 847214 " ] }, "metadata": {}, @@ -5560,7 +5711,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 132, "id": "7472ba04-7def-46ef-814b-bf63c1016f3b", "metadata": {}, "outputs": [ @@ -5816,8 +5967,9 @@ "**Conclusion**\n", "\n", "Based on these findings, The average cost of a ZEB, throughout the US, is ~$1,000,000, roughly twice the price of a conventional, non-ZEB.\n", - "Reasons for the variance in cost depends mainly on the options the Trasnit\n", - "Agencies chose for their bus. Unfortunately, analyzing the cost of configuable options is outside the scope of data provided. \n", + "The variance in cost depends mainly on the options the Trasnit\n", + "Agencies chooses. Highly optioned/customized buses contribute to high cost.\n", + "Unfortunately, analyzing the cost of configuable options is outside the scope of data provided. \n", "\"\"\"\n", "display(\n", " Markdown(conclusion)\n", From 2ea2948315263c2ac3329def6fd308cd5cf96ca2 Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Tue, 25 Jun 2024 17:14:07 +0000 Subject: [PATCH 22/36] final changes before overwriting initial scripts --- bus_procurement_cost/refactor_bus_cost.ipynb | 834 +++++++------------ 1 file changed, 312 insertions(+), 522 deletions(-) diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index fade5a2da..b22fd122d 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 5, "id": "e1b1f367-1dac-463f-8790-2e5134b7e79b", "metadata": {}, "outputs": [ @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 6, "id": "c6dacaba-c6f7-4cb0-afef-a84f77de25fc", "metadata": {}, "outputs": [], @@ -399,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "id": "fa387f41-c9b3-455a-9829-cfabb3f98c9b", "metadata": { "tags": [] @@ -463,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "fb1ae513-a8bf-4eb1-9e7b-71f828ebb9ea", "metadata": { "tags": [] @@ -487,7 +487,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "4a5bc209-a660-4c18-86b0-574640391a7d", "metadata": { "tags": [] @@ -1064,6 +1064,7 @@ "cell_type": "markdown", "id": "24ab982a-afff-4c07-a19a-703ab82d27b1", "metadata": { + "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -1272,7 +1273,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "id": "c9ffd5a9-b772-4509-b84c-9a96760b3112", "metadata": {}, "outputs": [], @@ -1284,7 +1285,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 19, "id": "f8aa3674-78fe-4ba9-8f5e-697d91ff4011", "metadata": {}, "outputs": [ @@ -1326,7 +1327,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 20, "id": "d2bca15a-c12c-4cf5-a5a9-d591ee73a359", "metadata": {}, "outputs": [ @@ -1363,36 +1364,36 @@ " \n", " \n", " \n", - " 27\n", - " Foothill Transit, West Covina, CA\n", - " 0\n", + " 53\n", + " San Antonio Metropolitan Transit Authority\n", " 1\n", - " 37642044\n", - " 33.0\n", - " 1140668\n", - " 0.890182\n", + " 0\n", + " 3187200\n", + " 15.0\n", + " 212480\n", + " -1.426794\n", " False\n", " \n", " \n", - " 24\n", - " Dallas Area Rapid Transit (DART)\n", - " 1\n", + " 20\n", + " City of Wasco\n", " 0\n", - " 103000000\n", - " 90.0\n", - " 1144444\n", - " 0.899608\n", + " 1\n", + " 1543000\n", + " 3.0\n", + " 514333\n", + " -0.673298\n", " False\n", " \n", " \n", - " 28\n", - " GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA)\n", + " 58\n", + " Santa Rosa City Bus\n", " 0\n", " 1\n", - " 5406355\n", - " 5.0\n", - " 1081271\n", - " 0.741913\n", + " 4068202\n", + " 4.0\n", + " 1017050\n", + " 0.581602\n", " False\n", " \n", " \n", @@ -1400,23 +1401,23 @@ "" ], "text/plain": [ - " transit_agency total_project_count \\\n", - "27 Foothill Transit, West Covina, CA 0 \n", - "24 Dallas Area Rapid Transit (DART) 1 \n", - "28 GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA) 0 \n", + " transit_agency total_project_count \\\n", + "53 San Antonio Metropolitan Transit Authority 1 \n", + "20 City of Wasco 0 \n", + "58 Santa Rosa City Bus 0 \n", "\n", " total_project_count_ppno total_agg_cost total_bus_count \\\n", - "27 1 37642044 33.0 \n", - "24 0 103000000 90.0 \n", - "28 1 5406355 5.0 \n", + "53 0 3187200 15.0 \n", + "20 1 1543000 3.0 \n", + "58 1 4068202 4.0 \n", "\n", " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", - "27 1140668 0.890182 False \n", - "24 1144444 0.899608 False \n", - "28 1081271 0.741913 False " + "53 212480 -1.426794 False \n", + "20 514333 -0.673298 False \n", + "58 1017050 0.581602 False " ] }, - "execution_count": 24, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1428,136 +1429,10 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 21, "id": "637032e4-d855-4190-a6f5-ff695f77143f", "metadata": {}, "outputs": [ - { - "data": { - "text/plain": [ - "(82, 8)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)10100000008.012500001.163100False
1Alameda County Transit Authority012284664020.011423320.894336False
2Antelope Valley Transit Authority (AVTA)013947800029.013613101.440957False
3CITY OF PORTERVILLE (PORTERVILLE, CA)0127818913.09272970.357558False
4CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...0136235364.09058840.304106False
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transit_agencyproject_titleprop_typebus_size_typedescriptionnew_project_typetotal_costbus_countsourceppnoproject_descriptioncost_per_buszscore_cost_per_busis_cpb_outlier?
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)Puerto Rico Initiative Minimizing Emissions Pl...electric (not specified)not specifiedThe Metropolitan Bus Authority will receive fu...bus only100000008.0ftaNoneNone12500000.917956False
1Cape Fear Public Transportation AuthorityWave Transit Low Emissions Replacement VehiclesCNGnot specifiedWave Transit will receive funding to buy compr...bus only28602505.0ftaNoneNone572050-0.529139False
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" - ], "text/plain": [ - " transit_agency \\\n", - "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) \n", - "1 Cape Fear Public Transportation Authority \n", - "\n", - " project_title \\\n", - "0 Puerto Rico Initiative Minimizing Emissions Pl... \n", - "1 Wave Transit Low Emissions Replacement Vehicles \n", - "\n", - " prop_type bus_size_type \\\n", - "0 electric (not specified) not specified \n", - "1 CNG not specified \n", - "\n", - " description new_project_type \\\n", - "0 The Metropolitan Bus Authority will receive fu... bus only \n", - "1 Wave Transit will receive funding to buy compr... bus only \n", - "\n", - " total_cost bus_count source ppno project_description cost_per_bus \\\n", - "0 10000000 8.0 fta None None 1250000 \n", - "1 2860250 5.0 fta None None 572050 \n", - "\n", - " zscore_cost_per_bus is_cpb_outlier? \n", - "0 0.917956 False \n", - "1 -0.529139 False " + "(88, 14)" ] }, - "execution_count": 48, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "merged_data.head(2)" + "merged_data.shape" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 36, "id": "4753f3ea-00b6-4d5e-a3f0-73b3d3593acb", "metadata": {}, "outputs": [ @@ -4211,7 +3990,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 37, "id": "2007be9d-13ec-4d0d-a642-d9a42448b924", "metadata": {}, "outputs": [ @@ -4332,7 +4111,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 38, "id": "8645cf77-b30a-4c45-b943-ac81e8b5a613", "metadata": {}, "outputs": [ @@ -4361,7 +4140,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 39, "id": "cefa6800-df50-4eda-95f8-74363ef942d0", "metadata": {}, "outputs": [ @@ -4391,7 +4170,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 40, "id": "563304d2-2d98-44e6-b3a4-fd54f63fc0d8", "metadata": {}, "outputs": [ @@ -4419,7 +4198,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 41, "id": "aa916127-57d9-4c1c-b5eb-8b7b7e4ac672", "metadata": {}, "outputs": [ @@ -4537,7 +4316,7 @@ "4 887323 0.356283 False " ] }, - "execution_count": 137, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -4548,7 +4327,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 42, "id": "5f11c857-ddbe-4871-aeca-e27fa00fbde8", "metadata": {}, "outputs": [ @@ -4687,7 +4466,7 @@ "4 ethanol 111861 9.0" ] }, - "execution_count": 142, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -4724,7 +4503,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 53, "id": "2472461d-7663-4b66-9bde-4c2a199707a5", "metadata": {}, "outputs": [ @@ -4743,6 +4522,13 @@ "2. TIRCP project data (state-funded, California only)\n", "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", "\n", + "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", + "The resulting dataset only contained projects that were solely used to procure buses. \n", + "\n", + "88 projects were determined to contain solely bus purchases. \n", + "These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", + "\n", + "\n", "Breakdown of each data souce:\n", "| source | bus_count | total_cost | cost_per_bus |\n", "|:------------|------------:|-------------:|---------------:|\n", @@ -4751,8 +4537,6 @@ "| tircp | 233 | 187250513 | 803650 |\n", "| Grand Total | 1352 | 828620391 | 612884 |\n", "\n", - "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", - "The resulting dataset only contained projects that were solely used to procure buses. 87 projects remained. These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", "\n", "**ZEB buses include:**\n", "- zero-emission (not specified) \n", @@ -4794,11 +4578,16 @@ "2. TIRCP project data (state-funded, California only)\n", "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", "\n", + "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", + "The resulting dataset only contained projects that were solely used to procure buses. \n", + "\n", + "{len(merged_data)} projects were determined to contain solely bus purchases. \n", + "These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", + "\n", + "\n", "Breakdown of each data souce:\n", "{pivot_source.to_markdown(index=False)}\n", "\n", - "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", - "The resulting dataset only contained projects that were solely used to procure buses. 87 projects remained. These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", "\n", "**ZEB buses include:**\n", "- zero-emission (not specified) \n", @@ -4826,7 +4615,228 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 48, + "id": "7472ba04-7def-46ef-814b-bf63c1016f3b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "**ZEB Summary**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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prop_typebus_counttotal_costcost_per_bus
0BEB163.01672324891025966
1FCEB102.01209513351185797
2electric (not specified)44.0566780001288136
3zero-emission bus (not specified)143.0128156513896199
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" + ], + "text/plain": [ + " prop_type bus_count total_cost cost_per_bus\n", + "0 BEB 163.0 167232489 1025966\n", + "1 FCEB 102.0 120951335 1185797\n", + "2 electric (not specified) 44.0 56678000 1288136\n", + "3 zero-emission bus (not specified) 143.0 128156513 896199\n", + "4 Grand Total 452.0 473018337 1046500" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Non-ZEB Summary**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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prop_typebus_counttotal_costcost_per_bus
0CNG252.0176039140698568
1ethanol9.01006750111861
2low emission (hybrid)145.091824361633271
3low emission (propane)44.08403969190999
4mix (zero and low emission)125.036775430294203
5Grand Total575.0314049650546173
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" + ], + "text/plain": [ + " prop_type bus_count total_cost cost_per_bus\n", + "0 CNG 252.0 176039140 698568\n", + "1 ethanol 9.0 1006750 111861\n", + "2 low emission (hybrid) 145.0 91824361 633271\n", + "3 low emission (propane) 44.0 8403969 190999\n", + "4 mix (zero and low emission) 125.0 36775430 294203\n", + "5 Grand Total 575.0 314049650 546173" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "The remaining buses did not specify a propulsion type" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(\n", + " Markdown(\"**ZEB Summary**\"),\n", + " pivot_zeb_prop,\n", + " \n", + " Markdown(\"**Non-ZEB Summary**\"),\n", + " pivot_non_zeb_prop,\n", + " \n", + " Markdown(\"The remaining buses did not specify a propulsion type\")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 44, "id": "91d0361d-b165-4607-b22e-66ae4234863d", "metadata": {}, "outputs": [ @@ -5230,7 +5240,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 45, "id": "6896d09d-a8e8-4351-bf69-6538d031bf93", "metadata": {}, "outputs": [ @@ -5392,7 +5402,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 46, "id": "743b25a2-8693-44f7-98fe-384e910620a7", "metadata": {}, "outputs": [ @@ -5557,7 +5567,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 47, "id": "45a95018-0ac8-450d-97d2-aa394e94779a", "metadata": {}, "outputs": [ @@ -5709,227 +5719,6 @@ "bus_min_max_summary(data=zeb_projects, col1=\"total_cost\", col_list=new_cols)" ] }, - { - "cell_type": "code", - "execution_count": 132, - "id": "7472ba04-7def-46ef-814b-bf63c1016f3b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "**ZEB Summary**" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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prop_typebus_counttotal_costcost_per_bus
0BEB163.01672324891025966
1FCEB102.01209513351185797
2electric (not specified)44.0566780001288136
3zero-emission bus (not specified)143.0128156513896199
4Grand Total452.04730183371046500
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" - ], - "text/plain": [ - " prop_type bus_count total_cost cost_per_bus\n", - "0 BEB 163.0 167232489 1025966\n", - "1 FCEB 102.0 120951335 1185797\n", - "2 electric (not specified) 44.0 56678000 1288136\n", - "3 zero-emission bus (not specified) 143.0 128156513 896199\n", - "4 Grand Total 452.0 473018337 1046500" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "**Non-ZEB Summary**" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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prop_typebus_counttotal_costcost_per_bus
0CNG252.0176039140698568
1ethanol9.01006750111861
2low emission (hybrid)145.091824361633271
3low emission (propane)44.08403969190999
4mix (zero and low emission)125.036775430294203
5Grand Total575.0314049650546173
\n", - "
" - ], - "text/plain": [ - " prop_type bus_count total_cost cost_per_bus\n", - "0 CNG 252.0 176039140 698568\n", - "1 ethanol 9.0 1006750 111861\n", - "2 low emission (hybrid) 145.0 91824361 633271\n", - "3 low emission (propane) 44.0 8403969 190999\n", - "4 mix (zero and low emission) 125.0 36775430 294203\n", - "5 Grand Total 575.0 314049650 546173" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "The remaining buses did not specify a propulsion type" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(\n", - " Markdown(\"**ZEB Summary**\"),\n", - " pivot_zeb_prop,\n", - " \n", - " Markdown(\"**Non-ZEB Summary**\"),\n", - " pivot_non_zeb_prop,\n", - " \n", - " Markdown(\"The remaining buses did not specify a propulsion type\")\n", - ")" - ] - }, { "cell_type": "markdown", "id": "ee2f5572-0683-4909-8e2a-deea22c006fb", @@ -5940,7 +5729,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 49, "id": "e39c89a1-a726-44f9-808b-bcf936c77254", "metadata": {}, "outputs": [ @@ -5950,9 +5739,10 @@ "\n", "**Conclusion**\n", "\n", - "Based on these findings, The average cost of a ZEB, throughout the US, is ~$1,000,000, roughly twice the price of a convention, non-ZEB.\n", - "Reasons for variance in cost depends on the options the Trasnit\n", - "Agencies chose for their bus. Unfortunately, analyzing the cost of configuable options is outside the scope of this analysis. \n" + "Based on these findings, The average cost of a ZEB, throughout the US, is ~$1,000,000, roughly twice the price of a conventional, non-ZEB.\n", + "The variance in cost depends mainly on the options the Trasnit\n", + "Agencies chooses. Highly optioned/customized buses contribute to high cost.\n", + "Unfortunately, analyzing the cost of configuable options is outside the scope of data provided. \n" ], "text/plain": [ "" From 9ba8be890bd1cf342d00dd894e286bc555333445 Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Tue, 25 Jun 2024 17:57:57 +0000 Subject: [PATCH 23/36] overwrote fta cleaner script. double checked and ensured script is good to go and is exporting to GCS --- bus_procurement_cost/fta_data_cleaner.py | 24 ++++------ bus_procurement_cost/refactor_bus_cost.ipynb | 46 +++++++++----------- 2 files changed, 30 insertions(+), 40 deletions(-) diff --git a/bus_procurement_cost/fta_data_cleaner.py b/bus_procurement_cost/fta_data_cleaner.py index c3deda5c9..a519be09c 100644 --- a/bus_procurement_cost/fta_data_cleaner.py +++ b/bus_procurement_cost/fta_data_cleaner.py @@ -2,10 +2,7 @@ import pandas as pd import shared_utils from calitp_data_analysis.sql import to_snakecase -from dgs_data_cleaner import new_bus_size_finder, new_prop_finder, project_type_checker -from tircp_data_cleaner import col_row_updater - -gcs_path = "gs://calitp-analytics-data/data-analyses/bus_procurement_cost/" +from bus_cost_utils import * def col_splitter( df: pd.DataFrame, @@ -26,8 +23,7 @@ def col_splitter( return df - -def agg_just_bus(df: pd.DataFrame) -> pd.DataFrame: +def fta_agg_bus_only(df: pd.DataFrame) -> pd.DataFrame: """ filters FTA data to only show projects with bus procurement (bus count > 0). then filters projects for new_project_type = bus only @@ -57,17 +53,16 @@ def agg_just_bus(df: pd.DataFrame) -> pd.DataFrame: return df2 - def clean_fta_columns() -> pd.DataFrame: """ Main function to clean FTA data. Reads in data, changes datatypes, change specific values. """ # params - file = "data-analyses_bus_procurement_cost_fta_press_release_data_csv.csv" + file = "raw_data-analyses_bus_procurement_cost_fta_press_release_data_csv.csv" # read in data - df = pd.read_csv(f"{gcs_path}{file}") + df = pd.read_csv(f"{GCS_PATH}{file}") # snakecase df df = to_snakecase(df) @@ -83,14 +78,14 @@ def clean_fta_columns() -> pd.DataFrame: # rename initial propulsion type col to propulsion category df = df.rename(columns={"propulsion_type": "prosulsion_category"}) - # splittign `approx_#_of_buses col to get bus count + # splitting `approx_#_of_buses` col to get bus count df1 = col_splitter(df, "approx_#_of_buses", "bus_count", "extract_prop_type", "(") # assign new columns via new_prop_finder and new_bus_size_finder df2 = df1.assign( new_prop_type_finder=df1["description"].apply(new_prop_finder), new_bus_size_type=df1["description"].apply(new_bus_size_finder), - new_project_type=df1["description"].apply(project_type_checker) + new_project_type=df1["description"].apply(project_type_finder) ) # cleaning specific values @@ -115,15 +110,14 @@ def clean_fta_columns() -> pd.DataFrame: return df2 - if __name__ == "__main__": # initial df (all projects) all_projects = clean_fta_columns() # projects with bus count > 0 only. - just_bus = agg_just_bus(all_projects) + just_bus = fta_agg_bus_only(all_projects) # export both DFs - all_projects.to_parquet(f"{gcs_path}fta_all_projects_clean.parquet") - just_bus.to_parquet(f"{gcs_path}fta_bus_cost_clean.parquet") \ No newline at end of file + all_projects.to_parquet(f"{GCS_PATH}clean_fta_all_projects.parquet") + just_bus.to_parquet(f"{GCS_PATH}clean_fta_bus_only.parquet") \ No newline at end of file diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index b22fd122d..cb119ad4e 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -552,7 +552,6 @@ "cell_type": "markdown", "id": "84e4d8ed-131b-4a03-a2af-55c2bd4efc66", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -569,6 +568,7 @@ "outputs": [], "source": [ "# FTA\n", + "# copied over 6/25/2024\n", "import numpy as np\n", "import pandas as pd\n", "import shared_utils\n", @@ -635,7 +635,7 @@ " file = \"raw_data-analyses_bus_procurement_cost_fta_press_release_data_csv.csv\"\n", "\n", " # read in data\n", - " df = pd.read_csv(f\"{gcs_path}{file}\")\n", + " df = pd.read_csv(f\"{GCS_PATH}{file}\")\n", "\n", " # snakecase df\n", " df = to_snakecase(df)\n", @@ -651,14 +651,14 @@ " # rename initial propulsion type col to propulsion category\n", " df = df.rename(columns={\"propulsion_type\": \"prosulsion_category\"})\n", "\n", - " # splittign `approx_#_of_buses col to get bus count\n", + " # splitting `approx_#_of_buses` col to get bus count\n", " df1 = col_splitter(df, \"approx_#_of_buses\", \"bus_count\", \"extract_prop_type\", \"(\")\n", "\n", " # assign new columns via new_prop_finder and new_bus_size_finder\n", " df2 = df1.assign(\n", " new_prop_type_finder=df1[\"description\"].apply(new_prop_finder),\n", " new_bus_size_type=df1[\"description\"].apply(new_bus_size_finder),\n", - " new_project_type=df1[\"description\"].apply(project_type_checker)\n", + " new_project_type=df1[\"description\"].apply(project_type_finder)\n", " )\n", "\n", " # cleaning specific values\n", @@ -692,15 +692,14 @@ "# just_bus = fta_agg_bus_only(all_projects)\n", "\n", " # export both DFs\n", - "# all_projects.to_parquet(f\"{gcs_path}clean_fta_all_projects.parquet\")\n", - "# just_bus.to_parquet(f\"{gcs_path}clean_fta_bus_only.parquet\")" + "# all_projects.to_parquet(f\"{GCS_PATH}clean_fta_all_projects.parquet\")\n", + "# just_bus.to_parquet(f\"{GCS_PATH}clean_fta_bus_only.parquet\")" ] }, { "cell_type": "markdown", "id": "0a60d451-7532-4053-b0de-3fc7c5a55792", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -727,12 +726,12 @@ " \"\"\"\n", " main function that reads in and cleans TIRCP data.\n", " \"\"\"\n", - " from fta_data_cleaner import gcs_path\n", + " \n", " file_name = \"raw_TIRCP Tracking Sheets 2_1-10-2024.xlsx\"\n", " tircp_name = \"Project Tracking\"\n", "\n", " # read in data\n", - " df = pd.read_excel(f\"{gcs_path}{file_name}\", sheet_name=tircp_name)\n", + " df = pd.read_excel(f\"{GCS_PATH}{file_name}\", sheet_name=tircp_name)\n", "\n", " # keep specific columns\n", " keep_col = [\n", @@ -802,7 +801,7 @@ " df4 = df3.assign(\n", " prop_type = df3['project_description'].apply(new_prop_finder),\n", " bus_size_type = df3['project_description'].apply(new_bus_size_finder),\n", - " new_project_type = df3['project_description'].apply(project_type_checker)\n", + " new_project_type = df3['project_description'].apply(project_type_finder)\n", " )\n", "\n", " return df4\n", @@ -844,15 +843,14 @@ "# df2 = tircp_agg_bus_only(df1)\n", " \n", " # export both df's as parquets to GCS\n", - "# df1.to_parquet(f'{gcs_path}clean_tircp_all_project.parquet')\n", - "# df2.to_parquet(f'{gcs_path}clean_tircp_bus_only_clean.parquet')" + "# df1.to_parquet(f'{GCS_PATH}clean_tircp_all_project.parquet')\n", + "# df2.to_parquet(f'{GCS_PATH}clean_tircp_bus_only_clean.parquet')" ] }, { "cell_type": "markdown", "id": "0a57f455-8b86-47c4-9cda-2114cac504db", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -892,7 +890,7 @@ " merged first becaues the snakecase function messes with the dtypes for some reason\n", " \"\"\"\n", " \n", - " from fta_data_cleaner import gcs_path\n", + " \n", " \n", " # params\n", " file_17c = \"raw_17c compiled-Proterra Compiled Contract Usage Report .xlsx\"\n", @@ -934,8 +932,8 @@ " ]\n", " \n", " # read in data\n", - " dgs_17c = pd.read_excel(f\"{gcs_path}{file_17c}\", sheet_name=sheet_17c)\n", - " dgs_17b = pd.read_excel(f\"{gcs_path}{file_17b}\", sheet_name=sheet_17b)\n", + " dgs_17c = pd.read_excel(f\"{GCS_PATH}{file_17c}\", sheet_name=sheet_17c)\n", + " dgs_17b = pd.read_excel(f\"{GCS_PATH}{file_17b}\", sheet_name=sheet_17b)\n", "\n", " # add new column to identify source\n", " dgs_17c[\"source\"] = \"17c\"\n", @@ -1056,15 +1054,14 @@ "# bus_w_options = dgs_agg_by_agency_w_options(df1)\n", " \n", " #export serperate df's as parquet to GCS\n", - "# just_bus.to_parquet(f'{gcs_path}clean_dgs_all_projects.parquet')\n", - "# bus_w_options.to_parquet(f'{gcs_path}clean_dgs_bus_only_w_options.parquet')" + "# just_bus.to_parquet(f'{GCS_PATH}clean_dgs_all_projects.parquet')\n", + "# bus_w_options.to_parquet(f'{GCS_PATH}clean_dgs_bus_only_w_options.parquet')" ] }, { "cell_type": "markdown", "id": "24ab982a-afff-4c07-a19a-703ab82d27b1", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -1115,9 +1112,9 @@ "\n", " # reading in data\n", " # bus only projects for each datase\n", - " fta = pd.read_parquet(f\"{gcs_path}clean_fta_bus_only.parquet\")\n", - " tircp = pd.read_parquet(f\"{gcs_path}clean_tircp_bus_only_clean.parquet\")\n", - " dgs = pd.read_parquet(f\"{gcs_path}clean_dgs_bus_only_w_options.parquet\")\n", + " fta = pd.read_parquet(f\"{GCS_PATH}clean_fta_bus_only.parquet\")\n", + " tircp = pd.read_parquet(f\"{GCS_PATH}clean_tircp_bus_only_clean.parquet\")\n", + " dgs = pd.read_parquet(f\"{GCS_PATH}clean_dgs_bus_only_w_options.parquet\")\n", " \n", " # adding new column to identify source\n", " fta[\"source\"] = \"fta\"\n", @@ -1181,9 +1178,9 @@ " \n", " # export to gcs\n", " #full data, with outliers\n", - "# df1.to_parquet(f'{gcs_path}cleaned_cpb_analysis_data_merge.parquet')\n", + "# df1.to_parquet(f'{GCS_PATH}cleaned_cpb_analysis_data_merge.parquet')\n", " # no outliers\n", - "# df2.to_parquet(f'{gcs_path}cleaned_no_outliers_cpb_analysis_data_merge.parquet')" + "# df2.to_parquet(f'{GCS_PATH}cleaned_no_outliers_cpb_analysis_data_merge.parquet')" ] }, { @@ -1263,7 +1260,6 @@ "cell_type": "markdown", "id": "b5224cfe-6b3a-4c68-a7b5-58df0ff8f85e", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ From 5786212dc5dae172a69a6f6662f67fdd944ec280 Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Tue, 25 Jun 2024 18:27:11 +0000 Subject: [PATCH 24/36] overwrote TIRCP cleaner script. ran with no errors, files saving to GCS. GTG --- bus_procurement_cost/refactor_bus_cost.ipynb | 2 ++ bus_procurement_cost/tircp_data_cleaner.py | 30 +++++++------------- 2 files changed, 12 insertions(+), 20 deletions(-) diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index cb119ad4e..fc0112bc0 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -552,6 +552,7 @@ "cell_type": "markdown", "id": "84e4d8ed-131b-4a03-a2af-55c2bd4efc66", "metadata": { + "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -716,6 +717,7 @@ "outputs": [], "source": [ "# TIRCP\n", + "## copied over 6/25/24\n", "import numpy as np\n", "import pandas as pd\n", "import shared_utils\n", diff --git a/bus_procurement_cost/tircp_data_cleaner.py b/bus_procurement_cost/tircp_data_cleaner.py index 7c457bb50..cf74902cd 100644 --- a/bus_procurement_cost/tircp_data_cleaner.py +++ b/bus_procurement_cost/tircp_data_cleaner.py @@ -2,28 +2,18 @@ import pandas as pd import shared_utils from calitp_data_analysis.sql import to_snakecase - -from dgs_data_cleaner import new_prop_finder, new_bus_size_finder, project_type_checker - - -def col_row_updater(df: pd.DataFrame, col1: str, val1, col2: str, new_val): - """ - function used to update values at specificed columns and row value. - """ - df.loc[df[col1] == val1, col2] = new_val - - return +from bus_cost_utils import * def clean_tircp_columns() -> pd.DataFrame: """ main function that reads in and cleans TIRCP data. """ - from fta_data_cleaner import gcs_path - file_name = "TIRCP Tracking Sheets 2_1-10-2024.xlsx" + + file_name = "raw_TIRCP Tracking Sheets 2_1-10-2024.xlsx" tircp_name = "Project Tracking" # read in data - df = pd.read_excel(f"{gcs_path}{file_name}", sheet_name=tircp_name) + df = pd.read_excel(f"{GCS_PATH}{file_name}", sheet_name=tircp_name) # keep specific columns keep_col = [ @@ -93,12 +83,12 @@ def clean_tircp_columns() -> pd.DataFrame: df4 = df3.assign( prop_type = df3['project_description'].apply(new_prop_finder), bus_size_type = df3['project_description'].apply(new_bus_size_finder), - new_project_type = df3['project_description'].apply(project_type_checker) + new_project_type = df3['project_description'].apply(project_type_finder) ) return df4 -def agg_buses_only(df: pd.DataFrame) -> pd.DataFrame: +def tircp_agg_bus_only(df: pd.DataFrame) -> pd.DataFrame: """ filters df to only include projects with bus procurement and for project type = bus only does not include engineering, planning or construction only projects. @@ -126,14 +116,14 @@ def agg_buses_only(df: pd.DataFrame) -> pd.DataFrame: if __name__ == "__main__": - from fta_data_cleaner import gcs_path + # initial df df1 = clean_tircp_columns() # aggregate - df2 = agg_buses_only(df1) + df2 = tircp_agg_bus_only(df1) # export both df's as parquets to GCS - df1.to_parquet(f'{gcs_path}clean_tircp_project.parquet') - df2.to_parquet(f'{gcs_path}clean_tircp_project_bus_only.parquet') \ No newline at end of file + df1.to_parquet(f'{GCS_PATH}clean_tircp_all_project.parquet') + df2.to_parquet(f'{GCS_PATH}clean_tircp_bus_only_clean.parquet') \ No newline at end of file From d0b4691f373c62ba2f04bcb5856cd79a79b41339 Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Tue, 25 Jun 2024 20:11:37 +0000 Subject: [PATCH 25/36] overwrote dgs cleaner script. ran with no errors. wrote to GCS. GTG --- bus_procurement_cost/dgs_data_cleaner.py | 255 +------------------ bus_procurement_cost/refactor_bus_cost.ipynb | 1 + 2 files changed, 14 insertions(+), 242 deletions(-) diff --git a/bus_procurement_cost/dgs_data_cleaner.py b/bus_procurement_cost/dgs_data_cleaner.py index 8cd27c401..f55a2e768 100644 --- a/bus_procurement_cost/dgs_data_cleaner.py +++ b/bus_procurement_cost/dgs_data_cleaner.py @@ -2,7 +2,7 @@ import pandas as pd import shared_utils from calitp_data_analysis.sql import to_snakecase - +from bus_cost_utils import * def calculate_total_cost(row): """ @@ -14,246 +14,18 @@ def calculate_total_cost(row): return row["total_with_options_per_unit"] * row["quantity"] else: return row["contract_unit_price"] * row["quantity"] - - -def new_bus_size_finder(description: str) -> str: - """ - Similar to prop_type_find, matches keywords to item description col and return standardized bus size type. - now includes variable that make description input lowercase. - To be used with .assign() - """ - - articulated_list = [ - "60 foot", - "articulated", - ] - - standard_bus_list = [ - "30 foot", - "35 foot", - "40 foot", - "40ft", - "45 foot", - "standard", - ] - - cutaway_list = [ - "cutaway", - ] - - other_bus_size_list = ["feeder bus"] - - otr_bus_list = [ - "coach style", - "over the road", - ] - - item_description = description.lower().replace("-", " ").strip() - - if any(word in item_description for word in articulated_list): - return "articulated" - - elif any(word in item_description for word in standard_bus_list): - return "standard/conventional (30ft-45ft)" - - elif any(word in item_description for word in cutaway_list): - return "cutaway" - - elif any(word in item_description for word in otr_bus_list): - return "over-the-road" - - elif any(word in item_description for word in other_bus_size_list): - return "other" - - else: - return "not specified" - - -# new prop_finder function -def new_prop_finder(description: str) -> str: - """ - function that matches keywords from each propulsion type list against the item description col, returns a standardized prop type - now includes variable that make description input lowercase. - to be used with .assign() - """ - - BEB_list = [ - "battery electric", - "BEBs paratransit buses" - ] - - cng_list = [ - "cng", - "compressed natural gas" - - ] - - electric_list = [ - "electric buses", - "electric commuter", - "electric", - ] - - FCEB_list = [ - "fuel cell", - "hydrogen", - #"fuel cell electric", - #"hydrogen fuel cell", - #"fuel cell electric bus", - #"hydrogen electric bus", - ] - - # low emission (hybrid) - hybrid_list = [ - #"diesel electric hybrids", - #"diesel-electric hybrids", - #"hybrid electric", - #"hybrid electric buses", - #"hybrid electrics", - "hybrids", - "hybrid", - ] - - # low emission (propane) - propane_list = [ - #"propane buses", - #"propaned powered vehicles", - "propane", - ] - - mix_beb_list = [ - "2 BEBs and 4 hydrogen fuel cell buses", - ] - - mix_lowe_list = [ - "diesel and gas", - ] - - mix_zero_low_list = [ - "15 electic, 16 hybrid", - "4 fuel cell / 3 CNG", - "estimated-cutaway vans (PM- award will not fund 68 buses", - "1:CNGbus ;2 cutaway CNG buses", - ] - - zero_e_list = [ - #"zero emission buses", - #"zero emission electric", - #"zero emission vehicles", - "zero-emission", - "zero emission", - ] - - item_description = description.lower().replace("‐", " ").strip() - - if any(word in item_description for word in BEB_list) and not any( - word in item_description for word in ["diesel", "hybrid", "fuel cell"] - ): - return "BEB" - - elif any(word in item_description for word in FCEB_list): - return "FCEB" - - elif any(word in item_description for word in hybrid_list): - return "low emission (hybrid)" - - elif any(word in item_description for word in mix_beb_list): - return "mix (BEB and FCEB)" - - elif any(word in item_description for word in mix_lowe_list): - return "mix (low emission)" - - elif any(word in item_description for word in mix_zero_low_list): - return "mix (zero and low emission)" - - elif any(word in item_description for word in zero_e_list): - return "zero-emission bus (not specified)" - - elif any(word in item_description for word in propane_list): - return "low emission (propane)" - - elif any(word in item_description for word in electric_list): - return "electric (not specified)" - - elif any(word in item_description for word in cng_list): - return "CNG" - - else: - return "not specified" - -#project type checker -def project_type_checker(description: str) -> str: - """ - function to match keywords to project description col to identy projects that only have bus procurement. - used to identify projects into diffferent categories: bus only, bus + others, no bus procurement. - use with .assign() to get a new col. - """ - bus_list =[ - "bus", - "transit vehicles",# for fta list - "cutaway vehicles",# for fta list - "zero-emission vehicles", # for tircp list - "zero emission vehicles", - "zero‐emissions vans", - "hybrid-electric vehicles", - "battery-electric vehicles", - "buy new replacement vehicles", # specific string for fta list - ] - - exclude_list =[ - "facility", - #"station", - "stops", - "installation", - "depot", - "construct", - "infrastructure", - "signal priority", - "improvements", - "build", - "chargers", - "charging equipment", - "install", - "rail", - "garage", - "facilities", - "bus washing system", - "build a regional transit hub" # specific string needed for fta list - #"associated infrastructure" may need to look at what is associated infrastructure is for ZEB - - ] - proj_description = description.lower().strip() - - if any(word in proj_description for word in bus_list) and not any( - word in proj_description for word in exclude_list - ): - return "bus only" - - elif any(word in proj_description for word in exclude_list) and not any( - word in proj_description for word in bus_list - ): - return "non-bus components" - elif any(word in proj_description for word in exclude_list) and any( - word in proj_description for word in bus_list - ): - return "includes bus and non-bus components" - - else: - return "needs review" - -# included assign columns def clean_dgs_columns() -> pd.DataFrame: """ reads in 2 dgs sheets, adds source column, merges both DFs, snakecase columns, update dtypes for monetary columns. merged first becaues the snakecase function messes with the dtypes for some reason """ - from fta_data_cleaner import gcs_path + # params - file_17c = "17c compiled-Proterra Compiled Contract Usage Report .xlsx" - file_17b = "17b compiled.xlsx" + file_17c = "raw_17c compiled-Proterra Compiled Contract Usage Report .xlsx" + file_17b = "raw_17b compiled.xlsx" sheet_17c = "Proterra " sheet_17b = "Usage Report Template" @@ -291,8 +63,8 @@ def clean_dgs_columns() -> pd.DataFrame: ] # read in data - dgs_17c = pd.read_excel(f"{gcs_path}{file_17c}", sheet_name=sheet_17c) - dgs_17b = pd.read_excel(f"{gcs_path}{file_17b}", sheet_name=sheet_17b) + dgs_17c = pd.read_excel(f"{GCS_PATH}{file_17c}", sheet_name=sheet_17c) + dgs_17b = pd.read_excel(f"{GCS_PATH}{file_17b}", sheet_name=sheet_17b) # add new column to identify source dgs_17c["source"] = "17c" @@ -319,7 +91,7 @@ def clean_dgs_columns() -> pd.DataFrame: return dgs_17bc2 -def agg_by_agency(df: pd.DataFrame) -> pd.DataFrame: +def dgs_agg_by_agency(df: pd.DataFrame) -> pd.DataFrame: """ function that aggregates the DGS data frame by transit agency and purchase order number (PPNO) to get total cost of just buses without options. first, dataframe is filtered for rows containing buses (does not include rows with 'not specified'). @@ -363,8 +135,7 @@ def agg_by_agency(df: pd.DataFrame) -> pd.DataFrame: return agg_agency_bus_count3 - -def agg_by_agency_w_options(df: pd.DataFrame) -> pd.DataFrame: +def dgs_agg_by_agency_w_options(df: pd.DataFrame) -> pd.DataFrame: """ similar to the previous function, aggregates the DGS dataframe by transit agency to get total cost of buses with options. agencies may order buses with different configurations, resulting in different total cost. @@ -403,16 +174,16 @@ def agg_by_agency_w_options(df: pd.DataFrame) -> pd.DataFrame: if __name__ == "__main__": - from fta_data_cleaner import gcs_path + # initial df df1 = clean_dgs_columns() #df of just bus cost (no options) - just_bus = agg_by_agency(df1) + just_bus = dgs_agg_by_agency(df1) #df of bus cost+options - bus_w_options = agg_by_agency_w_options(df1) + bus_w_options = dgs_agg_by_agency_w_options(df1) #export serperate df's as parquet to GCS - just_bus.to_parquet(f'{gcs_path}dgs_agg_clean.parquet') - bus_w_options.to_parquet(f'{gcs_path}dgs_agg_w_options_clean.parquet') \ No newline at end of file + just_bus.to_parquet(f'{GCS_PATH}clean_dgs_all_projects.parquet') + bus_w_options.to_parquet(f'{GCS_PATH}clean_dgs_bus_only_w_options.parquet') \ No newline at end of file diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index fc0112bc0..8e86cf208 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -701,6 +701,7 @@ "cell_type": "markdown", "id": "0a60d451-7532-4053-b0de-3fc7c5a55792", "metadata": { + "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ From b44a240d6dac5d16c7b9e747e9ec036126f80f5b Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Tue, 25 Jun 2024 20:46:14 +0000 Subject: [PATCH 26/36] added min max summary and outlier flag to utils file. cpb cleaner script ran no errors. files saved to GCS. GTG --- bus_procurement_cost/bus_cost_utils.py | 25 +++++++++++- bus_procurement_cost/cost_per_bus_cleaner.py | 40 ++++++++++++++------ bus_procurement_cost/refactor_bus_cost.ipynb | 8 +++- 3 files changed, 59 insertions(+), 14 deletions(-) diff --git a/bus_procurement_cost/bus_cost_utils.py b/bus_procurement_cost/bus_cost_utils.py index 2ed4ca501..8cc903607 100644 --- a/bus_procurement_cost/bus_cost_utils.py +++ b/bus_procurement_cost/bus_cost_utils.py @@ -232,4 +232,27 @@ def col_row_updater(df: pd.DataFrame, col1: str, val1, col2: str, new_val): """ df.loc[df[col1] == val1, col2] = new_val - return \ No newline at end of file + return + +def bus_min_max_summary(data:pd.DataFrame, col1:str, col_list=["transit_agency", + "total_agg_cost", + "total_bus_count", + "new_cost_per_bus"]): + """ + function to display min/max of specific column in aggregated bus df. + + """ + + return display( + Markdown(f"**Max {col1}**"), + data[data[col1] == data[col1].max()][col_list], + Markdown(f"**Min {col1}**"), + data[data[col1] == data[col1].min()][col_list] + ) + +def outlier_flag(col): + """ + function to flag outlier rows. use with .apply() + """ + + return col <= -3 or col >= 3 \ No newline at end of file diff --git a/bus_procurement_cost/cost_per_bus_cleaner.py b/bus_procurement_cost/cost_per_bus_cleaner.py index d4f33f8cd..29a201f61 100644 --- a/bus_procurement_cost/cost_per_bus_cleaner.py +++ b/bus_procurement_cost/cost_per_bus_cleaner.py @@ -1,16 +1,17 @@ import pandas as pd -from fta_data_cleaner import gcs_path +from bus_cost_utils import * +from scipy.stats import zscore -def prepare_data() ->pd.DataFrame: + + +def prepare_all_data() ->pd.DataFrame: """ primary function to read-in, merge data across FTA, TIRCP and DGS data. standardizes columns names, then exports as parquet. """ # variables for file names - # all bus only projects for each dataset - fta_bus_data = "fta_bus_cost_clean.parquet" - tircp_bus_data = "clean_tircp_project_bus_only.parquet" - dgs_bus_data = "dgs_agg_w_options_clean.parquet" + + # dictionary to update columns names col_dict = { @@ -28,9 +29,10 @@ def prepare_data() ->pd.DataFrame: } # reading in data - fta = pd.read_parquet(f"{gcs_path}{fta_bus_data}") - tircp = pd.read_parquet(f"{gcs_path}{tircp_bus_data}") - dgs = pd.read_parquet(f"{gcs_path}{dgs_bus_data}") + # bus only projects for each datase + fta = pd.read_parquet(f"{GCS_PATH}clean_fta_bus_only.parquet") + tircp = pd.read_parquet(f"{GCS_PATH}clean_tircp_bus_only_clean.parquet") + dgs = pd.read_parquet(f"{GCS_PATH}clean_dgs_bus_only_w_options.parquet") # adding new column to identify source fta["source"] = "fta" @@ -71,13 +73,29 @@ def prepare_data() ->pd.DataFrame: ], how="outer", ) + #normalizing data with cost per bus + #calculating cost per bus here + merge2["cost_per_bus"] = (merge2["total_cost"] / merge2["bus_count"]).astype("int64") + #calculating zscore on cost per bus + merge2["zscore_cost_per_bus"] = zscore(merge2["cost_per_bus"]) + + #flag any outliers + merge2["is_cpb_outlier?"] = merge2["zscore_cost_per_bus"].apply(outlier_flag) return merge2 + + + if __name__ == "__main__": # initial df - df1 = prepare_data() + df1 = prepare_all_data() + #remove outliers based on cost per bus zscore + df2 = df1[df1["is_cpb_outlier?"]==False] # export to gcs - df1.to_parquet(f'{gcs_path}cpb_analysis_data_merge.parquet') \ No newline at end of file + #full data, with outliers + df1.to_parquet(f'{GCS_PATH}cleaned_cpb_analysis_data_merge.parquet') + # no outliers + df2.to_parquet(f'{GCS_PATH}cleaned_no_outliers_cpb_analysis_data_merge.parquet') \ No newline at end of file diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index 8e86cf208..52ac5a943 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -107,7 +107,7 @@ "outputs": [], "source": [ "# NEW FUNCTION\n", - "# move this to bus_cost_utils\n", + "# moved this to bus_cost_utils\n", "def bus_min_max_summary(data:pd.DataFrame, col1:str, col_list=[\"transit_agency\",\n", " \"total_agg_cost\",\n", " \"total_bus_count\",\n", @@ -406,7 +406,8 @@ }, "outputs": [], "source": [ - "# to new bus_cost_util\n", + "# Moved to new bus_cost_util\n", + "\n", "def outlier_flag(col):\n", " \"\"\"\n", " function to flag outlier rows. use with .apply()\n", @@ -854,6 +855,7 @@ "cell_type": "markdown", "id": "0a57f455-8b86-47c4-9cda-2114cac504db", "metadata": { + "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -870,6 +872,7 @@ "outputs": [], "source": [ "# DGS\n", + "# over wrote 6/25\n", "import numpy as np\n", "import pandas as pd\n", "import shared_utils\n", @@ -1082,6 +1085,7 @@ "source": [ "# cost per bus cleaner\n", "# rename to all_bus_cost_cleaner?\n", + "# overwrote 6/25/24\n", "\n", "import pandas as pd\n", "from bus_cost_utils import *\n", From 50fa908e8c960c85cce1382199276413497b4eac Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Tue, 25 Jun 2024 22:50:36 +0000 Subject: [PATCH 27/36] started to copy over cells, functions, variables and tables to the final NB. still need to get charts moved over --- .../cost_per_bus_analysis.ipynb | 472 ++++++++++++++++++ bus_procurement_cost/refactor_bus_cost.ipynb | 75 ++- 2 files changed, 531 insertions(+), 16 deletions(-) diff --git a/bus_procurement_cost/cost_per_bus_analysis.ipynb b/bus_procurement_cost/cost_per_bus_analysis.ipynb index 8f6efe450..9cd4f8e9b 100644 --- a/bus_procurement_cost/cost_per_bus_analysis.ipynb +++ b/bus_procurement_cost/cost_per_bus_analysis.ipynb @@ -20,6 +20,478 @@ "from scipy.stats import zscore" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "d53376d9-d4b4-48b7-9916-5b9f633fbaf0", + "metadata": {}, + "outputs": [], + "source": [ + "merged_data = pd.read_parquet(f'{GCS_PATH}cleaned_no_outliers_cpb_analysis_data_merge.parquet')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a45f2d3d-a600-4fe6-80cf-6b887036faab", + "metadata": {}, + "outputs": [], + "source": [ + "# for subsetting ZEB and nonZEB\n", + "zeb_list =[\n", + " \"BEB\",\n", + " \"FCEB\",\n", + " \"electric (not specified)\",\n", + " \"zero-emission bus (not specified)\",\n", + "]\n", + "\n", + "non_zeb_list =[\n", + " \"CNG\",\n", + " \"ethanol\",\n", + " \"low emission (hybrid)\",\n", + " \"low emission (propane)\",\n", + " \"mix (zero and low emission)\",\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ac40482-ba3e-4fde-8c05-806e3725de44", + "metadata": {}, + "outputs": [], + "source": [ + "# means and standard deviations\n", + "# for graphs\n", + "cpb_mean = merged_data[\"cost_per_bus\"].mean()\n", + "cpb_std = merged_data[\"cost_per_bus\"].std()\n", + "\n", + "#testing weighted average calculation for sub-set non-zeb and zeb\n", + "zeb_cpb_wt_avg = (merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"bus_count\"].sum())\n", + "non_zeb_cpb_wt_avg = (merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"bus_count\"].sum())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d450fd60-cced-453b-b20b-62cdade0d7a6", + "metadata": {}, + "outputs": [], + "source": [ + "def new_cpb_aggregate(df: pd.DataFrame, column=\"transit_agency\") -> pd.DataFrame:\n", + " \"\"\"\n", + " function to aggregate compiled data by different categories:\n", + " \"transit agency\", \n", + " \"propulsion type\", \n", + " \"bus_size_type\",\n", + " \"new_project_type\"\n", + " aggregate on columns:\n", + " \"project_title\"\n", + " \"ppno\"\n", + " \"total_cost\"\n", + " \"bus_count\"\n", + " \n", + " Then, cost per bus is calculated AFTER the aggregation.\n", + " \"\"\"\n", + " df_agg = (\n", + " df.groupby(column)\n", + " .agg(\n", + " total_project_count=(\"project_title\", \"count\"),\n", + " total_project_count_ppno=(\"ppno\", \"count\"),\n", + " total_agg_cost=(\"total_cost\", \"sum\"),\n", + " total_bus_count=(\"bus_count\", \"sum\"),\n", + " #new_prop_type=(\"prop_type\",\"max\")\n", + " )\n", + " .reset_index()\n", + " )\n", + " df_agg[\"new_cost_per_bus\"] = (df_agg[\"total_agg_cost\"] / df_agg[\"total_bus_count\"]).astype(\"int64\")\n", + " \n", + " #calculate zscore\n", + " df_agg[\"new_zscore_cost_per_bus\"] = zscore(df_agg[\"new_cost_per_bus\"])\n", + " \n", + " #flag outliers\n", + " df_agg[\"new_is_cpb_outlier?\"] = df_agg[\"new_zscore_cost_per_bus\"].apply(outlier_flag)\n", + " \n", + " return df_agg" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a6a7ecf-5180-4691-84fe-23aa68cdae93", + "metadata": {}, + "outputs": [], + "source": [ + "def make_chart(y_col: str, title: str, data: pd.DataFrame, x_col: str):\n", + " \"\"\"\n", + " function to create chart. sorts values by y_col ascending.\"\"\"\n", + " \n", + " data.sort_values(by=y_col, ascending=False).head(10).plot(\n", + " x=x_col, y=y_col, kind=\"bar\", color=\"skyblue\"\n", + " )\n", + " plt.title(title)\n", + " plt.xlabel(x_col)\n", + " plt.ylabel(y_col)\n", + "\n", + " plt.ticklabel_format(style=\"plain\", axis=\"y\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44d21201-223f-4e6c-b238-b72fba984544", + "metadata": {}, + "outputs": [], + "source": [ + "def dist_curve(\n", + " df: pd.DataFrame,\n", + " mean: str,\n", + " std: str,\n", + " title: str,\n", + " xlabel: str,\n", + "):\n", + " \"\"\"\n", + " function to make distribution curve. uses the \"cpb\" column of the df.\n", + " \"\"\"\n", + " sns.histplot(df[\"cost_per_bus\"], kde=True, color=\"skyblue\", bins=20)\n", + " # mean line\n", + " plt.axvline(\n", + " mean, color=\"red\", linestyle=\"dashed\", linewidth=2, label=f\"Mean: ${mean:,.2f}\"\n", + " )\n", + " # mean+1std\n", + " plt.axvline(\n", + " mean + std,\n", + " color=\"green\",\n", + " linestyle=\"dashed\",\n", + " linewidth=2,\n", + " label=f\"Standard Deviation: ${std:,.2f}\",\n", + " )\n", + " plt.axvline(mean - std, color=\"green\", linestyle=\"dashed\", linewidth=2)\n", + " plt.axvline(mean + (std * 2), color=\"green\", linestyle=\"dashed\", linewidth=2)\n", + " plt.axvline(mean + (std * 3), color=\"green\", linestyle=\"dashed\", linewidth=2)\n", + "\n", + " plt.title(title + \" with Mean and Standard Deviation\")\n", + " plt.xlabel(xlabel)\n", + " plt.ylabel(\"Frequency\")\n", + "\n", + " # Turn off scientific notation on x-axis?\n", + " plt.gca().xaxis.set_major_formatter(ScalarFormatter(useMathText=False))\n", + "\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "067a14a5-5c77-4914-82a8-c5eeb170cb08", + "metadata": {}, + "outputs": [], + "source": [ + "# aggregating by big categories\n", + "agg_agency = new_cpb_aggregate(merged_data)\n", + "agg_prop = new_cpb_aggregate(merged_data, column=\"prop_type\")\n", + "agg_bus_size = new_cpb_aggregate(merged_data, column=\"bus_size_type\")\n", + "agg_source = new_cpb_aggregate(merged_data, column=\"source\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49a97d01-b17e-475c-b351-67426f3741d9", + "metadata": {}, + "outputs": [], + "source": [ + "# subsetting ZEB and nonZEB data\n", + "zeb_projects = merged_data[merged_data[\"prop_type\"].isin(zeb_list)]\n", + "\n", + "non_zeb_projects = merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4faaa4ad-b16c-4e6b-87c7-d12f7e7db3c6", + "metadata": {}, + "outputs": [], + "source": [ + "#pivot table to get totals for each prop type\n", + "\n", + "pivot_prop_type = pd.pivot_table(\n", + " merged_data,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"prop_type\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ").reset_index()\n", + "pivot_prop_type[\"cost_per_bus\"] = (pivot_prop_type[\"total_cost\"] / pivot_prop_type[\"bus_count\"]).astype(\"int64\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8535e97-e7bf-4d7e-b718-24c5758b0ccd", + "metadata": {}, + "outputs": [], + "source": [ + "#pivot for ZEB data\n", + "\n", + "pivot_zeb_prop = pd.pivot_table(\n", + " #filted incoming DF for zeb prop types\n", + " zeb_projects,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"prop_type\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ").reset_index() \n", + "\n", + "pivot_zeb_prop[\"cost_per_bus\"] = (pivot_zeb_prop[\"total_cost\"] / pivot_zeb_prop[\"bus_count\"]).astype(\"int64\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "829e38c9-3f9b-4e82-92a8-c86f81051580", + "metadata": {}, + "outputs": [], + "source": [ + "#pivot for non-ZEB data\n", + "\n", + "pivot_non_zeb_prop = pd.pivot_table(\n", + " #filted incoming DF for non-zeb prop types\n", + " non_zeb_projects,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"prop_type\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ").reset_index()\n", + "\n", + "pivot_non_zeb_prop[\"cost_per_bus\"] = (pivot_non_zeb_prop[\"total_cost\"] / pivot_non_zeb_prop[\"bus_count\"]).astype(\"int64\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a2163e3-dac1-4e64-a551-3dc961e44714", + "metadata": {}, + "outputs": [], + "source": [ + "# pivot for bus sizes\n", + "\n", + "pivot_size = pd.pivot_table(\n", + " merged_data,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"bus_size_type\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ").reset_index()\n", + "\n", + "pivot_size[\"cost_per_bus\"] = (pivot_size[\"total_cost\"] / pivot_size[\"bus_count\"]).astype(\"int64\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "074acb8d-de54-43a0-b243-a070ecfbe1ce", + "metadata": {}, + "outputs": [], + "source": [ + "#pivot for data soruces\n", + "pivot_source = pd.pivot_table(\n", + " merged_data,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"source\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ").reset_index()\n", + "\n", + "pivot_source[\"cost_per_bus\"] = (pivot_source[\"total_cost\"] / pivot_source[\"bus_count\"]).astype(\"int64\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8356953-e32d-47ab-b67c-fa016cad9c50", + "metadata": {}, + "outputs": [], + "source": [ + "# new summary\n", + "\n", + "new_summary = f\"\"\"\n", + "\n", + "# Bus Procurement Cost Analysis\n", + "\n", + "## Summary\n", + "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", + "\n", + "Data was compiled from three data sources:\n", + "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", + "2. TIRCP project data (state-funded, California only)\n", + "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", + "\n", + "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", + "The resulting dataset only contained projects that were solely used to procure buses. \n", + "\n", + "{len(merged_data)} projects were determined to contain solely bus purchases. \n", + "These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", + "\n", + "\n", + "Breakdown of each data souce:\n", + "{pivot_source.to_markdown(index=False)}\n", + "\n", + "\n", + "**ZEB buses include:**\n", + "- zero-emission (not specified) \n", + "- electric (not specified)\n", + "- battery electric \n", + "- fuel cell electric\n", + "\n", + "**Non-ZEB buses include:**\n", + "- CNG \n", + "- ethanol \n", + "- ow emission (hybrid, propane) \n", + "- diesel \n", + "- gas\n", + "\n", + "Below are charts and tables that summarize the findings.\n", + "\n", + "\"\"\"\n", + "display(Markdown(new_summary))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "676cbd9a-db4b-4e86-b60b-900f14513468", + "metadata": {}, + "outputs": [], + "source": [ + "#summary stuff\n", + "display(\n", + " Markdown(\"**ZEB Summary**\"),\n", + " pivot_zeb_prop,\n", + " \n", + " Markdown(\"**Non-ZEB Summary**\"),\n", + " pivot_non_zeb_prop,\n", + " \n", + " Markdown(\"The remaining buses did not specify a propulsion type\")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0158cda6-4cab-416a-bfe0-a6896d6da997", + "metadata": {}, + "outputs": [], + "source": [ + "#min max values for all projects\n", + "bus_min_max_summary(data=agg_agency,col1=\"new_cost_per_bus\")\n", + "bus_min_max_summary(data=agg_agency,col1=\"total_bus_count\")\n", + "bus_min_max_summary(data=agg_agency,col1=\"total_agg_cost\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80dd3d1c-86f8-4c68-bdd1-8249e6494f2c", + "metadata": {}, + "outputs": [], + "source": [ + "## min max values of just ZEB projects\n", + "new_cols =[\n", + " \"transit_agency\",\n", + " \"prop_type\",\n", + " \"total_cost\",\n", + " \"bus_count\",\n", + " \"cost_per_bus\"]\n", + "\n", + "display(Markdown(\"**Which Agneices had the highest and lowest cost per bus?**\")),\n", + "bus_min_max_summary(data=zeb_projects, col1=\"cost_per_bus\", col_list=new_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75919ab8-7f14-49f2-bb4a-9765fdddc35c", + "metadata": {}, + "outputs": [], + "source": [ + "display(Markdown(\n", + " \"**Which agency procured the most and least amount of ZEBs?**\"\n", + "))\n", + "bus_min_max_summary(data=zeb_projects, col1=\"bus_count\", col_list=new_cols)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4f1b88e-f46c-4f69-888d-e116d2ce2ace", + "metadata": {}, + "outputs": [], + "source": [ + "display(Markdown(\n", + " \"**Which Agency had the most and least total ZEB cost?**\"\n", + "))\n", + "bus_min_max_summary(data=zeb_projects, col1=\"total_cost\", col_list=new_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f39733f-448e-4d3e-9981-feff4a13dbda", + "metadata": {}, + "outputs": [], + "source": [ + "# Charts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d58bf288-bfaa-4373-b72b-3f9d7859775f", + "metadata": {}, + "outputs": [], + "source": [ + "# charts " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63f90c48-e28d-4d88-8b90-891a3e3e3681", + "metadata": {}, + "outputs": [], + "source": [ + "conclusion = f\"\"\"\n", + "**Conclusion**\n", + "\n", + "Based on these findings, The average cost of a ZEB, throughout the US, is ~$1,000,000, roughly twice the price of a conventional, non-ZEB.\n", + "The variance in cost depends mainly on the options the Trasnit\n", + "Agencies chooses. Highly optioned/customized buses contribute to high cost.\n", + "Unfortunately, analyzing the cost of configuable options is outside the scope of data provided. \n", + "\"\"\"\n", + "display(\n", + " Markdown(conclusion)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8025c84b-428f-4c40-b4d3-c969af58ce63", + "metadata": {}, + "source": [ + "-------" + ] + }, { "cell_type": "code", "execution_count": 10, diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index 52ac5a943..6d162915f 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -415,7 +415,39 @@ " \n", " return col <= -3 or col >= 3\n", "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "d46d8747-5d4e-418d-a362-c80a093de4dd", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## save to analysis notebook\n", + "chart functions should stay in the analysis notebook since the charts only exist in the analysis notebook\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b2be581-f4e9-4f7e-bde5-01f2de183479", + "metadata": {}, + "outputs": [], + "source": [ + "## moved to analysis NB 6/25\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import shared_utils\n", + "from cost_per_bus_nb_scripts import *\n", + "from IPython.display import Markdown, display\n", + "from matplotlib.ticker import ScalarFormatter\n", "from scipy.stats import zscore\n", + "\n", "def new_cpb_aggregate(df: pd.DataFrame, column=\"transit_agency\") -> pd.DataFrame:\n", " \"\"\"\n", " function to aggregate compiled data by different categories:\n", @@ -450,16 +482,8 @@ " #flag outliers\n", " df_agg[\"new_is_cpb_outlier?\"] = df_agg[\"new_zscore_cost_per_bus\"].apply(outlier_flag)\n", " \n", - " return df_agg" - ] - }, - { - "cell_type": "markdown", - "id": "d46d8747-5d4e-418d-a362-c80a093de4dd", - "metadata": {}, - "source": [ - "## save to analysis notebook\n", - "chart functions should stay in the analysis notebook since the charts only exist in the analysis notebook\n" + " return df_agg\n", + "\n" ] }, { @@ -471,6 +495,7 @@ }, "outputs": [], "source": [ + "## moved to analysis NB 6/25\n", "def make_chart(y_col: str, title: str, data: pd.DataFrame, x_col: str):\n", " \"\"\"\n", " function to create chart. sorts values by y_col ascending.\"\"\"\n", @@ -495,6 +520,7 @@ }, "outputs": [], "source": [ + "## moved to analysis NB 6/25\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from matplotlib.ticker import ScalarFormatter\n", @@ -1068,6 +1094,7 @@ "cell_type": "markdown", "id": "24ab982a-afff-4c07-a19a-703ab82d27b1", "metadata": { + "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -1204,7 +1231,9 @@ { "cell_type": "markdown", "id": "c8fc1d6c-85b5-4890-84f1-66e33eb9d97c", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "## Notes Variable Categories\n", "- initial DF stuff (all cleaned merged data)\n", @@ -1267,6 +1296,7 @@ "cell_type": "markdown", "id": "b5224cfe-6b3a-4c68-a7b5-58df0ff8f85e", "metadata": { + "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -2555,6 +2585,7 @@ " merged_data.shape,\n", " merged_data.head(),\n", " merged_data[\"zscore_cost_per_bus\"].agg([\"min\",\"max\"])\n", + "\n", ")" ] }, @@ -2610,6 +2641,8 @@ } ], "source": [ + "## moved to final NB\n", + "\n", "# aggregating by big categories\n", "agg_agency = new_cpb_aggregate(merged_data)\n", "agg_prop = new_cpb_aggregate(merged_data, column=\"prop_type\")\n", @@ -3219,7 +3252,7 @@ ], "source": [ "# testing pivot table on `merged_data`\n", - "\n", + "# moved to final NB\n", "#pivot table to get totals for each prop type\n", "pivot_prop_type = pd.pivot_table(\n", " merged_data,\n", @@ -3247,6 +3280,8 @@ "metadata": {}, "outputs": [], "source": [ + "#moved to final NB 6/25\n", + "\n", "#pivot table to get grand total for zeb/non-zeb only data\n", "\n", "# keep this\n", @@ -3879,6 +3914,8 @@ } ], "source": [ + "# moved to final NB 6/25\n", + "\n", "# answers total buses and cost per grant type\n", "pivot_source = pd.pivot_table(\n", " merged_data,\n", @@ -4569,7 +4606,9 @@ } ], "source": [ - "summary = f\"\"\"\n", + "# moved to final NB 6/25\n", + "\n", + "new_summary = f\"\"\"\n", "\n", "# Bus Procurement Cost Analysis\n", "\n", @@ -4613,7 +4652,7 @@ "\"\"\"\n", "from IPython.display import Markdown, display\n", "\n", - "display(Markdown(summary))" + "display(Markdown(new_summary))" ] }, { @@ -4826,6 +4865,7 @@ } ], "source": [ + "# moved to final NB 6/25\n", "display(\n", " Markdown(\"**ZEB Summary**\"),\n", " pivot_zeb_prop,\n", @@ -5389,6 +5429,7 @@ } ], "source": [ + "# moved to final NB 6/25\n", "## min max values of just ZEB projects\n", "# YES I CAN!!\n", "new_cols =[\n", @@ -5561,11 +5602,11 @@ } ], "source": [ + "# moved to final NB 6/25\n", "display(Markdown(\n", " \"**Which agency procured the most and least amount of ZEBs?**\"\n", "))\n", - "bus_min_max_summary(data=zeb_projects, col1=\"bus_count\", col_list=new_cols)\n", - "\n" + "bus_min_max_summary(data=zeb_projects, col1=\"bus_count\", col_list=new_cols)\n" ] }, { @@ -5716,6 +5757,7 @@ } ], "source": [ + "# moved to final NB 6/25\n", "display(Markdown(\n", " \"**Which Agency had the most and least total ZEB cost?**\"\n", "))\n", @@ -5756,6 +5798,7 @@ } ], "source": [ + "# moved to final NB 6/25\n", "conclusion = f\"\"\"\n", "**Conclusion**\n", "\n", From c2cedd7edce0516f471ff7a64db1e51c9a5f3c5d Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Tue, 25 Jun 2024 23:07:07 +0000 Subject: [PATCH 28/36] minor bug fixed for markdown to work in final nb --- bus_procurement_cost/bus_cost_utils.py | 4 +- .../cost_per_bus_analysis.ipynb | 1441 +++-- bus_procurement_cost/refactor_bus_cost.ipynb | 4685 +++-------------- 3 files changed, 1902 insertions(+), 4228 deletions(-) diff --git a/bus_procurement_cost/bus_cost_utils.py b/bus_procurement_cost/bus_cost_utils.py index 8cc903607..e15586fbd 100644 --- a/bus_procurement_cost/bus_cost_utils.py +++ b/bus_procurement_cost/bus_cost_utils.py @@ -1,6 +1,7 @@ #script with shared functions used throughout the bus cost analysis. import pandas as pd +from IPython.display import Markdown, display GCS_PATH = "gs://calitp-analytics-data/data-analyses/bus_procurement_cost/" @@ -243,8 +244,7 @@ def bus_min_max_summary(data:pd.DataFrame, col1:str, col_list=["transit_agency", """ - return display( - Markdown(f"**Max {col1}**"), + return display(Markdown(f"**Max {col1}**"), data[data[col1] == data[col1].max()][col_list], Markdown(f"**Min {col1}**"), data[data[col1] == data[col1].min()][col_list] diff --git a/bus_procurement_cost/cost_per_bus_analysis.ipynb b/bus_procurement_cost/cost_per_bus_analysis.ipynb index 9cd4f8e9b..4ded09c66 100644 --- a/bus_procurement_cost/cost_per_bus_analysis.ipynb +++ b/bus_procurement_cost/cost_per_bus_analysis.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "id": "da041e43-e8e2-4d4b-a498-10a7c0afe43f", "metadata": { "tags": [] @@ -14,15 +14,17 @@ "import pandas as pd\n", "import seaborn as sns\n", "import shared_utils\n", + "from bus_cost_utils import *\n", "from cost_per_bus_nb_scripts import *\n", "from IPython.display import Markdown, display\n", "from matplotlib.ticker import ScalarFormatter\n", - "from scipy.stats import zscore" + "from scipy.stats import zscore\n", + "from IPython.display import Markdown, display" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "d53376d9-d4b4-48b7-9916-5b9f633fbaf0", "metadata": {}, "outputs": [], @@ -32,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "a45f2d3d-a600-4fe6-80cf-6b887036faab", "metadata": {}, "outputs": [], @@ -56,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "8ac40482-ba3e-4fde-8c05-806e3725de44", "metadata": {}, "outputs": [], @@ -73,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "d450fd60-cced-453b-b20b-62cdade0d7a6", "metadata": {}, "outputs": [], @@ -117,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "9a6a7ecf-5180-4691-84fe-23aa68cdae93", "metadata": {}, "outputs": [], @@ -139,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "44d21201-223f-4e6c-b238-b72fba984544", "metadata": {}, "outputs": [], @@ -186,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "067a14a5-5c77-4914-82a8-c5eeb170cb08", "metadata": {}, "outputs": [], @@ -200,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "49a97d01-b17e-475c-b351-67426f3741d9", "metadata": {}, "outputs": [], @@ -213,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "4faaa4ad-b16c-4e6b-87c7-d12f7e7db3c6", "metadata": {}, "outputs": [], @@ -233,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "b8535e97-e7bf-4d7e-b718-24c5758b0ccd", "metadata": {}, "outputs": [], @@ -255,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "829e38c9-3f9b-4e82-92a8-c86f81051580", "metadata": {}, "outputs": [], @@ -277,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "0a2163e3-dac1-4e64-a551-3dc961e44714", "metadata": {}, "outputs": [], @@ -298,7 +300,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "074acb8d-de54-43a0-b243-a070ecfbe1ce", "metadata": {}, "outputs": [], @@ -369,210 +371,96 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "676cbd9a-db4b-4e86-b60b-900f14513468", "metadata": {}, - "outputs": [], - "source": [ - "#summary stuff\n", - "display(\n", - " Markdown(\"**ZEB Summary**\"),\n", - " pivot_zeb_prop,\n", - " \n", - " Markdown(\"**Non-ZEB Summary**\"),\n", - " pivot_non_zeb_prop,\n", - " \n", - " Markdown(\"The remaining buses did not specify a propulsion type\")\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0158cda6-4cab-416a-bfe0-a6896d6da997", - "metadata": {}, - "outputs": [], - "source": [ - "#min max values for all projects\n", - "bus_min_max_summary(data=agg_agency,col1=\"new_cost_per_bus\")\n", - "bus_min_max_summary(data=agg_agency,col1=\"total_bus_count\")\n", - "bus_min_max_summary(data=agg_agency,col1=\"total_agg_cost\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "80dd3d1c-86f8-4c68-bdd1-8249e6494f2c", - "metadata": {}, - "outputs": [], - "source": [ - "## min max values of just ZEB projects\n", - "new_cols =[\n", - " \"transit_agency\",\n", - " \"prop_type\",\n", - " \"total_cost\",\n", - " \"bus_count\",\n", - " \"cost_per_bus\"]\n", - "\n", - "display(Markdown(\"**Which Agneices had the highest and lowest cost per bus?**\")),\n", - "bus_min_max_summary(data=zeb_projects, col1=\"cost_per_bus\", col_list=new_cols)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "75919ab8-7f14-49f2-bb4a-9765fdddc35c", - "metadata": {}, - "outputs": [], - "source": [ - "display(Markdown(\n", - " \"**Which agency procured the most and least amount of ZEBs?**\"\n", - "))\n", - "bus_min_max_summary(data=zeb_projects, col1=\"bus_count\", col_list=new_cols)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c4f1b88e-f46c-4f69-888d-e116d2ce2ace", - "metadata": {}, - "outputs": [], - "source": [ - "display(Markdown(\n", - " \"**Which Agency had the most and least total ZEB cost?**\"\n", - "))\n", - "bus_min_max_summary(data=zeb_projects, col1=\"total_cost\", col_list=new_cols)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1f39733f-448e-4d3e-9981-feff4a13dbda", - "metadata": {}, - "outputs": [], - "source": [ - "# Charts" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d58bf288-bfaa-4373-b72b-3f9d7859775f", - "metadata": {}, - "outputs": [], - "source": [ - "# charts " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "63f90c48-e28d-4d88-8b90-891a3e3e3681", - "metadata": {}, - "outputs": [], - "source": [ - "conclusion = f\"\"\"\n", - "**Conclusion**\n", - "\n", - "Based on these findings, The average cost of a ZEB, throughout the US, is ~$1,000,000, roughly twice the price of a conventional, non-ZEB.\n", - "The variance in cost depends mainly on the options the Trasnit\n", - "Agencies chooses. Highly optioned/customized buses contribute to high cost.\n", - "Unfortunately, analyzing the cost of configuable options is outside the scope of data provided. \n", - "\"\"\"\n", - "display(\n", - " Markdown(conclusion)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "8025c84b-428f-4c40-b4d3-c969af58ce63", - "metadata": {}, - "source": [ - "-------" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "c51fe7dd-22e2-4686-b1a5-57b2f5ad8602", - "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "\n", - "## Summary\n", - "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", - "\n", - "Data was compiled from three data sources:\n", - "\n", - "1. 130 projects from FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", - "2. 124 projects TIRCP project data (state-funded, California only)\n", - "3. 35 projects DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc..\n", - "\n", - "The compiled dataset includes **289** total transit related projects. However, the initial dataset included projects that encompassed bus procurement and other components such as charging installation and facility construction, as well as non-bus related projects (ferries, trains). The dataset was filtered to exclude projects that were not bus related, indicated 0 buses procured, and projects that contained construction/installation work. **87** projects remained that specified the number of buses to procure and explicitly described procuring buses (bus only projects). \n", - "\n", - "Number of bus only contracts from each dataset \n", - "- FTA: **43**\n", - "- TIRCP: **9**\n", - "- DGS: **35**\n", - "\n", - "\n", - "The remaining bus only projects were categorized into different propulsion types and bus sizes, a “cost per bus” value was calculated, and outliers removed.\n", - "\n", - "A overall summary is provided below:\n", - "- Total projects: **298**\n", - "- Number of projects with mix bus procurement and other components, also non-bus projects: **204** \n", - "- Number of bus only projects: **87**\n", - "- Total dollars awarded to bus only projects: **`$831,843,715.00`**\n", - "- Total number of buses: **1353.0**\n", - "- Most common propulsion type procured for bus only projects: **BEB** at **30** projects\n", - "- Number of ZEB buses* procured: **452.0**\n", - "- Number of non-ZEB buses** procured: **575.0**\n", - "- Overall average cost per bus (ZEB & non-ZEB) is `$792,635.34` (std `$396,712.61`)\n", - "- ZEB average cost per bus is `$1,056,659.30` (std `$253,737.82`)\n", - "- Non-ZEB average cost per bus is `$528,106.49` (std `$315,932.20`) \n", - "\n", - "`*`ZEB buses include: zero-emission (not specified), electric (not specified), battery electric, fuel cell electric\n", - "\n", - "`**`Non-ZEB buses include: CNG, ethanol, low emission (hybrid, propane), diesel, gas.\n", - "\n", - "\n", - "Below are key charts that visualize more findings:\n", - "\n", - "\n" + "**ZEB Summary**" ], "text/plain": [ "" ] }, - "execution_count": 10, "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Markdown(summary)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "4e553e15-dc1d-47d3-9818-7dec893c5294", - "metadata": { - "tags": [] - }, - "outputs": [ + "output_type": "display_data" + }, { "data": { - "text/markdown": [ + "text/html": [ + "
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prop_typebus_counttotal_costcost_per_bus
0BEB163.01672324891025966
1FCEB102.01209513351185797
2electric (not specified)44.0566780001288136
3zero-emission bus (not specified)143.0128156513896199
4Grand Total452.04730183371046500
\n", + "
" ], "text/plain": [ - "" + " prop_type bus_count total_cost cost_per_bus\n", + "0 BEB 163.0 167232489 1025966\n", + "1 FCEB 102.0 120951335 1185797\n", + "2 electric (not specified) 44.0 56678000 1288136\n", + "3 zero-emission bus (not specified) 143.0 128156513 896199\n", + "4 Grand Total 452.0 473018337 1046500" ] }, "metadata": {}, @@ -580,45 +468,98 @@ }, { "data": { - "image/png": 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xHMcSEhIMy0r3qceXXb16lQFgX3zxRXlpKpfVTnc+/leORqNBTk4OmjRpAldXV1y6dMlk2/H39zf0EAGAs7MzJkyYgMuXLyMzM7NG63J1dQUA/Prrr9BoNOW22b17N1xcXPDMM88gOzvb8BMdHQ0nJyccPXq02uuqLr1ej71792LIkCFG3b+lSv+K+/3339GxY0fD6S2g5K+cadOmISkpCTdv3jTElpqaivPnz9cpricJBAJMnz7d8FgkEmH69Ol4+PAhLl68CKAkfxEREWjevLlR/p5++mkAMOSvVI8ePRAZGVntGCy9P5Tn8OHDUKvV+L//+z+jv7Bfe+21Kl/r4uICADhw4ECZ0yM18fLLL1e7bUxMDKKjow2Pg4ODMWzYMBw4cKDMqTBTKs3Ta6+9Bh7vf4eqqVOnwtnZucxpeicnJ6MxMSKRCB07dsS9e/cq3U7btm3h5ORkOIV78uRJBAYGYsKECbh06RIUCgUYYzh16pThL+3amjx5stFptdL1VRXj40aNGoXi4mL8+uuvKCwsxK+//lrhqc7qHo8A42NyYWEhsrOz0b17dygUCty6dctovbXN9ZPbyc/PR3Z2Nnr06IF79+4ZTtmXioyMNMq5l5cXwsPDjbbz+++/o3PnzujYsaNRu3HjxlUZCwC89NJL2L9/P3r27IlTp05h0aJF6N69O5o2bYozZ85Uax2PvyelUons7Gx07twZAMr9Pnvy89e9e3fk5OSgoKDA8J4AYNasWUbtyjtG1PS7dOrUqUZjPy9cuICHDx/i5ZdfNto3J02aZDje1FVpj3JhYSGA6u+Xpacod+3aZdTjv3PnTnTu3BnBwcEVblMsFhuOGzqdDjk5OXByckJ4eHita4zff/8dfD6/zO/ljTfeAGPMMBSiVJ8+fYzOFERFRcHZ2blGn3erFWnFxcX44IMPDOd1PT094eXlhby8vDIf1Lpo0qRJma7mZs2aAUCNx7/16NEDzz33HGJjY+Hp6Ylhw4Zh8+bNRmOl7ty5g/z8fHh7e8PLy8voRy6XGwaiVmdd1ZWVlYWCgoIqTz0lJycjPDy8zPKIiAjD8wDw9ttvw8nJCR07dkTTpk0xY8YMnD59usZxPcnf37/MINUnfxd37tzBjRs3yuSutN2TA3nDwsJqFIOl94fylOa5adOmRsu9vLzg5uZW6WvDwsIwZ84c/Pe//4Wnpyf69euHL7/8ssafmZrk7ck4gZKcKRQKZGVl1Wi7NVGapyf3WZFIhEaNGhmeLxUYGFjmd+vm5lZm7NWT+Hw+YmJiDGNwTp48ie7du6Nbt27Q6XQ4d+4cbt68iUePHtW5SHvyS6X0911VjI/z8vJCnz59sH37duzZswc6nQ7PP/98uW2rezwCSsZ1DR8+HC4uLnB2doaXl5ehEHty/6ptrgHg9OnT6NOnDxwdHeHq6govLy+8++675W6nvC/hJ7eTnJxc7j5a3rGuIv369cOBAweQl5eHEydOYMaMGUhOTsbgwYOrdfHAo0ePMHv2bPj4+EAqlcLLy8vwGSvvs1nVfpCcnAwej1dmKEB576mm36VPfvYrOh4JhcIKx4PVlFwuBwDIZDIANdsvR48ejfv37+Ps2bMASsYQXrx4EaNHj650m3q93nDBwuN5uXbtWq1rjOTkZPj7+xveR6knv0NLVWf/rYrVxqT93//9HzZv3ozXXnsNMTExcHFxAcdxeOGFF6o94NNUKhos/WQvQelklOfOncMvv/yCAwcO4KWXXsKKFStw7tw5ODk5Qa/Xw9vbG9u2bSt3naXjtqqzLmuJiIhAfHw8fv31V+zfvx8//vgjvvrqK3zwwQeIjY0167b1ej1atWqFlStXlvv8k+MjqjvupCZMuT+Yw4oVKzBp0iTs27cPBw8exKxZs7B06VKcO3euwjE1TzJ13qqbM3Oq6MrQx/8Cr0i3bt3w0UcfQalU4uTJk3jvvffg6uqKli1b4uTJk/Dx8QGAOhdpdYnxcWPHjsXUqVORmZmJAQMGGHp1n1Td41FeXh569OgBZ2dnLFy4EI0bN4ZEIsGlS5fw9ttvlzkm1/Z93L17F71790bz5s2xcuVKBAUFQSQS4ffff8eqVatMtp3acnBwQPfu3dG9e3d4enoiNjYWf/zxByZOnFjp60aNGoUzZ87gzTffRJs2bQzfBf379y/3+8yU76um36XmOGZW5fr16wBK/kgGqr9fAiUX8Tg4OGDXrl3o0qULdu3aBR6Ph5EjR1a6zSVLluD999/HSy+9hEWLFsHd3R08Hg+vvfaaxWoMU/yerVak/fDDD5g4cSJWrFhhWKZUKk0+71ZCQgIYY0ZfIrdv3wYAwyDz0r9i8vLyjA52T1bFpTp37ozOnTvjo48+wvbt2zFu3Djs2LED//nPf9C4cWMcPnwYXbt2rdaHobJ1VZeXlxecnZ0NH4SKhISElDvnS+mpjMfnp3J0dMTo0aMxevRoqNVqjBgxAh999BHmzZsHiURS4ZdyZdLT08tc8v3k76Jx48a4evUqevfuXattVMXS+0N5SvN8584do79Us7Kyqv0XVqtWrdCqVSvMnz8fZ86cQdeuXbFu3TosXrwYQMVFU23cuXOnzLLbt2/DwcHBcDB1c3Mr97NbXs6qG1tpnuLj443ypFarkZiYiD59+lRrPdXRvXt3qNVqfP/990hLSzMUY0899ZShSGvWrJmhWKuIOfbZ8gwfPhzTp0/HuXPnsHPnzgrbVfd4dOzYMeTk5GDPnj1G874lJiaaNO5ffvkFKpUKP//8s1Evw5PDGGoiJCSk3H20rvNblQ4dycjIAFDx7zY3NxdHjhxBbGwsPvjgA8Py8mKqrpCQEOj1ety9e9eo96y891TX79LHj0elw0qAklOniYmJaN26dS3fRQm5XI6ffvoJQUFBhh6nmnxPOjo6YvDgwdi9ezdWrlyJnTt3onv37vD396/0dT/88AN69eqFjRs3Gi3Py8szXOwG1OwzGxISgsOHD6OwsNCoN62871BTsdrpTj6fX6aa/OKLL0z+l3d6ejp++uknw+OCggJ88803aNOmDXx9fQH87+qyx6cVKJ0m4HG5ubllYm7Tpg0AGE5xjRo1CjqdDosWLSoTi1arNXxwqrOu6uLxeHj22Wfxyy+/4MKFC2WeL93OwIED8ffffxu6jYGS97lhwwaEhoYaxnY9edm4SCRCZGQkGGOGsVelhVZNimqtVov169cbHqvVaqxfvx5eXl6GMU+jRo1CWlpauZMSFhcXG65Gqi1L7w/l6dOnD4RCIb744guj11dnEtSCggJotVqjZa1atQKPxzPapqOjo8n+4Dl79qzRGI779+9j37596Nu3r+EvxcaNGyM/Px/Xrl0ztMvIyDDKdU1j69OnD0QiET7//HOjPG3cuBH5+fnlXulWW506dYJQKMSyZcvg7u5uuAKte/fuOHfuHI4fP16tXjRHR0eTDteoiJOTE9auXYsFCxZgyJAhFbar7vGo9Pf4eJ7VajW++uork8Zd3nby8/OxefPmWq9z4MCBOHfuHP7++2/DsqysrAp7aZ505MiRcpeXjgsrLZJKr5J8ct8t7z0B1fs8V6T0Ctknr9Ytb511/S5t3749vLy8sG7dOqOry7ds2VLnY0hxcTFefPFFPHr0CO+9956hIKrufllq9OjRSE9Px3//+19cvXq1ylOdQPl52b17N9LS0oyW1eS7bODAgdDpdFizZo3R8lWrVoHjuCqvbK4Nq/WkDR48GN9++y1cXFwQGRmJs2fP4vDhw9W/LLWamjVrhilTpuD8+fPw8fHBpk2b8ODBA6ODQt++fREcHIwpU6bgzTffBJ/Px6ZNm+Dl5YWUlBRDu61bt+Krr77C8OHD0bhxYxQWFuLrr7+Gs7MzBg4cCKBknNL06dOxdOlSXLlyBX379oVQKMSdO3ewe/dufPbZZ3j++eertS6gZPDm1q1bkZiYWOn0EkuWLMHBgwfRo0cPw9QVGRkZ2L17N06dOgVXV1e88847hkv3Z82aBXd3d8O6f/zxR8Mgy759+8LX1xddu3aFj48P4uLisGbNGgwaNMjw10NpUfXee+/hhRdegFAoxJAhQyqdGNHf3x/Lli1DUlISmjVrhp07d+LKlSvYsGGDYRqAF198Ebt27cLLL7+Mo0ePomvXrtDpdLh16xZ27dqFAwcOlHtxRHVZen8oj5eXF+bOnYulS5di8ODBGDhwIC5fvow//vjD6C+88vz555+YOXMmRo4ciWbNmkGr1eLbb78Fn8/Hc889Z2gXHR2Nw4cPY+XKlfD390dYWBg6depUq5y1bNkS/fr1M5qCA4DRqe8XXngBb7/9NoYPH45Zs2ZBoVBg7dq1aNasWZlButWNzcvLC/PmzUNsbCz69++PoUOHIj4+Hl999RU6dOhQZuLMunBwcEB0dDTOnTtnmCMNKOlJKyoqQlFRUbWKtOjoaOzcuRNz5sxBhw4d4OTkVGkRVRdVnYIDqn886tKlC9zc3DBx4kTMmjULHMfh22+/Nflpxb59+0IkEmHIkCGYPn065HI5vv76a3h7ext6rGrqrbfewrfffov+/ftj9uzZhik4QkJCjP5oqMiwYcMQFhaGIUOGoHHjxigqKsLhw4fxyy+/oEOHDobfn1QqRWRkJHbu3IlmzZrB3d0dLVu2RMuWLfHUU0/hk08+gUajQUBAAA4ePFinXsg2bdpgzJgx+Oqrr5Cfn48uXbrgyJEj5c6pV9fvUqFQiMWLF2P69Ol4+umnMXr0aCQmJmLz5s01GpOWlpaG7777DkBJ79nNmzexe/duZGZm4o033jC6aKy6+2WpgQMHQiaTYe7cuWWOdRUZPHgwFi5ciMmTJ6NLly74559/sG3btjLvqXHjxnB1dcW6desgk8ng6OiITp06lTtud8iQIejVqxfee+89JCUloXXr1jh48CD27duH1157rdLphGqt2teBmlhubi6bPHky8/T0ZE5OTqxfv37s1q1bZS6jresUHIMGDWIHDhxgUVFRTCwWs+bNm5c7fcHFixdZp06dmEgkYsHBwWzlypVlpgS4dOkSGzNmDAsODmZisZh5e3uzwYMHG01PUGrDhg0sOjqaSaVSJpPJWKtWrdhbb73F0tPTa7Su5557jkmlUpabm1vl+01OTmYTJkxgXl5eTCwWs0aNGrEZM2YYXVZ99+5d9vzzzzNXV1cmkUhYx44d2a+//mq0nvXr17OnnnqKeXh4MLFYzBo3bszefPNNlp+fb9Ru0aJFLCAggPF4vCovde/Rowdr0aIFu3DhAouJiWESiYSFhISwNWvWlGmrVqvZsmXLWIsWLZhYLGZubm4sOjqaxcbGGsUAgM2YMaPKvJSy5v7wJJ1Ox2JjY5mfnx+TSqWsZ8+e7Pr161Xu//fu3WMvvfQSa9y4MZNIJMzd3Z316tWLHT582Gj9t27dYk899RSTSqVG03qUTgGQlZVVJqaKpuCYMWMG++6771jTpk2ZWCxmbdu2Nfo8ljp48CBr2bIlE4lELDw8nH333XflrrOi2J7Mb6k1a9aw5s2bM6FQyHx8fNgrr7xS5vNQun89qbrHCsYYe/PNNxkAtmzZMqPlTZo0YQDY3bt3jZaXd2ySy+Vs7NixzNXVlQEwbLu07ZP7WmJiYoWX/z/u8Sk4KlPRVAhVHY8YY+z06dOsc+fOTCqVMn9/f/bWW2+xAwcOlHmPdc31zz//zKKiophEImGhoaFs2bJlbNOmTWV+9xW9lx49epSZFuLatWusR48eTCKRsICAALZo0SLD1BpVTcHx/fffsxdeeIE1btyYSaVSJpFIWGRkJHvvvfdYQUGBUdszZ86w6OhoJhKJjKbCSE1NZcOHD2eurq7MxcWFjRw5kqWnp5eZLqOiz195+35xcTGbNWsW8/DwYI6OjmzIkCHs/v37ZdZZ3e/Sqvahr776ioWFhTGxWMzat2/PTpw4UW6uy1M6XQoAxnEcc3Z2Zi1atGBTp041mhrlSdXZL0uNGzeOAWB9+vSpMIYnp+B44403DMfYrl27srNnz5b7nvbt28ciIyOZQCAw+jyWt08XFhay119/nfn7+zOhUMiaNm3Kli9fbjSdEmMVfz89GWdVuH9XRmyUj48PJkyYUOmkffagZ8+eyM7OrnLcHCGEEEJK0A3WbdiNGzdQXFyMt99+29qhEEIIIcTC6AbrNqxFixaGyQ0JIYQQ0rBQTxohhBBCiA2iMWmEEEIIITaIetIIIYQQQmwQFWmEEEIIITaILhwoh16vR3p6OmQymcVu80IIIYSQumGMobCwEP7+/oYJ2u0ZFWnlSE9PL3Mjb0IIIYTYh/v37yMwMNDaYdQZFWnlKL310f379+Hs7GzlaEip5muaI6MwA34yP9yaecva4dRPzZsDGRmAnx9wi3JsarQPmxfl17zsIb8FBQUICgoyugG6PaMirRylpzidnZ2pSLMhC/otgFwth5PIiX4v5rJgASCXA05OAOXY5GgfNi/Kr3nZU37ry1AlmoKjHAUFBXBxcUF+fr7N74iEEEIIKVHfvr/tf1QdIYQQQkg9REUaIYQQQogNojFpxG5kFGZAx3Tgc3z4yfysHU79lJEB6HQAn19y8UAd6HQ6aDQaEwVWPzwsegg904PH8eDt6G3tcOodyq952UJ+hUIh+Hy+VbZtDVSkEbvR4esOSCtMQ4AsAKlzUq0dTv3UoQOQlgYEBACptcsxYwyZmZnIy8szbWz1QGpBKnR6Hfg8Poqci6wdTr1D+TUvW8mvq6srfH19683FAZWhIo0QYlKlBZq3tzccHBwaxIG0ulRZKmiZFgJOgDCvMGuHU+9Qfs3L2vlljEGhUODhw4cAAL869vbbAyrSCCEmo9PpDAWah4eHtcOxOZyQA/QAx+MgkUisHU69Q/k1L1vIr1QqBQA8fPgQ3t7e9f7UJ104QAgxmdIxaA4ODlaOhBBSX5UeXxrCmFcq0gghJkenOAkh5tKQji9UpBFCCCGE2CAq0gghpJ5ISkrCggULrB0GIcREqEgjhBAAkyZNAsdxePnll8s8N2PGDHAch0mTJlk+MBNISkoCx3Hl/uzevdvQ7siRI+jSpQtkMhl8fX3x9ttvQ6vVGp4/duwYhg0bBj8/Pzg6OqJNmzbYtm1btWLYsmULoqKiIJFI4O3tjRkzZhiei4+PR69eveDj4wOJRIJGjRph/vz5RmOOtmzZUib2qgavnzp1Cl27doWHhwekUimaN2+OVatWVdj+448/BsdxeO2116r1nggxN7q6kxBC/hUUFIQdO3Zg1apVhqvIlEoltm/fjuDgYCtHV7HExETMmTMHZ8+eRUFBAXbs2IGePXti3bp1AEreV0ZGhtFrNmzYgOXLl2PAgAEAgKtXr2LgwIF477338M033yAtLQ0vv/wydDodPv30UwDAmTNnEBUVhbfffhs+Pj749ddfMWHCBLi4uGDw4MEVxrdy5UqsWLECy5cvR6dOnVBUVISkpCTD80KhEBMmTEC7du3g6uqKq1evYurUqdDr9ViyZImhnbOzM+Lj4w2Pqxqb5OjoiJkzZyIqKgqOjo44deoUpk+fDkdHR0ybNs2o7fnz57F+/XpERUVVuk5CLImKNEII+Ve7du1w9+5d7NmzB+PGjQMA7NmzB8HBwQgLM54XSq/XY9myZdiwYQMyMzPRrFkzvP/++3j++ecBlExHMm3aNPz555/IzMxEcHAwho0fhlFTRhnWMWnSJOTl5aFbt25YsWIF1Go1XnjhBaxevRpCobDacU+YMAE6nQ5r167FoUOHMGzYMJw4ccLwPJ/Ph6+vr9FrfvrpJ4waNQpOTk4AgJ07dyIqKgoffPABAKBJkyb45JNPMGrUKHz44YeQyWR49913jdYxe/ZsHDx4EHv27KmwSMvNzcX8+fPxyy+/oHfv3obljxdDjRo1QqNGjQyPQ0JCcOzYMZw8edJoXRzHlXkflWnbti3atm1reBwaGoo9e/bg5MmTRkWaXC7HuHHj8PXXX2Px4sXVXj8h5kZFGrEbRyYcgVavhYBHu63ZHDkCaLWAoPo5TklJQXZ2NoCSySYFAgEUCgV0Ol2dQhEIBBCLxXVaR2289NJL2Lx5s6FI27RpEyZPnoxjx44ZtVu6dCm+++47rFu3Dk2bNsWJEycwfvx4eHl5oUePHtDr9QgMDMTu3bvh4eGBM2fOYNq0aYhqHIXnRz5vWM/Ro0fh5+eHo0ePIiEhAaNHj0abNm0wdepUAMCCBQuwZcsWo56nJ12+fBnr169H27ZtcfXqVfTr1w/9+vWrsP3Fixdx5coVfPnll4ZlKpWqzOlDqVQKpVKJixcvomfPnuWuKz8/HxERERVu69ChQ9Dr9UhLS0NERAQKCwvRpUsXrFixAkFBQeW+JiEhAfv378eIESOMlsvlcoSEhECv16Ndu3ZYsmQJWrRoYXg+3CMcDAwcyu9hu3z5Ms6cOVOmEJsxYwYGDRqEPn36UJFWiaryS0yPvu2I3Qj3DLd2CPVfeM1ynJKSgoiICCgUCgAlPSDr1q0rd/4in23b4LN9e5XrVISHI2HlSvB4PLRo0aKkUBs6FLh0qeqA5swp+amD8ePHY968eUhOTgYAnD59Gjt27DAq0lQqFZYsWYLDhw8jJiYGQElv0KlTp7B+/Xr06NEDQqEQsbGxhteEhYXh7Nmz2LtnL8aPHW9Y7ubmhjVr1oDP56N58+YYNGgQjhw5YijSPD090bhx40pj7tq1K1avXg29Xl+t97hx40ZERESgS5cuhmX9+vXD6tWr8f3332PUqFHIzMzEwoULAaDMqdJSu3btMpwmrMi9e/cMpy0/++wzuLi4YP78+XjmmWdw7do1iEQiQ9suXbrg0qVLUKlUmDZtmmH7ABAeHo5NmzYhKioK+fn5+PTTT9GlSxfcuHEDgYGBAACJsPwxaoGBgcjKyoJWq8WCBQvwn//8x/Dcjh07cOnSJZw/f74amWvYKsovMR8q0gghtZadnQ2FQoH5azYipEk4xHwO7u4O8A8KglBk3AvmIJZC9O/tXCrDhYTALzgUGSlJ0Gq1JUVaVlbJPUWrUlBQ27di4OXlhUGDBmHLli1gjGHQoEHw9PQ0apOQkACFQoFnnnnGaLlarTY6vfbll19i06ZNSElJQXFxMdRqNdq0aWP0mhYtWhjNmu7n54d//vnH8HjmzJmYOXNmpTFv27YNsbGxePfdd5GZmYkDBw7gjTfeMJx6fVxxcTG2b9+O999/32h53759sXz5crz88st48cUXIRaL8f777+PkyZPg8cpeY3b06FFMnjwZX3/9tVFv1pP0ej00Gg0+//xz9O3bFwDw/fffw9fXF0ePHjXq8du5cycKCwtx9epVvPnmm/j000/x1ltvAQBiYmIMBTFQUtBFRERg/fr1WLRoUaX5OXnyJORyOc6dO4d33nkHTZo0wZgxY3D//n3Mnj0bhw4dojsUEJtERRohpM5CmoQjPKoNOI0KwoIMiMUSiJ740uO7u0MfEFDlujhvH4jET3xhenmV3PS9Ks7ONQm7Qi+99JKhMHr8lGApuVwOAPjtt98Q8ERcpadod+zYgblz52LFihWIiYmBTCbD8uXL8ddffxm1f3LsGcdx1e4RK+Xp6YkvvvgCb7zxBj7++GOEhoZi9OjR+OOPPwyFUakffvgBCoUCEyZMKLOeOXPm4PXXX0dGRgbc3NyQlJSEefPmGY0XA4Djx49jyJAhWLVqVbnreVzp/RUjIyMNy7y8vODp6YmUlBSjtqWnPyMjIw1j+t54441yb/0jFArRtm1bJCQkVLp9AIbxhK1atcKDBw+wYMECjBkzBhcvXsTDhw/Rrl07Q1udTocTJ05gzZo1UKlU9f62Q8S2UZFG7Mb2f7ZDoVHAQeiAsa3GWjuc+mn7dkChABwcgLGmzbHmtdehee316jUuLjZ+/PPPJo2lKv3794darQbHceWO7YqMjIRYLEZKSgp69OhR7jpOnz6NLl264NVXXzUsu3X7FrR6LXIUOfBwMM+9TX19ffHOO+9g9+7dOHnyZJkibePGjRg6dCi8vLzKfT3HcfD39wdQ0uMVFBRkVMQcO3YMgwcPxrJly8pcIVmerl27AiiZZqP0tOSjR4+QnZ2NkJCQCl9X2gOn1+vLLZR0Oh3++ecfDBw40LAsR5EDPdODx/EqzK9er4dKpQIA9O7d26jXEgAmT56M5s2b4+2336YC7QnVyS8xLSrSiN1469BbSCtMQ4AsgIo0c3nrrZLTigEBJi/S7Amfz0dcXJzh/0+SyWSYO3cuXn/9dej1enTr1g35+fk4ffo0nJ2dMXHiRDRt2hTffPMNDhw4gLCwMHz77be4dPES/IL8kFqQWu0vuTVr1uCnn37CkSNHKmwzZcoUw9QSKpUKe/bswY0bN8qc0kxISMCJEyfw+++/l7ue5cuXo3///uDxeNizZw8+/vhj7Nq1y5CDo0ePYvDgwZg9ezaee+45ZGZmAgBEIhHc3d3LXWezZs0wbNgwzJ49Gxs2bICzszPmzZuH5s2bo1evXgBKTtcKhUK0atUKYrEYFy5cwLx58zB69GhDT+PChQvRuXNnNGnSBHl5eVi+fDmSk5ONxpe99fZbyMzIxJI1S+Dh4IEvv/wSwcHBaN68OQDgxIkT+PTTTzFr1iwAJb/Hli1bGsXr6OgIDw+PMssJkFqQCo1eAyFPSEWahVh1MtsTJ05gyJAh8Pf3B8dx2Lt3r9HzFU2+uHz58grXuWDBgjLtSz+ghBBSXc7OznCu5PTpokWL8P7772Pp0qWIiIhA//798dtvvxlOrU2fPh0jRozA6NGj0alTJ+Tk5GDUxFEVrq8i2dnZuHv3bqVtvL298dJLL6Fjx45Yvnw55s6di0WLFuHZZ581ardp0yYEBgaW6V0r9ccff6B79+5o3749fvvtN+zbt89oHVu3boVCocDSpUvh5+dn+Hn8Ksxjx46B4zijq1G/+eYbdOrUCYMGDTJcVLF//35DASYQCLBs2TJ07NgRUVFRiI2NxcyZM/Hf//7XsI7c3FxMnToVERERGDhwIAoKCnDmzBmj06hZD7OQmZ5peKzX6zFv3jy0adMG7du3x5dffolly5YZXZBAiC3jGGPMWhv/448/cPr0aURHR2PEiBH46aefjA4IpX+lPd5+ypQpSEhIKDNGotSCBQvwww8/4PDhw4ZlAoGgzMDfyhQUFMDFxQX5+fmVHqSJZQWuDDT0pKXOSbV2OPVTYOD/etJSq87xpUuXEB0dja/3nzKMSXMsyEBwcGiZMWk1oSwuRvKdW4iIiICjo2Ot12NrrmZeNfREtPZtbfL1JyUlYcuWLVa9NdTmzZuxZMkS3Lx5s0ZzvZmCufPb0NlKfpVKJRITExEWFlbmgo/69v1t1dOdAwYMMMx2XZ4nJy3ct28fevXqVWGBVkogENRowkNCCCGm8fvvv2PJkiUWL9AIqY/sZkzagwcP8Ntvv2Hr1q1Vtr1z5w78/f0hkUgQExODpUuX2vQtXQghxBRCQ0OtfoP1x+8FSgipG7sp0rZu3QqZTFZmBuonderUCVu2bEF4eDgyMjIQGxuL7t274/r165DJZOW+RqVSGa72AUq6SwkhhBBCrMluirRNmzZh3LhxVU44+Pjp06ioKHTq1AkhISHYtWsXpkyZUu5rli5dajQzOCGEEEKItVn16s7qOnnyJOLj440uta4uV1dXNGvWrNIJD+fNm4f8/HzDz/379+sSLiGEEEJIndlFkbZx40ZER0ejdeuaX00il8tx9+5dw6zX5RGLxYbL7au67J4QQgghxBKsWqTJ5XJcuXIFV65cAQAkJibiypUrRrcKKSgowO7duyvsRevduzfWrFljeDx37lwcP34cSUlJOHPmDIYPHw4+n48xY8aY9b0Q8/N18kWALAC+TnTlrtn4+pZMv0FXR5uFkC+EkCeEkE9XPpoD5de8KL+WZ9UxaRcuXDDMOA2U3DcOACZOnIgtW7YAKLn/HWOswiLr7t27yM7ONjxOTU3FmDFjkJOTAy8vL3Tr1g3nzp2r8BYoxH5cmHbB2iHUfxcox+YU6RVZdSNSa5Rf86L8Wp5Vi7SePXuiqrl0p02bVun94R6f1RooKeoIIYQQQuydXYxJI4SQhiYpKQkcxxmGg9jLumtjy5YtcHV1tZn11MWCBQvKdB4QUltUpBFCCICsrCy88sorCA4Ohlgshq+vL/r164fTp08b2pR3j+H6qmfPnob7H4vFYgQEBGDIkCHYs2ePybc1evRo3L59u0avCQ0NxerVq+u8HkuJj49Hr1694OPjA4lEgkaNGmH+/PnQaDSGNhqNBgsXLkTjxo0hkUjQunVr7N+/v8y60tLSMH78eHh4eEAqlaJVq1a4UMlQhUmTJpV7H+wWLVoY2lR1L22NRoO3334brVq1gqOjI/z9/TFhwgSkp6dX+d6rinfPnj3o27cvPDw8bOqPB1tARRqxG9N/mY6Ru0di+i/TrR1K/TV9OjByZMm/Dcxzzz2Hy5cvY+vWrbh9+zZ+/vln9OzZEzk5OSbbRnJeMu4+uovkvGSTrbMyarW6Tq+fOnUqMjIycPfuXfz444+IjIzECy+8UOkQlNqQSqXw9vau83oeqh6iUFBosfw+bvfu3YiOjsby5cvRoUMHdO7cGT/++KPheaFQiAkTJuDgwYOIj4/H6tWr8fXXX+PDDz80tJk/fz7Wr1+PL774Ajdv3sTLL7+M4cOH4/Lly4Y2ubm56Nq1K4RCIf744w/cvHkTK1asgJubW4WxffbZZ8jIyDD83L9/H+7u7hg5cqShTVFREVq3bo0vv/yy3HUoFAqc+fsMpr4+Fb8c+wV79uxBfHw8hg4dWmleqhNvUVERunXrhmXLllW6rgaJkTLy8/MZAJafn2/tUMhjAlYEMCwAC1gRYO1Q6q+AAMaAkn+r4eLFiwwA+3r/KXYiXc5OJuewS/9cZ9n5clag0tb652FeITt//jyTy+VmfsMlcnNzGQB27NixCtuEhIQwAIafkJAQxhhjCQkJbOjQoczb25s5Ojqy9u3bs0OHDpV57UcffcSGvTCMOTg6MF9/X7Z+/XqjNn/99Rdr06YNE4vFLDo6mu3Zs4cBYJcvX2aMMabVatlLL73EQkNDmUQiYc2aNWOrV682WsfEiRPZsGHD2OLFi5mfnx8LDQ2t1rrL06NHDzZ79uwyyzdt2sQAGL3HlJQUNnLkSObi4sLc3NzY0KFDWWJiImOMsQMHDjCxWMxyc3ON1jNr1izWq1cvxhhjmzdvZi4uLobnqsppjx49jH4XpV9lsatjmZOzE7uSccXQ9quvvmKNGjViQqGQNWvWjH3zzTdGcQBgX3/9NXv22WeZVCplTZo0Yfv27aswL+WJj49nfD6fvf/+++zVV19lv/zyC/vmm2/Y999/X+nrXn/9ddatWzfDYz8/P7ZmzRqjNiNGjGDjxo0zPH777beNXlMbP/30E+M4jiUlJZX7PAD2008/lVl+JeMKO5923pDfv//+mwFgycnJFW6rJvEmJiZWuV8yxlhxcTG7efMmKy4uLvNcffv+pp40QkiD5+TkBCcnJ+zdu9foFnGPO3/+PABg8+bNyMjIMDyWy+UYOHAgjhw5gsuXL6N///4YMmSI0VRCALBixQpEto7Edwe+w6hJo/DKK68gPj7esI7BgwcjMjISFy9exIIFCzB37lyj1+v1egQGBmL37t24efMmPvjgA7z77rvYtWuXUbsjR44gPj4ehw4dwq+//lqtddfExIkT4ebmZjjtqdFo0K9fP8hkMpw8eRKnT5+Gk5MT+vfvD7Vajd69e8PV1dWoV0mn02Hnzp0YN25cuduoKqd79uxBYGAgFi5caOgdKs9PP/2E2bNn44033sD169cxffp0TJ48GUePHjVqFxsbi1GjRuHatWsYOHAgxo0bh0ePHhmer+qeqNeuXQOPx0NsbCy8vLzQsmVLvPjii3jhhRcqfE1CQgL279+PHj16GJapVKoyd9WRSqU4deqU4fHPP/+M9u3bY+TIkfD29kbbtm3x9ddfV7id8mzcuBF9+vRBSEhIjV73pPz8fHAcV+k4QFPE26BZu0q0RfWtEq8vqCfNAszYk7bk5HLmvyKgyp8B3w0u05M2ZPsQFrAioMqfFWdW1Pqt//DDD8zNzY1JJBLWpUsXNm/ePHb16lWjNqigh+FJLVq0YF988YXhcUhICBs/fryhJ+Jy+mXm7e3N1q5dyxhjbP369czDw8OoZ2Dt2rVV9irMmDGDPffcc4bHEydOZD4+PkylUhmW1XbdFfWkMcZYp06d2IABAxhjjH377bcsPDyc6fV6w/MqlYpJpVJ24MABxhhjs2fPZk8//bTh+Sd7157sSStPeTldtWqVUZsne9K6dOnCpk6datRm5MiRbODAgYbHANj8+fMNj+VyOQPA/vjjD8Oyp59+2mjbT7p37x4Ti8XsjTfeYFOmTDH0IpYnJiaGicViBoBNmzaN6XQ6w3NjxoxhkZGR7Pbt20yn07GDBw8yqVTKRCKRoY1YLGZisZjNmzePXbp0ia1fv55JJBK2ZcuWCrf5uLS0NMbn89nOnTsrbFPRfv54T1pxcTFr164dGzt2bKXbq0m81JNWFvWkEUIsokBVgPTCtCp/shVZZV6bpchCWmFalT8FqoJax/fcc88hPT0dP//8M/r3749jx46hXbt2hjkbKyKXyzF37lxERETA1dUVTk5OiIuLK9OTFhUVZfg/x3Hw9fXFw4cPAQBxcXGIiooy6kWJiYkps60vv/wS0dHR8PLygpOTEzZs2FBmO61atYJIJDI8ru66a4IxBo7jAABXr15FQkICZDKZoUfS3d0dSqUSd+/eBQCMGzcOx44dMwwy37ZtGwYNGlRhD0x1c1qVuLg4dO3a1WhZ165dERcXZ7Ts8d+No6MjnJ2dDb8boKR3cubMmRVuJywsDIcOHcL169fx/fffo127dhg7dqzh/T9u586duHTpErZv347ffvsNn376qeG5zz77DE2bNkXz5s0hEokwc+ZMTJ48GTze/76q9Xo92rVrhyVLlqBt27aYNm0apk6dinXr1lUrJ1u3boWrqyueffbZarUvj0ajwahRo8AYw9q1ayttW9d4Gzq7ucE6IcS+OYud4S8LqLKdp0PZiae9HLwQUI3XOovrdks3iUSCZ555Bs888wzef/99/Oc//8GHH36ISZMmVfiauXPn4tChQ/j000/RpEkTSKVSPP/882UG7QuFxrO0cxwHvV5f7dh27NiBuXPnYsWKFYiJiYFMJsPy5cvx119/GbVzdHSs9jprQ6fT4c6dO+jQoQOAkoIqOjoa27ZtK9O2dBLxDh06oHHjxtixYwdeeeUV/PTTT5UWv9XNqanU9XcDAN27d8f+/fuxYMECtGjRAhs3bsTTTz+Nu3fvQiD431dtUFAQACAyMhI6nQ7Tpk3DG2+8AT6fDy8vL+zduxdKpRI5OTnw9/fHO++8g0aNGhle7+fnh8hI40llIyIijE4nV4Qxhk2bNuHFF180KuRrQqvR4t2X30VOeg7+/PPPKm+jWJd4CRVphBALmdnxdczs+Hq12iqLi40e/zzmZ3OEVKXIyEijqQiEQiF0Op1Rm9OnT2PSpEkYPnw4gJKipabzZEVERODbb7+FUqk09HidO3euzHa6dOmCV1991bCsvJ6a2qy7JrZu3Yrc3Fw899xzAIB27dph586d8Pb2rvQLe9y4cdi2bRsCAwPB4/EwaNCgCttWJ6cikajM7+JJEREROH36NCZOnGi07ieLBlPr0KEDmjdvjqioKCQnJ6Nx48blttPr9dBoNNDr9eDz+YblEokEAQEB0Gg0+PHHHzFq1CjDc127djWMZSx1+/btao0vO378OBISEjBlypRavS+NRoN5L8/D/cT7OHvyLDw8PKp8TV3iJTQFByGEICcnB08//TS+++47XLt2DYmJidi9ezc++eQTDBs2zNAuNDQUR44cQWZmJnJzcwEATZs2xZ49e3DlyhVcvXoVY8eOrXEvzNixY8FxHKZOnYqbN2/i999/NzoNVrqdCxcu4MCBA7h9+zbef/99w8ULdV13RRQKBTIzM5Gamopz587h7bffxssvv4xXXnnFcEu/cePGwdPTE8OGDcPJkyeRmJiIY8eOYdasWUhNTTWsa9y4cbh06RI++ugjPP/88xCLxRVutzo5DQ0NxYkTJ5CWlmZ0a8DHvfnmm9iyZQvWrl2LO3fuYOXKldizZ0+NL5x48h7RT9q/fz9WrVqFe/fuQa/X4+HDh/j888/h6emJ4OBgACWneHft2oW4uDjcu3cPu3btwrx58zB69GhDT95ff/2FPXv24N69ezh58iT69+8PvV6Pt956y7Ct119/HefOncOSJUuQkJCA7du3Y8OGDZgxY0aV72Pjxo3o1KkTWrZsWea5qu6lrdFo8ObUN3Hz6k0s+XIJdDodMjMzkZmZadTD+WSuqhPvo0ePcOXKFdy8eRNAyZxyV65cQWZmZpXvqd6z8pg4m1TfBh7WF3ThgAU00Ck4lEole+edd1i7du2Yi4sLc3BwYOHh4Wz+/PlMoVAY2v3888+sSZMmTCAQGKbgSExMZL169WJSqZQFBQWxNWvWlBl0XzrI/fGB161bt2Yffvihoc3Zs2dZ69atmUgkYm3atGE//vij0SBqpVLJJk2axFxcXJirqyt75ZVX2DvvvMNat25tWEfpFBxPqmrd5Xl8mguRSMT8/PzY4MGD2Z49e8q0zcjIYBMmTGCenp5MLBazRo0asalTp5Y5hnbs2JEBYH/++afR8icvHKhOTs+ePcuioqIMg/AZq/0UHE8OkndxcWGbN282PA4JCTH6XT0pLi6OjR49mgUEBDA+n8+cnJxY165d2blz5wxtduzYwdq1a8ecnJyYo6Mji4yMZEuWLDEa/H7s2DEWERHBxGIx8/DwYC+++CJLS0srs71ffvmFtWzZkonFYta8eXO2YcMGo+c//PBDw/5ZKi8vj0ml0jJtSx09erTMtCYA2MSJExlj/xvUX97P0aNHK81VVfFu3ry53PVWlPOGdOEAx1gVN89sgAoKCuDi4oL8/Pwqz7cTywlcGYi0wjQEyAKQOie16heQmgsMBNLSgIAAILXqHF+6dAnR0dH4ev8phEe1AadRwbEgA8HBoRA9MZVATSiLi5F85xYiIiLMPsbKkq5mXoVGr4GQJ0Rr39bWDqfesYX8LliwAJMmTUJoaKhVtg+UTJPCcVyVF73UlC3kFwCUSiUSExMRFhZWZsqS+vb9TWPSiN0Y03IMcpW5cJNUPLM2qaMxY4DcXKCS2ctJ7blL3aFjOvA5ftWNSY1RfksuDjh27JjR3GqmQvm1PCrSiN1Y3ne5tUOo/5ZTjs0pyCXI2iHUa7aQ38omvbUEjuOQnGye22LZQn4bGrpwgBBCCCHEBlGRRgghhBBig6hII4SYzr+z0DPQ9UiEEPNoSNc70pg0Yjear2mO9MJ0+Mv8cWvmLWuHUz81bw6kpwP+/sCtmueY8fhgDFAVF0MskZohQPt2/eF1qHVqiPgitPQuO1cVqRvKr3nZSn4VCgWAsneKqI+oSCN2Q66Wo1BdCLlabu1Q6i+5HCgsLPm3Nnh8qMROyM4quf+mWCoFB67Gq9GoVQAAlUplNBO7vdOqtNAzPbRaLZRKpbXDqXcov+Zl7fwyxqBQKPDw4UO4urrWq2NDRahII4SYlNbJAwo58ODhw9KznzWmUWvwKDsbQqGw1vcYtEVZBVnQ6XXg8/gQyyuecZ/UDuXXvGwlv66urvD19bXa9i2JijRCiGlxHLQyT2gd3cDpdUAtxo8k3r6F919+GT/++CPCw8PNEKR1TNo8CQ+KHsDH0QfHJx+3djj1DuXXvGwhv0KhsEH0oJWiIo0QYh48PhivdgdTlY4hOTkZHMeVmVHcnqUVpyGtKA1anrZevS9bQfk1L8qv5dHVnYQQQgghNoiKNEIIIYQQG0RFGiGEEEKIDaIijRBCCCHEBlGRRgghhBBig+jqTmI31g1eh2JNMaRCmsnebNatA4qLASnl2BxoHzYvyq95UX4tj4o0YjcGNxts7RDqv8GUY3Oifdi8KL/mRfm1PDrdSQghhBBig6hII4QQQgixQXS6k9iNi+kXodapIeKLEO0fbe1w6qeLFwG1GhCJgGjKsanRPmxelF/zovxaHhVpxG4M2zEMaYVpCJAFIHVOqrXDqZ+GDQPS0oCAACCVcmxqtA+bF+XXvCi/lkenOwkhhBBCbBAVaYQQQgghNoiKNEIIIYQQG0RFGiGEEEKIDaIijRBCCCHEBlm1SDtx4gSGDBkCf39/cByHvXv3Gj0/adIkcBxn9NO/f/8q1/vll18iNDQUEokEnTp1wt9//22md0AIIYQQYh5WLdKKiorQunVrfPnllxW26d+/PzIyMgw/33//faXr3LlzJ+bMmYMPP/wQly5dQuvWrdGvXz88fPjQ1OETQgghhJiNVedJGzBgAAYMGFBpG7FYDF9f32qvc+XKlZg6dSomT54MAFi3bh1+++03bNq0Ce+8806d4iWEEEIIsRSbH5N27NgxeHt7Izw8HK+88gpycnIqbKtWq3Hx4kX06dPHsIzH46FPnz44e/Zsha9TqVQoKCgw+iGEEEIIsSabvuNA//79MWLECISFheHu3bt49913MWDAAJw9exZ8Pr9M++zsbOh0Ovj4+Bgt9/Hxwa1btyrcztKlSxEbG2vy+Ilpxc2IAwMDB87aodRfcXEAYwBHOTYH2ofNi/JrXpRfy7PpIu2FF14w/L9Vq1aIiopC48aNcezYMfTu3dtk25k3bx7mzJljeFxQUICgoCCTrZ+Yhkwss3YI9Z+McmxOtA+bF+XXvCi/lmfzpzsf16hRI3h6eiIhIaHc5z09PcHn8/HgwQOj5Q8ePKh0XJtYLIazs7PRDyGEEEKINdlVkZaamoqcnBz4+fmV+7xIJEJ0dDSOHDliWKbX63HkyBHExMRYKkxCCCGEkDqz6ulOuVxu1CuWmJiIK1euwN3dHe7u7oiNjcVzzz0HX19f3L17F2+99RaaNGmCfv36GV7Tu3dvDB8+HDNnzgQAzJkzBxMnTkT79u3RsWNHrF69GkVFRYarPYn9Wnl2JQpUBXAWO2NOzJyqX0BqbuVKoKAAcHYG5lCOTY32YfOi/JoX5dfyrFqkXbhwAb169TI8Lh0XNnHiRKxduxbXrl3D1q1bkZeXB39/f/Tt2xeLFi2CWCw2vObu3bvIzs42PB49ejSysrLwwQcfIDMzE23atMH+/fvLXExA7M/KsyuRVpiGAFkAHSDMZeVKIC0NCAigIs0MaB82L8qveVF+Lc+qRVrPnj3BGKvw+QMHDlS5jqSkpDLLZs6caehZI4QQQgixR3Y1Jo0QQgghpKGgIo0QQgghxAZRkUYIIYQQYoOoSCOEEEIIsUFUpBFCCCGE2CAq0gghhBBCbBAVaYQQQgghNsimb7BOyOPa+bVDkEsQvBy8rB1K/dWuHRAUBHhRjs2B9mHzovyaF+XX8qhII3bj5zE/WzuE+u9nyrE50T5sXpRf86L8Wh6d7iSEEEIIsUFUpBFCCCGE2CAq0gghhBBCbBCNSSN2Y+j3Q5GlyIKXgxeNjTCXoUOBrKySCwdofJrJ0T5sXpRf86L8Wh4VacRuXMq4hLTCNATIAqwdSv116RKQlgYEUI7NgfZh86L8mhfl1/LodCchhBBCiA2iIo0QQgghxAZRkUYIIYQQYoOoSCOEEEIIsUFUpBFCCCGE2CAq0gghhBBCbBAVaYQQQgghNoiKNEIIIYQQG0ST2RK7MSdmDgpUBXAWO1s7lPprzhygoABwphybA+3D5kX5NS/Kr+VRkUbsxpyYOdYOof6bQzk2J9qHzYvya16UX8uj052EEEIIITaIijRCCCGEEBtEpzuJ3ShUFYKBgQMHmVhm7XDqp8JCgDGA4wAZ5djUaB82L8qveVF+LY+KNGI3Ir6MQFphGgJkAUidk2rtcOqniAggLQ0ICABSKcemRvuweVF+zYvya3l0upMQQgghxAZRkUYIIYQQYoOoSCOEEEIIsUFUpBFCCCGE2CC6cIAQYrPi4uLMsl5PT08EBwebZd2EEGIqVKQRQmxOzsNMgOMwfvx4s6zfwcEBcXFxVKgRQmwaFWmEEJsjz88HGMPMRSvQukMnk647OSEei2dOQXZ2NhVphBCbRkUaIcRmBYQ1RnhUG2uHQQghVkEXDhBCCCGE2CDqSSN2Y98L+6DWqSHii6wdSv21bx+gVgMiyrE50D5sXpRf86L8Wp5Ve9JOnDiBIUOGwN/fHxzHYe/evYbnNBoN3n77bbRq1QqOjo7w9/fHhAkTkJ6eXuk6FyxYAI7jjH6aN29u5ndCLCHaPxoxQTGI9o+2dij1V3Q0EBNT8i8xOdqHzYvya16UX8uzapFWVFSE1q1b48svvyzznEKhwKVLl/D+++/j0qVL2LNnD+Lj4zF06NAq19uiRQtkZGQYfk6dOmWO8AkhhBBCzMaqpzsHDBiAAQMGlPuci4sLDh06ZLRszZo16NixI1JSUiq9KksgEMDX19eksRJCCCGEWJJdjUnLz88Hx3FwdXWttN2dO3fg7+8PiUSCmJgYLF26tNKiTqVSQaVSGR4XFBSYKmRiQr/e/hXFmmJIhVIMbjbY2uHUT7/+ChQXA1IpMJhybGq0D5sX5de8KL+WZzdFmlKpxNtvv40xY8bA2dm5wnadOnXCli1bEB4ejoyMDMTGxqJ79+64fv06ZDJZua9ZunQpYmNjzRU6MZGXf30ZaYVpCJAFIHVOqrXDqZ9efhlISwMCAoBUyrGp0T5sXpRf86L8Wp5dTMGh0WgwatQoMMawdu3aStsOGDAAI0eORFRUFPr164fff/8deXl52LVrV4WvmTdvHvLz8w0/9+/fN/VbIIQQQgipEZvvSSst0JKTk/Hnn39W2otWHldXVzRr1gwJCQkVthGLxRCLxXUNlRBCCCHEZGy6J620QLtz5w4OHz4MDw+PGq9DLpfj7t278PPzM0OEhBBCCCHmYdUiTS6X48qVK7hy5QoAIDExEVeuXEFKSgo0Gg2ef/55XLhwAdu2bYNOp0NmZiYyMzOhVqsN6+jduzfWrFljeDx37lwcP34cSUlJOHPmDIYPHw4+n48xY8ZY+u0RQgghhNSaVU93XrhwAb169TI8njNnDgBg4sSJWLBgAX7++WcAQJs2bYxed/ToUfTs2RMAcPfuXWRnZxueS01NxZgxY5CTkwMvLy9069YN586dg5eXl3nfDCGEEEKICVm1SOvZsycYYxU+X9lzpZKSkowe79ixo65hEUIIIYRYnU2PSSOEEEIIaaioSCOEEEIIsUFUpBG74SRygkwkg5PIydqh1F9OToBMVvIvMTnah82L8mtelF/Ls/l50ggpdWvmLWuHUP/dohybE+3D5kX5NS/Kr+VRTxohhBBCiA2iIo0QQgghxAZRkUYIIYQQYoNoTBqxG28efBO5yly4SdywvO9ya4dTP735JpCbC7i5Acspx6ZG+7B5UX7Ni/JreVSkEbvx/fXvkVaYhgBZAB0gzOX774G0NCAggIo0M6B92Lwov+ZF+bU8Ot1JCCGEEGKDqEgjhBBCCLFBVKQRQgghhNggKtIIIYQQQmwQFWmEEEIIITaIijRCCCGEEBtERRohhBBCiA2iIo0QQgghxAbRZLbEbgxqOgiPlI/gLnG3dij116BBwKNHgDvl2BxoHzYvyq95UX4tj4o0YjfWD1lv7RDqv/WUY3Oifdi8KL/mRfm1PDrdSQghhBBig2pVpN27d8/UcRBCCCGEkMfUqkhr0qQJevXqhe+++w5KpdLUMRFCCCGENHi1KtIuXbqEqKgozJkzB76+vpg+fTr+/vtvU8dGiJH2G9ojcGUg2m9ob+1Q6q/27YHAwJJ/icnRPmxelF/zovxaXq2KtDZt2uCzzz5Deno6Nm3ahIyMDHTr1g0tW7bEypUrkZWVZeo4CUGmPBNphWnIlGdaO5T6KzMTSEsr+ZeYHO3D5kX5NS/Kr+XV6cIBgUCAESNGYPfu3Vi2bBkSEhIwd+5cBAUFYcKECcjIyDBVnIQQQgghDUqdirQLFy7g1VdfhZ+fH1auXIm5c+fi7t27OHToENLT0zFs2DBTxUkIIYQQ0qDUap60lStXYvPmzYiPj8fAgQPxzTffYODAgeDxSmq+sLAwbNmyBaGhoaaMlRBCCCGkwahVkbZ27Vq89NJLmDRpEvz8/Mpt4+3tjY0bN9YpOEIIIYSQhqpWRdqdO3eqbCMSiTBx4sTarJ4QQgghpMGr1Zi0zZs3Y/fu3WWW7969G1u3bq1zUIQQQgghDV2tirSlS5fC09OzzHJvb28sWbKkzkERQgghhDR0tSrSUlJSEBYWVmZ5SEgIUlJS6hwUIYQQQkhDV6sxad7e3rh27VqZqzevXr0KDw8PU8RFSBmfPPMJFBoFHIQO1g6l/vrkE0ChABwox+ZA+7B5UX7Ni/JrebUq0saMGYNZs2ZBJpPhqaeeAgAcP34cs2fPxgsvvGDSAAkpNbbVWGuHUP+NpRybE+3D5kX5NS/Kr+XVqkhbtGgRkpKS0Lt3bwgEJavQ6/WYMGECjUkjhBBCCDGBWhVpIpEIO3fuxKJFi3D16lVIpVK0atUKISEhpo6PEEIIIaRBqlWRVqpZs2Zo1qyZqWIhpFLx2fHQ6rUQ8AQI9wy3djj1U3w8oNUCAgEQTjk2NdqHzYvya16UX8urVZGm0+mwZcsWHDlyBA8fPoRerzd6/s8//zRJcIQ8rvc3vZFWmIYAWQBS56RaO5z6qXdvIC0NCAgAUinHpkb7sHlRfs2L8mt5tZqCY/bs2Zg9ezZ0Oh1atmyJ1q1bG/1U14kTJzBkyBD4+/uD4zjs3bvX6HnGGD744AP4+flBKpWiT58+1brbwZdffonQ0FBIJBJ06tQJf//9d03fIiGEEEKIVdWqJ23Hjh3YtWsXBg4cWKeNFxUVoXXr1njppZcwYsSIMs9/8skn+Pzzz7F161aEhYXh/fffR79+/XDz5k1IJJJy17lz507MmTMH69atQ6dOnbB69Wr069cP8fHx8Pb2rlO8hBBCCCGWUqueNJFIhCZNmtR54wMGDMDixYsxfPjwMs8xxrB69WrMnz8fw4YNQ1RUFL755hukp6eX6XF73MqVKzF16lRMnjwZkZGRWLduHRwcHLBp06Y6x0sIIYQQYim1KtLeeOMNfPbZZ2CMmToeg8TERGRmZqJPnz6GZS4uLujUqRPOnj1b7mvUajUuXrxo9Boej4c+ffpU+BoAUKlUKCgoMPohhFiWjjEUafTIUeog8A5Cm4HPAzIPFGn00JvxWEMIIbaqVqc7T506haNHj+KPP/5AixYtIBQKjZ7fs2dPnQPLzMwEAPj4+Bgt9/HxMTz3pOzsbOh0unJfc+vWrQq3tXTpUsTGxtYxYkJITal0DFnFWuSq9CjU6FFaiombtsXoxWsBAFdyVOAAyIQ8eEj48JTwIeJzVouZEEIspVZFmqura7mnKO3VvHnzMGfOHMPjgoICBAUFWTEiQuq3ArUOaUVaPFIZXxku4AAxn0P+w0wkxt9E09btwXeUQceAAo0eBRo9kgo18JbyEeAogFRQq5MBhBBiF2pVpG3evNnUcZTh6+sLAHjw4AH8/PwMyx88eIA2bdqU+xpPT0/w+Xw8ePDAaPmDBw8M6yuPWCyGWCyue9CEkEoptHokF2qMijNnEQ+eEj5cRTxI+Bw4jsPB439h0/9NwdLv9qJTr95Q6hgeqfTIVmoh1zA8KNbhQbEOAY4CBDkJwOeoZ40QUv/U+s9QrVaLw4cPY/369SgsLAQApKenQy6XmySwsLAw+Pr64siRI4ZlBQUF+OuvvxATE1Pua0QiEaKjo41eo9frceTIkQpfQwgxPz1juC/X4Eq2ylCgeUv5aOspRit3MfwcSnrFuHKKLY7jIBXwEOAoQGsPCVq5i+AmKjl0pRVpcTlbhXy1zqLvhxBCLKFWPWnJycno378/UlJSoFKp8Mwzz0Amk2HZsmVQqVRYt25dtdYjl8uRkJBgeJyYmIgrV67A3d0dwcHBeO2117B48WI0bdrUMAWHv78/nn32WcNrevfujeHDh2PmzJkAgDlz5mDixIlo3749OnbsiNWrV6OoqAiTJ0+uzVslhNSRUqvHrTw1irQlI87cRDyEOgvhUMtTlc4iPiLd+Xik1OFugQYqHcP1R2qEygTwdxCUW+gRQog9qlWRNnv2bLRv3x5Xr16Fh4eHYfnw4cMxderUaq/nwoUL6NWrl+Fx6biwiRMnYsuWLXjrrbdQVFSEadOmIS8vD926dcP+/fuN5ki7e/cusrOzDY9Hjx6NrKwsfPDBB8jMzESbNm2wf//+MhcTEPtzfup56JgOfI5v7VDqr/PnAZ0O4Jsmx4+UOtzOV0PHSsabhTkL4SXhm6SQcpfw4Szi4V6BBllKHZIKS06FNnURgmejhRrtw+ZF+TUvyq/l1apIO3nyJM6cOQORSGS0PDQ0FGlpadVeT8+ePSudxoPjOCxcuBALFy6ssE1SUlKZZTNnzjT0rJH6w0/mV3UjUjd+pskxYwypRVqkyLUASq7MDHcVQWziqzIFPA5NXYSQCXlILNQgW6mDVs/Q3FUEPs/2CjXah82L8mtelF/Lq9X5Br1eD52u7BiQ1NRUyGSyOgdFCLFfjDHcLdAYCjQ/Bz5aupu+QCvFcRz8HAWIcBOBxwF5aj1u5Kqh1dPcaoQQ+1arIq1v375YvXq14THHcZDL5fjwww/rfKsoQoj90jOGW3lqPCgu+SOukUyIRs4ii5x+dBPz0dJNDAEHFGr0iMtTQ0eT4BJC7FitTneuWLEC/fr1Q2RkJJRKJcaOHYs7d+7A09MT33//valjJAQAsOHiBsjVcjiJnDAtepq1w6mfNmwA5HLAyQmYVrMc6xjDrVw18tR6cADCXUXwkFh27IpMxEOkuxg3HqlQoNYjPk+N5q6WKRKrg/Zh86L8mhfl1/JqVaQFBgbi6tWr2LFjB65duwa5XI4pU6Zg3LhxkEqlpo6READAwuMLkVaYhgBZAB0gzGXhQiAtDQgIqFGRxsAhLleNfLUePA6IcBXBVWydwcUyIQ8RbiLcfKRGrkqPewUaNHYW2sRVn7QPmxfl17wov5ZXqyINAAQCAcaPH2/KWAghdojj8VDo7Af1vwVapJsILiLrXv3lIuKjmavIcOrVQcCDv2OtD3eEEGIVtTpqffPNN5U+P2HChFoFQwixLwzAs+9+CrVEBg4lPWjWLtBKeUj4CJUJkFSoRWKhBlIBBzcr9e4RQkht1HqetMdpNBooFAqIRCI4ODhQkUZIA5EAF3Qc8SLAGMLdxFY7xVkRfwcBFFqGh8U63M5To40n3f6NEGI/anV1Z25urtGPXC5HfHw8unXrRhcOENJA/PVAgUTOBQDgVPjA4hcJVAfHcWjsLISjgIOWAfF5GtD1noQQe1Hre3c+qWnTpvj444/L9LIRQuqfG4+UOJquAAD88dlCSJT5Vo6oYjyOQ7irCPx/p+ZQOHpZOyRCCKkWkxVpQMnFBOnp6aZcJSHExqQXafB7ihwAEMIKcGLrF1aOqGpSAQ9NXUrukFLs6I5G7btaOSJCCKlarcak/fzzz0aPGWPIyMjAmjVr0LUrHfwIqa8K1Dr8eK8AOgY0cREhLC/P2iFVm4eEDx8pHw+KdRgZuwZ04pMQYutqVaQ9++yzRo85joOXlxeefvpprFixwhRxEUJsjFrH8OO9AhRpGbwkfAwJccKNPGtHVTNhMiGyCovh6heIW0yOTtYOiBBCKlGrIk2v15s6DkKq1MyjGVwkLvBx9LF2KGaTkpKC7Oxsk6/X09MTwcHBVTds1gxwcQF8jHPMGMNvKYV4UKyDVMDhuUbOEPNNOlrCIvg8DrKCDOS6BCKD74Q7+So0dbHcFZ8NYR+2JsqveVF+LY9mdyR248+Jf1o7BLNKSUlBREQEFAqFydft4OCAuLi4qgu1P8vP8enMYsTnqcHjgBFhzjY31UZNCLVKnPzuK/SY+H84eL8IwU5CixWc9X0ftjbKr3lRfi2vVkXanDlzqt125cqVtdkEIQ1OdnY2FAoF5q/ZiJAm4SZbb3JCPBbPnILs7Ozq9aY9ISFfjVOZJYVjvyAnBDkJTRabtRxZvxz9J7yMQo0Qx9MV6BvkZO2QCCGkjFoVaZcvX8bly5eh0WgQHl7yZXL79m3w+Xy0a9fO0M4W7pVHiL0JaRKO8Kg21g4DAJCv1uHX5EIAQDtPCVp7SKwckWlolMVogUe4AB9cylYi0k2MwHpQfBJC6pdaFWlDhgyBTCbD1q1b4ebmBqBkgtvJkyeje/fueOONN0waJCHE8rR6hr2JhVDqGPwcBHg6wNHaIZmUO1SI8hDjWo4KB1PlmBTuCh79YUkIsSG1KtJWrFiBgwcPGgo0AHBzc8PixYvRt29fKtKIWYzbMw7Zimx4Onhi24ht1g6nfho3DsjOBjw98efH65Gh0ELC5/BsmAwCXv0rYHr6OyI+T42HxTpczVGirafUrNujfdi8KL/mRfm1vFoVaQUFBcjKyiqzPCsrC4WFhXUOipDyHE86jrTCNATIAqwdSv11/DiQlgaNXwAuZSsBAENCZDZz03RTcxDw8JSfAw6lFuF4ugLNXcWQCsx3EQHtw+ZF+TUvyq/l1epoNHz4cEyePBl79uxBamoqUlNT8eOPP2LKlCkYMWKEqWMkhFiYUlcyzU4XHyka/ztTf33V1lMCLwkfSh3DyQzTX1lLCCG1Vasibd26dRgwYADGjh2LkJAQhISEYOzYsejfvz+++uorU8dICLEQ9ti/IU5CdPNzsGY4FsHjOPQJLBlvdzlbiQcKrZUjIoSQErUq0hwcHPDVV18hJyfHcKXno0eP8NVXX8HRsX4NLiakoWCMQakrKdN4AIaGyhrMQPoQmQjNXUVgAA6nycEY3TKKEGJ9dRp8kZGRgYyMDDRt2hSOjo50YCPEjl3JUUKjL/kMSwQ8OArt744CddErwBECDrgv1yIuT23tcAghpHZFWk5ODnr37o1mzZph4MCByMjIAABMmTKFruwkxA5lKDQ4nFpkeCxoGB1oRlxEfMT4lpzePZpWZChYCSHEWmpVpL3++usQCoVISUmBg8P/xqyMHj0a+/fvN1lwhBDzK9bq8VNiIXQM9XKajZro5C2Fs5CHQo0eF7OKrR0OIaSBq1WRdvDgQSxbtgyBgYFGy5s2bYrk5GSTBEYIMT/GGH5NLkSBWg9XEQ9SfsMu0gQ8Dt3/vVji7INiKLV6K0dECGnIalWkFRUVGfWglXr06BHEYnGdgyKEWMZfD4txt0ADPgc8G+aMhl2ilWjhLoaXhA+VjuHcA+pNI4RYT60ms+3evTu++eYbLFq0CEDJPTr1ej0++eQT9OrVy6QBElJqarupyFflw0XsYu1Q6oXkQjWOp5fMC/ZMoBN8HQTA1KlAfj7g0nBzzOM49PB3xA/3CnAhqxjtvCRwNtFkvrQPmxfl17wov5ZXqyLtk08+Qe/evXHhwgWo1Wq89dZbuHHjBh49eoTTp0+bOkZCAAAf9vzQ2iHUG3KNHj8nFYIBaOkuRmuPf3vAP6QcA0BjZyECHQVILdLidKYCA4JlJlkv7cPmRfk1L8qv5dXqdGfLli1x+/ZtdOvWDcOGDUNRURFGjBiBy5cvo3HjxqaOkRBiQnrGsC+pAEVaBi8JH30DncA1kPnQqovjOPT0L5nz8VqOCjlKmuCWEGJ5Ne5J02g06N+/P9atW4f33nvPHDERQszoRIYC9+VaiHglN04XNfCLBSoS6CREUxcR7uSXnBYe0cjZ2iERQhqYGhdpQqEQ165dM0cspI5SUlKQnZ1tlnV7enoiODjYLOsmlnMnX2UYDD8g2AkeklqNeGgwevg5ICFfjdv5aqQVaRDgKLR2SISQBqRWR+jx48dj48aN+Pjjj00dD6mllJQUREREQKEwzw2iHRwcEBcXZ9VCLXBlINIK0xAgC0DqnFSrxWGvFBDgeLIcABDtJUGEWzlXYgcGAmlpQEAAkEo59pQK0NJdjH8eqXAyQ4EXmtRtwDTtw+ZF+TUvyq/l1apI02q12LRpEw4fPozo6Ogy9+tcuXKlSYIj1ZednQ2FQoH5azYipEm4SdednBCPxTOnIDs7m3rT7JRALMFVeEKlYwhwFOBpf7rHbnV19XXAjUcqJBVqkCrXINCJetMIIZZRoyLt3r17CA0NxfXr19GuXTsAwO3bt43a0ABk6wppEo7wqDbWDoPYEAZg2LxPUMiJ4CDgMCxUBn4Dv7NATbiK+WjlIcbVnJLetDFNafoBQohl1KhIa9q0KTIyMnD06FEAJbeB+vzzz+Hj42OW4AghdaeSuKD90DEAYxga6myyOb8aki6+DvjnkQrJcg3uyzUIot40QogF1GgKDsaMbzj8xx9/oKioqILWhBBrK9ToIZd5AwCaIh+hMpGVI7JPLiI+otwlAICTGeYZ90kIIU+q1TxppZ4s2gghtkOjZ4jPVQMcDzeO/o5QFFg7JLsW4ysFjwNS5BqkFGqsHQ4hpAGoUZHGcVyZMWfmHoMWGhpq2O7jPzNmzCi3/ZYtW8q0lUgkZo2REFvDGMPtPDVUegaeVo3dH86k+3LWkYuIj9YeJceSU5nUm0YIMb8ajUljjGHSpEmGm6grlUq8/PLLZa7u3LNnj8kCPH/+PHQ6neHx9evX8cwzz2DkyJEVvsbZ2Rnx8fGGx3QxA2lo7su1yFPrwQPgnJ8OlbzQ2iHVCzE+UlzLUSJFrkFyoRohdPqYEGJGNSrSJk6caPR4/PjxJg2mPF5eXkaPP/74YzRu3Bg9evSo8DUcx8HX19fcoRFikx4pdbhfVHIbo8YuQuQ+VFk5ovrD+d/etEvZSpzKVCDYSUh/BBJCzKZGRdrmzZvNFUe1qNVqfPfdd5gzZ06lB0a5XI6QkBDo9Xq0a9cOS5YsQYsWLSpsr1KpoFL974usoIDG7hD7pNDqcTtfDQDwlfLhLRUg18ox1TedfaS4mqPEfbkW9+VaBMvoSk9CiHnY1T1h9u7di7y8PEyaNKnCNuHh4di0aROioqKQn5+PTz/9FF26dMGNGzcQGBhY7muWLl2K2NhYM0VNTOW7Ed9BpVVBLChnpnwCrZ4hLlcNHQOchTyEOdeiePjuO0ClAsSU44o4i/ho5S7BlRwlzjxQIFhW/XnTaB82L8qveVF+Lc+uirSNGzdiwIAB8Pf3r7BNTEwMYmJiDI+7dOmCiIgIrF+/HosWLSr3NfPmzcOcOXMMjwsKChAUFGS6wIlJ9Aztae0QbBZjDPF5aih1DGIeh+ZuIvBqcxquZ0+Tx1YflfamJRVqkFGkgV817+lJ+7B5UX7Ni/JreXZTpCUnJ+Pw4cM1vihBKBSibdu2SEhIqLCNWCw2XAxBiD1KKtSUXCjAARFuIgjpjgJm5SrmI9JNjBu5Kpx5UIznGtEpT0KI6dVpnjRL2rx5M7y9vTFo0KAavU6n0+Gff/6Bn5+fmSIjxLoeKLRIV5RcAd3URQRHod18rO1ajK8UAHAnX42HxVorR0MIqY/soidNr9dj8+bNmDhxIgQC45AnTJiAgIAALF26FACwcOFCdO7cGU2aNEFeXh6WL1+O5ORk/Oc//7FG6MSEjiUdM4yHoG73EgVqHe4WlEysGuQogKekjrd8Onbsf2PS6NRnpTwlAoS7ihCfp8a5B8UYGiqr8jW0D5sX5de8KL+WZxdF2uHDh5GSkoKXXnqpzHMpKSng8f7Xc5Cbm4upU6ciMzMTbm5uiI6OxpkzZxAZGWnJkIkZjN8zHmmFaQiQBSB1Tqq1w7E6lY7hVp4aDIC7mIcgJxN8nMePB9LSgIAAIJVyXJUYHwfE56kRl6tCdz8HuIkrL5JpHzYvyq95UX4tzy6KtL59+1Z4C6pjx44ZPV61ahVWrVplgagIsR4dY7iVq4JGDzgIODRzEdF8XVbg6yBAY2ch7hZocPaBAgODq+5NI4SQ6qLBK4TYmdJbPsm1DAIOiHAVgU8XClhNjI8DAOD6IxUK1LoqWhNCSPVRkUaInUkq1OCRSg8OQHM3ESQC+hhbU6CTEMFOQugZ8NfDYmuHQwipR+zidGd9k5KSguzsbJOuMy4uzqTrI7YpvejxKzmFcBHV8UKBBsyUnxkviJHC+eDyQwWCtY8QHhpssnUTQhouKtIsLCUlBREREVAoFGZZv1wuN8t6ifXlKHVILCy5kjPESQAvKX18ayPnYSbAcSa/9/CrW/cjqFU05n6xBV/OnoTgYCrUCCF1Q0d5C8vOzoZCocD8NRsR0iTcZOs9d/QgNi5bCKVSabJ1EttRqNHjdl7JPTl9pHwEONJHt7bk+fkAY5i5aAVad+hksvWqRI4oBBD97IvIyM6hIo0QUmd0pLeSkCbhCI9qY7L1Jd+JN9m6iG1RavWIy1VBD8BVxENjZyFdyWkCAWGNTfoZZIzhr7R8SJxkSGE6mK78I4Q0VDTimBAbptUz3MxVQ6MHHAUcwl1pqg1bxXEcHIpyAAApkEGtK3/aIEIIqS4q0gixUXrGEJenRrGOQcQDItzEENBUGzZNpCpEdso9aDg+ruTQ0ANCSN3Q6U5iNxrSDNelc6EVqPXgc0CkmxhivgUKNLrLQJ1wAI5v+QLPfbAKfz8sRjtPiVFh3ZD2YWug/JoX5dfyqCeNEBvDANwt0CCndC40V7ppuj25/NsuiJkWco0e1x+prB0OIcSO0ZGfEBujcPTEg+KSudDCXUVwreJ+kMS26DRqhKIQAHDugQL6Cm5pRwghVaEijRAb0mXMNBQ7egAAGjsL4SGhAs0eBUAOKZ9DnlqPW/9OnUIIITVFY9KI3Yg9Fot8VT5cxC74sOeH1g7H5DLggCFvfgQACHYSwNfBCh/P2FggPx9wcQE+rH85thQBGNp7S3EyQ4GzmQpE/HtVbn3fh62N8mtelF/LoyKN2I2vL32NtMI0BMgC6t0B4l6BGtdR0oMmUTxCoI+/dQL5+msgLQ0ICKAirY6iPSX460ExspQ63C3QoImLqF7vw7aA8mtelF/Lo9OdhFhZWpEGPyUWgHEcrvz+AxzlWTQXWj0gEfDQ1lMCADj7QAFGY9MIITVERRohVvRAocWuuwXQ6AEPVowfFswClWf1RwdvKfgckFakxf0irbXDIYTYGSrSCLGSHKUWO+/mQ6VjCHAUoDWyodNqrB0WMSEnIQ9RHv/2pmUqrBwNIcTeUJFGiBXkqXTYkVAAhZbBR8rHyMbOEIBOh9VHnbyl4AAkFmqgp18xIaQGqEgjxMIK1Tp8n5CPQo0enhI+RjdxgYRPH8X6ylXMR6SbGACgoiqNEFID9M1AiAUVafTYkVCAfLUeriIeXmjiAgcBfQzru84+UgCAloo0QkgN0LcDIRai1Oqx824+clQ6OAt5GNPUBU50u6cGwUsqQFMXkbXDIITYGZonjZAaSklJQXZ2do1eowWHi/BGPieGiOnQSp2Ou9eTjNrExcWZMEpia2L+7U0DQKMPCSHVQkUasRs9QnsgW5ENTwdPq8WQkpKCiIgIKBTVv1JPIJZg0uffo3GHICjyHmH1tGfxIKHigkwul5si1Nrp0QPIzgY8rZfj+srfUYiWPt2QpchCoLO3tcOpl2zhGFGfUX4tj4o0Yje2jdhm7RCQnZ0NhUKB+Ws2IqRJeJXtGYAClwBoxE7g9Dr46QuweM3X5bY9d/QgNi5bCKVSaeKoa2Cb9XNcn2177jvsSCiAgCsZn+hIp7tNyhaOEfUZ5dfyqEgjpBZCmoQjPKpNpW0YY4jPU0Oj0oMHoIWnFM7+ERW2T74Tb9ogic0JcRLCz0GADIUWF7KK0cPf0dohEUJsGP0ZR4gZMMZwJ1+DHJUeHIAINxGcRXxrh0WsjOM4w9i0S1lKKHV6K0dECLFlVKQRYmKMMdwr0CBLqQMAhLuK4CqmAo2UaOoigqeED5We4XKWFU9tE0JsHp3uJHbj6a1P40HRA/g4+uDPiX9aO5xyMcaQWKhBZnFJgdbMRQgPiR0VaE8/DTx4APj4AH/aZo7tWek+7CT2wvB2P+B8VjHae0sh5NEdW03BHo4R9ozya3lUpBG7cTvnNtIK05CvzLd2KOVijCFZrkWGoqRAa+IshJfUzj5it28DaWlAvm3m2N6V7sMBsgC4iHjIV+txLUeJaC9p1S8mVbL1Y4S9o/xaHp3uJMREUuRapBVpAQCNnIXwcbCzAo1YVCfvksLsrwfF0DGaOY0QUhYVaYSYwH25Bqn/FmhhspIr+AipTCsPCRwFHAo0elx/pLJ2OIQQG0RFGiF1lCrXIEVeUqCFygTwd6QCjVRNyOPQ8d/etLOZCupNI4SUQUUaIXWQVqRF8r8FWoiTAAGOQitHROxJW08pHAQc8tR63KDeNELIE6hII6SWMoq0SCrUAACCnAQIdKICjdSMiM8ZxqadyVRAT71phJDHUJFGSC0oJS6492+BFugoQBCd4iS11NZTCin1phFCykFFGiE11G7IC5DLfAAA/g4CBDsJwHE0zxWpHaPetAfUm0YI+R8q0gipgQw44LkPPwM4Dn4OfITKqEAjddfOUwopn0OuSo+budSbRggpQedoiN34oMcHkKvlcBI5WWX7t3JV+Ace4PE4SBR5CPPxrX8F2gcfAHI54GSdHNd3Fe3DIn7JlZ7HMxQ4k1mMSDcxePVt37IAax8j6jvKr+XZdJG2YMECxMbGGi0LDw/HrVu3KnzN7t278f777yMpKQlNmzbFsmXLMHDgQHOHSixgWvQ0q207Pk+FfUmFAMfh/N5t6N+lPTjOz2rxmM006+W4IahsH27nJcFfD4vxSKVDXK4KLdwlFoysfrDmMaIhoPxans2f7mzRogUyMjIMP6dOnaqw7ZkzZzBmzBhMmTIFly9fxrPPPotnn30W169ft2DEpL65lavC3sRCMAB+rAg/LZ4D6uMgpibm8wzzpp3OLKaxaYQQ2y/SBAIBfH19DT+enp4Vtv3ss8/Qv39/vPnmm4iIiMCiRYvQrl07rFmzxoIRk/rk5qOSHjQGoIWbGC2RA6bXWzssUk9Fe0kg4XN4pNLhVq7a2uEQQqzMpk93AsCdO3fg7+8PiUSCmJgYLF26FMHBweW2PXv2LObMmWO0rF+/fti7d2+l21CpVFCp/jdYt6CgoM5xE9PLKMyAjunA5/jwk5n/VOP1R0r8liwHA9DKXYwBwU648sjsm7WujAxApwP4fMCvHp7OtZC4uLhyl2cps6BnevA4HrwkXuW2CYAz7nKuOJL0CMVJGYZeW09PzwqPfaSEpY8RDQ3l1/Jsukjr1KkTtmzZgvDwcGRkZCA2Nhbdu3fH9evXIZPJyrTPzMyEj4+P0TIfHx9kZmZWup2lS5eWGftGbE+HrzsgrTANAbIApM5JNeu2/slR4rcUOQCgtYcY/YOc6t9FAuXp0AFISwMCAoBU8+a4Psp5mAlwHMaPH19+gzkAnAEUAFhZfhOxkwxv/3oJcHbF5HcW4Z+DewEADg4OiIuLo0KtEpY8RjRElF/Ls+kibcCAAYb/R0VFoVOnTggJCcGuXbswZcoUk21n3rx5Rj1wBQUFCAoKMtn6iX25mqPEH/8WaG09Jegb6NgwCjRSZ/L8fIAxzFy0Aq07dCrz/JuXhiNPnQVXTy8s3/9ThetR8LRQAHhx8Rq4zpmLlIR4LJ45BdnZ2VSkEdKA2HSR9iRXV1c0a9YMCQkJ5T7v6+uLBw8eGC178OABfH19K12vWCyGWCw2WZzEfl3JVmL//ZICrZ2nBM9QgUZqISCsMcKj2pRZLvxHCKgBoVBY7vOltHqGC1lK6ARiuDdtSReqENJA2fyFA4+Ty+W4e/cu/CoYKxMTE4MjR44YLTt06BBiYmIsER6xc+cfFhsKtPZeVKAR6xHwOAT+e6ux+3It6DpPQhommy7S5s6di+PHjyMpKQlnzpzB8OHDwefzMWbMGADAhAkTMG/ePEP72bNnY//+/VixYgVu3bqFBQsW4MKFC5g5c6a13gKxA4wxnEgvwpG0IgBAR28pegdQgUasy89BACEPUOoYlBIXa4dDCLECmz7dmZqaijFjxiAnJwdeXl7o1q0bzp07By+vkquiUlJSwOP9r87s0qULtm/fjvnz5+Pdd99F06ZNsXfvXrRs2dJab4HYOMYYDqUW4VK2EgDwlJ8DYnykVKARq+PzOAQ6CpFYqEGxowcEYprclpCGxqaLtB07dlT6/LFjx8osGzlyJEaOHGmmiEh9omMMvyXLDfdK7BvoiHZeUitHRcj/+DrwkV6khQpCdB452drhEEIszKZPdxJiLiqdHj/eK8DNXBV4AIaGyKhAIzaHx3EIcir5W7rn5NnQ0iUEhDQoVKSRBqdQrcO2O/m4V6CBgANGNHJGpDtd3Utsk7eUD75WBUc3DyTB2drhEEIsiIo00qA8UGjxze18PCzWwUHAYWxTFzRxEVk7LEIqxHEcHIqyAQDJkEGhpduSEdJQ2PSYNEIed2TCEWj1Wgh4tdtt7+arsS+pEGo9g4eEj5GNnOEq5ps4Sjt35Aig1QICOjSYw6pBv0Kn14Jfw31YpJIjLe4qAiJa40ymAn0CncwUoX2r6zGCVI7ya3mUaWI3wj3Da/U6xhj+fliMY+kKMADBTkKMCJNBIqCO5DLCa5djUj3Brs1q9ToOwP7PF2HK2h9wKVuJaC8p3OgPjDJqe4wg1UP5tTz6liL1mkqnx0+JhTj6b4HW0l2M0Y2dqUAjdifhr+PwYMXQM+BYepG1wyGEWAB9U5F6K6tYi63x+bidrwaPK5liY1CwE/g8ukKO2KdmyAMHID5PjVS5xtrhEELMjE53Erux/Z/tUGgUcBA6YGyrsRW2Y4zhRq4KB+7LodEDMiEPz4bJEOAotGC0dmr7dkChABwcgLEV55jUzqGEXVBpFRALHPBMk1E1fr0MGkR5iHE1R4U/04rwYjMXmnj5MdU9RpDaofxaHhVpxG68degtpBWmIUAWUOEBQq7R48B9Oe7kqwEAoTIhhobI4CCkTuNqeestIC0NCAigIs0M1v01H1lF6fBy9K9VkQYA3f0ccTNXhXSFFvF5ajR3o+ljSlXnGEFqj/JreVSkkXqBMYa4XDUOpsqh1DHwOKCrb8ktnnjU00DqESchD528HXAqU4Fj6UVo4iKCgE7hE1IvUZFG7F6BWofDqUW4/W/vmbeUj8EhMnhLafcm9VNHbymuZCuRp9bjUrYSHb3pbhmE1Ef0LUbslkZfMrXGuQcKaPQlV8F08XVAjK8UfOo9I/WYiM+hu78D/kiR43SmAi3dxXCgK5YJqXeoSCN26WJWMc5mFkP+7+zrgY4CPBPoBB+H/+3SKSkpyM7ONul24+LiTLo+QmqrlbsYFx4WI0upw4l0BfoH0wS3hNQ3VKTVc2odg1yjR7FOD6WOQa1j0OoBLWMAAAaAzwECjoOAx0HAA4QcB4mAg4TPQSrggVn3LRiUxlGk1eNQask8Uc5CHnr6OyLCTWR0lVtKSgoiIiKgUCjMEotcLjfLegmpLh7HoW+QE7bdyceVHCVae4rh50BXMBNSn1CRVt/w+Mgq1iJPrUeeSg+1vrolVsXtOM8meGXL77gBd2gfFsNLwoenlA8nAc/sl//rGUOKXIN/clQo0uj/XVYyeLqLjxRRHpJyB01nZ2dDoVBg/pqNCGliulmyzx09iI3LFkKpVJpsnYTUVpCTEC3cxLiRq8LB+0WYQFNyEFKvUJFWDzDGwHfzxpiP/ws064Db+caTXEoFHBwFPEj4HER8DkIeBz5XcqsZoKTo0bKSHjaNnkGjZ1DqGIq1DGo9A+PxERzVAWkA0tL+N9O5mM+VFGwSAbykfHj++3/HOk53UaDW4b5cg8RCDe7mq1Gs+1+vHwBI+BxeiXSr1qS0IU3CER7Vpk7xPC75TrzJ1kWIKfQKcMSdfDUyFFpce6RCaw+JtUMihJgIFWl2TM8YHih0SFdoIYnsjKjIkuVSPgc3MR9uYh5kQl6dZtjXMYZbN+Pw1ScfYf7HKyBw9UK2UodclQ4qHUNqkRapRVqj1zgIOHhKBPCU8OEg4EEqKDltKv23QOQ4gDFArWdQ6RgKNXoUqHXIUeqQpdRB/m+PWSkJn0OEW8nA6AKgpMikKQcIAVDSq9zVV4qj6QocTy9CuIuIbntGSD1BRZod0jOGB8U6pMo1UP9bzzCtBqd3bkS3mM5oG9PZZKc8+BwHgU6Nfw7tQ5OPP0C7Ro0BAFo9Q45Sh2ylFtn/FlfZ/55mVWhLTlGm1PK2NRwAXwcBgpyEaOIsQqCTADyOQ4DMF3wO8HXyNcl7I+Xw9TX+l5iUu9TH6F9Tae8txbVHKuQodTiZqcAzgQ3zIoLSYwMdI8yD8mt5VKTZmTyVDvcKNIZTgCIeh0AnAf458Bt+W/E+un231yJjUgQ8Dj4OAqOrKYGS06U5Sh2yirV4pNJBodWjWMtKLlzQlpxKZSgpxER8DmI+BycBDzIRH+5iPrykfHhJBBDxy76HC9MumP19NXgXKMfm9PWIk2ZZL5/j8EygI3YkFOBSlhJR7pIyn82GgI4R5kX5tbyG9ym2Uxo9w70CDbKVOgCAgAOCnYTwceCDx3H4R6+zcoQlhDwOvg4C+DbALwhCrClUJkJzVxFu5alx4L4c45u50N02CLFzNHDBDuQodbicrTQUaH4OfER7SeDnKKCDMCHEoHeAI8Q8DukKLS5m0RXIhNg7KtJsmI4x3M1X41aeGhp9yYD8KA8xGjnTvfoIIWXJRHz0DHAAAJzIKEKeyjZ62AkhtUPnpGxUsVaPW3lqKLQlY88CHAUIdmrYPWfTf5mOR8pHcJe4Y/2Q9dYOp36aPh149AhwdwfWU45NbfmJ/0OhKhcysRvefOoLs2yjjYcEN3NVuC/X4sB9OUY1dm4wc6fRMcK8KL+WR0WaDcpV6RCfp4aOAUIe0MxFBFcx39phWd1vd35DWmEaAmQB1g6l/vrtNyAtDQigHJvDufsHkFWUDi9Hf7Ntg+M4DAiSYeOtXCQWanAjV4WW7g1j7jQ6RpgX5dfy6HSnjcko0uJmbkmBJhPy0NpDQgUaIaRG3CV8dPMtOe15OLXIcLcOQoh9oSLNRjDGkFSowb3CkrnFfKR8tHQXQVzOVBSEEFKVjj5SeEv5UOoYDqfSvWYJsUdUpNkAPWO4k69B2r8z9wc7CdDYWdigx58RQuqGz3EYGCwDByAuT41buSprh0QIqSEq0qxMq2eIy1Uj69/pNZo4CxHkJGwwA30JIebj6yBAjI8UALD/vhyFarrakxB7QkWaFWn1DDdyVchT68HjgEg3UYOcJZwQYj5d/Rzg6yCAUsfwa7IcjDFrh0QIqSYq0qxEz/Fw45EKcg2DgANauonhRhcIEEJMjM9xGBoig5AHJMs1OE+T3BJiN6hIswKJzAUFroGQa/8t0NzFkInoV0EIMQ93CR9PBzgCAI6nF+FhsdbKERFCqoPOrVmYBhymfLUbWqHUUKA5CqlAI+YXFxdXZZuWGg1EANQaDa5fumSSdRLb0MZDgrv5GiQUqPFLUiEmhrvSnUsIsXFUpFmQUqvHRXgjsEUQOL0WLb0cqUCrgTEtxyBXmQs3iZu1Q7ErOQ8zAY7D+PHjq2z7CQA3ALkPH+Kt6Ohqb0MupykeqqN345EoVOVBJna1+LY5jsOAYCdsvJWLLKUOR9OL8Eygk8XjMCc6RpgX5dfyqEizoIOpRSjgxJDnZiNQXwhH/0hrh2RXlvddbu0Q7JI8Px9gDDMXrUDrDp2q9Ro3AF9Xo925owexcdlCKJU0zqk6Xu38kVW37yjkYVCwDLvvFeBilhIBDkJEuoutGpMp0THCvCi/lkdFmgU9HeCI9Ef5WD19BBavqc5XICGmExDWGOFRbUy6zuQ78SZdHzG/xi4idPGR4syDYvxxvxBeUj68pPRVQIgtonNtFuQk5KEDHuJBAo3jIYRYTzc/B4TKhNDogZ8SC6HS0W2jCLFFVKRZGA3TJYRYG4/jMDRUBmchD49UOvxG86cRYpOoSCN2o/ma5nBe6ozma5pbO5R6K7p7W8Q080N097bWDqVeGr+zLfpv9sP4ndbPr4OAh2fDZOBzwO18Nf5+WGztkOqMjhHmRfm1PJsu0pYuXYoOHTpAJpPB29sbzz77LOLjKx8Ds2XLFnAcZ/QjkUgsFDExJ7lajkJ1IeRqupLQXPiKIgjkheAriqwdSr1UrC2CQlOIYq1t5NffUYg+gSXzpx1LV+BuvtrKEdUNHSPMi/JreTZdpB0/fhwzZszAuXPncOjQIWg0GvTt2xdFRZUf4JydnZGRkWH4SU5OtlDEhBBiX9p4SBDlLgYDsDepAJkKmuiWEFth05f07N+/3+jxli1b4O3tjYsXL+Kpp56q8HUcx8HX19fc4RFCiN3jOA79gpyQr9YjWa7BD/cKMKGZC5xFdJs6QqzNpnvSnpSfnw8AcHd3r7SdXC5HSEgIgoKCMGzYMNy4caPS9iqVCgUFBUY/hBDSUPB5HIaHyeAp4UOu0WP33QK64pMQG2A3RZper8drr72Grl27omXLlhW2Cw8Px6ZNm7Bv3z5899130Ov16NKlC1JTUyt8zdKlS+Hi4mL4CQoKMsdbIIQQmyUR8DCysTMcBRyylDr8lFgIHV3xSYhV2U2RNmPGDFy/fh07duyotF1MTAwmTJiANm3aoEePHtizZw+8vLywfv36Cl8zb9485OfnG37u379v6vAJIcTmuYj4GNnYBUIekFSowR8pNDUHIdZk02PSSs2cORO//vorTpw4gcDAwBq9VigUom3btkhISKiwjVgshlhcf26NQgghteXrIMCwUGf8eK8A1x+pwOeA/kFO4Dia5ZEQS7PpnjTGGGbOnImffvoJf/75J8LCwmq8Dp1Oh3/++Qd+fn5miJAQQuqfJi4iDAmVgQNwNUeFQ6lF1KNGiBXYdE/ajBkzsH37duzbtw8ymQyZmZkAABcXF0ilUgDAhAkTEBAQgKVLlwIAFi5ciM6dO6NJkybIy8vD8uXLkZycjP/85z9Wex+EEGJvIt3E0OkZfkuR41K2Enyu5P7D1KNGiOXYdJG2du1aAEDPnj2Nlm/evBmTJk0CAKSkpIDH+1+HYG5uLqZOnYrMzEy4ubkhOjoaZ86cQWRkpKXCJmaybvA6FGuKIRVKrR1KvZXw8WfgKYuhl1COzeGNbp9BpSuGmG8f+W3lIYEewB8pcpzPUoLPcejh72CzhRodI8yL8mt5Nl2kVad7/dixY0aPV61ahVWrVpkpImJNg5sNtnYI9d6jZwZYO4R6rUuI/eW3tYcEOj3DwdQinHtYDLWeoU+gI3g2WKjRMcK8KL+WZ9Nj0gghhFhfOy8pnvn39lGXspX4OakQWj2NUSPE3KhII4QQUqVoLymGhcrA44BbeWqa8JYQC7Dp052k/ktJSUF2dna12sblxUGj10DIEyLCNaLytnFxpgivwXG6dhmcWg0mEkEe1dba4dQ78VmXodGrIeSJEO5lO/mtyeewLcS4Ai8kyzX479VMtMNDiFF+sebp6Yng4GBThmpQXsw1OUZUxJwx27uL6Reh1qkh4osQ7R9t7XAaBCrSiNWkpKQgIiICCoWiei+YA8AZQAGAldV7iVwur2V0DVPk5NEQZ6RD5eePvy/etnY49c67B0cjqygdXo7++HGcbeS3xp9DAP4RUZj0+feAhzf2ZQDfzp2M9LhrZdo5ODggLi7O5EVPhTHX4hjxJHPFXB8M2zEMaYVpCJAFIHVOxXfxIaZDRRqxmuzsbCgUCsxfsxEhTcKrbP/mpeHIU2fB1dMLy/f/VGnbc0cPYuOyhVAqlaYKl5B6qaafw1I6yJGvdYWrXyD+77tDcCp8AInyf/c9Tk6Ix+KZU5CdnW3ygqeimGtyjCiPOWMmpDaoSCNWF9IkHOFRbapsJ/xHCKhL7iJRVfvkO/GmCY6QBqK6n8PHafUMt/PVyFUBcmc/yHwDESoTWuzKzydjrskxghB7QBcOEEIIqRUBj0OEqwhBjiV/72codLjxSA2Vjq78JMQUqEgjhBBSaxzHIVgmRHNXEfgcUKDR43K2EiqxzNqhEWL3qEgjhBBSZx4SPlp7iOEk4KBjQKGLP0Yt/goa2N6kt4TYCyrSCCGEmIRUwEMrDzECHQUAY2g7cCTOwg9JhWprh0aIXaIijRBCiMnwOA4hMiFcclOQcz8RSk6AHQkF+CWpEEUamvyWkJqgIo0QQojJCbVKfD6mF4JYIQDgRq4KG+JycSmrGPpq3JeZEEJFGiGEEDNRK4oQgVxMaOYCHykfKl3Jjdq/vZ2PVLnG2uERYvNonjRiN74deREMDBwNRDabi8cvAowBFprnqqFpqPuwv6MQE8NdcSlbiRPpCmQotPjuTj6auojQ098BHhLTfBU11PxaStyMOMqvhVGRRuyGg4gu6Tc3nRPl2Jwa8j7M4zi095KiuasYJzOKcC1HhTv5aiTkqxHlIUY3PwfIhPw6baMh59cSZDStisVRkUYIIcRinIQ8DAiWoYOXFMcyFEjIV+NqjgrXH6kQ5SFBJ28pXMV1K9YIqS+oSCOEEGJxnlIBnm/kjPtyDY6nFyG1SIvL2UpczVaihbsYMT4OcJdQsUYaNirSiN3Yee0LFKkL4Chyxuio/7N2OPVSwPovwC8sgE7mjLTplGNTo324rCAnIcY1dUGKXIOzD4qRVKjBP49U+OeRCk1cROjgJUGwkxBcNcZJUn7Na+XZlShQFcBZ7Iw5MXOsHU6DQEUasRu7/vkCWUXp8HL0pwOwmQRs+ALijHSo/PypSDMD2ofLx3EcQmQihMhESC/S4ExmMRIKSsarJeSr4SXho723FJFuYgh5FRdrlF/zWnl2JdIK0xAgC6AizUKoSCOEEGIz/B2FeL6xEDlKLS5kKXH9kRJZSh3+SJHjaFoRWriLIYLQ2mESYhFUpBFCCLE5HhIB+gU5oYefA67mKHExW4kCtR4Xs5QA54cZ3x5EscQFWj2DoJLeNULsGRVphBBiJ+Li4uxinaYkEfDQyccBHbylSCrU4FqOEvG5KgS2aIsiAOcfKuEh4cNbShcZmENKSgqys7MBABqNxvDvpUuX6rReT09PBAcH1zm++o6KNEIIsXE5DzMBjsP48ePNtg25XG62dZsCj+PQyFmERs4inMu9gjdXrMfQ2fOhE4iRpdQhS6mDWldyuyk9Axhj1brYgFQsJSUFERERUCgUJQvmAHAGHj58iOjo6Dqt28HBAXFxcVSoVYGKNEIIsXHy/HyAMcxctAKtO3Qy6brPHT2IjcsWQqlUmnS95iSCHqe2rcOEF8fDv3krPCzWIVupQ+kdQTV6hkvZKnhJ+PCU8uEgoDsg1kZ2djYUCgXmr9mIkCbhePPScOSps+Dq6YXl+3+q9XqTE+KxeOYUZGdnU5FWBSrSCCHETgSENUZ4VBuTrjP5TrxJ12dJHABnER/OIj4aOTOjKz+VOob7RVrcL9LCQcDBXcyHh4QPRwFHPWw1FNIkHOFRbSD8RwioAaFQaPL9kJSPijRCCCF2j8dxKK3RRDwOTV2EyFbqkKfSQ6FlUGi1SC3SQszn4CHmwV3Ch7OQRwUbsWlUpBFCCKlXOA7wlgrgLRVAq2d4pNIh59+CTaVjSFfokK7QQciDoYfNRUSnRIntoSKN2I2mHm3g7RgIF4mntUOpt+Qt20DlHwiNO+XYHGgfNq/y8ivgcYaCTccY8lR65Ch1eKTSQaMHHhTr8KBYBx4H8F0C0HnkZChAV4qWh/Zfy6MijdiNj/vvsnYI9d7NrZRjc/r/9u49Lop6/x/4a/a+3EGQiyloCgoq3g58wVNqgmjkT36dvJWKKerpJ+dkKpWdb5LHEk1Ly3josWOgdRDNQkvNLBQtxEouKhdROISXRPNCstzZff/+QEZH7giyrO/n4zEP2M98Zvb9ns8s+2Z2Zof34Y7V3PaVCwK6aWqPnBmIcLvqbsFWZQAMagtMWvYufgSQk30LfayUeNxKhccslPxdbOD9tzNwkcYYY+yRIxME2KjlsFHL0YcIZTWEc4WXkHUuD32G/g9uVOpx43c9fvm9AkoZ4GqhgpuVEq4WSthr5HwuG3souEhjjDH2SBMEAeZKAWZlN7El7P/gRGoarN08kX+7Cv+9XYXSGqq9l+jtKgCAmUKAq4USvSyVcLVQwVbNFyCwjsFFGmOMMXYPJQj9bdXob6sGEeFquR4Ft6tQqKvGJV01ymoIOcVVyCmuAlAKS6UMPcwV6GGuRA9zBRy1Csj541HWDrhIY13G6wen4I+K67DW2PO5ER3EM3QKlDevo9rOns9P6wC8D3esjti+giDAyUwBJzMF/ADUGAi/ldXgQkk1CnVV+K20BiXVBpwtrsLZ4tojbXIBcDZTwMVcCZc7RZuNqusfbeP99+HjIo11GedvZOD30t/gYO7S2aGYLIvMDKiv/IZKZ97GHYH34Y71MLavQiagl4USvSyU+DPMUG0g/FZajculNbh852eFnnCptPZ72eqoZAK6a+XilabdtXLYaxRQybtO4cb778PHRRpjjDHWRkqZAFdLFVwtVQBq7xl6q9IgFmxXyqpxvUKPKkP9wg0ALJQy2KplsFXLYauSw1ZT+9NaJYNazndHeNRxkcYYY4y1E0EQYKeRw04jx6ButW16Itys0ONaeQ2ultf+vFZeg7Iagq7aAF21ARd1NfXWpRBqi7i6yVwpg4VCBq2itoBTyQSo5dJJLgiQC3ig4o6IQAAMAJQaMxgEGaoNBKK6+UB5jQF053fCnenOctK2u+s03Fl/hdoS3kH/FyVQtjnGRwUXaYwxxlgHkgsCHLQKOGgV8LqnvbzGgFuV+jtT7e837zyu0BNqCCiuMqC4ytDouhsjoPbcOLmstmiTQQCBJMUTofZBbVttEWYgiMUUhF745/FC3ATw87UKVBlqK66qOzewbzNrF0yL2oIi+qPt63hEdIkiLTo6GmvXrkVRURG8vb2xceNG+Pj4NNr/888/x5tvvolff/0V/fr1w5o1a/D0008/xIgZY4yxpmnvHBVzMa9/RKnaQCi9c5RNnGpqf1bq6c507+93j1QBtYVXDQE1erqnpf3IhdpCUKj7CeHu73d+AoBMqJ0HsR9QqitBTvpJePoMbNeYTJHRF2k7d+7E4sWLsXnzZvj6+mLDhg0ICgpCbm4uunfvXq//8ePHMX36dERFReGZZ55BXFwcQkJCkJaWhoEDeYdgjDFm/JSyu1+22xJEBD3VfrQq/jTUHhnTE8FA9xZUuKdoEuoVVfI7N6s/ffoUnhj5Z0TvOQSPQd5Q37nIQS0X8D+O2jbnlnspB1tfeg7/LzW1zet4VBj9HWXff/99zJs3Dy+++CI8PT2xefNmmJmZ4ZNPPmmw/wcffIDx48cjIiICAwYMwMqVKzFs2DB89NFHDzlyxhhj7OEQBAEKmQC1XAYzhQyWytoCz04jh4NWAUez2qtKHbQK2GsVsNco0E2jgJ1GDts7xaCNWg4rlRzmytojfEoQqivK7hwd4wsYOoNRF2lVVVVITU1FQECA2CaTyRAQEICUlJQGl0lJSZH0B4CgoKBG+zPGGGOMGSOj/rjz+vXr0Ov1cHR0lLQ7Ojri7NmzDS5TVFTUYP+ioqJGn6eyshKVlXdPgvzjj9qTGW/fvt3W0Bul0+kAAOfOZKC8tLTd1luYfw4AUJCTBXNt2w9DN+Tif88DAFJTU8X420Nubi6Alm+LqpJKoAqoMlQiIyW5yb4dtT06cjsbQ8x9KythDqC0svlt3Np1t0ZX3M4tWXdr9uHWrPdBdNS6O+rvBtD43462bt86HRkzUHuQwWBo/UUAnbXe+7fzg27fOnXbWafTtfv7bN36iNr3HLxOQ0bs8uXLBICOHz8uaY+IiCAfH58Gl1EqlRQXFydpi46Opu7duzf6PJGRkeIFLzzxxBNPPPHEU9eeLl68+OBFiBEw6iNp9vb2kMvluHr1qqT96tWrcHJyanAZJyenVvUHgGXLlmHx4sXiY4PBgJs3b6Jbt26t/hz+9u3b6NmzJy5evAgrK6tWLWvsTDk3wLTzM+XcANPOz5RzA0w7P1PODTDO/IgIJSUlcHExjbsiGHWRplKpMHz4cCQmJiIkJARAbQGVmJiI8PDwBpfx8/NDYmIiFi1aJLZ999138PPza/R51Go11Gq1pM3GxuaBYreysjKanba9mXJugGnnZ8q5AaadnynnBph2fqacG2B8+VlbW3d2CO3GqIs0AFi8eDFCQ0MxYsQI+Pj4YMOGDSgtLcWLL74IAJg1axZ69OiBqKgoAMDLL7+MUaNG4b333kNwcDDi4+Nx8uRJbNmypTPTYIwxxhhrFaMv0qZOnYrff/8dy5cvR1FREYYMGYKDBw+KFwdcuHABMtndi1T9/f0RFxeH//3f/8Ubb7yBfv36Yc+ePfwdaYwxxhjrUoy+SAOA8PDwRj/eTEpKqtc2efJkTJ48uYOjapharUZkZGS9j09NgSnnBph2fqacG2Da+ZlyboBp52fKuQGmn58xEIhM5TpVxhhjjDHTYdRfZssYY4wx9qjiIo0xxhhjzAhxkcYYY4wxZoS4SGOMMcYYM0JcpDUjOjoabm5u0Gg08PX1xc8//9xk/88//xz9+/eHRqPBoEGDcODAAcl8IsLy5cvh7OwMrVaLgIAAnD9/viNTaFJr8vv444/xxBNPwNbWFra2tggICKjXf/bs2RAEQTKNHz++o9NoUGtyi42NrRe3RqOR9OnKYzd69Oh6+QmCgODgYLGPsYzdsWPHMHHiRLi4uEAQBOzZs6fZZZKSkjBs2DCo1Wr07dsXsbGx9fq09rXcEVqb25dffonAwEA4ODjAysoKfn5++PbbbyV93nrrrXrj1r9//w7MonGtzS8pKanB/fL+ey13xbFr6PUkCAK8vLzEPsYydlFRUfjTn/4ES0tLdO/eHSEhIeJ9O5vS1d7vuiIu0pqwc+dOLF68GJGRkUhLS4O3tzeCgoJw7dq1BvsfP34c06dPx9y5c5Geno6QkBCEhIQgMzNT7PPuu+/iww8/xObNm/HTTz/B3NwcQUFBqKioeFhpiVqbX1JSEqZPn44jR44gJSUFPXv2xLhx43D58mVJv/Hjx+PKlSvitGPHjoeRjkRrcwNqvzX73rgLCwsl87vy2H355ZeS3DIzMyGXy+t9VY0xjF1paSm8vb0RHR3dov4FBQUIDg7GmDFjkJGRgUWLFiEsLExSzLRlf+gIrc3t2LFjCAwMxIEDB5CamooxY8Zg4sSJSE9Pl/Tz8vKSjNuPP/7YEeE3q7X51cnNzZXE3717d3FeVx27Dz74QJLTxYsXYWdnV+81Zwxjd/ToUSxcuBAnTpzAd999h+rqaowbNw6l99y8/n5d7f2uy+rMG4caOx8fH1q4cKH4WK/Xk4uLC0VFRTXYf8qUKRQcHCxp8/X1pQULFhARkcFgICcnJ1q7dq04v7i4mNRqNe3YsaMDMmhaa/O7X01NDVlaWtK2bdvEttDQUJo0aVJ7h9pqrc0tJiaGrK2tG12fqY3d+vXrydLSknQ6ndhmLGN3LwCUkJDQZJ9XX32VvLy8JG1Tp06loKAg8fGDbq+O0JLcGuLp6UkrVqwQH0dGRpK3t3f7BdZOWpLfkSNHCADdunWr0T6mMnYJCQkkCAL9+uuvYpuxjt21a9cIAB09erTRPl3t/a6r4iNpjaiqqkJqaioCAgLENplMhoCAAKSkpDS4TEpKiqQ/AAQFBYn9CwoKUFRUJOljbW0NX1/fRtfZUdqS3/3KyspQXV0NOzs7SXtSUhK6d+8ODw8PvPTSS7hx40a7xt6ctuam0+ng6uqKnj17YtKkScjKyhLnmdrYbd26FdOmTYO5ubmkvbPHri2ae921x/YyFgaDASUlJfVec+fPn4eLiwv69OmDF154ARcuXOikCNtmyJAhcHZ2RmBgIJKTk8V2Uxq7rVu3IiAgAK6urpJ2Yxy7P/74AwDq7Wf36krvd10ZF2mNuH79OvR6vXj7qTqOjo71zpeoU1RU1GT/up+tWWdHaUt+93vttdfg4uIieRGOHz8e27dvR2JiItasWYOjR49iwoQJ0Ov17Rp/U9qSm4eHBz755BPs3bsXn332GQwGA/z9/XHp0iUApjV2P//8MzIzMxEWFiZpN4axa4vGXne3b99GeXl5u+zrxmLdunXQ6XSYMmWK2Obr64vY2FgcPHgQmzZtQkFBAZ544gmUlJR0YqQt4+zsjM2bN+OLL77AF198gZ49e2L06NFIS0sD0D5/p4zBb7/9hm+++abea84Yx85gMGDRokUYOXJkk7dT7Ervd11Zl7gtFDM+q1evRnx8PJKSkiQn2E+bNk38fdCgQRg8eDAef/xxJCUlYezYsZ0Raov4+fnBz89PfOzv748BAwbgX//6F1auXNmJkbW/rVu3YtCgQfDx8ZG0d9Wxe1TExcVhxYoV2Lt3r+ScrQkTJoi/Dx48GL6+vnB1dcWuXbswd+7czgi1xTw8PODh4SE+9vf3R35+PtavX49PP/20EyNrX9u2bYONjQ1CQkIk7cY4dgsXLkRmZmanndfIpPhIWiPs7e0hl8tx9epVSfvVq1fh5OTU4DJOTk5N9q/72Zp1dpS25Fdn3bp1WL16NQ4dOoTBgwc32bdPnz6wt7dHXl7eA8fcUg+SWx2lUomhQ4eKcZvK2JWWliI+Pr5FbwCdMXZt0djrzsrKClqttl32h84WHx+PsLAw7Nq1q95HTPezsbGBu7u70Y9bY3x8fMTYTWHsiAiffPIJZs6cCZVK1WTfzh678PBw7Nu3D0eOHMFjjz3WZN+u9H7XlXGR1giVSoXhw4cjMTFRbDMYDEhMTJQccbmXn5+fpD8AfPfdd2L/3r17w8nJSdLn9u3b+OmnnxpdZ0dpS35A7dU6K1euxMGDBzFixIhmn+fSpUu4ceMGnJ2d2yXulmhrbvfS6/U4c+aMGLcpjB1Qe8l8ZWUlZsyY0ezzdMbYtUVzr7v22B86044dO/Diiy9ix44dkq9MaYxOp0N+fr7Rj1tjMjIyxNi7+tgBtVdO5uXltegfo84aOyJCeHg4EhIScPjwYfTu3bvZZbrS+12X1tlXLhiz+Ph4UqvVFBsbS9nZ2TR//nyysbGhoqIiIiKaOXMmvf7662L/5ORkUigUtG7dOsrJyaHIyEhSKpV05swZsc/q1avJxsaG9u7dS6dPn6ZJkyZR7969qby83OjzW716NalUKtq9ezdduXJFnEpKSoiIqKSkhJYuXUopKSlUUFBA33//PQ0bNoz69etHFRUVRp3bihUr6Ntvv6X8/HxKTU2ladOmkUajoaysLEn+XXXs6vz5z3+mqVOn1ms3prErKSmh9PR0Sk9PJwD0/vvvU3p6OhUWFhIR0euvv04zZ84U+//3v/8lMzMzioiIoJycHIqOjia5XE4HDx4U+zS3vYw1t//85z+kUCgoOjpa8porLi4W+yxZsoSSkpKooKCAkpOTKSAggOzt7enatWsPNbe25Ld+/Xras2cPnT9/ns6cOUMvv/wyyWQy+v7778U+XXXs6syYMYN8fX0bXKexjN1LL71E1tbWlJSUJNnPysrKxD5d/f2uq+IirRkbN26kXr16kUqlIh8fHzpx4oQ4b9SoURQaGirpv2vXLnJ3dyeVSkVeXl60f/9+yXyDwUBvvvkmOTo6klqtprFjx1Jubu7DSKVBrcnP1dWVANSbIiMjiYiorKyMxo0bRw4ODqRUKsnV1ZXmzZv30P+Y1mlNbosWLRL7Ojo60tNPP01paWmS9XXlsSMiOnv2LAGgQ4cO1VuXMY1d3dcy3D/V5RMaGkqjRo2qt8yQIUNIpVJRnz59KCYmpt56m9peD0trcxs1alST/Ylqv27E2dmZVCoV9ejRg6ZOnUp5eXkPN7E7WpvfmjVr6PHHHyeNRkN2dnY0evRoOnz4cL31dsWxI6r9ygmtVktbtmxpcJ3GMnYN5QVA8joyhfe7rkggIuqww3SMMcYYY6xN+Jw0xhhjjDEjxEUaY4wxxpgR4iKNMcYYY8wIcZHGGGOMMWaEuEhjjDHGGDNCXKQxxhhjjBkhLtIYY4wxxowQF2mMMaPy66+/QhAEZGRkdHYootmzZ3fo+t3c3LBhwwbxsSAI2LNnD4D62yMpKQmCIKC4uLjd49i6dSvGjRvXor6bN2/GxIkT2z0GZtqOHTuGiRMnwsXFRbKftwYRYd26dXB3d4darUaPHj3wzjvvtH+wRoCLNMYeYW+99RaGDBnS2WE88n755RfMnz+/RX39/f1x5coVWFtbt2sMFRUVePPNNxEZGdmi/nPmzEFaWhp++OGHdo2DmbbS0lJ4e3sjOjq6zet4+eWX8e9//xvr1q3D2bNn8dVXX8HHx6cdozQeis4OgDH2aCIi6PV6KBTG+WeovLwcr732Gvbt24dLly4hKSkJgwYNwscffwwnJ6d2fS4HB4cW91WpVO3+/ACwe/duWFlZYeTIkS2O4/nnn8eHH36IJ554ot3jYaZpwoQJmDBhQqPzKysr8Y9//AM7duxAcXExBg4ciDVr1mD06NEAgJycHGzatAmZmZnw8PAAgBbdEL6r4iNpjBk5g8GAd999F3379oVarUavXr0kh/bPnDmDp556ClqtFt26dcP8+fOh0+nE+UlJSfDx8YG5uTlsbGwwcuRIFBYWIjY2FitWrMCpU6cgCAIEQUBsbGyDMcyePRshISFYsWIFHBwcYGVlhb/+9a+oqqqSxBkVFYXevXtDq9XC29sbu3fvlsQhCAK++eYbDB8+HGq1Gj/++GOjeZ89exb+/v7QaDQYOHAgjh49Ks6LjY2FjY2NpP+ePXsgCIL4+NSpUxgzZgwsLS1hZWWF4cOH4+TJk81u7zqrVq3Czp07sXHjRjzzzDP47LPP4OPjI8n5fnVx7du3Dx4eHjAzM8Nzzz2HsrIybNu2DW5ubrC1tcXf//536PV6cbn7P+5sSkMfd37xxRfw8vKCWq2Gm5sb3nvvPckybm5uWLVqFebMmQNLS0v06tULW7ZskfSJj4+v9/FlY/tOnYkTJ+Krr75CeXl5i2JnrDnh4eFISUlBfHw8Tp8+jcmTJ2P8+PE4f/48AODrr79Gnz59sG/fPvTu3Rtubm4ICwvDzZs3OznyDtK5tw5ljDXn1VdfJVtbW4qNjaW8vDz64Ycf6OOPPyYiIp1OR87OzvTss8/SmTNnKDExkXr37i3eCLm6upqsra1p6dKllJeXR9nZ2RQbG0uFhYVUVlZGS5YsIS8vL7py5QpduXKFysrKGowhNDSULCwsaOrUqZSZmUn79u0jBwcHeuONN8Q+b7/9NvXv358OHjxI+fn5FBMTQ2q1mpKSkojo7g2qBw8eTIcOHaK8vDy6ceNGvecqKCggAPTYY4/R7t27KTs7m8LCwsjS0pKuX79OREQxMTFkbW0tWS4hIYHu/ZPm5eVFM2bMoJycHDp37hzt2rWLMjIyWrzdg4ODKSwsTMy/JWJiYkipVFJgYCClpaXR0aNHqVu3bjRu3DiaMmUKZWVl0ddff00qlYri4+PF5VxdXWn9+vXiYwCUkJAg2R7p6elEdHc73rp1i4iITp48STKZjP75z39Sbm4uxcTEkFarldwc29XVlezs7Cg6OprOnz9PUVFRJJPJ6OzZs2Ifa2trSUxN7Tt1SktLSSaT0ZEjR1q0fRi71737ORFRYWEhyeVyunz5sqTf2LFjadmyZUREtGDBAlKr1eTr60vHjh2jI0eO0JAhQ2jMmDEPM/SHhos0xozY7du3Sa1Wi0XZ/bZs2UK2trak0+nEtv3795NMJqOioiK6ceMGARALpftFRkaSt7d3s3GEhoaSnZ0dlZaWim2bNm0iCwsL0uv1VFFRQWZmZnT8+HHJcnPnzqXp06cT0d3iYs+ePU0+V11Rsnr1arGturqaHnvsMVqzZg0RtaxIs7S0pNjY2GZza8yqVavI3t6eduzYIebQnJiYGAJAeXl5YtuCBQvIzMyMSkpKxLagoCBasGCB+PhBirTnn3+eAgMDJXFERESQp6enZP0zZswQHxsMBurevTtt2rSJiIhu3bpFAOjYsWNin+b2nTp1/0Aw1lr3F2n79u0jAGRubi6ZFAoFTZkyhYiI5s2bRwAoNzdXXC41NZUASP7pMBXGeTIIYwxA7fkXlZWVGDt2bKPzvb29YW5uLraNHDkSBoMBubm5ePLJJzF79mwEBQUhMDAQAQEBmDJlCpydnVsdi7e3N8zMzMTHfn5+0Ol0uHjxInQ6HcrKyhAYGChZpqqqCkOHDpW0jRgxokXP5+fnJ/6uUCgwYsQI5OTktDjexYsXIywsDJ9++ikCAgIwefJkPP744y1ePiIiAgqFAu+88w6ysrKQlpaGWbNmISIiAkqlstHlzMzMJM/j6OgINzc3WFhYSNquXbvW4liakpOTg0mTJknaRo4ciQ0bNkCv10MulwMABg8eLM4XBAFOTk5iDHUfV2o0GrGPnZ1di/YdrVaLsrKydsmFPdp0Oh3kcjlSU1PF/bZO3evH2dkZCoUC7u7u4rwBAwYAAC5cuCCep2Yq+Jw0xoyYVqt94HXExMQgJSUF/v7+2LlzJ9zd3XHixIl2iO6uunPg9u/fj4yMDHHKzs6WnJcGQFJQtpVMJgMRSdqqq6slj9966y1kZWUhODgYhw8fhqenJxISElr8HAqFAhEREThz5gyee+45REZGYsOGDc1e/Xh/AScIQoNtBoOhxbG0h6Zi6NatGwRBwK1btyR9WrLv3Lx5s1UXPjDWmKFDh0Kv1+PatWvo27evZKq7WGbkyJGoqalBfn6+uNy5c+cAAK6urp0Sd0fiIo0xI9avXz9otVokJiY2OH/AgAE4deoUSktLxbbk5GTIZDLJf5RDhw7FsmXLcPz4cQwcOBBxcXEAaq/Qu/cE9qacOnVKcoL4iRMnYGFhgZ49e8LT0xNqtRoXLlyo98e1Z8+ebUldUgzU1NQgNTVV/I/ZwcEBJSUlkrwb+l41d3d3vPLKKzh06BCeffZZxMTEtCkWMzMzTJ8+HTNnzjS6r5wYMGAAkpOTJW3Jyclwd3evdzSiMSqVCp6ensjOzq43r7F9BwDy8/NRUVFR72gpY43R6XTiP3EAUFBQgIyMDFy4cAHu7u544YUXMGvWLHz55ZcoKCjAzz//jKioKOzfvx8AEBAQgGHDhmHOnDlIT09HamoqFixYgMDAQMnRNVPBRRpjRkyj0eC1117Dq6++iu3btyM/Px8nTpzA1q1bAQAvvPACNBoNQkNDkZmZiSNHjuBvf/sbZs6cCUdHRxQUFGDZsmVISUlBYWEhDh06hPPnz4vFjpubm/hH8vr166isrGw0lqqqKsydOxfZ2dk4cOAAIiMjER4eDplMBktLSyxduhSvvPIKtm3bhvz8fKSlpWHjxo3Ytm1bm3KPjo5GQkICzp49i4ULF+LWrVuYM2cOAMDX1xdmZmZ44403kJ+fj7i4OMmVqeXl5QgPD0dSUhIKCwuRnJyMX375Rcy7JSIjI3HgwAHcuHEDRISTJ09i7969GD58eJvy6ShLlixBYmIiVq5ciXPnzmHbtm346KOPsHTp0latJygoSHK1bXP7DgD88MMP6NOnT6s+RmaPtpMnT2Lo0KFiYb948WIMHToUy5cvB1B79HbWrFlYsmQJPDw8EBISgl9++QW9evUCUHsU/euvv4a9vT2efPJJBAcHY8CAAYiPj++0nDpUZ58Uxxhrml6vp7fffptcXV1JqVRSr169aNWqVeL806dP05gxY0ij0ZCdnR3NmzdPPEm9qKiIQkJCyNnZmVQqFbm6utLy5ctJr9cTEVFFRQX95S9/IRsbGwIguSLwXqGhoTRp0iRavnw5devWjSwsLGjevHlUUVEh9jEYDLRhwwby8PAgpVJJDg4OFBQUREePHiWi+ie8N6buRPm4uDjy8fEhlUpFnp6edPjwYUm/hIQE6tu3L2m1WnrmmWdoy5Yt4oUDlZWVNG3aNOrZsyepVCpycXGh8PBwKi8vF5dvKl8iou3bt5O/vz/Z2NiQIAjk6OhIc+fOlVwAcL+GLmho6OKMuu1Z50EuHCAi2r17N3l6eor7x9q1ayXPd//6iYi8vb0pMjJSfJyVlUVarZaKi4uJqPl9h4ho3LhxFBUV1ej2YIw9GIHovhM7GGPsPrNnz0ZxcXGbbuFijAoKCuDu7o7s7Gz069ev2f6zZ89u9DvkTMnkyZMxbNgwLFu2rNm+WVlZeOqpp3Du3Ll2v/sBY6wWf9zJGHvkHDhwAPPnz29RgfYoWbt2reQq1KZcuXIF27dv5wKNsQ7ER9IYY80ytSNpjDHWFXCRxhhjjDFmhPjjTsYYY4wxI8RFGmOMMcaYEeIijTHGGGPMCHGRxhhjjDFmhLhIY4wxxhgzQlykMcYYY4wZIS7SGGOMMcaMEBdpjDHGGGNGiIs0xhhjjDEj9P8B17QIucOMbooAAAAASUVORK5CYII=", + "text/markdown": [ + "**Non-ZEB Summary**" + ], "text/plain": [ - "
" + "" ] }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "# all bus distribution\n", - "display(Markdown(all_bus_desc))\n", - "\n", - "dist_curve(\n", - " df=no_outliers,\n", - " mean=cpb_mean,\n", - " std=cpb_std,\n", - " title=\"all buses, cost per bus distribution\",\n", - " xlabel=\"cost per bus, $ million(s)\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "dda584ca-76fa-4e88-9b1c-f70cc438dce6", - "metadata": { - "tags": [] - }, - "outputs": [ + }, { "data": { - "text/markdown": [ + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
prop_typebus_counttotal_costcost_per_bus
0CNG252.0176039140698568
1ethanol9.01006750111861
2low emission (hybrid)145.091824361633271
3low emission (propane)44.08403969190999
4mix (zero and low emission)125.036775430294203
5Grand Total575.0314049650546173
\n", + "
" ], "text/plain": [ - "" + " prop_type bus_count total_cost cost_per_bus\n", + "0 CNG 252.0 176039140 698568\n", + "1 ethanol 9.0 1006750 111861\n", + "2 low emission (hybrid) 145.0 91824361 633271\n", + "3 low emission (propane) 44.0 8403969 190999\n", + "4 mix (zero and low emission) 125.0 36775430 294203\n", + "5 Grand Total 575.0 314049650 546173" ] }, "metadata": {}, @@ -626,9 +567,11 @@ }, { "data": { - "image/png": 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YkyZN2NixYxlj+mNGJBKxjRs3Go7hXbt2GV43duxY5uPjw5KTk422N3jwYObk5MQUCgVjTP+3rFQqjdZJTU1lXl5ebMyYMYZlJc11Tk4O02q1Rsuio6OZWCw2yk3ue6lVq5ZRXD/88AMDwG7evMkY0x8Pnp6eLCwszGi9NWvWMABGx1N+dDoda9euHQPAvLy82JAhQ9hPP/3EYmJi8qy7ePFio3Pnq3Lz+KquXbuyqlWrGi3L/fs7ffq0Ydnz58+ZWCxmH330kWHZhx9+yACwixcvGq3n5OSUJ4b89j1hwgRmb2/PcnJyDMty3+eqVauM1s39nFm0aJFhmUajYW3atDE695mS33H3ugYNGjAXFxfDz+Yel6tXrzb6feeqXbs269ixY54YXj2v5JeXBQsWMI7jjH6/kyZNYqbKkdf/nvv168dEIhF79OiRYVl8fDyTSqWsbdu2hmW5587OnTsbfRZOmzaN8fl8lpaWlu/+8mOTXW9bt27Fr7/+infffTffwdq5+vbti4iIiDyPDh06GK1nZ2dneO7o0aNYvXo1HB0d0aNHD9y/f7/AWA4dOgQ+n2/4ppvro48+AmMMhw8fNlreuXNnhISEGH6uX78+ZDIZoqKiTO6Dx+Nh2LBh2L9/PzIyMgzLt2zZgpYtWyI4ONjka69evYro6Gh8+OGHeQY35rY0JCQk4Nq1axg1ahRcXV2NYnvzzTdx6NAhwzJnZ2dcvHgR8fHxJvdZkIMHD8LJyQmtW7fO89zkyZONYps8eTJUKhWOHz9epH1IJBKIRCKcOnUqT/dTSeW2jr36e3ids7MzsrKyjLrniio4OBhdu3Y1e/3x48cbtRK8//77EAgERr+70nDo0CF4e3tjyJAhhmVCoRBTpkxBZmYm/vrrL6P1Bw0aBBcXF8PPua0/BR3/ues9e/YMkZGRAPQtR23btkWbNm0MXThnz54FY8xki5K5Xr0qNnffhcX3uqFDh2Lv3r1QqVTYvXs3+Hw++vfvn2c9xhj27NmD3r17gzGG5ORkw6Nr165IT083dGvz+XxDS7hOp0NKSgo0Gg2aNGliWOdVxc21WCw2XDCg1Wrx4sULODo6IjQ0NN/9jB492qiF/vX9/Pvvv3j+/Dnee+89o/Vyu5EKw3Ecjh49im+++QYuLi7Ytm0bJk2ahMDAQAwaNMjs7qdXW3rS09ORnJyMdu3aISoqCunp6Ubr1q5d2+g48vDwQGhoqFHuDh06hObNmxuNRfPw8MCwYcMK3HdGRgaSk5PRpk0bKBQKo6v2AH3+R48ebbTs0KFDEAgERq2afD4fH3zwgVnv3RyOjo6G81pRjssBAwZAIBAYtcDcunULd+7cwaBBgwrc56t5ycrKQnJyMlq2bAnGGK5evVrk96DVanHs2DH069cPVatWNSz38fHB0KFDcfbsWcjlcqPXjB8/3qjVvU2bNtBqtYiJiTF7vzZXKD148ADvvfceatSogZ9//rnAdf39/dG5c+c8Dy8vL6P1+Hy+4bkuXbpg/PjxOH78ONLT0/H5558XuI+YmBj4+vpCKpUaLc/tqng92VWqVMmzDRcXl0I/0EeMGIHs7Gzs27cPgL5r5vLly3j33XcLfF1u/37dunULfA8A8u3eqVWrFpKTk5GVlQUAWLRoEW7duoWAgAA0a9YMs2bNKtKHyMGDB9GlS5c8zec8Hs/owAb+uzqxqOOKxGIxFi5ciMOHD8PLywtt27bFokWLkJiYWKTt5CczMxMA8vy+XzVx4kTUqFED3bt3h7+/P8aMGVPoOLTXFVT85uf1S9IdHR3h4+NT6pf4x8TEoHr16nmuxDP3+M/9IC/s+M/90Dpz5gyysrJw9epVtGnTBm3btjUUSmfOnIFMJkODBg2K/X7s7OwMXTWvxljUgnvw4MFIT0/H4cOHsWXLFvTq1SvfYyYpKQlpaWlYs2YNPDw8jB65H5bPnz83rL9x40bUr18fdnZ2cHNzg4eHBw4ePJjngx4ofq51Oh2+//57VK9eHWKxGO7u7vDw8MCNGzeKtZ/cY+D1Y1QoFOb5mzdFLBbjyy+/xN27dxEfH49t27ahefPm2Llzp9EXrIKcO3cOnTt3NozB9PDwMIz/ef19mXOezj32X5ffefT27dvo378/nJycIJPJ4OHhYbiA6PV9+/n55RkaEhMTAx8fnzzDGMwZn2quzMxMwzFalOPS3d0dnTp1ws6dOw3b2rFjBwQCgaF725TY2FjDF/TcMYHt2rUDkDcv5khKSoJCoTD5WabT6fDkyROj5cX9O3mVTRVKSqUSgwYNgkqlwvbt20t1cjh/f3+Ehobi9OnTFt0un8/Pdzl7beD362rXro3GjRtj8+bNAIDNmzdDJBKZHBxaWgYOHIioqCj8+OOP8PX1xeLFi1GnTp08LWf5USgUOHXqVLHnTzI11ia/gbYffvgh7t+/jwULFsDOzg4zZsxArVq1ivUt5VW3bt0CAFSrVs3kOp6enrh27Rr2799vGL/WvXv3Io0nyG+cQ2kpykDlkiru8e/r64vg4GCcPn0aFy5cAGMMLVq0QJs2bfDkyRPExMTgzJkzaNmyZYmmTzAVX1H5+Pigffv2WLJkCU6fPo2hQ4fmu17uwN/hw4fn2/odERFhGMS8efNmjBo1CiEhIVi3bh2OHDmCiIgIdOzYMd8BxMXN9fz58xEeHo62bdti8+bNOHr0KCIiIlCnTh2L7qe4fHx8MHjwYJw+fRrVq1fHzp07odFoCnzNo0eP0KlTJyQnJ2Pp0qU4ePAgIiIiMG3aNADI874s+Z7S0tLQrl07XL9+HXPmzMEff/yBiIgIw/jF1/ddln/7udRqNe7fv284rxXluAT0Xwzu37+Pa9euAQB27tyJTp06GV05/DqtVos333wTBw8exKefforffvsNERERhotyijrlSnFZ4ndtU4O5p0+fjqtXrxomHittGo3G0IJgSmBgII4fP46MjAyjb4y5zam5AxEtYcSIEQgPD0dCQgK2bt2Knj17GjWt5ye3m+/WrVvo3LlzvuvkxpjbrfGqe/fuwd3dHQ4ODoZlPj4+mDhxIiZOnIjnz5+jUaNGmDdvnmF6BlMFzZ9//gmlUplnGgdA/0cRFRVlNMdVbrdn7uDr3Pf6elO7qSbSkJAQfPTRR/joo4/w4MEDhIWFYcmSJYZiszhyBxMW1i0mEonQu3dv9O7dGzqdDhMnTsTq1asxY8YMVKtWrUgDrM3x4MEDoy7lzMxMJCQkGBWlLi4ueXKnUqmQkJBgtKwosQUGBuLGjRvQ6XRGBUppHP9t2rTB6dOnERwcjLCwMEilUjRo0ABOTk44cuQIrly5ku/VN6+ydN4LMnToUPzf//0fnJ2dTX458PDwgFQqhVarNfn3mWv37t2oWrUq9u7da/Q+CruYo6h2796NDh06YN26dUbL09LSCvzgMyX3GHjw4IHRPD1qtRrR0dHFbgEUCoWoX78+Hjx4gOTkZHh7e5v8/f7xxx9QKpXYv3+/UQvC61cMFkVgYCAePHiQZ/nr59FTp07hxYsX2Lt3L9q2bWtYHh0dXaR9nThxApmZmUYNBPmds4tj9+7dyM7ONpzXinJcAkC/fv0wYcIEQ/fb/fv3C+2NuXnzJu7fv4+NGzcaDaHJb8iCuX+3Hh4esLe3N/lZxuPxCr14oDhspkVp3759WLFiBfr06ZNnPFBpuH//PiIjIwv9I+7Rowe0Wm2eyyq///57cByXb1FQXEOGDAHHcZg6dSqioqLMmiOqUaNGCA4ONlz++arcitnHxwdhYWHYuHGj0Tq3bt3CsWPHDCd5rVabpznU09MTvr6+RjNWOzg45NtseujQITRp0iRP12euV3PIGMOKFSsgFArRqVMnAPqTBZ/Pz9PK93oXrEKhyDOpX0hICKRSaYlm1t66dSt++eUXtGjRwhBTfl68eGH0M4/HM1xZlbv/3MLTUnPArFmzxmj6iJUrV0Kj0RgdfyEhIXlyt2bNmjwtSkWJrUePHkhMTDQan6DRaPDjjz/C0dHR0IxuCW3atMHjx4+xY8cOQ1ccj8dDy5YtsXTpUqjV6kLHJ+XOR1MWc++8/fbbmDlzJn7++WeT80/x+Xy89dZb2LNnj6G18lWvXqac+8331W+6Fy9exIULFywaN5/Pz/NteteuXYiLiyvW9po0aQIPDw+sWrUKKpXKsHzDhg1m/R4ePHiA2NjYPMvT0tJw4cIFuLi4GLpLTR27+eUuPT0d69evL+rbMejRowf+/vtv/PPPP4ZlSUlJ2LJlS6H7VqlUhQ4deX1fGo3GaDoDrVaLH3/8sbjhG1y/fh0ffvghXFxcDFf/FeW4BPTjMrt27YqdO3di+/btEIlE6NevX4H7zS8vjLE809YA5p+T+Hw+unTpgt9//91o2MGzZ8+wdetWtG7dGjKZrMBtFIdNtCglJCRg7Nix4PP56NSpk8kWgZCQELRo0cLw8/379/Nd18vLy+jSbo1GY1hPp9Ph8ePHWLVqFXQ6XaHf1nr37o0OHTrgyy+/xOPHj9GgQQMcO3YMv//+Oz788EOjgdsl5eHhgW7dumHXrl1wdnY2a8JGHo+HlStXonfv3ggLC8Po0aPh4+ODe/fu4fbt2zh69CgAYPHixejevTtatGiBsWPHGqYHcHJyMsxRkZGRAX9/f7z99tto0KABHB0dcfz4cVy6dAlLliwx7LNx48bYsWMHwsPD0bRpUzg6OqJ37944dOhQnkGKuezs7HDkyBGMHDkSb7zxBg4fPoyDBw/iiy++MJwEnZyc8M477+DHH38Ex3EICQnBgQMHjMZwAPrfe6dOnTBw4EDUrl0bAoEA+/btw7NnzzB48GCzcr179244OjpCpVIZZuY+d+4cGjRoYLhs1ZT/+7//Q0pKCjp27Ah/f3/ExMTgxx9/RFhYmGHsTlhYGPh8PhYuXIj09HSIxWLDHC/FoVKpDO85MjISP//8M1q3bo0+ffoYxfXee+/hrbfewptvvonr16/j6NGjeVoJihLb+PHjsXr1aowaNQqXL19GUFAQdu/ejXPnzmHZsmUFjuUqqtwiKDIyEvPnzzcsb9u2LQ4fPmyYK6ggEokEtWvXxo4dO1CjRg24urqibt26BY7hK65X/3YK8u233+LkyZN44403MG7cONSuXRspKSm4cuUKjh8/jpSUFABAr169sHfvXvTv3x89e/ZEdHQ0Vq1ahdq1axfa8l0UvXr1wpw5czB69Gi0bNkSN2/exJYtW8weT/Q6oVCIb775BhMmTEDHjh0xaNAgREdHY/369WZt8/r16xg6dCi6d++ONm3awNXVFXFxcdi4cSPi4+OxbNkyw4du48aNAQBffvklBg8eDKFQiN69e6NLly6GVt4JEyYgMzMTa9euhaenZ54WVXN98skn+N///odu3bph6tSphukBcltZc7Vs2RIuLi4YOXIkpkyZAo7j8L///a9IXTu9e/dGq1at8Nlnn+Hx48eoXbs29u7dW+RxPGfOnEFOTo5hkP65c+ewf/9+ODk5Yd++fUZzkJl7XOYaNGgQhg8fjp9//hldu3YtdGb0mjVrIiQkBNOnT0dcXBxkMhn27NmT79ig3N/rlClT0LVrV/D5fJPn8m+++QYRERFo3bo1Jk6cCIFAgNWrV0OpVGLRokVFypfZzL4+rhTlXlZY2OPVS5wLWu/Vy1Hzmx5AJpOxTp06sePHj5sVX0ZGBps2bRrz9fVlQqGQVa9enS1evNjoksPcmPK7tN7UJd/5XeK6c+dOBoCNHz/erNhynT17lr355ptMKpUyBwcHVr9+faMpCRhj7Pjx46xVq1ZMIpEwmUzGevfuze7cuWN4XqlUso8//pg1aNDAsJ0GDRqwn3/+2Wg7mZmZbOjQoczZ2ZkBYIGBgezWrVsMAPvnn3/yxDZy5Ejm4ODAHj16xLp06cLs7e2Zl5cXmzlzZp7LlJOSkthbb73F7O3tmYuLC5swYYJh27mXyCYnJ7NJkyaxmjVrMgcHB+bk5MTeeOMNtnPnzkLzlDs9QO7Dzs6O+fv7s169erFff/3V6FLeV+N/9RL03bt3sy5dujBPT08mEolYlSpV2IQJE1hCQoLR69auXcuqVq3K+Hy+0WWzgYGBrGfPnvnGZ+pY+euvv9j48eOZi4sLc3R0ZMOGDTOa6oEx/TQTn376KXN3d2f29vasa9eu7OHDh3m2WVBsr08PwBhjz549Y6NHj2bu7u5MJBKxevXq5blcOfeS9fymbICJy/Xz4+npyQCwZ8+eGZadPXuWAWBt2rTJs/7rvxvGGDt//jxr3LgxE4lERvvOPQ5fl3tMFObV6QFMMXWZ9rNnz9ikSZNYQEAAEwqFzNvbm3Xq1ImtWbPGsI5Op2Pz589ngYGBTCwWs4YNG7IDBw7keY8lzXVOTg776KOPmI+PD5NIJKxVq1bswoULeX73pt7L69N15Pr5559ZcHAwE4vFrEmTJuz06dP5Hk+ve/bsGfv2229Zu3btmI+PDxMIBMzFxYV17NiR7d69O8/6c+fOZX5+fozH4xmdR/fv38/q16/P7OzsWFBQEFu4cCH79ddf85xrTf395RfrjRs3WLt27ZidnR3z8/Njc+fOZevWrcuzzXPnzrHmzZsziUTCfH192SeffMKOHj2a53L5go6hFy9esHfffZfJZDLm5OTE3n33XXb16tUiTQ+Q+xAKhczDw4O1bduWzZs3jz1//jzf15lzXOaSy+VMIpHkmS7k9Rhefb937txhnTt3Zo6Ojszd3Z2NGzfOMGXOq+9Jo9GwDz74gHl4eDCO44z+HvM7pq9cucK6du3KHB0dmb29PevQoQM7f/680Tq5585Lly4VGmdhuJeBEBvx+++/o1+/fjh9+nSJL4MuS4sWLcLSpUuRkJBQpuNECCGEkNJEhZKN6dWrF+7evYuHDx+Wq4Jj586d0Gq1RvPtEEIIIeWdTYxRIvp7h924cQMHDx7EDz/8UK6KJABlPo0BIYQQUhaoRclGcBwHR0dHDBo0CKtWrTLrZr2EEEIIKV30aWwjqF4lhBBCbI/NzKNECCGEEGJrqFAihBBCCDGhwne96XQ6xMfHQyqVlrsB0oQQQkhlxRhDRkYGfH19S3SPx5Kq8IVSfHx8qdz7hRBCCCGl78mTJ/D397fa/it8oZR7i4UnT56Uyj1giGXUXFETCRkJ8JH64N7ke9YOx3bVrAkkJAA+PsA9ypMpdDwVjnJkHsqT9cjlcgQEBFj0VknFUeELpdzuNplMRoWSDZvVdRYyVZlwFDnS76kgs2YBmZmAoyNAeTKJjqfCUY7MQ3myPmsPm6nw8yjJ5XI4OTkhPT2dDnJCCCGknLCVz2+66o0QQgghxAQqlAghhBBCTKjwY5RI+ZCQkQAt04LP8eEj9bF2OLYrIQHQagE+Xz+gu4zodDqoVKoy219JPc96Dh3Tgcfx4Ongae1wbBLlyDyUp9IjFArB5/OtHUahqFAiNqHp2qaIy4iDn9QPT8OfWjsc29W0KRAXB/j5AU/LJk8qlQrR0dHQ6XRlsj9LeCp/Cq1OCz6PjyxZlrXDsUmUI/NQnkqXs7MzvL29rT5guyBUKBFCTGKMISEhAXw+HwEBAVad9K0olElKaJgGAk6AYI9ga4djkyhH5qE8lQ7GGBQKBZ4/fw4A8CnDFvKiokKJEGKSRqOBQqGAr68v7O3trR2O2TghB+gAjsfBzs7O2uHYJMqReShPpUcikQAAnj9/Dk9PT5vthisfXw8JIVah1WoBACKRyMqREEIqotwvYGq12sqRmEaFEiGkULY8foAQUn6Vh3MLFUqEEEIIISZQoUQIITbo8ePHmDVrlrXDIKTSo0KJEFLhzJg6A039muKbT77J89ykSZPAcRxGjRpV9oFZyJo1a9C+fXvIZDJwHIe0tDSzXvfTTz8hKCgIdnZ2GN5jOG5fvW30fPv27cFxnNHjvffey7OdDRs2oH79+rCzs4OnpycmTZpU6L4vXLiAjh07wsHBATKZDG3btkV2drbh+aCgoDz7/vbbb422sXPnToSFhcHe3h6BgYFYvHhxofvt06cPqlSpAjs7O/j4+ODdd99FfHy80To3btxAmzZtYGdnh4CAACxatKjQ7ZLKgwolQkiF5OXrhSO/HzH6MM7JycHWrVtRpUoVK0ZWsOjoaPTv3x/NmzfHokWLULNmzTzFikKhQLdu3fDFF1+Yvd0dO3YgPDwcM2fOxJUrV1Cjdg18MOwDpCSnGK03btw4JCQkGB6vFw1Lly7Fl19+ic8++wy3b9/G8ePH0bVr1wL3feHCBXTr1g1dunTBP//8g0uXLmHy5Ml5ppuYM2eO0b4/+OADw3OHDx/GsGHD8N577+HWrVv4+eef8f3332PFihUF7rtDhw7YuXMnIiMjsWfPHjx69Ahvv/224Xm5XI4uXbogMDAQly9fxuLFizFr1iysWbOmwO2SSoRVcOnp6QwAS09Pt3YopAB+S/wYZoH5LfGzdii2zc+PMUD/bxnIzs5md+7cYdnZ2WWyP0vpPbA3a9e1HatWsxrbvHmzYfmWLVtY/fr1Wd++fdnIkSMNy7VaLZs/fz4LCgpidnZ2rH79+mzXrl2G5zUaDRszZozh+Ro1arBly5YZ7XPkyJGsb9++bPHixczb25u5urqyiRMnMpVKVaTYW7duzVq0aMH27t3L3n//fXbkyBH2xRdf5LvuyZMnGQCWmppa6HabNWvGJk2aZPj5StwV5uHtwaZ8McWwrF27dmzq1Kkmt5GSksIkEgk7fvy42e+HMcbeeOMN9tVXXxW4TmBgIPv+++9NPj9kyBD29ttvGy1bvnw58/f3ZzqdzuxYfv/9d8ZxnOH38vPPPzMXFxemVCoN63z66acsNDSUMcbYtYRr7FLcJXYt4ZrZ+yDmK+gcYyuf3zSPErEJJ0acgEangYBXtodkbGwskpOTS2Xb7u7ulm+5OHEC0GgAAf3pFsRJ7AQmYhg3dhzWr1+PYcOGAQB+/fVXjB49GqdOnTJaf8GCBdi8eTNWrVqF6tWr4/Tp0xg+fDg8PDzQrl076HQ6+Pv7Y9euXXBzc8P58+cxfvx4+Pj4YODAgYbtnDx5Ej4+Pjh58iQePnyIQYMGISwsDOPGjQMAzJo1Cxs2bMDjx49Nxn716lWsXr0aDRs2xPXr19G1a9dCW2wKo1KpcPnyZXz++eeGZbU8aqFL5y6IuhFltO6WLVuwefNmeHt7o3fv3pgxY4bhEu6IiAjodDrExcWhVq1ayMjIQMuWLbFkyRIEBATku+/nz5/j4sWLGDZsGFq2bIlHjx6hZs2amDdvHlq3bm207rfffou5c+eiSpUqGDp0KKZNmwbBy2NdqVTmmctLIpHg6dOniImJQVBQUKF5SElJwZYtW9CyZUsIhUIA+tautm3bGk2B0bVrVyxcuBCpqakIdQsFAwMH2786i5QOOtsSmxDqHlrm+4yNjUWtWrWgUChKZfv29va4e/euZYul0LLPU76WLtU/CtOoEbB/v/GyPn2AK1cKf214uP5RDHweH3weH6NHjsbXX32NmJgYAMC5c+ewfft2o0JJqVRi/vz5OH78OFq0aAEAqFq1Ks6ePYvVq1ejXbt2EAqFmD17tuE1wcHBuHDhAnbu3GlUKLm4uGDFihXg8/moWbMmevbsiRMnThgKJXd3d4SEhBQYe6tWrbBs2TKL3jImOTkZWq0WXl5ehmV2Qjv4+vjiwf0HhmVDhw5FYGAgfH19cePGDXz66aeIjIzE3r17AQBRUVHQ6XSYP38+fvjhBzg5OeGrr77Cm2++iRs3buQ731ZUlL4QmzVrFr777juEhYVh06ZN6NSpE27duoXq1asDAKZMmYJGjRrB1dUV58+fx+eff46EhAQsfXmcde3aFdOmTcOoUaPQoUMHPHz4EEuWLAEAJCQkFFgoffrpp1ixYgUUCgWaN2+OAwcOGJ5LTExEcLDxjNu5eUpMTEStWrXMzjOpmKhQIpVWcnIyFAoFvlqxDoHVLFuAxDyMxDeTxyI5Odmmx8MUm1yuv+dcYfJrZUhKMu+1cnnR43qNh4cHevbsiQ0bNoAxhp49e8Ld3d1onYcPH0KhUODNN980Wq5SqdCwYUPDzz/99BN+/fVXxMbGIjs7GyqVCmFhYUavqVOnjtHswj4+Prh586bh58mTJ2Py5MkFxrxlyxbMnj0bX3zxBRITE3H06FF89NFHRuNqSsv48eMN/69Xrx58fHzQqVMnPHr0CCEhIdDpdFCr1Vi+fDm6dOkCANi2bRu8vb1x8uTJfFu+cgu+CRMmYPTo0QCAhg0b4sSJE/j111+xYMECAED4K0Vx/fr1IRKJMGHCBCxYsABisRjjxo3Do0eP0KtXL6jVashkMkydOhWzZs0q9NY6H3/8McaOHYuYmBjMnj0bI0aMwIEDB8rFHD7E+qhQIpVeYLVQhNYPs3YY5YtMpr8xb2E8PPJfZs5rZbKix5WPMWPGGIqTn376Kc/zmZmZAICDBw/C77W4xGIxAGD79u2YPn06lixZghYtWkAqlWLx4sW4ePGi0fq53Tm5OI4rcsuQu7s7fvzxR3z00Uf49ttvERQUhEGDBuHw4cOG4qSo3N3dwefz8ezZM6Plz549g7e3t8nXvfHGGwD0xWRISIjhfly1a9c2rOPh4QF3d3fExsbmu438XgMAtWrVMvma3H1rNBo8fvwYoaGh4DgOCxcuxPz585GYmAgPDw+cOHECgL4FsCDu7u5wd3dHjRo1UKtWLQQEBODvv/9GixYt4O3tnW9eABSYG1J5UKFEbMLWm1uhUCtgL7TH0HpDrR2O7dq6FVAoAHt7YKgV81SCbrE8XXGlQKlRQqlV4oXiBbp16waVSgWO4/Jt8ahduzbEYjFiY2PRrl27fLd37tw5tGzZEhMnTjQse/ToUanFn8vb2xufffYZdu3ahTNnzhS7UBKJRGjcuDFOnDiBfv36AQCSMpMQcTwC/zfh/0y+7tq1awD+K3ZatWoFAIiMjIS/vz8A/bif5ORkBAYG5ruNoKAg+Pr6IjIy0mj5/fv30b179wL3zePx4OnpabScz+cbCtpt27ahRYsW8MivIDcht3BVKpUAgBYtWuDLL7+EWq02FLoREREIDQ2Fi4sLXiheQMd04HE8uNm7mb0fUnFQoURswicRnyAuIw5+Uj8qlAryySf6bis/P+sWSjYuS52FbHU2nsqfws3bDXfv3gWAfG+6KZVKMX36dEybNg06nQ6tW7dGeno6zp07B5lMhpEjR6J69erYtGkTjh49iuDgYPzvf//DpUuX8oxtKcyKFSuwb98+Q0tIfsaOHYsJEybAwcEBSqUSe/fuxe3btzFjxgzDOomJiUhMTMTDhw8BADdv3oRUKkWVKlXg6uqa73bDw8MxcuRINGnSBM2aNcOM+TOQmZWJ9v3bA9AXflu3bkWPHj3g5uaGGzduYNq0aWjbti3q168PAKhRowb69u2LqVOnYs2aNZDJZPj8889Rs2ZNdOjQId/9chyHjz/+GDNnzkSDBg0QFhaGjRs34t69e9i9ezcA/YDqixcvokOHDpBKpbhw4QKmTZuG4cOHw8XFBYC+q3z37t1o3749cnJysH79euzatQt//fWXYV///PMPRowYgRMnTsDPzw8XL17EpUuX0Lp1a7i4uODRo0eYMWMGQkJCDOPRhg4ditmzZ2Ps2LH49NNPcevWLfzwww/4/vvvAQBP5U+h1qkh5AmpUKqkqFAihFR4skK68ebOnQsPDw8sWLAAUVFRcHZ2RqNGjQzzFE2YMAFXr17FoEGDwHEchgwZgokTJ+Lw4cNFiiM5ObnQlihPT0+MGTMG0dHRUCqVqFKlCubOnWtoCQKAVatWGQ0ub9u2LQBg/fr1hok027dvj6CgIGzYsAEAMGjQICQlJeHrr79GYmIiatSpgeWbl8PNQ//hLxKJcPz4cSxbtgxZWVkICAjAW2+9ha+++soovk2bNmHatGno2bMneDwe2rVrhyNHjhh1O3IcZxTLhx9+iJycHEybNg0pKSlo0KABIiIiDAPbxWIxtm/fjlmzZkGpVCI4OBjTpk0zGrcEABs3bsT06dPBGEOLFi1w6tQpNGvWzPC8QqFAZGSk4Qar9vb22Lt3L2bOnImsrCz4+PigW7du+Oqrrwzdqk5OTjh27BgmTZqExo0bw93dHV9//bXReC1SuXGMMWbtIEqTXC6Hk5MT0tPTCz1ZEuvxX+pvaFF6Gv60TPZ55coVNG7cGGuPnLX4GKXIG9cwrltrXL58GY0aNbLchv39/2tRelr6ecrJyUF0dDSCg4NhZ2dX6vuzlOuJ1w2tAA28G1g7nGJ5/PgxNmzYUOzbmAQGBmL27NkmZyAvrRxFR0ejRo0auHPnjuGKtvKsIhxLtqygc4ytfH7TzNyEEFLB3L59G05OThgxYkSZ7/vQoUMYP358hSiSCAGo640QQmxSUFBQsVuT6tSpgxs3blg2IDOZc983QsoTalEihBBCCDGBCiVCCCGEEBOoUCKEEEIIMYEKJUIIIYQQE2gwN7EJ3o7eRv8SE3JvqUC3ViiQkC80+pfkRTkyD+WJUKFEbMK/4/+1dgjlw7+UJ3PU9qhd+EqVHOXIPJQnQl1vhBBCCCEmUKFECCGl7PHjx+A4znCT2fKy7eLYsGEDnJ2dbWY7JTFr1iw8fvzYqjEQ66NCiRBS4SQlJeH9999HlSpVIBaL4e3tja5du+LcuXOGdTiOw2+//Wa9IMtQ+/btwXEcOI6DWCyGn58fevfujb1791p8X4MGDcL9+/eL9JqgoCAsW7asxNspK6dOnULfvn3h4+MDBwcHhIWFYcuWLUbrbNiwwZDz3Mfrt+iYNWsWatasCQcHB7i4uKBz5864ePFigfsOCgrKs12O44wm+pwwYQJCQkIgkUjg4eGBvn374t69ewXGlvt4/vy5yX3fv38fffv2hbu7O2QyGVq3bo2TJ08anr9+/TqGDBmCgIAASCQS1KpVCz/88INZObVlVCgRmzDhjwl4Z9c7mPDHBGuHYtsmTADeeUf/LzGpV99euHDpAhb+tBD379/H/v370b59e7x48cLaoRWbSqUq0evHjRuHhIQEPHr0CHv27IFfVT8MHjwYQ0cNtVCEehKJBJ6enjazneLYtWsXGjdujMWLF6Nxk8YIaxKGVZtWGZ4/f/486tevjz179uDGjRsYPXo0RowYgQMHDhhtRyaTISEhwfCIiYkxer5GjRpYsWIFbt68ibNnzyIoKAhdunRBUlKSydguXbpktM2IiAgAwDvvvGNYp3Hjxli/fj3u3r2Lo0ePgjGGLl26QKvVAtAXoa9uIyEhAV27dkW7du0KzHmvXr2g0Wjw559/4vLly2jQoAF69eqFxMREAMDly5fh6emJzZs34/bt2/jyyy/x+eefY8WKFWZm3kaxCi49PZ0BYOnp6dYOhRTAb4kfwywwvyV+ZbbPy5cvMwBs7ZGz7HR8pkUfa4+cZQDY5cuXLRu0nx9jgP7fMpCdnc3u3LnDsrOzy2R/lpCamsoAsFW7V7FrCdfyXScwMJABMDwCAwMZY4w9fPiQ9enTh3l6ejIHBwfWpEkTFhERkee18+bNY6NHj2aOjo4sICCArV692midixcvsrCwMCYWi1njxo3Z3r17GQB29epVxhhjGo2GjRkzhgUFBTE7OztWo0YNtmzZMqNtjBw5kvXt25d98803zMfHhwUFBZm17fy0a9eOTZ061WjZtYRrbMbSGQyA0XuMjY1l77zzDnNycmIuLi6sT58+LDo6mjHG2NGjR5lYLGapqalG25oyZQrr0KEDY4yx9evXMycnJ8NzheW0Xbt2Rr+L3I+l17fDGGM///wzq1q1KhMKhaxGjRps06ZNRs8DYGvXrmX9+vVjEomEVatWjf3+++8m85KfyMhIxufz2YwZM9jEiRPZ8k3L2ewfZrNvV35b4Ot69OjBRo8ebfg5v/gLk/t5dfz4cbNfM3XqVBYSEsJ0Op3Jda5fv84AsIcPH+b7/PPnz5lQKMyTz1clJSUxAOz06dOGZXK5PM/x87qJEycajo38FHSOsZXPb2pRIoRUKI6OjrB3sMdfR/6CSpl/K8ylS5cAAOvXr0dCQoLh58zMTPTo0QMnTpzA1atX0a1bN/Tu3RuxsbFGr1+yZAmaNGmCq1evYuLEiXj//fcRGRlp2EavXr1Qu3ZtXL58GbNmzcL06dONXq/T6eDv749du3bhzp07+Prrr/HFF19g586dRuudOHECkZGRiIiIwIEDB8zadlH0eqcXZM4yQxecWq1G165dIZVKcebMGZw7dw6Ojo7o1q0bVCoVOnXqBGdnZ+zZs8ewDa1Wix07dmDYsGH57qOwnO7duxf+/v6YM2eOoXUjP/v27cPUqVPx0Ucf4datW5gwYQJGjx5t1PUDALNnz8bAgQNx48YN9OjRA8OGDUNKSorh+cLuoXfjxg3weDzMnj0bHh4eCKkZgh5v90C3ft0KzGV6ejpcXV3zvPfAwEAEBASgb9++uH37tsnXq1QqrFmzBk5OTmjQoEGB+3r1NZs3b8aYMWPAcVy+62RlZWH9+vUIDg5GQEBAvuts2rQJ9vb2ePvtt03uy83NDaGhodi0aROysrKg0WiwevVqeHp6onHjxiZfl19eyhuaHoAQUmRLLyzF0gtLC12vkU8j7B+y32hZn219cCXhSqGvDW8RjvAW4UWOTSAQYM6yOZjz8Rzs3bwXjRs1Rrt27TB48GDUr18fAODh4QEAcHZ2hvcrc1I1aNDA6ENq7ty52LdvH/bv34/Jkycblvfo0QMTJ04EAHz66af4/vvvcfLkSYSGhmLr1q3Q6XRYt24d7OzsUKdOHTx9+hTvv/++4fVCoRCzZ882/BwcHIwLFy5g586dGDhwoGG5g4MDfvnlF4hEIgDAmjVrCt12UfB4PARWDTQMWN6xYwd0Oh1++eUXwwfv+vXr4ezsjFOnTqFLly4YPHgwtm7dirFjxwLQF3NpaWl466238t1HYTl1dXUFn8+HVCo1+l287rvvvsOoUaMMeQ8PD8fff/+N7777Dh06dDCsN2rUKAwZMgQAMH/+fCxfvhz//PMPunXTFzohISFwd3c3uZ/GjRuDx+Ph448/RlpaGpqhWWFpxM6dO3Hp0iWsXr3asCw0NBS//vor6tevj/T0dHz33Xdo2bIlbt++DX9/f8N6Bw4cwODBg6FQKODj44OIiIgC43vVb7/9hrS0NIwaNSrPcz///DM++eQTZGVlITQ0FBEREYbj6HXr1q3D0KFDIZFITO6L4zgcP34c/fr1g1QqBY/Hg6enJ44cOQIXF5d8X3P+/Hns2LEDBw8eNOv92CoqlAghRSZXyhGXEVfoegFOeb/BJimSzHqtXCkvVmwA0LlXZzTv2Bw3L91E0v0kHD58GIsWLcIvv/yS74dKrszMTMyaNQsHDx5EQkICNBoNsrOz87Qo5RZcgP4DxNvb2zAI9u7du6hfv77RwN0WLVrk2ddPP/2EX3/9FbGxscjOzoZKpUJYWJjROvXq1TP6cDN320XBGDMURdevX8fDhw8hlUqN1snJycGjR48AAMOGDUPz5s0RHx8PX19fbNmyBT179jR5hZq5OS3M3bt3MX78eKNlrVq1yjNY+NXfjYODA2QymdEA5RMnThS4n+DgYERERGDevHk4c+YMdu3ZhRbtW2Dyp5PRwDtvS8/JkycxevRorF27FnXq1DEsb9GihdHvpmXLlqhVqxZWr16NuXPnGpZ36NAB165dQ3JyMtauXYuBAwfi4sWLZo3PWrduHbp37w5fX988zw0bNgxvvvkmEhIS8N1332HgwIE4d+5cngHlFy5cwN27d/G///2vwH0xxjBp0iR4enrizJkzkEgk+OWXX9C7d29cunQJPj4+RuvfunULffv2xcyZM9GlS5dC34sts2rX2+nTp9G7d2/4+vrmuQJFrVbj008/Rb169eDg4ABfX1+MGDEC8fHx1guYEAIAkIll8JP6FfrwsPfI81oPew+zXisTy0oUo9hOjBbtWmDGjBk4f/48Ro0ahZkzZxb4munTp2Pfvn2YP38+zpw5g2vXrqFevXp5BlILhcazNHMcB51OZ3Zs27dvx/Tp0zF27FgcO3YM165dw+jRo/Psx8HBwextFodWq0VsdCyCg4MB6Iuaxo0b49q1a0aP+/fvY+hQ/aDvpk2bIiQkBNu3b0d2djb27dtnstsNMD+nllLS3w0AtGnTBkeOHMHHH3+MrxZ9hfS0dIx/ezw0Go3Ren/99Rd69+6N77//HiNGjCg0roYNG+Lhw4dGyx0cHFCtWjU0b94c69atg0AgwLp16wqNMSYmBsePH8f//d//5fu8k5MTqlevjrZt22L37t24d+8e9u3bl2e9X375BWFhYQV2nwHAn3/+iQMHDmD79u1o1aoVGjVqhJ9//hkSiQQbN240WvfOnTvo1KkTxo8fj6+++qrQ92LrrNqilJWVhQYNGmDMmDEYMGCA0XMKhQJXrlzBjBkz0KBBA6SmpmLq1Kno06cP/qXZiQmxquJ2iwHI0xVXVmrXrm30ZUwoFBquAsp17tw5jBo1Cv379wegLxyKOo9OrVq18L///Q85OTmGb+9///13nv20bNnS0I0EwNBiU9JtF8XBXQchT5Mbus0aNWqEHTt2wNPTEzKZ6UJ12LBh2LJlC/z9/cHj8dCzZ0+T65qTU5FIlOd38bpatWrh3LlzGDlypNG2a9cu3Zmz64TVQUDVAAzpPAQxMTEICQkBoJ8ioFevXli4cGGelq78aLVa3Lx5Ez169ChwPZ1OB6VSWej21q9fD09PzwJzn4sxBsZYnu1mZmZi586dWLBgQaHbUCgUAPTdta/i8XhGhejt27fRsWNHjBw5EvPmzSt0u+WBVVuUunfvjm+++cbwB/QqJycnREREYODAgQgNDUXz5s2xYsUKXL58uchNtoSQyuPFixcY9/Y4HNpzCPfv3Ed0dDR27dqFRYsWoW/fvob1goKCcOLECSQmJiI1NRUAUL16dezduxfXrl3D9evXMXTo0CK3RgwdOhQcx2HcuHG4c+cODh06hO+++85onerVq+Pff//F0aNHcf/+fcyYMcMwoLyk2zZFoVAgMTERT58+xd9//41l3yzDgs8W4J2R7xjG+AwbNgzu7u7o27cvzpw5g+joaJw6dQpTpkzB06dPDdsaNmwYrly5gnnz5uHtt9+GWCw2uV9zchoUFITTp08jLi4OycnJ+W7n448/xoYNG7By5Uo8ePAAS5cuxd69e4s8mL1Tp04FXq5+5MgRfP/994iKioJOp0NKcgq2/7odLq4uqFKlCgB9d1vPnj0xZcoUvPXWW0hMTERiYqLRoPE5c+bg2LFjiIqKwpUrVzB8+HDExMQYWoCysrLwxRdf4O+//0ZMTAwuX76MMWPGIC4uzuhS//zodDqsX78eI0eOhEBg3N4RFRWFBQsWGD4rz58/j3feeQcSiSRPkbZjxw5oNBoMHz48zz7++ecf1KxZE3Fx+m7yFi1awMXFBSNHjsT169dx//59fPzxx4iOjjYUa7du3UKHDh3QpUsXhIeHG/JS0HQH5UG5uuotPT0dHMcVOFurUqmEXC43ehBCKg9HR0fUa1gP29Zuw5j+Y1C3bl3MmDED48aNM/qAXLJkCSIiIhAQEICGDRsCAJYuXQoXFxe0bNkSvXv3RteuXdGoUaMi7/+PP/7AzZs30bBhQ3z55ZdYuHCh0ToTJkzAgAEDMGjQILzxxht48eKFUetSSbZtytq1a+Hj44OQkBAMGDAAUfejMH/lfHz57ZeGdezt7XH69GlUqVIFAwYMQK1atTB27Fjk5OQYtTBVq1YNzZo1w40bNwrsdgPMy+mcOXPw+PFjhISEGAbav65fv3744Ycf8N1336FOnTpYvXo11q9fj/bt25v1/nM9evTIZDEG6Iu2ixcvom3btpg/fz4mvDMBjx88xvL/LTd0623cuBEKhQILFiyAj4+P4fFqz0hqairGjRuHWrVqoUePHpDL5Th//ryhBYzP5+PevXt46623UKNGDfTu3RsvXrzAmTNnjMY6tW/fPs+4uuPHjyM2NhZjxozJE7+dnR3OnDmDHj16oFq1ahg0aBCkUinOnz+fZ9zTunXrMGDAgHw/UxUKBSIjI6FWqwEA7u7uOHLkCDIzM9GxY0c0adIEZ8+exe+//24YrL97924kJSVh8+bNRnlp2rRpAb8R28cxxpi1gwD0/cj79u1Dv3798n0+JycHrVq1Qs2aNfPMgPqqWbNmGV1Nkis9Pb3ApmRiXf5L/RGXEQc/qR+ehj8t/AUWcOXKFTRu3Bhrj5xFaP0wi2478sY1jOvWGpcvXy7yB22B/P2BuDjAzw94Wvp5ysnJQXR0NIKDg/MMArVl1xOvQ61TQ8gT5jsAl1COzDFr1iw069kMnn6eVstTYGAgZs+eXeBFCOVZQecYuVwOJycnq39+l4ur3tRqNQYOHAjGGFauXFngup9//jnCw/8bOyGXy03OHUFsx5C6Q5CakwoXu/wvMyUvDRkCpKYCJi7HJXquEldomRZ8jm/tUGwW5cg8zmJnuNu7WyVPt2/fhpOTU6EDxUnpsvlCKbdIiomJwZ9//lloVSkWiwvsLye2aXGXxdYOoXxYTHkyR37TEhBjlKPCFTQxZVmoU6cObty4YdUYiI0XSrlF0oMHD3Dy5Em4ublZOyRCCCGEVCJWLZQyMzON5pSIjo7GtWvX4OrqCh8fH7z99tu4cuUKDhw4AK1Wa7jxnqurq8kZRgkhhBBCLMWqhdK///5rNPV87tiikSNHYtasWdi/Xz/fyuuz1Z48ebLIVzoQQorPRq75IIRUMOXh3GLVQql9+/YFJqk8JJBYRs0VNRGfEQ9fqS/uTb5n7XBsV82aQHw84OsL3Cv9PPH5+gGsKpWqwPtA2Zpbz29BpVVBxBehrmdda4djkyhH5qE8la7ciSxfn1Hdltj0GCVSeWSqMpGhykCmKtPaodi2zEwgI0P/bxkQCASwt7dHUlIShEJhnll5bZVGqYGO6aDRaJCTk2PtcGwS5cg8lKfSwRiDQqHA8+fP4ezsbPhSZouoUCKEmMRxHHx8fBAdHY2YmBhrh2O2JHkStDot+Dw+xJl0FWx+KEfmoTyVLmdnZ3h7e1s7jAJRoUQIKZBIJEL16tVL7SampWHU+lF4lvUMXg5e+Gv0X9YOxyZRjsxDeSo9QqHQpluSclGhRAgpFI/HK1czc8dlxyEuKw4anqZcxV2WKEfmoTyR8jHggBBCCCHECqhQIoQQQggxgQolQgghhBATqFAihBBCCDGBCiVCCCGEEBPoqjdiE1b1WoVsdTYkwvIz+7NVrFoFZGcD5WiWbGug46lwlCPzUJ4IFUrEJvSq0cvaIZQPvShP5qDjqXCUI/NQngh1vRFCCCGEmECFEiGEEEKICdT1RmzC5fjLhjt0N/ZtbO1wbNfly4BKBYhEQGPKkyl0PBWOcmQeyhOhQonYhL7b+yIuIw5+Uj88DX9q7XBsV9++QFwc4OcHPKU8mULHU+EoR+ahPBHqeiOEEEIIMYEKJUIIIYQQE6hQIoQQQggxgQolQgghhBATqFAihBBCCDGBCiVCCCGEEBOoUCKEEEIIMYEKJUIIIYQQE6hQIoQQQggxgWbmJjbh7qS7YGDgwFk7FNt29y7AGMBRngpCx1PhKEfmoTwRKpSITZCKpdYOoXyQUp7MQcdT4ShH5qE8Eep6I4QQQggxgQolQgghhBATqOuN2ISlF5ZCrpRDJpYhvEW4tcOxXUuXAnI5IJMB4ZQnU+h4KhzlyDyUJ0KFErEJSy8sRVxGHPykfnQyKsjSpUBcHODnR4VSAeh4KhzlyDyUJ0Jdb4QQQgghJlChRAghhBBiAhVKhBBCCCEmUKFECCGEEGICFUqEEEIIISZQoUQIIYQQYgIVSoQQQgghJlChRAghhBBiAk04SWxCI59GCHAKgIe9h7VDsW2NGgEBAYAH5akgdDwVjnJkHsoToUKJ2IT9Q/ZbO4TyYT/lyRx0PBWOcmQeyhOhrjdCCCGEEBOoUCKEEEIIMYEKJUIIIYQQE6xaKJ0+fRq9e/eGr68vOI7Db7/9ZvQ8Ywxff/01fHx8IJFI0LlzZzx48MA6wZJS1WdbH7RY1wJ9tvWxdii2rU8foEUL/b/EJDqeCkc5Mg/liVh1MHdWVhYaNGiAMWPGYMCAAXmeX7RoEZYvX46NGzciODgYM2bMQNeuXXHnzh3Y2dlZIWJSWq4kXEFcRhz8pH7WDsW2XbkCxMUBfpSngtDxVDjKkXkoT8SqhVL37t3RvXv3fJ9jjGHZsmX46quv0LdvXwDApk2b4OXlhd9++w2DBw8uy1AJIYQQUgnZ7Bil6OhoJCYmonPnzoZlTk5OeOONN3DhwgWTr1MqlZDL5UYPQgghhJDisNlCKTExEQDg5eVltNzLy8vwXH4WLFgAJycnwyMgIKBU4ySEEEJIxWWzhVJxff7550hPTzc8njx5Yu2QCCGEEFJO2Wyh5O3tDQB49uyZ0fJnz54ZnsuPWCyGTCYzehBCCCGEFIfNFkrBwcHw9vbGiRMnDMvkcjkuXryIFi1aWDEyQgghhFQWVr3qLTMzEw8fPjT8HB0djWvXrsHV1RVVqlTBhx9+iG+++QbVq1c3TA/g6+uLfv36WS9oQgghhFQaVi2U/v33X3To0MHwc3h4OABg5MiR2LBhAz755BNkZWVh/PjxSEtLQ+vWrXHkyBGaQ4kQQgghZcKqhVL79u3BGDP5PMdxmDNnDubMmVOGURFrCG8RDrlSDpmYxpQVKDwckMsBGntXIDqeCkc5Mg/liVi1UCIkV3iLcGuHUD6EU57MQcdT4ShH5qE8EZsdzE0IIYQQYm1UKBFCCCGEmEBdb8QmZCgzwMDAgYNULLV2OLYrIwNgDOA4QEp5MoWOp8JRjsxDeSJUKBGbUOunWoY7dD8Nf2rtcGxXrVpAXBzg5wc8pTyZQsdT4ShH5qE8Eep6I4QQQggxgQolQgghhBATqFAihBBCCDGBCiVCCCGEEBOoUCKEEEIIMYEKJUIIIYQQE6hQIoQQQggxgQolQgghhBATqFAihBBCCDGBZuYmNuH3wb9DpVVBxBdZOxTb9vvvgEoFiChPBaHjqXCUI/NQnggVSsQmNPZtbO0QyofGlCdz0PFUOMqReShPhLreCCGEEEJMoEKJEEIIIcQE6nojNuHA/QPIVmdDIpSgV41e1g7Hdh04AGRnAxIJ0IvyZAodT4WjHJmH8kSoUCI24b0D7yEuIw5+Uj88DX9q7XBs13vvAXFxgJ8f8JTyZAodT4WjHJmH8kSo640QQgghxAQqlAghhBBCTKBCiRBCCCHEBCqUCCGEEEJMoEKJEEIIIcQEKpQIIYQQQkygQokQQgghxAQqlAghhBBCTKBCidgER5EjpCIpHEWO1g7Ftjk6AlKp/l9iEh1PhaMcmYfyRGhmbmIT7k2+Z+0Qyod7lCdz0PFUOMqReShPhFqUCCGEEEJMoEKJEEIIIcQEKpQIIYQQQkygMUrEJnx87GOk5qTCxc4Fi7sstnY4tuvjj4HUVMDFBVhMeTKFjqfCUY7MQ3kiVCgRm7Dt1jbEZcTBT+pHJ6OCbNsGxMUBfn5UKBWAjqfCUY7MQ3ki1PVGCCGEEGICFUqEEEIIISZQoUQIIYQQYgIVSoQQQgghJlChRAghhBBiAhVKhBBCCCEmUKFECCGEEGICFUqEEEIIISbY9ISTWq0Ws2bNwubNm5GYmAhfX1+MGjUKX331FTiOs3Z4xIJ6Vu+JlJwUuNq5WjsU29azJ5CSArhSngpCx1PhKEfmoTwRmy6UFi5ciJUrV2Ljxo2oU6cO/v33X4wePRpOTk6YMmWKtcMjFrS692prh1A+rKY8mYOOp8JRjsxDeSI2XSidP38effv2Rc+ePQEAQUFB2LZtG/755x8rR0YIIYSQyqBYhVJUVBSqVq1q6VjyaNmyJdasWYP79++jRo0auH79Os6ePYulS5eafI1SqYRSqTT8LJfLSz1OQky5e/euxbfp7u6OKlWqWHy7hBBC8ipWoVStWjW0a9cOY8eOxdtvvw07OztLxwUA+OyzzyCXy1GzZk3w+XxotVrMmzcPw4YNM/maBQsWYPbs2aUSDyHmevE8EeA4DB8+3OLbtre3x927d6lYIoSQMlCsQunKlStYv349wsPDMXnyZAwaNAhjx45Fs2bNLBrczp07sWXLFmzduhV16tTBtWvX8OGHH8LX1xcjR47M9zWff/45wsPDDT/L5XIEBARYNC5ieU3WNEFiZiK8Hb3x7/h/rR1OiWWmpwOMYfLcJWjQ9A2Lbbf7uGFgT2Lg0q0bcOeOxbZb0VS046k0UI7MQ3kixSqUwsLC8MMPP2DJkiXYv38/NmzYgNatW6NGjRoYM2YM3n33XXh4eJQ4uI8//hifffYZBg8eDACoV68eYmJisGDBApOFklgshlgsLvG+SdlKzExEXEactcOwOL/gEITWD7PY9pyzFXAAoHrxwmLbrIgq6vFkSZQj81CeSInmURIIBBgwYAB27dqFhQsX4uHDh5g+fToCAgIwYsQIJCQklCg4hUIBHs84RD6fD51OV6LtEkIIIYSYo0SF0r///ouJEyfCx8cHS5cuxfTp0/Ho0SNEREQgPj4effv2LVFwvXv3xrx583Dw4EE8fvwY+/btw9KlS9G/f/8SbZcQQgghxBzF6npbunQp1q9fj8jISPTo0QObNm1Cjx49DK0/wcHB2LBhA4KCgkoU3I8//ogZM2Zg4sSJeP78OXx9fTFhwgR8/fXXJdouIYQQQog5ilUorVy5EmPGjMGoUaPg4+OT7zqenp5Yt25diYKTSqVYtmwZli1bVqLtEEIIIYQUR7EKpQcPHhS6jkgkMjngmhBCCCGkPCjWGKX169dj165deZbv2rULGzduLHFQhBBCCCG2oFiF0oIFC+Du7p5nuaenJ+bPn1/ioAghhBBCbEGxCqXY2FgEBwfnWR4YGIjY2NgSB0UIIYQQYguKNUbJ09MTN27cyHNV2/Xr1+Hm5maJuEgls+jNRVCoFbAX2ls7FJv2z9j3sWXhHHw5ZQryflUhueh4KhzlyDyUJ1KsQmnIkCGYMmUKpFIp2rZtCwD466+/MHXqVMMs2oQUxdB6Q60dQrnwqEMXrFs4BxO7d6dCqQB0PBWOcmQeyhMpVqE0d+5cPH78GJ06dYJAoN+ETqfDiBEjaIwSIYQQQiqMYhVKIpEIO3bswNy5c3H9+nVIJBLUq1cPgYGBlo6PEEIIIcRqilUo5apRowZq1KhhqVhIJRaZHAmNTgMBT4BQ91Brh2OznJ7EojYA8ePHQKNG1g7HZtHxVDjKkXkoT6RYhZJWq8WGDRtw4sQJPH/+PM9Nav/880+LBEcqj06bOiEuIw5+Uj88DX9q7XBsVo/Pp+IdAKr33wcGDLB2ODaLjqfCUY7MQ3kixSqUpk6dig0bNqBnz56oW7cuOI6zdFyEEEIIIVZXrEJp+/bt2LlzJ3r06GHpeAghhBBCbEaxB3NXq1bN0rEQUm6odQzpKh0yVDpoGIOWAQIOkAh4cBRSCyshhFQUxSqUPvroI/zwww9YsWIFdbuRSiVDpcPTLDVSlDoTa2gBAJKmXdBj2mxAICq74AghhFhcsQqls2fP4uTJkzh8+DDq1KkDoVBo9PzevXstEhwhtkKpZXgkVyH1lQJJwufgJOZBzOPA4ziodQzZGh3SVTpoRHZo8+5EQKdFTIYafg4CCHj0pYIQQsqbYhVKzs7O6N+/v6VjIcQmJedo8ShdBQ0DOAAeEj78HASwF+R/q0QdYzh56jQScnQIbtgcT7M0SMrRooaTEDIRv2yDJ4QQUiLFKpTWr19v6TgIsTmMMTzJ0uBJpgYA4CDgUMNZZLJAysXjOGhTErHmg7FY8NtpiINrQ6lluJmiQqCjAH4OAuqyJoSQcqLgM34BNBoNjh8/jtWrVyMjIwMAEB8fj8zMTIsFR4i1MMYQlaE2FEl+DgLUdxMXWiTlkZmCMDcx3O30LUkxmRo8lKvBGLN0yIQQQkpBsVqUYmJi0K1bN8TGxkKpVOLNN9+EVCrFwoULoVQqsWrVKkvHSUiZYYwhSq5GYrZ+YHZVqRA+DsWfxF7A4152u/EQJVfjebYWah1DqLMIfGpZIoQQm1bsCSebNGmC69evw83NzbC8f//+GDdunMWCI5XHpXGXoGVa8Ln8x/DExsYiOTnZovu8e/duvsvjsjSGIqm6kxCekhLd6QcAwHEcfOwFEPE43E/TDwq/l6pCLRcReEUoln77YS0+HdYPhzZtQv0SR1VxFXY8EcqRuShPpFifAGfOnMH58+chEhlf+hwUFIS4uDiLBEYqFx+pj8nnYmNjUatWLSgUilLZ96vdxUnZGsS87G4LllqmSHqVmx0ftV1FuJOiQppKh8g0FUKdzS+Wst3cEQdA4+Fh0bgqmoKOJ6JHOTIP5YkU61NAp9NBq9XmWf706VNIpdISB0XIq5KTk6FQKPDVinUIrGa5m1L+ffIY1i2cg5ycHACAQq3Dw3Q1AMDXXgDfEnS3FcRJxEctFxHupKqQotThkVyNajIhDfAmhBAbVKxPgi5dumDZsmVYs2YNAH23QmZmJmbOnEm3NSGlJrBaKELrh1lsezEPIg3/1+oY7qWroAPgLOIhSFo6RVIuZzEfoc4i3EtT4Xm2FhIBB38HYeEvJIQQUqaK9WmwZMkSdO3aFbVr10ZOTg6GDh2KBw8ewN3dHdu2bbN0jKQSWHN5DTJVmXAUOWJ84/Flvv+oDDWyNQxCHlDdSVQmrTtudnwES4WIzlAjJkMDCZ8HN7uCx0GEHvod0wC47d0LNGpU6jGWV9Y+nsoDypF5KE+kWIWSv78/rl+/ju3bt+PGjRvIzMzE2LFjMWzYMEgkEkvHSCqBOX/NQVxGHPykfmV/MnJ0wfOXg7dDnUQQ8cuuC8zHno9srQ6JCi0epKtgLxBDUsAUBI22bkAbAKq1a4FvvimzOMsbqx5P5QTlyDyUJ1Ls/gWBQIDhw4dbMhZCypzYwRHwCQGgH5fkJC7bK1s4jkNVqRAKNYNcrcO9NBXqu4rBp9udEEKITShWobRp06YCnx8xYkSxgiGkrHX7YAYgFMOOz6FKKY9LMoXjOIQ6i3DtRQ4UGoZHcjWqO9HgbkIIsQXFnkfpVWq1GgqFAiKRCPb29lQokXKBJ3VB84F9AAAhMqFVJ38U8fXF0q0UFZJytHAW8yw+NQEhhJCiK9YtTFJTU40emZmZiIyMROvWrWkwNykXGGMQVa2n/yH1GZzLuMstP04iPgIc9cVRlFyNHI3OyhERQggp9r3eXle9enV8++23eVqbCLFFz7O14Ds6IyczA3geY+1wDAIcBJAKedAy4H463ROOEEKszWKFEqAf4B0fH2/JTRJicRodQ0ymfmLJP39ZAmjVVo7oPxynvy8cnwMy1DrEKzTWDokQQiq1Yg2C2L9/v9HPjDEkJCRgxYoVaNWqlUUCI6S0xGVpoNYBuuwsnN+2Fj26vGntkIzYCXgIlgrxUK5GbIYGrmJ+gVMGEEIIKT3FKpT69etn9DPHcfDw8EDHjh2xZMkSS8RFSKlQaZmhlUb1+A60apWVI8qfp4SPpBwt0lX6W5zUcSmbSTAJIYQYK/a93gixpBpuNeBk5wQvB69S3U9clho6BjgKOGSlJJTqvkqC4ziEyIS4lqxEukqHZ9laeNsLkO4XgMfJSahSpQpEhW+m0iqr46k8oxyZh/JE6PpjYhP+HPlnqe9DqWVIUOhn4K4iFeJZqe+xZCQCHqpIBXicocHjDDVcxHwcWrgc47q1xuXVq0E3MDGtLI6n8o5yZB7KEylWoRQeHm72ukuXLi3OLgixuCeZajAAMiEPzqLyMebH116A5BwtMtUMUXIVqPONEELKVrEKpatXr+Lq1atQq9UIDQ0FANy/fx98Ph+NXrlRJ42pILZCqWWG+7lVkQrKzbHJcRyqyUS4/kKJFKUOUrGjtUMihJBKpViFUu/evSGVSrFx40a4uLgA0E9COXr0aLRp0wYfffSRRYMkpKTisl62Jol4cBJZf3LJonAQ8uDnIMDTLA0yHb3096cjhBBSJopVKC1ZsgTHjh0zFEkA4OLigm+++QZdunShQokU2bC9w5CsSIa7vTu2DNhi0W2rdQzPXo5NCnAon8PyAhz1XXBvfjkZnd28EPTll8Dhw9YOy2aV5vFUUVCOzEN5IsX61JDL5UhKSsqzPCkpCRkZGSUOilQ+fz3+C3EZcfCT+ll82/FZGuigv9LNqZyMTXodj+NQVSZEwOXzkD1PgDKH/s4KUprHU0VBOTIP5YkU61Ojf//+GD16NPbu3YunT5/i6dOn2LNnD8aOHYsBAwZYOkZCik2jY0h4OW+Sv6Ow3IxNyo+LmA/u5S1N1ODR7U0IIaQMFKtFadWqVZg+fTqGDh0KtVp/+weBQICxY8di8eLFFg2QkJJ4nq2FlgESPgdXcflsTXoVx/RdiDpwiExToaaL2MoREUJIxVasQsne3h4///wzFi9ejEePHgEAQkJC4ODgYNHgCCkJxv6bhdvXofxc6VaQV9/Bn3FZqCoTQcQv/++LEEJsVYm+YickJCAhIQHVq1eHg4NDqXQFxMXFYfjw4XBzc4NEIkG9evXw77//Wnw/pOJ5odRBqWUQcICHpHxd6VYYDoBcrcPfzxTWDoUQQiq0YhVKL168QKdOnVCjRg306NEDCQn6W0GMHTvWole8paamolWrVhAKhTh8+DDu3LmDJUuWGF1tR4gp8Vn61iRvewH4FaA16VVC6G8jdPF5NlKVWitHQwghFVexCqVp06ZBKBQiNjYW9vb2huWDBg3CkSNHLBbcwoULERAQgPXr16NZs2YIDg5Gly5dEBISYrF9kIopQ6VDhloHDoCPffmcEqAgPDAES4XQMuBkXJa1wyGEkAqrWJ8gx44dw9GjR+Hv72+0vHr16oiJibFIYACwf/9+dO3aFe+88w7++usv+Pn5YeLEiRg3bpzJ1yiVSiiVSsPPcrncYvGQ8iP3SjcPO36FHcPTyc8B6+6l4X66CrEZalSRCku8zdjYWCQnJ1sgOmPu7u6oUqWKxbdLCCGlrViFUlZWllFLUq6UlBSIxZa7CicqKgorV65EeHg4vvjiC1y6dAlTpkyBSCTCyJEj833NggULMHv2bIvFQMoftY4hOUffHeVdAVuTcrlLBAhzt8PV5ByciMvEqFDnEg1Yj42NRa1ataBQWH7ck729Pe7evUvFEiGk3CnWp0ibNm2wadMmzJ07F4D+flQ6nQ6LFi1Chw4dLBacTqdDkyZNMH/+fABAw4YNcevWLaxatcpkofT5558b3bRXLpcjICDAYjGR0jGu0TikK9PhJHYq8baeZ2vAADgIODgKK1Zr0r1uvfHX5l8xuH9/+AJo7W2POylKPMvW4laKEvXc7Iq97eTkZCgUCny1Yh0Cq4VaLOaYh5H4ZvJYJCcnl1mhZMnjqaKiHJmH8kSKVSgtWrQInTp1wr///guVSoVPPvkEt2/fRkpKCs6dO2ex4Hx8fFC7dm2jZbVq1cKePXtMvkYsFlu0VYuUjZntZ1pkO4wxJCr+a02qCFMCvOrq8DH4aPOvaD9+PHyhvw9cC28JTsUrcDpBgVBncYm7GgOrhSK0fphF4rUWSx1PFRnlyDyUJ1Kswdx169bF/fv30bp1a/Tt2xdZWVkYMGAArl69atGB1q1atUJkZKTRsvv37yMwMNBi+yAVS5pKhxwtA5/Tj0+qDJp4SOAk4iFDrcM/z7OtHQ4hhFQoRW5RUqvV6NatG1atWoUvv/yyNGIymDZtGlq2bIn58+dj4MCB+Oeff7BmzRqsWbOmVPdLyq/El4O4PSV88HkVqzXJFAGPQ3tfB/z+OAMXnyvQwF0MqbByFImEEFLaityiJBQKcePGjdKIJY+mTZti37592LZtG+rWrYu5c+di2bJlGDZsWJnsn5QvSi1DilI/v5C3pOIO4s5PTWcR/BwEUOuA0/E0CSUhhFhKsbrehg8fjnXr1lk6lnz16tULN2/eRE5ODu7evVvg1ACk/PJf6g9uNgf/pf6Fr2zCs5etSTIhD/bC8n9ft/wMGd4fDEDd7t2NlnMch45++lsI3UxRGnJRWVnieKroKEfmoTyRYn3t1mg0+PXXX3H8+HE0btw4zz3eli5dapHgCDGXjjE8y86dibtydjv5OQhR20WMO6lK/BmXhcHVZBVuMDshhJS1IhVKUVFRCAoKwq1bt9CoUSMA+sHVr6ITM7GGVKUOKh0g5AFulWQQd37a+dojMk2JmEw1HspVqO5EV4ASQkhJFKlQql69OhISEnDy5EkA+luWLF++HF5eXqUSHCHm+m8QtwC8SlysO4n4aOYpwYVn2TgZp0BVmajC3eeOEELKUpEGcjDGjH4+fPgwsrLoPlPEunK0OqSpcgdxV97WpFzNvSSwF3BIUWpxLTnH2uEQQki5VqIRr68XToRYQ1K2foJJJxEPdoKKOYi7KMR8Htr46G8xdDZBgRytzsoREUJI+VWkTxWO4/KMQaIxScSaGGN4/rJQ8qTWJIMGbnZws+MjW8vwdyJNQkkIIcVVpDFKjDGMGjXKcIuQnJwcvPfee3muetu7d6/lIiSkAHL1fzNxu4mpUMrF4zh08HXA7ig5LiVlo6GHHZxElB9CCCmqIhVKr9+Idvjw4RYNhpCiym1NcrOrPDNxmytEJkSgoxAxmWr8Fa9AnyCptUMihJByp0iF0vr160srDkKKTKtjSM7RF0pe1O2WB8dx6ODngA2RabiTqkRTDzv4OAitHRYhhJQrles+D8RmbR6wGUqNEmKB+fP+JOdooWOAHZ+DtILOxP26Ux/PwPJPp+DHuXNRw4z1ve0FqOsqxq0UJf6Mz8LQak6VYlxhcY6nyoZyZB7KE6FCidiE9kHti/yaVwdxV4YPfwBIaNAIxwBkNmli9mva+tjjXqoSTzI1eJCuQg3nin/CL87xVNlQjsxDeSKV42s4qXCyNTrI1frL3j0r2Q1wi0om4qOppwQAcCpeAS1N60EIIWajQomUS7mtSc4iHsT8ytGaVBI0CSUhhBQPfRUnNuHU41OGcQCFNXVX5rmTfK5fQRcAjv/+C7y836I5xHweWnvb49jTLJxNVKCOqxh2/Ir7Pakox1NlRTkyD+WJUKFEbMLwvcMRlxEHP6kfnoY/LXDddJUOKt3LuZMq2Q1w2y+ei54AVDNmAOPHF+m1Ye52uJyUgxdKLf5OzEZ7P4fCX1ROFeV4qqwoR+ahPJGK+5WSVFi5rUkedvxKfQPcouK9nC4AAC4lZSNdpbVyRIQQYvuoUCLlipYxvFC+LJQqWbebJYTIhKjiKISWAafjFdYOhxBCbB4VSqRcSXk5d5K4Es2dZEkcx6Hjy1al26lKJCjUVo6IEEJsG33SkHIlKee/brfKMneSpXnbC1DHRT+X0p9xWWA0XQAhhJhEhRIpN3QcH2lK/dxJ1O1WMm197SHggCeZGjyUq6wdDiGE2CwqlEi5obSTggFwEHCwF9ChWxJOr0xCeTKOJqEkhBBT6NOGlBtKOxkAwINm4raIVyehvE6TUBJCSL6oUCLlgqt/EDRCfQuIRyWbO6m05E5CCQBnEhVQg8Z8EULI66hQIuVCWLcBAPS3LBHRLUsspoG7HVzFfGRrGB5DZu1wCCHE5lAfBrEJBc14ywCE9XgHAA3i3rZ5H8Z1a43Lhw/D/BuYmMbnOHTws8eeqAzEQAYnbz8LbNX6aAblwlGOzEN5ItSiRGyeHCJ4BFUDmA6u4spdKJWGajIRAhwF0HEcuk76wtrhEEKITaFCidi8BOjH0YiUmRDwqNvN0l6dhLJhz4HQCMRWjogQQmwHFUrEpukYQyL0H+J2OXIrR1Nx+dgL4cOyAACZjp40CSUhhLxEY5SITZh9ajbSlelwEjthZvuZhuWPM9RQcXxkpibDTZ1lxQhtQ8PNv2IJAO81a4BVqyy67WpIQ2wOD7CzxwulDu7l+OpCU8cT+Q/lyDyUJ0ItSsQmrL2yFt///T3WXllrtPx2ihIAcPPY73TxOoCaR/5AOAD3ffssvm0JtPhr448AgMdydbmehNLU8UT+QzkyD+WJUKFEbJZKy3A/XV8oXTu828rRVA6nN64AT6uGUscQn6WxdjiEEGJ1VCgRm/UgXQm1DpAwNWJv/GvtcCoFdU42HDKTAABPMzVQanVWjogQQqyLCiVis+6k6luTfKCwciSVi0iZAZmQBx2AxxnUqkQIqdyoUCI2KUutQ5RcDQDwAQ3iLkscgGCZEACQnKOFXKW1bkCEEGJFVCgRm3QvTQkGwMdeAAdQq0ZZcxTy4PVyFvQouZqmCyCEVFpUKBGblHu1W20XmvzQWqo4CsHngCwNw/NsalUihFROVCgRm5OSo0W8QgMOVChZk4jPIcBRP9VaTKYaGh21KhFCKh8qlIjNyR3EHSQVwkFIh6g1+dgLIOFzUOuAJ5nUBUoIqXxoZm5iE9oFtUOyIhnuEnfcTs0BANRxpdak1yXUC8OjkxFo2qgRXMtgfzyOQ7BMiDupKiQoNPC250MisP3i1XA82btbOxSbRTkyD+WJUKFEbMKWAVsAAPFZamy6nw4hD6jhRIXS6059OhPjTkbg8rx5ZVIoAYCLmA8XMQ+pSv2ViLVdROA4254nPfd4IqZRjsxDeSK2/9WQVCq3X3a7VXcSQ8S37Q/jyiRYKgQHIE2lwwslTUJJCKk8qFAiNkPLGO6+LJTq0CBumyIR8ODnoG+AjparoaWB3YSQSoIKJWIzouVqKDQM9gLOMOEhsR3+jgKI+RxUOoYndB84QkglUa4KpW+//RYcx+HDDz+0dijEwjpu7IiO6xtg7fn+qO0iBs/Gx8BYS49Pp+AWgOoTJpT5vvkch6pSfQEbn6WBQm27XXAdN3ZEnZ/roOPGjtYOxWZRjsxDeSLlZjD3pUuXsHr1atSvX9/aoZBScP/FfcRlxEFm50NXuxXAKe4JfAGoYmOtsn9XOz5cxTykKHV4JFejrqttDuzOPZ7Sc9KtHYrNohyZh/JEykWLUmZmJoYNG4a1a9fCxcXF2uGQUqB+OeaFxwHeknJTv1dKwTIheADkah2ScmjGbkJIxVYuCqVJkyahZ8+e6Ny5c6HrKpVKyOVyowexfZqXvThCHmeTLRTkP3Z8nmHG7scZNGM3IaRis/lCafv27bhy5QoWLFhg1voLFiyAk5OT4REQEFDKEZKSylBpoXl501Uhj4qk8sDX4b8Zu2Mz1dYOhxBCSo1NF0pPnjzB1KlTsWXLFtjZ2Zn1ms8//xzp6emGx5MnT0o5SlJSubcsAQAqk8oHHseh6ssrExMUWmTa8MBuQggpCZseDHL58mU8f/4cjRo1MizTarU4ffo0VqxYAaVSCT6fb/QasVgMsZgGA5cnt1KUha9EbI6zmA93Oz6Sc7R4JFehvquYuk0JIRWOTRdKnTp1ws2bN42WjR49GjVr1sSnn36ap0gi5c/zbA2ScrTUklROBUmFSFVqkalmSMzWwsfepk8phBBSZDZ9VpNKpahbt67RMgcHB7i5ueVZTsqn2y9bk/g0NqlcEvM5VHEUIjpDjZgMNVzFfIjp1jOEkArEpscokYpNx5hhfBIN4i6/fOz5cBRy0DIgSq4CY3QVHCGk4rDpFqX8nDp1ytohEAuJzVQjQ62DmM9hZrsZyFZnwVHkaO2wbNqVoaOwb/liTB03DoHWDuYljuNQTSbC9RdKpCj1N811t7Nut/jX7b5GpiqTjqcCUI7MQ3ki5a5QIhVHbrdbLWcxulUp+1tylEeRPfri++WLMXzAAJsplADAQai/ae7TLA2i5Co4i+wgsGIr4fjG46227/KCcmQeyhOhrjdiFWodQ2SaCgDoliUVRIDjf3MrPc6guZUIIRUDFUrEKh6mq6DSMTiJePB3oIbNioDHcQhx0s+t9Cxbi3QV3d6EEFL+0ScUsYpbKTkAgDou+rl3EjISoGVa8Dk+fKQ+Vo7OdkleJMMPgCApydqh5MtJxIeXhI9n2Vo8TFejobt1vovR8VQ4ypF5KE+ECiVS5hRqHaLl+q6Z2i+73ZqubYq4jDj4Sf3wNPypNcOzaf2mjsMwAKoRI4Bnz6wdTr6CpEKkKLXI0TI8ydRYJQY6ngpHOTIP5YlQ1xspc7dTldAB8JYI4G5HtXpFI+BxCJGJAABxWRpo+DQGjRBSflGhRMrczZfdbvXc6AO0onKz48NNzAMDkCnzAsejUw0hpHyisxcpU4kKDZ5na8HngNouVChVZMEyEfgcoBFK0ObdSdYOhxBCioUKJVKmcluTqjuJIBHQ4VeRifkcgqX6q+DefP9TZEBo5YgIIaTo6JOKlBmNjhkmmazvZmflaEhZ8JTwIVRmQiAS4xbcoKXbmxBCyhkqlEiZeZiuQo6WQSrkIUhKrQuVAcdxkGYkQpGeigxOhAuJ2dYOiRBCioQKJVJmbrzsdqvrKgaPo5vgVhY8nRb7F34GADifqECiwjpTBhBCSHFQoUTKRIZKa5g7qZ4rdbtVNteP7IUnU0AH4GBMBjQ66oIjhJQPVCiRMnErRQkGwN9BAFcr31meWEctpEAi4JCUo8W5RIW1wyGEELPQbH+k1DHGcPPlIO56JgZxnxhxAhqdBgIeHZIFObTgB8waNxQ7V65EHWsHU0Ri6NA1wBG/RWfg72fZqO4kgq9D6YxVo+OpcJQj81CeCP3mSamLy9IgRamFkAfUdBblu06oe2gZR1U+pQdUwR0AyqAga4dSLDWdxajtosKdVCUOxmRiVE1nCHmWH69Gx1PhKEfmoTwR6nojpS537qSazmKI+XTIVXZv+jvAQcDhhVKLMwnUBUcIsW30qUVKlVKrw91UFQDT3W6kcpEIeOhWxREA8M/zbMRkqKwcESGEmEZdb6RU3UlVQqVjcBPzEeBg+nDbenMrFGoF7IX2GFpvaBlGWL6EnDyGsQBcDh8GGjWydjjFVt1JjAZuKlx/ocSBmEyMqels0Zna6XgqHOXIPJQnQoUSKTWMMVxN1ne7hbnbgStg7qRPIj5BXEYc/KR+dDIqQLN1K9EBgGr5cuDLL60dTpHcvXvX6Gc3cLCHNzLUQmy/8RT1kYzijFZyd3dHlSpVjJbR8VQ4ypF5KE+ECiVSauJf3gBXwAH1XOkGuJXVi+eJAMdh+PDheZ7zrx2G99YfwjOhPcbPXIwrf2wv8vbt7e1x9+7dPMUSIYRYAhVKpNTktibVchHDjm6AW2llpqcDjGHy3CVo0PSNPM8rlGlQCD0wcOYPGD9lGvhatdnbjnkYiW8mj0VycjIVSoSQUkGFEikV2Rod7qXq505q6E6DuAngFxyC0PpheZYzxnArRQW5GlB7h6Am3eKGEGJD6Gs+KRW3UpTQMP3d433sqR4npnEchxrOQvA5IFPN8CST7gVHCLEdVCgRi3t1EHfDQgZxEwIAYj4P1Zz0k5E+zdIgVam1ckSEEKJHhRKxuNhMNVKUWoh4HGq70CBuYh53Oz68JPr7AD5IV0GlpRvnEkKsjwolYnHXXrYm1XGlmbhJ0QTLhLAXcFDrgPvpKjBGxRIhxLroU4xYVJZah8h0/UzLYTQTNykiPsch1FkEHgekq3R4kkXjlQgh1kWjbIlFXXuRAx0DfO0F8CrCIG5vR2+jf0n+FC6uSE1OgoubG/K/vXD5Zy/gIUQmxIN0NZ5kauAk5MFJzC/SNuh4KhzlyDyUJ0KFErEYjY7hSlI2AKCxR9Fak/4d/29phFTh/P7jOozr1hqXN29G+b2BSeE8JQKkq3R4nq1FZLoKYW52EPHNvyiAjqfCUY7MQ3ki1PVGLOZOqhJZGgapkIeaNIiblFBVqRCSl+OVItNU0NF4JUKIFVChRCyCMYZLz/9rTeLTlACkhPg8DjWdReBzgFytQ0wGjVcihJQ9KpSIRcRkqpGUo4WQR4O4ieXYC3io/nJ+pXiFBsnZVCwRQsoWjVEiFpHbmlTP1a5Y93Wb8McEpOSkwNXOFat7r7Z0eBVGqx8WYSeAgHnzgD17rB1OmXCz48PPQYC4LA0eyNWwF/JgX8gxRsdT4ShH5qE8ESqUSIm9yNHgkVx/I9MmHpJibePgg4OIy4iDn9TPkqFVOFUuXUAtAKqzZ60dSpkKdBQgU61DukqHe6kq1HcTQ8Az3b1Lx1PhKEfmoTwR6nojJfZvkn6CyWpOIrjaFe0ybkLMwXEcajiJIOJxyNYymoySEFJmqFAiJZKt0eHmC32h1LSIUwIQUhQivn5wNwcgValDDN08lxBSBqhQIiVyNTkHGgZ4Sfio4ii0djikgpOKeKjupD/O4rI0yBHLrBwRIaSio0KJFJtGx3D55QSTTT0l4GhKAFIGPCQC+Dvoh1dmyrwQUK+xlSMihFRkVCiRYrvxIgdZGgaZkIdazjTBJCk7VRwFcBXzAI6Hd5dsQg5obBwhpHRQoUSKRcsY/n45JUAzLwn4BVyBRIil5Q7u5qtzIHX3xFV4QKWlwd2EEMujQokUy50UJeQqHewFHBrQBJPECvg8DrL0OGSmJCGDE+G3aDm0dCUcIcTCqFAiRaZjDBeevWxN8pRASK1JxEr4Og02Th0GHtMhKkONI7GZNG0AIcSibHrCyQULFmDv3r24d+8eJBIJWrZsiYULFyI0NNTaoVVq99JUSFFqIeZzaOhumdakIXWHIDUnFS52LhbZXkX1qF1n/LNnG3p37QovawdjI57evooGSMY1eOJmihIyEY+OJzNQjsxDeSI2XSj99ddfmDRpEpo2bQqNRoMvvvgCXbp0wZ07d+Dg4GDt8ColHWM4m6AAoG9NEvMt0yi5uMtii2ynovtn3CSM27MNlz/8kAqlV3ggB10DHHHkSSbOJWZjWKO5CLNQEV9R0d+ceShPxKYLpSNHjhj9vGHDBnh6euLy5cto27atlaKq3O6kKpGi1MKOz6EJTTBJbEiYux3kKi3OP8vG0SeZcBByqO5EV2MSQkqmXI1RSk9PBwC4urpaOZLK6dXWpDcs2JpEiKW08bFHPVcxGIDfojPwWK6ydkiEkHLOpluUXqXT6fDhhx+iVatWqFu3rsn1lEollEql4We5XF4W4VUKN1OUSFPpIBFwaFzMm98Sy7h792652GZZ4zgO3ao4IkfL8CBdhT3RcgwMcUIAzRpPCCmmclMoTZo0Cbdu3cLZQu6avmDBAsyePbuMoqo81Lr/WpOae0og4lv2SreaK2oiPiMevlJf3Jt8z6Lbrkj6jx6IgQDihw9HrVLaR2ZmZiltuWzwOQ5fHmqG2PR4ONp5gcPfGFJNBh8HKpZeRX9z5qE8kXJRKE2ePBkHDhzA6dOn4e/vX+C6n3/+OcLDww0/y+VyBAQElHaIFd6/z7ORodZBJuSVSmtSpioTGaoMZKrK94d0aRNkZ0MGQCtzwtqdBy267b9PHsO6hXOQk5Nj0e1aQ6YqE9maDNhppVDpGHY8kmNodSd4SsrFKa9M0N+ceShPxKbPGowxfPDBB9i3bx9OnTqF4ODgQl8jFoshFtMATkvK1ugMs3C39bWHgOZNsjo+X4DQ+mEW3WbMg0iLbs8W2As4+NoLEK/QYPvDdAyuRsUSIaRobHo07qRJk7B582Zs3boVUqkUiYmJSExMRHZ2trVDq1TOJyqg1DJ4Svio40JFKClfBobI4CXhQ6Fh2PogHQlZamuHRAgpR2y6UFq5ciXS09PRvn17+Pj4GB47duywdmiVxoscDS4n6bti2vs6gOOoNYmUL3YCHoZUc4KvvQA5WoZtD+WIzaRiiRBiHptug6ZbEVjfn3FZ0AEIkQlRVSaydjiEFIudgIdB1WTYE5WB2Ew1dj5Mx4CqMjqmCSGFsukWJWJdj9JVeCRXg8cBnfwcrR0OISUi5vPwTogMITIhNAzYEyXHvTRl4S8khFRqVCiRfGl0DMfj9Fd5NPGQwNWOb+WICCk5IY/DgGAZQp1F0DL9pJQXnymo9ZoQYhIVSiRffz/LRqpSBwcBh1beNLkkqTj4PA59g6Ro9PJecCfjFTj2NAs6KpYIIfmgQonk8SJHgwvP9JNLdvZ3pFuVkAqHx3F4098Bnfz0N9e+mpyD3Y/kUGp1Vo6MEGJrbHowNyl7jDEcfZIFLQOqyoSo6Vw2g11X9VqFbHU2JEJqvSrI/kHvYv/q5Rg45j14WzsYG2bO8cRxHJp6SuAk4uGPmAxEZaix+b5+kLeLuOJ3NdPfnHkoT4QKJWLk+gslYjPVEHBAF3/HMpsOoFeNXmWyn/Luft0G2A2gccOmVCgVoCjHUw1nMYaKeNj9SI6kHC023EtDj0BHhDpX7DnD6G/OPJQnQn0qxCBdpcWfcVkAgLa+DnCuBN+qCQEAH3shRoU6w99BAKWOYV90Bk48zYSWxi0RUulRoUQA6LvcDsVkQqVj8HcQoImHnbVDIqRMSUV8DKnuhGae+i6WS0k52PYgHXKV1sqREUKsibreCADgclIOYl52ufUMlIJXxjNwX46/DJVWBRFfhMa+jct03+WJb+xjNAfgFf0QQGdrh2Ozins88TkOHf0c4OcgwKGYTDzN0mDdvTR09nNAXVdxhZqZnv7mzEN5IlQoETxTaHAyXt/l1sHPwSoDWftu74u4jDj4Sf3wNPxpme+/vBi6dgXeByBfugDXx75n7XBsVkmPp1BnMTwlAux/nIEEhQYHYzNxL02JblUcIRVWjC5p+pszD+WJUNdbJafSMvz+OANaBlRzEhnmliGksnMR8/FuDSe087EHnwMeydX45W4abr7IoQkqCalEqFCqxPRTAWQiRamFVMhDzypld5UbIeUBj+PQwtseo0Kd4S0RQKllOBibiS0P0pGo0Fg7PEJIGaBCqRK7kpyD26lKcAB6B0khEdDhQEh+PCQCjAjVty4JOOBplgYbItNwODYDWWqapJKQiozGKFVSsZlqnHj637ikKo5CK0dEiG3LbV2q4yrGqXgF7qQqcf2FEvdSVXjDS4LGHnY0iz0hFRD9VVdCaUotfouWQwegtosYTWkqAELMJhPx0SdIimHVneAl4UOpYzidoMDK26k4l6hAjoZamAipSKhQqmSyNTrsfCSHQsPgKeGjO41LIqRYAhz1k1T2CnSEq5iPHC3DmZcF0+n4LGRSlxwhFQJ1vVUiGh3Dnig5UpRayIQ8vBMig5BHRRIhxcVxHOq62qG2ixj30lQ4n6hAco4W559l4+9n2ajurL+StIqjkL6QEFJOUaFUSWh1DPui5XiapYGYx+GdEFmFmQ+GEGvjcRxqu4hRy1mEyHQV/n2ejadZGkSmqRCZpoKbmI/6bmLUchFDJqK/O0LKEyqUKgEdY/gjJgOP5PqZt9+qKoOHhH71hFgax3Go6SxGTWcxnmdrcDU5B7dTlHih1OJkvAIn4xXwdxCglot+HQchjX4gxNbRp2UFp9UxHIjJwL00FfgcMKCqDFWktneF291Jd8HAwIG6Jwqy/Mu5WPzxZMxatAJNrR2MDbOF48lTIkDXAEe097XHnVQl7qQq8SRTg6dZ+kfE0yz42gtQVSZCiEwIb3tBmXbP2UKOygPKE6FCqQRiY2ORnJxcKtt2d3dHlSpVSrQNtY7ht2g5HsnV4HFA3yApqspEpRZ3SWKWiqUWjqZiUtlJkAFAJZFYOxSbcvfuXYtv0xJ/gwAg5vPQ0F2Chu4SyFVa3EtT4fozOV5o+IhXaBCv0OBsIiBkWrghBy5QwgVKOEBdrI9mpVIJsVhc4rjLaruA5XJdGirSuckWz/3lARVKxRQbG4tatWpBoVCUyvbt7e1x9+7dYh982Rod9kTpxyQJXrYk5RZJpRV3SWMmpKhePE8EOA7Dhw+3+LZL43iWifjwzklCh+a1IHB0Qo2WnRDaqhOqvdEOcJQiEQ5IhAMAQJGWgsfXLuLJ7auIu3MNcXevQ5GWUug+OI4rlVuslNZ2ATp3lAU69xcfFUrFlJycDIVCga9WrENgtVCLbjvmYSS+mTwWycnJxTrwUnK02BWVjlSlDmIeh7dCZIYJJUsr7pLGTEhxZKanA4xh8twlaND0DYtttzSPZ8Pf4KIfDX+DTBEPtVoCjdAeapEEaqEE9s6uqN2+O2q37254LU+rhkCdA4Em5+W/SvCY1vD83yePYd3CORbPR2ltF6BzR1mhc3/xUaFUQoHVQhFaP8zaYRg8TFfhQEwGcrQMMhEP75gYuG1rcS+9sBRypRwysQzhLcKtHY7NavnnUcwE0PTQ70DHztYOx2b4BYcYHc87bvyILJUcDiIZBtX/wHqBFaCgv0EdY8hSM8jVWmSqGTLVOuRoGXR8IVR8IVT4rztIxAMchDw4CHjwT8mAe5WqefKRn6LkKOZBJIC8ea4MKtq5ydbO/eUBFUoVhI4xnE1Q4PyzbACAr70Ab1WVlZurapZeWIq4jDj4Sf0qxMmotLQ8GYHuAOSH9+P6dz9aOxybtfPmj0jKioeHg6/NFkoF4XEcpCIOUtF/f78anb5gytLojIonlQ5QKXVIVepgV7MJPvrtIqDT4sYLJRwEnKGIshdy4L8yWLy856is0LmJUKFUAaTkaHEgJgPxL+9m3sjdDh39HCCgySQJqTAEPA7OYj6cxf/Nw6TVMWRpdMjSMGSpdYh/ngyd0A5COwky1DpkqAFk/9c1J+HnFk4cdKUz3IiQCqd8NDeQfOkYwz/Ps/HrvVTEKzQQ8zn0CZKiS4AjFUmEVAJ8HgeZiA8fewGqOYmQc+MMZrYOAh5eQQ0nIfwcBHAW8ZDbsJytZUjO0SImUwP1y0pJpWW4naLE4ww1XuRoodJSBUXIq6hFqZyKy1Lj6JNMPH/5bTHQUYiegY406y8hlRzT6QBVNjwkAni8slylfdn6pNa3QOV+lWIA0lQ6pKn+uzedHZ+DVMiDTMSDtJx03xNSWqhQKmdSlVqcis9CZJoKgP6E1sHXAfXdxHQvKUKISSI+BxGfD5eXXXcivv58IeRxCJEJkanWIUOtg0LDkKNlyNFqkZSj/yJm/0Y3DF+yEXDxgUKjg4TP0fmGVBpUKJUTKTlaXHimwK0UJXIbxuu7itHe1wH29I2PEFJMPA7wtv/vo0CjY8hQ6yBX6V6Oc9JBJxChToceAICryUoIeYCziA8nEQ/OYh7EfDoHkYqLCiUbxgDEZqhxKSkbD9NVhgKpqkyI9r4O8KT7tRFCLEzA4+Ai/q/liTGGE8eO4a+//kK3MR+A5+gMtQ5IynmlxUmQ+xoeZEIetTaRCoU+aW2QlidA+7HTcA4+UDxMNywPkQnRytsevg62d682QkjFxHEcdJlp+GvDcnTr3BFvdOgEuUqHdJUOaSr9PE8KDYNCo0FcFiDgAOeXRZOLmA8hXVhCyjkqlGyESsuQotR/Q5O7h6DrpC+ggP6kU9fVDk087OBOLUiEECvjcf9NUxAIIdQ6hjSlFilKHdKUWmgYkJyjRXKOFoAaUiEPLmIeXMV82AtobBMpf+iT10oYY8hU64ujVKUWWRrjS3IfXTqDPk1qo0v96pWi/7+RTyMEOAXAw96j8JUrsQT/KriTlgr3oKrWDsWmVXcLg6eDP5zs3K0dis2yVI6EPE5/hZ1Ef17LUOsnv8w9r+WOc4rN1EDM4+Bqp29pchLxwCsHRROdmwgVSmVEyxgyVTrIXxkk+fp0JY5CDm5iPuSPI/HLhAF4//LlSlEkAcD+IfutHUK5sGXCFHzzwVgs+OhLtLJ2MDbs2247rR2CzSuNHHGcfl4nmYiPQKkQSi1DqlKLFKUW6UodlDqGBIUWCQot+BzgLOJBaSeDvbOrxWOxFDo3ESqUSoFGx6B4eZuBLLUOmRr9Jbev47/sy3cV8+As4hsu143Uaco6ZEIIsTgxn4O3vQDe9gJoGUO6UoeUl4WTWge8UOoAmQ++jLiDf6CG+pkC1ZxEcBXzqYuO2AwqlEpAZO8AtcAOzxSal4MZ9QWRysS9AUQ86L9tvZzIjfrrCSGVBZ/j4GrHh6sdXz/0QMOQkqNFQlomILRDGvg4Ga/AyXgFXMQ8VJOJUM1JhABHYbnooiMVFxVKxXQLrph99jHSAaTL1Xmez72jt6OAp/9XyIOIByqMCCGVHsdxkAr1s3/nRMdg+qhB2HDwTyhlXojNVCNVqcOlpBxcSsqBmM8h5GXRVFUqhJ2gcgxHILaDCqViEkE/fwin1UAmEcFewMFewDP8S/daK5o+2/ogSZEED3sPGhNQgGGrl6MHAPcl85DYsbO1w7FZnx0ZiPScZDjZudN4JRNsKUfpiXGogkw0qlYDSq0O0RlqPExX4VG6CtlahjupStxJVYID4OcgQKBUiCCpCL4OAvBL+csnnZsIFUrFFIQMjGnfBMt3HkBo/TBrh1PuXUm4griMOPhJ/awdik3zeRqLmgDkj6OQaO1gbNiDF9eQlBUPDwdfa4dis2w1R2I+DzWdxajpLIaOMcRnafAwXYUHchVe5GjxNEuDp1kanEvMhojHIcBRgCCpCIFSIdzt+BbvpquM5ybGGDRMP95Ww/Q3YNcxQMf0z+mQ+38gW+KMlkPGIwMVd34/KpSKSQQdsuVp1g6DEEIqLB7Hwd9RCH9HIdr7OSBNqUVMphqP5So8zlQjW8PwSK7Go5fDH8Q8Dr4OAvg5COHnIICvg6DSXDlcGAZA5uENtcAOL3K0UGr142lVL//V6Bg0OkDDWJ4rsgsk9ULvj+chnb0ordCtjgolQggh5ULuRJcN3OzAGMPzbC0eZ6jwOEONuCwNlDqG6Aw1ojP+GzfqYceHr4MAnhIBvCQCeEj4Fa54UmnZy5saa/XzVr2cgib3RscZah0yEYDPj97Uj6t9eVP1wvA5/SB8Hof/HvjvZ47jkJGWin9ORqBxt9al+yatiAolQggh5Q7HcfCyF8DLXoA3vPTdQ0nZWsRl6YumuCw10lS6V+5JpzS81lnEg5sdH65iPtzsBHB9ecsVRxu7T51Wx5Cl0Rc8rz5eL4KU5jQBcRy0Gg2EHIO9nQhiHgcRn4Po5b9CHgcBx0HA09/vT8CZd/FRZGwCdnz5Hj7pdtkC79g2lYtC6aeffsLixYuRmJiIBg0a4Mcff0SzZs2sHRYhhBAbwXulcGr0chLtTLUOcVlqPFNo8Cxbg+fZ+haXNJX+8Qjq17YBSF9O3yIT8uEg5Bmme9EwIC5LDRFPX1SIXxYXPA7gkH9RwV52Y+WO9dHoGNQvHzlahmyN7uW/DDla/f+zcgsijQ7Z+cy/Z4qQB0iFfEhfXmUtFfEgFf73iI68g9bNG2HNodM0rraIbL5Q2rFjB8LDw7Fq1Sq88cYbWLZsGbp27YrIyEh4enpaOzxCCCE2ylHIQ6izGKHOYsMyhUaH59kapOToJ75MydHihVILuUoHHQPSX97wF9BP/JvbWpOt0eF/99Pz242BvmB6+S/0xVVJ8TjAUaAvfnKnmnEUGhdBjiIexLyC5+VLgBZMpyt5QJWQzRdKS5cuxbhx4zB69GgAwKpVq3Dw4EH8+uuv+Oyzz6wcHSGEkPLEXsBDkFSEIKnxct3L+9TJVTrIVfqWpyy1DsKXU73wOQ5OIh7ULwdA51cEMeivBDNFyAMEnL4lyk7AQcLnvfYvB4ngv2LIUciDhE8TE1ubTRdKKpUKly9fxueff25YxuPx0LlzZ1y4cMGKkRFCCKlIeBwHJxEfTiI+8Mql7nYvby1lL+Dwfp3/7kmnZQxqrf5SecZeFklg//2fvRzrwwOELwdEU8FTPtl0oZScnAytVgsvLy+j5V5eXrh3716+r1EqlVAq/xu0l56ubyqVy+UWjS0zMxMAcP/mNWRnZVl020+iHgAALl++bNiPpURGRgKwfNwljVmZoQRyACWUOH36tNFzpRVzzKP7AIDou7fhIJFYbLulue0AtRocgFSNGtcunLPYdoHSi9kaeVZlKAEVoNIpi5Wn8vg3CBQt10XJUWn+Dksz1zweD7oSdjeZOjdZYtv5Ka3tlva5PzMz0+Kfs7nbYwU105UFZsPi4uIYAHb+/Hmj5R9//DFr1qxZvq+ZOXOmvrCnBz3oQQ960IMe5f7x5MmTsig5TLLpFiV3d3fw+Xw8e/bMaPmzZ8/g7e2d72s+//xzhIeHG37W6XRISUmBm5tbuWv2lMvlCAgIwJMnTyCTyawdTqmh91nxVJb3Su+zYqH3aVsYY8jIyICvr3Vnj7fpQkkkEqFx48Y4ceIE+vXrB0Bf+Jw4cQKTJ0/O9zVi8f+3d+9BUd1XHMC/C8sui/LwAbq+QNQl4ANfhcFtYlWQJoQJkypUjUIRHxXaRBTjo3G1UWiiTRktMaO1oK2ASsUqEGsGBRRlVB5GAUEeYmxFK5GGp+Du6R8ZbrLK4oILC3g+Mzvj/e3v7p5zvdw9e+/v7k8KqVSq1WZjY9PNkXYvKyurXr0zGwrn2f+8Krlynv0L59l7WFtbGzuE3l0oAUB4eDgCAwMxc+ZMuLm5ITo6Gg0NDcJdcIwxxhhj3aXXF0oBAQH473//i61bt6K6uhpTp07FmTNnnhvgzRhjjDFmaL2+UAKAsLAwnZfa+jOpVAqVSvXcpcT+hvPsf16VXDnP/oXzZO0RERn7vjvGGGOMsd6pf02hzBhjjDFmQFwoMcYYY4zpwIUSY4wxxpgOXCgxxhhjjOnAhZKRxcTEwMHBAebm5nB3d8eVK1c67F9bW4vQ0FDI5XJIpVIoFAqkpaX1ULRd19k8o6Oj4eTkBJlMhtGjR2Pt2rVobm7uoWi7JisrC76+vhgxYgREIhFOnjz5wnUyMjIwffp0SKVSjB8/HnFxcd0e58vqbJ4nTpyAl5cXbG1tYWVlBQ8PD/zrX//qmWBfQlf+P9tkZ2dDLBZj6tSp3RafoXQlzydPnmDLli2wt7eHVCqFg4MD/vrXv3Z/sC+hK3keOXIErq6usLCwgFwuR3BwMGpqaro/2JcQFRWFn/zkJ7C0tISdnR38/PyEed46cvz4cbz22mswNzfH5MmT+8TnSk/hQsmIjh49ivDwcKhUKuTl5cHV1RXe3t54+PBhu/1bWlrg5eWFO3fuICkpCSUlJThw4ABGjhzZw5F3TmfzjI+Px8aNG6FSqVBcXIyDBw/i6NGj2Lx5cw9H3jkNDQ1wdXVFTEyMXv0rKyvh4+ODOXPmoKCgAB988AFCQkJ6fRHR2TyzsrLg5eWFtLQ05ObmYs6cOfD19UV+fn43R/pyOptnm9raWixbtgzz5s3rpsgMqyt5+vv7Iz09HQcPHkRJSQkSEhLg5OTUjVG+vM7mmZ2djWXLlmH58uUoLCzE8ePHceXKFaxYsaKbI305mZmZCA0NRU5ODr766iu0trZi/vz5aOhgItxLly5h0aJFWL58OfLz8+Hn5wc/Pz/cvHmzByPvxYw609wrzs3NjUJDQ4VltVpNI0aMoKioqHb779u3jxwdHamlpaWnQjSIzuYZGhpKc+fO1WoLDw8npVLZrXEaEgBKTk7usM+GDRto4sSJWm0BAQHk7e3djZEZlj55tsfFxYW2b99u+IC6SWfyDAgIoN/97nekUqnI1dW1W+MyNH3y/PLLL8na2ppqamp6JqhuoE+eu3btIkdHR622PXv20MiRI7sxMsN7+PAhAaDMzEydffz9/cnHx0erzd3dnVatWtXd4fUJfEbJSFpaWpCbmwtPT0+hzcTEBJ6enrh8+XK765w6dQoeHh4IDQ3FsGHDMGnSJERGRkKtVvdU2J3WlTxnzZqF3Nxc4fJcRUUF0tLS8NZbb/VIzD3l8uXLWtsFALy9vXVul/5Co9Ggrq4OgwcPNnYoBhcbG4uKigqoVCpjh9JtTp06hZkzZ+LTTz/FyJEjoVAosH79ejQ1NRk7NIPy8PDAN998g7S0NBARHjx4gKSkpD53HPrf//4HAB3+vb2qxyJ99Ylf5u6PHj16BLVa/dxULMOGDcOtW7faXaeiogLnzp3DkiVLkJaWhrKyMqxZswatra299sDclTwXL16MR48e4ac//SmICE+fPsXq1at7/aW3zqqurm53u3z33XdoamqCTCYzUmTda/fu3aivr4e/v7+xQzGo27dvY+PGjbhw4QLE4v57aK2oqMDFixdhbm6O5ORkPHr0CGvWrEFNTQ1iY2ONHZ7BKJVKHDlyBAEBAWhubsbTp0/h6+vb6UuxxqTRaPDBBx9AqVRi0qRJOvvpOhZVV1d3d4h9Ap9R6kM0Gg3s7Oywf/9+zJgxAwEBAdiyZQu++OILY4dmUBkZGYiMjMTnn3+OvLw8nDhxAqmpqfj444+NHRp7SfHx8di+fTuOHTsGOzs7Y4djMGq1GosXL8b27duhUCiMHU630mg0EIlEOHLkCNzc3PDWW2/hs88+w6FDh/rVWaWioiK8//772Lp1K3Jzc3HmzBncuXMHq1evNnZoegsNDcXNmzeRmJho7FD6tP77taeXGzp0KExNTfHgwQOt9gcPHmD48OHtriOXy2FmZgZTU1OhzdnZGdXV1WhpaYFEIunWmLuiK3l+9NFHWLp0KUJCQgAAkydPRkNDA1auXIktW7bAxKR/1PfDhw9vd7tYWVn1y7NJiYmJCAkJwfHjx587zd/X1dXV4dq1a8jPzxfmpdRoNCAiiMVinD17FnPnzjVylIYhl8sxcuRIWFtbC23Ozs4gIty7dw8TJkwwYnSGExUVBaVSiYiICADAlClTMGDAALz++uvYsWMH5HK5kSPsWFhYGFJSUpCVlYVRo0Z12FfXsUjXMfpV0z8+cfogiUSCGTNmID09XWjTaDRIT0+Hh4dHu+solUqUlZVBo9EIbaWlpZDL5b2ySAK6lmdjY+NzxVBbcUj9aGpCDw8Pre0CAF999ZXO7dKXJSQk4Fe/+hUSEhLg4+Nj7HAMzsrKCjdu3EBBQYHwWL16NZycnFBQUAB3d3djh2gwSqUS//nPf1BfXy+0lZaWwsTE5IUfyH1JXz0OERHCwsKQnJyMc+fOYezYsS9c51U6FnWJEQeSv/ISExNJKpVSXFwcFRUV0cqVK8nGxoaqq6uJiGjp0qW0ceNGof/du3fJ0tKSwsLCqKSkhFJSUsjOzo527NhhrBT00tk8VSoVWVpaUkJCAlVUVNDZs2dp3Lhx5O/vb6wU9FJXV0f5+fmUn59PAOizzz6j/Px8qqqqIiKijRs30tKlS4X+FRUVZGFhQREREVRcXEwxMTFkampKZ86cMVYKeulsnkeOHCGxWEwxMTF0//594VFbW2usFPTS2Tyf1VfueutsnnV1dTRq1ChasGABFRYWUmZmJk2YMIFCQkKMlYJeOptnbGwsicVi+vzzz6m8vJwuXrxIM2fOJDc3N2OloJdf//rXZG1tTRkZGVp/b42NjUKfZ4+52dnZJBaLaffu3VRcXEwqlYrMzMzoxo0bxkih1+FCycj27t1LY8aMIYlEQm5ubpSTkyM8N3v2bAoMDNTqf+nSJXJ3dyepVEqOjo60c+dOevr0aQ9H3XmdybO1tZW2bdtG48aNI3Nzcxo9ejStWbOGHj9+3POBd8L58+cJwHOPttwCAwNp9uzZz60zdepUkkgk5OjoSLGxsT0ed2d1Ns/Zs2d32L+36sr/54/1lUKpK3kWFxeTp6cnyWQyGjVqFIWHh2t9EPdGXclzz5495OLiQjKZjORyOS1ZsoTu3bvX88F3Qns5AtA6trT32XLs2DFSKBQkkUho4sSJlJqa2rOB92Iiol58DpExxhhjzIh4jBJjjDHGmA5cKDHGGGOM6cCFEmOMMcaYDlwoMcYYY4zpwIUSY4wxxpgOXCgxxhhjjOnAhRJjjDHGmA5cKDHGus2dO3cgEolQUFBg7FAEQUFB3fr6Dg4OiI6OFpZFIhFOnjwJ4PntkZGRAZFIhNraWoPHcfDgQcyfP1+vvl988QV8fX0NHgPr37KysuDr64sRI0Zo7eedQUTYvXs3FAoFpFIpRo4ciZ07dxo+2JfAhRJj/cS2bdswdepUY4fxyrt69SpWrlypV99Zs2bh/v37WhPMGkJzczM++ugjqFQqvfoHBwcjLy8PFy5cMGgcrH9raGiAq6srYmJiuvwa77//Pv7yl79g9+7duHXrFk6dOgU3NzcDRvnyxMYOgDHW9xER1Go1xOLeeUhpamrChx9+iJSUFNy7dw8ZGRmYPHkyDhw4YPAZ0m1tbfXuK5FIumWG9qSkJFhZWUGpVOodx+LFi7Fnzx68/vrrBo+H9U9vvvkm3nzzTZ3PP3nyBFu2bEFCQgJqa2sxadIkfPLJJ/jZz34GACguLsa+fftw8+ZNODk5AYBek/j2ND6jxFgP0mg0+PTTTzF+/HhIpVKMGTNG6zTzjRs3MHfuXMhkMgwZMgQrV67UmqU9IyMDbm5uGDBgAGxsbKBUKlFVVYW4uDhs374d169fh0gkgkgkQlxcXLsxBAUFwc/PD9u3b4etrS2srKywevVqtLS0aMUZFRWFsWPHQiaTwdXVFUlJSVpxiEQifPnll5gxYwakUikuXryoM+9bt25h1qxZMDc3x6RJk5CZmSk8FxcXBxsbG63+J0+ehEgkEpavX7+OOXPmwNLSElZWVpgxYwauXbv2wu3dJjIyEkePHsXevXvx9ttv4+9//zvc3Ny0cn5WW1wpKSlwcnKChYUFFixYgMbGRhw6dAgODg4YNGgQfvvb30KtVgvrPXvprSPtXXr7xz/+gYkTJ0IqlcLBwQF//OMftdZxcHBAZGQkgoODYWlpiTFjxmD//v1afRITE5+7lKZr32nj6+uLU6dOoampSa/YGXuRsLAwXL58GYmJifj666+xcOFC/PznP8ft27cBAKdPn4ajoyNSUlIwduxYODg4ICQkBN9++62RI3+GcaeaY+zVsmHDBho0aBDFxcVRWVkZXbhwgQ4cOEBERPX19SSXy+ndd9+lGzduUHp6Oo0dO1aYvLK1tZWsra1p/fr1VFZWRkVFRRQXF0dVVVXU2NhI69ato4kTJ7Y7W/iPBQYG0sCBAykgIIBu3rxJKSkpZGtrS5s3bxb67Nixg1577TU6c+YMlZeXU2xsLEmlUsrIyCCiHyYYnTJlCp09e5bKysqopqbmufeqrKwkADRq1ChKSkqioqIiCgkJIUtLS3r06BERfT9Lu7W1tdZ6ycnJ9OPD08SJE+m9996j4uJiKi0tpWPHjlFBQYHe293Hx0eY3V7fyXhjY2PJzMyMvLy8KC8vjzIzM2nIkCE0f/588vf3p8LCQjp9+jRJJBJKTEwU1rO3t6c//elPwjIASk5O1toe+fn5RPTDdmyb8PnatWtkYmJCv//976mkpIRiY2NJJpNpTWhqb29PgwcPppiYGLp9+zZFRUWRiYkJ3bp1S+hjbW2tFVNH+06bhoYGMjExofPnz+u1fRj7sR/v50REVVVVZGpqSv/+97+1+s2bN482bdpERESrVq0iqVRK7u7ulJWVJUwSPmfOnJ4M/YW4UGKsh3z33XcklUqFwuhZ+/fvp0GDBlF9fb3QlpqaSiYmJlRdXU01NTUEQChWnqXvbPWBgYE0ePBgamhoENr27dtHAwcOJLVaTc3NzWRhYUGXLl3SWm/58uW0aNEiIvrhA/7kyZMdvldbYfCHP/xBaGttbaVRo0bRJ598QkT6FUqWlpYUFxf3wtx0iYyMpKFDh1JCQoKQw4vExsYSACorKxPaVq1aRRYWFlRXVye0eXt706pVq4TllymUFi9eTF5eXlpxREREkIuLi9brv/fee8KyRqMhOzs72rdvHxERPX78mABQVlaW0OdF+06btiKesc56tlBKSUkhADRgwACth1gsJn9/fyIiWrFiBQGgkpISYb3c3FwCoFX4G1vvHFDAWD9UXFyMJ0+eYN68eTqfd3V1xYABA4Q2pVIJjUaDkpISvPHGGwgKCoK3tze8vLzg6ekJf39/yOXyTsfi6uoKCwsLYdnDwwP19fX45ptvUF9fj8bGRnh5eWmt09LSgmnTpmm1zZw5U6/38/DwEP4tFosxc+ZMFBcX6x1veHg4QkJC8Le//Q2enp5YuHAhxo0bp/f6EREREIvF2LlzJwoLC5GXl4dly5YhIiICZmZmOtezsLDQep9hw4bBwcEBAwcO1Gp7+PCh3rF0pLi4GO+8845Wm1KpRHR0NNRqNUxNTQEAU6ZMEZ4XiUQYPny4EEPbpTNzc3Ohz+DBg/Xad2QyGRobGw2SC3u11dfXw9TUFLm5ucJ+26bt70cul0MsFkOhUAjPOTs7AwDu3r0rjFsyNh6jxFgPkclkL/0asbGxuHz5MmbNmoWjR49CoVAgJyfHANH9oG1MVGpqKgoKCoRHUVGR1jglAFpFXVeZmJiAiLTaWltbtZa3bduGwsJC+Pj44Ny5c3BxcUFycrLe7yEWixEREYEbN25gwYIFUKlUiI6OfuFdYc8WUSKRqN02jUajdyyG0FEMQ4YMgUgkwuPHj7X66LPvfPvtt50ajM6YLtOmTYNarcbDhw8xfvx4rUfbDQxKpRJPnz5FeXm5sF5paSkAwN7e3ihxt4cLJcZ6yIQJEyCTyZCent7u887Ozrh+/ToaGhqEtuzsbJiYmGh9s5o2bRo2bdqES5cuYdKkSYiPjwfw/Z1LPx5U3JHr169rDdrNycnBwIEDMXr0aLi4uEAqleLu3bvPHeBGjx7dldS1PpCfPn2K3Nxc4Zujra0t6urqtPJu73eXFAoF1q5di7Nnz+Ldd99FbGxsl2KxsLDAokWLsHTp0l53O7yzszOys7O12rKzs6FQKJ77Vq6LRCKBi4sLioqKnntO174DAOXl5Whubn7urCFjutTX1wtfpACgsrISBQUFuHv3LhQKBZYsWYJly5bhxIkTqKysxJUrVxAVFYXU1FQAgKenJ6ZPn47g4GDk5+cjNzcXq1atgpeXl9ZZJmPjQomxHmJubo4PP/wQGzZswOHDh1FeXo6cnBwcPHgQALBkyRKYm5sjMDAQN2/exPnz5/Gb3/wGS5cuxbBhw1BZWYlNmzbh8uXLqKqqwtmzZ3H79m2h4HBwcBAOVI8ePcKTJ090xtLS0oLly5ejqKgIaWlpUKlUCAsLg4mJCSwtLbF+/XqsXbsWhw4dQnl5OfLy8rB3714cOnSoS7nHxMQgOTkZt27dQmhoKB4/fozg4GAAgLu7OywsLLB582aUl5cjPj5e6469pqYmhIWFISMjA1VVVcjOzsbVq1eFvPWhUqmQlpaGmpoaEBGuXbuGf/7zn5gxY0aX8uku69atQ3p6Oj7++GOUlpbi0KFD+POf/4z169d36nW8vb217kJ80b4DABcuXICjo2OnLmmyV9u1a9cwbdo0obgODw/HtGnTsHXrVgDfn8VctmwZ1q1bBycnJ/j5+eHq1asYM2YMgO/PJp8+fRpDhw7FG2+8AR8fHzg7OyMxMdFoObXL2IOkGHuVqNVq2rFjB9nb25OZmRmNGTOGIiMjhee//vprmjNnDpmbm9PgwYNpxYoVwsDh6upq8vPzI7lcThKJhOzt7Wnr1q2kVquJiKi5uZl+8YtfkI2NDQHQulPqxwIDA+mdd96hrVu30pAhQ2jgwIG0YsUKam5uFvpoNBqKjo4mJycnMjMzI1tbW/L29qbMzEwien4Qsi5tg5fj4+PJzc2NJBIJubi40Llz57T6JScn0/jx40kmk9Hbb79N+/fvFwZzP3nyhH75y1/S6NGjSSKR0IgRIygsLIyampqE9TvKl4jo8OHDNGvWLLKxsSGRSETDhg2j5cuXaw3KflZ7g8zbGzDftj3bvMxgbiKipKQkcnFxEfaPXbt2ab3fs69PROTq6koqlUpYLiwsJJlMRrW1tUT04n2HiGj+/PkUFRWlc3sw9qoSET0zOIAx1q8FBQWhtra2S9MN9EaVlZVQKBQoKirChAkTXtg/KChI529M9ScLFy7E9OnTsWnTphf2LSwsxNy5c1FaWmrwXwlnrK/jS2+MsT4tLS0NK1eu1KtIepXs2rVL6+68jty/fx+HDx/mIomxdvAZJcZeMf3tjBJjjHUnLpQYY4wxxnTgS2+MMcYYYzpwocQYY4wxpgMXSowxxhhjOnChxBhjjDGmAxdKjDHGGGM6cKHEGGOMMaYDF0qMMcYYYzpwocQYY4wxpgMXSowxxhhjOvwftzBsL3gfuKEAAAAASUVORK5CYII=", + "text/markdown": [ + "The remaining buses did not specify a propulsion type" + ], "text/plain": [ - "
" + "" ] }, "metadata": {}, @@ -636,33 +579,28 @@ } ], "source": [ - "# ZEB dist curve\n", - "display(Markdown(zeb_desc))\n", - "\n", - "dist_curve(\n", - " df=zeb_no_outliers,\n", - " mean=zeb_only_mean,\n", - " std=zeb_only_std,\n", - " title=\"ZEB only cost/bus Distribution\",\n", - " xlabel=\"cost per bus, $ million(s)\",\n", + "#summary stuff\n", + "display(\n", + " Markdown(\"**ZEB Summary**\"),\n", + " pivot_zeb_prop,\n", + " \n", + " Markdown(\"**Non-ZEB Summary**\"),\n", + " pivot_non_zeb_prop,\n", + " \n", + " Markdown(\"The remaining buses did not specify a propulsion type\")\n", ")" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "679d8261-85d6-4d68-9905-e4b048ebc61a", - "metadata": { - "tags": [] - }, + "execution_count": 17, + "id": "0158cda6-4cab-416a-bfe0-a6896d6da997", + "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "\n", - "## non-ZEB cost/bus Distribution. \n", - "Chart of projects with non-ZEB bus procurement (hybrids, diesel, cng)\n", - "This distrubtion is wider than the ZEB projects." + "**Max new_cost_per_bus**" ], "text/plain": [ "" @@ -673,41 +611,476 @@ }, { "data": { - "image/png": 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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
71Transit Joint Powers Authority for Merced County32233242.01611662
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" + ], "text/plain": [ - "
" + " transit_agency total_agg_cost \\\n", + "71 Transit Joint Powers Authority for Merced County 3223324 \n", + "\n", + " total_bus_count new_cost_per_bus \n", + "71 2.0 1611662 " ] }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "# non_zeb distribution\n", - "display(Markdown(non_zeb_desc))\n", - "\n", - "dist_curve(\n", - " non_zeb_no_outliers,\n", - " non_zeb_only_mean,\n", - " non_zeb_only_std,\n", - " title=\"non-ZEB only cost/bus Distribution\",\n", - " xlabel='\"cost per bus, $ million(s)\"',\n", - ")" - ] - }, + }, + { + "data": { + "text/markdown": [ + "**Min new_cost_per_bus**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
45Oregon Department of Transportation on behalf ...1812505.036250
\n", + "
" + ], + "text/plain": [ + " transit_agency total_agg_cost \\\n", + "45 Oregon Department of Transportation on behalf ... 181250 \n", + "\n", + " total_bus_count new_cost_per_bus \n", + "45 5.0 36250 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Max total_bus_count**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
61South Carolina Department of Transportation on...15423904160.096399
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" + ], + "text/plain": [ + " transit_agency total_agg_cost \\\n", + "61 South Carolina Department of Transportation on... 15423904 \n", + "\n", + " total_bus_count new_cost_per_bus \n", + "61 160.0 96399 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Min total_bus_count**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
9City of Beloit6531841.0653184
16City of San Luis Obispo8592701.0859270
49SLO TRANSIT (SAN LUIS OBISPO, CA)8472141.0847214
\n", + "
" + ], + "text/plain": [ + " transit_agency total_agg_cost total_bus_count \\\n", + "9 City of Beloit 653184 1.0 \n", + "16 City of San Luis Obispo 859270 1.0 \n", + "49 SLO TRANSIT (SAN LUIS OBISPO, CA) 847214 1.0 \n", + "\n", + " new_cost_per_bus \n", + "9 653184 \n", + "16 859270 \n", + "49 847214 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Max total_agg_cost**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
24Dallas Area Rapid Transit (DART)10300000090.01144444
\n", + "
" + ], + "text/plain": [ + " transit_agency total_agg_cost total_bus_count \\\n", + "24 Dallas Area Rapid Transit (DART) 103000000 90.0 \n", + "\n", + " new_cost_per_bus \n", + "24 1144444 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Min total_agg_cost**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
45Oregon Department of Transportation on behalf ...1812505.036250
\n", + "
" + ], + "text/plain": [ + " transit_agency total_agg_cost \\\n", + "45 Oregon Department of Transportation on behalf ... 181250 \n", + "\n", + " total_bus_count new_cost_per_bus \n", + "45 5.0 36250 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#min max values for all projects\n", + "bus_min_max_summary(data=agg_agency,col1=\"new_cost_per_bus\")\n", + "bus_min_max_summary(data=agg_agency,col1=\"total_bus_count\")\n", + "bus_min_max_summary(data=agg_agency,col1=\"total_agg_cost\")\n" + ] + }, { "cell_type": "code", - "execution_count": 14, - "id": "31c592b0-e37e-4da4-8726-36b0a1d3e6f5", - "metadata": { - "tags": [] - }, + "execution_count": 18, + "id": "80dd3d1c-86f8-4c68-bdd1-8249e6494f2c", + "metadata": {}, "outputs": [ { "data": { "text/markdown": [ + "**Which Agneices had the highest and lowest cost per bus?**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Max cost_per_bus**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencyprop_typetotal_costbus_countcost_per_bus
76University of California - San DiegoBEB41340002.02067000
\n", + "
" + ], + "text/plain": [ + " transit_agency prop_type total_cost bus_count \\\n", + "76 University of California - San Diego BEB 4134000 2.0 \n", + "\n", + " cost_per_bus \n", + "76 2067000 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Min cost_per_bus**" ], "text/plain": [ "" @@ -718,9 +1091,51 @@ }, { "data": { - "image/png": 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UqlUL3bt3h4WFBe7evYtdu3ahYcOGWLBgwWd9zW7duoUOHTqgdevWCAsLw5o1a9CzZ0+l+bL69++PGTNmoH///nB3d0doaKh05ibL8+fPUa5cOXzzzTeoWbMmDA0NceDAAZw5cwazZ88usCwA4ObmBmdnZ2zatAnVqlVDrVq18ny8VapUQb9+/XDmzBlYWVlh+fLliI+Px4oVK3J9rYWFBcaPH4/JkyejdevW6NChg/R9+/LLL7MVgDY2Npg5cyZu376NKlWqYOPGjTh//jyWLl0q3VRRvXp11KtXD+PHj8fTp09RunRpbNiwAW/evMk1T//+/fH06VO0aNEC5cqVw507d/DHH3/A1dUV1apVy/E1JUuWxMyZM9GnTx80bdoUPXr0QHx8PObNmwd7e3uMHDkyD1/FvKlduzYWL16MX375BZUqVYKlpaXSGK7evXtj/vz5OHToEGbOnPnB98ntc1GxYkX88ssvGD9+PG7fvg0vLy8YGRnh1q1b2Lp1K3x9fTF69OgCOy5SI/LdUElUvFy/fl0MGDBA2NvbCx0dHWFkZCQaNmwo/vjjD5GWlib1e/36tZg8ebJwcHAQJUuWFLa2tmL8+PFKfcLDw0WPHj1E+fLlha6urrC0tBRff/21OHv2rNI+T5w4IWrXri10dHRynVoiawqBI0eOCF9fX2FmZiYMDQ2Ft7e3ePLkSbb+hw4dEp6ensLExETo6emJihUriu+++04pg4+PjzAwMMjz1yjrVv2oqCjxzTffCCMjI2FmZiaGDBmiNM2AEEKkpqaKfv36CRMTE2FkZCS6du0qEhISlI7z1atXYsyYMaJmzZrCyMhIGBgYiJo1a4pFixYVaJYsWVNvTJs2Lc/HbGdnJ9q1ayf27t0ratSoIXR1dYWjo6PYtGmTUr+s78+HphBZsGCBcHR0FCVLlhRWVlZi0KBB4tmzZ0p9mjZtKqpXry7Onj0r6tevL/T09ISdnZ1YsGBBtve7ceOG8PDwELq6usLKykr88MMPYv/+/blOJ7F582bRqlUrYWlpKXR0dET58uXF999/Lx4+fCj1yWlaCiGE2Lhxo3BzcxO6urqidOnSwtvbW9y7d0+pz4c+U1nfr9zExcWJdu3aCSMjIwEgx6klqlevLrS0tLLt+9395PVz8c8//4hGjRoJAwMDYWBgIBwdHYWfn5+4du1arllJMymEeO8cKBEVS8HBwejTpw/OnDkDd3d3ueOopXnz5mHkyJG4ffs2ypcvn6fX2Nvbw9nZGTt37izkdG9nrn/8+HG+b/Yobtzc3FC6dGmEhIRk2zZp0iRMnjwZjx49grm5uQzpSN1xjBcRUQEQQuCvv/5C06ZN81x0UdFz9uxZnD9/Hr1795Y7CmkojvEiIvoMKSkp2L59Ow4dOoSLFy/i33//lTsSfYJLly7h3LlzmD17NsqWLYtu3brJHYk0FAsvIqLP8OjRI/Ts2ROmpqb44YcfpJsQSL1s3rwZU6ZMQdWqVbF+/XqlGfSJChLHeBERERGpCMd4EREREakICy8iIiIiFeEYryImMzMTDx48gJGR0Scv+UJERESqJYTA8+fPYWNj89H1PVl4FTEPHjyAra2t3DGIiIjoE8TGxqJcuXIf3M7Cq4gxMjIC8PYbZ2xsLHMaIiIiyovk5GTY2tpKv8c/hIVXEZN1edHY2JiFFxERkZrJbZgQB9cTERERqQgLLyIiIiIVYeFFREREpCIc40VERETIyMjA69ev5Y5RZJUsWRLa2tqf/T4svIiIiIoxIQTi4uKQmJgod5Qiz9TUFNbW1p81zyYLLyIiomIsq+iytLREqVKlOHl3DoQQSE1NRUJCAgCgbNmyn/xeLLyIiIiKqYyMDKnoKlOmjNxxijR9fX0AQEJCAiwtLT/5siMH1xMRERVTWWO6SpUqJXMS9ZD1dfqcsXAsvIiIiIo5Xl7Mm4L4OrHwIiIiIlIRFl5ERERU7Bw+fBgKhULld3NycD0RERFlMyPisUr3F+BmrtL9yYVnvIiIiIhUhIUXERERqaXMzEzMmjULlSpVgq6uLsqXL4+pU6fi9u3bUCgU2LBhAxo0aAA9PT04OzvjyJEj2d7j+PHjqFGjBvT09FCvXj1cunSpUDOz8CIiIiK1NH78eMyYMQMTJkxAVFQU1q1bBysrK2n7mDFjMGrUKERERKB+/fpo3749njx5ovQeY8aMwezZs3HmzBlYWFigffv2hbp0Esd4aQBVXIcvLtfeiYhIPTx//hzz5s3DggUL4OPjAwCoWLEiGjVqhNu3bwMAhgwZgi5dugAAFi9ejD179uCvv/7C2LFjpfeZOHEivvrqKwDAypUrUa5cOWzduhVdu3YtlNw840VERERq58qVK3j16hVatmz5wT7169eX/l2iRAm4u7vjypUrH+xTunRpVK1aNVufgsTCi4iIiNRO1hI+6oaFFxEREamdypUrQ19fHyEhIR/sc/LkSenfb968wblz51CtWrUP9nn27BmuX7+erU9B4hgvIiIiUjt6enoYN24cxo4dCx0dHTRs2BCPHj3C5cuXpcuPCxcuROXKlVGtWjX8/vvvePbsGfr27av0PlOmTEGZMmVgZWWFH3/8Eebm5vDy8iq03Cy8iIiISC1NmDABJUqUQGBgIB48eICyZcti4MCB0vYZM2ZgxowZOH/+PCpVqoTt27fD3Fz5ZrEZM2Zg+PDhiI6OhqurK3bs2AEdHZ1Cy6wQQohCe3fKt+TkZJiYmCApKQnGxsZ5eg3vaiQiok+RlpaGW7duwcHBAXp6enLHKTC3b9+Gg4MDIiIi4OrqWmDv+7GvV15/f3OMFxEREZGKsPAiIiIiUhGO8SIiIiKNYm9vj6I6kopnvIiIiIhUhIUXERFRMVdUzw4VNQXxdWLhRUREVEyVLFkSAJCamipzEvWQ9XXK+rp9Co7xIiIiKqa0tbVhamqKhIQEAECpUqWgUChkTlX0CCGQmpqKhIQEmJqaQltb+5Pfi4UXERFRMWZtbQ0AUvFFH2Zqaip9vT4VCy8iIqJiTKFQoGzZsrC0tMTr16/ljlNklSxZ8rPOdGVh4UVERETQ1tYukMKCPo6D64mIiIhUhIUXERERkYqw8CIiIiJSEVkLr9DQULRv3x42NjZQKBTYtm2btO3169cYN24cXFxcYGBgABsbG/Tu3RsPHjxQeo+nT5/C29sbxsbGMDU1Rb9+/fDixQulPhcuXEDjxo2hp6cHW1tbzJo1K1uWTZs2wdHREXp6enBxccHu3buVtgshEBgYiLJly0JfXx8eHh6Ijo7OdxYiIiIqvmQtvFJSUlCzZk0sXLgw27bU1FSEh4djwoQJCA8Px5YtW3Dt2jV06NBBqZ+3tzcuX76M/fv3Y+fOnQgNDYWvr6+0PTk5Ga1atYKdnR3OnTuHX3/9FZMmTcLSpUulPidOnECPHj3Qr18/REREwMvLC15eXrh06ZLUZ9asWZg/fz6CgoJw6tQpGBgYwNPTE2lpaXnOQkRERMWbQhSRdQIUCgW2bt0KLy+vD/Y5c+YM6tSpgzt37qB8+fK4cuUKnJyccObMGbi7uwMA9uzZg7Zt2+LevXuwsbHB4sWL8eOPPyIuLg46OjoAgICAAGzbtg1Xr14FAHTr1g0pKSnYuXOntK969erB1dUVQUFBEELAxsYGo0aNwujRowEASUlJsLKyQnBwMLp3756nLHmRnJwMExMTJCUlwdjYOE+vmRHxOE/9PkeAm3mh74OIiEhd5fX3t1qN8UpKSoJCoYCpqSkAICwsDKamplKhAwAeHh7Q0tLCqVOnpD5NmjSRii4A8PT0xLVr1/Ds2TOpj4eHh9K+PD09ERYWBgC4desW4uLilPqYmJigbt26Up+8ZMnJq1evkJycrPQgIiIizaQ2hVdaWhrGjRuHHj16SJVkXFwcLC0tlfqVKFECpUuXRlxcnNTHyspKqU/W89z6vLv93dd9qE9uWXIyffp0mJiYSA9bW9tcvhJERESkrtSi8Hr9+jW6du0KIQQWL14sd5wCNX78eCQlJUmP2NhYuSMRERFRISnyM9dnFV137tzBwYMHla6bWltbZ1tb6s2bN3j69Km0lpK1tTXi4+OV+mQ9z63Pu9uz2sqWLavUx9XVNc9ZcqKrqwtdXd2PfxGIiIhIIxTpM15ZRVd0dDQOHDiAMmXKKG2vX78+EhMTce7cOant4MGDyMzMRN26daU+oaGhSutP7d+/H1WrVoWZmZnUJyQkROm99+/fj/r16wMAHBwcYG1trdQnOTkZp06dkvrkJQsREREVb7Ke8Xrx4gViYmKk57du3cL58+dRunRplC1bFt988w3Cw8Oxc+dOZGRkSGOlSpcuDR0dHVSrVg2tW7fGgAEDEBQUhNevX2PIkCHo3r27dBdhz549MXnyZPTr1w/jxo3DpUuXMG/ePPz+++/SfocPH46mTZti9uzZaNeuHTZs2ICzZ89KU04oFAqMGDECv/zyCypXrgwHBwdMmDABNjY20l2YeclCH1fYd2fyzkwiIpKbrIXX2bNn0bx5c+m5v78/AMDHxweTJk3C9u3bAUC6nJfl0KFDaNasGQBg7dq1GDJkCFq2bAktLS106dIF8+fPl/qamJhg37598PPzQ+3atWFubo7AwECl+bUaNGiAdevW4aeffsIPP/yAypUrY9u2bXB2dpb6jB07FikpKfD19UViYiIaNWqEPXv2QE9PT+qTWxYiIiIq3orMPF70VnGex4tnvIiISF1p5DxeREREROqMhRcRERGRirDwIiIiIlIRFl5EREREKsLCi4iIiEhFWHgRERERqQgLLyIiIiIVYeFFREREpCIsvIiIiIhUhIUXERERkYqw8CIiIiJSERZeRERERCrCwouIiIhIRVh4EREREakICy8iIiIiFWHhRURERKQiLLyIiIiIVISFFxEREZGKsPAiIiIiUhEWXkREREQqwsKLiIiISEVYeBERERGpCAsvIiIiIhVh4UVERESkIiy8iIiIiFSEhRcRERGRirDwIiIiIlIRFl5EREREKsLCi4iIiEhFWHgRERERqQgLLyIiIiIVYeFFREREpCIsvIiIiIhUhIUXERERkYqw8CIiIiJSERZeRERERCrCwouIiIhIRVh4EREREakICy8iIiIiFWHhRURERKQiJeQOQKRJZkQ8LvR9BLiZF/o+iIiocPCMFxEREZGKsPAiIiIiUhFZC6/Q0FC0b98eNjY2UCgU2LZtm9J2IQQCAwNRtmxZ6Ovrw8PDA9HR0Up9nj59Cm9vbxgbG8PU1BT9+vXDixcvlPpcuHABjRs3hp6eHmxtbTFr1qxsWTZt2gRHR0fo6enBxcUFu3fvLpQsREREVHzJWnilpKSgZs2aWLhwYY7bZ82ahfnz5yMoKAinTp2CgYEBPD09kZaWJvXx9vbG5cuXsX//fuzcuROhoaHw9fWVticnJ6NVq1aws7PDuXPn8Ouvv2LSpElYunSp1OfEiRPo0aMH+vXrh4iICHh5ecHLywuXLl0q0CxERERUvCmEEELuEACgUCiwdetWeHl5AXh7hsnGxgajRo3C6NGjAQBJSUmwsrJCcHAwunfvjitXrsDJyQlnzpyBu7s7AGDPnj1o27Yt7t27BxsbGyxevBg//vgj4uLioKOjAwAICAjAtm3bcPXqVQBAt27dkJKSgp07d0p56tWrB1dXVwQFBRVYlrxITk6GiYkJkpKSYGxsnKfXaMqA7sI+Dk04BoCD64mIiqK8/v4usmO8bt26hbi4OHh4eEhtJiYmqFu3LsLCwgAAYWFhMDU1lQodAPDw8ICWlhZOnTol9WnSpIlUdAGAp6cnrl27hmfPnkl93t1PVp+s/RRUlpy8evUKycnJSg8iIiLSTEW28IqLiwMAWFlZKbVbWVlJ2+Li4mBpaam0vUSJEihdurRSn5ze4919fKjPu9sLIktOpk+fDhMTE+lha2v7wb5ERESk3ops4VVcjB8/HklJSdIjNjZW7khERERUSIps4WVtbQ0AiI+PV2qPj4+XtllbWyMhIUFp+5s3b/D06VOlPjm9x7v7+FCfd7cXRJac6OrqwtjYWOlBREREmqnIFl4ODg6wtrZGSEiI1JacnIxTp06hfv36AID69esjMTER586dk/ocPHgQmZmZqFu3rtQnNDQUr1+/lvrs378fVatWhZmZmdTn3f1k9cnaT0FlISIiouJN1sLrxYsXOH/+PM6fPw/g7SD28+fP4+7du1AoFBgxYgR++eUXbN++HRcvXkTv3r1hY2Mj3flYrVo1tG7dGgMGDMDp06dx/PhxDBkyBN27d5fuIuzZsyd0dHTQr18/XL58GRs3bsS8efPg7+8v5Rg+fDj27NmD2bNn4+rVq5g0aRLOnj2LIUOGAECBZSEiIqLiTda1Gs+ePYvmzZtLz7OKIR8fHwQHB2Ps2LFISUmBr68vEhMT0ahRI+zZswd6enrSa9auXYshQ4agZcuW0NLSQpcuXTB//nxpu4mJCfbt2wc/Pz/Url0b5ubmCAwMVJpfq0GDBli3bh1++ukn/PDDD6hcuTK2bdsGZ2dnqU9BZCEiIqLircjM40VvcR6vwqMJxwBwHi8ioqJI7efxIiIiItI0LLyIiIiIVISFFxEREZGKsPAiIiIiUhEWXkREREQqwsKLiIiISEVYeBERERGpCAsvIiIiIhVh4UVERESkIiy8iIiIiFSEhRcRERGRirDwIiIiIlIRFl5EREREKsLCi4iIiEhFWHgRERERqQgLLyIiIiIVYeFFREREpCIsvIiIiIhUhIUXERERkYqw8CIiIiJSERZeRERERCrCwouIiIhIRUrIHYCIip4ZEY8LfR8BbuaFvg8ioqKGZ7yIiIiIVISFFxEREZGKsPAiIiIiUhEWXkREREQqwsKLiIiISEVYeBERERGpCAsvIiIiIhVh4UVERESkIiy8iIiIiFSEhRcRERGRirDwIiIiIlIRFl5EREREKsLCi4iIiEhFWHgRERERqQgLLyIiIiIVYeFFREREpCIsvIiIiIhUhIUXERERkYqw8CIiIiJSERZeRERERCrCwouIiIhIRYp04ZWRkYEJEybAwcEB+vr6qFixIn7++WcIIaQ+QggEBgaibNmy0NfXh4eHB6Kjo5Xe5+nTp/D29oaxsTFMTU3Rr18/vHjxQqnPhQsX0LhxY+jp6cHW1hazZs3KlmfTpk1wdHSEnp4eXFxcsHv3bqXteclCRERExVeRLrxmzpyJxYsXY8GCBbhy5QpmzpyJWbNm4Y8//pD6zJo1C/Pnz0dQUBBOnToFAwMDeHp6Ii0tTerj7e2Ny5cvY//+/di5cydCQ0Ph6+srbU9OTkarVq1gZ2eHc+fO4ddff8WkSZOwdOlSqc+JEyfQo0cP9OvXDxEREfDy8oKXlxcuXbqUryxERERUfCnEu6ePipivv/4aVlZW+Ouvv6S2Ll26QF9fH2vWrIEQAjY2Nhg1ahRGjx4NAEhKSoKVlRWCg4PRvXt3XLlyBU5OTjhz5gzc3d0BAHv27EHbtm1x79492NjYYPHixfjxxx8RFxcHHR0dAEBAQAC2bduGq1evAgC6deuGlJQU7Ny5U8pSr149uLq6IigoKE9Z8iI5ORkmJiZISkqCsbFxnl4zI+Jxnvp9jgA380LfR2EfhyYcA8DjICIqivL6+7tIn/Fq0KABQkJCcP36dQBAZGQkjh07hjZt2gAAbt26hbi4OHh4eEivMTExQd26dREWFgYACAsLg6mpqVR0AYCHhwe0tLRw6tQpqU+TJk2kogsAPD09ce3aNTx79kzq8+5+svpk7ScvWXLy6tUrJCcnKz2IiIhIM5WQO8DHBAQEIDk5GY6OjtDW1kZGRgamTp0Kb29vAEBcXBwAwMrKSul1VlZW0ra4uDhYWloqbS9RogRKly6t1MfBwSHbe2RtMzMzQ1xcXK77yS1LTqZPn47Jkyfn8pUgIiIiTVCkz3j9/fffWLt2LdatW4fw8HCsXLkSv/32G1auXCl3tAIzfvx4JCUlSY/Y2Fi5IxEREVEhKdJnvMaMGYOAgABpfJSLiwvu3LmD6dOnw8fHB9bW1gCA+Ph4lC1bVnpdfHw8XF1dAQDW1tZISEhQet83b97g6dOn0uutra0RHx+v1CfreW593t2eW5ac6OrqQldXN/cvBhEREam9In3GKzU1FVpayhG1tbWRmZkJAHBwcIC1tTVCQkKk7cnJyTh16hTq168PAKhfvz4SExNx7tw5qc/BgweRmZmJunXrSn1CQ0Px+vVrqc/+/ftRtWpVmJmZSX3e3U9Wn6z95CULERERFW9FuvBq3749pk6dil27duH27dvYunUr5syZg06dOgEAFAoFRowYgV9++QXbt2/HxYsX0bt3b9jY2MDLywsAUK1aNbRu3RoDBgzA6dOncfz4cQwZMgTdu3eHjY0NAKBnz57Q0dFBv379cPnyZWzcuBHz5s2Dv7+/lGX48OHYs2cPZs+ejatXr2LSpEk4e/YshgwZkucsREREVLwV6UuNf/zxByZMmIDBgwcjISEBNjY2+P777xEYGCj1GTt2LFJSUuDr64vExEQ0atQIe/bsgZ6entRn7dq1GDJkCFq2bAktLS106dIF8+fPl7abmJhg37598PPzQ+3atWFubo7AwEClub4aNGiAdevW4aeffsIPP/yAypUrY9u2bXB2ds5XFiIiIiq+ivQ8XsUR5/EqPJpwDACPg4ioKNKIebyIiIiINAkLLyIiIiIVYeFFREREpCIsvIiIiIhUhIUXERERkYqw8CIiIiJSERZeRERERCrCwouIiIhIRVh4EREREakICy8iIiIiFWHhRURERKQiLLyIiIiIVISFFxEREZGKsPAiIiIiUhEWXkREREQqwsKLiIiISEVYeBERERGpSIlPfWFGRga2bt2KK1euAACqVasGLy8vlCjxyW9JREREpNE+qUq6fPkyOnTogLi4OFStWhUAMHPmTFhYWGDHjh1wdnYu0JBEREREmuCTLjX2798f1atXx7179xAeHo7w8HDExsaiRo0a8PX1LeiMRERERBrhk854nT9/HmfPnoWZmZnUZmZmhqlTp+LLL78ssHBEREREmuSTCq8qVaogPj4e1atXV2pPSEhApUqVCiQYEdHnmBHxuND3EeBmXuj7ICLNkudLjcnJydJj+vTpGDZsGDZv3ox79+7h3r172Lx5M0aMGIGZM2cWZl4iIiIitZXnM16mpqZQKBTScyEEunbtKrUJIQAA7du3R0ZGRgHHJCIiIlJ/eS68Dh06VJg5iIiIiDRenguvpk2bFmYOIiIiIo33ybOdPnv2DH/99Zc0gaqTkxP69OmD0qVLF1g4IiIiIk3ySfN4hYaGwt7eHvPnz8ezZ8/w7NkzzJ8/Hw4ODggNDS3ojEREREQa4ZPOePn5+aFbt25YvHgxtLW1AbxdQmjw4MHw8/PDxYsXCzQkERERkSb4pDNeMTExGDVqlFR0AYC2tjb8/f0RExNTYOGIiIiINMknFV61atWSxna968qVK6hZs+ZnhyIiIiLSRJ90qXHYsGEYPnw4YmJiUK9ePQDAyZMnsXDhQsyYMQMXLlyQ+taoUaNgkhIRERGpuU8qvHr06AEAGDt2bI7bFAoFhBBQKBScTJWIiIjo//ukwuvWrVsFnYOIiIhI431S4bVu3TpYWVmhb9++Su3Lly/Ho0ePMG7cuAIJR0RERKRJPmlw/ZIlS+Do6JitvXr16ggKCvrsUERERESa6JMKr7i4OJQtWzZbu4WFBR4+fPjZoYiIiIg00ScVXra2tjh+/Hi29uPHj8PGxuazQxERERFpok8a4zVgwACMGDECr1+/RosWLQAAISEhGDt2LEaNGlWgAYmIiIg0xScVXmPGjMGTJ08wePBgpKenAwD09PQwbtw4jB8/vkADEhEVZzMiHhf6PgLczAt9H0T01icVXgqFAjNnzsSECRNw5coV6Ovro3LlytDV1S3ofEREREQa45MKryyGhob48ssvCyoLERERkUb7pMH1RERERJR/Rb7wun//Pv73v/+hTJky0NfXh4uLC86ePSttF0IgMDAQZcuWhb6+Pjw8PBAdHa30Hk+fPoW3tzeMjY1hamqKfv364cWLF0p9Lly4gMaNG0NPTw+2traYNWtWtiybNm2Co6Mj9PT04OLigt27dyttz0sWIiIiKr6KdOH17NkzNGzYECVLlsR///2HqKgozJ49G2ZmZlKfWbNmYf78+QgKCsKpU6dgYGAAT09PpKWlSX28vb1x+fJl7N+/Hzt37kRoaCh8fX2l7cnJyWjVqhXs7Oxw7tw5/Prrr5g0aRKWLl0q9Tlx4gR69OiBfv36ISIiAl5eXvDy8sKlS5fylYWIiIiKr88a41XYZs6cCVtbW6xYsUJqc3BwkP4thMDcuXPx008/oWPHjgCAVatWwcrKCtu2bUP37t1x5coV7NmzB2fOnIG7uzsA4I8//kDbtm3x22+/wcbGBmvXrkV6ejqWL18OHR0dVK9eHefPn8ecOXOkAm3evHlo3bo1xowZAwD4+eefsX//fixYsABBQUF5ykJERETFW5E+47V9+3a4u7vj22+/haWlJdzc3PDnn39K22/duoW4uDh4eHhIbSYmJqhbty7CwsIAAGFhYTA1NZWKLgDw8PCAlpYWTp06JfVp0qQJdHR0pD6enp64du0anj17JvV5dz9ZfbL2k5csREREVLwV6cLr5s2bWLx4MSpXroy9e/di0KBBGDZsGFauXAng7dJFAGBlZaX0OisrK2lbXFwcLC0tlbaXKFECpUuXVuqT03u8u48P9Xl3e25ZcvLq1SskJycrPYiIiEgzFelLjZmZmXB3d8e0adMAAG5ubrh06RKCgoLg4+Mjc7qCMX36dEyePFnuGERERKQCRfqMV9myZeHk5KTUVq1aNdy9excAYG1tDQCIj49X6hMfHy9ts7a2RkJCgtL2N2/e4OnTp0p9cnqPd/fxoT7vbs8tS07Gjx+PpKQk6REbG/vBvkRERKTeinTh1bBhQ1y7dk2p7fr167CzswPwdqC9tbU1QkJCpO3Jyck4deoU6tevDwCoX78+EhMTce7cOanPwYMHkZmZibp160p9QkND8fr1a6nP/v37UbVqVekOyvr16yvtJ6tP1n7ykiUnurq6MDY2VnoQERGRZirShdfIkSNx8uRJTJs2DTExMVi3bh2WLl0KPz8/AG+XLhoxYgR++eUXbN++HRcvXkTv3r1hY2MDLy8vAG/PkLVu3RoDBgzA6dOncfz4cQwZMgTdu3eHjY0NAKBnz57Q0dFBv379cPnyZWzcuBHz5s2Dv7+/lGX48OHYs2cPZs+ejatXr2LSpEk4e/YshgwZkucsREREVLwV6TFeX375JbZu3Yrx48djypQpcHBwwNy5c+Ht7S31GTt2LFJSUuDr64vExEQ0atQIe/bsgZ6entRn7dq1GDJkCFq2bAktLS106dIF8+fPl7abmJhg37598PPzQ+3atWFubo7AwEClub4aNGiAdevW4aeffsIPP/yAypUrY9u2bXB2ds5XFiIiIiq+FEIIIXcI+j/JyckwMTFBUlJSni87zoh4XMipgAA380LfR2EfhyYcA8DjyCtNOAZAc46DSNPl9fd3kb7USERERKRJWHgRERERqQgLLyIiIiIVYeFFREREpCIsvIiIiIhUhIUXERERkYqw8CIiIiJSERZeRERERCrCwouIiIhIRVh4EREREakICy8iIiIiFWHhRURERKQiLLyIiIiIVISFFxEREZGKsPAiIiIiUhEWXkREREQqwsKLiIiISEVYeBERERGpCAsvIiIiIhVh4UVERESkIiy8iIiIiFSEhRcRERGRirDwIiIiIlIRFl5EREREKsLCi4iIiEhFWHgRERERqQgLLyIiIiIVYeFFREREpCIsvIiIiIhUhIUXERERkYqw8CIiIiJSERZeRERERCrCwouIiIhIRVh4EREREakICy8iIiIiFWHhRURERKQiLLyIiIiIVISFFxEREZGKsPAiIiIiUhEWXkREREQqwsKLiIiISEVYeBERERGpCAsvIiIiIhVh4UVERESkImpVeM2YMQMKhQIjRoyQ2tLS0uDn54cyZcrA0NAQXbp0QXx8vNLr7t69i3bt2qFUqVKwtLTEmDFj8ObNG6U+hw8fRq1ataCrq4tKlSohODg42/4XLlwIe3t76OnpoW7dujh9+rTS9rxkISIiouJLbQqvM2fOYMmSJahRo4ZS+8iRI7Fjxw5s2rQJR44cwYMHD9C5c2dpe0ZGBtq1a4f09HScOHECK1euRHBwMAIDA6U+t27dQrt27dC8eXOcP38eI0aMQP/+/bF3716pz8aNG+Hv74+JEyciPDwcNWvWhKenJxISEvKchYiIiIo3tSi8Xrx4AW9vb/z5558wMzOT2pOSkvDXX39hzpw5aNGiBWrXro0VK1bgxIkTOHnyJABg3759iIqKwpo1a+Dq6oo2bdrg559/xsKFC5Geng4ACAoKgoODA2bPno1q1aphyJAh+Oabb/D7779L+5ozZw4GDBiAPn36wMnJCUFBQShVqhSWL1+e5yxERERUvKlF4eXn54d27drBw8NDqf3cuXN4/fq1UrujoyPKly+PsLAwAEBYWBhcXFxgZWUl9fH09ERycjIuX74s9Xn/vT09PaX3SE9Px7lz55T6aGlpwcPDQ+qTlyw5efXqFZKTk5UeREREpJlKyB0gNxs2bEB4eDjOnDmTbVtcXBx0dHRgamqq1G5lZYW4uDipz7tFV9b2rG0f65OcnIyXL1/i2bNnyMjIyLHP1atX85wlJ9OnT8fkyZM/uJ2IiIg0R5E+4xUbG4vhw4dj7dq10NPTkztOoRg/fjySkpKkR2xsrNyRiIiIqJAU6cLr3LlzSEhIQK1atVCiRAmUKFECR44cwfz581GiRAlYWVkhPT0diYmJSq+Lj4+HtbU1AMDa2jrbnYVZz3PrY2xsDH19fZibm0NbWzvHPu++R25ZcqKrqwtjY2OlBxEREWmmIl14tWzZEhcvXsT58+elh7u7O7y9vaV/lyxZEiEhIdJrrl27hrt376J+/foAgPr16+PixYtKdx/u378fxsbGcHJykvq8+x5ZfbLeQ0dHB7Vr11bqk5mZiZCQEKlP7dq1c81CRERExVuRHuNlZGQEZ2dnpTYDAwOUKVNGau/Xrx/8/f1RunRpGBsbY+jQoahfvz7q1asHAGjVqhWcnJzQq1cvzJo1C3Fxcfjpp5/g5+cHXV1dAMDAgQOxYMECjB07Fn379sXBgwfx999/Y9euXdJ+/f394ePjA3d3d9SpUwdz585FSkoK+vTpAwAwMTHJNQsREREVb0W68MqL33//HVpaWujSpQtevXoFT09PLFq0SNqura2NnTt3YtCgQahfvz4MDAzg4+ODKVOmSH0cHBywa9cujBw5EvPmzUO5cuWwbNkyeHp6Sn26deuGR48eITAwEHFxcXB1dcWePXuUBtznloWIiIiKN4UQQsgdgv5PcnIyTExMkJSUlOfxXjMiHhdyKiDAzbzQ91HYx6EJxwDwOPJKE44B0JzjINJ0ef39XaTHeBERERFpEhZeRERERCrCwouIiIhIRVh4EREREakICy8iIiIiFWHhRURERKQiLLyIiIiIVISFFxEREZGKsPAiIiIiUhEWXkREREQqwsKLiIiISEVYeBERERGpCAsvIiIiIhVh4UVERESkIiy8iIiIiFSEhRcRERGRirDwIiIiIlIRFl5EREREKsLCi4iIiEhFWHgRERERqQgLLyIiIiIVYeFFREREpCIl5A5ARESab0bE40J9/wA380J9f6KCwjNeRERERCrCwouIiIhIRVh4EREREakICy8iIiIiFWHhRURERKQiLLyIiIiIVISFFxEREZGKsPAiIiIiUhEWXkREREQqwsKLiIiISEVYeBERERGpCAsvIiIiIhVh4UVERESkIiy8iIiIiFSEhRcRERGRirDwIiIiIlIRFl5EREREKsLCi4iIiEhFWHgRERERqQgLLyIiIiIVYeFFREREpCJFuvCaPn06vvzySxgZGcHS0hJeXl64du2aUp+0tDT4+fmhTJkyMDQ0RJcuXRAfH6/U5+7du2jXrh1KlSoFS0tLjBkzBm/evFHqc/jwYdSqVQu6urqoVKkSgoODs+VZuHAh7O3toaenh7p16+L06dP5zkJERETFV5EuvI4cOQI/Pz+cPHkS+/fvx+vXr9GqVSukpKRIfUaOHIkdO3Zg06ZNOHLkCB48eIDOnTtL2zMyMtCuXTukp6fjxIkTWLlyJYKDgxEYGCj1uXXrFtq1a4fmzZvj/PnzGDFiBPr374+9e/dKfTZu3Ah/f39MnDgR4eHhqFmzJjw9PZGQkJDnLERERFS8lZA7wMfs2bNH6XlwcDAsLS1x7tw5NGnSBElJSfjrr7+wbt06tGjRAgCwYsUKVKtWDSdPnkS9evWwb98+REVF4cCBA7CysoKrqyt+/vlnjBs3DpMmTYKOjg6CgoLg4OCA2bNnAwCqVauGY8eO4ffff4enpycAYM6cORgwYAD69OkDAAgKCsKuXbuwfPlyBAQE5CkLERERFW9F+ozX+5KSkgAApUuXBgCcO3cOr1+/hoeHh9TH0dER5cuXR1hYGAAgLCwMLi4usLKykvp4enoiOTkZly9flvq8+x5ZfbLeIz09HefOnVPqo6WlBQ8PD6lPXrLk5NWrV0hOTlZ6EBERkWZSm8IrMzMTI0aMQMOGDeHs7AwAiIuLg46ODkxNTZX6WllZIS4uTurzbtGVtT1r28f6JCcn4+XLl3j8+DEyMjJy7PPue+SWJSfTp0+HiYmJ9LC1tc3DV4OIiIjUkdoUXn5+frh06RI2bNggd5QCNX78eCQlJUmP2NhYuSMRERFRISnSY7yyDBkyBDt37kRoaCjKlSsntVtbWyM9PR2JiYlKZ5ri4+NhbW0t9Xn/7sOsOw3f7fP+3Yfx8fEwNjaGvr4+tLW1oa2tnWOfd98jtyw50dXVha6ubh6/EkRERKTOinThJYTA0KFDsXXrVhw+fBgODg5K22vXro2SJUsiJCQEXbp0AQBcu3YNd+/eRf369QEA9evXx9SpU5GQkABLS0sAwP79+2FsbAwnJyepz+7du5Xee//+/dJ76OjooHbt2ggJCYGXlxeAt5c+Q0JCMGTIkDxnISIi9TUj4nGh7yPAzbzQ90HyKtKFl5+fH9atW4d///0XRkZG0lgpExMT6Ovrw8TEBP369YO/vz9Kly4NY2NjDB06FPXr15fuImzVqhWcnJzQq1cvzJo1C3Fxcfjpp5/g5+cnnWkaOHAgFixYgLFjx6Jv3744ePAg/v77b+zatUvK4u/vDx8fH7i7u6NOnTqYO3cuUlJSpLsc85KFiIiIirciXXgtXrwYANCsWTOl9hUrVuC7774DAPz+++/Q0tJCly5d8OrVK3h6emLRokVSX21tbezcuRODBg1C/fr1YWBgAB8fH0yZMkXq4+DggF27dmHkyJGYN28eypUrh2XLlklTSQBAt27d8OjRIwQGBiIuLg6urq7Ys2eP0oD73LIQERFR8VakCy8hRK599PT0sHDhQixcuPCDfezs7LJdSnxfs2bNEBER8dE+Q4YMkS4tfmoWIiIiKr7U5q5GIiIiInXHwouIiIhIRVh4EREREakICy8iIiIiFWHhRURERKQiLLyIiIiIVISFFxEREZGKsPAiIiIiUhEWXkREREQqwsKLiIiISEVYeBERERGpCAsvIiIiIhVh4UVERESkIiXkDkBERESqMyPicaHvI8DNvND3oa54xouIiIhIRVh4EREREakICy8iIiIiFeEYLyIiIlIr6jxOjWe8iIiIiFSEhRcRERGRirDwIiIiIlIRFl5EREREKsLCi4iIiEhFWHgRERERqQgLLyIiIiIVYeFFREREpCIsvIiIiIhUhIUXERERkYqw8CIiIiJSERZeRERERCrCwouIiIhIRVh4EREREakICy8iIiIiFWHhRURERKQiLLyIiIiIVISFFxEREZGKsPAiIiIiUhEWXkREREQqwsKLiIiISEVYeBERERGpCAsvIiIiIhVh4UVERESkIiy8iIiIiFSEhVchWLhwIezt7aGnp4e6devi9OnTckciIiKiIoCFVwHbuHEj/P39MXHiRISHh6NmzZrw9PREQkKC3NGIiIhIZiy8CticOXMwYMAA9OnTB05OTggKCkKpUqWwfPlyuaMRERGRzFh4FaD09HScO3cOHh4eUpuWlhY8PDwQFhYmYzIiIiIqCkrIHUCTPH78GBkZGbCyslJqt7KywtWrV3N8zatXr/Dq1SvpeVJSEgAgOTk5z/tNe/H8E9LmT3KyTqHvo7CPQxOOAeBx5JUmHAPA48grTTgGgMeRV0XxGLJ+bwshPt5RUIG5f/++ACBOnDih1D5mzBhRp06dHF8zceJEAYAPPvjggw8++NCAR2xs7EdrBZ7xKkDm5ubQ1tZGfHy8Unt8fDysra1zfM348ePh7+8vPc/MzMTTp09RpkwZKBSKAs+YnJwMW1tbxMbGwtjYuMDfX1V4HEWHJhwDoBnHoQnHAPA4ihJNOAZANcchhMDz589hY2Pz0X4svAqQjo4OateujZCQEHh5eQF4W0iFhIRgyJAhOb5GV1cXurq6Sm2mpqaFnBQwNjZW6x+iLDyOokMTjgHQjOPQhGMAeBxFiSYcA1D4x2FiYpJrHxZeBczf3x8+Pj5wd3dHnTp1MHfuXKSkpKBPnz5yRyMiIiKZsfAqYN26dcOjR48QGBiIuLg4uLq6Ys+ePdkG3BMREVHxw8KrEAwZMuSDlxblpquri4kTJ2a7vKlueBxFhyYcA6AZx6EJxwDwOIoSTTgGoGgdh0KI3O57JCIiIqKCwAlUiYiIiFSEhRcRERGRirDwIiIiIlIRFl5EREREKsK7GomIiArYq1evisQddHmRn7WBNWESVbnxrkYNlpmZiSNHjuDo0aO4c+cOUlNTYWFhATc3N3h4eMDW1lbuiKRmEhMTsXXr1hw/U56enmjQoIHcEYsNTfpeXLlyBRs2bPjgsXTp0qXIFzH//fefdAyxsbHIzMyEgYEB3Nzc0KpVK/Tp0yfXpWTkoqWllecl6jIyMgo5zee5cOFCnvvWqFGjEJN8GAsvDfTy5UvMnj0bixcvxtOnT+Hq6gobGxvo6+vj6dOnuHTpEh48eIBWrVohMDAQ9erVkzvyJ0lPT0d6ejoMDQ3ljqLxHjx4gMDAQKxduxY2NjaoU6dOts/UuXPnYGdnh4kTJ6Jbt25yR/6oxMRErF+/HoMGDQIAeHt74+XLl9J2bW1t/PnnnypZviu/NOl7ER4ejrFjx+LYsWNo2LBhjsdy9OhRJCcnY+zYsRgxYkSRK8C2bt2KcePG4fnz52jbtu0HjyEsLAzfffcdfv75Z1hYWMgdW8mRI0ekf9++fRsBAQH47rvvUL9+fQBAWFgYVq5cienTp8PHx0eumHmSVUR+qLTJ2qZQKOQrIj+6hDappXLlyolvv/1W7Nq1S6Snp+fY5/bt22LatGnCzs5OLF26VMUJ82/58uViyJAhYs2aNUIIIQICAoSOjo7Q0tISHh4e4vHjxzInzJuDBw+K3377TRw7dkwIIURQUJCwtbUV5ubmon///iI1NVXmhDmztLQUY8aMEZcvX/5gn9TUVLFu3TpRr1498euvv6owXf7NmjVL9OzZU3puaGgounTpIr777jvx3XffiapVq4qJEyfKF/AjNOl7YW9vLxYuXCiePXv20X4nTpwQ3bp1E1OnTlVNsHyoV6+e2Llzp8jIyPhov3v37olx48aJOXPmqCjZp2nRooVYt25dtva1a9eKpk2bqj5QPt2+fTvPD7mw8NJAUVFRee6bnp4uYmJiCjHN5/vll1+Evr6+8PDwEKVLlxYDBw4U1tbWYsaMGWLWrFmiXLlyYuDAgXLHzNXSpUuFtra2qFSpktDV1RXTpk0TBgYGYuDAgWLw4MHC2NhYjBs3Tu6YOcpvYVvUC+E6deqI/fv3S88NDQ3FjRs3pOdbtmwRrq6uckTLlSZ9Lz70h2FB9af809fXF9evX8/Wfu3aNaGvry9DIs3DwouKvEqVKkl/gZ05c0ZoaWmJzZs3S9t3794typcvL1e8PKtevbqYP3++EEKI//77T5QoUUIEBwdL2//++29RsWJFueIVK+bm5uLu3bvS89q1a4vY2Fjp+Y0bN4SBgYEc0UgDvHr1Sly9elW8fv1a7ij5VqVKFTFmzJhs7WPGjBFVqlSRIdHniYmJEUOGDBEtW7YULVu2FEOHDpX9ZAPHeGkgdRhcmB+6urqIiYmRbgbQ1dXFhQsXULVqVQDA/fv34eDggPT0dDlj5qpUqVK4cuUK7OzsAAA6OjqIjIxEtWrVAAB3795F5cqV8erVKzlj5mj79u157tuhQ4dCTFIwSpUqhdOnT8PZ2TnH7RcvXkTdunWRmpqq4mS507TvxbtCQkIQEhKChIQEZGZmKm1bvny5TKnyLjU1FUOHDsXKlSsBANevX0eFChUwdOhQfPHFFwgICJA5Ye52796NLl26oFKlSqhbty4A4PTp04iOjsY///yDtm3bypww7/bu3YsOHTrA1dUVDRs2BAAcP34ckZGR2LFjB7766itZcnE6CQ3k6uqqNIDwY4r6HSoA8Pr1a6UBtTo6OihZsqT0vESJEmpxHGlpadDX15ee6+rqKh2Xrq4u3rx5I0e0XHl5eSk9f3/w6rufM3X4XlSoUAHh4eEfLLzOnj0LBwcHFafKG037XmSZPHkypkyZAnd3d5QtWzbPd9kVJePHj0dkZCQOHz6M1q1bS+0eHh6YNGmSWhRebdu2xfXr17F48WJcvXoVANC+fXsMHDhQ7e6EDwgIwMiRIzFjxoxs7ePGjZOt8OKlRg307uDBrVu3iooVK4qgoCARGRkpIiMjRVBQkKhcubLYunWr3FHzRKFQiEOHDkn5DQwMxK5du6TnISEhQktLS+6YudLS0hIxMTEiKSlJJCYmCiMjIxEZGSmSkpJEUlKSuH79ulocx/79+0WtWrXEnj17pOx79uwR7u7uYt++fXLHy5OffvpJ2Nrairi4uGzbHj58KGxtbcWPP/4oQ7L80YTvRRZra2uxatUquWN8lvLly4uwsDAhhPK4wejoaGFkZCRntGJJV1f3g+PVdHV1ZUj0FgsvDffll1+KXbt2ZWvftWuXqFWrlgyJ8k+hUAgtLS2hUCiyPbLa1aFgycqZ9fjQ86KuevXq4ujRo9naQ0NDhaOjowyJ8i85OVlUq1ZNGBkZicGDB4u5c+eKuXPnikGDBgkjIyPh6OgokpOT5Y6ZK034XmQpXbq07GNvPpe+vr5UbL1beJ0/f14YGxvLGS1fQkNDhbe3t6hfv764d++eEEKIVatW5fhZK8rKlSsn/v7772ztGzduFLa2tjIkeouXGjXcxYsXc7xk4uDggKioKBkS5d+tW7fkjlAgDh06JHeEAnHjxo0c57cyMTHB7du3VZ7nUxgZGeH48eMYP3481q9fj8TERACAqakpevbsiWnTpsHIyEjekHmgCd+LLP3798e6deswYcIEuaN8Mnd3d+zatQtDhw4F8H+XfZctWybNiVXU/fPPP+jVqxe8vb0RHh4ujTlNSkrCtGnTsHv3bpkT5t2AAQPg6+uLmzdvShMKHz9+HDNnzoS/v79suTi4XsPVqlULzs7OWLZsGXR0dAC8nXi0f//+uHTpEsLDw2VOSOqmSZMm0NPTw+rVq2FlZQUAiI+PR+/evZGWlqY0GaM6EELg0aNHAAALCwu1GlukSd+L4cOHY9WqVahRowZq1KihNI4TAObMmSNTsrw7duwY2rRpg//9738IDg7G999/j6ioKJw4cQJHjhxB7dq15Y6YKzc3N4wcORK9e/eGkZERIiMjUaFCBURERKBNmzaIi4uTO2KeCSEwd+5czJ49Gw8ePAAA2NjYYMyYMRg2bJhsP+ssvDTc6dOn0b59ewghpDsYL1y4AIVCgR07dqBOnToyJ8zdrFmzMHToUGlg+vHjx+Hu7i4NTH/+/DnGjRuHRYsWyRkzV3///Te8vLykAvjevXuwsbGBltbbtepTU1OxYMECjB07Vs6YuYqJiUGnTp1w/fp1abBtbGwsKleujG3btqFSpUoyJyw+NOl70bx58w9uUygUOHjwoArTfLobN25gxowZiIyMxIsXL1CrVi2MGzcOLi4uckfLk1KlSiEqKgr29vZKhdfNmzfh5OSEtLQ0uSN+kufPnwNAkTiTzcKrGEhJScHatWulO1SqVauGnj17wsDAQOZkeaOtrY2HDx/C0tISwNtFWs+fP48KFSoAePsXvo2NTZG/g0tTjgN4+5fk/v37lT5THh4eanO2qHnz5rlmVSgUCAkJUVGiT6fu3wsqWipUqIClS5fCw8NDqfBatWoVZsyYoTZDVIoyjvEqBgwMDODr6yt3jE/2/t8G6vq3gqYcB/C2KGnVqhWaNGkCXV1dtfsl7+rq+sFtz58/x7p164rkfGo5yfpetGrVSu4oBebevXsAgHLlysmcJP8yMzMRExOT41xkTZo0kSlV3g0YMADDhw/H8uXLoVAo8ODBA4SFhWH06NFqN/4uPj4eo0ePluaGe///XLn+yGXhVQysXr0aS5Yswc2bNxEWFgY7Ozv8/vvvqFChAjp27Ch3PFIzmZmZmDp1KoKCghAfHy9NEjlhwgTY29ujX79+ckfM1e+//56t7c2bN1i4cCGmTp2KL774Aj///LMMyXI3f/58+Pr6Qk9PD/Pnz/9o32HDhqko1efLzMzEL7/8gtmzZ+PFixcA3l4WGjVqFH788UfpknxRdvLkSfTs2RN37tzJ9kte1kWZ8yEgIACZmZlo2bIlUlNTpT+uRo8eLd00oC6+++473L17FxMmTChac8Op/D5KUqlFixYJc3Nz8csvvwg9PT3p9uYVK1aIZs2ayZwubxQKhYiPj5eev7+uXlxcnFpMw6ApxzF58mRRoUIFsWbNGqXb5zds2CDq1asnc7pPs2bNGlGhQgVRtmxZsXDhwiK91Iu9vb20/qK9vf0HHw4ODjInzZ+AgABhYWEhFi1aJM3Rt3DhQmFhYSF++OEHuePlSc2aNcW3334roqKixLNnz0RiYqLSQ528evVKXL58WZw6dUo8f/5c7jifxNDQUERERMgdIxue8dJwf/zxB/788094eXkpzd7r7u6O0aNHy5gsf5YtWwZDQ0MAb89MBAcHw9zcHMD/DZpUB3v37oWJiQmAt3/hh4SE4NKlSwAgTWlQ1K1atQpLly5Fy5YtMXDgQKm9Zs2a0jgjdbFnzx4EBATg1q1bGD16NPz9/Yv82Md3p1fRlKlWAGDlypVYtmyZ0jJHNWrUwBdffIHBgwdj6tSpMqbLm+joaGzevFmtbmr4EB0dHTg5Ockd47PY2toWySEdLLw03K1bt+Dm5patXVdXFykpKTIkyr/y5cvjzz//lJ5bW1tj9erV2fqoAx8fH6Xn33//vdLzInMq/CPu37+f4y+WzMxMvH79WoZE+Xf69GmMGzcOJ0+exMCBA3HgwAGpkFcXr1+/hqOjI3bu3Cmt96nOnj59CkdHx2ztjo6OePr0qQyJ8q9u3bqIiYlRu8Krc+fOCA4OhrGxMTp37vzRvlu2bFFRqs83d+5cBAQEYMmSJbC3t5c7joSFl4ZzcHDA+fPnpYWZs+zZs0dt/rNWt4kgP+T9gbbqysnJCUePHs32mdq8eXOORX5RVK9ePejr62PgwIFwcHDAunXrcuxXlMdIlSxZUm1v7c9JzZo1sWDBgmzj1hYsWICaNWvKlCp/hg4dilGjRiEuLg4uLi7Z5iLLmtKnqDExMZH+6DM2NlaLPwDzolu3bkhNTUXFihVRqlSpbN8PuQp6Fl4azt/fH35+fkhLS4MQAqdPn8b69esxffp0LFu2TO54xdKTJ09QpkwZAG/nXPrzzz+RlpaG9u3bo3HjxjKny11gYCB8fHxw//59ZGZmYsuWLbh27RpWrVqFnTt3yh0vT8qXLw+FQoFt27Z9sI9CoSjShRcA+Pn5YebMmVi2bBlKlFDv/85nzZqFdu3a4cCBA9Is72FhYYiNjVWb2dK7dOkCAOjbt6/UlrWIeVEeXN+pUyfo6ekBAIKDg+UNU4Dmzp0rd4QccR6vYmDt2rWYNGkSbty4AeDtzL2TJ09Wi7vPAODgwYMYMmQITp48CWNjY6VtSUlJaNCgARYvXlzkb9W+ePEi2rdvL01wuWHDBrRu3RopKSnQ0tJCSkoKNm/eDC8vL7mj5uro0aOYMmWK0iSRgYGBGjWlgTro1KkTQkJCYGhoCBcXl2zj09TpshAAPHjwAAsXLlSak2zw4MGwsbGROVne3Llz56Pb3z9LXFRoa2sjLi4OFhYW2eYbpILHwqsYSU1NxYsXL9TuB6pDhw5o3rw5Ro4cmeP2+fPn49ChQ9i6dauKk+VPmzZtUKJECQQEBGD16tXYuXMnPD09pfFrQ4cOxblz53Dy5EmZk2q+vBTzQUFBRf4MZJ8+fT66fcWKFSpKQurM2toaf/75J9q3bw8tLS3Ex8fDwsJC7lgFKi0tDenp6Upt7//sqwoLLyry7OzsPjom7erVq2jVqhXu3r2r4mT5Y25ujoMHD6JGjRp48eIFjI2NcebMGWn9tqtXr6JevXpqc3ejOtOUYl7dXbhwAc7OztDS0sKFCxc+2reojo/KSVRUFO7evZvtF/27d2wWJZMmTcKUKVPyNLarqF4uzUlKSgrGjRuHv//+G0+ePMm2nROoUoGpVasWQkJCYGZmBjc3t4/+MKnDItnx8fHZBkW+q0SJEtIix0XZ06dPYW1tDQAwNDSEgYEBzMzMpO1mZmZFdmqM0qVL4/r16zA3N4eZmdlHP1PqcAdaZGQkZs6c+cHtrVq1wm+//abCRJ8nISEB165dAwBUrVpVbc5qu7q6Ii4uDpaWlnB1dZXGQ72vKI+PetfNmzfRqVMnXLx4UelYsn5eiuoxTJo0Cd27d0dMTAw6dOiAFStWwNTUVO5Yn23s2LE4dOgQFi9ejF69emHhwoW4f/8+lixZojS9kqqx8NJAHTt2lBaQVofxQrn54osvcOnSpQ/eon3hwgWULVtWxak+zfsFi7rcPfT7779Li8sW1QGr+aEpxXxycjL8/PywYcMG6Ze6trY2unXrhoULF0pzxhVVt27dki5pacKcZMOHD4eDgwNCQkLg4OCA06dP48mTJxg1alSRL+QdHR3h6OiIiRMn4ttvv0WpUqXkjvTZduzYgVWrVqFZs2bo06cPGjdujEqVKsHOzg5r166Ft7e3LLlYeGkgMzMzaXmNPn36oFy5cmqx3MaHtG3bFhMmTEDr1q2lO2+yvHz5EhMnTsTXX38tU7r8+e6776SiOC0tDQMHDpQGRBfltQEjIyPxzTffQFdXFw4ODmjQoIFa30WnKcX8gAEDEBERgZ07dyrdCTh8+HB8//332LBhg8wJP+7dweZFdeB5foSFheHgwYMwNzeHlpYWtLS00KhRI0yfPh3Dhg1DRESE3BFzNXHiRLkjFJinT5+iQoUKAN6O58o6G9+oUSMMGjRItlwc46WBSpQogQcPHsDS0lIj7lCJj49HrVq1oK2tjSFDhqBq1aoA3o6JWrhwITIyMhAeHg4rKyuZk35cbgOhsxTFAdElS5bEvXv3YGVlpRGfqaFDh+Lw4cM4c+ZMjsV8nTp10Lx581zXQpSbgYEB9u7di0aNGim1Hz16VLpjVl2sXLkS5ubmaNeuHYC3l4mWLl0KJycnrF+/Xi0KMzMzM4SHh8PBwQEVK1bEsmXL0Lx5c9y4cQMuLi5ITU2VO2KONG14SpYaNWrgjz/+QNOmTeHh4QFXV1f89ttvmD9/PmbNmiUtxq5q6vsnK32QjY0N/vnnH7Rt2xZCCNy7d++DEy2qw4zvVlZWOHHiBAYNGoTx48crjZvw9PTEwoULi3zRBRTNgiqv7O3tMX/+fLRq1QpCCISFhSmNT3tXUZ/WAwB++uknbNmyBVWqVPlgMf/jjz/KnDJ3ZcqUyfFyoomJyQe/P0XVtGnTsHjxYgBvzxwtWLAAc+fOxc6dOzFy5Ei1mBrD2dkZkZGRcHBwQN26dTFr1izo6Ohg6dKl0pmXokjThqdk6dOnDyIjI9G0aVMEBASgffv2WLBgAV6/fo05c+bIF0z1y0NSYVuyZInQ0dERWlpaH3woFAq1WJBZCCFu3LghMjMzhRBCPH36VJw+fVqcOnVKPH36VOZkxcfWrVuFlZWV9LlRKBQ5PtTlMyWEELdv3xZt2rRROh4tLS3Rpk0bcfPmTbnj5cmSJUuEh4eHePjwodT28OFD0apVKxEUFCRjsvzT19cXd+7cEUIIMXbsWNGrVy8hhBCXLl0S5ubmckbLsz179oh//vlHCCFEdHS0qFq1qlAoFMLc3FyEhITInI5u374t/vnnHxEZGSlrDl5q1FDPnz/HnTt3UKNGDRw4cECaKf196rAUx/uXtrp164b58+erxVkuTZM1Dca1a9c+eKmxqA/oft+zZ88QExMDIQQqV65c5M8UvX8pKDo6Gq9evZLOXt+9exe6urqoXLmyWl0WsrS0xN69e+Hm5gY3Nzf4+/ujV69euHHjBmrWrIkXL17IHfGTPH36NNc7gYuSM2fOIDMzE3Xr1lVqP3XqFLS1teHu7i5TMs3BS40aysjICM7OzlixYgUaNmwonUZWR+//bbB7925Mnz5dpjTFm6GhIQ4dOgQHBwe1Hlz/LjMzM3z55Zdyx8gzTboU9K6vvvoK/fv3h5ubG65fv462bdsCAC5fvlykFjjOq9jYWACAra2tzEnyx8/PD2PHjs1WeN2/fx8zZ87EqVOnZEr2aUJCQhASEoKEhIRs6+UuX75clkya8T8nfZCPj4/cEUgDJCcnS7M8u7m5fXSQsFyzQRcXmnTX2bsWLlyIn376CbGxsfjnn3+ks/Tnzp1Djx49ZE6XN2/evMHkyZMxf/586QydoaEhhg4diokTJ350CpOiIioqCrVq1crW7ubmhqioKBkSfbrJkydjypQpcHd3R9myZYvMWUcWXhpI0ya7VCgUajv/laYwMzOTLveamprm+PUXRXwhYE3k4+ODfv36qcUNDbkxNTXFggULsrVPnjxZhjSfZujQodiyZQtmzZqlNL3HpEmT8OTJE+nmgaJMV1cX8fHx2W4GePjwodqd5Q4KCkJwcDB69eoldxQl6vVVpDx5d7LL33//Xe2LFCHER+e/yqIOdz2pq4MHD6J06dLSv9X9M6UpkpKS4OHhATs7O/Tp0wc+Pj744osv5I71Sfbs2QNDQ0NpaoyFCxfizz//hJOTExYuXFjkx94BwLp167Bhwwa0adNGaqtRowZsbW3Ro0cPtSi8WrVqhfHjx+Pff/+VxmsmJibihx9+wFdffSVzuvxJT09HgwYN5I6RDQfXU5GnzvNfERW2R48eYfXq1Vi5ciWioqLg4eGBfv36oWPHjmpxaSuLi4sLZs6cibZt2+LixYv48ssv4e/vj0OHDsHR0VEtfr4tLS1x5MiRbOvKXrlyBU2aNFGL1RDu37+PJk2a4MmTJ3BzcwMAnD9/HlZWVti/f79ajVkbN24cDA0NMWHCBLmjKGHhpeF2794NbW1teHp6KrXv27cPGRkZSn+ZEeXFihUrYGhoiG+//VapfdOmTUhNTeW4QhmFh4djxYoVWLZsGQwNDfG///0PgwcPRuXKleWOlitDQ0NcunQJ9vb2mDRpEi5duoTNmzcjPDwcbdu2RVxcnNwRczVlyhRcvXoVK1askM7Qv3r1Cv369UPlypXVZnxeSkoK1q5di8jISOjr66NGjRro0aOHWhTy/v7+0r8zMzOxcuVK1KhRAzVq1MiWX665vHipUcMFBATkuBhoZmYmAgICWHhRvk2fPh1LlizJ1m5paQlfX18WXjJ5+PAh9u/fj/3790NbW1s6c+Tk5IRZs2Zh5MiRckf8KB0dHemmjQMHDqB3794A3o5ZTU5OljNankVERCAkJATlypWTpuqJjIxEeno6WrZsic6dO0t9i/LQCAMDA/j6+sod45O8vyyTq6srAODSpUsypMkZCy8NFx0dDScnp2ztjo6OiImJkSERqbu7d+/CwcEhW7udnR3u3r0rQ6Li6/Xr19i+fTtWrFiBffv2oUaNGhgxYgR69uwp3V26detW9O3bt8gXXo0aNYK/vz8aNmyI06dPY+PGjQCA69evo1y5cjKnyxtTU1N06dJFqU2dLs1lWb16NZYsWYKbN28iLCwMdnZ2+P3331GhQgV07NhR7ngfdejQIbkj5IqFl4YzMTHBzZs3s82DExMTk21wOlFeWFpa4sKFC9k+U5GRkR+cqJcKR9myZZGZmYkePXrg9OnT0l/372revDlMTU1Vni2/FixYgMGDB2Pz5s1YvHixdJPAf//9h9atW8ucLm/UYRxabhYvXozAwECMGDECv/zyi3SXspmZGebOnVvkC6939e3bF/PmzZNuNsuSkpKCoUOHyjaPF5cM0nC+vr7CxcVFxMTESG3R0dGiRo0aol+/fjImI3U1duxYYWdnJw4ePCjevHkj3rx5I0JCQoSdnZ0YNWqU3PGKlVWrVomXL1/KHYPek5CQII4ePSqOHj0qEhIS5I6TL9WqVRNbt24VQghhaGgobty4IYQQ4uLFi6JMmTIyJss/LS0tER8fn6390aNHQltbW4ZEb/GMl4abNWsWWrduDUdHR+l0/b1799C4cWP89ttvMqcjdfTzzz/j9u3baNmypTSvT2ZmJnr37o1p06bJnK54KWrzE+XXuxPz5jaOSx0m5s06k7Jq1SpplnRtbW307t0bf/zxB0qVKiVzwtzdunVLupvxXbq6ukhJSZEhUf4lJydDCAEhBJ4/fw49PT1pW0ZGBnbv3v3BJc9UgYWXhjMxMcGJEyewf/9+pTtUNGHCRZKHjo4ONm7ciJ9//ln6TLm4uMDOzk7uaMVOSkoKZsyY8cElUW7evClTsrzRtIl5/f39ceTIEezYsQMNGzYEABw7dgzDhg3DqFGj1GIeLwcHB5w/fz7bz/OePXuyTZNRVGV9lhQKBapUqZJtu0KhkHViXhZexYBCoUCrVq3QpEkT6OrqcvJLKhD29vYQQqBixYpqN6O1pujfvz+OHDmCXr16FaklUfLq3Yl51WFQdG7++ecfbN68Gc2aNZPa2rZtC319fXTt2lUtCi9/f3/4+fkhLS0NQgicPn0a69evx/Tp07Fs2TK54+XJoUOHIIRAixYt8M8//0ifMeDtH452dnawsbGRL6BsFzlJJTIyMsSUKVOEjY2N0NbWlq7X//TTT2LZsmUypyN1lJKSIvr27Su0tbWVPlNDhgwR06dPlzld8WJiYiKOHTsmdwz6//T19UVUVFS29kuXLolSpUrJkOjTrFmzRlSqVEkoFAqhUCjEF198oZa/L27fvi1CQ0OFt7e3qFevnrh3754Q4u3YyKNHj8qWS0u+ko9U4ZdffkFwcDBmzZoFHR0dqd3Z2Vlt/nqhomX8+PGIjIzE4cOHlcZOeHh4SFMAkGqYmZkp/TWv7tLS0nD69Gns3LkT27dvV3qog/r162PixIlIS0uT2l6+fInJkydLazeqA29vb0RHR+PFixeIi4vDvXv30K9fP7lj5dvZs2fh6ekJfX19RERE4NWrVwDeLrUl63hU2Uo+UomKFSuKAwcOCCGU71C5cuWKMDU1lTMaqany5cuLsLAwIYTyZyo6OloYGRnJGa3YWb16tfjmm29ESkqK3FE+23///ScsLCyksyzvPrS0tOSOlycXLlwQNjY2okyZMqJFixaiRYsWokyZMuKLL74Qly5dkjtevsTHx4vQ0FARGhqqdndmZnF1dRUrV64UQij/XxUeHi6srKxky8WBGRru/v37qFSpUrb2zMxMvH79WoZEpO4ePXqU4x1BKSkpajfGSB25ubkpfZ1jYmJgZWUFe3v7bEuihIeHqzreJxs6dCi+/fZbBAYGwsrKSu44n8TFxQXR0dFYu3Ytrl69CgDo0aMHvL29oa+vL3O6vHn+/DkGDx6M9evXK92Z2a1bNyxcuFBaOFsdXLt2LccbyUxMTJCYmKj6QP8fCy8N5+TkhKNHj2a7Q2Xz5s053jJMlBt3d3fs2rULQ4cOBQCpCFi2bJlaXU5RV15eXnJHKBTx8fHw9/dX26Lr9evXcHR0xM6dOzFgwAC543yy/v37IyIiArt27ZJ+nsPCwjB8+HB8//332LBhg8wJ887a2hoxMTHZJns+duwYKlSoIE8osPDSeIGBgfDx8cH9+/eRmZmJLVu24Nq1a1i1ahV27twpdzxSQ9OmTUObNm0QFRWFN2/eYN68eYiKisKJEydw5MgRueNpPHVZaDm/vvnmGxw+fBgVK1aUO8onKVmypNLYLnW1c+dO7N27F40aNZLaPD098eeff6rNCgJZBgwYgOHDh2P58uVQKBR48OABwsLCMHr0aEyYMEG2XAohhJBt76QSR48exZQpUxAZGYkXL16gVq1aCAwMRKtWreSORmrqxo0bmDFjhtJnaty4cXBxcZE7msYT/39eK02TmpqKb7/9FhYWFnBxccl22XTYsGEyJcu7adOm4fr161i2bJnaTrFSvnx57Nq1K9vP8oULF9C2bVvcu3dPpmT5J4TAtGnTMH36dGkBdl1dXYwePRo///yzbLlYeBERqREnJycEBgaic+fOSncqvy86Ohpz5syBnZ0dAgICVJjw0/z1118YOHAg9PT0UKZMGaXiUqFQFPnJYAGgU6dOCAkJgaGhIVxcXLKth7tlyxaZkuXd0qVLsWnTJqxevRrW1tYAgLi4OPj4+KBz5874/vvvZU6Yf+np6YiJicGLFy/g5OQEQ0NDWfOw8Comzp49iytXrgB4+x937dq1ZU5E6iwjIwNbt25V+kx17NhRbf/KVychISEYN24cbt68ia+++gru7u6wsbGBnp4enj17hqioKBw7dgyXL1/GkCFD8MMPP6jFgGhra2sMGzYMAQEB0NJSz5mO+vTp89Ht6rCItpubG2JiYvDq1SuUL18eAHD37l3o6uqicuXKSn3V6eaNooSFl4a7d+8eevTogePHj8PU1BQAkJiYiAYNGmDDhg3S+o1EeXX58mV06NABcXFxqFq1KgDg+vXrsLCwwI4dO+Ds7CxzwuLh2LFj2LhxI44ePYo7d+7g5cuXMDc3h5ubGzw9PeHt7Q0zMzO5Y+ZZ6dKlcebMGbUd46Up8rOUjqaONyxsLLw0XOvWrZGYmIiVK1dKvySvXbuGPn36wNjYGHv27JE5Iamb+vXrw8LCAitXrpR+sT979gzfffcdHj16hBMnTsickNTRyJEjYWFhgR9++EHuKJ8tISEB165dAwBUrVpV1gWZqehh4aXh9PX1ceLEiWxTR5w7dw6NGzeWBhwS5ZW+vj7Onj2L6tWrK7VfunQJX375JV6+fClTMlJnw4YNw6pVq1CzZk3UqFEj2+D6OXPmyJQs75KTk+Hn54cNGzZIi3qr2xxYsbGxUCgU0tWQ06dPY926dXBycoKvr6/M6TSDel5IpzyztbXNcaLUjIwMeRcJJbVVpUoVxMfHZ2tPSEjIcbJeory4ePEi3NzcoKWlhUuXLiEiIkJ6nD9/Xu54eTJgwACcOnUKO3fuRGJiIhITE7Fz506cPXtWbQal9+zZU1qwPC4uDh4eHjh9+jR+/PFHTJkyReZ0GkKO6fJJdbZt2ybq1Kkjzpw5I7WdOXNG1KtXT2zdulW+YKS2du3aJapXry42bdokYmNjRWxsrNi0aZNwcXERu3btEklJSdKDqDgpVapUjosvh4aGqs0i2aampuLq1atCCCHmzZsnGjRoIIQQYu/evcLBwUHOaBqDlxo1nJmZGVJTU/HmzRvpjrOsf79/q/PTp0/liEhq5t07zrJu+c/6b+Td5wqFQrrcQpRXMTExuHHjBpo0aQJ9fX21mrdME+bAMjQ0xKVLl2Bvb48OHTqgYcOGGDduHO7evYuqVatyKEEB4L3fGm7u3LlyRyANk3UZgqggPXnyBF27dsWhQ4egUCgQHR2NChUqoF+/fjAzM8Ps2bPljpirn376Cf7+/tnmwBozZoysM6XnR/Xq1REUFIR27dph//790kSjDx48QJkyZWROpxl4xouISI1lZmYiJiYGCQkJ0qLGWXJaILio6t27NxISErBs2TJUq1YNkZGRqFChAvbu3Qt/f39cvnxZ7oi50oQ5sA4fPoxOnTohOTkZPj4+WL58OQDghx9+wNWrV9ViEtiijme8NFx4eDhKliwpnfr+999/sWLFCjg5OWHSpEkfnfmaKCd79uyBoaGhtJbbwoUL8eeff8LJyQkLFy5Uq7mj1N3JkyfRs2dP3LlzB+//Da1ul3r37duHvXv3ZptbsHLlyrhz545MqfJHExYwb9asGR4/fozk5GSln2VfX1+UKlVKxmSag2e8NNyXX36JgIAAdOnSBTdv3oSTkxM6d+6MM2fOoF27drwUSfnm4uKCmTNnom3btrh48SLc3d0xatQoHDp0CI6OjmoxO7emcHV1RZUqVTB58mSULVs221godZi+IIuRkRHCw8NRuXJlGBkZSWe8zp49C09PTzx58kTuiEQFgoWXhjMxMUF4eDgqVqyImTNn4uDBg9i7dy+OHz+O7t27IzY2Vu6IpGbeHXw7adIkXLp0CZs3b0Z4eDjatm2LuLg4uSMWGwYGBoiMjNSIaTzatm2L2rVr4+eff4aRkREuXLgAOzs7dO/eHZmZmdi8ebPcEXOkToP/qWjgPF4aTgghjfs4cOAA2rZtC+Dt/F6PHz+WMxqpKR0dHWni3QMHDqBVq1YA3i75kpycLGe0Yqdu3bqIiYmRO0aBmDVrFpYuXYo2bdogPT0dY8eOhbOzM0JDQzFz5ky5431Q9erVsWHDBqSnp3+0X3R0NAYNGoQZM2aoKBkVVRzjpeHc3d3xyy+/wMPDA0eOHMHixYsBALdu3YKVlZXM6UgdNWrUCP7+/mjYsCFOnz6NjRs3Ani7XiPX/lStoUOHYtSoUYiLi4OLi0u22d5r1KghU7L8c3Z2xvXr17FgwQIYGRnhxYsX6Ny5M/z8/FC2bFm5433QH3/8gXHjxmHw4MF5WrR80KBBckcmmfFSo4a7cOECvL29cffuXfj7+0uLmg4dOhRPnjzBunXrZE5I6ubu3bsYPHgwYmNjMWzYMPTr1w/A27X2MjIyMH/+fJkTFh/vzqmWRaFQcB41GWjKouWrVq1Ct27doKurq9Senp6ODRs2oHfv3jIl0xwsvIqptLQ0aGtrZ/sLmYjUR253+9nZ2akoCWkKbW1tPHz4MNvC3k+ePIGlpSWL+QLAS43FlJ6entwRiOgzsbCigvahmwXu3bunVnfJFmUsvIiI1NiNGzcwd+5cXLlyBQDg5OSE4cOHo2LFijInI3Xi5uYGhUIBhUKBli1bSkvMAUBGRgZu3bqF1q1by5hQc7DwIiJSU3v37kWHDh3g6uqKhg0bAgCOHz+O6tWrY8eOHfjqq69kTkjqImvy1/Pnz8PT0xOGhobSNh0dHdjb26NLly4ypdMsHONFRKSmsgZuvz9FQUBAAPbt21dkl6XJyfLly9G8eXM4ODjIHaVYW7lyJbp168bhKIWI83hpuClTpkhzLr3r5cuXmDJligyJSN317dsXz58/z9aekpKCvn37ypCo+Lpy5Yp0V+m7+vbti6ioKBkSfbrp06ejUqVKKF++PHr16oVly5ZpzBxl6sTHxwd6eno4d+4c1qxZgzVr1iAiIkLuWBqFZ7w0HO9QoYL2oc/U48ePYW1tjTdv3siUrPixtbXFnDlz8O233yq1//333xg9ejTu3r0rU7JPc//+fRw+fBihoaE4cuQIoqOjUbZsWTRr1gxr1qyRO16eqPui5QkJCejevTsOHz4MU1NTAEBiYiKaN2+ODRs2wMLCQt6AGoBjvDTch+5QiYyMROnSpWVIROoqOTkZQggIIfD8+XOlSxEZGRnYvXt3tmKMCteAAQPg6+uLmzdvokGDBgDejvGaOXMm/P39ZU6Xf1988QW8vb3RqVMnHD16FOvXr8fatWuxYcMGtSi8NGHR8qFDh+L58+e4fPkyqlWrBgCIioqCj48Phg0bhvXr18ucUP3xjJeGMjMzg0KhQFJSEoyNjZWKr4yMDLx48QIDBw7EwoULZUxJ6kRLS+uja9IpFApMnjwZP/74owpTFW9CCMydOxezZ8/GgwcPAAA2NjYYM2YMhg0bplZrCO7btw+HDx/G4cOHERERgWrVqqFp06Zo1qwZmjRpohaTj2rCouUmJiY4cOAAvvzyS6X206dPo1WrVkhMTJQnmAZh4aWhVq5cCSEE+vbti7lz5yr9wGfdoVK/fn0ZE5K6OXLkCIQQaNGiBf755x+lM6Y6Ojqws7ODjY2NjAmLt6xxd0ZGRjIn+TRaWlqwsLDAqFGj4OvrK13mUieasGi5kZERjh49CldXV6X2iIgING3alOuxFgAWXhruyJEjaNCgAWeopwJz584d2Nra5rhcDdGnmjt3LkJDQxEaGgpdXV3pbFezZs1QpUoVuePlSYsWLTB27Fi1nu+qY8eOSExMxPr166U/pO7fvy8tebR161aZE6o/Fl7FQEZGBrZt2yZNsFi9enV06NAB2traMicjdZWYmIi//vpL6TPVt29ftbiUou5q1aqFkJAQmJmZSZNefog6TSfxrosXL+LIkSM4ePAgdu7cCUtLS9y7d0/uWLnaunUrfvrpJ4wZM0ZtFy2PjY1Fhw4dcPnyZdja2kptzs7O2L59O8qVKydzQvXHwkvDxcTEoG3btrh//z6qVq0KALh27RpsbW2xa9cuzm5N+Xb27Fl4enpCX18fderUAQCcOXMGL1++xL59+1CrVi2ZE2q2yZMnY8yYMShVqhQmT5780b4TJ05UUaqCIYRAREQEDh8+jEOHDuHYsWN4/vw5XFxc1GJKA01ZtFwIgQMHDuDq1asAgGrVqsHDw0PmVJqDhZeGa9u2LYQQWLt2rTQm58mTJ/jf//4HLS0t7Nq1S+aEpG4aN26MSpUq4c8//5SWFXnz5g369++PmzdvIjQ0VOaEpI7at2+P48ePIzk5GTVr1kSzZs3QtGlTNGnSRG3Ge3HRcsoLFl4azsDAACdPnoSLi4tSe2RkJBo2bIgXL17IlIzUlb6+PiIiIuDo6KjUHhUVBXd39xwn7KXCERsbC4VCIV3+OX36NNatWwcnJyf4+vrKnC5/xowZg6ZNm6Jx48a8ZC2zkJAQhISE5DgX2fLly2VKpTk4j5eG09XVzXGW8RcvXkBHR0eGRKTujI2Ncffu3WyFV2xsrNreUaeuevbsCV9fX/Tq1QtxcXHw8PCAs7Mz1q5di7i4OAQGBsodMc9+/fVXuSMUCHVftHzy5MmYMmUK3N3dc5wSgwqAII3Wq1cvUb16dXHy5EmRmZkpMjMzRVhYmHB2dhY+Pj5yxyM1NHToUFGuXDmxYcMGcffuXXH37l2xfv16Ua5cOTF8+HC54xUrpqam4urVq0IIIebNmycaNGgghBBi7969wsHBQc5on+Tw4cPi66+/FhUrVhQVK1YU7du3F6GhoXLHyrM9e/YIHR0dUadOHTFy5EgxcuRIUadOHaGrqyv27dsnd7w8sba2FqtWrZI7hkZj4aXhnj17Jjp06CAUCoXQ0dEROjo6QktLS3h5eYnExES545EaevXqlRg2bJj0WdLS0hK6urpixIgRIi0tTe54xYqBgYG4deuWEEKI9u3bixkzZgghhLhz547Q09OTMVn+rV69WpQoUUJ07dpVzJs3T8ybN0907dpVlCxZUqxdu1bueHni6uoqxo0bl6193Lhxws3NTYZE+Ve6dGkRExMjdwyNxjFexUR0dLTSHSrqPMEfFQ2pqam4ceMGAKBixYooVaqUzImKn7p166J58+Zo164dWrVqhZMnT6JmzZo4efIkvvnmG7WYgiFLtWrV4Ovri5EjRyq1z5kzB3/++ad06a4o09PTw8WLF1G5cmWl9uvXr6NGjRpIS0uTKVnejRs3DoaGhpgwYYLcUTQWx3gVE5UrV872nwHR5yhVqlS2mzZItWbOnIlOnTrh119/hY+PD2rWrAkA2L59uzTVh7q4efMm2rdvn629Q4cO+OGHH2RIlH8WFhY4f/58tv9rz58/rzbrmKalpWHp0qU4cOAAatSokW0usjlz5siUTHOw8NJwGRkZCA4O/uAdKgcPHpQpGamrlJQUzJgx44OfqZs3b8qUrPhp1qwZHj9+jOTkZKW1DH19fdXuDKStrS1CQkKynY0/cOCANJFnUacJi5ZfuHBBWi7o0qVLSts40L5gsPDScMOHD0dwcDDatWsHZ2dn/uDQZ+vfvz+OHDmCXr168a4nmb18+RJCCKnounPnDrZu3Ypq1arB09NT5nT5M2rUKAwbNgznz59XKlqCg4Mxb948mdPlzYQJE2BkZITZs2dj/PjxAN4uWj5p0iQMGzZM5nR5c+jQIbkjaDyO8dJw5ubmWLVqFdq2bSt3FNIQpqam2LVrFxo2bCh3lGKvVatW6Ny5MwYOHIjExEQ4OjqiZMmSePz4MebMmYNBgwbJHTFftm7ditmzZ0vjuapVq4YxY8agY8eOMifLP3VftJwKD1e51XA6OjocSE8FyszMTFoFgeQVHh6Oxo0bAwA2b94MKysr3LlzB6tWrcL8+fNlTpd/nTp1wrFjx/DkyRM8efIEx44dU8uiC3hbcLHoopzwjJeGmz17Nm7evIkFCxbwkhAViDVr1uDff//FypUr1W4ckaYpVaoUrl69ivLly6Nr166oXr06Jk6ciNjYWFStWpWrCKhAcVi0nAoWx3hpuGPHjuHQoUP477//UL169Wx3qGzZskWmZKSuZs+ejRs3bsDKygr29vbZPlP85aI6lSpVwrZt29CpUyfs3btXmoohISEBxsbGMqfLnZmZWZ7/IHz69Gkhp/k0HTt2hK6uLgDAy8tL3jCkFlh4aThTU1N06tRJ7hikQfjLpegIDAxEz549MXLkSLRs2RL169cHAOzbtw9ubm4yp8vd3Llz5Y7w2SZOnJjjv4k+hJcaiYjUWFxcHB4+fIiaNWtCS+vtsN3Tp0/D2Ng423qaVLg0adFyKjwsvIgoV0IIjhEkykXjxo2VFi2vUqUKnJ2dER0djaFDh6rVouVUeFh4aaDWrVtj0qRJqFev3kf7PX/+HIsWLYKhoSH8/PxUlI7UkZOTEwIDA9G5c2fo6Oh8sF90dDTmzJkDOzs7BAQEqDBh8dG5c2cEBwfD2NgYnTt3/mhfjuFULTMzM5w8eRJVq1bF/PnzsXHjRhw/fhz79u3DwIEDObkwAeAYL4307bffokuXLjAxMUH79u3h7u4OGxsb6Onp4dmzZ4iKisKxY8ewe/dutGvXDr/++qvckamI++OPPzBu3DgMHjwYX3311Qc/U5cvX8aQIUPUbv4odWJiYiKdfTQxMZE5Db3r9evX0kD7AwcOoEOHDgAAR0dHPHz4UM5oVITwjJeGevXqFTZt2oSNGzfi2LFjSEpKAvB2yQcnJyd4enqiX79+qFatmsxJSZ0cO3YMGzduxNGjR3Hnzh28fPkS5ubmcHNzg6enJ7y9vZWWriEqTjRp0XIqPCy8iomkpCS8fPkSZcqUyXb7PxERfb7Dhw+jU6dOSE5Oho+PD5YvXw4A+OGHH3D16lVe+iUALLyIiNTWkydPEBgYiEOHDuW4YHlRnfsqS25j1N6lLkVLRkZGtkXLb9++jVKlSsHS0lLGZFRUcIwXEZGa6tWrF2JiYtCvXz9YWVmp3Z2n745RE0Jg69atMDExgbu7OwDg3LlzSExMzFeBJidNWrScCg/PeBERqSkjIyMcO3YMNWvWlDvKZxs3bhyePn2KoKAgaGtrA3h79mjw4MEwNjZWi5uANG3RciocXCSbiEhNOTo64uXLl3LHKBDLly/H6NGjpaILALS1teHv7y+NlSrqNG3RciocLLyIiNTUokWL8OOPP+LIkSN48uQJkpOTlR7q5M2bN7h69Wq29qtXr2Ybu1ZUpaamwsjICMDbZZs6d+4MLS0t1KtXD3fu3JE5HRUVHOOl4c6cOYPMzEzUrVtXqf3UqVPQ1taWxlIQ5VV4eDhKliwJFxcXAMC///6LFStWwMnJCZMmTfroBKtUsExNTZGcnIwWLVootWetNJCRkSFTsvzr06cP+vXrhxs3bqBOnToA3v4/NWPGDPTp00fmdHmj7ouWk2qw8NJwfn5+GDt2bLbC6/79+5g5cyZOnTolUzJSV99//z0CAgLg4uKCmzdvonv37ujUqRM2bdqE1NRUjVj4WF14e3ujZMmSWLdunVoOrn/Xb7/9Bmtra8yePVuabLRs2bIYM2YMRo0aJXO6vFH3RctJNTi4XsMZGhriwoULqFChglL7rVu3UKNGDTx//lymZKSuTExMEB4ejooVK2LmzJk4ePAg9u7di+PHj6N79+6IjY2VO2KxUapUKURERKBq1apyRylQWZdJ1fEsERctp9zwjJeG09XVRXx8fLbC6+HDhyhRgt9+yj8hhDTm5sCBA/j6668BALa2tnj8+LGc0Yodd3d3xMbGalzhpY4FVxZra2tYW1srtWVdOiUCeMZL4/Xo0QMPHz7Ev//+K82Zk5iYCC8vL1haWuLvv/+WOSGpmxYtWsDW1hYeHh7o168foqKiUKlSJRw5cgQ+Pj64ffu23BGLjU2bNmHSpEkYM2YMXFxcsq1KUaNGDZmS5V98fDxGjx6NkJAQJCQk4P1fTUV1vBoXLaf84ikPDffbb7+hSZMmsLOzk8YYnD9/HlZWVli9erXM6UgdzZ07F97e3ti2bRt+/PFHVKpUCcDb2+cbNGggc7ripVu3bgCAvn37Sm0KhUItB9d/9913uHv3LiZMmICyZcuqzXg1LlpO+cUzXsVASkoK1q5di8jISOjr66NGjRro0aMH12ykApWWlgZtbW1+rlQotykK7OzsVJTk8xkZGeHo0aNwdXWVOwpRoeIZr2LAwMAAvr6+cscgDaenpyd3hGJHnQqr3Nja2ma7vEikiXjGSwNt374dbdq0QcmSJbF9+/aP9u3QoYOKUpGm0NLS+uhlIHW6vKUJVq9ejaCgINy6dQthYWGws7PD3Llz4eDggI4dO8odL8/27duH2bNnY8mSJbC3t5c7zidR90XLSTV4xksDeXl5IS4uDpaWlvDy8vpgP3UbA0JFw9atW5Wev379GhEREVi5ciUmT54sU6riafHixQgMDMSIESMwdepU6efZ1NQUc+fOVavCq1u3bkhNTUXFihVRqlSpbJes1aFoUfdFy0k1eMaLiArEunXrsHHjRvz7779yRyk2nJycMG3aNHh5ecHIyAiRkZGoUKECLl26hGbNmqnV9B4rV6786HYfHx8VJfl0mrRoORUenvHSYK9fv0br1q0RFBSEypUryx2HNFy9evU4llDFbt26leOM6Lq6ukhJSZEh0adTh8IqN5q0aDkVHi6SrcFKliyJCxcuyB2DioGXL19i/vz5+OKLL+SOUqw4ODjg/Pnz2dr37NmDatWqqT5QAUlLS1PLBb81adFyKjw846Xh/ve//+Gvv/7CjBkz5I5CGsLMzExp7IoQAs+fP0epUqWwZs0aGZMVP/7+/vDz80NaWhqEEDh9+jTWr1+P6dOnY9myZXLHy5eUlBSMGzcOf//9N548eZJtuzqMR9WkRcup8LDw0nBv3rzB8uXLceDAAdSuXRsGBgZK2+fMmSNTMlJX7y+CraWlBQsLC9StWxdmZmbyhCqm+vfvD319ffz0009ITU1Fz549YWNjg3nz5qF79+5yx8uXsWPH4tChQ1i8eDF69eqFhQsX4v79+1iyZIna/OGoSYuWU+Hh4HoN17x5849uP3TokIqSEFFhSk1NxYsXL2BpaSl3lE9Svnx5rFq1Cs2aNYOxsTHCw8NRqVIlrF69GuvXr8fu3bvljpgrTV20nAoWz3hpOBZWVBiePXuGv/76C1euXAHw9u66Pn36oHTp0jInK75KlSqFUqVKyR3jkz19+hQVKlQA8HaR7KzpIxo1aoRBgwbJGS3PNHXRcipYHFyv4fr27Yvnz59na09JSVFa340or0JDQ2Fvb4/58+fj2bNnePbsGebPnw8HBweEhobKHY/UVIUKFXDr1i0Ab+8O/PvvvwEAO3bsgKmpqYzJ8m7o0KEYPnw4goODce7cOVy4cEHpQQTwUqPG09bWxsOHD7Ndfnj8+DGsra3x5s0bmZKRunJxcUH9+vWxePFiaGtrA3g78Hnw4ME4ceIELl68KHNCUke///47tLW1MWzYMBw4cADt27eHEAKvX7/GnDlzMHz4cLkj5kpLK/u5DHVdtJwKDwsvDZWcnAwhBMzMzBAdHQ0LCwtpW0ZGBnbs2IGAgAA8ePBAxpSkjvT19XH+/Plsl1OuXbsGV1dXzmNEBeLOnTs4d+4cKlWqhBo1asgdJ080adFyKjwc46WhTE1NoVAooFAoUKVKlWzbFQoFl3ehT1KrVi1cuXIlW+F15coVztitYmlpaRq7OLmdnZ3aFSrqlpfkwcJLQx06dAhCCLRo0QL//POP0qBnHR0d2NnZwcbGRsaEpE7eHZ8ybNgwDB8+HDExMahXrx4A4OTJk1i4cKHa3PavKUxNTVGnTh00bdoUzZo1Q4MGDaCvry93rGJNUxYtp8LDS40a7s6dOyhfvjznk6HPoqWlJY1V+RiOY1GtY8eOITQ0FIcPH8aJEyfw5s0buLu7S4XYV199JXfEYuX9RcsvXbqEChUqIDg4GCtXruRd5gSAhZfGW7FiBQwNDfHtt98qtW/atAmpqakasT4aFb7cxq68i5db5PHmzRucOXMGS5Yswdq1a5GZmckiWMU0adFyKjy81Kjhpk+fjiVLlmRrt7S0hK+vLwsvyhMWU0XX9evXcfjwYenx6tUrfP3112jWrJnc0YodTVq0nAoPCy8Nd/fuXTg4OGRrt7Ozw927d2VIREQF5YsvvsDLly/RrFkzNGvWDOPGjUONGjXUdmhBRkYGtm3bJk3MW716dXTo0EGatqSoy1q0/P0/VNR90XIqWCy8NJylpSUuXLgAe3t7pfbIyEiUKVNGnlBEVCAsLCxw9epVxMXFIS4uDvHx8Xj58qVazmAfExODdu3a4d69e9Ids9OnT4etrS127dqFihUrypwwd5q0aDkVHo7x0nDjxo3Dxo0bsWLFCjRp0gQAcOTIEfTt2xfffPMNfvvtN5kTEtHnSExMRGhoKI4cOYIjR44gKioKrq6uaN68OaZOnSp3vDxr27YthBBYu3atdBf2kydP8L///Q9aWlrYtWuXzAnzZu3atZg0aRJu3LgBALCxscHkyZPRr18/mZNRUcHCS8Olp6ejV69e2LRpE0qUeHuCMzMzE71790ZQUBB0dHRkTkhEBeHJkyc4fPgw/v33X6xfv17tBtcbGBjg5MmTcHFxUWqPjIxEw4YN8eLFC5mSfRp1X7ScCg8vNWo4HR0dbNy4ET///DMiIyOhr68PFxcXDpamz5aeno6EhARkZmYqtZcvX16mRMXPli1bpEH1UVFRKF26NBo1aoTZs2ejadOmcsfLF11d3RzXlX3x4oVa/oGo7ouWU+HhGa9iIj09Hbdu3ULFihWlM19EnyI6Ohp9+/bFiRMnlNq5Hp3qWVpaokmTJmjWrBmaNm2a7WyROunduzfCw8Px119/oU6dOgCAU6dOYcCAAahduzaCg4PlDUhUQFh4abjU1FQMHToUK1euBPD21vMKFSpg6NCh+OKLLxAQECBzQlI3DRs2RIkSJRAQEICyZctmu4OOywbRp0hMTISPjw927NiBkiVLAng7N1mHDh0QHBwMExMTmRMSFQwWXhpu+PDhOH78OObOnYvWrVvjwoULqFChAv79919MmjQJERERckckNWNgYIBz587B0dFR7iiE7FMwODk5oWPHjmozBQPw9mxpbGwsLCwscP/+felYqlWrhkqVKsmcjqhg8ZqThtu2bRs2btyIevXqKZ2ZqF69unTXDVF+ODk5cQbuIiImJgZt27bF/fv31XYKBuBt4VWpUiVcvnwZlStXVttiS5MXLaeCoyV3ACpcjx49yvGumpSUFLWdZJHkNXPmTIwdOxaHDx/GkydPkJycrPQg1Rk2bBgqVqyI2NhYhIeHIzw8XJo0ediwYXLHyzMtLS1UrlwZT548kTvKZzE1NUWTJk0wYcIEhISE4OXLl3JHoiKIlxo1XJMmTfDtt99i6NChMDIywoULF+Dg4IChQ4ciOjoae/bskTsiqRktrbd/r71fuHNwvepp0hQMO3bswKxZs7B48WI4OzvLHeeTcNFyygsWXhru2LFjaNOmDf73v/8hODgY33//PaKionDixAkcOXIEtWvXljsiqZkjR458dLu6TWOgzkqXLo2dO3eiQYMGSu3Hjx9H+/bt8fTpU5mS5Z+ZmRlSU1Px5s0b6OjoQF9fX2m7Oh0LwEXL6cM4xkvDNWrUCOfPn8eMGTPg4uKCffv2oVatWggLC1PrW89JPiysio6vv/4avr6+2aZgGDhwIDp06CBzuvyZO3eu3BEKBBctp9zwjBcR5VtiYiL++usvpcWM+/bty1v+VYxTMBQt7y9a3rRpU7VetJwKBwsvDZSfAc7GxsaFmIQ00dmzZ+Hp6Ql9fX3pLMuZM2fw8uVL6YwqqVZ0dDSuXr0KQL2nYLhx4wZWrFiBGzduYN68ebC0tMR///2H8uXLo3r16nLHy5WrqyuuXr2KWrVqScVXo0aNOIM9KWHhpYG0tLRy/QuLA6HpUzVu3BiVKlXCn3/+Ka2C8ObNG/Tv3x83b95EaGiozAlJHR05cgRt2rRBw4YNERoaiitXrqBChQqYMWMGzp49i82bN8sdMU80ZdFyKjwsvDRQboOf38XxOpRf+vr6iIiIyDaBalRUFNzd3ZGamipTsuLB398/z33nzJlTiEkKVv369fHtt9/C398fRkZGiIyMRIUKFXD69Gl07twZ9+7dkztivqj7ouVUeDi4XgOxmKLCZGxsjLt372YrvGJjY2FkZCRTquIjr6tNqNu4oosXL2LdunXZ2i0tLdVmwl5NWrScCg8Lr2Lg6NGjWLJkCW7evIlNmzbhiy++wOrVq+Hg4IBGjRrJHY/UTLdu3dCvXz/89ttv0jQGx48fx5gxY9CjRw+Z02m+Q4cOyR2hUJiamuLhw4dwcHBQao+IiMAXX3whU6r8GThwIJo0aQJfX1+1X7ScCg8LLw33zz//oFevXvD29kZ4eDhevXoFAEhKSsK0adOwe/dumROSuvntt9+gUCjQu3dvvHnzBgBQsmRJDBo0CDNmzJA5Hamr7t27Y9y4cdi0aRMUCgUyMzNx/PhxjB49Gr1795Y7Xp4kJCTIHYHUAMd4aTg3NzeMHDkSvXv3Vho3ERERgTZt2iAuLk7uiKSmUlNTpfU+K1asyDu36LOkp6fDz88PwcHByMjIQIkSJZCRkYGePXsiODhYbRb91oRFy6lwsfDScKVKlUJUVBTs7e2VCq+bN2/CyckJaWlpckckIpLExsbi4sWLePHiBdzc3FC5cmW5I+VZTouWX7t2Te0WLafCxUuNGs7a2hoxMTGwt7dXaj927BgqVKggTyhSO507d0ZwcDCMjY3RuXPnj/bdsmWLilKRJgkNDf1/7d17UJV1HsfxzzkmFzE187Y0BFqsAgKLWANRhFpettldwjazVmRCqZ1FUKGRmoFSNsexlQkNZ2tMZNtMdBzd2SGN1oSEcPOCGKsjWl52CyyLLAQD5dk/Ws92BisewvOcg+/XDDOc33M5n2EY5svv+Z7fT+PGjVNAQIACAgIc4x0dHaqpqVF8fLyF6brnyqble/fu1dChQyV9++nG3/3ud8rIyFBZWZnFCeEOKLz6uPnz5yszM1Pr16+XzWbTJ598opqaGmVnZys3N9fqePAQgwcPdnxKjtXQcS0kJCRo5MiR2rZtm2JiYhzjX3zxhSZNmuQRSzFUVlY6FV2SdPPNN2vFihWKi4uzMBncCYVXH5eTk6POzk5NmTJFra2tio+Pl7e3t7Kzs7VgwQKr48FDFBcXX/V7oDc98sgjmjJlioqKipSSkuIY95SOGG9vb3399dddxltaWuTl5WVBIrgjeryuE+3t7Tpx4oRaWloUGhqqgQMHWh0JHqqtrU2GYTia6U+fPq1t27YpNDRUU6dOtTgdPFW/fv3U2NioqqoqJScnKy0tTatWrdKnn34qf39/j5jxSk5O1sGDB7tsWj5//nxFR0drw4YN1gaEW6DwAmDK1KlTlZSUpCeffFJffvmlxo4dKy8vL507d04FBQX6/e9/b3VEeCC73a6mpiaNGDFCtbW1+s1vfqPQ0FAVFhYqNDTUIwovNi1Hd1B4ATBl2LBhqqysVFhYmNatW6c1a9aotrZWW7duVV5enuNj9IAZ3y28JKmpqUmJiYn6z3/+o8bGRo8ovK7oK5uW49qgxwuAKa2trY6tgcrLy5WUlCS73a6YmBidPn3a4nTwVHPnzpWvr6/j9ahRo1RZWam0tDSP23g9ODjYo5bBgGsx4wXAlIiICM2bN08PPvigxo8fr507dyo2NlYHDhzQAw88wKK8uK701U3Lce0w4wXAlLy8PD366KNatGiRpkyZotjYWEnfzn5FRUVZnA6e5MyZM7r11lu7ff7HH3/sdvs29tVNy3HtMOMFwLSmpiY1NjYqMjJSdrtdkvT+++9r0KBBGjdunMXp4ClGjhypxMREzZs3T3fcccdVzzl//rw2b96swsJCpaWlKSMjw8Upgd5F4QXgJ/nqq6/0zjvvaOzYsQoJCbE6DjzI559/rueff17r16+Xj4+PoqOj5e/vLx8fHzU3N+vIkSP617/+pQkTJig3N1e//OUvrY4M/GQUXgBMefjhhxUfH6/09HS1tbUpMjJSp06dkmEY2rRpk2bOnGl1RHiYtrY2lZWVqaqqSqdPn1ZbW5uGDRumqKgoTZs2TePHj7c6ItBrKLwAmDJq1Ci99dZbioyM1MaNG/Xss8+qrq5OJSUleuWVV7rd8wIA1yO71QEAeJbz58879qLbuXOnZs6cqQEDBuiBBx7Q8ePHLU4HAO6NwguAKQEBAaqpqdGFCxe0c+dOxzZBzc3N8vHxsTgdALg3lpMAYMrChQv12GOPaeDAgQoMDFRCQoIk6d1331V4eLi14QDAzdHjBcC0/fv369///rfuv/9+x4brZWVlGjJkiOLi4ixOBwDui8ILAODWDMNgAVL0GTxqBPCjFi9erPz8fPn5+f3oFilsi4KeSElJUVFRkfz8/JzGT506pTlz5mjPnj0WJQN6F4UXgB9VW1urjo4Ox/ffh1kJ9FRdXZ0iIiL017/+1bENVUlJiTIyMjR58mSL0wG9h0eNAADLdXR06JlnntHq1auVlZWlEydOaMeOHSooKND8+fOtjgf0GgovAIDbePbZZ5Wfn68bbrhBlZWVjtkvoK+g8AJgysWLF7VmzRrt3r1bn376qTo7O52OHzx40KJk8GQdHR3KyclRUVGRsrKyVFVVpYaGBr366qvs0Yg+hR4vAKakpqaqvLxcDz30kO688076utArJk6cqNbWVlVUVCgmJkaGYWjlypVKSkrS448/rrVr11odEegVzHgBMGXw4MF68803Wa8LvSo1NVWrV6/u8qnG2tpazZkzR/X19RYlA3oXhRcAU0JDQ7Vp0yZFRERYHQXXiW+++Ube3t5WxwB6BYUXAFN27Nih1atX689//rMCAwOtjgMP9tVXX2nQoEGO73/IlfMAT0ePFwBTJk6cqIsXL2rMmDEaMGCA+vfv73T8iy++sCgZPM1NN92kxsZGjRgxQkOGDLlqv+CVVesvX75sQUKg91F4ATBl9uzZ+vjjj7V8+XKNHDmS5nr02DvvvKOhQ4dKknbv3m1xGsA1eNQIwJQBAwaopqZGkZGRVkcBAI/DjBcAU8aNG6e2tjarY6APunjxog4fPnzV9eF+/etfW5QK6F3MeAEwpby8XEuXLtXzzz+v8PDwLj1eNEGjJ3bu3Knk5GSdO3euyzF6vNCXUHgBMMVut0vquiE2TdD4KYKDgzV16lTl5eVp5MiRVscBrhkeNQIwhSZoXAtnz57V4sWLKbrQ51F4ATDl3nvvtToC+qCHHnpIFRUVuu2226yOAlxTPGoEYNqePXv08ssv66OPPtKWLVt0yy236LXXXtPo0aN19913Wx0PHqi1tVW//e1vNXz48Kv2DmZkZFiUDOhdzHgBMGXr1q2aM2eOHnvsMR08eFDffPONJOn8+fNavny53nzzTYsTwhO98cYbKi8vl4+PjyoqKpx6CG02G4UX+gxmvACYEhUVpUWLFik5OVk33nij6urqNGbMGNXW1mrGjBlqamqyOiI80KhRo5SRkaGcnBzHBziAvojfbgCmHDt2TPHx8V3GBw8erC+//NL1gdAntLe3a9asWRRd6PP4DQdgyqhRo3TixIku41VVVRozZowFidAXzJ07V6WlpVbHAK45erwAmDJ//nxlZmZq/fr1stls+uSTT1RTU6Ps7Gzl5uZaHQ8e6vLly1q5cqXeeustRUREdGmuLygosCgZ0LsovACYkpOTo87OTk2ZMkWtra2Kj4+Xt7e3srOztWDBAqvjwUN98MEHioqKkiTV19c7HWMjdvQlNNcD6JH29nadOHFCLS0tCg0N1cCBA62OBABujx4vAD3i5eWluro6hYWFUXQBQDcx4wWgxwYNGqRDhw7RVA8A3cSMF4Ae4/82ADCHwgsAAMBFKLwA9NiOHTvk7+9vdQwA8Bj0eAHosSt/Pvi4PwB0DzNeAEz7y1/+ovDwcPn6+srX11cRERF67bXXrI4FAG6PBVQBmFJQUKDc3Fylp6crLi5O0rfbBT355JM6d+6cFi1aZHFCAHBfPGoEYMro0aO1dOlSJScnO42XlJT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transit_agencyprop_typetotal_costbus_countcost_per_bus
45City of Wascozero-emission bus (not specified)15430003.0514333
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" + ], "text/plain": [ - "
" + " transit_agency prop_type total_cost bus_count \\\n", + "45 City of Wasco zero-emission bus (not specified) 1543000 3.0 \n", + "\n", + " cost_per_bus \n", + "45 514333 " ] }, "metadata": {}, @@ -728,24 +1143,104 @@ } ], "source": [ - "# COST PER BUS BY PROP TYPE\n", - "display(Markdown(cpb_prop_type_desc))\n", - "make_chart(\"cpb\", \"Cost per bus by propulsion type\", x_col=\"prop_type\", data=prop_agg)" + "## min max values of just ZEB projects\n", + "new_cols =[\n", + " \"transit_agency\",\n", + " \"prop_type\",\n", + " \"total_cost\",\n", + " \"bus_count\",\n", + " \"cost_per_bus\"]\n", + "\n", + "display(Markdown(\"**Which Agneices had the highest and lowest cost per bus?**\")),\n", + "bus_min_max_summary(data=zeb_projects, col1=\"cost_per_bus\", col_list=new_cols)" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "7462b55c-29ef-4909-a7dd-27e1c84157d0", - "metadata": { - "tags": [] - }, + "execution_count": 19, + "id": "75919ab8-7f14-49f2-bb4a-9765fdddc35c", + "metadata": {}, "outputs": [ { "data": { "text/markdown": [ + "**Which agency procured the most and least amount of ZEBs?**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Max bus_count**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencyprop_typetotal_costbus_countcost_per_bus
44City of Los Angeles (LA DOT)zero-emission bus (not specified)102790000112.0917767
\n", + "
" + ], + "text/plain": [ + " transit_agency prop_type \\\n", + "44 City of Los Angeles (LA DOT) zero-emission bus (not specified) \n", + "\n", + " total_cost bus_count cost_per_bus \n", + "44 102790000 112.0 917767 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Min bus_count**" ], "text/plain": [ "" @@ -756,9 +1251,61 @@ }, { "data": { - "image/png": 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transit_agencyprop_typetotal_costbus_countcost_per_bus
70SLO TRANSIT (SAN LUIS OBISPO, CA)BEB8472141.0847214
82City of San Luis ObispoBEB8592701.0859270
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" + ], "text/plain": [ - "
" + " transit_agency prop_type total_cost bus_count \\\n", + "70 SLO TRANSIT (SAN LUIS OBISPO, CA) BEB 847214 1.0 \n", + "82 City of San Luis Obispo BEB 859270 1.0 \n", + "\n", + " cost_per_bus \n", + "70 847214 \n", + "82 859270 " ] }, "metadata": {}, @@ -766,33 +1313,98 @@ } ], "source": [ - "# bus count BY PROP TYPE\n", - "display(Markdown(bus_count_prop_type_desc))\n", - "make_chart(\n", - " \"total_bus_count\", \n", - " \"Bus count by propulsion type\",\n", - " x_col=\"prop_type\",\n", - " data=prop_agg\n", - ")" + "display(Markdown(\n", + " \"**Which agency procured the most and least amount of ZEBs?**\"\n", + "))\n", + "bus_min_max_summary(data=zeb_projects, col1=\"bus_count\", col_list=new_cols)\n" ] }, { "cell_type": "code", - "execution_count": 16, - "id": "4f092539-c4c6-4579-aa02-fbee65414ec3", - "metadata": { - "tags": [] - }, + "execution_count": 20, + "id": "c4f1b88e-f46c-4f69-888d-e116d2ce2ace", + "metadata": {}, "outputs": [ { "data": { "text/markdown": [ + "**Which Agency had the most and least total ZEB cost?**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Max total_cost**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencyprop_typetotal_costbus_countcost_per_bus
44City of Los Angeles (LA DOT)zero-emission bus (not specified)102790000112.0917767
\n", + "
" + ], + "text/plain": [ + " transit_agency prop_type \\\n", + "44 City of Los Angeles (LA DOT) zero-emission bus (not specified) \n", + "\n", + " total_cost bus_count cost_per_bus \n", + "44 102790000 112.0 917767 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Min total_cost**" ], "text/plain": [ "" @@ -800,8 +1412,229 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencyprop_typetotal_costbus_countcost_per_bus
70SLO TRANSIT (SAN LUIS OBISPO, CA)BEB8472141.0847214
\n", + "
" + ], + "text/plain": [ + " transit_agency prop_type total_cost bus_count \\\n", + "70 SLO TRANSIT (SAN LUIS OBISPO, CA) BEB 847214 1.0 \n", + "\n", + " cost_per_bus \n", + "70 847214 " + ] + }, + "metadata": {}, + "output_type": "display_data" } ], + "source": [ + "display(Markdown(\n", + " \"**Which Agency had the most and least total ZEB cost?**\"\n", + "))\n", + "bus_min_max_summary(data=zeb_projects, col1=\"total_cost\", col_list=new_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f39733f-448e-4d3e-9981-feff4a13dbda", + "metadata": {}, + "outputs": [], + "source": [ + "# Charts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d58bf288-bfaa-4373-b72b-3f9d7859775f", + "metadata": {}, + "outputs": [], + "source": [ + "# charts " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63f90c48-e28d-4d88-8b90-891a3e3e3681", + "metadata": {}, + "outputs": [], + "source": [ + "conclusion = f\"\"\"\n", + "**Conclusion**\n", + "\n", + "Based on these findings, The average cost of a ZEB, throughout the US, is ~$1,000,000, roughly twice the price of a conventional, non-ZEB.\n", + "The variance in cost depends mainly on the options the Trasnit\n", + "Agencies chooses. Highly optioned/customized buses contribute to high cost.\n", + "Unfortunately, analyzing the cost of configuable options is outside the scope of data provided. \n", + "\"\"\"\n", + "display(\n", + " Markdown(conclusion)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8025c84b-428f-4c40-b4d3-c969af58ce63", + "metadata": {}, + "source": [ + "-------" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c51fe7dd-22e2-4686-b1a5-57b2f5ad8602", + "metadata": {}, + "outputs": [], + "source": [ + "Markdown(summary)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e553e15-dc1d-47d3-9818-7dec893c5294", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# all bus distribution\n", + "display(Markdown(all_bus_desc))\n", + "\n", + "dist_curve(\n", + " df=no_outliers,\n", + " mean=cpb_mean,\n", + " std=cpb_std,\n", + " title=\"all buses, cost per bus distribution\",\n", + " xlabel=\"cost per bus, $ million(s)\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dda584ca-76fa-4e88-9b1c-f70cc438dce6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# ZEB dist curve\n", + "display(Markdown(zeb_desc))\n", + "\n", + "dist_curve(\n", + " df=zeb_no_outliers,\n", + " mean=zeb_only_mean,\n", + " std=zeb_only_std,\n", + " title=\"ZEB only cost/bus Distribution\",\n", + " xlabel=\"cost per bus, $ million(s)\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "679d8261-85d6-4d68-9905-e4b048ebc61a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# non_zeb distribution\n", + "display(Markdown(non_zeb_desc))\n", + "\n", + "dist_curve(\n", + " non_zeb_no_outliers,\n", + " non_zeb_only_mean,\n", + " non_zeb_only_std,\n", + " title=\"non-ZEB only cost/bus Distribution\",\n", + " xlabel='\"cost per bus, $ million(s)\"',\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31c592b0-e37e-4da4-8726-36b0a1d3e6f5", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# COST PER BUS BY PROP TYPE\n", + "display(Markdown(cpb_prop_type_desc))\n", + "make_chart(\"cpb\", \"Cost per bus by propulsion type\", x_col=\"prop_type\", data=prop_agg)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7462b55c-29ef-4909-a7dd-27e1c84157d0", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# bus count BY PROP TYPE\n", + "display(Markdown(bus_count_prop_type_desc))\n", + "make_chart(\n", + " \"total_bus_count\", \n", + " \"Bus count by propulsion type\",\n", + " x_col=\"prop_type\",\n", + " data=prop_agg\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f092539-c4c6-4579-aa02-fbee65414ec3", + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ "display(Markdown(conclusion))" ] diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index 6d162915f..4fe422a29 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "910ee0fa-38ce-44f3-8e18-4cdf740e1fd0", "metadata": {}, "outputs": [], @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "8a1fd0b4-14a6-4cad-bb15-0ce0437ed125", "metadata": {}, "outputs": [], @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "c3552c45-8b28-4bbe-ae82-f2d726a45937", "metadata": {}, "outputs": [], @@ -58,19 +58,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "e1b1f367-1dac-463f-8790-2e5134b7e79b", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.9/site-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Data Validation extension is not supported and will be removed\n", - " warn(msg)\n" - ] - } - ], + "outputs": [], "source": [ "# links to all Raw Data\n", "fta_raw = pd.read_csv(f\"{GCS_PATH}raw_data-analyses_bus_procurement_cost_fta_press_release_data_csv.csv\")\n", @@ -101,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "id": "c6dacaba-c6f7-4cb0-afef-a84f77de25fc", "metadata": {}, "outputs": [], @@ -128,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "5513b941-ecdb-405e-bfd6-952df6b8f8b4", "metadata": { "tags": [] @@ -249,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "8e18cbe6-bde7-4c30-8a8a-aefd8d619821", "metadata": { "tags": [] @@ -311,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "8004cc2d-957f-4e9a-9ca8-2a6f9aba9ffb", "metadata": { "tags": [] @@ -381,7 +372,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "c0ca9254-2983-4cab-845c-f9bfb0229417", "metadata": { "tags": [] @@ -399,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "fa387f41-c9b3-455a-9829-cfabb3f98c9b", "metadata": { "tags": [] @@ -422,7 +413,6 @@ "cell_type": "markdown", "id": "d46d8747-5d4e-418d-a362-c80a093de4dd", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -432,7 +422,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "0b2be581-f4e9-4f7e-bde5-01f2de183479", "metadata": {}, "outputs": [], @@ -488,7 +478,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "fb1ae513-a8bf-4eb1-9e7b-71f828ebb9ea", "metadata": { "tags": [] @@ -513,7 +503,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "4a5bc209-a660-4c18-86b0-574640391a7d", "metadata": { "tags": [] @@ -588,7 +578,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "ea9c3269-d53d-4d94-bc22-c6768cb63d91", "metadata": { "tags": [] @@ -737,7 +727,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "4e16119d-f6f3-478b-a419-7c4989557910", "metadata": { "tags": [] @@ -890,7 +880,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "359f3b7a-d691-446f-9a14-424c47fc0929", "metadata": { "tags": [] @@ -1103,7 +1093,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "19f4bd75-f614-4937-880a-1e1a6ff2eb7f", "metadata": { "tags": [] @@ -1306,7 +1296,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "c9ffd5a9-b772-4509-b84c-9a96760b3112", "metadata": {}, "outputs": [], @@ -1318,32 +1308,10 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "f8aa3674-78fe-4ba9-8f5e-697d91ff4011", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(82, 8)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Index(['transit_agency', 'total_project_count', 'total_project_count_ppno',\n", - " 'total_agg_cost', 'total_bus_count', 'new_cost_per_bus',\n", - " 'new_zscore_cost_per_bus', 'new_is_cpb_outlier?'],\n", - " dtype='object')" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# testing the improved cpb agg function\n", "# default grouby column is `transit_agency`\n", @@ -1360,101 +1328,10 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "d2bca15a-c12c-4cf5-a5a9-d591ee73a359", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
53San Antonio Metropolitan Transit Authority10318720015.0212480-1.426794False
20City of Wasco0115430003.0514333-0.673298False
58Santa Rosa City Bus0140682024.010170500.581602False
\n", - "
" - ], - "text/plain": [ - " transit_agency total_project_count \\\n", - "53 San Antonio Metropolitan Transit Authority 1 \n", - "20 City of Wasco 0 \n", - "58 Santa Rosa City Bus 0 \n", - "\n", - " total_project_count_ppno total_agg_cost total_bus_count \\\n", - "53 0 3187200 15.0 \n", - "20 1 1543000 3.0 \n", - "58 1 4068202 4.0 \n", - "\n", - " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", - "53 212480 -1.426794 False \n", - "20 514333 -0.673298 False \n", - "58 1017050 0.581602 False " - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# confirming the default cpb_agg is working\n", "agg1.sample(3)" @@ -1462,101 +1339,10 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "637032e4-d855-4190-a6f5-ff695f77143f", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False 81\n", - "True 1\n", - "Name: new_is_cpb_outlier?, dtype: int64" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "-1.8667057821355477" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "3.4069219663792882" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
71Transit Joint Powers Authority for Merced County0264466483.021488823.406922True
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" - ], - "text/plain": [ - " transit_agency total_project_count \\\n", - "71 Transit Joint Powers Authority for Merced County 0 \n", - "\n", - " total_project_count_ppno total_agg_cost total_bus_count \\\n", - "71 2 6446648 3.0 \n", - "\n", - " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", - "71 2148882 3.406922 True " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# agg looks good\n", "# double checked it against the old agg function, CPB numbers match between this new function and old one\n", @@ -1571,7 +1357,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "ce1f4c86-e85d-41d8-83f6-14aadce48d5c", "metadata": {}, "outputs": [], @@ -1582,7 +1368,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "59298193-fc78-4ffb-bfc3-326593c19edb", "metadata": {}, "outputs": [], @@ -1596,14 +1382,83 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "01883fc0-4f6d-4e6a-a88f-97a5914b281b", "metadata": {}, + "outputs": [], + "source": [ + "#EVERYTHING CHECKS OUT!\n", + "display(\n", + " old_prop_agg.shape,\n", + " agg_prop_type.shape,\n", + " old_prop_agg.head(),\n", + " agg_prop_type.head()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "52204752-3932-4ce6-98ac-de8ad3a1f8e8", + "metadata": {}, + "outputs": [], + "source": [ + "# double checking the bus size agg new vs old\n", + "#EVERYTHING CHECKS OUT!\n", + "new_agg_bus_size = new_cpb_aggregate(test, \"bus_size_type\")\n", + "old_size_agg = cpb_aggregate(no_outliers, \"bus_size_type\")\n", + "display(\n", + " old_size_agg.shape,\n", + " new_agg_bus_size.shape,\n", + " old_size_agg,\n", + " new_agg_bus_size\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0391dd4d-23e1-49cb-8123-509954c796e8", + "metadata": {}, + "outputs": [], + "source": [ + "#EVERYTHING CHECKS OUT!\n", + "# move forward with `new_cpb_aggregate` function\n", + "new_agg_agency = new_cpb_aggregate(test)\n", + "old_agency_agg = cpb_aggregate(no_outliers, \"transit_agency\")\n", + "display(\n", + " old_agency_agg.shape,\n", + " new_agg_agency.shape,\n", + " old_agency_agg.head(),\n", + " new_agg_agency.head()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3428875a-6a64-41bc-8f9c-81902006d7f0", + "metadata": { + "tags": [] + }, + "source": [ + "## Testing variables rework\n", + "there are a lot of variables in the initial script. need to cut down the amount of variables or at least make it more efficient. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f21298ee-0efb-4f91-ba63-55fc2645a4d2", + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(10, 6)" + "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", + " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", + " 'ppno', 'project_description', 'cost_per_bus', 'zscore_cost_per_bus',\n", + " 'is_cpb_outlier?'],\n", + " dtype='object')" ] }, "metadata": {}, @@ -1612,7 +1467,7 @@ { "data": { "text/plain": [ - "(10, 8)" + "(88, 14)" ] }, "metadata": {}, @@ -1639,78 +1494,154 @@ " \n", " \n", " \n", + " transit_agency\n", + " project_title\n", " prop_type\n", - " total_project_count\n", - " total_project_count_ppno\n", - " total_agg_cost\n", - " total_bus_count\n", - " cpb\n", - " \n", + " bus_size_type\n", + " description\n", + " new_project_type\n", + " total_cost\n", + " bus_count\n", + " source\n", + " ppno\n", + " project_description\n", + " cost_per_bus\n", + " zscore_cost_per_bus\n", + " is_cpb_outlier?\n", + " \n", " \n", " \n", " \n", " 0\n", - " BEB\n", - " 0\n", - " 30\n", - " 167232489\n", - " 163.0\n", - " 1025966\n", + " AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)\n", + " Puerto Rico Initiative Minimizing Emissions Pl...\n", + " electric (not specified)\n", + " not specified\n", + " The Metropolitan Bus Authority will receive fu...\n", + " bus only\n", + " 10000000\n", + " 8.0\n", + " fta\n", + " None\n", + " None\n", + " 1250000\n", + " 0.917956\n", + " False\n", " \n", " \n", " 1\n", + " Cape Fear Public Transportation Authority\n", + " Wave Transit Low Emissions Replacement Vehicles\n", " CNG\n", - " 12\n", - " 1\n", - " 176039140\n", - " 252.0\n", - " 698568\n", + " not specified\n", + " Wave Transit will receive funding to buy compr...\n", + " bus only\n", + " 2860250\n", + " 5.0\n", + " fta\n", + " None\n", + " None\n", + " 572050\n", + " -0.529139\n", + " False\n", " \n", " \n", " 2\n", - " FCEB\n", - " 2\n", - " 6\n", - " 120951335\n", - " 102.0\n", - " 1185797\n", + " Central Oklahoma Transportation and Parking Au...\n", + " COTPA, dba EMBARK Elimination of Fixed Route D...\n", + " CNG\n", + " not specified\n", + " The Central Oklahoma Transportation and Parkin...\n", + " bus only\n", + " 4278772\n", + " 9.0\n", + " fta\n", + " None\n", + " None\n", + " 475419\n", + " -0.735399\n", + " False\n", " \n", " \n", " 3\n", - " electric (not specified)\n", - " 1\n", - " 2\n", - " 56678000\n", - " 44.0\n", - " 1288136\n", + " Champaign-Urbana Mass Transit District\n", + " MTD 40-Foot Hybrid Replacement Buses\n", + " low emission (hybrid)\n", + " not specified\n", + " The Champaign-Urbana Mass Transit District wil...\n", + " bus only\n", + " 6635394\n", + " 10.0\n", + " fta\n", + " None\n", + " None\n", + " 663539\n", + " -0.333854\n", + " False\n", " \n", " \n", " 4\n", - " ethanol\n", - " 1\n", - " 0\n", - " 1006750\n", - " 9.0\n", - " 111861\n", + " City of Beaumont\n", + " Beaumont Municipal Transit Zips to Improve Low...\n", + " CNG\n", + " not specified\n", + " Beaumont Municipal Transit will receive fundin...\n", + " bus only\n", + " 2819460\n", + " 5.0\n", + " fta\n", + " None\n", + " None\n", + " 563892\n", + " -0.546552\n", + " False\n", " \n", " \n", "\n", "" ], "text/plain": [ - " prop_type total_project_count total_project_count_ppno \\\n", - "0 BEB 0 30 \n", - "1 CNG 12 1 \n", - "2 FCEB 2 6 \n", - "3 electric (not specified) 1 2 \n", - "4 ethanol 1 0 \n", + " transit_agency \\\n", + "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) \n", + "1 Cape Fear Public Transportation Authority \n", + "2 Central Oklahoma Transportation and Parking Au... \n", + "3 Champaign-Urbana Mass Transit District \n", + "4 City of Beaumont \n", + "\n", + " project_title \\\n", + "0 Puerto Rico Initiative Minimizing Emissions Pl... \n", + "1 Wave Transit Low Emissions Replacement Vehicles \n", + "2 COTPA, dba EMBARK Elimination of Fixed Route D... \n", + "3 MTD 40-Foot Hybrid Replacement Buses \n", + "4 Beaumont Municipal Transit Zips to Improve Low... \n", "\n", - " total_agg_cost total_bus_count cpb \n", - "0 167232489 163.0 1025966 \n", - "1 176039140 252.0 698568 \n", - "2 120951335 102.0 1185797 \n", - "3 56678000 44.0 1288136 \n", - "4 1006750 9.0 111861 " + " prop_type bus_size_type \\\n", + "0 electric (not specified) not specified \n", + "1 CNG not specified \n", + "2 CNG not specified \n", + "3 low emission (hybrid) not specified \n", + "4 CNG not specified \n", + "\n", + " description new_project_type \\\n", + "0 The Metropolitan Bus Authority will receive fu... bus only \n", + "1 Wave Transit will receive funding to buy compr... bus only \n", + "2 The Central Oklahoma Transportation and Parkin... bus only \n", + "3 The Champaign-Urbana Mass Transit District wil... bus only \n", + "4 Beaumont Municipal Transit will receive fundin... bus only \n", + "\n", + " total_cost bus_count source ppno project_description cost_per_bus \\\n", + "0 10000000 8.0 fta None None 1250000 \n", + "1 2860250 5.0 fta None None 572050 \n", + "2 4278772 9.0 fta None None 475419 \n", + "3 6635394 10.0 fta None None 663539 \n", + "4 2819460 5.0 fta None None 563892 \n", + "\n", + " zscore_cost_per_bus is_cpb_outlier? \n", + "0 0.917956 False \n", + "1 -0.529139 False \n", + "2 -0.735399 False \n", + "3 -0.333854 False \n", + "4 -0.546552 False " ] }, "metadata": {}, @@ -1718,116 +1649,10 @@ }, { "data": { - 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prop_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
0BEB031170455813164.010393640.923505False
1CNG121176039140252.06985680.122141False
2FCEB26120951335102.011857971.267835False
3electric (not specified)125667800044.012881361.508480False
4ethanol1010067509.0111861-1.257470False
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" - ], "text/plain": [ - " prop_type total_project_count total_project_count_ppno \\\n", - "0 BEB 0 31 \n", - "1 CNG 12 1 \n", - "2 FCEB 2 6 \n", - "3 electric (not specified) 1 2 \n", - "4 ethanol 1 0 \n", - "\n", - " total_agg_cost total_bus_count new_cost_per_bus new_zscore_cost_per_bus \\\n", - "0 170455813 164.0 1039364 0.923505 \n", - "1 176039140 252.0 698568 0.122141 \n", - "2 120951335 102.0 1185797 1.267835 \n", - "3 56678000 44.0 1288136 1.508480 \n", - "4 1006750 9.0 111861 -1.257470 \n", - "\n", - " new_is_cpb_outlier? \n", - "0 False \n", - "1 False \n", - "2 False \n", - "3 False \n", - "4 False " + "min -1.672813\n", + "max 2.661856\n", + "Name: zscore_cost_per_bus, dtype: float64" ] }, "metadata": {}, @@ -1835,25 +1660,31 @@ } ], "source": [ - "#EVERYTHING CHECKS OUT!\n", + "# read in all cleaned project data without outliers\n", + "# cpb_analysis_data_merge is bus only projects. all DGS rows were Bus only projects anyways\n", + "\n", + "merged_data = pd.read_parquet(f'{GCS_PATH}cleaned_no_outliers_cpb_analysis_data_merge.parquet')\n", "display(\n", - " old_prop_agg.shape,\n", - " agg_prop_type.shape,\n", - " old_prop_agg.head(),\n", - " agg_prop_type.head()\n", + " merged_data.columns,\n", + " merged_data.shape,\n", + " merged_data.head(),\n", + " merged_data[\"zscore_cost_per_bus\"].agg([\"min\",\"max\"])\n", + "\n", ")" ] }, { "cell_type": "code", - "execution_count": 25, - "id": "52204752-3932-4ce6-98ac-de8ad3a1f8e8", + "execution_count": 11, + "id": "ece95fb7-cbb8-46bd-a5f9-2b68a47a4817", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(5, 6)" + "min 36250\n", + "max 1611662\n", + "Name: new_cost_per_bus, dtype: int64" ] }, "metadata": {}, @@ -1862,7 +1693,9 @@ { "data": { "text/plain": [ - "(5, 8)" + "min 1.0\n", + "max 160.0\n", + "Name: total_bus_count, dtype: float64" ] }, "metadata": {}, @@ -1870,97 +1703,10 @@ }, { "data": { - "text/html": [ - "
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bus_size_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpb
0articulated025823757641.01420428
1cutaway3016694500152.0109832
2not specified406509919038881.0578795
3over-the-road01951600014.0679714
4standard/conventional (30ft-45ft)036234253277264.0887323
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" - ], "text/plain": [ - " bus_size_type total_project_count \\\n", - "0 articulated 0 \n", - "1 cutaway 3 \n", - "2 not specified 40 \n", - "3 over-the-road 0 \n", - "4 standard/conventional (30ft-45ft) 0 \n", - "\n", - " total_project_count_ppno total_agg_cost total_bus_count cpb \n", - "0 2 58237576 41.0 1420428 \n", - "1 0 16694500 152.0 109832 \n", - "2 6 509919038 881.0 578795 \n", - "3 1 9516000 14.0 679714 \n", - "4 36 234253277 264.0 887323 " + "min 181250\n", + "max 103000000\n", + "Name: total_agg_cost, dtype: int64" ] }, "metadata": {}, @@ -1968,116 +1714,10 @@ }, { "data": { - "text/html": [ - "
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bus_size_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
0articulated025823757641.014204281.598471False
1cutaway3016694500152.0109832-1.466801False
2not specified406509919038881.0578795-0.369972False
3over-the-road01951600014.0679714-0.133939False
4standard/conventional (30ft-45ft)037237476601265.08961380.372242False
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" - ], "text/plain": [ - " bus_size_type total_project_count \\\n", - "0 articulated 0 \n", - "1 cutaway 3 \n", - "2 not specified 40 \n", - "3 over-the-road 0 \n", - "4 standard/conventional (30ft-45ft) 0 \n", - "\n", - " total_project_count_ppno total_agg_cost total_bus_count \\\n", - "0 2 58237576 41.0 \n", - "1 0 16694500 152.0 \n", - "2 6 509919038 881.0 \n", - "3 1 9516000 14.0 \n", - "4 37 237476601 265.0 \n", - "\n", - " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", - "0 1420428 1.598471 False \n", - "1 109832 -1.466801 False \n", - "2 578795 -0.369972 False \n", - "3 679714 -0.133939 False \n", - "4 896138 0.372242 False " + "min -1.939451\n", + "max 2.182513\n", + "Name: new_zscore_cost_per_bus, dtype: float64" ] }, "metadata": {}, @@ -2085,3224 +1725,461 @@ } ], "source": [ - "# double checking the bus size agg new vs old\n", - "#EVERYTHING CHECKS OUT!\n", - "new_agg_bus_size = new_cpb_aggregate(test, \"bus_size_type\")\n", - "old_size_agg = cpb_aggregate(no_outliers, \"bus_size_type\")\n", + "## moved to final NB\n", + "\n", + "# aggregating by big categories\n", + "agg_agency = new_cpb_aggregate(merged_data)\n", + "agg_prop = new_cpb_aggregate(merged_data, column=\"prop_type\")\n", + "agg_bus_size = new_cpb_aggregate(merged_data, column=\"bus_size_type\")\n", + "agg_source = new_cpb_aggregate(merged_data, column=\"source\")\n", + "\n", + "#overall agency info\n", "display(\n", - " old_size_agg.shape,\n", - " new_agg_bus_size.shape,\n", - " old_size_agg,\n", - " new_agg_bus_size\n", + " #min max,\n", + " agg_agency[\"new_cost_per_bus\"].agg([\"min\",\"max\"]),\n", + " agg_agency[\"total_bus_count\"].agg([\"min\",\"max\"]),\n", + " agg_agency[\"total_agg_cost\"].agg([\"min\",\"max\"]),\n", + " agg_agency[\"new_zscore_cost_per_bus\"].agg([\"min\",\"max\"]),\n", + " \n", ")" ] }, { "cell_type": "code", - "execution_count": 26, - "id": "0391dd4d-23e1-49cb-8123-509954c796e8", + "execution_count": null, + "id": "03940ccb-d1e6-439d-a930-13dae17537b2", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(82, 6)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "(82, 8)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpb
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)10100000008.01250000
1Alameda County Transit Authority012284664020.01142332
2Antelope Valley Transit Authority (AVTA)013947800029.01361310
3CITY OF PORTERVILLE (PORTERVILLE, CA)0127818913.0927297
4CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...0136235364.0905884
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" - ], - "text/plain": [ - " transit_agency total_project_count \\\n", - "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) 1 \n", - "1 Alameda County Transit Authority 0 \n", - "2 Antelope Valley Transit Authority (AVTA) 0 \n", - "3 CITY OF PORTERVILLE (PORTERVILLE, CA) 0 \n", - "4 CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ... 0 \n", - "\n", - " total_project_count_ppno total_agg_cost total_bus_count cpb \n", - "0 0 10000000 8.0 1250000 \n", - "1 1 22846640 20.0 1142332 \n", - "2 1 39478000 29.0 1361310 \n", - "3 1 2781891 3.0 927297 \n", - "4 1 3623536 4.0 905884 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)10100000008.012500001.163100False
1Alameda County Transit Authority012284664020.011423320.894336False
2Antelope Valley Transit Authority (AVTA)013947800029.013613101.440957False
3CITY OF PORTERVILLE (PORTERVILLE, CA)0127818913.09272970.357558False
4CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...0136235364.09058840.304106False
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" - ], - "text/plain": [ - " transit_agency total_project_count \\\n", - "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) 1 \n", - "1 Alameda County Transit Authority 0 \n", - "2 Antelope Valley Transit Authority (AVTA) 0 \n", - "3 CITY OF PORTERVILLE (PORTERVILLE, CA) 0 \n", - "4 CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ... 0 \n", - "\n", - " total_project_count_ppno total_agg_cost total_bus_count \\\n", - "0 0 10000000 8.0 \n", - "1 1 22846640 20.0 \n", - "2 1 39478000 29.0 \n", - "3 1 2781891 3.0 \n", - "4 1 3623536 4.0 \n", - "\n", - " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", - "0 1250000 1.163100 False \n", - "1 1142332 0.894336 False \n", - "2 1361310 1.440957 False \n", - "3 927297 0.357558 False \n", - "4 905884 0.304106 False " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "#EVERYTHING CHECKS OUT!\n", - "# move forward with `new_cpb_aggregate` function\n", - "new_agg_agency = new_cpb_aggregate(test)\n", - "old_agency_agg = cpb_aggregate(no_outliers, \"transit_agency\")\n", "display(\n", - " old_agency_agg.shape,\n", - " new_agg_agency.shape,\n", - " old_agency_agg.head(),\n", - " new_agg_agency.head()\n", + " merged_data.shape,\n", + " agg_agency.shape,\n", + " merged_data.head(),\n", + " agg_agency.head()\n", ")" ] }, - { - "cell_type": "markdown", - "id": "3428875a-6a64-41bc-8f9c-81902006d7f0", - "metadata": { - "tags": [] - }, - "source": [ - "## Testing variables rework\n", - "there are a lot of variables in the initial script. need to cut down the amount of variables or at least make it more efficient. " - ] - }, { "cell_type": "code", - "execution_count": 27, - "id": "f21298ee-0efb-4f91-ba63-55fc2645a4d2", + "execution_count": null, + "id": "1696d78f-7018-417b-9847-d82edac3acdf", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", - " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", - " 'ppno', 'project_description', 'cost_per_bus', 'zscore_cost_per_bus',\n", - " 'is_cpb_outlier?'],\n", - " dtype='object')" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "(88, 14)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencyproject_titleprop_typebus_size_typedescriptionnew_project_typetotal_costbus_countsourceppnoproject_descriptioncost_per_buszscore_cost_per_busis_cpb_outlier?
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)Puerto Rico Initiative Minimizing Emissions Pl...electric (not specified)not specifiedThe Metropolitan Bus Authority will receive fu...bus only100000008.0ftaNoneNone12500000.917956False
1Cape Fear Public Transportation AuthorityWave Transit Low Emissions Replacement VehiclesCNGnot specifiedWave Transit will receive funding to buy compr...bus only28602505.0ftaNoneNone572050-0.529139False
2Central Oklahoma Transportation and Parking Au...COTPA, dba EMBARK Elimination of Fixed Route D...CNGnot specifiedThe Central Oklahoma Transportation and Parkin...bus only42787729.0ftaNoneNone475419-0.735399False
3Champaign-Urbana Mass Transit DistrictMTD 40-Foot Hybrid Replacement Buseslow emission (hybrid)not specifiedThe Champaign-Urbana Mass Transit District wil...bus only663539410.0ftaNoneNone663539-0.333854False
4City of BeaumontBeaumont Municipal Transit Zips to Improve Low...CNGnot specifiedBeaumont Municipal Transit will receive fundin...bus only28194605.0ftaNoneNone563892-0.546552False
\n", - "
" - ], - "text/plain": [ - " transit_agency \\\n", - "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) \n", - "1 Cape Fear Public Transportation Authority \n", - "2 Central Oklahoma Transportation and Parking Au... \n", - "3 Champaign-Urbana Mass Transit District \n", - "4 City of Beaumont \n", - "\n", - " project_title \\\n", - "0 Puerto Rico Initiative Minimizing Emissions Pl... \n", - "1 Wave Transit Low Emissions Replacement Vehicles \n", - "2 COTPA, dba EMBARK Elimination of Fixed Route D... \n", - "3 MTD 40-Foot Hybrid Replacement Buses \n", - "4 Beaumont Municipal Transit Zips to Improve Low... \n", - "\n", - " prop_type bus_size_type \\\n", - "0 electric (not specified) not specified \n", - "1 CNG not specified \n", - "2 CNG not specified \n", - "3 low emission (hybrid) not specified \n", - "4 CNG not specified \n", - "\n", - " description new_project_type \\\n", - "0 The Metropolitan Bus Authority will receive fu... bus only \n", - "1 Wave Transit will receive funding to buy compr... bus only \n", - "2 The Central Oklahoma Transportation and Parkin... bus only \n", - "3 The Champaign-Urbana Mass Transit District wil... bus only \n", - "4 Beaumont Municipal Transit will receive fundin... bus only \n", - "\n", - " total_cost bus_count source ppno project_description cost_per_bus \\\n", - "0 10000000 8.0 fta None None 1250000 \n", - "1 2860250 5.0 fta None None 572050 \n", - "2 4278772 9.0 fta None None 475419 \n", - "3 6635394 10.0 fta None None 663539 \n", - "4 2819460 5.0 fta None None 563892 \n", - "\n", - " zscore_cost_per_bus is_cpb_outlier? \n", - "0 0.917956 False \n", - "1 -0.529139 False \n", - "2 -0.735399 False \n", - "3 -0.333854 False \n", - "4 -0.546552 False " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "min -1.672813\n", - "max 2.661856\n", - "Name: zscore_cost_per_bus, dtype: float64" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "# read in all cleaned project data without outliers\n", - "# cpb_analysis_data_merge is bus only projects. all DGS rows were Bus only projects anyways\n", + "# testing pivot table on `merged_data`\n", + "# moved to final NB\n", + "#pivot table to get totals for each prop type\n", + "pivot_prop_type = pd.pivot_table(\n", + " merged_data,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"prop_type\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ").reset_index()\n", + "pivot_prop_type[\"cost_per_bus\"] = (pivot_prop_type[\"total_cost\"] / pivot_prop_type[\"bus_count\"]).astype(\"int64\")\n", "\n", - "merged_data = pd.read_parquet(f'{GCS_PATH}cleaned_no_outliers_cpb_analysis_data_merge.parquet')\n", "display(\n", - " merged_data.columns,\n", - " merged_data.shape,\n", - " merged_data.head(),\n", - " merged_data[\"zscore_cost_per_bus\"].agg([\"min\",\"max\"])\n", - "\n", - ")" + " #from new_cpb_agg\n", + " agg_prop[[\"prop_type\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]],\n", + " #pivot\n", + " pivot_prop_type\n", + ")\n", + "# same data, dont need the pivot table anymore, but the pivot table does have grand total" ] }, { "cell_type": "code", - "execution_count": 28, - "id": "ece95fb7-cbb8-46bd-a5f9-2b68a47a4817", + "execution_count": null, + "id": "ca709e43-4947-4a34-970f-216d4b6ab7cc", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "min 36250\n", - "max 1611662\n", - "Name: new_cost_per_bus, dtype: int64" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "min 1.0\n", - "max 160.0\n", - "Name: total_bus_count, dtype: float64" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "min 181250\n", - "max 103000000\n", - "Name: total_agg_cost, dtype: int64" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "min -1.939451\n", - "max 2.182513\n", - "Name: new_zscore_cost_per_bus, dtype: float64" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "## moved to final NB\n", + "#moved to final NB 6/25\n", "\n", - "# aggregating by big categories\n", - "agg_agency = new_cpb_aggregate(merged_data)\n", - "agg_prop = new_cpb_aggregate(merged_data, column=\"prop_type\")\n", - "agg_bus_size = new_cpb_aggregate(merged_data, column=\"bus_size_type\")\n", - "agg_source = new_cpb_aggregate(merged_data, column=\"source\")\n", + "#pivot table to get grand total for zeb/non-zeb only data\n", "\n", - "#overall agency info\n", + "# keep this\n", + "zeb_list =[\n", + " \"BEB\",\n", + " \"FCEB\",\n", + " \"electric (not specified)\",\n", + " \"zero-emission bus (not specified)\",\n", + "]\n", + "\n", + "zeb_projects = merged_data[merged_data[\"prop_type\"].isin(zeb_list)]\n", + "\n", + "#keep this\n", + "non_zeb_list =[\n", + " \"CNG\",\n", + " \"ethanol\",\n", + " \"low emission (hybrid)\",\n", + " \"low emission (propane)\",\n", + " \"mix (zero and low emission)\",\n", + "]\n", + "\n", + "non_zeb_projects = merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)]\n", + "\n", + "#keep this\n", + "pivot_zeb_prop = pd.pivot_table(\n", + " #filted incoming DF for zeb prop types\n", + " zeb_projects,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"prop_type\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ").reset_index() \n", + "\n", + "pivot_zeb_prop[\"cost_per_bus\"] = (pivot_zeb_prop[\"total_cost\"] / pivot_zeb_prop[\"bus_count\"]).astype(\"int64\")\n", + "\n", + "#keep this\n", + "pivot_non_zeb_prop = pd.pivot_table(\n", + " #filted incoming DF for non-zeb prop types\n", + " non_zeb_projects,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"prop_type\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ").reset_index()\n", + "\n", + "pivot_non_zeb_prop[\"cost_per_bus\"] = (pivot_non_zeb_prop[\"total_cost\"] / pivot_non_zeb_prop[\"bus_count\"]).astype(\"int64\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01647d83-8b4f-47a9-ab57-a1db7cd501dd", + "metadata": {}, + "outputs": [], + "source": [ "display(\n", - " #min max,\n", - " agg_agency[\"new_cost_per_bus\"].agg([\"min\",\"max\"]),\n", - " agg_agency[\"total_bus_count\"].agg([\"min\",\"max\"]),\n", - " agg_agency[\"total_agg_cost\"].agg([\"min\",\"max\"]),\n", - " agg_agency[\"new_zscore_cost_per_bus\"].agg([\"min\",\"max\"]),\n", + " #zeb data 3 different methods\n", + " #1. filtering agg_prop by zeb list, no grand totas\n", + " #2. filtering pivot talbe by zeb list, without grand totals\n", + " #3. dedicated pivot table for zeb, with grand totals\n", + " #agg_prop[agg_prop[\"prop_type\"].isin(zeb_list)],\n", + " #pivot_prop_type.loc[zeb_list],\n", + " pivot_zeb_prop,\n", " \n", + " #non-zeb same 3 methods\n", + " #agg_prop[agg_prop[\"prop_type\"].isin(non_zeb_list)],\n", + " #pivot_prop_type.loc[non_zeb_list],\n", + " pivot_non_zeb_prop\n", + ")\n", + "# confirmed all data is the same, but need pivot for grand total rows" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3be3ef4f-0bf3-4770-a8b7-340d372ae1ce", + "metadata": {}, + "outputs": [], + "source": [ + "# answers total buses sizes\n", + "pivot_size = pd.pivot_table(\n", + " merged_data,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"bus_size_type\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ").reset_index()\n", + "\n", + "pivot_size[\"cost_per_bus\"] = (pivot_size[\"total_cost\"] / pivot_size[\"bus_count\"]).astype(\"int64\")\n", + "\n", + "display(\n", + " agg_bus_size[[\"bus_size_type\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]],\n", + " pivot_size,\n", + " pivot_prop_type[pivot_prop_type[\"prop_type\"] == \"Grand Total\"]\n", + ")\n", + "\n", + "#same data, dont need pivot for this one because the grand totals will be the same as pivot_prop_type. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c933257-bdc2-4007-9571-58475118073c", + "metadata": {}, + "outputs": [], + "source": [ + "# moved to final NB 6/25\n", + "\n", + "# answers total buses and cost per grant type\n", + "pivot_source = pd.pivot_table(\n", + " merged_data,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"source\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ").reset_index()\n", + "\n", + "pivot_source[\"cost_per_bus\"] = (pivot_source[\"total_cost\"] / pivot_source[\"bus_count\"]).astype(\"int64\")\n", + "\n", + "display(\n", + " agg_source[[\"source\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]],\n", + " pivot_source\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "11547020-dd35-4745-98f8-bbd02fccaa23", + "metadata": { + "tags": [] + }, + "source": [ + "## Testing Charts\n", + "\n", + "using `merged_data`, now without outliers.\n", + "charts looking good, similar results to initial charts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aace38a4-3f2d-460d-a258-59efa659f852", + "metadata": {}, + "outputs": [], + "source": [ + "merged_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4753f3ea-00b6-4d5e-a3f0-73b3d3593acb", + "metadata": {}, + "outputs": [], + "source": [ + "# means and standard deviations\n", + "# for graphs\n", + "cpb_mean = merged_data[\"cost_per_bus\"].mean()\n", + "cpb_std = merged_data[\"cost_per_bus\"].std()\n", + "\n", + "#testing weighted average calculation for sub-set non-zeb and zeb\n", + "zeb_cpb_wt_avg = (merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"bus_count\"].sum())\n", + "non_zeb_cpb_wt_avg = (merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"bus_count\"].sum())\n", + "display(\n", + " cpb_mean,\n", + " cpb_std,\n", + " zeb_cpb_wt_avg,\n", + " non_zeb_cpb_wt_avg\n", ")" ] }, { "cell_type": "code", - "execution_count": 29, - "id": "03940ccb-d1e6-439d-a930-13dae17537b2", + "execution_count": null, + "id": "2007be9d-13ec-4d0d-a642-d9a42448b924", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(88, 14)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "(82, 8)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencyproject_titleprop_typebus_size_typedescriptionnew_project_typetotal_costbus_countsourceppnoproject_descriptioncost_per_buszscore_cost_per_busis_cpb_outlier?
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)Puerto Rico Initiative Minimizing Emissions Pl...electric (not specified)not specifiedThe Metropolitan Bus Authority will receive fu...bus only100000008.0ftaNoneNone12500000.917956False
1Cape Fear Public Transportation AuthorityWave Transit Low Emissions Replacement VehiclesCNGnot specifiedWave Transit will receive funding to buy compr...bus only28602505.0ftaNoneNone572050-0.529139False
2Central Oklahoma Transportation and Parking Au...COTPA, dba EMBARK Elimination of Fixed Route D...CNGnot specifiedThe Central Oklahoma Transportation and Parkin...bus only42787729.0ftaNoneNone475419-0.735399False
3Champaign-Urbana Mass Transit DistrictMTD 40-Foot Hybrid Replacement Buseslow emission (hybrid)not specifiedThe Champaign-Urbana Mass Transit District wil...bus only663539410.0ftaNoneNone663539-0.333854False
4City of BeaumontBeaumont Municipal Transit Zips to Improve Low...CNGnot specifiedBeaumont Municipal Transit will receive fundin...bus only28194605.0ftaNoneNone563892-0.546552False
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" - ], - "text/plain": [ - " transit_agency \\\n", - "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) \n", - "1 Cape Fear Public Transportation Authority \n", - "2 Central Oklahoma Transportation and Parking Au... \n", - "3 Champaign-Urbana Mass Transit District \n", - "4 City of Beaumont \n", - "\n", - " project_title \\\n", - "0 Puerto Rico Initiative Minimizing Emissions Pl... \n", - "1 Wave Transit Low Emissions Replacement Vehicles \n", - "2 COTPA, dba EMBARK Elimination of Fixed Route D... \n", - "3 MTD 40-Foot Hybrid Replacement Buses \n", - "4 Beaumont Municipal Transit Zips to Improve Low... \n", - "\n", - " prop_type bus_size_type \\\n", - "0 electric (not specified) not specified \n", - "1 CNG not specified \n", - "2 CNG not specified \n", - "3 low emission (hybrid) not specified \n", - "4 CNG not specified \n", - "\n", - " description new_project_type \\\n", - "0 The Metropolitan Bus Authority will receive fu... bus only \n", - "1 Wave Transit will receive funding to buy compr... bus only \n", - "2 The Central Oklahoma Transportation and Parkin... bus only \n", - "3 The Champaign-Urbana Mass Transit District wil... bus only \n", - "4 Beaumont Municipal Transit will receive fundin... bus only \n", - "\n", - " total_cost bus_count source ppno project_description cost_per_bus \\\n", - "0 10000000 8.0 fta None None 1250000 \n", - "1 2860250 5.0 fta None None 572050 \n", - "2 4278772 9.0 fta None None 475419 \n", - "3 6635394 10.0 fta None None 663539 \n", - "4 2819460 5.0 fta None None 563892 \n", - "\n", - " zscore_cost_per_bus is_cpb_outlier? \n", - "0 0.917956 False \n", - "1 -0.529139 False \n", - "2 -0.735399 False \n", - "3 -0.333854 False \n", - "4 -0.546552 False " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)10100000008.012500001.236248False
1Alameda County Transit Authority012284664020.011423320.954542False
2Antelope Valley Transit Authority (AVTA)013947800029.013613101.527483False
3CITY OF PORTERVILLE (PORTERVILLE, CA)0127818913.09272970.391916False
4CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...0136235364.09058840.335891False
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" - ], - "text/plain": [ - " transit_agency total_project_count \\\n", - "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) 1 \n", - "1 Alameda County Transit Authority 0 \n", - "2 Antelope Valley Transit Authority (AVTA) 0 \n", - "3 CITY OF PORTERVILLE (PORTERVILLE, CA) 0 \n", - "4 CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ... 0 \n", - "\n", - " total_project_count_ppno total_agg_cost total_bus_count \\\n", - "0 0 10000000 8.0 \n", - "1 1 22846640 20.0 \n", - "2 1 39478000 29.0 \n", - "3 1 2781891 3.0 \n", - "4 1 3623536 4.0 \n", - "\n", - " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", - "0 1250000 1.236248 False \n", - "1 1142332 0.954542 False \n", - "2 1361310 1.527483 False \n", - "3 927297 0.391916 False \n", - "4 905884 0.335891 False " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ + "# why is the average different when i use .mean() vs. total cost / bus cout\n", + "\n", "display(\n", - " merged_data.shape,\n", - " agg_agency.shape,\n", - " merged_data.head(),\n", - " agg_agency.head()\n", + " #this is the arithmatic mean, sums all the `cost_per_bus` rows, the divide by the number of rows. (row-wise)\n", + " zeb_projects[\"cost_per_bus\"].mean(),\n", + " \n", + " #this is like the accounting method of calculating average (Total Cost and Total Quantity Approach (Weighted Average))\n", + " pivot_zeb_prop,\n", + " \n", + " # calculating mean by weighted average the long way (total cost / total bus count, similar to pivot table)\n", + " (zeb_projects[\"total_cost\"].sum() / zeb_projects[\"bus_count\"].sum())\n", + ")\n", + "\n", + "# so the calculated grand total cost_per_bus is equivilent to the weighted average cost per bus\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8645cf77-b30a-4c45-b943-ac81e8b5a613", + "metadata": {}, + "outputs": [], + "source": [ + "# chart of all cost per bus in the analysis.\n", + "\n", + "dist_curve(\n", + " df=merged_data,\n", + " mean=cpb_mean,\n", + " std=cpb_std,\n", + " title=\"all buses, cost per bus distribution\",\n", + " xlabel=\"cost per bus, $ million(s)\",\n", ")" ] }, { "cell_type": "code", - "execution_count": 30, - "id": "1696d78f-7018-417b-9847-d82edac3acdf", + "execution_count": null, + "id": "cefa6800-df50-4eda-95f8-74363ef942d0", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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prop_typetotal_agg_costtotal_bus_countnew_cost_per_bus
0BEB167232489163.01025966
1CNG176039140252.0698568
2FCEB120951335102.01185797
3electric (not specified)5667800044.01288136
4ethanol10067509.0111861
5low emission (hybrid)91824361145.0633271
6low emission (propane)840396944.0190999
7mix (zero and low emission)36775430125.0294203
8not specified41552404325.0127853
9zero-emission bus (not specified)128156513143.0896199
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" - ], - "text/plain": [ - " prop_type total_agg_cost total_bus_count \\\n", - "0 BEB 167232489 163.0 \n", - "1 CNG 176039140 252.0 \n", - "2 FCEB 120951335 102.0 \n", - "3 electric (not specified) 56678000 44.0 \n", - "4 ethanol 1006750 9.0 \n", - "5 low emission (hybrid) 91824361 145.0 \n", - "6 low emission (propane) 8403969 44.0 \n", - "7 mix (zero and low emission) 36775430 125.0 \n", - "8 not specified 41552404 325.0 \n", - "9 zero-emission bus (not specified) 128156513 143.0 \n", - "\n", - " new_cost_per_bus \n", - "0 1025966 \n", - "1 698568 \n", - "2 1185797 \n", - "3 1288136 \n", - "4 111861 \n", - "5 633271 \n", - "6 190999 \n", - "7 294203 \n", - "8 127853 \n", - "9 896199 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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prop_typebus_counttotal_costcost_per_bus
0BEB163.01672324891025966
1CNG252.0176039140698568
2FCEB102.01209513351185797
3electric (not specified)44.0566780001288136
4ethanol9.01006750111861
5low emission (hybrid)145.091824361633271
6low emission (propane)44.08403969190999
7mix (zero and low emission)125.036775430294203
8not specified325.041552404127853
9zero-emission bus (not specified)143.0128156513896199
10Grand Total1352.0828620391612884
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" - ], - "text/plain": [ - " prop_type bus_count total_cost cost_per_bus\n", - "0 BEB 163.0 167232489 1025966\n", - "1 CNG 252.0 176039140 698568\n", - "2 FCEB 102.0 120951335 1185797\n", - "3 electric (not specified) 44.0 56678000 1288136\n", - "4 ethanol 9.0 1006750 111861\n", - "5 low emission (hybrid) 145.0 91824361 633271\n", - "6 low emission (propane) 44.0 8403969 190999\n", - "7 mix (zero and low emission) 125.0 36775430 294203\n", - "8 not specified 325.0 41552404 127853\n", - "9 zero-emission bus (not specified) 143.0 128156513 896199\n", - "10 Grand Total 1352.0 828620391 612884" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "# testing pivot table on `merged_data`\n", - "# moved to final NB\n", - "#pivot table to get totals for each prop type\n", - "pivot_prop_type = pd.pivot_table(\n", - " merged_data,\n", - " values = [\"bus_count\", \"total_cost\"],\n", - " index = \"prop_type\",\n", - " aggfunc = \"sum\",\n", - " margins = True,\n", - " margins_name = \"Grand Total\"\n", - ").reset_index()\n", - "pivot_prop_type[\"cost_per_bus\"] = (pivot_prop_type[\"total_cost\"] / pivot_prop_type[\"bus_count\"]).astype(\"int64\")\n", - "\n", - "display(\n", - " #from new_cpb_agg\n", - " agg_prop[[\"prop_type\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]],\n", - " #pivot\n", - " pivot_prop_type\n", - ")\n", - "# same data, dont need the pivot table anymore, but the pivot table does have grand total" + "# ZEB cost per bus \n", + "dist_curve(\n", + " df=zeb_projects,\n", + " #using the accounting, weighted average approach to mean (total cost/total number of buses)\n", + " mean=zeb_cpb_wt_avg,\n", + " # need to investigate if std needs to be weighted as well?\n", + " std=zeb_projects[\"cost_per_bus\"].std(),\n", + " title=\"ZEB buses, cost per bus distribution\",\n", + " xlabel=\"cost per bus, $ million(s)\",\n", + ")" ] }, { "cell_type": "code", - "execution_count": 31, - "id": "ca709e43-4947-4a34-970f-216d4b6ab7cc", + "execution_count": null, + "id": "563304d2-2d98-44e6-b3a4-fd54f63fc0d8", "metadata": {}, "outputs": [], "source": [ - "#moved to final NB 6/25\n", + "# non-zeb cost per bus\n", + "dist_curve(\n", + " df=non_zeb_projects,\n", + " mean=non_zeb_cpb_wt_avg,\n", + " std=non_zeb_projects[\"cost_per_bus\"].std(),\n", + " title=\"non-ZEB costper bus Distribution\",\n", + " xlabel='\"cost per bus, $ million(s)\"',\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aa916127-57d9-4c1c-b5eb-8b7b7e4ac672", + "metadata": {}, + "outputs": [], + "source": [ + "agg_bus_size" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f11c857-ddbe-4871-aeca-e27fa00fbde8", + "metadata": {}, + "outputs": [], + "source": [ + "# multiple bar charts in one cell\n", "\n", - "#pivot table to get grand total for zeb/non-zeb only data\n", + "# cpb by prop type\n", + "make_chart(\"new_cost_per_bus\", \"Cost per bus by propulsion type\", x_col=\"prop_type\", data=agg_prop)\n", "\n", - "# keep this\n", - "zeb_list =[\n", - " \"BEB\",\n", - " \"FCEB\",\n", - " \"electric (not specified)\",\n", - " \"zero-emission bus (not specified)\",\n", - "]\n", + "# bus count by prop type\n", + "make_chart(\"total_bus_count\", \"Bus count by propulsion type\", x_col=\"prop_type\", data=agg_prop)\n", "\n", - "zeb_projects = merged_data[merged_data[\"prop_type\"].isin(zeb_list)]\n", + "#bus size bar chart\n", + "make_chart(\"total_bus_count\", \"\"\"Amount of buses procured by bus size.\n", + "excluding 'not specified' responses.\"\"\", x_col=\"bus_size_type\",data=agg_bus_size[agg_bus_size[\"bus_size_type\"]!=\"not specified\"])\n", "\n", - "#keep this\n", - "non_zeb_list =[\n", - " \"CNG\",\n", - " \"ethanol\",\n", - " \"low emission (hybrid)\",\n", - " \"low emission (propane)\",\n", - " \"mix (zero and low emission)\",\n", - "]\n", + "# pivot table to\n", + "agg_prop[[\"prop_type\",\"new_cost_per_bus\",\"total_bus_count\"]].sort_values(by=\"new_cost_per_bus\", ascending=False)\n" + ] + }, + { + "cell_type": "markdown", + "id": "9270ab8f-25ff-4de3-aca5-7ef4637a4f9c", + "metadata": { + "tags": [] + }, + "source": [ + "## Testing summary and conclusion\n", + "time to rework the summary section.\n", "\n", - "non_zeb_projects = merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)]\n", + "no more long expositions and variables. try to get the same point across using tables instead." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2472461d-7663-4b66-9bde-4c2a199707a5", + "metadata": {}, + "outputs": [], + "source": [ + "# moved to final NB 6/25\n", "\n", - "#keep this\n", - "pivot_zeb_prop = pd.pivot_table(\n", - " #filted incoming DF for zeb prop types\n", - " zeb_projects,\n", - " values = [\"bus_count\", \"total_cost\"],\n", - " index = \"prop_type\",\n", - " aggfunc = \"sum\",\n", - " margins = True,\n", - " margins_name = \"Grand Total\"\n", - ").reset_index() \n", + "new_summary = f\"\"\"\n", "\n", - "pivot_zeb_prop[\"cost_per_bus\"] = (pivot_zeb_prop[\"total_cost\"] / pivot_zeb_prop[\"bus_count\"]).astype(\"int64\")\n", + "# Bus Procurement Cost Analysis\n", "\n", - "#keep this\n", - "pivot_non_zeb_prop = pd.pivot_table(\n", - " #filted incoming DF for non-zeb prop types\n", - " non_zeb_projects,\n", - " values = [\"bus_count\", \"total_cost\"],\n", - " index = \"prop_type\",\n", - " aggfunc = \"sum\",\n", - " margins = True,\n", - " margins_name = \"Grand Total\"\n", - ").reset_index()\n", + "## Summary\n", + "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", "\n", - "pivot_non_zeb_prop[\"cost_per_bus\"] = (pivot_non_zeb_prop[\"total_cost\"] / pivot_non_zeb_prop[\"bus_count\"]).astype(\"int64\")" + "Data was compiled from three data sources:\n", + "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", + "2. TIRCP project data (state-funded, California only)\n", + "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", + "\n", + "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", + "The resulting dataset only contained projects that were solely used to procure buses. \n", + "\n", + "{len(merged_data)} projects were determined to contain solely bus purchases. \n", + "These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", + "\n", + "\n", + "Breakdown of each data souce:\n", + "{pivot_source.to_markdown(index=False)}\n", + "\n", + "\n", + "**ZEB buses include:**\n", + "- zero-emission (not specified) \n", + "- electric (not specified)\n", + "- battery electric \n", + "- fuel cell electric\n", + "\n", + "**Non-ZEB buses include:**\n", + "- CNG \n", + "- ethanol \n", + "- ow emission (hybrid, propane) \n", + "- diesel \n", + "- gas\n", + "\n", + "Below are charts and tables that summarize the findings.\n", + "\n", + "\n", + "\n", + "\n", + "\"\"\"\n", + "from IPython.display import Markdown, display\n", + "\n", + "display(Markdown(new_summary))" ] }, { "cell_type": "code", - "execution_count": 32, - "id": "01647d83-8b4f-47a9-ab57-a1db7cd501dd", + "execution_count": null, + "id": "7472ba04-7def-46ef-814b-bf63c1016f3b", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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prop_typebus_counttotal_costcost_per_bus
0BEB163.01672324891025966
1FCEB102.01209513351185797
2electric (not specified)44.0566780001288136
3zero-emission bus (not specified)143.0128156513896199
4Grand Total452.04730183371046500
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" - ], - "text/plain": [ - " prop_type bus_count total_cost cost_per_bus\n", - "0 BEB 163.0 167232489 1025966\n", - "1 FCEB 102.0 120951335 1185797\n", - "2 electric (not specified) 44.0 56678000 1288136\n", - "3 zero-emission bus (not specified) 143.0 128156513 896199\n", - "4 Grand Total 452.0 473018337 1046500" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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prop_typebus_counttotal_costcost_per_bus
0CNG252.0176039140698568
1ethanol9.01006750111861
2low emission (hybrid)145.091824361633271
3low emission (propane)44.08403969190999
4mix (zero and low emission)125.036775430294203
5Grand Total575.0314049650546173
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" - ], - "text/plain": [ - " prop_type bus_count total_cost cost_per_bus\n", - "0 CNG 252.0 176039140 698568\n", - "1 ethanol 9.0 1006750 111861\n", - "2 low emission (hybrid) 145.0 91824361 633271\n", - "3 low emission (propane) 44.0 8403969 190999\n", - "4 mix (zero and low emission) 125.0 36775430 294203\n", - "5 Grand Total 575.0 314049650 546173" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ + "# moved to final NB 6/25\n", "display(\n", - " #zeb data 3 different methods\n", - " #1. filtering agg_prop by zeb list, no grand totas\n", - " #2. filtering pivot talbe by zeb list, without grand totals\n", - " #3. dedicated pivot table for zeb, with grand totals\n", - " #agg_prop[agg_prop[\"prop_type\"].isin(zeb_list)],\n", - " #pivot_prop_type.loc[zeb_list],\n", + " Markdown(\"**ZEB Summary**\"),\n", " pivot_zeb_prop,\n", " \n", - " #non-zeb same 3 methods\n", - " #agg_prop[agg_prop[\"prop_type\"].isin(non_zeb_list)],\n", - " #pivot_prop_type.loc[non_zeb_list],\n", - " pivot_non_zeb_prop\n", - ")\n", - "# confirmed all data is the same, but need pivot for grand total rows" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "3be3ef4f-0bf3-4770-a8b7-340d372ae1ce", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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bus_size_typetotal_agg_costtotal_bus_countnew_cost_per_bus
0articulated5823757641.01420428
1cutaway16694500152.0109832
2not specified509919038881.0578795
3over-the-road951600014.0679714
4standard/conventional (30ft-45ft)234253277264.0887323
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" - ], - "text/plain": [ - " bus_size_type total_agg_cost total_bus_count \\\n", - "0 articulated 58237576 41.0 \n", - "1 cutaway 16694500 152.0 \n", - "2 not specified 509919038 881.0 \n", - "3 over-the-road 9516000 14.0 \n", - "4 standard/conventional (30ft-45ft) 234253277 264.0 \n", - "\n", - " new_cost_per_bus \n", - "0 1420428 \n", - "1 109832 \n", - "2 578795 \n", - "3 679714 \n", - "4 887323 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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bus_size_typebus_counttotal_costcost_per_bus
0articulated41.0582375761420428
1cutaway152.016694500109832
2not specified881.0509919038578795
3over-the-road14.09516000679714
4standard/conventional (30ft-45ft)264.0234253277887323
5Grand Total1352.0828620391612884
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" - ], - "text/plain": [ - " bus_size_type bus_count total_cost cost_per_bus\n", - "0 articulated 41.0 58237576 1420428\n", - "1 cutaway 152.0 16694500 109832\n", - "2 not specified 881.0 509919038 578795\n", - "3 over-the-road 14.0 9516000 679714\n", - "4 standard/conventional (30ft-45ft) 264.0 234253277 887323\n", - "5 Grand Total 1352.0 828620391 612884" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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prop_typebus_counttotal_costcost_per_bus
10Grand Total1352.0828620391612884
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" - ], - "text/plain": [ - " prop_type bus_count total_cost cost_per_bus\n", - "10 Grand Total 1352.0 828620391 612884" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# answers total buses sizes\n", - "pivot_size = pd.pivot_table(\n", - " merged_data,\n", - " values = [\"bus_count\", \"total_cost\"],\n", - " index = \"bus_size_type\",\n", - " aggfunc = \"sum\",\n", - " margins = True,\n", - " margins_name = \"Grand Total\"\n", - ").reset_index()\n", - "\n", - "pivot_size[\"cost_per_bus\"] = (pivot_size[\"total_cost\"] / pivot_size[\"bus_count\"]).astype(\"int64\")\n", - "\n", - "display(\n", - " agg_bus_size[[\"bus_size_type\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]],\n", - " pivot_size,\n", - " pivot_prop_type[pivot_prop_type[\"prop_type\"] == \"Grand Total\"]\n", - ")\n", - "\n", - "#same data, dont need pivot for this one because the grand totals will be the same as pivot_prop_type. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "2c933257-bdc2-4007-9571-58475118073c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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sourcetotal_agg_costtotal_bus_countnew_cost_per_bus
0dgs250112853236.01059800
1fta391257025883.0443099
2tircp187250513233.0803650
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" - ], - "text/plain": [ - " source total_agg_cost total_bus_count new_cost_per_bus\n", - "0 dgs 250112853 236.0 1059800\n", - "1 fta 391257025 883.0 443099\n", - "2 tircp 187250513 233.0 803650" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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sourcebus_counttotal_costcost_per_bus
0dgs236.02501128531059800
1fta883.0391257025443099
2tircp233.0187250513803650
3Grand Total1352.0828620391612884
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" - ], - "text/plain": [ - " source bus_count total_cost cost_per_bus\n", - "0 dgs 236.0 250112853 1059800\n", - "1 fta 883.0 391257025 443099\n", - "2 tircp 233.0 187250513 803650\n", - "3 Grand Total 1352.0 828620391 612884" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# moved to final NB 6/25\n", - "\n", - "# answers total buses and cost per grant type\n", - "pivot_source = pd.pivot_table(\n", - " merged_data,\n", - " values = [\"bus_count\", \"total_cost\"],\n", - " index = \"source\",\n", - " aggfunc = \"sum\",\n", - " margins = True,\n", - " margins_name = \"Grand Total\"\n", - ").reset_index()\n", - "\n", - "pivot_source[\"cost_per_bus\"] = (pivot_source[\"total_cost\"] / pivot_source[\"bus_count\"]).astype(\"int64\")\n", - "\n", - "display(\n", - " agg_source[[\"source\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]],\n", - " pivot_source\n", - ")\n" - ] - }, - { - "cell_type": "markdown", - "id": "11547020-dd35-4745-98f8-bbd02fccaa23", - "metadata": { - "tags": [] - }, - "source": [ - "## Testing Charts\n", - "\n", - "using `merged_data`, now without outliers.\n", - "charts looking good, similar results to initial charts" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "aace38a4-3f2d-460d-a258-59efa659f852", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(88, 14)" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged_data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "4753f3ea-00b6-4d5e-a3f0-73b3d3593acb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "792635.3409090909" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "396712.6067531972" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "1046500.7455752213" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "546173.304347826" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# means and standard deviations\n", - "# for graphs\n", - "cpb_mean = merged_data[\"cost_per_bus\"].mean()\n", - "cpb_std = merged_data[\"cost_per_bus\"].std()\n", - "\n", - "#testing weighted average calculation for sub-set non-zeb and zeb\n", - "zeb_cpb_wt_avg = (merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"bus_count\"].sum())\n", - "non_zeb_cpb_wt_avg = (merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"bus_count\"].sum())\n", - "display(\n", - " cpb_mean,\n", - " cpb_std,\n", - " zeb_cpb_wt_avg,\n", - " non_zeb_cpb_wt_avg\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "2007be9d-13ec-4d0d-a642-d9a42448b924", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1056659.3043478262" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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prop_typebus_counttotal_costcost_per_bus
0BEB163.01672324891025966
1FCEB102.01209513351185797
2electric (not specified)44.0566780001288136
3zero-emission bus (not specified)143.0128156513896199
4Grand Total452.04730183371046500
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" - ], - "text/plain": [ - " prop_type bus_count total_cost cost_per_bus\n", - "0 BEB 163.0 167232489 1025966\n", - "1 FCEB 102.0 120951335 1185797\n", - "2 electric (not specified) 44.0 56678000 1288136\n", - "3 zero-emission bus (not specified) 143.0 128156513 896199\n", - "4 Grand Total 452.0 473018337 1046500" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "1046500.7455752213" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# why is the average different when i use .mean() vs. total cost / bus cout\n", - "\n", - "display(\n", - " #this is the arithmatic mean, sums all the `cost_per_bus` rows, the divide by the number of rows. (row-wise)\n", - " zeb_projects[\"cost_per_bus\"].mean(),\n", - " \n", - " #this is like the accounting method of calculating average (Total Cost and Total Quantity Approach (Weighted Average))\n", - " pivot_zeb_prop,\n", - " \n", - " # calculating mean by weighted average the long way (total cost / total bus count, similar to pivot table)\n", - " (zeb_projects[\"total_cost\"].sum() / zeb_projects[\"bus_count\"].sum())\n", - ")\n", - "\n", - "# so the calculated grand total cost_per_bus is equivilent to the weighted average cost per bus\n" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "8645cf77-b30a-4c45-b943-ac81e8b5a613", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# chart of all cost per bus in the analysis.\n", - "\n", - "dist_curve(\n", - " df=merged_data,\n", - " mean=cpb_mean,\n", - " std=cpb_std,\n", - " title=\"all buses, cost per bus distribution\",\n", - " xlabel=\"cost per bus, $ million(s)\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "cefa6800-df50-4eda-95f8-74363ef942d0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# ZEB cost per bus \n", - "dist_curve(\n", - " df=zeb_projects,\n", - " #using the accounting, weighted average approach to mean (total cost/total number of buses)\n", - " mean=zeb_cpb_wt_avg,\n", - " # need to investigate if std needs to be weighted as well?\n", - " std=zeb_projects[\"cost_per_bus\"].std(),\n", - " title=\"ZEB buses, cost per bus distribution\",\n", - " xlabel=\"cost per bus, $ million(s)\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "563304d2-2d98-44e6-b3a4-fd54f63fc0d8", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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wie9D4efwu+++My3TarXC399fDBw48J7xCVHw3vTq1Uvo9Xrh7+8v5s6dK4QQ4uzZswKA+OOPP0yf4Tv3106dOomGDRuKvLw80zKj0Shat24tateubVqWl5dnej8LXblyRajVajFnzhzTsrK+1zk5OUWWHThwoMh7U7gtnTt3Nh23hBDixRdfFEqlUqSlpQkhCo7ZKpVK9OrVy6zda6+9JgCYfZ5KiicqKkoAEKGhoWL06NFi6dKl4saNG0XaTpo0SZT0J+zu7dLpdKJBgwaiY8eOZssBCJVKJS5dumRadvLkSQFAfPHFF6Zl/fv3FxqNRly7ds207OzZs0KpVBaJobj3tFu3bqJmzZpmy0o6dn766acCgFi3bp1pWXZ2tqhVq5bZsa8kxX3u7ubh4SEeeugh031LP5czZswQjo6O4vbt26ZlWq1WeHp6mtUDxR2/i3tfJkyYIJydnc1et1evXiI0NLRI2+KOa4V/N27dumVadvLkSaFQKMSoUaNMyyytWSxRplGed15lBgBt27bFrVu3kJGRAaBgoB4ATJ061azdSy+9BABFuvXr1atn+kYIFHzjjYqKQmxsbKniWrVqFb799luMHDmy2IHehfr164cdO3YUuXXo0MGsnUajMT22fft2LFmyBK6urujZs6fZqZvibNy4EZIkYebMmUUeK/wWZen7VDhQ9+eff0Z+fv49X/deJk+ebBbD5MmTodPp8PvvvwMA1q9fDw8PD3Tp0gUpKSmmW7NmzeDq6ordu3ebrS88PPye3zDu5uDggAkTJpjuq1QqTJgwAcnJyTh69GiptqWwJ2n79u2l7ma9n8JvOPe62sTT0xNxcXE4fPiw1a/Trl071KtXz+L2d39bfe655wD8/+eovGzduhUtWrTAo48+alrm6uqKp59+GlevXi1yNemYMWPMemwL9+377c9t27bFsWPHkJ2dDaCgJ6Bnz55o0qQJ9u3bB6Cg90mSJLNYSsvV1dVsPJtKpUKLFi1KdbxRKpUYPHgwVq9eDaBgAHhISIjZcazQ7du3sWvXLgwePBiZmZmm/erWrVvo1q0bLl68iPj4eAAF48kKB+EbDAbcunULrq6uiIqKwrFjx4qs29r3+s4xLfn5+bh16xZq1aoFT0/PYl/n6aefNuv9adu2LQwGA65duwag4Ju/TqfDc889Z9bu7kG994rn0KFDptPGy5cvx7hx4xAQEIDnnnvO4lNUd25Xamoq0tPTTZ+ru3Xu3NnUSwgU9My7u7ub3juDwYDt27ejf//+qFGjhqld3bp1iz3u3fna6enpSElJQbt27RAbG1tkyEBxx86tW7ciICAAgwYNMi1zdnYusVfVGq6urqbjWmk+l0OGDEF+fr7p7AZQcAFGWloahgwZcs/XvPN9KXydtm3bIicnB+fOnSv1NiQmJuLEiRMYPXo0qlWrZlreqFEjdOnSpdjj4f1qFkuUqWi68wMEAF5eXgBgGvdy7do1KBQK1KpVy6ydv78/PD09TTtaSesrXGfh+gwGA5KSksxud3f5Xrx4Ec888wwiIyPx5Zdf3jP+4OBgdO7cucjt7i5XpVJpeqxr1654+umn8fvvvyM9PR0zZsy452tcvnwZgYGBZkm9m6XvU7t27TBw4EDMnj0bPj4+6NevH5YtW1aqc90KhQI1a9Y0W1Z46qnw/PPFixeRnp4OPz8/+Pr6mt2ysrKQnJxs9vzw8HCLXx8AAgMD4eLics8YLBUeHo6pU6fim2++gY+PD7p164aFCxeWaTxToaysLACAm5tbiW1effVVuLq6okWLFqhduzYmTZqE/fv3l+p1Svv+1a5d2+x+REQEFAqFTedEKc61a9eKPfVat25d0+N3ut/xoSRt27aFXq/HgQMHcP78eSQnJ6Nt27Z47LHHzIqmevXq3XO/up/g4OAip3/uPN5YatiwYTh79ixOnjyJVatW4cknnyz2tNKlS5cghMCbb75ZZL8q/FJVuG8ZjUZ88sknqF27NtRqNXx8fODr64tTp04V+9m29r3Ozc3FW2+9hZCQELPXSUtLs+p1Cj8Dd39GfX19TW3vx8PDAx988AGuXr2Kq1evYunSpYiKisKCBQswd+5ci9bx888/45FHHoFGo0G1atVMQxcs2abC7Srcpps3byI3N7fINgEodn/Yv38/OnfubBpj4+vri9deew0Aii2a7nbt2jXUqlWryGfofsMeSiMrK8t0XCvN57Jx48aoU6eO2enYtWvXwsfHBx07drzna545cwYDBgyAh4cH3N3d4evra/rSYs3xuvCzVtIxKSUlxfTFq5C1+8mdyjSmqaTR8eKuQcWWTv51v/X9+++/RT5ku3fvNg3+1Gq1GDJkiOkc9r3OhZZVcHAwoqKisHfvXput837vkyRJ2LBhAw4ePIj//e9/2L59O8aOHYuPP/4YBw8etNn2Go1G+Pn5YeXKlcU+fveYl/K4AqOk9+LOQYOFPv74Y4wePRo//fQTfvvtN0yZMsU03ic4ONjqGE6fPg0ARYrZO9WtWxfnz5/Hzz//jG3btmHjxo348ssv8dZbb5mmfrifsr5/d79XpXnvypOlx4e7NW/eHBqNBnv37kWNGjXg5+eHyMhItG3bFl9++SW0Wq3p6jQ54rtby5YtERERgRdeeAFXrlwpctVXocJ5v6ZNm1Ziz2zhZ+3dd9/Fm2++ibFjx2Lu3LmoVq0aFAoFXnjhhWLnD7N2W5577jksW7YML7zwAlq1agUPDw9IkoQnn3zSpq9jrdDQUIwdOxYDBgxAzZo1sXLlSrz99tv3fM6+ffvQt29fPPbYY/jyyy8REBAAR0dHLFu2rMigfcC223T58mV06tQJderUwfz58xESEgKVSoWtW7fik08+KfKeynGVcVxcHNLT002ftdJ8LoGC3qZ33nkHKSkpcHNzw5YtWzB06NB7XhSVlpaGdu3awd3dHXPmzEFERAQ0Gg2OHTuGV1991WZz4t2PLXJdrlMOhIaGwmg04uLFi6Zvo0DBgNK0tDTToD9L+fv7Y8eOHWbLGjdubPr/tGnTcPz4cXz22Wd46KGHyha8BfR6vak3oiQRERHYvn07bt++XeK34tK+T4888ggeeeQRvPPOO1i1ahWGDx+ONWvW4Kmnnrpv4WU0GhEbG2s2sLnwFGPhoLmIiAj8/vvvaNOmTbns1AkJCcjOzjbrbbo7hsJvAHdPunh3b0ahhg0bomHDhnjjjTfw119/oU2bNli8ePF9D7D38v3330OSJHTp0uWe7VxcXDBkyBBTwf7444/jnXfewYwZM6DRaGw+Y/DFixfNvjxcunQJRqPRqveuNLGFhobi/PnzRZYXdq2Xdn8uSeFpsn379qFGjRqmU01t27aFVqvFypUrcePGDTz22GP3XE9FzdQMAEOHDsXbb7+NunXrljgnVGEPr6OjY4mTYhbasGEDOnTogKVLl5otT0tLMw3Yt4UNGzYgOjoaH3/8sWlZXl6e1ZOdFn4GLl68aNajffPmzVL34N3Jy8sLERERpi8yQMn53bhxIzQaDbZv3252AdCyZcusem1fX184OTnh4sWLRR67e3/43//+B61Wiy1btpj1atw9pOFeQkNDcfr0aQghzLaxuH3PGoUD5wsLpNJ8LoGComn27NnYuHEjqlevjoyMDDz55JP3fM6ePXtw69YtbNq0yWy/vXLlSpG2lu63hZ+1ko5JPj4+Rc5o2EK5zlzXs2dPACgyu+f8+fMBoMhVDPej0WiKnEor/AOxefNmLFiwAH379sWUKVPKHvx9XLhwAefPnzcr2oozcOBACCGK7XUorG4tfZ9SU1OLVMSFB+jCU3SFV7vc66C3YMECsxgWLFgAR0dHdOrUCUDBFQYGg6HYrnC9Xl/m2aP1er3Z5bo6nQ5LliyBr68vmjVrBgCmMQZ39uQZDAZ89dVXZuvKyMiAXq83W9awYUMoFIoyXaL73nvv4bfffsOQIUOK7ZYvdOvWLbP7KpUK9erVgxDCNO6scMe11azbhZdaF/riiy8AwHTFl7u7O3x8fIr0ghZ3uro0sfXs2RN///03Dhw4YFqWnZ2Nr776CmFhYaUal3U/bdu2xaFDh7B7925T0eTj44O6deuarnQpbtzQnWz9vt/LU089hZkzZ5oVH3fz8/ND+/btsWTJEiQmJhZ5/M5L95VKZZF9ff369aaxJbZS3Ot88cUXVvdKdu7cGY6Ojvjiiy/M1mvpDM8nT54s9orma9eu4ezZs2anYkrKr1KphCRJZttw9epV/Pjjj5ZvyF3r69atG3788Udcv37dtDwmJgbbt28v0hYw77lIT08vVcHWs2dPJCQkmE2RkJOTU+TYZ41du3Zh7ty5CA8Px/DhwwGU7nMJFPSuN2zYEGvXrsXatWsREBBw3y8wxb0vOp2uxGOSJafrAgIC0KRJE6xYscLsM3D69Gn89ttvpr+rtlauPU2NGzdGdHQ0vvrqK1P33N9//40VK1agf//+RQZcWysxMRHjxo2DUqlEp06disw/USgiIgKtWrUy3b9w4UKxbatXr27Wu6DX603tjEYjrl69isWLF8NoNBY7wPtOHTp0wMiRI/H555/j4sWL6N69O4xGI/bt24cOHTpg8uTJFr9PK1aswJdffokBAwYgIiICmZmZ+Prrr+Hu7m76gDg5OaFevXpYu3YtIiMjUa1aNTRo0MD0O1sajQbbtm1DdHQ0WrZsiV9//RW//PILXnvtNdNpt3bt2mHChAmYN28eTpw4ga5du8LR0REXL17E+vXr8dlnn5kNUiytwMBAvP/++7h69SoiIyOxdu1anDhxAl999ZXpMu369evjkUcewYwZM0y9dGvWrClSIO3atQuTJ0/GE088gcjISOj1enz//fdQKpUYOHDgfWO5M7d5eXm4du0atmzZglOnTqFDhw73PVB17doV/v7+aNOmDapXr46YmBgsWLAAvXr1Mo0ZKCwEX3/9dTz55JNwdHREnz59rP4WdOXKFfTt2xfdu3fHgQMH8MMPP2DYsGFmBfxTTz2F9957D0899RSaN2+OvXv3FnvRQmlimz59OlavXo0ePXpgypQpqFatGlasWIErV65g48aNNp09vG3btnjnnXfw77//mhVHjz32GJYsWYKwsLD7nnqNiIiAp6cnFi9eDDc3N7i4uKBly5alHkNmidDQUItmpl64cCEeffRRNGzYEOPHj0fNmjVx48YNHDhwAHFxcaZ5mHr37o05c+ZgzJgxaN26Nf755x+sXLmyyHjEsurduze+//57eHh4oF69ejhw4AB+//1306X2peXr64tp06Zh3rx56N27N3r27Injx4/j119/taiHbMeOHZg5cyb69u2LRx55xDSv3LfffgutVmv2Hhd+dqdMmYJu3bpBqVTiySefRK9evTB//nx0794dw4YNQ3JyMhYuXIhatWrh1KlTVm3X7NmzsW3bNrRt2xYTJ06EXq83zc925zq7du0KlUqFPn36YMKECcjKysLXX38NPz+/YguS4owfPx4LFizAqFGjcPToUQQEBOD7778vMv3D/fz66684d+4c9Ho9bty4gV27dmHHjh0IDQ3Fli1bzCaKtPRzWWjIkCF46623oNFoMG7cuPvu+61bt4aXlxeio6MxZcoUSJKE77//vtjTYs2aNcPatWsxdepUPPzww3B1dUWfPn2KXe+HH36IHj16oFWrVhg3bpxpygEPD4/ymym+VNfa3XX5XuEl6oWKu8wwPz9fzJ49W4SHhwtHR0cREhIiZsyYYXaJoRD/fwnv3e6+jLg4hZfd3u925+Wu92p35+sVN+WAu7u76NSpk/j9998ter/0er348MMPRZ06dYRKpRK+vr6iR48eZlMWWPI+HTt2TAwdOlTUqFFDqNVq4efnJ3r37i2OHDli9np//fWXaNasmVCpVGaXmBdeYn/58mXRtWtX4ezsLKpXry5mzpxZ5PJmIYT46quvRLNmzYSTk5Nwc3MTDRs2FK+88opISEgwtSkpbyVp166dqF+/vjhy5Iho1aqV0Gg0IjQ0VCxYsKBI28uXL4vOnTsLtVotqlevLl577TWxY8cOs8tuY2NjxdixY0VERITQaDSiWrVqokOHDhbl5u7cOjs7i7CwMDFw4ECxYcOGYt+Tuz+PS5YsEY899pjw9vYWarVaREREiJdfflmkp6ebPW/u3LkiKChIKBQKs30EgJg0aVKx8d2ZOyH+f787e/asGDRokHBzcxNeXl5i8uTJIjc31+y5OTk5Yty4ccLDw0O4ubmJwYMHi+Tk5GKnoygptrunHBCiICeDBg0Snp6eQqPRiBYtWoiff/7ZrE3h/nj3NBD3mgrhbhkZGUKpVAo3Nzeh1+tNy3/44QcBQIwcObLIc4o7Vvz000+iXr16wsHBwey1Cz+Hd4uOji72cue7WfK5L+nS78uXL4tRo0YJf39/4ejoKIKCgkTv3r3Fhg0bTG3y8vLESy+9JAICAoSTk5No06aNOHDgQJFtLOt7nZqaKsaMGSN8fHyEq6ur6Natmzh37lyR3Je0LXdPASKEEAaDQcyePdsUe/v27cXp06eL/TzdLTY2Vrz11lvikUceEX5+fsLBwUH4+vqKXr16mU1RI0TBcfW5554Tvr6+QpIks0v/ly5dKmrXri3UarWoU6eOWLZsmWn/uVNJ+19xsf7xxx+m42rNmjXF4sWLi13nli1bRKNGjYRGoxFhYWHi/fffF99++22Rv433+gxdu3ZN9O3bVzg7OwsfHx/x/PPPm6Z8sXTKgcKbSqUS/v7+okuXLuKzzz4TGRkZxT7Pks9loYsXL5rW/+eff5YYw53bu3//fvHII48IJycnERgYKF555RWxffv2ItuUlZUlhg0bJjw9PU1TTwhR8mf6999/F23atBFOTk7C3d1d9OnTR5w9e9asTWlqlvuRhCinEXxkd0aPHo0NGzbcdxwWERERFVV5fo2TiIiISEYsmoiIiIgswKKJiIiIyAIc00RERERkAfY0EREREVmARRMRERGRBcp1csvyZjQakZCQADc3twr9yQQiIiKynhACmZmZCAwMtOnEuOWtUhdNCQkJCAkJkTsMIiIissK///5bph9Wr2iVumgq/JmKf//9F+7u7jJHU7nVWVAHiZmJCHALwLnJ5+QOp2qqUwdITAQCAoBzzIFcuC/YB+bBPpRXHjIyMhASEmL6O15ZVOqiqfCUnLu7O4umMprVbRaydFlwVbnyvZTLrFlAVhbg6gowB7LhvmAfmAf7UN55qGxDayr1lAMZGRnw8PBAeno6dyoiIqJKorL+/a48o6+IiIiIZMSiiYiIiMgClXpME9lOYmYiDMIApaREgFuA3OFUTYmJgMEAKJUFg8ErkMFgQH5+foW+pr1Kzk6GURihkBTwc/GTO5wqi3mwD9bmwdHREUqlshwjkweLJgIAPPz1w4jPjEeQWxDipsbJHU7V9PDDQHw8EBQExFVMDoQQSEpKQlpaWoW8XmUQlxEHg9EApUKJbPdsucOpspgH+1CWPHh6esLf37/SDfa+FxZNRFVYYcHk5+cHZ2fnB+rgZi3tTS30Qg8HyQHhvuFyh1NlMQ/2wZo8CCGQk5OD5ORkAEBABfeclycWTURVlMFgMBVM3t7ecodjNyRHCTACkkKCRqORO5wqi3mwD9bmwcnJCQCQnJwMPz+/B+ZUHQeCE1VRhWOYnJ2dZY6EiB5EhceWB2m8JIsmoiqOp+SIqDw8iMcWFk1EREREFpC9aIqPj8eIESPg7e0NJycnNGzYEEeOHJE7LCKicnP16lXMmjVL7jCIqJRkLZpSU1PRpk0bODo64tdff8XZs2fx8ccfw8vLS86wiMiOjR49GpIk4Zlnniny2KRJkyBJEkaPHl3xgdlI+/btIUmS2a24bQWAW7duITg4GJIkFZk2QqvV4vXXX0doaCjUajXCwsLw7bff3vO1p0yZgmbNmkGtVqNJkyZFHp81a1aR2CRJgouLi6nNpk2b0Lx5c3h6esLFxQVNmjTB999/f8/X/fPPP9GmTRvTl+c6derg+yVFn7Nw4UKEhYVBo9GgZcuW+Pvvv++5XiJbk/Xquffffx8hISFYtmyZaVl4OC8tJaJ7CwkJwZo1a/DJJ5+YrtLJy8vDqlWrUKNGDZmjK9mVK1cwdepUHDhwABkZGVizZg3at2+PxYsXm7UbP3485syZY7pf0mD9cePGoVGjRoiPjy/y2ODBg3Hjxg0sXboUtWrVQmJiIoxG431jHDt2LA4dOoRTp04VeWzatGlFCrhOnTrh4YcfNt2vVq0aXn/9ddSpUwcqlQo///wzxowZAz8/P3Tr1q3Y13RxccHkyZPRqFEjuLi44M8//8T4p8dD5aTCkFFDAABr167F1KlTsXjxYrRs2RKffvopunXrhvPnz8PPj5NfUsWQtadpy5YtaN68OZ544gn4+fnhoYcewtdffy1nSERUCTRt2hQhISHYtGmTadmmTZtQo0YNPPTQQ2ZtjUYj5s2bh/DwcDg5OaFx48bYsGGD6XGDwYBx48aZHu/3aD+s/ma12TpGjx6N/v3746OPPkJAQAC8vb0xadKkUl8VNGrUKNy4cQOLFi3C6NGj8dlnnxU73YOzszP8/f1Nt+J+0HTRokVIS0vDtGnTijy2bds2/PHHH9i6dSs6d+6MsLAwtGrVCm3atLlnfJ9//jkmTZqEmjVrFvu4q6urWVw3btzA2bNnMW7cOFOb9u3bY8CAAahbty4iIiLw/PPPo1GjRvjzzz9LfN2HHnoIQ4cORf369REWFoYRI0agdfvWOHHohKnN/PnzMX78eIwZMwb16tXD4sWL4ezsfN/eMyJbkrVoio2NxaJFi1C7dm1s374dzz77LKZMmYIVK1YU216r1SIjI8PsRraxc9ROnH72NHaO2il3KFXXzp3A6dMF/8pAq9UiOzu7TDetVlth8Y4dO9asl/rbb7/FmDFjirSbN28evvvuOyxevBhnzpzBiy++iBEjRuCPP/4AUFBUBQcHY/369Th79ixmvjkTi99fjH92/WO2nt27d+Py5cvYvXs3VqxYgeXLl2P58uWmx2fNmoWwsLB7xnz8+HFMmjQJDz30kKnn5Z133inSbuXKlfDx8UGDBg0wY8YM5OTkmD1+9uxZzJkzB9999x0UiqKH8cIvpB988AGCgoIQGRmJadOmITc3957xldY333yDyMhItG3bttjHhRDYuXMnzp8/j8cee8zi9R4/fhxnjp1B7669EeUdBZ1Oh6NHj6Jz586mNgqFAp07d8aBAwfKvB1UsijvKNT3rY8o7yi5Q7ELsp6eMxqNaN68Od59910ABd82Tp8+jcWLFyM6OrpI+3nz5mH27NkVHWaVEOXDHUJ2UfLlQKvV4syZMzAajai+ciWqr1p13+fkREXh0vz5ZstqvfQSVJcv474XGk+dWnArgxEjRmDGjBm4du0aAGD//v1Ys2YN9uzZY2qj1Wrx7rvv4vfff0erVq0AADVr1sSff/6JJUuWoF27dnB0dDQ7roSHh+Po4aP4cdOPGDFshGm5l5cXFixYAKVSiTp16qBXr17YuXMnxo8fDwDw8fFBRETEPWNu06YNPv3003ueJhs2bBhCQ0MRGBiIU6dO4dVXX8X58+dNvWparRZDhw7Fhx9+iBo1aiA2NrbIOmJjY/Hnn39Co9Fg8+bNSElJwcSJE3Hr1i2zQrMs8vLysHLlSkyfPr3IY+np6QgKCoJWq4VSqcSXX36JLl263HedwcHBuHnzJvR6PWbNmoWJEyYCABISEmAwGFC9enWz9tWrV8e5c+dssj1UPI0jJxa9k6xFU0BAAOrVq2e2rG7duti4cWOx7WfMmIGpdxxoMzIyEBISUq4xElUFer0eRqMRATXC4Kl2guq/nz+4Fyk0FKG165ju67R5cEhNhVTM+JoibNBL7Ovri169emH58uUQQqBXr17w8fExa3Pp0iXk5OQU+YOt0+nMTuMtXLgQ3377La5fv47c3FzodLoiA6Hr169vNqtxQEAA/vnn/3ujJk+ejMmTJ98z5pUrV2L27Nl47bXXkJSUhO3bt+Oll17CoEGDTG2efvpp0/8bNmyIgIAAdOrUCZcvX0ZERARmzJiBunXrYsSIEcW9BICCL6SSJGHlypXw8PAAUHB6a9CgQfjyyy9N48DKYvPmzcjMzCz2C66bmxtOnDiBrKws7Ny5E1OnTkXNmjXRvn37e65z3759yMrKwsGDBzF9+nTUqlULQ4cOLXOsRLYia9HUpk0bnD9/3mzZhQsXEBoaWmx7tVoNtVpdEaERVUkqtQbKatVgDAq6b1vJrzo0d/3x1Xt6whgYCMX9JrUrZoyONcaOHWsqVBYuXFjk8aysLADAL7/8gqC7tqnwWLJmzRpMmzYNH3/8MVq1agU3Nzd8+OGHOHTokFl7R0dHs/uSJFk0sPpOPj4++OKLL/DSSy/hvffeQ1hYGIYMGYJff/0VXbt2LfY5LVu2BFBQAEZERGDXrl34559/TOOyhBCmdb/++uuYPXs2AgICEBQUZCqYgIIvpEIIxMXFoXbt2qWKuzjffPMNevfuXaT3Byg4dVarVi0AQJMmTRATE4N58+bdt2gqvBCoYcOGuHHjBmbNmoWhQ4fCx8cHSqUSN27cMGt/48YN+Pv7l3lbiCwla9H04osvonXr1nj33XcxePBg/P333/jqq6/w1VdfyRlWlbTqn1XIyc+Bs6MzhjUcJnc4VdOqVUBODuDsDAyTLwf5L7yI/BdetOq5l+bPR926dc0uQS9P3bt3h06ngyRJxV6ZVa9ePajValy/fh3t2rUrdh379+9H69atMXFiwamgWzm3EHMhBgajoVxj9/f3x/Tp07F+/Xrs27evxKLpxIkTAP7/R083btxoNjbp8OHDGDt2LPbt22c6PdimTRusX78eWVlZcHV1BVDwhVShUCA4OLjMsV+5cgW7d+/Gli1bLGpvNBpLPd4tS5uF3Lxc3Mq5BW9nbzRr1gw7d+5E//79TevcuXPnfXv3qGxu5dyCURihkBTwduZvVMpaND388MPYvHkzZsyYgTlz5iA8PByffvophg8fLmdYVdIrO15BfGY8gtyCWDTJ5ZVXgPh4IChI1qKpMlEqlYiJiTH9/25ubm6YNm0aXnzxRRiNRjz66KNIT0/H/v374e7ujujoaNSuXRvfffcdtm/fjvDwcHy06CMcO3oMQTXu39t2pwULFmDz5s3YeY+B/OPGjcOECRPg4uICrVaLTZs24cyZM3jzzTcBAJcvX8aqVavQs2dPeHt749SpU3jxxRfx2GOPoVGjRgBQZNxUSkoKgIKeJE9PTwAF46Lmzp2LMWPGYPbs2UhJScHLL7+MsWPH3vPU3KVLl5CVlYWkpCTk5uaaCrZ69epBpVKZ2n377bcICAhAjx49iqxj3rx5aN68OSIiIqDVarF161Z8//33WLRokanNjBkzEB8fj++++w5AQS9hjRo1UKdOwenevXv3YsGnCzBk7BDEZcTB29kbU6dORXR0NJo3b44WLVrg008/RXZ2drGD/8l24jLikG/Mh6PCkUUTZC6aAKB3797o3bu33GEQUSVV3OX4d5o7dy58fX0xb948xMbGwtPTE02bNsVrr70GAJgwYQKOHz+OIUOGQJIkdOnXBYOiB+HA7tJdlZWSkoLLly/fs42fnx/Gjh2LK1euQKvVokaNGpg7d66p90SlUuH33383FQQhISEYOHAg3njjjVLF4urqih07duC5555D8+bN4e3tjcGDB+Ptt982tdmzZw86dOiAK1eumK76e+qpp0xXFQIwjfu6s43RaMTy5csxevToYgvV7OxsTJw4EXFxcaaJKn/44QcMGTLE1CYxMRHXr1833TcajZgxYwauXLkCBweHgqkK3nge/Yb3M7UZMmQIbt68ibfeegtJSUlo0qQJtm3bVuzpQaLyIonCE+KVUEZGBjw8PJCenn7fAyfdW/D8YFNPU9zUOLnDqZqCg/+/pymu/HOQl5eHK1euIDw8HAaDATExMQitXafIOCWL15ebi2sXz1Xo6bnycDLppOmbdWP/xuXyGlevXsXy5ctl/SmVZcuW4d1338XZs2eLjNeyBxWRB7q/suThzmOMRmN+FV5l/fst+2/PERFRxdu6dSveffdduyyYiOyV7KfniIiqmrCwMNl/sHf9+vWyvj5RZcSeJiIiIiILsGgiIiIisgCLJiIiIiILsGgiIiIisgAHghMAwN/V3+xfkkHhz0HwZyFk5ah0NPuX5ME82AfmwRyLJgIAHHn6iNwh0BHmwB7U8613/0ZU7pgH+8A8mOPpOSIiIiILsGgiIiqDq1evQpIk0++0VZZ1W2P58uWm37ezh/WUxaxZs3D16lVZY6DKh0UTEVUqN2/exLPPPosaNWpArVbD398f3bp1w/79+01tJEnCjz/+KF+QFah9+/aQJAmSJEGtViMoKAh9+vTBpk2bbP5aQ4YMwYULF0r1nLCwMHz66adlXk9FOX/+PDp06IDq1atDo9GgZs2aeOONN5Cfn29qc+bMGQwcOBBhYWGQJKnI9gEFRVlhXgpvhT9IXJL8/HzMmTMHERER0Gg0aNy4MbZt22bWZtGiRWjUqBHc3d3h7u6OVq1a4ddffzU9fvv2bTz33HOIioqCk5MTatSogSlTpiA9Pf2er/vqq6+iYcOGcHFxQWBgIEaNGoWEhASzdrdv38bw4cPh7u4OT09PjBs3DllZWffcpgcNxzQRAGDC/ybgdt5tVNNUw5I+S+QOp2qaMAG4fRuoVg1YwhyUZODAgdDpdFixYgVq1qyJGzduYOfOnbh165ZN1n8t7Rr0Rj0cFA4I9Qy1yTrvR6fTQaVSWf388ePHY86cOdDr9YiLi8PmzZvx5JNPYvTo0fjqq69sFqeTkxOcrPxtwtKup7zysH79erz33ns4d+4cFi5ciIiICLz88ssYOHAgAMDR0RGjRo1C06ZN4enpiZMnT2L8+PEwGo149913AQA5OTmoWbMmnnjiCbz44oslvlb9+vXx+++/m+47ONz7T+4bb7yBH374AV9//TXq1KmD7du3Y8CAAfjrr79MP54cHByM9957D7Vr14YQAitWrEC/fv1w/Phx1K9fHwkJCUhISMBHH32EevXq4dq1a3jmmWeQkJCADRs2FPu6OTk5OHbsGN588000btwYqampeP7559G3b19s/H2jKQ/PDH8GiYmJ2LFjB/Lz8zFmzBg8/fTTWLVqValyUKmJSiw9PV0AEOnp6XKHUukFfRwkMAsi6OMguUOpuoKChAAK/q0Aubm54uzZsyI3N1dkZWWJw4cPi+S0TJGh1Vt1S07LFIcPHxZZWVnlFnNqaqoAIPbs2VNim9DQUAHAdAsNDRVCCHHp0iXRt29f4efnJ1xcXETz5s3Fjh07ijx38vTJos+QPsLZxVmEhISIJUuWmLU5dOiQaNKkiVCr1aJZs2Zi06ZNAoA4fvy4EEIIvV4vxo4dK8LCwoRGoxGRkZHi008/NVtHdHS06Nevn3j77bdFQECACAsLs2jdxWnXrp14/vnniyz/9ttvBQCzbbx+/bp44oknhIeHh/Dy8hJ9+/YVV65cEUIIsX37dqFWq0VqaqrZeqZMmSI6dOgghBBi2bJlwsPDw/TY/d7Tdu3ameWi8E/O3esRQogvv/xS1KxZUzg6OorIyEjx9udvi8Pxh8WJxBNCCCEAiK+//lr0799fODk5iVq1aomffvqpxPelOOfPnxdKpVK8+eabYuLEieJ///uf+O6778Tq1avv+bwXX3xRPProo8U+FhoaKj755JMiy2fOnCkaN25cqvgCAgLEggULzJY9/vjjYvjw4fd8npeXl/jmm29KfHzdunVCpVKJ/Px8i2P5+++/BQDx6+FfxeH4w2LTHwWfxcOHD5va/Prrr0KSJBEfH1/sOu48xtytsv795uk5Iqo0XF1d4erqih9//BFarbbYNocPHwYALFu2DImJiab7WVlZ6NmzJ3bu3Injx4+je/fu6NOnD65fv272/O+XfI+6jepizY41mDhxIp599lmcP3/etI7evXujXr16OHr0KGbNmoVp06aZPd9oNCI4OBjr16/H2bNn8dZbb+G1117DunXrzNrt3LkT58+fx44dO/Dzzz9btO7SiI6OhpeXl+k0XX5+Prp16wY3Nzfs27cP+/fvh6urK7p37w6dTodOnTrB09MTGzduNK3DYDBg7dq1GD58eLGvcb/3dNOmTQgODsacOXOQmJiIxMTEYtezefNmPP/883jppZdw+vRpTJgwATNfnIkj+82vKJ09ezYGDx6MU6dOoWfPnhg+fDhu375tevx+v+l36tQpKBQKzJ49G76+vmjQoAFGjhyJJ598ssTnXLp0Cdu2bUO7du1KbFOSixcvIjAwEDVr1sTw4cOLfNbuptVqodFozJY5OTnhzz//LLa9wWDAmjVrkJ2djVatWpW43vT0dLi7u9+3p+vu50iSBDcPNwDAqaOn4OnpiebNm5vadO7cGQqFAocOHbJ4vZWe3FVbWVTWStUesafJDthJT9O7+z4UgR8H3ffW44feRXqa2n7ZVgR+FCiCPg665+3jvz62Ou4NGzYILy8vodFoROvWrcWMGTPEyZMnzdoAEJs3b77vuurXry+++OIL0/3Q0FDRa2AvUw+H0WgUfn5+YtGiRUIIIZYsWSK8vb3NvjkvWrTovr1BkyZNEgMHDjTdj46OFtWrVxdarda0zNp1l9TTJIQQLVu2FD169BBCCPH999+LqKgoYTQaTY9rtVrh5OQktm/fLoQQ4vnnnxcdO3Y0PX5371NxPUR3K+49vbsn5u71tG7dWowfP96sTZc+XUSbjm3MepreeOMN0+NZWVkFPSG//mpa1rFjR7PXvltsbKxQq9XipZdeEuPGjTP1shWnVatWQq1WCwDi6aefFgaDodh2JfU0bd26Vaxbt06cPHlSbNu2TbRq1UrUqFFDZGRklPiaQ4cOFfXq1RMXLlwQBoNB/Pbbb8LJyUmoVCqzdqdOnRIuLi5CqVQKDw8P8csvv5S4zps3b4oaNWqI1157rcQ2d8vNzRVNmzYVw4YNEycST4jD8YfF5OmTRWRkZJG2vr6+4ssvvyxxPexpIqIHWoY2AwmZ8fe9peTcLPLcNF0aErISEJ8Zf89bhjbD6vgGDhyIhIQEbNmyBd27d8eePXvQtGlTLF++/J7Py8rKwrRp01C3bl14enrC1dUVMTExRb79165X2/R/SZLg7++P5ORkAEBMTAwaNWpk1htQ3Df8hQsXolmzZvD19YWrqyu++uqrIq/TsGFDs3FMlq67NIQQkCQJAHDy5ElcunQJbm5uph67atWqIS8vD5cvXwYADB8+HHv27DENAF65ciV69epV4pVulr6n9xMTE4M2bdqYLWvycBNcuXTFbFmjRo1M/3dxcYG7u7spN0BB793kyZNLfJ3w8HDs2LEDp0+fxurVq9G0aVMMGzbMtP13Wrt2LY4dO4ZVq1bhl19+wUcffVSqberRoweeeOIJNGrUCN26dcPWrVuRlpZWpMfxTp999hlq166NOnXqQKVSYfLkyRgzZgwUCvM/1VFRUThx4gQOHTqEZ599FtHR0Th79myR9WVkZKBXr16oV6/ePXvg7pSfn4/BgwdDCIFFixaVapurAg4EJyIz7mp3BLoF3bedj7NvkWWeKk8Eugaa/lDf6zXKQqPRoEuXLujSpQvefPNNPPXUU5g5cyZGjx5d4nOmTZuGHTt24KOPPkKtWrXg5OSEQYMGQafTmbW7+xSGJEkwGo0Wx7ZmzRpMmzYNH3/8MVq1agU3Nzd8+OGHRU5huLi4WLxOaxgMBly8eBEPP/wwgIICp1mzZli5cmWRtr6+Bbl8+OGHERERgTVr1uDZZ5/F5s2b71mMWvqe2oqjo/ms1KXNDQC0bdsW27Ztw6xZs1C/fn0sXboUHTt2xOXLl81yHxISAgCoV68eDAYDnn76abz00ktQKpVWxe7p6YnIyEhcunSpxDa+vr748ccfkZeXh1u3biEwMBDTp09HzZo1zdqpVCrUqlULANCsWTMcPnwYn332GZbccQFJZmYmunfvDjc3N2zevLnIe1ecwoLp2rVr2LVrF9zd3YGcgsd8/HzMClQA0Ov1uH37Nvyr0K8YsGgiIjOTW7yIyS1KviLoXuY/PB9169Yt94LgbvXq1TObYsDR0REGg8Gszf79+zF69GgMGDAAQEERUdp5eurWrYvvv/8eeXl5ph6hgwcPFnmd1q1bY+LEiaZlxfVkWLPu0lixYgVSU1NNV4U1bdoUa9euhZ+fX8EfwxIMHz4cK1euRHBwMBQKBXr16lViW0veU5VKVSQXd6tbty7279+P6Oho07ITh0+gZu2a93hW2T388MOoU6cOGjVqhGvXriEiIqLYdkajEfn5+TAajVYXTVlZWbh8+TJGjhx537YajQZBQUHIz8/Hxo0bMXjw4Hu2NxqNZmP8MjIy0K1bN6jVamzZsqXIOKniFBZMFy9exO7du+Ht7W32eKNmjZCWloajR4+iWbNmAIBdu3bBaDSiZcuW913/g4Kn54io0rh16xY6duyIH374AadOncKVK1ewfv16fPDBB+jXr5+pXVhYGHbu3ImkpCSkpqYCAGrXro1NmzbhxIkTOHnyJIYNG1bqXophw4ZBkiSMHz8eZ8+exdatW4uctqlduzaOHDmC7du348KFC3jzzTdNg9HLuu6S5OTkICkpCXFxcTh48CBeffVVPPPMM3j22WfRoUMHAAXFkI+PD/r164d9+/bhypUr2LNnD6ZMmYK4uDjTuoYPH45jx47hnXfewaBBg6BWq0t8XUve07CwMOzduxfx8fFISUkpdj0vv/wyli9fjkWLFuHixYuYP38+dm3dhRHPjLBo+wt16tQJCxYsKPHxbdu24ZNPPkFsbCyMRiOSk5Px+eefw8fHBzVq1ABQcEpy3bp1iImJQWxsLNatW4cZM2ZgyJAhpt4anU6HEydO4MSJE9DpdIiPj8eJEyfMepGmTZuGP/74A1evXsVff/2FAQMGQKlUYujQoSXGd+jQIWzatAmxsbHYt28funfvDqPRiFdeecXUZsaMGdi7dy+uXr2Kf/75BzNmzMCePXtMg/UzMjLQtWtXZGdnY+nSpcjIyEBSUhKSkpLMitc6depg8+bNAAoKpkGDBuHIkSNYuXIlDAaD6Tn5uoL5qWpG1kT37t0xfvx4/P3339i/fz8mT56MJ598EoGBgaXKU6Um96CqsqisA8nsEQeC2wE7GQhuz1MO5OXlienTp4umTZsKDw8P4ezsLKKiosQbb7whcnJyTO22bNkiatWqJRwcHExTDly5ckV06NBBODk5iZCQELFgwYIig6hDQ0PFtNnTzC51b9y4sZg5c6apzYEDB0Tjxo2FSqUSTZo0ERs3bjQbrJ2XlydGjx4tPDw8hKenp3j22WfF9OnTzS4/L5xy4G73W3dx7rysX6VSiYCAANG7d2+xadOmIm0TExPFqFGjhI+Pj1Cr1aJmzZpi/PjxRY6hLVq0EADErl27zJbfPYDbkvf0wIEDolGjRqZB1cWtRwjLphy4e3C/h4eHWLZsmel+aGioWa7uFhMTI4YMGSKCgoKEUqkUrq6uok2bNuLgwYOmNmvWrBFNmzYVrq6uwsXFRdSrV0+8++67ZoOZr1y5UmQqBQCiXbt2pjZDhgwRAQEBQqVSiaCgIDFkyBBx6dIls3iio6PNnrNnzx5Rt25doVarhbe3txg5cmSRy/nHjh0rQkNDhUqlEr6+vqJTp07it99+Mz2+e/fuYmMDYDbwHYDpvStpewCIrzd+bcrDrVu3xNChQ4Wrq6twd3cXY8aMEZmZmSW+3w/iQHBJCCEqqD6zuYyMDHh4eJgupyTrBc8PRnxmPILcghA3Ne7+TyDbCw4G4uOBoCAgrvxzkJeXhytXriA8PBwGgwExMTEIrV0HGisnL8zLzcW1i+dkOT1nSyeTTiLfmA9HhSMa+zeWO5wqq7zzMGvWLIwePRphYWE2X7el2rVrhw4dOlg8SFsOZcnDnceYu08RVta/3xzTRACAoQ2GIjUvFV4aL7lDqbqGDgVSUwEv5kBO1ZyqwSAMUErWjV0h23jQ85Ceno7Lly/jl19+kTuUe3rQ81BaLJoIAPBh1w/lDoE+ZA7sQYhHiNwhEMo/D3L37nh4eJiNJbNX3B/McSA4ERERkQVYNBERERFZgEUTURVXia8FISI79iAeWzimiQAAdRbUQUJmAgLdAnFu8jm5w6ma6tQBEhKAwEDgXPnnoHDOmZycHIsmv6sqTiefhs6gg0qpQgO/BnKHU2UxD/ahLHnIySmYTtyS2cgrCxZNBADI0mUhU5eJLF2W3KFUXVlZQGZmwb8VQKlUwtPTE8nJyfDw8AAA5Ou0UNznJ1BKkq8rmJFYq9VaPWuyPdBr9TAKI/R6PfLy8uQOp8piHuyDNXkQQiAnJwfJycnw9PSs1MeDu7FoIqrCCn8zKikpCTdv3oRR4QBHlXXfCvN1+bidkgJHR0ezH6KtbG5m3ITBaIBSoYQ6q+TZsKl8MQ/2oSx58PT0fOB+l45FE1EVJkkSAgICkJCQgGeeeQZvL12N8Mg6Vq3ryoVzePOZZ7Bx40ZERUXZONKKM3rZaNzIvoHqLtXxx5g/5A6nymIe7IO1eXB0dHygepgKsWgiIkiShOvXr0NrEBCO1n2r1xoErl27BkmSKvUYqfjceMRnx0Ov0Ffq7ajsmAf7wDyY49VzRERERBZg0URERERkARZNRERERBZg0URERERkARZNRERERBbg1XMEAFjcezFy83Ph5OgkdyhV1+LFQG4u4MQcyIn7gn1gHuwD82CORRMBAHpH9pY7BOrNHNgD7gv2gXmwD8yDOZ6eIyIiIrIAiyYiIiIiC/D0HAEAjiYcNf2SdbPAZnKHUzUdPQrodIBKBTRjDuTCfcE+MA/2gXkwx6KJAAD91vRDfGY8gtyCEDc1Tu5wqqZ+/YD4eCAoCIhjDuTCfcE+MA/2gXkwx9NzRERERBZg0URERERkARZNRERERBZg0URERERkARZNRERERBaQtWiaNWsWJEkyu9WpU0fOkIiIiIiKJfuUA/Xr18fvv/9uuu/gIHtIREREREXIXqE4ODjA399f7jCIiIiI7kn2ounixYsIDAyERqNBq1atMG/ePNSoUaPYtlqtFlqt1nQ/IyOjosIkKjfXr19HSkoKGuTnQwVAl5+P08eOlWodPj4+Je43RERkG7IWTS1btsTy5csRFRWFxMREzJ49G23btsXp06fh5uZWpP28efMwe/ZsGSJ98MVMioGAgARJ7lCqlOvXr6Nu3brIycmBKwAJgEhORlYpf0bF2dkZMTExLJxsgPuCfWAe7APzYE7WoqlHjx6m/zdq1AgtW7ZEaGgo1q1bh3HjxhVpP2PGDEydOtV0PyMjAyEhIRUS64POTV20SKXyl5KSgpycHLyxYClCa0VZtY5rl87j7cnjkJKSwqLJBrgv2AfmwT4wD+ZkPz13J09PT0RGRuLSpUvFPq5Wq6FWqys4KqLyF1orClGNmsgdBhER3YNdzdOUlZWFy5cvIyAgQO5QiIiIiMzI2tM0bdo09OnTB6GhoUhISMDMmTOhVCoxdOhQOcOqkuYfmI8MbQbc1e6Y2mrq/Z9ANhe05AsoMzNgcHNH/ITn5A6nyuK+YB+YB/vAPJiTtWiKi4vD0KFDcevWLfj6+uLRRx/FwYMH4evrK2dYVdL8A/MRnxmPILcg7hgyCfrqC6gTE6ANCGTRJCPuC/aBebAPzIM5WYumNWvWyPnyRERERBazqzFNRERERPaKRRMRERGRBVg0EREREVmARRMRERGRBVg0EREREVmARRMRERGRBVg0EREREVnArn57juTTNKApQjxC4OvMiUXlktWgCbSBwciv5iN3KFUa9wX7wDzYB+bBHIsmAgBsGbpF7hCqvLMr1skdAoH7gr1gHuwD82COp+eIiIiILMCiiYiIiMgCLJqIiIiILMAxTQQA6Lu6L27m3ISvsy/PYcukXvRgON5OQX41H45vkhH3BfvAPNgH5sEciyYCABxLPIb4zHgEuQXJHUqV5Xr6BNSJCdAGBModSpXGfcE+MA/2gXkwx9NzRERERBZg0URERERkARZNRERERBZg0URERERkARZNRERERBZg0URERERkARZNRERERBZg0URERERkAU5uSQCAqa2mIkObAXe1u9yhVFnxTz8HZWYGDG7MgZy4L9gH5sE+MA/mWDQRgIIdg+QVP+E5uUMgcF+wF8yDfWAezPH0HBEREZEFWDQRERERWYCn5wgAkKnNhICABAluaje5w6mSlFmZgBCAJMHgyhzIhfuCfWAe7APzYI5FEwEA6i6sa/ol67ipcXKHUyU1a9cM6sQEaAMC8ffRC3KHU2VxX7APzIN9YB7M8fQcERERkQVYNBERERFZgEUTERERkQVYNBERERFZgEUTERERkQVYNBERERFZgEUTERERkQVYNBERERFZgEUTERERkQU4IzgBAH568ifoDDqolCq5Q6myzi5bC0mng1AxB3LivmAfmAf7wDyYY9FEAIBmgc3kDqHKy2r0kNwhELgv2AvmwT4wD+Z4eo6IiIjIAiyaiIiIiCzA03MEAPj5ws/Izc+Fk6MTekf2ljucKqnajl+hyMuFUeOE2116yB1OlcV9wT4wD/aBeTDHookAAM/8/AziM+MR5BaEuKlxcodTJdWa/jzUiQnQBgTibxZNsuG+YB+YB/vAPJjj6TkiIiIiC7BoIiIiIrIAiyYiIiIiC7BoIiIiIrIAiyYiIiIiC9hN0fTee+9BkiS88MILcodCREREVIRdFE2HDx/GkiVL0KhRI7lDISIiIiqW7EVTVlYWhg8fjq+//hpeXl5yh0NERERULNknt5w0aRJ69eqFzp074+23375nW61WC61Wa7qfkZFR3uGV2fXr15GSklKmdWi1WqjV6jKtw8fHBzVq1CjxcVeVK9xUbnBVuZbpdch6BmcX6F3dYHB2kTuUKo37gn1gHuwD82BO1qJpzZo1OHbsGA4fPmxR+3nz5mH27NnlHJXtXL9+HXXr1kVOTk6Z1iNJEoQQZVqHs7MzYmJiSiyczk0+V6b1U9kd3Xdc7hAI3BfsBfNgH5gHc7IVTf/++y+ef/557NixAxqNxqLnzJgxA1OnTjXdz8jIQEhISHmFWGYpKSnIycnBGwuWIrRWlFXrOLj7Nyx9fw4mz/0YjR9uadU6rl06j7cnj0NKSso9e5uIiIioZLIVTUePHkVycjKaNm1qWmYwGLB3714sWLAAWq0WSqXS7DlqtbrMp6nkEForClGNmlj13GsXzwMAgsIjrF4HERERlZ1sRVOnTp3wzz//mC0bM2YM6tSpg1dffbVIwUREREQkJ9mKJjc3NzRo0MBsmYuLC7y9vYssp/L38m8vIzUvFV4aL3zY9UO5w6mSwue8Dof0NOg9PHHlrXfkDqfK4r5gH5gH+8A8mJP96jmyD6tPr0Z8ZjyC3IK4Y8jE96f1UCcmQBsQyKJJRtwX7APzYB+YB3N2VTTt2bNH7hCIiIiIiiX75JZERERElQGLJiIiIiILsGgiIiIisgCLJiIiIiILsGgiIiIisgCLJiIiIiILsGgiIiIisoBdzdNE8ulVuxdu591GNU01uUOpsm536gaHtFToPb3kDqVK475gH5gH+8A8mGPRRACAJX2WyB1ClXfpgy/kDoHAfcFeMA/2gXkwx9NzRERERBawqmiKjY21dRxEREREds2qoqlWrVro0KEDfvjhB+Tl5dk6JiIiIiK7Y1XRdOzYMTRq1AhTp06Fv78/JkyYgL///tvWsVEFav5VcwTPD0bzr5rLHUqV1aR7W7RoFokm3dvKHUqVxn3BPjAP9oF5MGdV0dSkSRN89tlnSEhIwLfffovExEQ8+uijaNCgAebPn4+bN2/aOk4qZ0lZSYjPjEdSVpLcoVRZqps3oE5MgOrmDblDqdK4L9gH5sE+MA/myjQQ3MHBAY8//jjWr1+P999/H5cuXcK0adMQEhKCUaNGITEx0VZxEhEREcmqTEXTkSNHMHHiRAQEBGD+/PmYNm0aLl++jB07diAhIQH9+vWzVZxEREREsrJqnqb58+dj2bJlOH/+PHr27InvvvsOPXv2hEJRUIOFh4dj+fLlCAsLs2WsRERERLKxqmhatGgRxo4di9GjRyMgIKDYNn5+fli6dGmZgiMiIiKyF1YVTRcvXrxvG5VKhejoaGtWT0RERGR3rBrTtGzZMqxfv77I8vXr12PFihVlDoqIiIjI3lhVNM2bNw8+Pj5Flvv5+eHdd98tc1BERERE9saqoun69esIDw8vsjw0NBTXr18vc1BERERE9saqMU1+fn44depUkavjTp48CW9vb1vERRXsgy4fICc/B86OznKHUmVdef1tKHJzYHRiDuTEfcE+MA/2gXkwZ1XRNHToUEyZMgVubm547LHHAAB//PEHnn/+eTz55JM2DZAqxrCGw+QOocq7+fhguUMgcF+wF8yDfWAezFlVNM2dOxdXr15Fp06d4OBQsAqj0YhRo0ZxTBMRERE9kKwqmlQqFdauXYu5c+fi5MmTcHJyQsOGDREaGmrr+IiIiIjsglVFU6HIyEhERkbaKhaS0fmU89Ab9XBQOCDKJ0rucKokp0sXIBn0EEoH5NbifiUX7gv2gXmwD8yDOauKJoPBgOXLl2Pnzp1ITk6G0Wg0e3zXrl02CY4qTqfvOiE+Mx5BbkGImxondzhVUsMhvaFOTIA2IBB/H70gdzhVFvcF+8A82AfmwZxVRdPzzz+P5cuXo1evXmjQoAEkSbJ1XERERER2xaqiac2aNVi3bh169uxp63iIiIiI7JJVk1uqVCrUqlXL1rEQERER2S2riqaXXnoJn332GYQQto6HiIiIyC5ZdXruzz//xO7du/Hrr7+ifv36cHR0NHt806ZNNgmOiIiIyF5YVTR5enpiwIABto6FiIiIyG5ZVTQtW7bM1nEQERER2TWrxjQBgF6vx++//44lS5YgMzMTAJCQkICsrCybBUdERERkL6zqabp27Rq6d++O69evQ6vVokuXLnBzc8P7778PrVaLxYsX2zpOIiIiIllZPbll8+bNcfLkSXh7e5uWDxgwAOPHj7dZcFRxDo8/DIMwQCkp5Q6lyjq+dS8kgwFCyRzIifuCfWAe7APzYM6qomnfvn3466+/oFKpzJaHhYUhPj7eJoFRxQpwC5A7hCovv7q/3CEQuC/YC+bBPjAP5qwa02Q0GmEwGIosj4uLg5ubW5mDIiIiIrI3VhVNXbt2xaeffmq6L0kSsrKyMHPmTP60ChERET2QrDo99/HHH6Nbt26oV68e8vLyMGzYMFy8eBE+Pj5YvXq1rWOkCvDV0a+QpcuCq8oVTzd7Wu5wqiT/H76FMjsbBhcXJI0YK3c4VRb3BfvAPNgH5sGcVUVTcHAwTp48iTVr1uDUqVPIysrCuHHjMHz4cDg5Odk6RqoAc/6Yg/jMeAS5BXHHkEmNT96DOjEB2oBAFk0y4r5gH5gH+8A8mLOqaAIABwcHjBgxwpaxEBEREdktq4qm77777p6Pjxo1yqpgiIiIiOyV1fM03Sk/Px85OTlQqVRwdnZm0UREREQPHKuunktNTTW7ZWVl4fz583j00Uc5EJyIiIgeSFb/9tzdateujffee69IL9S9LFq0CI0aNYK7uzvc3d3RqlUr/Prrr7YKiYiIiMhmbFY0AQWDwxMSEixuHxwcjPfeew9Hjx7FkSNH0LFjR/Tr1w9nzpyxZVhEREREZWbVmKYtW7aY3RdCIDExEQsWLECbNm0sXk+fPn3M7r/zzjtYtGgRDh48iPr161sTGhEREVG5sKpo6t+/v9l9SZLg6+uLjh074uOPP7YqEIPBgPXr1yM7OxutWrWyah1ERERE5cWqosloNNosgH/++QetWrVCXl4eXF1dsXnzZtSrV6/YtlqtFlqt1nQ/IyPDZnFUdZHekfDQeKC6S3W5Q6mycmvWgt7NHfm+fnKHUqVxX7APzIN9YB7MWT25pa1ERUXhxIkTSE9Px4YNGxAdHY0//vij2MJp3rx5mD17tgxRPvh2Re+SO4Qq75/1W+UOgcB9wV4wD/aBeTBnVdE0depUi9vOnz//no+rVCrUqlULANCsWTMcPnwYn332GZYsWVKk7YwZM8xeOyMjAyEhIRbHQkRERGQtq4qm48eP4/jx48jPz0dUVBQA4MKFC1AqlWjatKmpnSRJpV630Wg0OwV3J7VaDbVabU3IRERERGViVdHUp08fuLm5YcWKFfDy8gJQMOHlmDFj0LZtW7z00ksWrWfGjBno0aMHatSogczMTKxatQp79uzB9u3brQmLiIiIqNxYVTR9/PHH+O2330wFEwB4eXnh7bffRteuXS0umpKTkzFq1CgkJibCw8MDjRo1wvbt29GlSxdrwqIyGL5pOFJyUuDj7IOVj6+UO5wqKWrSWDjevoX8at44v/BbucOpsrgv2AfmwT4wD+asKpoyMjJw8+bNIstv3ryJzMxMi9ezdOlSa16eysEfV/9AfGY8gtyC5A6lyvI4+CfUiQnQBgTKHUqVxn3BPjAP9oF5MGfVjOADBgzAmDFjsGnTJsTFxSEuLg4bN27EuHHj8Pjjj9s6RiIiIiLZWdXTtHjxYkybNg3Dhg1Dfn5+wYocHDBu3Dh8+OGHNg2QiIiIyB5YVTQ5Ozvjyy+/xIcffojLly8DACIiIuDi4mLT4IiIiIjsRZl+sDcxMRGJiYmoXbs2XFxcIISwVVxEREREdsWqounWrVvo1KkTIiMj0bNnTyQmJgIAxo0bZ/GVc0RERESViVVF04svvghHR0dcv34dzs7OpuVDhgzBtm3bbBYcERERkb2wakzTb7/9hu3btyM4ONhsee3atXHt2jWbBEZERERkT6zqacrOzjbrYSp0+/Zt/swJERERPZCs6mlq27YtvvvuO8ydOxdAwW/MGY1GfPDBB+jQoYNNA6SKMb7peKRr0+Gh9pA7lCoradhoKDMzYHBzlzuUKo37gn1gHuwD82DOqqLpgw8+QKdOnXDkyBHodDq88sorOHPmDG7fvo39+/fbOkaqADPbz5Q7hCrv+kuvyR0CgfuCvWAe7APzYM6q03MNGjTAhQsX8Oijj6Jfv37Izs7G448/juPHjyMiIsLWMRIRERHJrtQ9Tfn5+ejevTsWL16M119/vTxiIiIiIrI7pe5pcnR0xKlTp8ojFiIiIiK7ZdXpuREjRmDp0qW2joVkFDw/GNJsCcHzg+/fmMpFi2aRaBvoihbNIuUOpUrjvmAfmAf7wDyYs2oguF6vx7fffovff/8dzZo1K/Kbc/Pnz7dJcERERET2olRFU2xsLMLCwnD69Gk0bdoUAHDhwgWzNpIk2S46IiIiIjtRqqKpdu3aSExMxO7duwEU/GzK559/jurVq5dLcERERET2olRjmoQQZvd//fVXZGdn2zQgIiIiIntk1UDwQncXUUREREQPqlIVTZIkFRmzxDFMREREVBWUakyTEAKjR482/ShvXl4ennnmmSJXz23atMl2ERIRERHZgVIVTdHR0Wb3R4wYYdNgiIiIiOxVqYqmZcuWlVccRERERHbNqskt6cHzw+M/QKvXQu2gljuUKuv8F99A0ukgVCq5Q6nSuC/YB+bBPjAP5lg0EQCgfVh7uUOo8tJbPyZ3CATuC/aCebAPzIO5Mk05QERERFRVsGgiIiIisgBPzxEAYM/VPabz1uyOlYfHX3tNY5p4qk4+3BfsA/NgH5gHcyyaCAAwYtMIxGfGI8gtCHFT4+QOp0qKeu4pqBMToA0IxN9HL9z/CVQuuC/YB+bBPjAP5nh6joiIiMgCLJqIiIiILMCiiYiIiMgCLJqIiIiILMCB4EQPMCEE8o0F/3dUAJIkyRsQEVElxqKJqJITAEIaNsO/cMXtuCzcyjMgTWdAnkFAqxcw3tFWKQFqpQQPlRKeKgU81Ur4OTkgD0q5wiciqjRYNBFVQjl6I27nGZCuMyLNpzYmrtiGGAC4mXfP5xkEkKMXyNHrkZhzxwNSEF75+Sgy3TxwK88AT7UCSvZKERGZYdFEVEnkGYxIyTUgJc+AbL34/wcUCmSn3kKopzPC/bzgo3GAl1oJJwcJGqUEtbJg6KLeKJAvBPL0Auk6A1K1BqRqjUjIyUdyjh5egTWgBXAuTQeFBHipFPBzcoCXWsHTekREYNFEZNeEEEjTGZGYo0eq9v9PtEmA6fRa6tWLeK1rCxw5cgRNg0NKXJdKWVD4eKiA6s7mu/7fx45jxMSpmPjREghXb2iNAre0RtzS6qBWSPB3VqK6swMcFSyeiKjqYtFEAMCZXu3AnbOAG4XAzVwD4rL1yDP8f6+Sh0oBH40S3hqlqYDJNGghhCiyvtJwgMDFg3vgmnUTkeGByNILpOQakJyrh9YocC1Lj3+z9KjurESwi6OpAHsQcV+wD8yDfWAezLFoIrIjRiGQ/F+xpP2vWFJKgJ+TEv7ODnB2KP9ZQiRJgpujBDdHBWq4OeBWngGJ2Xpk6QUScwy4kWOoEsUTEdHdWDQR2QEBICXPgGuZ+aaeJUcFEOTiAH8nByhlOi2mlCT4OTnAV6NEus6I61l6ZOYbkZhjQHKuASGuDghwdoCCY56IqApg0UQks+B6TZDuGYJbaToAhcWSI/ydlXZzBZskSfBUK+GhUiBdZ8TVzHxk6wWuZuqRlGNAuJuj3CESEZU7Fk0EAJi9ZzbStenwUHtgZvuZcodTJeTqjTiDapj0ww7oATy65EN4a7OgqeaJuGmvyR1esQqLp8YqBZJzDbiWVdAzFpOmg8o9AK7VfOUOscy4L9gH5sE+MA/mWDQRAODrY18jPjMeQW5B3DHKmRACp29rsSshG7mSKwBAnZuO5j/9AE1SArQBgXZbNBWSJAnVnR3grVEiLluP+Gw9dBp3vLhxP+JhxENCVNppCrgv2AfmwT4wD+b423NEFShDZ8CaSxn45XoWcvUCLkKHxWN7wy0zCZWxxnBQSAhzc0RjbzWU+Xlw9vDCGckbG2MzkZ1vvP8KiIgqERZNRBXk7G0tlp5Lw7WsfDhIQIdAZ7RCEq6dOCR3aGXm6qiAZ+o1/PrZHEhC4FKGDkvPpeJyuk7u0IiIbIZFE1E5y9MbseVqJrZcy4TWIBDg7ICxdbzQsrrzA7UDSgD2rvgCjyAJvholcvQC62Mz8Nu/WdAbyzaPFBGRPeCYJqJydDVTh1+uZSEz3wgJQBt/Z7T2d3qgL9F3Qz6iozzxR0I2Dt/Mw7GUPCTm6NE/3A0eKv4wMBFVXrJ+0Z03bx4efvhhuLm5wc/PD/3798f58+flDInIJvRGgZ1xWVhzKQOZ+UZ4qRUYGemBRwOcH+iCqZCDQkKnYFc8UdMdGqWExBw9lp9LQ2wGT9cRUeUla9H0xx9/YNKkSTh48CB27NiB/Px8dO3aFdnZ2XKGRVQmN3L0WH4+DYdv5gEAHvLRYEyUFwJdqt5cRhEeKoyO8oS/kwNyDQLrLmdgX2I2jGX82RciIjnIenpu27ZtZveXL18OPz8/HD16FI899phMURFZxygE/k7Oxd7EHBgF4OIgoWcNN0R4qOQOTVaeaiVGRHpgZ3w2jqfkYX9SLhKy9egX5gZNBfwsDBGRrdjVmKb09HQAQLVq1Yp9XKvVQqvVmu5nZGSUazzXr19HSkqK1c+PiYmxYTRld6948vPzTf8eO3as2DY+Pj6oUaNGucQmh7LmF/j/9yRNa8DP1zIRl60HANT2UKFHiCucHVkUAAWn67qFuCLIxQHbrmfhSmY+vruQjkE13VFNw3FO92PLz+qDEAeRXOymaDIajXjhhRfQpk0bNGjQoNg28+bNw+zZsysknuvXr6Nu3brIyckp87qysrJsEJH1biUnAZKEESNGlNzocQDOQHJOMppNb1ZsE2dnZ8TExDwQBzxb5dfZ2Rm/HL+AY7ka6IwCKoWEzsEuaFhNXerJHdMfeRSOt28hv5p3mWKyZw2qaeCrccDG2Azc1hqw4kIa+oe5Idzdfnrj2oW1Q0pOCnycfeQOBYBtP6tl2X8rOg57y0NVxTyYs5uiadKkSTh9+jT+/PPPEtvMmDEDU6dONd3PyMhASEhIucSTkpKCnJwcvLFgKUJrRVm1joO7f8PS9+cgLy/PxtGVTlZ6OiAEJs/9GI0fbnn/JzxddNG1S+fx9uRxSElJeSCKJlvk99rVK7iSqcfBbDUAgWAXB/QOdYOn2rqek/MLv7XqeZVNdWcHREd5YtOVDMRn67HucgY6B7ugqY/GLmYRX/n4SrlDMGOTz6oN9t+KjsPe8lBVMQ/m7KJomjx5Mn7++Wfs3bsXwcHBJbZTq9VQq9UVGBkQWisKUY2aWPXcaxft60rAoPAIq7flQWVtflO1BtzyjkADpQMkIfBYoAtaVn+wpxKwJRdHBYbW8sC2f7Nw+rYWO+KycTPXgC4hLnbzI8X2pizHogcxDiI5yDrgQgiByZMnY/Pmzdi1axfCw8PlDIfovgxGgcsZOpxN1UEoHXAj9jxaIgmt/KvGVAK25KCQ0KuGKzoEOgMATtzKw4bLGdAa+PMrRGSfZC2aJk2ahB9++AGrVq2Cm5sbkpKSkJSUhNzcXDnDIipWps6IE7e0SMoxAAA0ObexYHhnuCNf5sgqL0mS0LK6MwbWdIOjAriSmY8fLqQjQ2eQOzQioiJkPT23aNEiAED79u3Nli9btgyjR4+u+ICqsOd/7onU3GR4Ofnhs95b5Q7HrhiFQFyWHv/+d2WcSlFwddyN5JvQa203Xq3hEz3heDMZ+b5++Gd91cpBbQ81htVWYMPlDNzMM+D7C+l4IsIdfk4Vf4jquKIjbmTfQHWX6tgVvavCX58KMA/2gXkwJ2vRJDjBnd2IS7+Em9kJyNaV7zQOlU12vhEX03XI1hd8Vn00StR0d4SjQsING7+WU+wlqBMToM2smjkIcHbEyEhPrI/NwK08A364kI4B4RV/Zd2FWxcQnxmP9Lz0Cn1dMsc82AfmwRwnkSEqhhAC/2bl4+QtLbL1Ag4SEOnhiEiPgoKJyoenWomRtT1Qw9UROqPA+ssZOHlL3qtPiYgKsWgiuktOvhGnbmlxPUsPAaCaWoGHfDTwdXKwi0viH3QaBwUGR7ijvpcaRgC/Xs/C3oRs9kwTkezsYsoBIntgMAr8m61HQnZBsaSUgJrujvDVKFksVTAHhYTeoa7wUCnw141c/HUjFxn5RvQIcYWSPX1EJBMWTUQAbuUZcCUjH1pjQW9GNbUCNd1VUCv5B1oukiThsUAXeKiUpvmcsvKNGBDuBrWSneREVPF45KEqzaBwQEyqFufSdNAaBdQKCXU8VajrpWbBZCca+2gwqKY7HBXA1f+mJMjklAREJAP2NFViRiGQZxDI0xf8qzUIGISAQQCG/8Z/SADUkc0wcOZngF8oErL1cFQAGqUCGgepyg5qzoeEbs+9iVTvcEBrhAQg0MUBIS4OPP1jhyI8VBhe2xPrL6ebTUngK8OUBERUdfGIU4nojQJpOiMydUZk5huRlW+EJUNjHXyD0LzfMAAFkweaPSYBzg4K6P87LSVEwZVjD+oYHr1R4FhKHv5EINqPmQIAcFcpEOHuCGcHdrzaM39nB4yM9MS6ywU/9vvDxXQ8Hu6GUDf7+bFfInqwsWiyd44qtBg4CqhRD38n5xUpkpQSoFFK0CglqJUSHBQSlJIEhVTQy2QEEHPiGPZs3YJuQ8fAJygEOkNBz5TOKKAXQEa+EYb/VqwzChxKzoOLowIejgq4qxRwU1X+YkJnEDhxKw+HbuQUzLkkKXHj8jnU9nZFg6haD2yR+KDxVCsxMtIDG2MzEPffj/32CnVDPa+K/U1KIqqaWDTZISEEMvKNSMoxwLl5Vwxo0b1gOQAnpQSP/woZN0cFNErpvn/w/0m8gj3ffopuHdsjqn6EabnBWFA8ZeuNeLzBy0jXZkOSnGEQQIbOiAydEcguKL6UniHo8ux03IIa+UZRaU7rZeUbcSIlD0dTcpH73wSV7o4KBOtu4vUn22PJL3/YTcF0/cXpUGZnw+DiIncods3JQYEhtTzw87VMnE/TYcvVTGTqDGjh52STXL7V7i1k6bLgqnK1QbRkLebBPjAP5lg02REhBG7mGRCXrTf9gZcUCsSdPYFgb088VD/KpqeQlAoJLgoJLo4KjG36tCmGHL1AZn5B0ZSuM0BnBPQqZ3Qc/xKOAjh+6hYCnR1Qw9URNVwdEejiCJUdDZoumJhSj2MpubiQpkPhz796qhRo5e+MBl5qnDxxFUaDfQ0mThoxVu4QKg1HhYR+YW7YFZ+NIzfzsDshB+k6IzoHu5T5h5Ofbva0jaKksmAe7APzYI5Fkx0QQiAlz4B/s/TI/e88mUICfDVKXD2wEwvHPoF5P/wIZ4e65R6LJElwcSwopPydC2LLMwhciL2Gffv24pGej0MLB8Rl6xGXrcdfN3KhABDg8v9FVJAMRZQQAjdyDTibqsW5VC0y8o2mx4JcHNDMxwl1vFRl/oNK9kMhSegc7Ap3lRK74rNxLCUPmflG9A1zqzQ9oURUubBoklmGzoDYjHzTb5s5SAVXcQU4O8BBISE2W97fIZMkCU4OEjR56Vj35iS80vMR1KzXGNez8nE9Mx//ZuUjI9+I+Gw94rP1OPBfEeXn7AB/Jwf4OxfcfDVKm1+VlplvwPXMfFz975Z5R6GkUkio66VCUx8nVHfmx/xB1sLPCe6OCvzvWiYupuuw+mI6BkW4c2A/Edkc/5rIRGcQuJaZj+S8glNESqmgR6SwWKpoKTlJMBoNUCiU8HH2L7GdBMBLrYSXWonG3hoIIZCuMxYUUf/dMnRGJOXokZSjB24VPE8pAd4aJaqpC26eaiXcHBVwdlDA5b+xWUoJpjEp4r+pE/KNBWOuMnVGZOQbkao1IDlHjxu5elOhWchBAmp5FMyxFOGukuV9LAvHG0mQDAYIpRL51UvOARVVx0sNF0cFNsZmICFHj+8vpGFwhAe81MpSrysxMxEGYYBSUiLALaAcoiVLMA/2gXkwx6KpghWOW4rNyDddsebnpESYm7w/BDth82O4mZ0AX5dAbBx+weLnSZIEz/+KoEbeGgBAmtaAxP+KpqQcPZJy9dAaBJJzDUjOvfc4IqVUcGpSb4RF0ylUd1IizE2FcDdHBLlW7h/TfajnY1AnJkAbEIi/j1qeAyoQ4uqIEZEeWHc5A6laI76/kIYnarojwMWxVOt5+OuHEZ8ZjyC3IMRNjSunaOl+mAf7wDyYY9FUgfKNApfTdbilLTiN5OIgIcJd9UBc0n+nwiKq7n+XgRf2Rt3M0yNVa8TtPAPSdAZk5xuRrTci544eo4KJOc3Xp1ZKcHNUwN1RAQ+1En5OSvg5OcBX42BXA9BJfj4aB4yKLJgE80auAasupaNfmDtqeXAuJyIqOxZNFSRVa8DFdB3yjQWnuEJcHRDs4mA3l7uXpzt7o4pjFAVzRhmMgF4IGAXgoCi4Qsrxv3mniCzl6qjAsNoe+PFKJq5k5mNjbAa6BLugqa+T3KERUSXHoqmcSQoF4FsDZ1N1AABnBwm1PVRwdXywepfKQiEVTM6J0g8/ISqWWqnAoAh3bL+ehVO3tfgtLhspeQabTElARFUX/3KXJwcVxnyxBvANAQD4OynR2FvNgomoAiglCT1quKJdgDMA4FhKHtZdzkCe3nifZxIRFY9/vctJdr4RTo0fQ+1WHQCjAbU9HBHhwXmCiCqSJElo5e+Mx8Pd4KgArmbm47sL6bidZ18TmxJR5cCiqRzc1hpw6rYWCo0zUq7HAldOwY+/xk4km0hPNUbU9oS7owK3tQasuJCGq5k6ucMiokqGRZONJWbrEZOqg1EAhvQUfDmqG6DNkTssoiqvurMDRkV5ItDZAVqDwNpLGTh2M1fusIioEmHRZCNCCFzJyEdsZj6AgrmX8s4cQG5GmryBEZFJ4ZV19b3UEAB+i8vGtutZ0BstmRWMiKo6Fk02IITApYx8JOToAQA1XB1Qy90REDwQE9kbB4WE3qH/P0D8xK08rLqYjkwdxzkR0b1xoE0ZGYXA+TQdbv83YWUtd8dK+Vtnn/T6GQajHkpF5Yv9QfHP2p8hGfQQSuagvBUOEPdzcsCWa5lIyNFj2fk09A93x85RO6E36uHAfUFWzIN9YB7M8V0oA4MQiEnVIV1nhAQgylMFb03lnGyohmek3CFUebm1mIOKFuGhwugoT2yKzcDNPANWX0xHx6AaaO6rqRITz9qzKJ8ouUMgMA934+k5K91ZMCkkoJ5X5S2YiKoyL7USo6I8Ue+/cU4747Px09VMaA2cz4mIzLFossLdBVN9L1WJPxFCRPbPUSGhT6grOge5QAHgXJoOy86lIem/cYpERABPz5VacQWTu6ryF0w7Lq2DVp8DtYMzutQaLHc4VZLvpnVQ5ObA6OSMm48zBxVNkiQ093PCwevrcSAxFQZokJk/EB2DXNDUh6frKtqqf1YhJz8Hzo7OGNZwmNzhVFnMgzkWTaXwoBZMALD40Bu4mZ0AX5dAFk0yCX/nDagTE6ANCGTRJKP39r6G+Mx4VHMORJPggdgRl41rmfnoWcMVGgd2zleUV3a8gvjMeAS5BfGPtYyYB3M8AljIKATOPaAFExEV5aSU0CnIBQoJuJCuw7fn0nD9v3nYiKhqYtFkAfHftAJpLJiIqpSH/ZwwMtIDnioFMvKNWHUpHbvjszkZJlEVxaLpPgSASxn5uK0tmFagricLJqKqJMDZEWPqeKKRtxoAcCg5F99dSMPNXA4SJ6pqWDTdR46rL5JzC2YKjvLkVXJEVZFaqUDPGm54PNwNTg4SknMNWH4+DX8n58LImf+JqgwWTffQbszzyHWuBqBgpm/Ow0RUtUV6qjGujhdqujvCIIBd8dlYeTEdt/LY60RUFbBoKsG/cEX3594AAIS5Vc6fRiEi23N1VOCJmu7oFuIClUJCfLYe355Lw8EbOex1InrAsWgqRkyqFjHwAgA4Zd9CkAsLJiL6f5Ik4SEfJ4yr64lwt4Jepz0JOfjufDqSOdaJ6IHFoqkYGqUEJQQOrl8G5+wUucMhIjvloVJicIQ7etVwhVopISlXj+Xn0rAnIRv5vMKO6IHDLpRihLur8AiS8Or709G73V65w6kQ1Zyqm/1LFU/nW93sX5KHv6u/2b/3I0kSGnprEO6uwvZ/s3AxXYeDN3JxNlWLLsEuqO2hLs9wH1ilzQOVD+bBHIumErhAD2GsOj/Y+fXj++QOoco7sY05sAdHnj5i1fNcHRUYWNMdF9O12PFvNjJ0RmyMzURtDy06B7vAg1OVlIq1eSDbYh7MsWgiIrKh2h5qhLqq8FdSDv5OzsXFdB2uZurwSHVntPBzgqOCv2FHVFlxTBMRkY2plBLaB7lgTB1PBLs4IN8I7EvMwdcxqYhJ1ULwKjuiSolFExFROfF1csDw2h7oG+oGN0cFMnRG/HQ1EysvpiMph1fZEVU2PD1HAIAP9z6HTG0q3NReePmxL+QOp0qq9cpzcEhLhd7TC5c+YA7kMuF/E3A77zaqaaphSZ8lZV6fJEmoV02N2p4qHLqRi4M3chCXrcfy82loWE2NdoEucHXk99e72ToPZB3mwRyLJgIAHPx3O25mJ8DXJVDuUKqsaju3Q52YAG0AcyCnXy7+gvjMeAS5Bdl0vY4KCY8GOKORtxp/JOTgTKoW/9zW4nyaDg/7adDCzwlqJYunQuWVByod5sEc91AiogrkrlKiT5gbRkZ6IMDZATqjwP6kXCw5m4ojN3Nh4PxORHaLRRMRkQyCXBwxKtID/cPc4KVWIEcv8HtcNr6KScWZ23kcLE5kh3h6johIJpIkoY5XwXinU7fysD8xF+k6I/53LQuHknPRLsAFLJ2I7AeLJiIimSn/+y27+l4aHLmZi0M3cpGca8D62Ax4oDpqtWzH4onIDsh6em7v3r3o06cPAgMDIUkSfvzxRznDISKSlUopobW/M56p74UWfk5wkIB0SY1xizYg3TMEaVoDT9sRyUjWoik7OxuNGzfGwoUL5QyDiMiuODko0DHIBc/Ur4YaIgP52jzoVc44k6rD6ds6pOsMcodIVCXJenquR48e6NGjh5whEBHZLVdHBeogDc/27YXZm/dC6+yFjHwjTt/WwUOlQIirA3/TjqgCVaoxTVqtFlqt1nQ/IyNDxmhITtevX0dKSorVz4+JibFZLGVZF+MonlarhVqtlmUd+fn5pn8PHDggWxyFYmJikHEzCa5ZyWgQ6o+4LD1u5BqQrjMi/b/iqYarA9xZPBGVu0pVNM2bNw+zZ8+WO4wHUqeIJ5CpTYOb2lPuUO7r+vXrqFu3LnJycsq8rqysLKufeys5CZAkjBgxwiZx3Oz3BBzS06D38JQ1DmvZMg5Jkso8dsfqdXQB4AQk5yajzYw28sVxl6ysLKiVCkR4qBDkakRclh7J/xVP/9zWwfO/nqcHpXga2mAoUvNS4aXxkjuUKo15MFepiqYZM2Zg6tSppvsZGRkICQmRMaIHx8RH3pE7BIulpKQgJycHbyxYitBaUVat4+Du37D0/TnIy8uzOo6s9HRACEye+zEaP9yyzHFcecu6HNg6DmvZIo47Y7HF9pR5HTvsJI67cqNRKlDLQ4VgFyP+zS4ontJ0RqQ9QD1PH3b9UO4QCMzD3SpV0aRWq8vcVU4PjtBaUYhq1MSq5167eN5mcQSFRzAOG8VxZyy22J4HaR3F0TgoUNtDhRAXI+KyzXueOOaJyPYqVdFERERFaRz+v+fpzuKpcMwTHJ3kDpHogSBr0ZSVlYVLly6Z7l+5cgUnTpxAtWrVUKNGDRkjIyKqfEzF011jnuBVA08t2YzbYE89UVnIWjQdOXIEHTp0MN0vHK8UHR2N5cuXyxRV1TRi7UNIyUmCj7M/fhhyXO5wqqRmbR+C6kYSdNX9cXQfcyCXwn3ByeAidyhWKxzzFPJf8ZSUo0fEw4/iCIAbF9PwqL8zarg6QpIkuUMtUZ0FdZCQmYBAt0Ccm3xO7nCqLObBnKxFU/v27Tm7rZ3I1WcjJz8TuXo3uUOpspQ52XDIyoTBjTmQU+G+oFRU/tELhVfbaf+9gK27/kDrJ8bg3yw9Vl/KQLCLAx4NcEaonRZPWbosZOoykaWz/opOKjvmwZysM4ITEVH5Uxr12PLeq2iLBDT10UApAXHZeqy5lIGVF9NxNUPHL7BEFqj8X6WIiMgiGhjQOsQVj1R3wsEbuTh5K6+geLqcgSAXBzzq74wwN/vseSKyByyaiIiqGHeVEl1DXNHKv6B4OpGSh/hsPdb+Vzy18XdGOIsnoiJYNBERVVFujkp0CS7oeTp0R/G07nIGAp0LxjyxeCL6fyyaiIiqODdHJToHu+KR6s44dCMHx1PykJBTUDwFOBectuOIJyIWTURE9B9XRwU6Bbui5R3FU2KOHutjM+CO6oh6tAuLJ6rSWDQREZGZwuLpkerOOJSci+MpucgwqjH681VIz8/F7TwDvNQKnrajKodTDhARUbFcHBXoGOSCZ+pVQ5jIgC43G3pHJ8Sk6XDylha38wycqoCqFPY0EQDgpUc/g9aQC7WSv1Ell0vvfQZFXi6MGuZAToX7wtlDR7AWn8sdjl1wcVQgEml4qldXvPO//dC6eiNbLxCTpoObo4Qaro7wVNv2h4EX916M3PxcOPF382TFPJhj0UQAgNahPeQOocq73YU5sAeF+0L+8XyZI7E/2Wm34JKdgobhQYjP1iMxW4/MfIEzqQU/DBzq6gg3lW1OYPSO7G2T9VDZMA/meHqOiIhKxVEhIczNEc18NQhwVkICkK4z4tRtLWJStcjON8odIlG5YE8TERFZRaWUUNNdhUBnI/7N1iM514DbWiNua7Xw0ShRw9UBTg78bk4PDhZNBAA4f/M48o06OCpUiPJ9SO5wqiTXU8ch6XQQKhWyGjEHcincFxL1V+UOpdLQOChQ20OFIBcjrmfpcSvPgJT/btWdlAhxdYBaWbri6WjCUegMOqiUKjQLbFZOkdP9MA/mWDQRAOC134bgZnYCfF0CsXH4BbnDqZLqjRkCdWICtAGB+PsocyCXwn3BTeEldyiVjrODAnU8VcjKN+J6Vj5StUbcyDUgOdcAf2cljJLlg8X7remH+Mx4BLkFIW5qXDlGTffCPJhj0URERDbl6qhAPS81MnQGXMvUIyPfiMQcA+BdE10nvYZ8cH4nqpx4spmIiMqFu0qJBtVUqO+lgqujBCgU6DDuRZyGt9yhEVmFRRMREZUbSZLgqVaiUTU13NLikXTxLMKRIXdYRFZh0UREROVOkiSodVn4/Mn28IRO7nCIrMKiiYiIKgx/doUqMxZNRERERBZg0URERERkARZNRERERBZg0URERERkAU5uSQCA7584CgEBiZPOyeboH0cBIQCJOZBT4b6wa8smfIjJcodTZcVMiuExyQ4wD+ZYNBEAwFnlJncIVZ7BlTmwB4X7glpykjmSqs1Nzf3BHjAP5nh6joiIiMgCLJqIiIiILMDTcwQAWHvqC2TrMuCicseQRs/JHU6VFLTkCygzM2Bwc0f8BOZALoX7QlzuZblDqdLmH5iPDG0G3NXumNpqqtzhVFnMgzkWTQQAWPfPF7iZnQBfl0AWTTIJ+uoLqBMToA0IZNEko8J9wU3hJXcoVdr8A/MRnxmPILcg/rGWEfNgjqfniIiIiCzAoomIiIjIAiyaiIiIiCzAoomIiIjIAiyaiIiIiCzAoomIiIjIAiyaiIiIiCzAoomIiIjIApzckgAAtb2bwM8lGB4aH7lDqbKyGjSBNjAY+dWYAzkV7gu6VC0ykSp3OFVW04CmCPEIga+zr9yhVGnMgzkWTQQAeK/7OrlDqPLOrmAO7EHhvvDbxrV4G+Nkjqbq2jJ0i9whEJiHu/H0HBEREZEFWDQRERERWYBFExEREZEFOKaJAADTtw1Gel4KPDQ+HN8kk3rRg+F4OwX51Xw4vklGhfuCLkMrdyhVWt/VfXEz5yZ8nX05rkZGzIM5Fk0EALh46wRuZifA1yVQ7lCqLNfTJ6BOTIA2gDmQU+G+4KbwkjuUKu1Y4jHEZ8YjyC1I7lCqNObBHE/PEREREVmARRMRERGRBVg0EREREVnALoqmhQsXIiwsDBqNBi1btsTff/8td0hEREREZmQvmtauXYupU6di5syZOHbsGBo3boxu3bohOTlZ7tCIiIiITGQvmubPn4/x48djzJgxqFevHhYvXgxnZ2d8++23codGREREZCJr0aTT6XD06FF07tzZtEyhUKBz5844cOCAjJERERERmZN1nqaUlBQYDAZUr17dbHn16tVx7ty5Iu21Wi202v+fcC49PR0AkJGRYfPYsrKyAAAX/jmB3Oxsq9Zx7fIFAMCVmDNwcXKy63XoMrWADtAZtThxYH+Rx/+NvQgAOHr0qOm9sYZCoYDRaLT6+QBw/vx5AA9ebm5rtXABkK0tPgcVFYec67CHWAr3BT3yZY3Dluuwxf5ri/2uMI6srKz7HreNeUYgDzA6GsvlGE+WKa88FK5LCGGzdVYIIaP4+HgBQPz1119my19++WXRokWLIu1nzpwpAPDGG2+88cYbbw/A7d9//62oksMmZO1p8vHxgVKpxI0bN8yW37hxA/7+/kXaz5gxA1OnTjXdNxqNuH37Nry9vSFJ0j1fKyMjAyEhIfj333/h7u5umw2oBLjd3O6qgNvN7a4KHqTtFkIgMzMTgYGV6xcQZC2aVCoVmjVrhp07d6J///4ACgqhnTt3YvLkyUXaq9VqqNVqs2Wenp6lek13d/dK/2GzBre7auF2Vy3c7qrlQdluDw8PuUMoNdl/e27q1KmIjo5G8+bN0aJFC3z66afIzs7GmDFj5A6NiIiIyET2omnIkCG4efMm3nrrLSQlJaFJkybYtm1bkcHhRERERHKSvWgCgMmTJxd7Os6W1Go1Zs6cWeT03oOO283trgq43dzuqqCqbrc9kYSobNf7EREREVU82WcEJyIiIqoMWDQRERERWYBFExEREZEFWDQRERERWeCBKpoWLlyIsLAwaDQatGzZEn///fc9269fvx516tSBRqNBw4YNsXXr1gqK1LZKs91ff/012rZtCy8vL3h5eaFz5873fZ/sVWnzXWjNmjWQJMk0oWplU9rtTktLw6RJkxAQEAC1Wo3IyMhK+Vkv7XZ/+umniIqKgpOTE0JCQvDiiy8iLy+vgqItu71796JPnz4IDAyEJEn48ccf7/ucPXv2oGnTplCr1ahVqxaWL19e7nHaWmm3e9OmTejSpQt8fX3h7u6OVq1aYfv27RUTrA1Zk+9C+/fvh4ODA5o0aVJu8VGBB6ZoWrt2LaZOnYqZM2fi2LFjaNy4Mbp164bk5ORi2//1118YOnQoxo0bh+PHj6N///7o378/Tp8+XcGRl01pt3vPnj0YOnQodu/ejQMHDiAkJARdu3ZFfHx8BUdeNqXd7kJXr17FtGnT0LZt2wqK1LZKu906nQ5dunTB1atXsWHDBpw/fx5ff/01goKCKjjysintdq9atQrTp0/HzJkzERMTg6VLl2Lt2rV47bXXKjhy62VnZ6Nx48ZYuHChRe2vXLmCXr16oUOHDjhx4gReeOEFPPXUU5WugCjtdu/duxddunTB1q1bcfToUXTo0AF9+vTB8ePHyzlS2yrtdhdKS0vDqFGj0KlTp3KKjMzI/Nt3NtOiRQsxadIk032DwSACAwPFvHnzim0/ePBg0atXL7NlLVu2FBMmTCjXOG2ttNt9N71eL9zc3MSKFSvKK8RyYc126/V60bp1a/HNN9+I6Oho0a9fvwqI1LZKu92LFi0SNWvWFDqdrqJCLBel3e5JkyaJjh07mi2bOnWqaNOmTbnGWV4AiM2bN9+zzSuvvCLq169vtmzIkCGiW7du5RhZ+bJku4tTr149MXv2bNsHVEFKs91DhgwRb7zxhpg5c6Zo3LhxucZFQjwQPU06nQ5Hjx5F586dTcsUCgU6d+6MAwcOFPucAwcOmLUHgG7dupXY3h5Zs913y8nJQX5+PqpVq1ZeYdqctds9Z84c+Pn5Ydy4cRURps1Zs91btmxBq1atMGnSJFSvXh0NGjTAu+++C4PBUFFhl5k12926dWscPXrUdAovNjYWW7duRc+ePSskZjk8CMc0WzAajcjMzKxUxzRrLVu2DLGxsZg5c6bcoVQZdjEjeFmlpKTAYDAU+emV6tWr49y5c8U+Jykpqdj2SUlJ5RanrVmz3Xd79dVXERgYWORga8+s2e4///wTS5cuxYkTJyogwvJhzXbHxsZi165dGD58OLZu3YpLly5h4sSJyM/PrzQHWmu2e9iwYUhJScGjjz4KIQT0ej2eeeaZSnV6rrRKOqZlZGQgNzcXTk5OMkVWsT766CNkZWVh8ODBcodSri5evIjp06dj3759cHB4IP6UVwoPRE8TWee9997DmjVrsHnzZmg0GrnDKTeZmZkYOXIkvv76a/j4+MgdToUyGo3w8/PDV199hWbNmmHIkCF4/fXXsXjxYrlDK1d79uzBu+++iy+//BLHjh3Dpk2b8Msvv2Du3Llyh0blaNWqVZg9ezbWrVsHPz8/ucMpNwaDAcOGDcPs2bMRGRkpdzhVygNRnvr4+ECpVOLGjRtmy2/cuAF/f/9in+Pv71+q9vbImu0u9NFHH+G9997D77//jkaNGpVnmDZX2u2+fPkyrl69ij59+piWGY1GAICDgwPOnz+PiIiI8g3aBqzJd0BAABwdHaFUKk3L6tati6SkJOh0OqhUqnKN2Ras2e4333wTI0eOxFNPPQUAaNiwIbKzs/H000/j9ddfh0Lx4H1fLOmY5u7uXiV6mdasWYOnnnoK69evr1Q959bIzMzEkSNHcPz4cdPvthqNRggh4ODggN9++w0dO3aUOcoH0wNx5FCpVGjWrBl27txpWmY0GrFz5060atWq2Oe0atXKrD0A7Nixo8T29sia7Qa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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# non-zeb cost per bus\n", - "dist_curve(\n", - " df=non_zeb_projects,\n", - " mean=non_zeb_cpb_wt_avg,\n", - " std=non_zeb_projects[\"cost_per_bus\"].std(),\n", - " title=\"non-ZEB costper bus Distribution\",\n", - " xlabel='\"cost per bus, $ million(s)\"',\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "aa916127-57d9-4c1c-b5eb-8b7b7e4ac672", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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bus_size_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
0articulated025823757641.014204281.605005False
1cutaway3016694500152.0109832-1.464878False
2not specified406509919038881.0578795-0.366399False
3over-the-road01951600014.0679714-0.130011False
4standard/conventional (30ft-45ft)036234253277264.08873230.356283False
\n", - "
" - ], - "text/plain": [ - " bus_size_type total_project_count \\\n", - "0 articulated 0 \n", - "1 cutaway 3 \n", - "2 not specified 40 \n", - "3 over-the-road 0 \n", - "4 standard/conventional (30ft-45ft) 0 \n", - "\n", - " total_project_count_ppno total_agg_cost total_bus_count \\\n", - "0 2 58237576 41.0 \n", - "1 0 16694500 152.0 \n", - "2 6 509919038 881.0 \n", - "3 1 9516000 14.0 \n", - "4 36 234253277 264.0 \n", - "\n", - " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", - "0 1420428 1.605005 False \n", - "1 109832 -1.464878 False \n", - "2 578795 -0.366399 False \n", - "3 679714 -0.130011 False \n", - "4 887323 0.356283 False " - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "agg_bus_size" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "5f11c857-ddbe-4871-aeca-e27fa00fbde8", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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prop_typenew_cost_per_bustotal_bus_count
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2FCEB1185797102.0
0BEB1025966163.0
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1CNG698568252.0
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7mix (zero and low emission)294203125.0
6low emission (propane)19099944.0
8not specified127853325.0
4ethanol1118619.0
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" - ], - "text/plain": [ - " prop_type new_cost_per_bus total_bus_count\n", - "3 electric (not specified) 1288136 44.0\n", - "2 FCEB 1185797 102.0\n", - "0 BEB 1025966 163.0\n", - "9 zero-emission bus (not specified) 896199 143.0\n", - "1 CNG 698568 252.0\n", - "5 low emission (hybrid) 633271 145.0\n", - "7 mix (zero and low emission) 294203 125.0\n", - "6 low emission (propane) 190999 44.0\n", - "8 not specified 127853 325.0\n", - "4 ethanol 111861 9.0" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# multiple bar charts in one cell\n", - "\n", - "# cpb by prop type\n", - "make_chart(\"new_cost_per_bus\", \"Cost per bus by propulsion type\", x_col=\"prop_type\", data=agg_prop)\n", - "\n", - "# bus count by prop type\n", - "make_chart(\"total_bus_count\", \"Bus count by propulsion type\", x_col=\"prop_type\", data=agg_prop)\n", - "\n", - "#bus size bar chart\n", - "make_chart(\"total_bus_count\", \"\"\"Amount of buses procured by bus size.\n", - "excluding 'not specified' responses.\"\"\", x_col=\"bus_size_type\",data=agg_bus_size[agg_bus_size[\"bus_size_type\"]!=\"not specified\"])\n", - "\n", - "# pivot table to\n", - "agg_prop[[\"prop_type\",\"new_cost_per_bus\",\"total_bus_count\"]].sort_values(by=\"new_cost_per_bus\", ascending=False)\n" - ] - }, - { - "cell_type": "markdown", - "id": "9270ab8f-25ff-4de3-aca5-7ef4637a4f9c", - "metadata": { - "tags": [] - }, - "source": [ - "## Testing summary and conclusion\n", - "time to rework the summary section.\n", - "\n", - "no more long expositions and variables. try to get the same point across using tables instead." - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "2472461d-7663-4b66-9bde-4c2a199707a5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "\n", - "\n", - "# Bus Procurement Cost Analysis\n", - "\n", - "## Summary\n", - "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", - "\n", - "Data was compiled from three data sources:\n", - "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", - "2. TIRCP project data (state-funded, California only)\n", - "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", - "\n", - "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", - "The resulting dataset only contained projects that were solely used to procure buses. \n", - "\n", - "88 projects were determined to contain solely bus purchases. \n", - "These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", - "\n", - "\n", - "Breakdown of each data souce:\n", - "| source | bus_count | total_cost | cost_per_bus |\n", - "|:------------|------------:|-------------:|---------------:|\n", - "| dgs | 236 | 250112853 | 1059800 |\n", - "| fta | 883 | 391257025 | 443099 |\n", - "| tircp | 233 | 187250513 | 803650 |\n", - "| Grand Total | 1352 | 828620391 | 612884 |\n", - "\n", - "\n", - "**ZEB buses include:**\n", - "- zero-emission (not specified) \n", - "- electric (not specified)\n", - "- battery electric \n", - "- fuel cell electric\n", - "\n", - "**Non-ZEB buses include:**\n", - "- CNG \n", - "- ethanol \n", - "- ow emission (hybrid, propane) \n", - "- diesel \n", - "- gas\n", - "\n", - "Below are charts and tables that summarize the findings.\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# moved to final NB 6/25\n", - "\n", - "new_summary = f\"\"\"\n", - "\n", - "# Bus Procurement Cost Analysis\n", - "\n", - "## Summary\n", - "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", - "\n", - "Data was compiled from three data sources:\n", - "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", - "2. TIRCP project data (state-funded, California only)\n", - "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", - "\n", - "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", - "The resulting dataset only contained projects that were solely used to procure buses. \n", - "\n", - "{len(merged_data)} projects were determined to contain solely bus purchases. \n", - "These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", - "\n", - "\n", - "Breakdown of each data souce:\n", - "{pivot_source.to_markdown(index=False)}\n", - "\n", - "\n", - "**ZEB buses include:**\n", - "- zero-emission (not specified) \n", - "- electric (not specified)\n", - "- battery electric \n", - "- fuel cell electric\n", - "\n", - "**Non-ZEB buses include:**\n", - "- CNG \n", - "- ethanol \n", - "- ow emission (hybrid, propane) \n", - "- diesel \n", - "- gas\n", - "\n", - "Below are charts and tables that summarize the findings.\n", - "\n", - "\n", - "\n", - "\n", - "\"\"\"\n", - "from IPython.display import Markdown, display\n", - "\n", - "display(Markdown(new_summary))" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "7472ba04-7def-46ef-814b-bf63c1016f3b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "**ZEB Summary**" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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prop_typebus_counttotal_costcost_per_bus
0BEB163.01672324891025966
1FCEB102.01209513351185797
2electric (not specified)44.0566780001288136
3zero-emission bus (not specified)143.0128156513896199
4Grand Total452.04730183371046500
\n", - "
" - ], - "text/plain": [ - " prop_type bus_count total_cost cost_per_bus\n", - "0 BEB 163.0 167232489 1025966\n", - "1 FCEB 102.0 120951335 1185797\n", - "2 electric (not specified) 44.0 56678000 1288136\n", - "3 zero-emission bus (not specified) 143.0 128156513 896199\n", - "4 Grand Total 452.0 473018337 1046500" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "**Non-ZEB Summary**" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
prop_typebus_counttotal_costcost_per_bus
0CNG252.0176039140698568
1ethanol9.01006750111861
2low emission (hybrid)145.091824361633271
3low emission (propane)44.08403969190999
4mix (zero and low emission)125.036775430294203
5Grand Total575.0314049650546173
\n", - "
" - ], - "text/plain": [ - " prop_type bus_count total_cost cost_per_bus\n", - "0 CNG 252.0 176039140 698568\n", - "1 ethanol 9.0 1006750 111861\n", - "2 low emission (hybrid) 145.0 91824361 633271\n", - "3 low emission (propane) 44.0 8403969 190999\n", - "4 mix (zero and low emission) 125.0 36775430 294203\n", - "5 Grand Total 575.0 314049650 546173" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "The remaining buses did not specify a propulsion type" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# moved to final NB 6/25\n", - "display(\n", - " Markdown(\"**ZEB Summary**\"),\n", - " pivot_zeb_prop,\n", - " \n", - " Markdown(\"**Non-ZEB Summary**\"),\n", - " pivot_non_zeb_prop,\n", - " \n", - " Markdown(\"The remaining buses did not specify a propulsion type\")\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "91d0361d-b165-4607-b22e-66ae4234863d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "**Max new_cost_per_bus**" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
71Transit Joint Powers Authority for Merced County32233242.01611662
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" - ], - "text/plain": [ - " transit_agency total_agg_cost \\\n", - "71 Transit Joint Powers Authority for Merced County 3223324 \n", - "\n", - " total_bus_count new_cost_per_bus \n", - "71 2.0 1611662 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "**Min new_cost_per_bus**" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
45Oregon Department of Transportation on behalf ...1812505.036250
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" - ], - "text/plain": [ - " transit_agency total_agg_cost \\\n", - "45 Oregon Department of Transportation on behalf ... 181250 \n", - "\n", - " total_bus_count new_cost_per_bus \n", - "45 5.0 36250 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "**Max total_bus_count**" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
61South Carolina Department of Transportation on...15423904160.096399
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" - ], - "text/plain": [ - " transit_agency total_agg_cost \\\n", - "61 South Carolina Department of Transportation on... 15423904 \n", - "\n", - " total_bus_count new_cost_per_bus \n", - "61 160.0 96399 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "**Min total_bus_count**" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
9City of Beloit6531841.0653184
16City of San Luis Obispo8592701.0859270
49SLO TRANSIT (SAN LUIS OBISPO, CA)8472141.0847214
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" - ], - "text/plain": [ - " transit_agency total_agg_cost total_bus_count \\\n", - "9 City of Beloit 653184 1.0 \n", - "16 City of San Luis Obispo 859270 1.0 \n", - "49 SLO TRANSIT (SAN LUIS OBISPO, CA) 847214 1.0 \n", - "\n", - " new_cost_per_bus \n", - "9 653184 \n", - "16 859270 \n", - "49 847214 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "**Max total_agg_cost**" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
24Dallas Area Rapid Transit (DART)10300000090.01144444
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" - ], - "text/plain": [ - " transit_agency total_agg_cost total_bus_count \\\n", - "24 Dallas Area Rapid Transit (DART) 103000000 90.0 \n", - "\n", - " new_cost_per_bus \n", - "24 1144444 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "**Min total_agg_cost**" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
45Oregon Department of Transportation on behalf ...1812505.036250
\n", - "
" - ], - "text/plain": [ - " transit_agency total_agg_cost \\\n", - "45 Oregon Department of Transportation on behalf ... 181250 \n", - "\n", - " total_bus_count new_cost_per_bus \n", - "45 5.0 36250 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#min max values for all projects\n", - "bus_min_max_summary(data=agg_agency,col1=\"new_cost_per_bus\")\n", - "bus_min_max_summary(data=agg_agency,col1=\"total_bus_count\")\n", - "bus_min_max_summary(data=agg_agency,col1=\"total_agg_cost\")\n" + " Markdown(\"**Non-ZEB Summary**\"),\n", + " pivot_non_zeb_prop,\n", + " \n", + " Markdown(\"The remaining buses did not specify a propulsion type\")\n", + ")" ] }, { "cell_type": "code", - "execution_count": 45, - "id": "6896d09d-a8e8-4351-bf69-6538d031bf93", + "execution_count": 12, + "id": "91d0361d-b165-4607-b22e-66ae4234863d", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "**Which Agneices had the highest and lowest cost per bus?**" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "**Max cost_per_bus**" + "**Max new_cost_per_bus**" ], "text/plain": [ "" @@ -5333,31 +2210,29 @@ " \n", " \n", " transit_agency\n", - " prop_type\n", - " total_cost\n", - " bus_count\n", - " cost_per_bus\n", + " total_agg_cost\n", + " total_bus_count\n", + " new_cost_per_bus\n", " \n", " \n", " \n", " \n", - " 76\n", - " University of California - San Diego\n", - " BEB\n", - " 4134000\n", + " 71\n", + " Transit Joint Powers Authority for Merced County\n", + " 3223324\n", " 2.0\n", - " 2067000\n", + " 1611662\n", " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency prop_type total_cost bus_count \\\n", - "76 University of California - San Diego BEB 4134000 2.0 \n", + " transit_agency total_agg_cost \\\n", + "71 Transit Joint Powers Authority for Merced County 3223324 \n", "\n", - " cost_per_bus \n", - "76 2067000 " + " total_bus_count new_cost_per_bus \n", + "71 2.0 1611662 " ] }, "metadata": {}, @@ -5366,7 +2241,7 @@ { "data": { "text/markdown": [ - "**Min cost_per_bus**" + "**Min new_cost_per_bus**" ], "text/plain": [ "" @@ -5397,66 +2272,29 @@ " \n", " \n", " transit_agency\n", - " prop_type\n", - " total_cost\n", - " bus_count\n", - " cost_per_bus\n", + " total_agg_cost\n", + " total_bus_count\n", + " new_cost_per_bus\n", " \n", " \n", " \n", " \n", " 45\n", - " City of Wasco\n", - " zero-emission bus (not specified)\n", - " 1543000\n", - " 3.0\n", - " 514333\n", + " Oregon Department of Transportation on behalf ...\n", + " 181250\n", + " 5.0\n", + " 36250\n", " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency prop_type total_cost bus_count \\\n", - "45 City of Wasco zero-emission bus (not specified) 1543000 3.0 \n", + " transit_agency total_agg_cost \\\n", + "45 Oregon Department of Transportation on behalf ... 181250 \n", "\n", - " cost_per_bus \n", - "45 514333 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# moved to final NB 6/25\n", - "## min max values of just ZEB projects\n", - "# YES I CAN!!\n", - "new_cols =[\n", - " \"transit_agency\",\n", - " \"prop_type\",\n", - " \"total_cost\",\n", - " \"bus_count\",\n", - " \"cost_per_bus\"]\n", - "\n", - "display(Markdown(\"**Which Agneices had the highest and lowest cost per bus?**\")),\n", - "bus_min_max_summary(data=zeb_projects, col1=\"cost_per_bus\", col_list=new_cols)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "743b25a2-8693-44f7-98fe-384e910620a7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "**Which agency procured the most and least amount of ZEBs?**" - ], - "text/plain": [ - "" + " total_bus_count new_cost_per_bus \n", + "45 5.0 36250 " ] }, "metadata": {}, @@ -5465,7 +2303,7 @@ { "data": { "text/markdown": [ - "**Max bus_count**" + "**Max total_bus_count**" ], "text/plain": [ "" @@ -5496,31 +2334,29 @@ " \n", " \n", " transit_agency\n", - " prop_type\n", - " total_cost\n", - " bus_count\n", - " cost_per_bus\n", + " total_agg_cost\n", + " total_bus_count\n", + " new_cost_per_bus\n", " \n", " \n", " \n", " \n", - " 44\n", - " City of Los Angeles (LA DOT)\n", - " zero-emission bus (not specified)\n", - " 102790000\n", - " 112.0\n", - " 917767\n", + " 61\n", + " South Carolina Department of Transportation on...\n", + " 15423904\n", + " 160.0\n", + " 96399\n", " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency prop_type \\\n", - "44 City of Los Angeles (LA DOT) zero-emission bus (not specified) \n", + " transit_agency total_agg_cost \\\n", + "61 South Carolina Department of Transportation on... 15423904 \n", "\n", - " total_cost bus_count cost_per_bus \n", - "44 102790000 112.0 917767 " + " total_bus_count new_cost_per_bus \n", + "61 160.0 96399 " ] }, "metadata": {}, @@ -5529,7 +2365,7 @@ { "data": { "text/markdown": [ - "**Min bus_count**" + "**Min total_bus_count**" ], "text/plain": [ "" @@ -5560,68 +2396,47 @@ " \n", " \n", " transit_agency\n", - " prop_type\n", - " total_cost\n", - " bus_count\n", - " cost_per_bus\n", + " total_agg_cost\n", + " total_bus_count\n", + " new_cost_per_bus\n", " \n", " \n", " \n", " \n", - " 70\n", - " SLO TRANSIT (SAN LUIS OBISPO, CA)\n", - " BEB\n", - " 847214\n", + " 9\n", + " City of Beloit\n", + " 653184\n", " 1.0\n", - " 847214\n", + " 653184\n", " \n", " \n", - " 82\n", + " 16\n", " City of San Luis Obispo\n", - " BEB\n", " 859270\n", " 1.0\n", " 859270\n", " \n", + " \n", + " 49\n", + " SLO TRANSIT (SAN LUIS OBISPO, CA)\n", + " 847214\n", + " 1.0\n", + " 847214\n", + " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency prop_type total_cost bus_count \\\n", - "70 SLO TRANSIT (SAN LUIS OBISPO, CA) BEB 847214 1.0 \n", - "82 City of San Luis Obispo BEB 859270 1.0 \n", + " transit_agency total_agg_cost total_bus_count \\\n", + "9 City of Beloit 653184 1.0 \n", + "16 City of San Luis Obispo 859270 1.0 \n", + "49 SLO TRANSIT (SAN LUIS OBISPO, CA) 847214 1.0 \n", "\n", - " cost_per_bus \n", - "70 847214 \n", - "82 859270 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# moved to final NB 6/25\n", - "display(Markdown(\n", - " \"**Which agency procured the most and least amount of ZEBs?**\"\n", - "))\n", - "bus_min_max_summary(data=zeb_projects, col1=\"bus_count\", col_list=new_cols)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "45a95018-0ac8-450d-97d2-aa394e94779a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "**Which Agency had the most and least total ZEB cost?**" - ], - "text/plain": [ - "" + " new_cost_per_bus \n", + "9 653184 \n", + "16 859270 \n", + "49 847214 " ] }, "metadata": {}, @@ -5630,7 +2445,7 @@ { "data": { "text/markdown": [ - "**Max total_cost**" + "**Max total_agg_cost**" ], "text/plain": [ "" @@ -5661,31 +2476,29 @@ " \n", " \n", " transit_agency\n", - " prop_type\n", - " total_cost\n", - " bus_count\n", - " cost_per_bus\n", + " total_agg_cost\n", + " total_bus_count\n", + " new_cost_per_bus\n", " \n", " \n", " \n", " \n", - " 44\n", - " City of Los Angeles (LA DOT)\n", - " zero-emission bus (not specified)\n", - " 102790000\n", - " 112.0\n", - " 917767\n", + " 24\n", + " Dallas Area Rapid Transit (DART)\n", + " 103000000\n", + " 90.0\n", + " 1144444\n", " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency prop_type \\\n", - "44 City of Los Angeles (LA DOT) zero-emission bus (not specified) \n", + " transit_agency total_agg_cost total_bus_count \\\n", + "24 Dallas Area Rapid Transit (DART) 103000000 90.0 \n", "\n", - " total_cost bus_count cost_per_bus \n", - "44 102790000 112.0 917767 " + " new_cost_per_bus \n", + "24 1144444 " ] }, "metadata": {}, @@ -5694,7 +2507,7 @@ { "data": { "text/markdown": [ - "**Min total_cost**" + "**Min total_agg_cost**" ], "text/plain": [ "" @@ -5725,37 +2538,84 @@ " \n", " \n", " transit_agency\n", - " prop_type\n", - " total_cost\n", - " bus_count\n", - " cost_per_bus\n", + " total_agg_cost\n", + " total_bus_count\n", + " new_cost_per_bus\n", " \n", " \n", " \n", " \n", - " 70\n", - " SLO TRANSIT (SAN LUIS OBISPO, CA)\n", - " BEB\n", - " 847214\n", - " 1.0\n", - " 847214\n", + " 45\n", + " Oregon Department of Transportation on behalf ...\n", + " 181250\n", + " 5.0\n", + " 36250\n", " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency prop_type total_cost bus_count \\\n", - "70 SLO TRANSIT (SAN LUIS OBISPO, CA) BEB 847214 1.0 \n", + " transit_agency total_agg_cost \\\n", + "45 Oregon Department of Transportation on behalf ... 181250 \n", "\n", - " cost_per_bus \n", - "70 847214 " + " total_bus_count new_cost_per_bus \n", + "45 5.0 36250 " ] }, "metadata": {}, "output_type": "display_data" } ], + "source": [ + "#min max values for all projects\n", + "bus_min_max_summary(data=agg_agency,col1=\"new_cost_per_bus\")\n", + "bus_min_max_summary(data=agg_agency,col1=\"total_bus_count\")\n", + "bus_min_max_summary(data=agg_agency,col1=\"total_agg_cost\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6896d09d-a8e8-4351-bf69-6538d031bf93", + "metadata": {}, + "outputs": [], + "source": [ + "# moved to final NB 6/25\n", + "## min max values of just ZEB projects\n", + "# YES I CAN!!\n", + "new_cols =[\n", + " \"transit_agency\",\n", + " \"prop_type\",\n", + " \"total_cost\",\n", + " \"bus_count\",\n", + " \"cost_per_bus\"]\n", + "\n", + "display(Markdown(\"**Which Agneices had the highest and lowest cost per bus?**\")),\n", + "bus_min_max_summary(data=zeb_projects, col1=\"cost_per_bus\", col_list=new_cols)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "743b25a2-8693-44f7-98fe-384e910620a7", + "metadata": {}, + "outputs": [], + "source": [ + "# moved to final NB 6/25\n", + "display(Markdown(\n", + " \"**Which agency procured the most and least amount of ZEBs?**\"\n", + "))\n", + "bus_min_max_summary(data=zeb_projects, col1=\"bus_count\", col_list=new_cols)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45a95018-0ac8-450d-97d2-aa394e94779a", + "metadata": {}, + "outputs": [], "source": [ "# moved to final NB 6/25\n", "display(Markdown(\n", @@ -5774,29 +2634,10 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "id": "e39c89a1-a726-44f9-808b-bcf936c77254", "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "\n", - "**Conclusion**\n", - "\n", - "Based on these findings, The average cost of a ZEB, throughout the US, is ~$1,000,000, roughly twice the price of a conventional, non-ZEB.\n", - "The variance in cost depends mainly on the options the Trasnit\n", - "Agencies chooses. Highly optioned/customized buses contribute to high cost.\n", - "Unfortunately, analyzing the cost of configuable options is outside the scope of data provided. \n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# moved to final NB 6/25\n", "conclusion = f\"\"\"\n", From 2a4120e70e3a81885cda92bc075935a8578c4967 Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Wed, 26 Jun 2024 17:11:03 +0000 Subject: [PATCH 29/36] moved charts over to final NB --- .../cost_per_bus_analysis.ipynb | 1035 +++- bus_procurement_cost/refactor_bus_cost.ipynb | 4605 ++++++++++++++--- 2 files changed, 4784 insertions(+), 856 deletions(-) diff --git a/bus_procurement_cost/cost_per_bus_analysis.ipynb b/bus_procurement_cost/cost_per_bus_analysis.ipynb index 4ded09c66..0829a5c22 100644 --- a/bus_procurement_cost/cost_per_bus_analysis.ipynb +++ b/bus_procurement_cost/cost_per_bus_analysis.ipynb @@ -215,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "4faaa4ad-b16c-4e6b-87c7-d12f7e7db3c6", "metadata": {}, "outputs": [], @@ -235,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "b8535e97-e7bf-4d7e-b718-24c5758b0ccd", "metadata": {}, "outputs": [], @@ -257,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "id": "829e38c9-3f9b-4e82-92a8-c86f81051580", "metadata": {}, "outputs": [], @@ -279,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "id": "0a2163e3-dac1-4e64-a551-3dc961e44714", "metadata": {}, "outputs": [], @@ -300,7 +300,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "id": "074acb8d-de54-43a0-b243-a070ecfbe1ce", "metadata": {}, "outputs": [], @@ -320,10 +320,65 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "d8356953-e32d-47ab-b67c-fa016cad9c50", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/markdown": [ + "\n", + "\n", + "# Bus Procurement Cost Analysis\n", + "\n", + "## Summary\n", + "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", + "\n", + "Data was compiled from three data sources:\n", + "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", + "2. TIRCP project data (state-funded, California only)\n", + "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", + "\n", + "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", + "The resulting dataset only contained projects that were solely used to procure buses. \n", + "\n", + "88 projects were determined to contain solely bus purchases. \n", + "These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", + "\n", + "\n", + "Breakdown of each data souce:\n", + "| source | bus_count | total_cost | cost_per_bus |\n", + "|:------------|------------:|-------------:|---------------:|\n", + "| dgs | 236 | 250112853 | 1059800 |\n", + "| fta | 883 | 391257025 | 443099 |\n", + "| tircp | 233 | 187250513 | 803650 |\n", + "| Grand Total | 1352 | 828620391 | 612884 |\n", + "\n", + "\n", + "**ZEB buses include:**\n", + "- zero-emission (not specified) \n", + "- electric (not specified)\n", + "- battery electric \n", + "- fuel cell electric\n", + "\n", + "**Non-ZEB buses include:**\n", + "- CNG \n", + "- ethanol \n", + "- ow emission (hybrid, propane) \n", + "- diesel \n", + "- gas\n", + "\n", + "Below are charts and tables that summarize the findings.\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# new summary\n", "\n", @@ -366,7 +421,9 @@ "Below are charts and tables that summarize the findings.\n", "\n", "\"\"\"\n", - "display(Markdown(new_summary))" + "display(\n", + " Markdown(new_summary)\n", + ")" ] }, { @@ -1475,76 +1532,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "1f39733f-448e-4d3e-9981-feff4a13dbda", "metadata": {}, - "outputs": [], - "source": [ - "# Charts" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d58bf288-bfaa-4373-b72b-3f9d7859775f", - "metadata": {}, - "outputs": [], - "source": [ - "# charts " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "63f90c48-e28d-4d88-8b90-891a3e3e3681", - "metadata": {}, - "outputs": [], - "source": [ - "conclusion = f\"\"\"\n", - "**Conclusion**\n", - "\n", - "Based on these findings, The average cost of a ZEB, throughout the US, is ~$1,000,000, roughly twice the price of a conventional, non-ZEB.\n", - "The variance in cost depends mainly on the options the Trasnit\n", - "Agencies chooses. Highly optioned/customized buses contribute to high cost.\n", - "Unfortunately, analyzing the cost of configuable options is outside the scope of data provided. \n", - "\"\"\"\n", - "display(\n", - " Markdown(conclusion)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "8025c84b-428f-4c40-b4d3-c969af58ce63", - "metadata": {}, - "source": [ - "-------" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c51fe7dd-22e2-4686-b1a5-57b2f5ad8602", - "metadata": {}, - "outputs": [], - "source": [ - "Markdown(summary)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4e553e15-dc1d-47d3-9818-7dec893c5294", - "metadata": { - "tags": [] - }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# all bus distribution\n", - "display(Markdown(all_bus_desc))\n", - "\n", + "# Charts\n", "dist_curve(\n", - " df=no_outliers,\n", + " df=merged_data,\n", " mean=cpb_mean,\n", " std=cpb_std,\n", " title=\"all buses, cost per bus distribution\",\n", @@ -1554,87 +1560,848 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "dda584ca-76fa-4e88-9b1c-f70cc438dce6", - "metadata": { - "tags": [] - }, - "outputs": [], + "execution_count": 30, + "id": "adebe10d-167c-480e-abff-313e8d8e91d4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# ZEB dist curve\n", - "display(Markdown(zeb_desc))\n", - "\n", + "# ZEB cost per bus \n", "dist_curve(\n", - " df=zeb_no_outliers,\n", - " mean=zeb_only_mean,\n", - " std=zeb_only_std,\n", - " title=\"ZEB only cost/bus Distribution\",\n", + " df=zeb_projects,\n", + " #using the accounting, weighted average approach to mean (total cost/total number of buses)\n", + " mean=zeb_cpb_wt_avg,\n", + " # need to investigate if std needs to be weighted as well?\n", + " std=zeb_projects[\"cost_per_bus\"].std(),\n", + " title=\"ZEB buses, cost per bus distribution\",\n", " xlabel=\"cost per bus, $ million(s)\",\n", ")" ] }, { "cell_type": "code", - "execution_count": null, - "id": "679d8261-85d6-4d68-9905-e4b048ebc61a", - "metadata": { - "tags": [] - }, - "outputs": [], + "execution_count": 31, + "id": "554eeee1-a3b6-47b0-912f-830885eb100b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# non_zeb distribution\n", - "display(Markdown(non_zeb_desc))\n", - "\n", + "# non-zeb cost per bus\n", "dist_curve(\n", - " non_zeb_no_outliers,\n", - " non_zeb_only_mean,\n", - " non_zeb_only_std,\n", - " title=\"non-ZEB only cost/bus Distribution\",\n", + " df=non_zeb_projects,\n", + " mean=non_zeb_cpb_wt_avg,\n", + " std=non_zeb_projects[\"cost_per_bus\"].std(),\n", + " title=\"non-ZEB costper bus Distribution\",\n", " xlabel='\"cost per bus, $ million(s)\"',\n", ")" ] }, { "cell_type": "code", - "execution_count": null, - "id": "31c592b0-e37e-4da4-8726-36b0a1d3e6f5", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# COST PER BUS BY PROP TYPE\n", - "display(Markdown(cpb_prop_type_desc))\n", - "make_chart(\"cpb\", \"Cost per bus by propulsion type\", x_col=\"prop_type\", data=prop_agg)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7462b55c-29ef-4909-a7dd-27e1c84157d0", - "metadata": { - "tags": [] - }, - "outputs": [], + "execution_count": 34, + "id": "8d030948-59ea-4ea5-9db6-5d8639f6f8f5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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bus_size_typebus_counttotal_costcost_per_bus
0articulated41.0582375761420428
1cutaway152.016694500109832
2not specified881.0509919038578795
3over-the-road14.09516000679714
4standard/conventional (30ft-45ft)264.0234253277887323
5Grand Total1352.0828620391612884
\n", + "
" + ], + "text/plain": [ + " bus_size_type bus_count total_cost cost_per_bus\n", + "0 articulated 41.0 58237576 1420428\n", + "1 cutaway 152.0 16694500 109832\n", + "2 not specified 881.0 509919038 578795\n", + "3 over-the-road 14.0 9516000 679714\n", + "4 standard/conventional (30ft-45ft) 264.0 234253277 887323\n", + "5 Grand Total 1352.0 828620391 612884" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# bus count BY PROP TYPE\n", - "display(Markdown(bus_count_prop_type_desc))\n", - "make_chart(\n", - " \"total_bus_count\", \n", - " \"Bus count by propulsion type\",\n", - " x_col=\"prop_type\",\n", - " data=prop_agg\n", - ")" + "pivot_size" ] }, { "cell_type": "code", - "execution_count": null, - "id": "4f092539-c4c6-4579-aa02-fbee65414ec3", - "metadata": { - "tags": [] - }, - "outputs": [], + "execution_count": 48, + "id": "5117c222-74a3-424c-9b13-1592a3f14eba", + "metadata": {}, + "outputs": [ + { + "data": { + 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\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
prop_typenew_cost_per_bus
3electric (not specified)1288136
2FCEB1185797
0BEB1025966
9zero-emission bus (not specified)896199
1CNG698568
5low emission (hybrid)633271
7mix (zero and low emission)294203
6low emission (propane)190999
8not specified127853
4ethanol111861
\n", + "
" + ], + "text/plain": [ + " prop_type new_cost_per_bus\n", + "3 electric (not specified) 1288136\n", + "2 FCEB 1185797\n", + "0 BEB 1025966\n", + "9 zero-emission bus (not specified) 896199\n", + "1 CNG 698568\n", + "5 low emission (hybrid) 633271\n", + "7 mix (zero and low emission) 294203\n", + "6 low emission (propane) 190999\n", + "8 not specified 127853\n", + "4 ethanol 111861" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(\n", + "# cpb by prop type\n", + "make_chart(\"new_cost_per_bus\", \"Cost per bus by propulsion type\", x_col=\"prop_type\", data=agg_prop),\n", + "\n", + "# pivot table to\n", + "agg_prop[[\"prop_type\",\"new_cost_per_bus\"]].sort_values(by=\"new_cost_per_bus\", ascending=False)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "65566782-7cc4-4ce0-987e-2d055f60ec57", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
prop_typetotal_bus_count
8not specified325.0
1CNG252.0
0BEB163.0
5low emission (hybrid)145.0
9zero-emission bus (not specified)143.0
7mix (zero and low emission)125.0
2FCEB102.0
3electric (not specified)44.0
6low emission (propane)44.0
4ethanol9.0
\n", + "
" + ], + "text/plain": [ + " prop_type total_bus_count\n", + "8 not specified 325.0\n", + "1 CNG 252.0\n", + "0 BEB 163.0\n", + "5 low emission (hybrid) 145.0\n", + "9 zero-emission bus (not specified) 143.0\n", + "7 mix (zero and low emission) 125.0\n", + "2 FCEB 102.0\n", + "3 electric (not specified) 44.0\n", + "6 low emission (propane) 44.0\n", + "4 ethanol 9.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(\n", + "# bus count by prop type\n", + "make_chart(\"total_bus_count\", \"Bus count by propulsion type\", x_col=\"prop_type\", data=agg_prop),\n", + "# pivot table to\n", + "agg_prop[[\"prop_type\",\"total_bus_count\"]].sort_values(by=\"total_bus_count\", ascending=False)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "7b56f81a-cf52-4309-ac8d-01d0389f9d4b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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7adOmAJDpisuzZs0CAI1aP1VqaiqWLFkiPU9OTsaSJUvg4OCAChUqAPj//7iPHTsmrZeWloalS5dqbCshIQGpqakabaVLl4aBgYF0WqBNmzYwNDREUFBQpp4uIQSePn2a7W197vt6n4oVK8LR0RGLFy/W2NeePXtw7do16fN2cHBArVq1sHz5ckRGRmZ6LxmKFCmC+Ph4XL58WWqLioqSZtF9jKGhIdq2bYstW7ZkGaweP34s/dy0aVM8fPgQmzdvltqSkpIyfVcf8+4fHPPnzwcANGnSRKP922+/xbNnz9CnTx8kJiaia9eu2d7Hw4cPNT6DhIQErF69GmXLloWzszOArI+9Fy9eYNWqVRrbevbsWabjKaNH5UPHS05fFxQUhL1792LDhg2ZTjsCQIUKFVCkSBHMmDEDiYmJmZa//Z0lJSXh+vXrOr9FB2UPe3ZIp3bu3Innz5/Dz88vy+VVq1aVLjDYoUOHz96fg4MDxo4di6CgIDRu3Bh+fn4ICwvDwoULUalSJY3/tHv27InNmzejcePGaN++PW7duoW1a9dK/xHnRIUKFfDHH39g+PDhqFSpEvLly4cWLVpkuW6ZMmXQvXt3LF26FHFxcahduzbOnj2LVatWoVWrVtKgyJxwdXXFzz//jDt37qBo0aL4448/EBoaiqVLl0pT70uWLImqVati7NixiI2NhZ2dHTZu3JgpjBw6dAgDBw7EN998g6JFiyI1NRVr1qyRflkDb355/fjjjxg7dizu3LmDVq1awdLSEhEREdi2bRt69+6NkSNHZmtbn/u+3sfY2Bg///wzAgICULt2bXTq1Emaeu7h4YFhw4ZJ686bNw9fffUVypcvj969e8PT0xN37tzB33//jdDQUABAx44dMXr0aLRu3RqDBw9GUlISFi1ahKJFi0oDiz9m2rRpOHz4MKpUqYJevXqhRIkSiI2NxcWLF3HgwAHpGlK9evXCggUL0K1bN1y4cAEuLi5Ys2ZNpp6Hj4mIiICfnx8aN26MU6dOYe3atejcuXOma+uUK1cOpUqVwp9//onixYujfPny2d5H0aJFERgYiHPnzsHJyQnLly/Ho0ePsGLFCmmdhg0bolChQggMDMSoUaNgaGiI5cuXw8HBQSNgrlq1CgsXLkTr1q1RpEgRPH/+HL/99husrKykPxaykpPXXblyBZMnT0atWrUQExODtWvXaizv2rUrDAwMsGzZMjRp0gQlS5ZEQEAAChQogAcPHuDw4cOwsrLCX3/9BQA4e/Ys6tatiwkTJvD+W3mBDDPASI+0aNFCmJqaihcvXrx3HX9/f2FsbCyePHkiTT3/5ZdfNNbJmE787pTljKmq704xXbBggfDx8RHGxsbCyclJ9OvXTzx79izTvmfOnCkKFCggTExMRI0aNcT58+ffO/X83X1n1LpixQqpLTExUXTu3FnY2NgIAB+dhp6SkiKCgoKEp6enMDY2Fm5ubmLs2LHSVPoMnzr1vGTJkuL8+fOiWrVqwtTUVLi7u4sFCxZkWvfWrVuiQYMGwsTERDg5OYn//e9/Yv/+/RrTmW/fvi169OghihQpIkxNTYWdnZ2oW7euOHDgQKbtbdmyRXz11VfCwsJCWFhYCB8fHzFgwAARFhb2ydvK6ft63/eV4Y8//hDlypUTJiYmws7OTnTp0kXcv38/03r//vuvaN26tbCxsRGmpqaiWLFiYty4cRrr7Nu3T5QqVUqo1WpRrFgxsXbt2vdOPR8wYECW9Tx69EgMGDBAuLm5CWNjY+Hs7Czq168vli5dqrHe3bt3hZ+fnzA3Nxf29vZiyJAhIjg4+JOmnl+9elW0a9dOWFpaCltbWzFw4ECNafhvmz59ugAgfvrppw9u+23u7u6iWbNmYu/evcLX11eYmJgIHx+fLL+LCxcuiCpVqgi1Wi0KFSokZs2alWnq+cWLF0WnTp1EoUKFhImJiXB0dBTNmzcX58+f/2Ad2X0d3pp6nnHcvO/xtpCQENGmTRuRP39+YWJiItzd3UX79u3FwYMHpXUytvehqe2Ue1RCfOboSiKiXFCnTh08efLko2NpSDvmzp2LYcOG4c6dO5lmIhJ9aThmh4iINAgh8Pvvv6N27doMOqQIHLNDREQA3gwS3rlzJw4fPowrV65gx44dcpdEpBUMO0REBODNbKLOnTvDxsYG//vf/947sYDoS8MxO0RERKRoHLNDREREisawQ0RERIrGsEP0Fn9//4/edkAb21SpVLzQ2Bdg4sSJmW4Bkpqaiu+++w5ubm4wMDBAq1atAGj/Oz1y5AhUKhWOHDmitW0S6SuGHSI9cufOnVz5BfrTTz9h+/btOt2HXJYvX45ffvkF7dq1w6pVqzSuvKxr/v7+qFOnTq7tj0gpOBuLSAYvX76EkZFy//n99NNPaNeundTr8aX64YcfMGbMGI22Q4cOoUCBApg9e7ZGu9K/U6IvGf9lEsnA1NRU7hIoG4yMjDIFmJiYGI07dWfIy9+pEAKvXr2CmZmZ3KUQyYKnsShPe/DgAXr06AEnJyeYmJigZMmSWL58ubT85cuX8PHxgY+PD16+fCm1x8bGwsXFBdWrV0daWprUvmfPHtSuXRuWlpawsrJCpUqVsH79+vfu/33jJjJOB61cuVKjffv27ShVqhRMTU1RqlSp994B+93xHRljQ8LDw+Hv7w8bGxtYW1sjICAASUlJGq99+fIlBg8eDHt7e1haWsLPzw8PHjzI8ZgRf39/5MuXDw8ePECrVq2QL18+ODg4YOTIkRqfHfDmonMjRoyAm5sbTExMUKxYMcyYMUPjDtMqlUq6g7VKpYJKpYK/v/8Ha5g/fz5KliwJc3Nz2NraomLFihrfS8bnc/36dbRv3x5WVlbInz8/hgwZglevXmXa3tq1a1GhQgWYmZnBzs4OHTt2zPJu9mfOnEHTpk1ha2sLCwsL+Pr6Yu7cuZn2C/z/d3748GH8999/0nvLODay+vw/dvxmuH//Plq1agULCws4Ojpi2LBh2boL/Pt4eHigefPm2Lt3LypWrAgzMzPpbvFxcXEYOnSo9B16eXnh559/Rnp6usY2Nm7ciAoVKkj/VkqXLq3x2axcuRIqlQrHjh1Dnz59kD9/flhZWaFbt2549uxZppoWLlyIkiVLwsTEBK6urhgwYADi4uI01qlTpw5KlSqFq1evom7dujA3N0eBAgUwffr0TNv72DEDZP/zz8626MvGnh3Ksx49eoSqVatCpVJh4MCBcHBwwJ49exAYGIiEhAQMHToUZmZmWLVqFWrUqIHvv/8es2bNAgAMGDAA8fHxWLlyJQwNDQG8+c+5R48eKFmyJMaOHQsbGxuEhIQgODgYnTt3/ux69+3bh7Zt26JEiRKYOnUqnj59ioCAABQsWDDb22jfvj08PT0xdepUXLx4EcuWLYOjoyN+/vlnaR1/f39s2rQJ3377LapWrYqjR4+iWbNmn1V7WloaGjVqhCpVqmDGjBk4cOAAZs6ciSJFiqBfv34A3vQO+Pn54fDhwwgMDETZsmWxd+9ejBo1Cg8ePJBO66xZswY9e/ZE5cqV0bt3bwD44J3kf/vtNwwePBjt2rWTwsvly5dx5syZTN9L+/bt4eHhgalTp+L06dOYN28enj17htWrV0vrTJkyBePGjUP79u3Rs2dPPH78GPPnz0etWrUQEhIi9crs378fzZs3h4uLC4YMGQJnZ2dcu3YNu3btwpAhQzLV6eDggDVr1mDKlClITEzE1KlTAQDFixfP8n1l5/gF3oTX+vXrIzIyEoMHD4arqyvWrFmDQ4cOZeObe7+wsDB06tQJffr0Qa9evVCsWDEkJSWhdu3aePDgAfr06YNChQrh5MmTGDt2LKKiojBnzhzps+nUqRPq168vHXvXrl3DiRMnMn02AwcOhI2NDSZOnIiwsDAsWrQId+/elf5QAN6ExqCgIDRo0AD9+vWT1jt37hxOnDihccf6Z8+eoXHjxmjTpg3at2+PzZs3Y/To0ShdujSaNGkCIHvHTHY//085/ugLJt89SIk+LDAwULi4uIgnT55otHfs2FFYW1uLpKQkqW3s2LHCwMBAHDt2TPz5558CgJgzZ460PC4uTlhaWooqVapkustzenq69HP37t017lSecefid+8qndUdz8uWLStcXFxEXFyc1LZv374s736Od+6GnHFX6h49emis17p1a5E/f37p+YULFwQAMXToUI31/P39c3yH5e7duwsAYtKkSRrt5cqVExUqVJCeb9++XQAQP/74o8Z67dq1EyqVSoSHh0ttFhYWonv37tnaf8uWLUXJkiU/uE7G5+Pn56fR3r9/fwFAXLp0SQghxJ07d4ShoaGYMmWKxnpXrlwRRkZGUntqaqrw9PQU7u7u4tmzZxrrvn08ZHUH84y7r7/r3c8/u8fvnDlzBACxadMmaZ0XL14ILy+vbN3RPCvu7u4CgAgODtZonzx5srCwsBA3btzQaB8zZowwNDQUkZGRQgghhgwZIqysrERqaup795Fxh/IKFSqI5ORkqT3jbuk7duwQQggRExMj1Gq1aNiwoUhLS5PWW7BggQAgli9fLrXVrl1bABCrV6+W2l6/fi2cnZ1F27ZtpbbsHDPZ/fyzsy368vE0FuVJQghs2bIFLVq0gBACT548kR6NGjVCfHw8Ll68KK0/ceJElCxZEt27d0f//v1Ru3ZtDB48WFq+f/9+PH/+HGPGjMk0tuLdqcU5ERUVhdDQUHTv3h3W1tZS+9dff40SJUpkezt9+/bVeF6zZk08ffoUCQkJAIDg4GAAQP/+/TXWGzRoUE5L/+C+b9++LT3fvXs3DA0NNT5XABgxYgSEENizZ0+O9mtjY4P79+/j3LlzH113wIABGs8z3vfu3bsBAFu3bkV6ejrat2+vccw4OzvD29sbhw8fBgCEhIQgIiICQ4cOzTT+RhvHw6ccv7t374aLiwvatWsnvd7c3FzqFcspT09PNGrUSKPtzz//RM2aNWFra6tRU4MGDZCWloZjx44BePOdvHjxAvv37//ofnr37q3RM9OvXz8YGRlJ38mBAweQnJyMoUOHwsDg/3/l9OrVC1ZWVvj77781tpcvXz507dpVeq5Wq1G5cmWNY/Fjx8ynfP6fcvzRl4thh/Kkx48fIy4uDkuXLoWDg4PGIyAgAMCbgaIZ1Go1li9fjoiICDx//hwrVqzQ+KV169YtAECpUqV0Uu/du3cBAN7e3pmWFStWLNvbefcO07a2tgAgjYG4e/cuDAwM4OnpqbGel5fXJ9X7LlNTUzg4OGTa99tjL+7evQtXV1dYWlpqrJdxGifjM/hUo0ePRr58+VC5cmV4e3tjwIABOHHiRJbrvvv5FilSBAYGBrhz5w4A4ObNmxBCwNvbO9Nxc+3aNemY0fXx8CnH7927d+Hl5ZUpZH3KcZOVd48R4M3nExwcnKmmBg0aaNTUv39/FC1aFE2aNEHBggXRo0cPKWi/693vJF++fHBxcZG+k4zj4t33o1arUbhw4UzHTcGCBTN9Fu8eix87Zj7l8/+U44++XByzQ3lSxmDJrl27onv37lmu4+vrq/F87969AIBXr17h5s2bWf5n/6ne91f+uwN3tSVjfNG7hI5vYfe+/eaG4sWLIywsDLt27UJwcDC2bNmChQsXYvz48QgKCvrga9/9ftLT06FSqbBnz54s31O+fPm0Wvv75OT41basZl6lp6fj66+/xnfffZfla4oWLQoAcHR0RGhoKPbu3Ys9e/Zgz549WLFiBbp164ZVq1bptO7s/Bv42DHzKZ//5xx/9OVg2KE8ycHBAZaWlkhLS5P+6vyQy5cvY9KkSQgICEBoaCh69uyJK1euSKeUMgbI/vvvv5/UC5LRs/LurJF3/xp1d3cH8OYv53eFhYVle38f4+7ujvT0dERERGj8RR0eHq61fXxo3wcOHMDz5881eneuX78uLc/wqaeCLCws0KFDB3To0AHJyclo06YNpkyZgrFjx2qcdnw3xIaHhyM9PV26QnWRIkUghICnp6f0izsrbx8P2Tm+PtWnHL/u7u74999/IYTQ+Ny0edxkKFKkCBITE7P1ntVqNVq0aIEWLVogPT0d/fv3x5IlSzBu3DiNf0M3b95E3bp1peeJiYmIiopC06ZNAfz/cREWFobChQtL6yUnJyMiIiLHn/+HjplP/f8ju8cffbl4GovyJENDQ7Rt2xZbtmzBv//+m2n548ePpZ9TUlLg7+8PV1dXzJ07FytXrsSjR480rmzbsGFDWFpaYurUqZmmKn+o18Td3R2GhobSWIYMCxcu1Hju4uKCsmXLYtWqVYiPj5fa9+/fj6tXr2bvTWdDxhiMd/c/f/58re3jfZo2bYq0tDQsWLBAo3327NlQqVTSTBngzS+PdwPi+zx9+lTjuVqtRokSJSCEQEpKisayX3/9VeN5xvvO2HebNm1gaGiIoKCgTN+rEELaV/ny5eHp6Yk5c+ZkqlMbvWifcvw2bdoUDx8+xObNm6W2pKQkLF269LPreFf79u1x6tQpqRf0bXFxcUhNTQWQ+TsxMDCQekLenRK/dOlSje9p0aJFSE1Nlb6TBg0aQK1WY968eRqf7e+//474+PgczST82DHzKZ9/do6/pKQkXL9+HU+ePPnkWilvYM8O5VnTpk3D4cOHUaVKFfTq1QslSpRAbGwsLl68iAMHDiA2NhYA8OOPPyI0NBQHDx6EpaUlfH19MX78ePzwww9o164dmjZtCisrK8yePRs9e/ZEpUqV0LlzZ9ja2uLSpUtISkp6b9e8tbU1vvnmG8yfPx8qlQpFihTBrl27NMYLZZg6dSqaNWuGr776Cj169EBsbKx0/Y7ExEStfCYVKlRA27ZtMWfOHDx9+lSaen7jxg0A2hlc+z4tWrRA3bp18f333+POnTsoU6YM9u3bhx07dmDo0KEa08srVKiAAwcOYNasWXB1dYWnpyeqVKmS5XYbNmwIZ2dn1KhRA05OTrh27RoWLFiAZs2aZRofFBERAT8/PzRu3BinTp3C2rVr0blzZ5QpUwbAm56LH3/8EWPHjsWdO3fQqlUrWFpaIiIiAtu2bUPv3r0xcuRIGBgYYNGiRWjRogXKli2LgIAAuLi44Pr16/jvv/+yDAOfKrvHb69evbBgwQJ069YNFy5cgIuLC9asWQNzc/PPruFdo0aNws6dO9G8eXP4+/ujQoUKePHiBa5cuYLNmzfjzp07sLe3R8+ePREbG4t69eqhYMGCuHv3LubPn4+yZctmmmqfnJyM+vXro3379ggLC8PChQvx1Vdfwc/PD8CbXq6xY8ciKCgIjRs3hp+fn7RepUqVNAYjZ1d2jpnsfv7Z2dbZs2dRt25dTJgwgfe0+1Ll7uQvok/z6NEjMWDAAOHm5iaMjY2Fs7OzqF+/vli6dKkQ4s1UbCMjIzFo0CCN16WmpopKlSoJV1dXjanFO3fuFNWrVxdmZmbCyspKVK5cWWzYsEFa/u7UcyGEePz4sWjbtq0wNzcXtra2ok+fPuLff//NNPVcCCG2bNkiihcvLkxMTESJEiXE1q1bs9wm3jP1/PHjxxrrZUzvjYiIkNpevHghBgwYIOzs7ES+fPlEq1atRFhYmAAgpk2blr0P9i3du3cXFhYWmdqzmnb9/PlzMWzYMOHq6iqMjY2Ft7e3+OWXXzSmawshxPXr10WtWrWEmZmZAPDBaehLliwRtWrVEvnz5xcmJiaiSJEiYtSoUSI+Pj5TLVevXhXt2rUTlpaWwtbWVgwcODDTpQSEePM9fPXVV8LCwkJYWFgIHx8fMWDAABEWFqax3vHjx8XXX38tLC0thYWFhfD19RXz58//4GeQ3annQnz8+M1w9+5d4efnJ8zNzYW9vb0YMmSICA4O/qyp582aNcty2fPnz8XYsWOFl5eXUKvVwt7eXlSvXl3MmDFDmkK+efNm0bBhQ+Ho6CjUarUoVKiQ6NOnj4iKipK2k3FsHj16VPTu3VvY2tqKfPnyiS5duoinT59m2u+CBQuEj4+PMDY2Fk5OTqJfv36Zpv2/77N9999Qdo4ZIbL3+WdnWxmXoMjJpR0ob1AJoeORj0Skc6GhoShXrhzWrl2LLl26yF2O1mVclO7x48ewt7eXuxzCm4t0BgQE4Ny5c6hYsaLc5RB9EMfsEH1h3r4tRoY5c+bAwMAAtWrVkqEiIqK8jWN2iL4w06dPx4ULF1C3bl0YGRlJU4N79+4NNzc3ucsjIspzGHaIvjDVq1fH/v37MXnyZCQmJqJQoUKYOHEivv/+e7lLIyLKkzhmh4iIiBSNY3aIiIhI0XgaC28uof7w4UNYWlrq9DolREREpD1CCDx//hyurq4aN5p9F8MOgIcPH3JgJxER0Rfq3r17KFiw4HuXM+wA0lUy7927BysrK5mrISIiouxISEiAm5tbpqutv4thB/9/iX0rKyuGHSIioi/Mx4agcIAyERERKRrDDhERESkaww4REREpGsfsEBFRtqWlpSElJUXuMkhPGBsbw9DQ8LO3w7BDREQfJYRAdHQ04uLi5C6F9IyNjQ2cnZ0/6zp4DDtERPRRGUHH0dER5ubmvAAr6ZwQAklJSYiJiQEAuLi45HhbDDtERPRBaWlpUtDJnz+/3OWQHjEzMwMAxMTEwNHRMcentDhAmYiIPihjjI65ubnMlZA+yjjuPmesGMMOERFlC09dkRy0cdwx7BAREZGiMewQERGRonGAMhER5di0kCe5tq8x5exzbV854e/vj7i4OGzfvv2j69apUwdly5bFnDlzdF4XsWeHiIgUrE6dOhg6dKjOX0OfbuXKlbCxscmVfTHsEBERkaIx7BARkSL5+/vj6NGjmDt3LlQqFVQqFe7cuYOjR4+icuXKMDExgYuLC8aMGYPU1NQPviYtLQ2BgYHw9PSEmZkZihUrhrlz535WfampqRg4cCCsra1hb2+PcePGQQghLVepVJlOidnY2GDlypUAgOTkZAwcOBAuLi4wNTWFu7s7pk6dmq19x8XFoU+fPnBycoKpqSlKlSqFXbt2Scu3bNmCkiVLwsTEBB4eHpg5c6bG6z9W2507d6BSqbB161bUrVsX5ubmKFOmDE6dOgUAOHLkCAICAhAfHy99zhMnTsxW7TnBMTt5QG6e885L8vr5dyL6ss2dOxc3btxAqVKlMGnSJABvLpDYtGlT+Pv7Y/Xq1bh+/Tp69eoFU1NTTJw4McvXODg4ID09HQULFsSff/6J/Pnz4+TJk+jduzdcXFzQvn37HNW3atUqBAYG4uzZszh//jx69+6NQoUKoVevXtl6/bx587Bz505s2rQJhQoVwr1793Dv3r2Pvi49PR1NmjTB8+fPsXbtWhQpUgRXr16VLth34cIFtG/fHhMnTkSHDh1w8uRJ9O/fH/nz54e/v/8nvcfvv/8eM2bMgLe3N77//nt06tQJ4eHhqF69OubMmYPx48cjLCwMAJAvX75P2vanYNghIiJFsra2hlqthrm5OZydnQG8+eXr5uaGBQsWQKVSwcfHBw8fPsTo0aMxfvz4LF8DAIaGhggKCpKee3p64tSpU9i0aVOOw46bmxtmz54NlUqFYsWK4cqVK5g9e3a2w05kZCS8vb3x1VdfQaVSwd3dPVuvO3DgAM6ePYtr166haNGiAIDChQtLy2fNmoX69etj3LhxAICiRYvi6tWr+OWXXz457IwcORLNmjUDAAQFBaFkyZIIDw+Hj48PrK2toVKpND5nXeFpLCIi0hvXrl1DtWrVNC5UV6NGDSQmJuL+/fsffO2vv/6KChUqwMHBAfny5cPSpUsRGRmZ41qqVq2qUUe1atVw8+ZNpKWlZev1/v7+CA0NRbFixTB48GDs27cvW68LDQ1FwYIFpaDzrmvXrqFGjRoabTVq1Pik2jL4+vpKP2fc2yrjXle5iWGHiIjoIzZu3IiRI0ciMDAQ+/btQ2hoKAICApCcnKyzfapUKo0xPIDmLRPKly+PiIgITJ48GS9fvkT79u3Rrl27j243435Tuqwtg7GxscZrgDen0XIbT2MREZFiqdVqjd6I4sWLY8uWLRBCSL98T5w4AUtLSxQsWDDL12SsU716dfTv319qu3Xr1mfVdubMGY3np0+fhre3tzR2xsHBAVFRUdLymzdvIikpSeM1VlZW6NChAzp06IB27dqhcePGiI2NhZ2d3Xv36+vri/v37+PGjRtZ9u4UL14cJ06c0Gg7ceIEihYt+km1fUxWn7OusGeHiIgUy8PDA2fOnMGdO3fw5MkT9O/fH/fu3cOgQYNw/fp17NixAxMmTMDw4cNhYGCQ5WvS09Ph7e2N8+fPY+/evbhx4wbGjRuHc+fOfVZtkZGRGD58OMLCwrBhwwbMnz8fQ4YMkZbXq1cPCxYsQEhICM6fP4++fftq9JTMmjULGzZswPXr13Hjxg38+eefcHZ2/ui1a2rXro1atWqhbdu22L9/PyIiIrBnzx4EBwcDAEaMGIGDBw9i8uTJuHHjBlatWoUFCxZg5MiR2a4tOzw8PJCYmIiDBw/iyZMnnxyWPgV7doiIKMfy+qzKkSNHonv37ihRogRevnyJiIgI7N69G6NGjUKZMmVgZ2eHwMBA/PDDDx98TZ8+fRASEoIOHTpApVKhU6dO6N+/P/bs2ZPj2rp164aXL1+icuXKMDQ0xJAhQ9C7d29p+cyZMxEQEICaNWvC1dUVc+fOxYULF6TllpaWmD59Om7evAlDQ0NUqlQJu3fvlkLbh2zZsgUjR45Ep06d8OLFC3h5eWHatGkA3pwe27RpE8aPH4/JkyfDxcUFkyZN0hic/LHasqN69ero27cvOnTogKdPn2LChAk6m36uEu+edNNDCQkJsLa2Rnx8PKysrHJ9/5x6TkR52atXrxAREQFPT0+YmprKXQ7pmQ8df9n9/c3TWERERKRoDDtERERaFBkZiXz58r338TnT1bNj3bp17913yZIldbrvvIpjdoiIiLTI1dUVoaGhH1yuS35+fqhSpUqWyz51ELFSMOwQERFpkZGREby8vGTbv6WlJSwtLWXbf17E01hERJQtnM9CctDGccewQ0REH5Rx6kOX10Ehep+M4+5zTsHxNBYREX2QoaEhbGxspHsamZuba9zTiUgXhBBISkpCTEwMbGxspKs354SsYWfq1KnYunUrrl+/DjMzM1SvXh0///wzihUrJq1Tp04dHD16VON1ffr0weLFi6XnkZGR6NevHw4fPox8+fKhe/fumDp1KoyMmOWIiLQh487UctzEkfSbjY3NZ98ZXdY0cPToUQwYMACVKlVCamoq/ve//6Fhw4a4evUqLCwspPV69eqFSZMmSc/Nzc2ln9PS0tCsWTM4Ozvj5MmTiIqKQrdu3WBsbIyffvopV98PEZFSqVQquLi4wNHRMcsbPhLpgrGx8Wf16GSQNexk3Icjw8qVK+Ho6IgLFy6gVq1aUru5ufl7U92+fftw9epVHDhwAE5OTihbtiwmT56M0aNHY+LEiVCr1Tp9D0RE+sTQ0FArv3yIclOeGqAcHx8PAJnu1rpu3TrY29ujVKlSGDt2rMYguVOnTqF06dJwcnKS2ho1aoSEhAT8999/We7n9evXSEhI0HgQERGRMuWZQS3p6ekYOnQoatSogVKlSkntnTt3hru7O1xdXXH58mWMHj0aYWFh2Lp1KwAgOjpaI+gAkJ5HR0dnua+pU6ciKChIR++EiIiI8pI8E3YGDBiAf//9F8ePH9dof/sOsKVLl4aLiwvq16+PW7duoUiRIjna19ixYzF8+HDpeUJCAtzc3HJWOBEREeVpeeI01sCBA7Fr1y4cPnwYBQsW/OC6GZfADg8PB/BmhsCjR4801sl4/r5xPiYmJrCystJ4EBERkTLJGnaEEBg4cCC2bduGQ4cOwdPT86OvybjfiIuLCwCgWrVquHLlisZ0yP3798PKygolSpTQSd1ERET05ZD1NNaAAQOwfv167NixA5aWltIYG2tra5iZmeHWrVtYv349mjZtivz58+Py5csYNmwYatWqBV9fXwBAw4YNUaJECXz77beYPn06oqOj8cMPP2DAgAEwMTGR8+0RERFRHiBrz86iRYsQHx+POnXqwMXFRXr88ccfAAC1Wo0DBw6gYcOG8PHxwYgRI9C2bVv89ddf0jYMDQ2xa9cuGBoaolq1aujatSu6deumcV0eIiIi0l+y9ux87OZebm5uma6enBV3d3fs3r1bW2URERGRguSJAcpEREREusKwQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIoma9iZOnUqKlWqBEtLSzg6OqJVq1YICwvTWOfVq1cYMGAA8ufPj3z58qFt27Z49OiRxjqRkZFo1qwZzM3N4ejoiFGjRiE1NTU33woRERHlUbKGnaNHj2LAgAE4ffo09u/fj5SUFDRs2BAvXryQ1hk2bBj++usv/Pnnnzh69CgePnyINm3aSMvT0tLQrFkzJCcn4+TJk1i1ahVWrlyJ8ePHy/GWiIiIKI9RCSGE3EVkePz4MRwdHXH06FHUqlUL8fHxcHBwwPr169GuXTsAwPXr11G8eHGcOnUKVatWxZ49e9C8eXM8fPgQTk5OAIDFixdj9OjRePz4MdRqdab9vH79Gq9fv5aeJyQkwM3NDfHx8bCyssqdN/uWaSFPcn2fecGYcvZyl0BERF+whIQEWFtbf/T3d54asxMfHw8AsLOzAwBcuHABKSkpaNCggbSOj48PChUqhFOnTgEATp06hdKlS0tBBwAaNWqEhIQE/Pfff1nuZ+rUqbC2tpYebm5uunpLREREJLM8E3bS09MxdOhQ1KhRA6VKlQIAREdHQ61Ww8bGRmNdJycnREdHS+u8HXQylmcsy8rYsWMRHx8vPe7du6fld0NERER5hZHcBWQYMGAA/v33Xxw/flzn+zIxMYGJiYnO90NERETyyxM9OwMHDsSuXbtw+PBhFCxYUGp3dnZGcnIy4uLiNNZ/9OgRnJ2dpXXenZ2V8TxjHSIiItJfsoYdIQQGDhyIbdu24dChQ/D09NRYXqFCBRgbG+PgwYNSW1hYGCIjI1GtWjUAQLVq1XDlyhXExMRI6+zfvx9WVlYoUaJE7rwRIiIiyrNkPY01YMAArF+/Hjt27IClpaU0xsba2hpmZmawtrZGYGAghg8fDjs7O1hZWWHQoEGoVq0aqlatCgBo2LAhSpQogW+//RbTp09HdHQ0fvjhBwwYMICnqoiIiEjesLNo0SIAQJ06dTTaV6xYAX9/fwDA7NmzYWBggLZt2+L169do1KgRFi5cKK1raGiIXbt2oV+/fqhWrRosLCzQvXt3TJo0KbfeBhEREeVheeo6O3LJ7jx9XeF1doiIiD7dF3mdHSIiIiJtY9ghIiIiRWPYISIiIkVj2CEiIiJFY9ghIiIiRWPYISIiIkVj2CEiIiJFY9ghIiIiRWPYISIiIkVj2CEiIiJFY9ghIiIiRWPYISIiIkVj2CEiIiJFY9ghIiIiRWPYISIiIkVj2CEiIiJFY9ghIiIiRWPYISIiIkVj2CEiIiJFY9ghIiIiRWPYISIiIkVj2CEiIiJFy1HYmTRpEpKSkjK1v3z5EpMmTfrsooiIiIi0JUdhJygoCImJiZnak5KSEBQU9NlFEREREWlLjsKOEAIqlSpT+6VLl2BnZ/fZRRERERFpi9GnrGxrawuVSgWVSoWiRYtqBJ60tDQkJiaib9++Wi+SiIiIKKc+KezMmTMHQgj06NEDQUFBsLa2lpap1Wp4eHigWrVqWi+SiIiIKKc+Kex0794dAODp6Ynq1avD2NhYJ0URERERacsnhZ0MtWvXRnp6Om7cuIGYmBikp6drLK9Vq5ZWiiMiIiL6XDkKO6dPn0bnzp1x9+5dCCE0lqlUKqSlpWmlOCIiIqLPlaOw07dvX1SsWBF///03XFxcspyZRURERJQX5Cjs3Lx5E5s3b4aXl5e26yEiIiLSqhxdZ6dKlSoIDw/Xdi1EREREWpejnp1BgwZhxIgRiI6ORunSpTPNyvL19dVKcURERESfK0dhp23btgCAHj16SG0qlUq6sjIHKBMREVFekaOwExERoe06iIiIiHQiR2HH3d1d23UQERER6USOws7q1as/uLxbt245KoaIiIhI23IUdoYMGaLxPCUlBUlJSVCr1TA3N2fYISIiojwjR1PPnz17pvFITExEWFgYvvrqK2zYsEHbNRIRERHlWI7CTla8vb0xbdq0TL0+RERERHLSWtgBACMjIzx8+FCbmyQiIiL6LDkas7Nz506N50IIREVFYcGCBahRo4ZWCiMiIiLShhyFnVatWmk8V6lUcHBwQL169TBz5kxt1EVERESkFTkKO+np6dqug4iIiEgnPnvMjhACQght1EJERESkdTkOO6tXr0bp0qVhZmYGMzMz+Pr6Ys2aNdqsjYiIiOiz5eg01qxZszBu3DgMHDhQGpB8/Phx9O3bF0+ePMGwYcO0WiQRERFRTuUo7MyfPx+LFi3SuFKyn58fSpYsiYkTJzLsEBERUZ6Ro9NYUVFRqF69eqb26tWrIyoq6rOLIiIiItKWHIUdLy8vbNq0KVP7H3/8AW9v788uioiIiEhbcnQaKygoCB06dMCxY8ekMTsnTpzAwYMHswxBRERERHLJUc9O27ZtcebMGdjb22P79u3Yvn077O3tcfbsWbRu3VrbNRIRERHlWI56dgCgQoUKWLt2rTZrISIiItK6HPXs7N69G3v37s3UvnfvXuzZs+eziyIiIiLSlhyFnTFjxiAtLS1TuxACY8aMyfZ2jh07hhYtWsDV1RUqlQrbt2/XWO7v7w+VSqXxaNy4scY6sbGx6NKlC6ysrGBjY4PAwEAkJibm5G0RERGRAuXoNNbNmzdRokSJTO0+Pj4IDw/P9nZevHiBMmXKoEePHmjTpk2W6zRu3BgrVqyQnpuYmGgs79KlC6KiorB//36kpKQgICAAvXv3xvr167NdB1FumhbyRO4SZDGmnL3cJRCRnspR2LG2tsbt27fh4eGh0R4eHg4LC4tsb6dJkyZo0qTJB9cxMTGBs7NzlsuuXbuG4OBgnDt3DhUrVgTw5oKHTZs2xYwZM+Dq6prtWoiIiEiZcnQaq2XLlhg6dChu3boltYWHh2PEiBHw8/PTWnEAcOTIETg6OqJYsWLo168fnj59Ki07deoUbGxspKADAA0aNICBgQHOnDnz3m2+fv0aCQkJGg8iIiJSphyFnenTp8PCwgI+Pj7w9PSEp6cnihcvjvz582PGjBlaK65x48ZYvXo1Dh48iJ9//hlHjx5FkyZNpPFC0dHRcHR01HiNkZER7OzsEB0d/d7tTp06FdbW1tLDzc1NazUTERFR3pLj01gnT57E/v37cenSJemu57Vq1dJqcR07dpR+Ll26NHx9fVGkSBEcOXIE9evXz/F2x44di+HDh0vPExISGHiIiIgUKsfX2VGpVGjYsCEaNmz43nVKly6N3bt3ay1IFC5cGPb29ggPD0f9+vXh7OyMmJgYjXVSU1MRGxv73nE+wJtxQO8OdCYiIiJlytFprOy6c+cOUlJStLa9+/fv4+nTp3BxcQEAVKtWDXFxcbhw4YK0zqFDh5Ceno4qVapobb9ERET05cpxz442JCYmakxVj4iIQGhoKOzs7GBnZ4egoCC0bdsWzs7OuHXrFr777jt4eXmhUaNGAIDixYujcePG6NWrFxYvXoyUlBQMHDgQHTt25EwsIiIiAqDjnp2POX/+PMqVK4dy5coBAIYPH45y5cph/PjxMDQ0xOXLl+Hn54eiRYsiMDAQFSpUwD///KNxCmrdunXw8fFB/fr10bRpU3z11VdYunSpXG+JiIiI8hhZe3bq1KkDIcR7l2d1S4p32dnZ8QKCRERE9F6y9uwQERER6RrDDhERESma1sJOXFxcprYlS5bAyclJW7sgIiIi+mQ5Cjs///wz/vjjD+l5+/btkT9/fhQoUACXLl2S2jt37vxJ98oiIiIi0rYchZ3FixdLFwrcv38/9u/fjz179qBJkyYYNWqUVgskIiIi+hw5mo0VHR0thZ1du3ahffv2aNiwITw8PHgxPyIiIspTctSzY2tri3v37gEAgoOD0aBBAwCAEEK6SScRERFRXpCjnp02bdqgc+fO8Pb2xtOnT9GkSRMAQEhICLy8vLRaIBEREdHnyFHYmT17Njw8PHDv3j1Mnz4d+fLlAwBERUWhf//+Wi2QiIiI6HPkKOwYGxtj5MiRmdqHDRv22QURERERaVOOws7q1as/uLxbt245KoaIiIhI23IUdoYMGaLxPCUlBUlJSVCr1TA3N2fYISIiojwjR7Oxnj17pvFITExEWFgYvvrqK2zYsEHbNRIRERHlmNZuF+Ht7Y1p06Zl6vUhIiIikpNWbwRqZGSEhw8fanOTRERERJ8lR2N2du7cqfFcCIGoqCgsWLAANWrU0EphRERERNqQo7DTqlUrjecqlQoODg6oV68eZs6cqY26iIiIiLQiR2EnPT1d23UQERER6cRnj9kRQkAIoY1aiIiIiLQux2Hn999/R6lSpWBqagpTU1OUKlUKy5Yt02ZtRERERJ8tR6exxo8fj1mzZmHQoEGoVq0aAODUqVMYNmwYIiMjMWnSJK0WSURERJRTOQo7ixYtwm+//YZOnTpJbX5+fvD19cWgQYMYdoiIiCjPyNFprJSUFFSsWDFTe4UKFZCamvrZRRERERFpS47CzrfffotFixZlal+6dCm6dOny2UURERERaUu2T2MNHz5c+lmlUmHZsmXYt28fqlatCgA4c+YMIiMjeRNQIiIiylOyHXZCQkI0nleoUAEAcOvWLQCAvb097O3t8d9//2mxPCIiIqLPk+2wc/jw4U/e+P379+Hq6goDA63egouIiIgo23SaQkqUKIE7d+7ochdEREREH6TTsMMrKxMREZHceH6JiIiIFI1hh4iIiBSNYYeIiIgUTadhR6VS6XLzRERERB/FAcpERESkaDm6EWh2Xb16Fa6urrrcBREREdEHZTvstGnTJtsb3bp1KwDAzc3t0ysiIiIi0qJshx1ra2td1kFERESkE9kOOytWrNBlHUREREQ6wannREREpGg5HqC8efNmbNq0CZGRkUhOTtZYdvHixc8ujIiIiEgbctSzM2/ePAQEBMDJyQkhISGoXLky8ufPj9u3b6NJkybarpGIiIgox3IUdhYuXIilS5di/vz5UKvV+O6777B//34MHjwY8fHx2q6RiIiIKMdyFHYiIyNRvXp1AICZmRmeP38OAPj222+xYcMG7VVHRERE9JlyFHacnZ0RGxsLAChUqBBOnz4NAIiIiOBVk4mIiChPyVHYqVevHnbu3AkACAgIwLBhw/D111+jQ4cOaN26tVYLJCIiIvocOZqNtXTpUqSnpwMABgwYgPz58+PkyZPw8/NDnz59tFogERER0efIUdi5f/++xq0gOnbsiI4dO0IIgXv37qFQoUJaK5CIiIjoc+ToNJanpyceP36cqT02Nhaenp6fXRQRERGRtuQo7AghoFKpMrUnJibC1NT0s4siIiIi0pZPOo01fPhwAIBKpcK4ceNgbm4uLUtLS8OZM2dQtmxZrRZIRERE9Dk+KeyEhIQAeNOzc+XKFajVammZWq1GmTJlMHLkSO1WSERERPQZPinsHD58GMCb6eZz586FlZWVTooiIiIi0pYczcZasWKF9PP9+/cBAAULFtRORURERERalKMByunp6Zg0aRKsra3h7u4Od3d32NjYYPLkydL1d4iIiIjyghz17Hz//ff4/fffMW3aNNSoUQMAcPz4cUycOBGvXr3ClClTtFokERERUU7lKOysWrUKy5Ytg5+fn9Tm6+uLAgUKoH///gw7RERElGfk6DRWbGwsfHx8MrX7+PhINwjNjmPHjqFFixZwdXWFSqXC9u3bNZYLITB+/Hi4uLjAzMwMDRo0wM2bNzPV0qVLF1hZWcHGxgaBgYFITEzMydsiIiIiBcpR2ClTpgwWLFiQqX3BggUoU6ZMtrfz4sULlClTBr/++muWy6dPn4558+Zh8eLFOHPmDCwsLNCoUSO8evVKWqdLly7477//sH//fuzatQvHjh1D7969P/1NERERkSLl6DTW9OnT0axZMxw4cADVqlUDAJw6dQr37t3D7t27s72dJk2aoEmTJlkuE0Jgzpw5+OGHH9CyZUsAwOrVq+Hk5ITt27ejY8eOuHbtGoKDg3Hu3DlUrFgRADB//nw0bdoUM2bMgKura07eHhERESlIju+NdePGDbRu3RpxcXGIi4tDmzZtEBYWBnd3d60UFhERgejoaDRo0EBqs7a2RpUqVXDq1CkAbwKWjY2NFHQAoEGDBjAwMMCZM2feu+3Xr18jISFB40FERETKlKOeHU9PT0RFRWUaiPz06VO4ubkhLS3tswuLjo4GADg5OWm0Ozk5Scuio6Ph6OiosdzIyAh2dnbSOlmZOnUqgoKCPrtGIiIiyvtyfCPQrHwpNwIdO3Ys4uPjpce9e/fkLomIiIh0JMc3Ah0/frxObwTq7OwMAHj06BFcXFyk9kePHkn7cHZ2RkxMjMbrUlNTERsbK70+KyYmJjAxMdFKnURERJS35dkbgXp6esLZ2RkHDx6Uwk1CQgLOnDmDfv36AQCqVauGuLg4XLhwARUqVAAAHDp0COnp6ahSpYpW6iAiIqIvm6w3Ak1MTER4eLj0PCIiAqGhobCzs0OhQoUwdOhQ/Pjjj/D29oanpyfGjRsHV1dXtGrVCgBQvHhxNG7cGL169cLixYuRkpKCgQMHomPHjpyJRURERAC0cCPQz3H+/HnUrVtXep5xmqx79+5YuXIlvvvuO7x48QK9e/dGXFwcvvrqKwQHB2uMC1q3bh0GDhyI+vXrw8DAAG3btsW8efO0Uh8RERF9+VTifaON9UhCQgKsra0RHx//2b1VOTEt5Emu7zMvGFPOXu4SZMHvm4hIO7L7+ztHs7GIiIiIvhQMO0RERKRoDDtERESkaAw7REREpGgMO0RERKRoDDtERESkaAw7REREpGgMO0RERKRoDDtERESkaAw7REREpGgMO0RERKRoDDtERESkaAw7REREpGgMO0RERKRoDDtERESkaAw7REREpGgMO0RERKRoDDtERESkaAw7REREpGgMO0RERKRoDDtERESkaAw7REREpGgMO0RERKRoDDtERESkaAw7REREpGgMO0RERKRoDDtERESkaAw7REREpGgMO0RERKRoDDtERESkaAw7REREpGgMO0RERKRoDDtERESkaAw7REREpGgMO0RERKRoDDtERESkaAw7REREpGgMO0RERKRoDDtERESkaAw7REREpGgMO0RERKRoDDtERESkaAw7REREpGgMO0RERKRoDDtERESkaAw7REREpGgMO0RERKRoDDtERESkaAw7REREpGgMO0RERKRoDDtERESkaAw7REREpGgMO0RERKRoDDtERESkaAw7REREpGgMO0RERKRoeT7sTJw4ESqVSuPh4+MjLX/16hUGDBiA/PnzI1++fGjbti0ePXokY8VERESUl+T5sAMAJUuWRFRUlPQ4fvy4tGzYsGH466+/8Oeff+Lo0aN4+PAh2rRpI2O1RERElJcYyV1AdhgZGcHZ2TlTe3x8PH7//XesX78e9erVAwCsWLECxYsXx+nTp1G1atUst/f69Wu8fv1aep6QkKCbwomIiEh2X0TPzs2bN+Hq6orChQujS5cuiIyMBABcuHABKSkpaNCggbSuj48PChUqhFOnTr13e1OnToW1tbX0cHNz0/l7ICIiInnk+bBTpUoVrFy5EsHBwVi0aBEiIiJQs2ZNPH/+HNHR0VCr1bCxsdF4jZOTE6Kjo9+7zbFjxyI+Pl563Lt3T8fvgoiIiOSS509jNWnSRPrZ19cXVapUgbu7OzZt2gQzM7McbdPExAQmJibaKpGIiIjysDzfs/MuGxsbFC1aFOHh4XB2dkZycjLi4uI01nn06FGWY3yIiIhI/3xxYScxMRG3bt2Ci4sLKlSoAGNjYxw8eFBaHhYWhsjISFSrVk3GKomIiCivyPOnsUaOHIkWLVrA3d0dDx8+xIQJE2BoaIhOnTrB2toagYGBGD58OOzs7GBlZYVBgwahWrVq752JRURERPolz4ed+/fvo1OnTnj69CkcHBzw1Vdf4fTp03BwcAAAzJ49GwYGBmjbti1ev36NRo0aYeHChTJXTURERHlFng87Gzdu/OByU1NT/Prrr/j1119zqSIiIiL6knxxY3aIiIiIPgXDDhERESkaww4REREpGsMOERERKVqeH6BMRPQlmxbyRO4SZDGmnL3cJRBJ2LNDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIpmJHcBRERESjEt5IncJchiTDl7uUv4IPbsEBERkaIx7BAREZGiMewQERGRojHsEBERkaIx7BAREZGiMewQERGRojHsEBERkaIx7BAREZGiMewQERGRojHsEBERkaIx7BAREZGiMewQERGRojHsEBERkaIx7BAREZGiKSbs/Prrr/Dw8ICpqSmqVKmCs2fPyl0SERER5QGKCDt//PEHhg8fjgkTJuDixYsoU6YMGjVqhJiYGLlLIyIiIpkpIuzMmjULvXr1QkBAAEqUKIHFixfD3Nwcy5cvl7s0IiIikpmR3AV8ruTkZFy4cAFjx46V2gwMDNCgQQOcOnUqy9e8fv0ar1+/lp7Hx8cDABISEnRb7Hu8Snwuy37llpCglrsEWfD71i/8vvULv+/c3u+b39tCiA+u98WHnSdPniAtLQ1OTk4a7U5OTrh+/XqWr5k6dSqCgoIytbu5uemkRspa5m+AlIzft37h961f5P6+nz9/Dmtr6/cu/+LDTk6MHTsWw4cPl56np6cjNjYW+fPnh0qlkrGy3JWQkAA3Nzfcu3cPVlZWcpdDOsbvW7/w+9Yv+vp9CyHw/PlzuLq6fnC9Lz7s2Nvbw9DQEI8ePdJof/ToEZydnbN8jYmJCUxMTDTabGxsdFVinmdlZaVX/zj0Hb9v/cLvW7/o4/f9oR6dDF/8AGW1Wo0KFSrg4MGDUlt6ejoOHjyIatWqyVgZERER5QVffM8OAAwfPhzdu3dHxYoVUblyZcyZMwcvXrxAQECA3KURERGRzBQRdjp06IDHjx9j/PjxiI6ORtmyZREcHJxp0DJpMjExwYQJEzKd0iNl4vetX/h96xd+3x+mEh+br0VERET0Bfvix+wQERERfQjDDhERESkaww4REREpGsMOERERKRrDDhERESmaIqaeU/a8fv0aZ86cwd27d5GUlAQHBweUK1cOnp6ecpdGOnL79m0ULlxY7jKIiGTFqed64MSJE5g7dy7++usvpKSkwNraGmZmZoiNjcXr169RuHBh9O7dG3379oWlpaXc5ZIWGRgYoHbt2ggMDES7du1gamoqd0mkZZcvX872ur6+vjqshCjvYthROD8/P1y8eBGdO3dGixYtULFiRZiZmUnLb9++jX/++QcbNmzApUuXsHr1anz99dcyVkzaFBoaihUrVmDDhg1ITk5Ghw4dEBgYiMqVK8tdGmmJgYEBVCoVhBAfvZFxWlpaLlVFulKuXLls37D64sWLOq7my8Gwo3BLlixBjx49YGxs/NF1r169iqioKNSvXz8XKqPclJqaip07d2LlypUIDg5G0aJF0aNHD3z77bdwcHCQuzz6DHfv3pV+DgkJwciRIzFq1Cjp3oCnTp3CzJkzMX36dLRq1UqmKklbgoKCpJ9fvXqFhQsXokSJEtL3ffr0afz333/o378/pk6dKleZeQ7DDpEeef36NRYuXIixY8ciOTkZarUa7du3x88//wwXFxe5y6PPVLlyZUycOBFNmzbVaN+9ezfGjRuHCxcuyFQZ6ULPnj3h4uKCyZMna7RPmDAB9+7dw/Lly2WqLO/hbCw9UrhwYTx9+jRTe1xcHAexKtz58+fRv39/uLi4YNasWRg5ciRu3bqF/fv34+HDh2jZsqXcJZIWXLlyJcsJB56enrh69aoMFZEu/fnnn+jWrVum9q5du2LLli0yVJR3MezokTt37mR5zv7169d48OCBDBWRrs2aNQulS5dG9erV8fDhQ6xevRp3797Fjz/+CE9PT9SsWRMrV67kuX2FKF68OKZOnYrk5GSpLTk5GVOnTkXx4sVlrIx0wczMDCdOnMjUfuLECU5GeAennuuBnTt3Sj/v3bsX1tbW0vO0tDQcPHgQHh4eMlRGurZo0SL06NED/v7+7z1N5ejoiN9//z2XKyNdWLx4MVq0aIGCBQtKM68uX74MlUqFv/76S+bqSNuGDh2Kfv364eLFi9KkgzNnzmD58uUYN26czNXlLRyzowcMDN7fgWdsbAwPDw/MnDkTzZs3z8WqiEgXXrx4gXXr1uH69esA3vT2dO7cGRYWFjJXRrqwadMmzJ07F9euXQPw5vseMmQI2rdvL3NleQvDjsJdvnwZJUuWhKGhITw9PXHu3DnY29vLXRblsqSkJERGRmqc3gB43RUi0g8cs6Nw5cqVQ2xsLABApVJl+/oMpAyPHz9Gs2bNYGlpiZIlS6JcuXIaD1KeNWvW4KuvvoKrq6s0LX327NnYsWOHzJURyYdhR+FsbGxw+/ZtAG+ux5Geni5zRZSbhg4divj4eJw5cwZmZmYIDg7GqlWr4O3trTGWi5Rh0aJFGD58OJo0aYJnz55JExJsbW0xZ84ceYsjrUtLS8OMGTNQuXJlODs7w87OTuNB/49hR+Hatm2L2rVrS9NRK1asiMKFC2f5IOU5dOgQZs2ahYoVK8LAwADu7u7o2rUrpk+fzguOKdD8+fPx22+/4fvvv4eR0f/PP6lYsSKuXLkiY2WkC0FBQZg1axY6dOiA+Ph4DB8+HG3atIGBgQEmTpwod3l5CmdjKdzSpUvRpk0bhIeHY/DgwejVqxfvf6VHXrx4AUdHRwBv/rp//PgxihYtitKlS3O6uQJFRERkeXrSxMQEL168kKEi0qV169bht99+Q7NmzTBx4kR06tQJRYoUga+vL06fPo3BgwfLXWKewbCjBxo3bgwAuHDhAoYMGcKwo0eKFSuGsLAweHh4oEyZMliyZAk8PDywePFiXjFZgTw9PREaGgp3d3eN9uDgYF5nR4Gio6NRunRpAEC+fPkQHx8PAGjevDmnnr+DYUePrFixQu4SKJcNGTIEUVFRAN5cQr5x48ZYt24d1Go1Vq5cKW9xpHXDhw/HgAED8OrVKwghcPbsWWzYsAFTp07FsmXL5C6PtKxgwYKIiopCoUKFUKRIEezbtw/ly5fHuXPnYGJiInd5eQqnnuuBmJgY6VQG8OZO2LNnz0Z4eDhcXFwwcOBA1KlTR74CKdckJSXh+vXrKFSoEC9BoFDr1q3DxIkTcevWLQCAq6srgoKCEBgYKHNlpG1jxoyBlZUV/ve//+GPP/5A165d4eHhgcjISAwbNgzTpk2Tu8Q8g2FHDxgaGiIqKgqOjo44efIk6tSpg+rVq6Ny5coIDQ3F4cOHcfDgQdSqVUvuUknLbt++zcHneiopKQmJiYkaf+iQsp06dQqnTp2Ct7c3WrRoIXc5eQrDjh4wMDBAdHQ0HB0d0bBhQ7i5uWncHmDo0KG4cuUKDh48KGOVpAsGBgYoWLAgateujTp16qB27drw8vKSuyzSkXr16mHr1q2wsbHRaE9ISECrVq1w6NAheQojkhnDjh54O+y4urpi69atqFq1qrT8v//+Q506dfD48WMZqyRdePDgAY4cOYKjR4/i6NGjuHnzJlxdXVG7dm3UrVsXPXv2lLtE0qK3/62/LSYmBgUKFEBKSopMlZGu3Lp1C3PmzJFuF1GyZEkMGTKEPbrv4ABlPfH8+XOYmprC1NQ008A1U1NTJCUlyVQZ6VKBAgXQpUsXdOnSBQBw8+ZNTJkyBevWrcPGjRsZdhTi8uXL0s9Xr15FdHS09DwtLQ3BwcEoUKCAHKWRDu3duxd+fn4oW7YsatSoAeDNHc+XLFmCv/76C19//bXMFeYdDDt6omjRogAAIQTOnz+vcS2O//77D66urnKVRjqUlJSE48eP48iRIzhy5AhCQkLg4+PDQekKU7ZsWel2MPXq1cu03MzMDPPnz5ehMtKlMWPGZDkQecyYMRg9ejTDzlt4GksPHD16VOO5i4uLFH4AYO7cuUhOTsaoUaNyuzTSMbVaDVtbW3Tp0gV16tRBzZo1YWtrK3dZpGV3796FEAKFCxfG2bNn4eDgIC1Tq9VwdHSEoaGhjBWSLpiamuLKlSvw9vbWaL9x4wZ8fX3x6tUrmSrLe9izowdq1679weVDhgzJpUootzVt2hTHjx/Hxo0bER0djejoaNSpU0cj7NKXL+Migrz3nX5xcHBAaGhoprATGhrKWXjvYNjRY0FBQRgwYACvt6Jg27dvB/BmTMfRo0exb98+jBs3DkZGRqhTpw7WrVsnb4GkE1evXkVkZCSSk5M12v38/GSqiHShV69e6N27N27fvo3q1asDeDNm5+eff8bw4cNlri5v4WksPZCQkJCpTQgBBwcHHD9+HD4+PgAAKyur3C6NcokQAiEhITh8+DAOHz6MvXv3QgiB1NRUuUsjLbp9+zZat26NK1euQKVSIeO/d5VKBQDSXdBJGYQQmDNnDmbOnImHDx8CeHMRyVGjRmHw4MHS904MO3rhfefqhRDSf4gqlYr/ESrQrFmzcOTIERw/fhzPnz9HmTJlUKtWLY7fUagWLVrA0NAQy5Ytg6enJ86ePYunT59ixIgRmDFjBmrWrCl3iaQlqampWL9+PRo1agQnJyc8f/4cAHjvw/dg2NEDBQsWRNmyZTFixAgYGBgAeBN0GjRoIP2nCHx8bA99eSpVqiRdULBmzZqwtraWuyTSIXt7exw6dAi+vr6wtrbG2bNnUaxYMRw6dAgjRoxASEiI3CWSFpmbm+PatWuZbvxKmXHMjh64fPkyAgMDMXnyZKxZs0a63oZKpULlypVRokQJmSskXTl37pzcJVAuSktLk/6yt7e3x8OHD1GsWDG4u7sjLCxM5upI2ypXroyQkBCGnWxg2NEDdnZ22LZtGxYtWoTKlStjxowZ6NSpk9xlUS5KSkrKcsCqr6+vTBWRLpQqVQqXLl2Cp6cnqlSpgunTp0OtVmPp0qW8oq4C9e/fHyNGjMD9+/dRoUIFWFhYaCznv+//x9NYeubq1avo3LkzSpQogT///BOXLl1iz46CPX78GP7+/ggODs5yOcdpKcvevXvx4sULtGnTBuHh4WjevDlu3LiB/Pnz448//sjygoP05coYlvA2jsPMGnt29EyJEiVw9uxZjBkzBqVKlYKZmZncJZEODR06FPHx8Thz5gzq1KmDbdu24dGjR/jxxx8xc+ZMucsjLWvUqJH0s5eXF65fv47Y2FjY2tpyZo4CRUREyF3CF4M9O0QK5uLigh07dqBy5cqwsrLC+fPnUbRoUezcuRPTp0/H8ePH5S6RiEjn2LOjp0qXLo3du3fDzc1N7lJIh168eCFdSdXW1haPHz9G0aJFUbp0aVy8eFHm6kgb2rRpk+11t27dqsNKSE5WVlYIDQ3l2Kz3YNjRU3fu3EFKSorcZZCOFStWDGFhYfDw8ECZMmWwZMkSeHh4YPHixXBxcZG7PNICXk6AAIAnaT6MYYdIwYYMGYKoqCgAwIQJE9C4cWOsW7cOarUaK1eulLc40ooVK1bIXQJRnsewo6dq1qzJwcl6oGvXrtLPFSpUwN27d3H9+nUUKlSI90QjUpCuXbvylj8fwAHKeuTYsWOoXr06jIw0M25qaipOnjyJWrVqyVQZ6cqkSZMwcuRImJuba7S/fPkSv/zyC8aPHy9TZaQLnp6eH5x1dfv27VyshnLTq1evYGpqKncZeRbDjh4xNDREVFSUNGA1w9OnT+Ho6MhrMigQv3P9MnfuXI3nKSkpCAkJQXBwMEaNGoUxY8bIVBnpQnp6OqZMmYLFixfj0aNHuHHjBgoXLoxx48bBw8MDgYGBcpeYZ/A0lh7JuNDUu54+fZrpypukDO/7zi9dugQ7OzsZKiJdGjJkSJbtv/76K86fP5/L1ZCu/fjjj1i1ahWmT5+OXr16Se2lSpXCnDlzGHbewp4dPZAxNXXHjh1o3LgxTExMpGVpaWm4fPkyihUr9t6r7NKXJ+MicvHx8bCystIIPGlpaUhMTETfvn3x66+/ylgl5Zbbt2+jbNmySEhIkLsU0iIvLy8sWbIE9evXh6WlJS5duoTChQvj+vXrqFatGp49eyZ3iXkGe3b0QMbUVCEELC0tNQYmq9VqVK1aVeOvAvryzZkzB0II9OjRA0FBQRrTk9VqNTw8PFCtWjUZK6TctHnzZvbkKdCDBw/g5eWVqT09PZ2XFnkHw47CDR8+HAsWLICFhQXu3LmDZcuWIV++fHKXRTrWvXt3AG8GrFavXh3GxsYyV0S5oVy5chq9eEIIREdH4/Hjx1i4cKGMlZEulChRAv/880+mu55v3rwZ5cqVk6mqvIlhR+Hmz5+P0aNHw8LCAseOHUNSUhLDjh7x9PSUrrOTlUKFCuViNaRrrVq10nhuYGAABwcH1KlTBz4+PvIURTozfvx4dO/eHQ8ePEB6ejq2bt2KsLAwrF69Grt27ZK7vDyFY3YUztvbG+3bt0fDhg1Rt25dbNu2Dba2tlmuy6nnymNgYPDBqcicjUX0Zfvnn38wadIkXLp0CYmJiShfvjzGjx+Phg0byl1ansKwo3Dbt29H3759ERMTA5VK9d5LiqtUKv7iU6BLly5pPM+Yijxr1ixMmTLlk+6rRHnf7t27YWhoqHH3cwDYu3cv0tPT0aRJE5kqI5IXw46eSExMhJWVFcLCwjJdcyUD77GjP/7++2/88ssvOHLkiNylkBb5+vpi2rRpaNq0qUZ7cHAwRo8enSn8kjIkJycjJiYG6enpGu08Tf3/OGZHT+TLlw+HDx+Gp6dnpisok/4pVqwYzp07J3cZpGU3b95EiRIlMrX7+PggPDxchopIl27evIkePXrg5MmTGu0Z19dib/3/4289PVKsWDH8/fffiI6OBgA4OzujSpUqcHZ2lrky0pV3r6sihEBUVBQmTpwIb29vmaoiXbG2tsbt27fh4eGh0R4eHs4LhyqQv78/jIyMsGvXLri4uHxwfJ6+Y9jRAy9evECfPn2wceNGqFQq6XobsbGxEEKgU6dOWLJkSab7J9GXz8bGJtN/gEIIuLm5YcOGDTJVRbrSsmVLDB06FNu2bUORIkUAvAk6I0aMgJ+fn8zVkbaFhobiwoULnGmXDQw7emDIkCE4e/Ys/v77bzRo0ACGhoYA3szEOXjwIAYNGoQhQ4bgt99+k7lS0rbDhw9rPM+Yiuzl5cXTmQo0ffp0NG7cGD4+PihYsCAA4P79+6hZsyZmzJghc3WkbSVKlMCTJ0/kLuOLwAHKesDW1hZ///03qlevnuXyEydOoHnz5ry0uAJNnToVTk5O6NGjh0b78uXL8fjxY4wePVqmykhXhBDYv38/Ll26BDMzM/j6+vKyEgry9qnp8+fP44cffsBPP/2E0qVLZ7p4qJWVVW6Xl2cx7OgBa2trHDx4EBUrVsxy+blz59CgQQPEx8fncmWkax4eHli/fn2moHvmzBl07NgRERERMlVGRDnx7rWzsrrZLwcoZ8Z+bD3QvHlz9O7dG7///numS4iHhISgX79+aNGihUzVkS5FR0fDxcUlU7uDg8MHr6xMX4558+ahd+/eMDU1xbx58z647uDBg3OpKtKVd09NU/awZ0cPPHv2DJ07d8bevXtha2srXWcnJiYGcXFxaNSoEdavXw8bGxt5CyWt8/b2xoQJE9C1a1eN9jVr1mDChAm4ffu2TJWRtnh6euL8+fPInz8/PD0937ueSqXi960wkZGRcHNzy7Jn5969e7zOzlvYs6MHbG1tsWfPHly7dg2nT5/WmHperVo1juRXsF69emHo0KFISUlBvXr1AAAHDx7Ed999hxEjRshcHWnD26cieVpSv2Tc++7dC8XGxsbC09OTp7HewrCjR4oXL47ixYvLXQblolGjRuHp06fo378/kpOTAQCmpqYYPXo0xo4dK3N1pG2TJk3CyJEjM11G4uXLl/jll18wfvx4mSojXchqvA7w5or5pqamMlSUd/E0lp54+vQpLl++jDJlysDOzg5PnjzB77//jtevX+Obb75hCFK4xMREXLt2DWZmZvD29oaJiYncJZEOGBoaZvmX/tOnT+Ho6Mi/9BVi+PDhAIC5c+eiV69eGuE2LS0NZ86cgaGhIU6cOCFXiXkOe3b0wNmzZ9GwYUMkJCTAxsYG+/fvxzfffAMjIyOkp6dj2rRpOH78OMqXLy93qaQj+fLlQ6VKleQug3TsfX/pX7p0SbqYKH35QkJCALz5vq9cuQK1Wi0tU6vVKFOmDEaOHClXeXkSe3b0wNdffw0PDw/MmjULS5Yswdy5c9G4cWPpIoI9evTAs2fPsG3bNpkrJaKcsLW1hUqlQnx8PKysrDQCT1paGhITE9G3b1/8+uuvMlZJ2hYQEIC5c+fyejrZwLCjB+zs7HDixAkUL14cKSkpMDU1xalTp1C5cmUAwMWLF+Hn54f79+/LXCkR5cSqVasghECPHj0wZ84cWFtbS8vUajU8PDxQrVo1GSskXduwYQP8/Px4D7T34GksPZCcnAwzMzMAgLGxMczNzWFvby8tt7e3x9OnT+Uqj4g+U/fu3ZGamgqVSoV69erBzc1N7pIol/Xp0wdVqlRB4cKF5S4lTzKQuwDSPTc3N43ra2zcuFHjQnNRUVEa4YeIvjxGRkbo168f0tPT5S6FZMCTNB/GsKMHOnbsiJiYGOl5s2bNpJ4eANi5c6d0SouIvlyVK1eWBq8S0f/jmB1CUlISDA0NOR2Z6Au3adMmjB07FsOGDUOFChUyjd/w9fWVqTLStpSUFJiZmSE0NBSlSpXC8ePHUalSJf4//h4MO0RECmFg8P7Oet4YUnkKFy6Mbdu2oUyZMnKXkucx7CjctGnTMHjw4ExXVM3KmTNn8OTJEzRr1iwXKiMibbt79+4Hl7u7u+dSJZQbfv/9d2zduhVr1qzhdZQ+grOxFO7q1atwd3fHN998gxYtWqBixYpwcHAAAKSmpuLq1as4fvw41q5di4cPH2L16tUyV0xEOZURZq5evYrIyEjpFiHAm54dhh1lWbBgAcLDw+Hq6gp3d/dMpy0vXrwoU2V5D8OOwq1evRqXLl3CggUL0LlzZyQkJEjjc5KSkgAA5cqVQ8+ePeHv78/7qRB9wW7fvo3WrVvjypUrUKlU0gydjIsM8jSWsrRq1UruEr4YPI2lR9LT03H58mXcvXsXL1++hL29PcqWLctp50QK0aJFCxgaGmLZsmXw9PTEmTNnEBsbixEjRmDGjBmoWbOm3CUSyYJhh4hIIezt7XHo0CH4+vrC2toaZ8+eRbFixXDo0CGMGDGC09IVKC4uDps3b8atW7cwatQo2NnZ4eLFi3ByckKBAgXkLi/P4GksIiKFSEtLg6WlJYA3wefhw4coVqwY3N3dERYWJnN1pG2XL19GgwYNYG1tjTt37qBXr16ws7PD1q1bERkZyTGYb+FFBYmIFKJUqVK4dOkSAKBKlSqYPn06Tpw4gUmTJvE2Ago0fPhw+Pv74+bNmxrjLZs2bYpjx47JWFnew54dIiKF+OGHH/DixQsAwKRJk9C8eXPUrFkT+fPnxx9//CFzdaRt586dw5IlSzK1FyhQANHR0TJUlHcx7BARKUSjRo2kn728vHD9+nXExsbC1tZWmpFFymFiYoKEhIRM7Tdu3JAuMUJv8DQWEZGC2dnZMegolJ+fHyZNmoSUlBQAby4xEBkZidGjR6Nt27YyV5e3cDaWwrVp0ybb627dulWHlRARkTbFx8ejXbt2OH/+PJ4/fw5XV1dER0ejWrVq2L17d6aLDOoznsZSOGtra7lLICIiHbC2tsb+/ftx/PhxXL58GYmJiShfvjwaNGggd2l5Dnt2iIiIvkD37t2Dm5ub3GV8EThmh4iI6Avk4eGB2rVr47fffsOzZ8/kLidPY8+Ontm8eTM2bdqU6SaBAG8aR0T0JQkJCcH69euxceNGPH78GI0bN0bXrl3RokULmJiYyF1ensKeHT0yb948BAQEwMnJCSEhIahcuTLy58+P27dvo0mTJnKXR0REn6BcuXL45ZdfEBkZiT179sDBwQG9e/eGk5MTevToIXd5eQp7dvSIj48PJkyYgE6dOsHS0hKXLl1C4cKFMX78eMTGxmLBggVyl0hERJ/h4sWLCAwMxOXLl3mX+7ewZ0ePREZGonr16gAAMzMzPH/+HADw7bffYsOGDXKWRkREOXT//n1Mnz4dZcuWReXKlZEvXz78+uuvcpeVp3DquR5xdnZGbGws3N3dUahQIZw+fRplypRBREQE2MFHRPRlWbJkCdavX4/jx4+jePHi6NKlC3bs2AF3d3e5S8tzGHb0SL169bBz506UK1cOAQEBGDZsGDZv3ozz589/0sUHiYhIfj/++CM6deqEefPmoUyZMnKXk6dxzI4eSU9PR3p6OoyM3mTcjRs34uTJk/D29kafPn2gVqtlrpCIiLJLCIH4+Hj8/vvvuHbtGgCgRIkSCAwM5AVl38GwQ0RE9AW6cOECGjVqBFNTU1SuXBnAmzuhv3z5Evv27UP58uVlrjDvYNjRM3FxcTh79ixiYmKQnp6usaxbt24yVUVERJ+qZs2a8PLywm+//Sb12KempqJnz564ffs2jh07JnOFeQfDjh7566+/0KVLFyQmJsLKykrjTsgqlQqxsbEyVkdERJ/CzMwMISEh8PHx0Wi/evUqKlasiKSkJJkqy3s49VyPjBgxAj169EBiYiLi4uLw7Nkz6cGgQ0T0ZbGyskJkZGSm9nv37sHS0lKGivIuhh098uDBAwwePBjm5uZyl0JERJ+pQ4cOCAwMxB9//IF79+7h3r172LhxI3r27IlOnTrJXV6ewqnneqRRo0Y4f/48ChcuLHcpRET0mWbMmAGVSoVu3bohNTUVAGBsbIx+/fph2rRpMleXt3DMjh75/fffMWnSJAQEBKB06dIwNjbWWO7n5ydTZURElFNJSUm4desWAKBIkSLsvc8Cw44eMTB4/1lLlUrF+6gQEZEiMewQERGRonGAMhERESkaw46eOXr0KFq0aAEvLy94eXnBz88P//zzj9xlERER6QzDjh5Zu3YtGjRoAHNzcwwePBiDBw+GmZkZ6tevj/Xr18tdHhERkU5wzI4eKV68OHr37o1hw4ZptM+aNQu//fabdCM5IiIiJWHY0SMmJib477//4OXlpdEeHh6OUqVK4dWrVzJVRkREpDs8jaVH3NzccPDgwUztBw4cgJubmwwVERER6R6voKxHRowYgcGDByM0NBTVq1cHAJw4cQIrV67E3LlzZa6OiIhIN3gaS89s27YNM2fOlMbnFC9eHKNGjULLli1lroyIiEg3GHaIiIhI0Thmh4iIiBSNY3YUzs7ODjdu3IC9vT1sbW2hUqneu25sbGwuVkZERJQ7GHYUbvbs2bC0tJR+/lDYISIiUiKO2SEiIiJF45gdPWJoaIiYmJhM7U+fPoWhoaEMFREREekew44eeV8n3uvXr6FWq3O5GiIiotzBMTt6YN68eQAAlUqFZcuWIV++fNKytLQ0HDt2DD4+PnKVR0REpFMcs6MHPD09AQB3795FwYIFNU5ZqdVqeHh4YNKkSahSpYpcJRIREekMw44eqVu3LrZu3QpbW1u5SyEiIso1DDtERESkaByzo0fS0tKwcuVKHDx4EDExMUhPT9dYfujQIZkqIyIi0h2GHT0yZMgQrFy5Es2aNUOpUqV4gUEiItILPI2lR+zt7bF69Wo0bdpU7lKIiIhyDa+zo0fUajW8vLzkLoOIiChXMezokREjRmDu3LnvvbggERGREvE0lh5p3bo1Dh8+DDs7O5QsWRLGxsYay7du3SpTZURERLrDAcp6xMbGBq1bt5a7DCIiolzFnh0iIiJSNI7Z0TOpqak4cOAAlixZgufPnwMAHj58iMTERJkrIyIi0g327OiRu3fvonHjxoiMjMTr169x48YNFC5cGEOGDMHr16+xePFiuUskIiLSOvbs6JEhQ4agYsWKePbsGczMzKT21q1b4+DBgzJWRkREpDscoKxH/vnnH5w8eRJqtVqj3cPDAw8ePJCpKiIiIt1iz44eSU9PR1paWqb2+/fvw9LSUoaKiIiIdI9hR480bNgQc+bMkZ6rVCokJiZiwoQJvIUEEREpFgco65H79++jUaNGEELg5s2bqFixIm7evAl7e3scO3YMjo6OcpdIRESkdQw7eiY1NRUbN27E5cuXkZiYiPLly6NLly4aA5aJiIiUhGFHj7x69QqmpqZyl0FERJSrOGZHjzg6OqJ79+7Yv38/0tPT5S6HiIgoVzDs6JFVq1YhKSkJLVu2RIECBTB06FCcP39e7rKIiIh0iqex9NDz58+xefNmbNiwAYcOHULhwoXRtWtXjB8/Xu7SiIiItI5hR89dvXoVXbp0weXLl7O8Bg8REdGXjqex9NCrV6+wadMmtGrVCuXLl0dsbCxGjRold1lEREQ6wdtF6JG9e/di/fr12L59O4yMjNCuXTvs27cPtWrVkrs0IiIineFpLD1ibm6O5s2bo0uXLmjatCmMjY3lLomIiEjnGHb0yPPnz3kPLCIi0jsMO3omPT0d4eHhiImJyXStHZ7OIiIiJeKYHT1y+vRpdO7cGXfv3sW7GVelUnE2FhERKRJ7dvRI2bJlUbRoUQQFBcHFxQUqlUpjubW1tUyVERER6Q7Djh6xsLDApUuX4OXlJXcpREREuYbX2dEjVapUQXh4uNxlEBER5SqO2dEjgwYNwogRIxAdHY3SpUtnmnru6+srU2VERES6w9NYesTAIHNHnkqlghCCA5SJiEix2LOjRyIiIuQugYiIKNexZ4eIiIgUjT07eubWrVuYM2cOrl27BgAoUaIEhgwZgiJFishcGRERkW5wNpYe2bt3L0qUKIGzZ8/C19cXvr6+OHPmDEqWLIn9+/fLXR4REZFO8DSWHilXrhwaNWqEadOmabSPGTMG+/btw8WLF2WqjIiISHcYdvSIqakprly5Am9vb432GzduwNfXF69evZKpMiIiIt3haSw94uDggNDQ0EztoaGhcHR0zP2CiIiIcgEHKOuRXr16oXfv3rh9+zaqV68OADhx4gR+/vlnDB8+XObqiIiIdIOnsfSIEAJz5szBzJkz8fDhQwCAq6srRo0ahcGDB2e6MSgREZESMOzoqefPnwMALC0tZa6EiIhItxh29EhERARSU1MzDVC+efMmjI2N4eHhIU9hREREOsQBynrE398fJ0+ezNR+5swZ+Pv7535BREREuYA9O3rEysoKFy9ehJeXl0Z7eHg4KlasiLi4OHkKIyIi0iH27OgRlUoljdV5W3x8PO94TkREisWeHT3SokULmJmZYcOGDTA0NAQApKWloUOHDnjx4gX27Nkjc4VERETax7CjR65evYpatWrBxsYGNWvWBAD8888/SEhIwKFDh1CqVCmZKyQiItI+hh098/DhQyxYsACXLl2CmZkZfH19MXDgQNjZ2cldGhERkU4w7OiB5cuXw8/PD/b29nKXQkRElOs4QFkPrF27FgULFkT16tXx888/4/r163KXRERElGvYs6Mnnj17hr///hs7d+5EcHAwnJyc4Ofnh5YtW+Krr76CgQFzLxERKRPDjh5KTk7GoUOHsHPnTvz11194+fIlmjZtCj8/PzRp0gQWFhZyl0hERKQ1DDuE8+fPY+fOndixYwfatWuHcePGyV0SERGR1jDskIaUlBQYGxvLXQYREZHWMOwo3PDhw7O97qxZs3RYCRERkTyM5C6AdCskJETj+cWLF5GamopixYoBAG7cuAFDQ0NUqFBBjvKIiIh0jmFH4Q4fPiz9PGvWLFhaWmLVqlWwtbUF8GaWVkBAgHRFZSIiIqXhaSw9UqBAAezbtw8lS5bUaP/333/RsGFDPHz4UKbKiIiIdIcXV9EjCQkJePz4cab2x48fZ3k3dCIiIiVg2NEjrVu3RkBAALZu3Yr79+/j/v372LJlCwIDA9GmTRu5yyMiItIJnsbSI0lJSRg5ciSWL1+OlJQUAICRkRECAwPxyy+/8GKCRESkSAw7eiItLQ0nTpxA6dKloVarcevWLQBAkSJFGHKIiEjRGHb0iKmpKa5duwZPT0+5SyEiIso1HLOjR0qVKoXbt2/LXQYREVGuYs+OHgkODsbYsWMxefJkVKhQIdPpKysrK5kqIyIi0h2GHT1iYPD/HXkqlUr6WQgBlUqFtLQ0OcoiIiLSKV5BWY+8fTVlIiIifcGeHSIiIlI09uzooaSkJERGRiI5OVmj3dfXV6aKiIiIdIdhR488fvwYAQEB2LNnT5bLOWaHiIiUiFPP9cjQoUMRFxeHM2fOwMzMDMHBwVi1ahW8vb2xc+dOucsjIiLSCfbs6JFDhw5hx44dqFixIgwMDODu7o6vv/4aVlZWmDp1Kpo1ayZ3iURERFrHnh098uLFCzg6OgIAbG1tpTugly5dGhcvXpSzNCIiIp1h2NEjxYoVQ1hYGACgTJkyWLJkCR48eIDFixfDxcVF5uqIiIh0g1PP9cjatWuRmpoKf39/XLhwAY0bN0ZsbCzUajVWrlyJDh06yF0iERGR1jHs6LGkpCRcv34dhQoVgr29vdzlEBER6QTDDhERESkaZ2Mp3PDhw7O97qxZs3RYCRERkTwYdhQuJCRE4/nFixeRmpqKYsWKAQBu3LgBQ0NDVKhQQY7yiIiIdI5hR+HevvnnrFmzYGlpiVWrVsHW1hYA8OzZMwQEBKBmzZpylUhERKRTHLOjRwoUKIB9+/ahZMmSGu3//vsvGjZsiIcPH8pUGRERke7wOjt6JCEhQbqQ4NseP36M58+fy1ARERGR7jHs6JHWrVsjICAAW7duxf3793H//n1s2bIFgYGBaNOmjdzlERER6QRPY+mRpKQkjBw5EsuXL0dKSgoAwMjICIGBgfjll19gYWEhc4VERETax7Cjh168eIFbt24BAIoUKcKQQ0REisawQ0RERIrGqed65MWLF5g2bRoOHjyImJgYpKenayy/ffu2TJURERHpDsOOHunZsyeOHj2Kb7/9Fi4uLlCpVHKXREREpHM8jaVHbGxs8Pfff6NGjRpyl0JERJRrOPVcj9ja2sLOzk7uMoiIiHIVw44emTx5MsaPH4+kpCS5SyEiIso1PI2lR8qVK4dbt25BCAEPDw8YGxtrLL948aJMlREREekOByjrkVatWsldAhERUa5jzw4REREpGsfsEBERkaLxNJYeSUtLw+zZs7Fp0yZERkYiOTlZY3lsbKxMlREREekOe3b0SFBQEGbNmoUOHTogPj4ew4cPR5s2bWBgYICJEyfKXR4REZFOcMyOHilSpAjmzZuHZs2awdLSEqGhoVLb6dOnsX79erlLJCIi0jr27OiR6OholC5dGgCQL18+xMfHAwCaN2+Ov//+W87SiIiIdIZhR48ULFgQUVFRAN708uzbtw8AcO7cOZiYmMhZGhERkc4w7OiR1q1b4+DBgwCAQYMGYdy4cfD29ka3bt3Qo0cPmasjIiLSDY7Z0WOnT5/GyZMn4e3tjRYtWshdDhERkU4w7OiRY8eOoXr16jAy0rziQGpqKk6ePIlatWrJVBkREZHuMOzoEUNDQ0RFRcHR0VGj/enTp3B0dERaWppMlREREekOx+zoESEEVCpVpvanT5/CwsJChoqIiIh0j1dQ1gNt2rQBAKhUKvj7+2vMvEpLS8Ply5dRvXp1ucojIiLSKYYdPWBtbQ3gTc+OpaUlzMzMpGVqtRpVq1ZFr1695CqPiIhIpzhmR48EBQVh5MiRPGVFRER6hWFHj7x8+RJCCJibmwMA7t69i23btqFEiRJo2LChzNURERHpBgco65GWLVti9erVAIC4uDhUrlwZM2fORMuWLbFo0SKZqyMiItINhh09cvHiRdSsWRMAsHnzZjg7O+Pu3btYvXo15s2bJ3N1REREusGwo0eSkpJgaWkJANi3bx/atGkDAwMDVK1aFXfv3pW5OiIiIt1g2NEjXl5e2L59O+7du4e9e/dK43RiYmJgZWUlc3VERES6wbCjR8aPH4+RI0fCw8MDVapUQbVq1QC86eUpV66czNURERHpBmdj6Zno6GhERUWhTJkyMDB4k3XPnj0LKysr+Pj4yFwdERGR9jHsEBERkaLxNBYREREpGsMOERERKRrDDhERESkaww4REREpGsMOEWVLnTp1MHToULnLyMTDwwNz5syRuwwiysOM5C6AiOhznDt3DhYWFjrfz507d+Dp6YmQkBCULVtW5/sjIu1h2CGiL5qDg4PcJRBRHsfTWESUbampqRg4cCCsra1hb2+PcePGIeNSXSqVCtu3b9dY38bGBitXrgQAJCcnY+DAgXBxcYGpqSnc3d0xderUj+5TCIGJEyeiUKFCMDExgaurKwYPHiwtf/s01sqVK6FSqTI9Jk6cKK2/bNkyFC9eHKampvDx8cHChQuz9d49PT0BAOXKlYNKpUKdOnVw7NgxGBsbIzo6WmPdoUOHSjfdXblyJWxsbLB9+3Z4e3vD1NQUjRo1wr179zRes2PHDpQvXx6mpqYoXLgwgoKCkJqamq3aiOjDGHaIKNtWrVoFIyMjnD17FnPnzsWsWbOwbNmybL123rx52LlzJzZt2oSwsDCsW7cOHh4eH33dli1bMHv2bCxZsgQ3b97E9u3bUbp06SzX7dChA6KioqTHhg0bYGRkhBo1agAA1q1bh/Hjx2PKlCm4du0afvrpJ4wbNw6rVq36aB1nz54FABw4cABRUVHYunUratWqhcKFC2PNmjXSeikpKVi3bh169OghtSUlJWHKlClYvXo1Tpw4gbi4OHTs2FFa/s8//6Bbt24YMmQIrl69iiVLlmDlypWYMmXKR+siomwQRETZULt2bVG8eHGRnp4utY0ePVoUL15cCCEEALFt2zaN11hbW4sVK1YIIYQYNGiQqFevnsbrs2PmzJmiaNGiIjk5Ocvl7u7uYvbs2Znaw8PDhZ2dnZg+fbrUVqRIEbF+/XqN9SZPniyqVav20ToiIiIEABESEqLR/vPPP0ufgRBCbNmyReTLl08kJiYKIYRYsWKFACBOnz4trXPt2jUBQJw5c0YIIUT9+vXFTz/9pLHdNWvWCBcXl4/WRUQfx54dIsq2qlWrQqVSSc+rVauGmzdvIi0t7aOv9ff3R2hoKIoVK4bBgwdj37592drnN998g5cvX6Jw4cLo1asXtm3b9tHTO/Hx8WjevDmaNWuGUaNGAQBevHiBW7duITAwEPny5ZMeP/74I27dupWtWt73vsLDw3H69GkAb05btW/fXmPQtJGRESpVqiQ99/HxgY2NDa5duwYAuHTpEiZNmqRRV69evRAVFYWkpKQc10ZEb3CAMhFphUqlksbvZEhJSZF+Ll++PCIiIrBnzx4cOHAA7du3R4MGDbB58+YPbtfNzQ1hYWE4cOAA9u/fj/79++OXX37B0aNHYWxsnGn9tLQ0dOjQAVZWVli6dKnUnpiYCAD47bffUKVKFY3XGBoafvL7zeDo6IgWLVpgxYoV8PT0xJ49e3DkyJFP2kZiYiKCgoLQpk2bTMtMTU1zXBsRvcGwQ0TZdubMGY3np0+fhre3NwwNDeHg4ICoqChp2c2bNzP1SlhZWaFDhw7o0KED2rVrh8aNGyM2NhZ2dnYf3K+ZmRlatGiBFi1aYMCAAfDx8cGVK1dQvnz5TOsOGzYMV65cwfnz5zWCgpOTE1xdXXH79m106dLlk9+7Wq0GgCx7sXr27IlOnTqhYMGCKFKkiDRGKENqairOnz+PypUrAwDCwsIQFxeH4sWLA3gTBMPCwuDl5fXJdRHRxzHsEFG2RUZGYvjw4ejTpw8uXryI+fPnY+bMmQCAevXqYcGCBahWrRrS0tIwevRojZ6XWbNmwcXFBeXKlYOBgQH+/PNPODs7w8bG5oP7XLlyJdLS0lClShWYm5tj7dq1MDMzg7u7e6Z1V6xYgYULF2Lbtm1QqVTSLKmMU0NBQUEYPHgwrK2t0bhxY7x+/Rrnz5/Hs2fPMHz48A/W4ejoCDMzMwQHB6NgwYIwNTWFtbU1AKBRo0awsrLCjz/+iEmTJmV6rbGxMQYNGoR58+bByMgIAwcORNWqVaXwM378eDRv3hyFChVCu3btYGBggEuXLuHff//Fjz/++MG6iCgb5B40RERfhtq1a4v+/fuLvn37CisrK2Frayv+97//SQOOHzx4IBo2bCgsLCyEt7e32L17t8YA5aVLl4qyZcsKCwsLYWVlJerXry8uXrz40f1u27ZNVKlSRVhZWQkLCwtRtWpVceDAAWn52wOUu3fvLgBkekyYMEFaf926daJs2bJCrVYLW1tbUatWLbF169ZsfQa//fabcHNzEwYGBqJ27doay8aNGycMDQ3Fw4cPNdpXrFghrK2txZYtW0ThwoWFiYmJaNCggbh7967GesHBwaJ69erCzMxMWFlZicqVK4ulS5dmqy4i+jCVEO+cZCciok8WGBiIx48fY+fOnRrtK1euxNChQxEXFydPYUTE01hERJ8jPj4eV65cwfr16zMFHSLKGzj1nIhktW7dOo0p128/SpYsmWt1/PTTT++to0mTJu99XcuWLdGwYUP07dsXX3/9da7VS0TZx9NYRCSr58+f49GjR1kuMzY2znIgsi7ExsYiNjY2y2VmZmYoUKBArtRBRNrHsENERESKxtNYREREpGgMO0RERKRoDDtERESkaAw7REREpGgMO0RERKRoDDtERESkaAw7REREpGj/B3eBDqXqoKFkAAAAAElFTkSuQmCC", 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bus_size_typetotal_bus_count
0articulated41.0
1cutaway152.0
2not specified881.0
3over-the-road14.0
4standard/conventional (30ft-45ft)264.0
\n", + "
" + ], + "text/plain": [ + " bus_size_type total_bus_count\n", + "0 articulated 41.0\n", + "1 cutaway 152.0\n", + "2 not specified 881.0\n", + "3 over-the-road 14.0\n", + "4 standard/conventional (30ft-45ft) 264.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#bus size bar chart\n", + "display(\n", + "make_chart(\"total_bus_count\", \"\"\"Amount of buses procured by bus size.\n", + "excluding 'not specified' responses.\"\"\", x_col=\"bus_size_type\",data=agg_bus_size[agg_bus_size[\"bus_size_type\"]!=\"not specified\"]),\n", + "agg_bus_size[[\"bus_size_type\",\"total_bus_count\"]]\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "63f90c48-e28d-4d88-8b90-891a3e3e3681", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "\n", + "**Conclusion**\n", + "\n", + "Based on these findings, The average cost of a ZEB, throughout the US, is ~$1,000,000, roughly twice the price of a conventional, non-ZEB.\n", + "The variance in cost depends mainly on the options the Trasnit\n", + "Agencies chooses. Highly optioned/customized buses contribute to high cost.\n", + "Unfortunately, analyzing the cost of configuable options is outside the scope of data provided. \n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "conclusion = f\"\"\"\n", + "**Conclusion**\n", + "\n", + "Based on these findings, The average cost of a ZEB, throughout the US, is ~$1,000,000, roughly twice the price of a conventional, non-ZEB.\n", + "The variance in cost depends mainly on the options the Trasnit\n", + "Agencies chooses. Highly optioned/customized buses contribute to high cost.\n", + "Unfortunately, analyzing the cost of configuable options is outside the scope of data provided. \n", + "\"\"\"\n", + "display(\n", + " Markdown(conclusion)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8025c84b-428f-4c40-b4d3-c969af58ce63", + "metadata": {}, + "source": [ + "-------\n", + "# Start of old stuff" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "c51fe7dd-22e2-4686-b1a5-57b2f5ad8602", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "\n", + "## Summary\n", + "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", + "\n", + "Data was compiled from three data sources:\n", + "\n", + "1. 130 projects from FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", + "2. 124 projects TIRCP project data (state-funded, California only)\n", + "3. 35 projects DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc..\n", + "\n", + "The compiled dataset includes **289** total transit related projects. However, the initial dataset included projects that encompassed bus procurement and other components such as charging installation and facility construction, as well as non-bus related projects (ferries, trains). The dataset was filtered to exclude projects that were not bus related, indicated 0 buses procured, and projects that contained construction/installation work. **87** projects remained that specified the number of buses to procure and explicitly described procuring buses (bus only projects). \n", + "\n", + "Number of bus only contracts from each dataset \n", + "- FTA: **43**\n", + "- TIRCP: **9**\n", + "- DGS: **35**\n", + "\n", + "\n", + "The remaining bus only projects were categorized into different propulsion types and bus sizes, a “cost per bus” value was calculated, and outliers removed.\n", + "\n", + "A overall summary is provided below:\n", + "- Total projects: **298**\n", + "- Number of projects with mix bus procurement and other components, also non-bus projects: **204** \n", + "- Number of bus only projects: **87**\n", + "- Total dollars awarded to bus only projects: **`$831,843,715.00`**\n", + "- Total number of buses: **1353.0**\n", + "- Most common propulsion type procured for bus only projects: **BEB** at **30** projects\n", + "- Number of ZEB buses* procured: **452.0**\n", + "- Number of non-ZEB buses** procured: **575.0**\n", + "- Overall average cost per bus (ZEB & non-ZEB) is `$792,635.34` (std `$396,712.61`)\n", + "- ZEB average cost per bus is `$1,056,659.30` (std `$253,737.82`)\n", + "- Non-ZEB average cost per bus is `$528,106.49` (std `$315,932.20`) \n", + "\n", + "`*`ZEB buses include: zero-emission (not specified), electric (not specified), battery electric, fuel cell electric\n", + "\n", + "`**`Non-ZEB buses include: CNG, ethanol, low emission (hybrid, propane), diesel, gas.\n", + "\n", + "\n", + "Below are key charts that visualize more findings:\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Markdown(summary)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "4e553e15-dc1d-47d3-9818-7dec893c5294", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/markdown": [ + "\n", + "## All buses (ZEB and non-ZEB) cost/bus distribution curve.\n", + "This chart shows the cost per bus distribution of all bus only projects.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "KeyError", + "evalue": "'cost_per_bus'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/core/indexes/base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3801\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3802\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/_libs/index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/_libs/index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'cost_per_bus'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[25], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# all bus distribution\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display(Markdown(all_bus_desc))\n\u001b[0;32m----> 4\u001b[0m \u001b[43mdist_curve\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mno_outliers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mmean\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcpb_mean\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mstd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcpb_std\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mtitle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mall buses, cost per bus distribution\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43mxlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcost per bus, $ million(s)\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[7], line 11\u001b[0m, in \u001b[0;36mdist_curve\u001b[0;34m(df, mean, std, title, xlabel)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdist_curve\u001b[39m(\n\u001b[1;32m 2\u001b[0m df: pd\u001b[38;5;241m.\u001b[39mDataFrame,\n\u001b[1;32m 3\u001b[0m mean: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 6\u001b[0m xlabel: \u001b[38;5;28mstr\u001b[39m,\n\u001b[1;32m 7\u001b[0m ):\n\u001b[1;32m 8\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;124;03m function to make distribution curve. uses the \"cpb\" column of the df.\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m sns\u001b[38;5;241m.\u001b[39mhistplot(\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcost_per_bus\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m, kde\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskyblue\u001b[39m\u001b[38;5;124m\"\u001b[39m, bins\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m)\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# mean line\u001b[39;00m\n\u001b[1;32m 13\u001b[0m plt\u001b[38;5;241m.\u001b[39maxvline(\n\u001b[1;32m 14\u001b[0m mean, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mred\u001b[39m\u001b[38;5;124m\"\u001b[39m, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdashed\u001b[39m\u001b[38;5;124m\"\u001b[39m, linewidth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMean: $\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmean\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m,.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 15\u001b[0m )\n", + "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/core/frame.py:3807\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3805\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 3806\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3807\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3808\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 3809\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n", + "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/core/indexes/base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3802\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m 3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3804\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3805\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3806\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3807\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3808\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3809\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", + "\u001b[0;31mKeyError\u001b[0m: 'cost_per_bus'" + ] + } + ], + "source": [ + "# all bus distribution\n", + "display(Markdown(all_bus_desc))\n", + "\n", + "dist_curve(\n", + " df=no_outliers,\n", + " mean=cpb_mean,\n", + " std=cpb_std,\n", + " title=\"all buses, cost per bus distribution\",\n", + " xlabel=\"cost per bus, $ million(s)\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dda584ca-76fa-4e88-9b1c-f70cc438dce6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# ZEB dist curve\n", + "display(Markdown(zeb_desc))\n", + "\n", + "dist_curve(\n", + " df=zeb_no_outliers,\n", + " mean=zeb_only_mean,\n", + " std=zeb_only_std,\n", + " title=\"ZEB only cost/bus Distribution\",\n", + " xlabel=\"cost per bus, $ million(s)\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "679d8261-85d6-4d68-9905-e4b048ebc61a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# non_zeb distribution\n", + "display(Markdown(non_zeb_desc))\n", + "\n", + "dist_curve(\n", + " non_zeb_no_outliers,\n", + " non_zeb_only_mean,\n", + " non_zeb_only_std,\n", + " title=\"non-ZEB only cost/bus Distribution\",\n", + " xlabel='\"cost per bus, $ million(s)\"',\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "31c592b0-e37e-4da4-8726-36b0a1d3e6f5", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/markdown": [ + "\n", + "## Cost per bus by propulsion type. \n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# COST PER BUS BY PROP TYPE\n", + "display(Markdown(cpb_prop_type_desc))\n", + "make_chart(\"cpb\", \"Cost per bus by propulsion type\", x_col=\"prop_type\", data=prop_agg)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "7462b55c-29ef-4909-a7dd-27e1c84157d0", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/markdown": [ + "\n", + "## Bus count by propulsion type. \n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# bus count BY PROP TYPE\n", + "display(Markdown(bus_count_prop_type_desc))\n", + "make_chart(\n", + " \"total_bus_count\", \n", + " \"Bus count by propulsion type\",\n", + " x_col=\"prop_type\",\n", + " data=prop_agg\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "4f092539-c4c6-4579-aa02-fbee65414ec3", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/markdown": [ + "\n", + "**Conclusion**\n", + "\n", + "Based on these findings, The average cost of a ZEB, throughout the US, is ~$1,000,000, roughly twice the price of a conventional, non-ZEB.\n", + "The variance in cost depends mainly on the options the Trasnit\n", + "Agencies chooses. Highly optioned/customized buses contribute to high cost.\n", + "Unfortunately, analyzing the cost of configuable options is outside the scope of data provided. \n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "display(Markdown(conclusion))" ] diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index 4fe422a29..ed390c240 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "910ee0fa-38ce-44f3-8e18-4cdf740e1fd0", "metadata": {}, "outputs": [], @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "8a1fd0b4-14a6-4cad-bb15-0ce0437ed125", "metadata": {}, "outputs": [], @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "c3552c45-8b28-4bbe-ae82-f2d726a45937", "metadata": {}, "outputs": [], @@ -58,10 +58,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "e1b1f367-1dac-463f-8790-2e5134b7e79b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.9/site-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Data Validation extension is not supported and will be removed\n", + " warn(msg)\n" + ] + } + ], "source": [ "# links to all Raw Data\n", "fta_raw = pd.read_csv(f\"{GCS_PATH}raw_data-analyses_bus_procurement_cost_fta_press_release_data_csv.csv\")\n", @@ -92,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "id": "c6dacaba-c6f7-4cb0-afef-a84f77de25fc", "metadata": {}, "outputs": [], @@ -119,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "5513b941-ecdb-405e-bfd6-952df6b8f8b4", "metadata": { "tags": [] @@ -240,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "8e18cbe6-bde7-4c30-8a8a-aefd8d619821", "metadata": { "tags": [] @@ -302,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "8004cc2d-957f-4e9a-9ca8-2a6f9aba9ffb", "metadata": { "tags": [] @@ -372,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "c0ca9254-2983-4cab-845c-f9bfb0229417", "metadata": { "tags": [] @@ -390,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "fa387f41-c9b3-455a-9829-cfabb3f98c9b", "metadata": { "tags": [] @@ -422,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "id": "0b2be581-f4e9-4f7e-bde5-01f2de183479", "metadata": {}, "outputs": [], @@ -478,7 +487,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "fb1ae513-a8bf-4eb1-9e7b-71f828ebb9ea", "metadata": { "tags": [] @@ -503,7 +512,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "4a5bc209-a660-4c18-86b0-574640391a7d", "metadata": { "tags": [] @@ -578,7 +587,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "ea9c3269-d53d-4d94-bc22-c6768cb63d91", "metadata": { "tags": [] @@ -727,7 +736,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "4e16119d-f6f3-478b-a419-7c4989557910", "metadata": { "tags": [] @@ -880,7 +889,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "359f3b7a-d691-446f-9a14-424c47fc0929", "metadata": { "tags": [] @@ -1093,7 +1102,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "19f4bd75-f614-4937-880a-1e1a6ff2eb7f", "metadata": { "tags": [] @@ -1296,7 +1305,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "c9ffd5a9-b772-4509-b84c-9a96760b3112", "metadata": {}, "outputs": [], @@ -1308,10 +1317,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "f8aa3674-78fe-4ba9-8f5e-697d91ff4011", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(82, 8)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Index(['transit_agency', 'total_project_count', 'total_project_count_ppno',\n", + " 'total_agg_cost', 'total_bus_count', 'new_cost_per_bus',\n", + " 'new_zscore_cost_per_bus', 'new_is_cpb_outlier?'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# testing the improved cpb agg function\n", "# default grouby column is `transit_agency`\n", @@ -1328,10 +1359,101 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "d2bca15a-c12c-4cf5-a5a9-d591ee73a359", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
3CITY OF PORTERVILLE (PORTERVILLE, CA)0127818913.09272970.357558False
48Rogue Valley Transportation District1039375006.0656250-0.319040False
80Whatcom Transportation Authority (WTA)10964486511.08768050.231518False
\n", + "
" + ], + "text/plain": [ + " transit_agency total_project_count \\\n", + "3 CITY OF PORTERVILLE (PORTERVILLE, CA) 0 \n", + "48 Rogue Valley Transportation District 1 \n", + "80 Whatcom Transportation Authority (WTA) 1 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count \\\n", + "3 1 2781891 3.0 \n", + "48 0 3937500 6.0 \n", + "80 0 9644865 11.0 \n", + "\n", + " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", + "3 927297 0.357558 False \n", + "48 656250 -0.319040 False \n", + "80 876805 0.231518 False " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# confirming the default cpb_agg is working\n", "agg1.sample(3)" @@ -1339,10 +1461,101 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "637032e4-d855-4190-a6f5-ff695f77143f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "False 81\n", + "True 1\n", + "Name: new_is_cpb_outlier?, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "-1.8667057821355477" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "3.4069219663792882" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
71Transit Joint Powers Authority for Merced County0264466483.021488823.406922True
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" + ], + "text/plain": [ + " transit_agency total_project_count \\\n", + "71 Transit Joint Powers Authority for Merced County 0 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count \\\n", + "71 2 6446648 3.0 \n", + "\n", + " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", + "71 2148882 3.406922 True " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# agg looks good\n", "# double checked it against the old agg function, CPB numbers match between this new function and old one\n", @@ -1357,7 +1570,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "ce1f4c86-e85d-41d8-83f6-14aadce48d5c", "metadata": {}, "outputs": [], @@ -1368,7 +1581,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "59298193-fc78-4ffb-bfc3-326593c19edb", "metadata": {}, "outputs": [], @@ -1382,83 +1595,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "01883fc0-4f6d-4e6a-a88f-97a5914b281b", "metadata": {}, - "outputs": [], - "source": [ - "#EVERYTHING CHECKS OUT!\n", - "display(\n", - " old_prop_agg.shape,\n", - " agg_prop_type.shape,\n", - " old_prop_agg.head(),\n", - " agg_prop_type.head()\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "52204752-3932-4ce6-98ac-de8ad3a1f8e8", - "metadata": {}, - "outputs": [], - "source": [ - "# double checking the bus size agg new vs old\n", - "#EVERYTHING CHECKS OUT!\n", - "new_agg_bus_size = new_cpb_aggregate(test, \"bus_size_type\")\n", - "old_size_agg = cpb_aggregate(no_outliers, \"bus_size_type\")\n", - "display(\n", - " old_size_agg.shape,\n", - " new_agg_bus_size.shape,\n", - " old_size_agg,\n", - " new_agg_bus_size\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0391dd4d-23e1-49cb-8123-509954c796e8", - "metadata": {}, - "outputs": [], - "source": [ - "#EVERYTHING CHECKS OUT!\n", - "# move forward with `new_cpb_aggregate` function\n", - "new_agg_agency = new_cpb_aggregate(test)\n", - "old_agency_agg = cpb_aggregate(no_outliers, \"transit_agency\")\n", - "display(\n", - " old_agency_agg.shape,\n", - " new_agg_agency.shape,\n", - " old_agency_agg.head(),\n", - " new_agg_agency.head()\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "3428875a-6a64-41bc-8f9c-81902006d7f0", - "metadata": { - "tags": [] - }, - "source": [ - "## Testing variables rework\n", - "there are a lot of variables in the initial script. need to cut down the amount of variables or at least make it more efficient. " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f21298ee-0efb-4f91-ba63-55fc2645a4d2", - "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", - " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", - " 'ppno', 'project_description', 'cost_per_bus', 'zscore_cost_per_bus',\n", - " 'is_cpb_outlier?'],\n", - " dtype='object')" + "(10, 6)" ] }, "metadata": {}, @@ -1467,7 +1611,7 @@ { "data": { "text/plain": [ - "(88, 14)" + "(10, 8)" ] }, "metadata": {}, @@ -1494,154 +1638,78 @@ " \n", " \n", " \n", - " transit_agency\n", - " project_title\n", " prop_type\n", - " bus_size_type\n", - " description\n", - " new_project_type\n", - " total_cost\n", - " bus_count\n", - " source\n", - " ppno\n", - " project_description\n", - " cost_per_bus\n", - " zscore_cost_per_bus\n", - " is_cpb_outlier?\n", + " total_project_count\n", + " total_project_count_ppno\n", + " total_agg_cost\n", + " total_bus_count\n", + " cpb\n", " \n", " \n", " \n", " \n", " 0\n", - " AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)\n", - " Puerto Rico Initiative Minimizing Emissions Pl...\n", - " electric (not specified)\n", - " not specified\n", - " The Metropolitan Bus Authority will receive fu...\n", - " bus only\n", - " 10000000\n", - " 8.0\n", - " fta\n", - " None\n", - " None\n", - " 1250000\n", - " 0.917956\n", - " False\n", + " BEB\n", + " 0\n", + " 30\n", + " 167232489\n", + " 163.0\n", + " 1025966\n", " \n", " \n", " 1\n", - " Cape Fear Public Transportation Authority\n", - " Wave Transit Low Emissions Replacement Vehicles\n", " CNG\n", - " not specified\n", - " Wave Transit will receive funding to buy compr...\n", - " bus only\n", - " 2860250\n", - " 5.0\n", - " fta\n", - " None\n", - " None\n", - " 572050\n", - " -0.529139\n", - " False\n", + " 12\n", + " 1\n", + " 176039140\n", + " 252.0\n", + " 698568\n", " \n", " \n", " 2\n", - " Central Oklahoma Transportation and Parking Au...\n", - " COTPA, dba EMBARK Elimination of Fixed Route D...\n", - " CNG\n", - " not specified\n", - " The Central Oklahoma Transportation and Parkin...\n", - " bus only\n", - " 4278772\n", - " 9.0\n", - " fta\n", - " None\n", - " None\n", - " 475419\n", - " -0.735399\n", - " False\n", + " FCEB\n", + " 2\n", + " 6\n", + " 120951335\n", + " 102.0\n", + " 1185797\n", " \n", " \n", " 3\n", - " Champaign-Urbana Mass Transit District\n", - " MTD 40-Foot Hybrid Replacement Buses\n", - " low emission (hybrid)\n", - " not specified\n", - " The Champaign-Urbana Mass Transit District wil...\n", - " bus only\n", - " 6635394\n", - " 10.0\n", - " fta\n", - " None\n", - " None\n", - " 663539\n", - " -0.333854\n", - " False\n", + " electric (not specified)\n", + " 1\n", + " 2\n", + " 56678000\n", + " 44.0\n", + " 1288136\n", " \n", " \n", " 4\n", - " City of Beaumont\n", - " Beaumont Municipal Transit Zips to Improve Low...\n", - " CNG\n", - " not specified\n", - " Beaumont Municipal Transit will receive fundin...\n", - " bus only\n", - " 2819460\n", - " 5.0\n", - " fta\n", - " None\n", - " None\n", - " 563892\n", - " -0.546552\n", - " False\n", + " ethanol\n", + " 1\n", + " 0\n", + " 1006750\n", + " 9.0\n", + " 111861\n", " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency \\\n", - "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) \n", - "1 Cape Fear Public Transportation Authority \n", - "2 Central Oklahoma Transportation and Parking Au... \n", - "3 Champaign-Urbana Mass Transit District \n", - "4 City of Beaumont \n", - "\n", - " project_title \\\n", - "0 Puerto Rico Initiative Minimizing Emissions Pl... \n", - "1 Wave Transit Low Emissions Replacement Vehicles \n", - "2 COTPA, dba EMBARK Elimination of Fixed Route D... \n", - "3 MTD 40-Foot Hybrid Replacement Buses \n", - "4 Beaumont Municipal Transit Zips to Improve Low... \n", - "\n", - " prop_type bus_size_type \\\n", - "0 electric (not specified) not specified \n", - "1 CNG not specified \n", - "2 CNG not specified \n", - "3 low emission (hybrid) not specified \n", - "4 CNG not specified \n", - "\n", - " description new_project_type \\\n", - "0 The Metropolitan Bus Authority will receive fu... bus only \n", - "1 Wave Transit will receive funding to buy compr... bus only \n", - "2 The Central Oklahoma Transportation and Parkin... bus only \n", - "3 The Champaign-Urbana Mass Transit District wil... bus only \n", - "4 Beaumont Municipal Transit will receive fundin... bus only \n", - "\n", - " total_cost bus_count source ppno project_description cost_per_bus \\\n", - "0 10000000 8.0 fta None None 1250000 \n", - "1 2860250 5.0 fta None None 572050 \n", - "2 4278772 9.0 fta None None 475419 \n", - "3 6635394 10.0 fta None None 663539 \n", - "4 2819460 5.0 fta None None 563892 \n", + " prop_type total_project_count total_project_count_ppno \\\n", + "0 BEB 0 30 \n", + "1 CNG 12 1 \n", + "2 FCEB 2 6 \n", + "3 electric (not specified) 1 2 \n", + "4 ethanol 1 0 \n", "\n", - " zscore_cost_per_bus is_cpb_outlier? \n", - "0 0.917956 False \n", - "1 -0.529139 False \n", - "2 -0.735399 False \n", - "3 -0.333854 False \n", - "4 -0.546552 False " + " total_agg_cost total_bus_count cpb \n", + "0 167232489 163.0 1025966 \n", + "1 176039140 252.0 698568 \n", + "2 120951335 102.0 1185797 \n", + "3 56678000 44.0 1288136 \n", + "4 1006750 9.0 111861 " ] }, "metadata": {}, @@ -1649,10 +1717,116 @@ }, { "data": { + "text/html": [ + "
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prop_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
0BEB031170455813164.010393640.923505False
1CNG121176039140252.06985680.122141False
2FCEB26120951335102.011857971.267835False
3electric (not specified)125667800044.012881361.508480False
4ethanol1010067509.0111861-1.257470False
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" + ], "text/plain": [ - "min -1.672813\n", - "max 2.661856\n", - "Name: zscore_cost_per_bus, dtype: float64" + " prop_type total_project_count total_project_count_ppno \\\n", + "0 BEB 0 31 \n", + "1 CNG 12 1 \n", + "2 FCEB 2 6 \n", + "3 electric (not specified) 1 2 \n", + "4 ethanol 1 0 \n", + "\n", + " total_agg_cost total_bus_count new_cost_per_bus new_zscore_cost_per_bus \\\n", + "0 170455813 164.0 1039364 0.923505 \n", + "1 176039140 252.0 698568 0.122141 \n", + "2 120951335 102.0 1185797 1.267835 \n", + "3 56678000 44.0 1288136 1.508480 \n", + "4 1006750 9.0 111861 -1.257470 \n", + "\n", + " new_is_cpb_outlier? \n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False " ] }, "metadata": {}, @@ -1660,31 +1834,25 @@ } ], "source": [ - "# read in all cleaned project data without outliers\n", - "# cpb_analysis_data_merge is bus only projects. all DGS rows were Bus only projects anyways\n", - "\n", - "merged_data = pd.read_parquet(f'{GCS_PATH}cleaned_no_outliers_cpb_analysis_data_merge.parquet')\n", + "#EVERYTHING CHECKS OUT!\n", "display(\n", - " merged_data.columns,\n", - " merged_data.shape,\n", - " merged_data.head(),\n", - " merged_data[\"zscore_cost_per_bus\"].agg([\"min\",\"max\"])\n", - "\n", + " old_prop_agg.shape,\n", + " agg_prop_type.shape,\n", + " old_prop_agg.head(),\n", + " agg_prop_type.head()\n", ")" ] }, { "cell_type": "code", - "execution_count": 11, - "id": "ece95fb7-cbb8-46bd-a5f9-2b68a47a4817", + "execution_count": 26, + "id": "52204752-3932-4ce6-98ac-de8ad3a1f8e8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "min 36250\n", - "max 1611662\n", - "Name: new_cost_per_bus, dtype: int64" + "(5, 6)" ] }, "metadata": {}, @@ -1693,9 +1861,7 @@ { "data": { "text/plain": [ - "min 1.0\n", - "max 160.0\n", - "Name: total_bus_count, dtype: float64" + "(5, 8)" ] }, "metadata": {}, @@ -1703,10 +1869,97 @@ }, { "data": { + "text/html": [ + "
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bus_size_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpb
0articulated025823757641.01420428
1cutaway3016694500152.0109832
2not specified406509919038881.0578795
3over-the-road01951600014.0679714
4standard/conventional (30ft-45ft)036234253277264.0887323
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" + ], "text/plain": [ - "min 181250\n", - "max 103000000\n", - "Name: total_agg_cost, dtype: int64" + " bus_size_type total_project_count \\\n", + "0 articulated 0 \n", + "1 cutaway 3 \n", + "2 not specified 40 \n", + "3 over-the-road 0 \n", + "4 standard/conventional (30ft-45ft) 0 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count cpb \n", + "0 2 58237576 41.0 1420428 \n", + "1 0 16694500 152.0 109832 \n", + "2 6 509919038 881.0 578795 \n", + "3 1 9516000 14.0 679714 \n", + "4 36 234253277 264.0 887323 " ] }, "metadata": {}, @@ -1714,315 +1967,2238 @@ }, { "data": { - "text/plain": [ - "min -1.939451\n", - "max 2.182513\n", - "Name: new_zscore_cost_per_bus, dtype: float64" - ] - }, + "text/html": [ + "
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bus_size_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
0articulated025823757641.014204281.598471False
1cutaway3016694500152.0109832-1.466801False
2not specified406509919038881.0578795-0.369972False
3over-the-road01951600014.0679714-0.133939False
4standard/conventional (30ft-45ft)037237476601265.08961380.372242False
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" + ], + "text/plain": [ + " bus_size_type total_project_count \\\n", + "0 articulated 0 \n", + "1 cutaway 3 \n", + "2 not specified 40 \n", + "3 over-the-road 0 \n", + "4 standard/conventional (30ft-45ft) 0 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count \\\n", + "0 2 58237576 41.0 \n", + "1 0 16694500 152.0 \n", + "2 6 509919038 881.0 \n", + "3 1 9516000 14.0 \n", + "4 37 237476601 265.0 \n", + "\n", + " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", + "0 1420428 1.598471 False \n", + "1 109832 -1.466801 False \n", + "2 578795 -0.369972 False \n", + "3 679714 -0.133939 False \n", + "4 896138 0.372242 False " + ] + }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "## moved to final NB\n", - "\n", - "# aggregating by big categories\n", - "agg_agency = new_cpb_aggregate(merged_data)\n", - "agg_prop = new_cpb_aggregate(merged_data, column=\"prop_type\")\n", - "agg_bus_size = new_cpb_aggregate(merged_data, column=\"bus_size_type\")\n", - "agg_source = new_cpb_aggregate(merged_data, column=\"source\")\n", - "\n", - "#overall agency info\n", + "# double checking the bus size agg new vs old\n", + "#EVERYTHING CHECKS OUT!\n", + "new_agg_bus_size = new_cpb_aggregate(test, \"bus_size_type\")\n", + "old_size_agg = cpb_aggregate(no_outliers, \"bus_size_type\")\n", "display(\n", - " #min max,\n", - " agg_agency[\"new_cost_per_bus\"].agg([\"min\",\"max\"]),\n", - " agg_agency[\"total_bus_count\"].agg([\"min\",\"max\"]),\n", - " agg_agency[\"total_agg_cost\"].agg([\"min\",\"max\"]),\n", - " agg_agency[\"new_zscore_cost_per_bus\"].agg([\"min\",\"max\"]),\n", - " \n", + " old_size_agg.shape,\n", + " new_agg_bus_size.shape,\n", + " old_size_agg,\n", + " new_agg_bus_size\n", ")" ] }, { "cell_type": "code", - "execution_count": null, - "id": "03940ccb-d1e6-439d-a930-13dae17537b2", + "execution_count": 27, + "id": "0391dd4d-23e1-49cb-8123-509954c796e8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(82, 6)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(82, 8)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpb
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)10100000008.01250000
1Alameda County Transit Authority012284664020.01142332
2Antelope Valley Transit Authority (AVTA)013947800029.01361310
3CITY OF PORTERVILLE (PORTERVILLE, CA)0127818913.0927297
4CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...0136235364.0905884
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" + ], + "text/plain": [ + " transit_agency total_project_count \\\n", + "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) 1 \n", + "1 Alameda County Transit Authority 0 \n", + "2 Antelope Valley Transit Authority (AVTA) 0 \n", + "3 CITY OF PORTERVILLE (PORTERVILLE, CA) 0 \n", + "4 CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ... 0 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count cpb \n", + "0 0 10000000 8.0 1250000 \n", + "1 1 22846640 20.0 1142332 \n", + "2 1 39478000 29.0 1361310 \n", + "3 1 2781891 3.0 927297 \n", + "4 1 3623536 4.0 905884 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)10100000008.012500001.163100False
1Alameda County Transit Authority012284664020.011423320.894336False
2Antelope Valley Transit Authority (AVTA)013947800029.013613101.440957False
3CITY OF PORTERVILLE (PORTERVILLE, CA)0127818913.09272970.357558False
4CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...0136235364.09058840.304106False
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" + ], + "text/plain": [ + " transit_agency total_project_count \\\n", + "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) 1 \n", + "1 Alameda County Transit Authority 0 \n", + "2 Antelope Valley Transit Authority (AVTA) 0 \n", + "3 CITY OF PORTERVILLE (PORTERVILLE, CA) 0 \n", + "4 CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ... 0 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count \\\n", + "0 0 10000000 8.0 \n", + "1 1 22846640 20.0 \n", + "2 1 39478000 29.0 \n", + "3 1 2781891 3.0 \n", + "4 1 3623536 4.0 \n", + "\n", + " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", + "0 1250000 1.163100 False \n", + "1 1142332 0.894336 False \n", + "2 1361310 1.440957 False \n", + "3 927297 0.357558 False \n", + "4 905884 0.304106 False " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "#EVERYTHING CHECKS OUT!\n", + "# move forward with `new_cpb_aggregate` function\n", + "new_agg_agency = new_cpb_aggregate(test)\n", + "old_agency_agg = cpb_aggregate(no_outliers, \"transit_agency\")\n", "display(\n", - " merged_data.shape,\n", - " agg_agency.shape,\n", - " merged_data.head(),\n", - " agg_agency.head()\n", + " old_agency_agg.shape,\n", + " new_agg_agency.shape,\n", + " old_agency_agg.head(),\n", + " new_agg_agency.head()\n", ")" ] }, + { + "cell_type": "markdown", + "id": "3428875a-6a64-41bc-8f9c-81902006d7f0", + "metadata": { + "tags": [] + }, + "source": [ + "## Testing variables rework\n", + "there are a lot of variables in the initial script. need to cut down the amount of variables or at least make it more efficient. " + ] + }, { "cell_type": "code", - "execution_count": null, - "id": "1696d78f-7018-417b-9847-d82edac3acdf", + "execution_count": 28, + "id": "f21298ee-0efb-4f91-ba63-55fc2645a4d2", "metadata": {}, - "outputs": [], - "source": [ - "# testing pivot table on `merged_data`\n", - "# moved to final NB\n", - "#pivot table to get totals for each prop type\n", - "pivot_prop_type = pd.pivot_table(\n", - " merged_data,\n", - " values = [\"bus_count\", \"total_cost\"],\n", - " index = \"prop_type\",\n", - " aggfunc = \"sum\",\n", - " margins = True,\n", - " margins_name = \"Grand Total\"\n", - ").reset_index()\n", - "pivot_prop_type[\"cost_per_bus\"] = (pivot_prop_type[\"total_cost\"] / pivot_prop_type[\"bus_count\"]).astype(\"int64\")\n", + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", + " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", + " 'ppno', 'project_description', 'cost_per_bus', 'zscore_cost_per_bus',\n", + " 'is_cpb_outlier?'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(88, 14)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencyproject_titleprop_typebus_size_typedescriptionnew_project_typetotal_costbus_countsourceppnoproject_descriptioncost_per_buszscore_cost_per_busis_cpb_outlier?
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)Puerto Rico Initiative Minimizing Emissions Pl...electric (not specified)not specifiedThe Metropolitan Bus Authority will receive fu...bus only100000008.0ftaNoneNone12500000.917956False
1Cape Fear Public Transportation AuthorityWave Transit Low Emissions Replacement VehiclesCNGnot specifiedWave Transit will receive funding to buy compr...bus only28602505.0ftaNoneNone572050-0.529139False
2Central Oklahoma Transportation and Parking Au...COTPA, dba EMBARK Elimination of Fixed Route D...CNGnot specifiedThe Central Oklahoma Transportation and Parkin...bus only42787729.0ftaNoneNone475419-0.735399False
3Champaign-Urbana Mass Transit DistrictMTD 40-Foot Hybrid Replacement Buseslow emission (hybrid)not specifiedThe Champaign-Urbana Mass Transit District wil...bus only663539410.0ftaNoneNone663539-0.333854False
4City of BeaumontBeaumont Municipal Transit Zips to Improve Low...CNGnot specifiedBeaumont Municipal Transit will receive fundin...bus only28194605.0ftaNoneNone563892-0.546552False
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" + ], + "text/plain": [ + " transit_agency \\\n", + "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) \n", + "1 Cape Fear Public Transportation Authority \n", + "2 Central Oklahoma Transportation and Parking Au... \n", + "3 Champaign-Urbana Mass Transit District \n", + "4 City of Beaumont \n", + "\n", + " project_title \\\n", + "0 Puerto Rico Initiative Minimizing Emissions Pl... \n", + "1 Wave Transit Low Emissions Replacement Vehicles \n", + "2 COTPA, dba EMBARK Elimination of Fixed Route D... \n", + "3 MTD 40-Foot Hybrid Replacement Buses \n", + "4 Beaumont Municipal Transit Zips to Improve Low... \n", + "\n", + " prop_type bus_size_type \\\n", + "0 electric (not specified) not specified \n", + "1 CNG not specified \n", + "2 CNG not specified \n", + "3 low emission (hybrid) not specified \n", + "4 CNG not specified \n", + "\n", + " description new_project_type \\\n", + "0 The Metropolitan Bus Authority will receive fu... bus only \n", + "1 Wave Transit will receive funding to buy compr... bus only \n", + "2 The Central Oklahoma Transportation and Parkin... bus only \n", + "3 The Champaign-Urbana Mass Transit District wil... bus only \n", + "4 Beaumont Municipal Transit will receive fundin... bus only \n", + "\n", + " total_cost bus_count source ppno project_description cost_per_bus \\\n", + "0 10000000 8.0 fta None None 1250000 \n", + "1 2860250 5.0 fta None None 572050 \n", + "2 4278772 9.0 fta None None 475419 \n", + "3 6635394 10.0 fta None None 663539 \n", + "4 2819460 5.0 fta None None 563892 \n", + "\n", + " zscore_cost_per_bus is_cpb_outlier? \n", + "0 0.917956 False \n", + "1 -0.529139 False \n", + "2 -0.735399 False \n", + "3 -0.333854 False \n", + "4 -0.546552 False " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "min -1.672813\n", + "max 2.661856\n", + "Name: zscore_cost_per_bus, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# read in all cleaned project data without outliers\n", + "# cpb_analysis_data_merge is bus only projects. all DGS rows were Bus only projects anyways\n", "\n", + "merged_data = pd.read_parquet(f'{GCS_PATH}cleaned_no_outliers_cpb_analysis_data_merge.parquet')\n", "display(\n", - " #from new_cpb_agg\n", - " agg_prop[[\"prop_type\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]],\n", - " #pivot\n", - " pivot_prop_type\n", - ")\n", - "# same data, dont need the pivot table anymore, but the pivot table does have grand total" + " merged_data.columns,\n", + " merged_data.shape,\n", + " merged_data.head(),\n", + " merged_data[\"zscore_cost_per_bus\"].agg([\"min\",\"max\"])\n", + "\n", + ")" ] }, { "cell_type": "code", - "execution_count": null, - "id": "ca709e43-4947-4a34-970f-216d4b6ab7cc", + "execution_count": 29, + "id": "ece95fb7-cbb8-46bd-a5f9-2b68a47a4817", "metadata": {}, - "outputs": [], - "source": [ - "#moved to final NB 6/25\n", - "\n", - "#pivot table to get grand total for zeb/non-zeb only data\n", - "\n", - "# keep this\n", - "zeb_list =[\n", - " \"BEB\",\n", - " \"FCEB\",\n", - " \"electric (not specified)\",\n", - " \"zero-emission bus (not specified)\",\n", - "]\n", - "\n", - "zeb_projects = merged_data[merged_data[\"prop_type\"].isin(zeb_list)]\n", - "\n", - "#keep this\n", - "non_zeb_list =[\n", - " \"CNG\",\n", - " \"ethanol\",\n", - " \"low emission (hybrid)\",\n", - " \"low emission (propane)\",\n", - " \"mix (zero and low emission)\",\n", - "]\n", - "\n", - "non_zeb_projects = merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)]\n", - "\n", - "#keep this\n", - "pivot_zeb_prop = pd.pivot_table(\n", - " #filted incoming DF for zeb prop types\n", - " zeb_projects,\n", - " values = [\"bus_count\", \"total_cost\"],\n", - " index = \"prop_type\",\n", - " aggfunc = \"sum\",\n", - " margins = True,\n", - " margins_name = \"Grand Total\"\n", - ").reset_index() \n", - "\n", - "pivot_zeb_prop[\"cost_per_bus\"] = (pivot_zeb_prop[\"total_cost\"] / pivot_zeb_prop[\"bus_count\"]).astype(\"int64\")\n", - "\n", - "#keep this\n", - "pivot_non_zeb_prop = pd.pivot_table(\n", - " #filted incoming DF for non-zeb prop types\n", - " non_zeb_projects,\n", - " values = [\"bus_count\", \"total_cost\"],\n", - " index = \"prop_type\",\n", - " aggfunc = \"sum\",\n", - " margins = True,\n", - " margins_name = \"Grand Total\"\n", - ").reset_index()\n", - "\n", - "pivot_non_zeb_prop[\"cost_per_bus\"] = (pivot_non_zeb_prop[\"total_cost\"] / pivot_non_zeb_prop[\"bus_count\"]).astype(\"int64\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "01647d83-8b4f-47a9-ab57-a1db7cd501dd", - "metadata": {}, - "outputs": [], - "source": [ - "display(\n", - " #zeb data 3 different methods\n", - " #1. filtering agg_prop by zeb list, no grand totas\n", - " #2. filtering pivot talbe by zeb list, without grand totals\n", - " #3. dedicated pivot table for zeb, with grand totals\n", - " #agg_prop[agg_prop[\"prop_type\"].isin(zeb_list)],\n", - " #pivot_prop_type.loc[zeb_list],\n", - " pivot_zeb_prop,\n", - " \n", - " #non-zeb same 3 methods\n", - " #agg_prop[agg_prop[\"prop_type\"].isin(non_zeb_list)],\n", - " #pivot_prop_type.loc[non_zeb_list],\n", - " pivot_non_zeb_prop\n", - ")\n", - "# confirmed all data is the same, but need pivot for grand total rows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3be3ef4f-0bf3-4770-a8b7-340d372ae1ce", - "metadata": {}, - "outputs": [], - "source": [ - "# answers total buses sizes\n", - "pivot_size = pd.pivot_table(\n", - " merged_data,\n", - " values = [\"bus_count\", \"total_cost\"],\n", - " index = \"bus_size_type\",\n", - " aggfunc = \"sum\",\n", - " margins = True,\n", - " margins_name = \"Grand Total\"\n", - ").reset_index()\n", - "\n", - "pivot_size[\"cost_per_bus\"] = (pivot_size[\"total_cost\"] / pivot_size[\"bus_count\"]).astype(\"int64\")\n", - "\n", - "display(\n", - " agg_bus_size[[\"bus_size_type\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]],\n", - " pivot_size,\n", - " pivot_prop_type[pivot_prop_type[\"prop_type\"] == \"Grand Total\"]\n", - ")\n", - "\n", - "#same data, dont need pivot for this one because the grand totals will be the same as pivot_prop_type. \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2c933257-bdc2-4007-9571-58475118073c", - "metadata": {}, - "outputs": [], - "source": [ - "# moved to final NB 6/25\n", - "\n", - "# answers total buses and cost per grant type\n", - "pivot_source = pd.pivot_table(\n", - " merged_data,\n", - " values = [\"bus_count\", \"total_cost\"],\n", - " index = \"source\",\n", - " aggfunc = \"sum\",\n", - " margins = True,\n", - " margins_name = \"Grand Total\"\n", - ").reset_index()\n", - "\n", - "pivot_source[\"cost_per_bus\"] = (pivot_source[\"total_cost\"] / pivot_source[\"bus_count\"]).astype(\"int64\")\n", - "\n", - "display(\n", - " agg_source[[\"source\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]],\n", - " pivot_source\n", - ")\n" - ] - }, - { - "cell_type": "markdown", - "id": "11547020-dd35-4745-98f8-bbd02fccaa23", - "metadata": { - "tags": [] - }, - "source": [ - "## Testing Charts\n", - "\n", - "using `merged_data`, now without outliers.\n", - "charts looking good, similar results to initial charts" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aace38a4-3f2d-460d-a258-59efa659f852", - "metadata": {}, - "outputs": [], - "source": [ - "merged_data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4753f3ea-00b6-4d5e-a3f0-73b3d3593acb", - "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "min 36250\n", + "max 1611662\n", + "Name: new_cost_per_bus, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "min 1.0\n", + "max 160.0\n", + "Name: total_bus_count, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "min 181250\n", + "max 103000000\n", + "Name: total_agg_cost, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "min -1.939451\n", + "max 2.182513\n", + "Name: new_zscore_cost_per_bus, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# means and standard deviations\n", - "# for graphs\n", - "cpb_mean = merged_data[\"cost_per_bus\"].mean()\n", - "cpb_std = merged_data[\"cost_per_bus\"].std()\n", + "## moved to final NB\n", "\n", - "#testing weighted average calculation for sub-set non-zeb and zeb\n", - "zeb_cpb_wt_avg = (merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"bus_count\"].sum())\n", - "non_zeb_cpb_wt_avg = (merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"bus_count\"].sum())\n", - "display(\n", - " cpb_mean,\n", - " cpb_std,\n", - " zeb_cpb_wt_avg,\n", - " non_zeb_cpb_wt_avg\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2007be9d-13ec-4d0d-a642-d9a42448b924", - "metadata": {}, - "outputs": [], - "source": [ - "# why is the average different when i use .mean() vs. total cost / bus cout\n", + "# aggregating by big categories\n", + "agg_agency = new_cpb_aggregate(merged_data)\n", + "agg_prop = new_cpb_aggregate(merged_data, column=\"prop_type\")\n", + "agg_bus_size = new_cpb_aggregate(merged_data, column=\"bus_size_type\")\n", + "agg_source = new_cpb_aggregate(merged_data, column=\"source\")\n", "\n", + "#overall agency info\n", "display(\n", - " #this is the arithmatic mean, sums all the `cost_per_bus` rows, the divide by the number of rows. (row-wise)\n", - " zeb_projects[\"cost_per_bus\"].mean(),\n", - " \n", - " #this is like the accounting method of calculating average (Total Cost and Total Quantity Approach (Weighted Average))\n", - " pivot_zeb_prop,\n", + " #min max,\n", + " agg_agency[\"new_cost_per_bus\"].agg([\"min\",\"max\"]),\n", + " agg_agency[\"total_bus_count\"].agg([\"min\",\"max\"]),\n", + " agg_agency[\"total_agg_cost\"].agg([\"min\",\"max\"]),\n", + " agg_agency[\"new_zscore_cost_per_bus\"].agg([\"min\",\"max\"]),\n", " \n", - " # calculating mean by weighted average the long way (total cost / total bus count, similar to pivot table)\n", - " (zeb_projects[\"total_cost\"].sum() / zeb_projects[\"bus_count\"].sum())\n", - ")\n", - "\n", - "# so the calculated grand total cost_per_bus is equivilent to the weighted average cost per bus\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8645cf77-b30a-4c45-b943-ac81e8b5a613", - "metadata": {}, - "outputs": [], - "source": [ - "# chart of all cost per bus in the analysis.\n", - "\n", - "dist_curve(\n", - " df=merged_data,\n", - " mean=cpb_mean,\n", - " std=cpb_std,\n", - " title=\"all buses, cost per bus distribution\",\n", - " xlabel=\"cost per bus, $ million(s)\",\n", ")" ] }, { "cell_type": "code", - "execution_count": null, - "id": "cefa6800-df50-4eda-95f8-74363ef942d0", + "execution_count": 30, + "id": "03940ccb-d1e6-439d-a930-13dae17537b2", "metadata": {}, - "outputs": [], - "source": [ - "# ZEB cost per bus \n", - "dist_curve(\n", - " df=zeb_projects,\n", + "outputs": [ + { + "data": { + "text/plain": [ + "(88, 14)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(82, 8)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencyproject_titleprop_typebus_size_typedescriptionnew_project_typetotal_costbus_countsourceppnoproject_descriptioncost_per_buszscore_cost_per_busis_cpb_outlier?
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)Puerto Rico Initiative Minimizing Emissions Pl...electric (not specified)not specifiedThe Metropolitan Bus Authority will receive fu...bus only100000008.0ftaNoneNone12500000.917956False
1Cape Fear Public Transportation AuthorityWave Transit Low Emissions Replacement VehiclesCNGnot specifiedWave Transit will receive funding to buy compr...bus only28602505.0ftaNoneNone572050-0.529139False
2Central Oklahoma Transportation and Parking Au...COTPA, dba EMBARK Elimination of Fixed Route D...CNGnot specifiedThe Central Oklahoma Transportation and Parkin...bus only42787729.0ftaNoneNone475419-0.735399False
3Champaign-Urbana Mass Transit DistrictMTD 40-Foot Hybrid Replacement Buseslow emission (hybrid)not specifiedThe Champaign-Urbana Mass Transit District wil...bus only663539410.0ftaNoneNone663539-0.333854False
4City of BeaumontBeaumont Municipal Transit Zips to Improve Low...CNGnot specifiedBeaumont Municipal Transit will receive fundin...bus only28194605.0ftaNoneNone563892-0.546552False
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" + ], + "text/plain": [ + " transit_agency \\\n", + "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) \n", + "1 Cape Fear Public Transportation Authority \n", + "2 Central Oklahoma Transportation and Parking Au... \n", + "3 Champaign-Urbana Mass Transit District \n", + "4 City of Beaumont \n", + "\n", + " project_title \\\n", + "0 Puerto Rico Initiative Minimizing Emissions Pl... \n", + "1 Wave Transit Low Emissions Replacement Vehicles \n", + "2 COTPA, dba EMBARK Elimination of Fixed Route D... \n", + "3 MTD 40-Foot Hybrid Replacement Buses \n", + "4 Beaumont Municipal Transit Zips to Improve Low... \n", + "\n", + " prop_type bus_size_type \\\n", + "0 electric (not specified) not specified \n", + "1 CNG not specified \n", + "2 CNG not specified \n", + "3 low emission (hybrid) not specified \n", + "4 CNG not specified \n", + "\n", + " description new_project_type \\\n", + "0 The Metropolitan Bus Authority will receive fu... bus only \n", + "1 Wave Transit will receive funding to buy compr... bus only \n", + "2 The Central Oklahoma Transportation and Parkin... bus only \n", + "3 The Champaign-Urbana Mass Transit District wil... bus only \n", + "4 Beaumont Municipal Transit will receive fundin... bus only \n", + "\n", + " total_cost bus_count source ppno project_description cost_per_bus \\\n", + "0 10000000 8.0 fta None None 1250000 \n", + "1 2860250 5.0 fta None None 572050 \n", + "2 4278772 9.0 fta None None 475419 \n", + "3 6635394 10.0 fta None None 663539 \n", + "4 2819460 5.0 fta None None 563892 \n", + "\n", + " zscore_cost_per_bus is_cpb_outlier? \n", + "0 0.917956 False \n", + "1 -0.529139 False \n", + "2 -0.735399 False \n", + "3 -0.333854 False \n", + "4 -0.546552 False " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)10100000008.012500001.236248False
1Alameda County Transit Authority012284664020.011423320.954542False
2Antelope Valley Transit Authority (AVTA)013947800029.013613101.527483False
3CITY OF PORTERVILLE (PORTERVILLE, CA)0127818913.09272970.391916False
4CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...0136235364.09058840.335891False
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" + ], + "text/plain": [ + " transit_agency total_project_count \\\n", + "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) 1 \n", + "1 Alameda County Transit Authority 0 \n", + "2 Antelope Valley Transit Authority (AVTA) 0 \n", + "3 CITY OF PORTERVILLE (PORTERVILLE, CA) 0 \n", + "4 CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ... 0 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count \\\n", + "0 0 10000000 8.0 \n", + "1 1 22846640 20.0 \n", + "2 1 39478000 29.0 \n", + "3 1 2781891 3.0 \n", + "4 1 3623536 4.0 \n", + "\n", + " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", + "0 1250000 1.236248 False \n", + "1 1142332 0.954542 False \n", + "2 1361310 1.527483 False \n", + "3 927297 0.391916 False \n", + "4 905884 0.335891 False " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(\n", + " merged_data.shape,\n", + " agg_agency.shape,\n", + " merged_data.head(),\n", + " agg_agency.head()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "1696d78f-7018-417b-9847-d82edac3acdf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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prop_typetotal_agg_costtotal_bus_countnew_cost_per_bus
0BEB167232489163.01025966
1CNG176039140252.0698568
2FCEB120951335102.01185797
3electric (not specified)5667800044.01288136
4ethanol10067509.0111861
5low emission (hybrid)91824361145.0633271
6low emission (propane)840396944.0190999
7mix (zero and low emission)36775430125.0294203
8not specified41552404325.0127853
9zero-emission bus (not specified)128156513143.0896199
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" + ], + "text/plain": [ + " prop_type total_agg_cost total_bus_count \\\n", + "0 BEB 167232489 163.0 \n", + "1 CNG 176039140 252.0 \n", + "2 FCEB 120951335 102.0 \n", + "3 electric (not specified) 56678000 44.0 \n", + "4 ethanol 1006750 9.0 \n", + "5 low emission (hybrid) 91824361 145.0 \n", + "6 low emission (propane) 8403969 44.0 \n", + "7 mix (zero and low emission) 36775430 125.0 \n", + "8 not specified 41552404 325.0 \n", + "9 zero-emission bus (not specified) 128156513 143.0 \n", + "\n", + " new_cost_per_bus \n", + "0 1025966 \n", + "1 698568 \n", + "2 1185797 \n", + "3 1288136 \n", + "4 111861 \n", + "5 633271 \n", + "6 190999 \n", + "7 294203 \n", + "8 127853 \n", + "9 896199 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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prop_typebus_counttotal_costcost_per_bus
0BEB163.01672324891025966
1CNG252.0176039140698568
2FCEB102.01209513351185797
3electric (not specified)44.0566780001288136
4ethanol9.01006750111861
5low emission (hybrid)145.091824361633271
6low emission (propane)44.08403969190999
7mix (zero and low emission)125.036775430294203
8not specified325.041552404127853
9zero-emission bus (not specified)143.0128156513896199
10Grand Total1352.0828620391612884
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" + ], + "text/plain": [ + " prop_type bus_count total_cost cost_per_bus\n", + "0 BEB 163.0 167232489 1025966\n", + "1 CNG 252.0 176039140 698568\n", + "2 FCEB 102.0 120951335 1185797\n", + "3 electric (not specified) 44.0 56678000 1288136\n", + "4 ethanol 9.0 1006750 111861\n", + "5 low emission (hybrid) 145.0 91824361 633271\n", + "6 low emission (propane) 44.0 8403969 190999\n", + "7 mix (zero and low emission) 125.0 36775430 294203\n", + "8 not specified 325.0 41552404 127853\n", + "9 zero-emission bus (not specified) 143.0 128156513 896199\n", + "10 Grand Total 1352.0 828620391 612884" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# testing pivot table on `merged_data`\n", + "# moved to final NB\n", + "#pivot table to get totals for each prop type\n", + "pivot_prop_type = pd.pivot_table(\n", + " merged_data,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"prop_type\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ").reset_index()\n", + "pivot_prop_type[\"cost_per_bus\"] = (pivot_prop_type[\"total_cost\"] / pivot_prop_type[\"bus_count\"]).astype(\"int64\")\n", + "\n", + "display(\n", + " #from new_cpb_agg\n", + " agg_prop[[\"prop_type\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]],\n", + " #pivot\n", + " pivot_prop_type\n", + ")\n", + "# same data, dont need the pivot table anymore, but the pivot table does have grand total" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "ca709e43-4947-4a34-970f-216d4b6ab7cc", + "metadata": {}, + "outputs": [], + "source": [ + "#moved to final NB 6/25\n", + "\n", + "#pivot table to get grand total for zeb/non-zeb only data\n", + "\n", + "# keep this\n", + "zeb_list =[\n", + " \"BEB\",\n", + " \"FCEB\",\n", + " \"electric (not specified)\",\n", + " \"zero-emission bus (not specified)\",\n", + "]\n", + "\n", + "zeb_projects = merged_data[merged_data[\"prop_type\"].isin(zeb_list)]\n", + "\n", + "#keep this\n", + "non_zeb_list =[\n", + " \"CNG\",\n", + " \"ethanol\",\n", + " \"low emission (hybrid)\",\n", + " \"low emission (propane)\",\n", + " \"mix (zero and low emission)\",\n", + "]\n", + "\n", + "non_zeb_projects = merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)]\n", + "\n", + "#keep this\n", + "pivot_zeb_prop = pd.pivot_table(\n", + " #filted incoming DF for zeb prop types\n", + " zeb_projects,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"prop_type\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ").reset_index() \n", + "\n", + "pivot_zeb_prop[\"cost_per_bus\"] = (pivot_zeb_prop[\"total_cost\"] / pivot_zeb_prop[\"bus_count\"]).astype(\"int64\")\n", + "\n", + "#keep this\n", + "pivot_non_zeb_prop = pd.pivot_table(\n", + " #filted incoming DF for non-zeb prop types\n", + " non_zeb_projects,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"prop_type\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ").reset_index()\n", + "\n", + "pivot_non_zeb_prop[\"cost_per_bus\"] = (pivot_non_zeb_prop[\"total_cost\"] / pivot_non_zeb_prop[\"bus_count\"]).astype(\"int64\")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "01647d83-8b4f-47a9-ab57-a1db7cd501dd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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prop_typebus_counttotal_costcost_per_bus
0BEB163.01672324891025966
1FCEB102.01209513351185797
2electric (not specified)44.0566780001288136
3zero-emission bus (not specified)143.0128156513896199
4Grand Total452.04730183371046500
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" + ], + "text/plain": [ + " prop_type bus_count total_cost cost_per_bus\n", + "0 BEB 163.0 167232489 1025966\n", + "1 FCEB 102.0 120951335 1185797\n", + "2 electric (not specified) 44.0 56678000 1288136\n", + "3 zero-emission bus (not specified) 143.0 128156513 896199\n", + "4 Grand Total 452.0 473018337 1046500" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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prop_typebus_counttotal_costcost_per_bus
0CNG252.0176039140698568
1ethanol9.01006750111861
2low emission (hybrid)145.091824361633271
3low emission (propane)44.08403969190999
4mix (zero and low emission)125.036775430294203
5Grand Total575.0314049650546173
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" + ], + "text/plain": [ + " prop_type bus_count total_cost cost_per_bus\n", + "0 CNG 252.0 176039140 698568\n", + "1 ethanol 9.0 1006750 111861\n", + "2 low emission (hybrid) 145.0 91824361 633271\n", + "3 low emission (propane) 44.0 8403969 190999\n", + "4 mix (zero and low emission) 125.0 36775430 294203\n", + "5 Grand Total 575.0 314049650 546173" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(\n", + " #zeb data 3 different methods\n", + " #1. filtering agg_prop by zeb list, no grand totas\n", + " #2. filtering pivot talbe by zeb list, without grand totals\n", + " #3. dedicated pivot table for zeb, with grand totals\n", + " #agg_prop[agg_prop[\"prop_type\"].isin(zeb_list)],\n", + " #pivot_prop_type.loc[zeb_list],\n", + " pivot_zeb_prop,\n", + " \n", + " #non-zeb same 3 methods\n", + " #agg_prop[agg_prop[\"prop_type\"].isin(non_zeb_list)],\n", + " #pivot_prop_type.loc[non_zeb_list],\n", + " pivot_non_zeb_prop\n", + ")\n", + "# confirmed all data is the same, but need pivot for grand total rows" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "3be3ef4f-0bf3-4770-a8b7-340d372ae1ce", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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bus_size_typetotal_agg_costtotal_bus_countnew_cost_per_bus
0articulated5823757641.01420428
1cutaway16694500152.0109832
2not specified509919038881.0578795
3over-the-road951600014.0679714
4standard/conventional (30ft-45ft)234253277264.0887323
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" + ], + "text/plain": [ + " bus_size_type total_agg_cost total_bus_count \\\n", + "0 articulated 58237576 41.0 \n", + "1 cutaway 16694500 152.0 \n", + "2 not specified 509919038 881.0 \n", + "3 over-the-road 9516000 14.0 \n", + "4 standard/conventional (30ft-45ft) 234253277 264.0 \n", + "\n", + " new_cost_per_bus \n", + "0 1420428 \n", + "1 109832 \n", + "2 578795 \n", + "3 679714 \n", + "4 887323 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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bus_size_typebus_counttotal_costcost_per_bus
0articulated41.0582375761420428
1cutaway152.016694500109832
2not specified881.0509919038578795
3over-the-road14.09516000679714
4standard/conventional (30ft-45ft)264.0234253277887323
5Grand Total1352.0828620391612884
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" + ], + "text/plain": [ + " bus_size_type bus_count total_cost cost_per_bus\n", + "0 articulated 41.0 58237576 1420428\n", + "1 cutaway 152.0 16694500 109832\n", + "2 not specified 881.0 509919038 578795\n", + "3 over-the-road 14.0 9516000 679714\n", + "4 standard/conventional (30ft-45ft) 264.0 234253277 887323\n", + "5 Grand Total 1352.0 828620391 612884" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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prop_typebus_counttotal_costcost_per_bus
10Grand Total1352.0828620391612884
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" + ], + "text/plain": [ + " prop_type bus_count total_cost cost_per_bus\n", + "10 Grand Total 1352.0 828620391 612884" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# answers total buses sizes\n", + "pivot_size = pd.pivot_table(\n", + " merged_data,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"bus_size_type\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ").reset_index()\n", + "\n", + "pivot_size[\"cost_per_bus\"] = (pivot_size[\"total_cost\"] / pivot_size[\"bus_count\"]).astype(\"int64\")\n", + "\n", + "display(\n", + " agg_bus_size[[\"bus_size_type\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]],\n", + " pivot_size,\n", + " pivot_prop_type[pivot_prop_type[\"prop_type\"] == \"Grand Total\"]\n", + ")\n", + "\n", + "#same data, dont need pivot for this one because the grand totals will be the same as pivot_prop_type. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "2c933257-bdc2-4007-9571-58475118073c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sourcetotal_agg_costtotal_bus_countnew_cost_per_bus
0dgs250112853236.01059800
1fta391257025883.0443099
2tircp187250513233.0803650
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" + ], + "text/plain": [ + " source total_agg_cost total_bus_count new_cost_per_bus\n", + "0 dgs 250112853 236.0 1059800\n", + "1 fta 391257025 883.0 443099\n", + "2 tircp 187250513 233.0 803650" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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sourcebus_counttotal_costcost_per_bus
0dgs236.02501128531059800
1fta883.0391257025443099
2tircp233.0187250513803650
3Grand Total1352.0828620391612884
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" + ], + "text/plain": [ + " source bus_count total_cost cost_per_bus\n", + "0 dgs 236.0 250112853 1059800\n", + "1 fta 883.0 391257025 443099\n", + "2 tircp 233.0 187250513 803650\n", + "3 Grand Total 1352.0 828620391 612884" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# moved to final NB 6/25\n", + "\n", + "# answers total buses and cost per grant type\n", + "pivot_source = pd.pivot_table(\n", + " merged_data,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"source\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ").reset_index()\n", + "\n", + "pivot_source[\"cost_per_bus\"] = (pivot_source[\"total_cost\"] / pivot_source[\"bus_count\"]).astype(\"int64\")\n", + "\n", + "display(\n", + " agg_source[[\"source\",\"total_agg_cost\",\"total_bus_count\",\"new_cost_per_bus\"]],\n", + " pivot_source\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "11547020-dd35-4745-98f8-bbd02fccaa23", + "metadata": { + "tags": [] + }, + "source": [ + "## Testing Charts\n", + "\n", + "using `merged_data`, now without outliers.\n", + "charts looking good, similar results to initial charts" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "aace38a4-3f2d-460d-a258-59efa659f852", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(88, 14)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "4753f3ea-00b6-4d5e-a3f0-73b3d3593acb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "792635.3409090909" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "396712.6067531972" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "1046500.7455752213" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "546173.304347826" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# means and standard deviations\n", + "# for graphs\n", + "cpb_mean = merged_data[\"cost_per_bus\"].mean()\n", + "cpb_std = merged_data[\"cost_per_bus\"].std()\n", + "\n", + "#testing weighted average calculation for sub-set non-zeb and zeb\n", + "zeb_cpb_wt_avg = (merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(zeb_list)][\"bus_count\"].sum())\n", + "non_zeb_cpb_wt_avg = (merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"total_cost\"].sum() / merged_data[merged_data[\"prop_type\"].isin(non_zeb_list)][\"bus_count\"].sum())\n", + "display(\n", + " cpb_mean,\n", + " cpb_std,\n", + " zeb_cpb_wt_avg,\n", + " non_zeb_cpb_wt_avg\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "2007be9d-13ec-4d0d-a642-d9a42448b924", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1056659.3043478262" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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prop_typebus_counttotal_costcost_per_bus
0BEB163.01672324891025966
1FCEB102.01209513351185797
2electric (not specified)44.0566780001288136
3zero-emission bus (not specified)143.0128156513896199
4Grand Total452.04730183371046500
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" + ], + "text/plain": [ + " prop_type bus_count total_cost cost_per_bus\n", + "0 BEB 163.0 167232489 1025966\n", + "1 FCEB 102.0 120951335 1185797\n", + "2 electric (not specified) 44.0 56678000 1288136\n", + "3 zero-emission bus (not specified) 143.0 128156513 896199\n", + "4 Grand Total 452.0 473018337 1046500" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "1046500.7455752213" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# why is the average different when i use .mean() vs. total cost / bus cout\n", + "\n", + "display(\n", + " #this is the arithmatic mean, sums all the `cost_per_bus` rows, the divide by the number of rows. (row-wise)\n", + " zeb_projects[\"cost_per_bus\"].mean(),\n", + " \n", + " #this is like the accounting method of calculating average (Total Cost and Total Quantity Approach (Weighted Average))\n", + " pivot_zeb_prop,\n", + " \n", + " # calculating mean by weighted average the long way (total cost / total bus count, similar to pivot table)\n", + " (zeb_projects[\"total_cost\"].sum() / zeb_projects[\"bus_count\"].sum())\n", + ")\n", + "\n", + "# so the calculated grand total cost_per_bus is equivilent to the weighted average cost per bus\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "8645cf77-b30a-4c45-b943-ac81e8b5a613", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# chart of all cost per bus in the analysis.\n", + "# moved to final NB 6/26\n", + "dist_curve(\n", + " df=merged_data,\n", + " mean=cpb_mean,\n", + " std=cpb_std,\n", + " title=\"all buses, cost per bus distribution\",\n", + " xlabel=\"cost per bus, $ million(s)\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "cefa6800-df50-4eda-95f8-74363ef942d0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ZEB cost per bus \n", + "# moved to final NB 6/26\n", + "dist_curve(\n", + " df=zeb_projects,\n", " #using the accounting, weighted average approach to mean (total cost/total number of buses)\n", " mean=zeb_cpb_wt_avg,\n", " # need to investigate if std needs to be weighted as well?\n", @@ -2034,12 +4210,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "id": "563304d2-2d98-44e6-b3a4-fd54f63fc0d8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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bus_size_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
0articulated025823757641.014204281.605005False
1cutaway3016694500152.0109832-1.464878False
2not specified406509919038881.0578795-0.366399False
3over-the-road01951600014.0679714-0.130011False
4standard/conventional (30ft-45ft)036234253277264.08873230.356283False
\n", + "
" + ], + "text/plain": [ + " bus_size_type total_project_count \\\n", + "0 articulated 0 \n", + "1 cutaway 3 \n", + "2 not specified 40 \n", + "3 over-the-road 0 \n", + "4 standard/conventional (30ft-45ft) 0 \n", + "\n", + " total_project_count_ppno total_agg_cost total_bus_count \\\n", + "0 2 58237576 41.0 \n", + "1 0 16694500 152.0 \n", + "2 6 509919038 881.0 \n", + "3 1 9516000 14.0 \n", + "4 36 234253277 264.0 \n", + "\n", + " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", + "0 1420428 1.605005 False \n", + "1 109832 -1.464878 False \n", + "2 578795 -0.366399 False \n", + "3 679714 -0.130011 False \n", + "4 887323 0.356283 False " + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "agg_bus_size" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "id": "5f11c857-ddbe-4871-aeca-e27fa00fbde8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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prop_typenew_cost_per_bustotal_bus_count
3electric (not specified)128813644.0
2FCEB1185797102.0
0BEB1025966163.0
9zero-emission bus (not specified)896199143.0
1CNG698568252.0
5low emission (hybrid)633271145.0
7mix (zero and low emission)294203125.0
6low emission (propane)19099944.0
8not specified127853325.0
4ethanol1118619.0
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" + ], + "text/plain": [ + " prop_type new_cost_per_bus total_bus_count\n", + "3 electric (not specified) 1288136 44.0\n", + "2 FCEB 1185797 102.0\n", + "0 BEB 1025966 163.0\n", + "9 zero-emission bus (not specified) 896199 143.0\n", + "1 CNG 698568 252.0\n", + "5 low emission (hybrid) 633271 145.0\n", + "7 mix (zero and low emission) 294203 125.0\n", + "6 low emission (propane) 190999 44.0\n", + "8 not specified 127853 325.0\n", + "4 ethanol 111861 9.0" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# multiple bar charts in one cell\n", + "# moved to final NB 6/26\n", "\n", "# cpb by prop type\n", "make_chart(\"new_cost_per_bus\", \"Cost per bus by propulsion type\", x_col=\"prop_type\", data=agg_prop)\n", @@ -2097,89 +4545,766 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "id": "2472461d-7663-4b66-9bde-4c2a199707a5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/markdown": [ + "\n", + "\n", + "# Bus Procurement Cost Analysis\n", + "\n", + "## Summary\n", + "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", + "\n", + "Data was compiled from three data sources:\n", + "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", + "2. TIRCP project data (state-funded, California only)\n", + "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", + "\n", + "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", + "The resulting dataset only contained projects that were solely used to procure buses. \n", + "\n", + "88 projects were determined to contain solely bus purchases. \n", + "These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", + "\n", + "\n", + "Breakdown of each data souce:\n", + "| source | bus_count | total_cost | cost_per_bus |\n", + "|:------------|------------:|-------------:|---------------:|\n", + "| dgs | 236 | 250112853 | 1059800 |\n", + "| fta | 883 | 391257025 | 443099 |\n", + "| tircp | 233 | 187250513 | 803650 |\n", + "| Grand Total | 1352 | 828620391 | 612884 |\n", + "\n", + "\n", + "**ZEB buses include:**\n", + "- zero-emission (not specified) \n", + "- electric (not specified)\n", + "- battery electric \n", + "- fuel cell electric\n", + "\n", + "**Non-ZEB buses include:**\n", + "- CNG \n", + "- ethanol \n", + "- ow emission (hybrid, propane) \n", + "- diesel \n", + "- gas\n", + "\n", + "Below are charts and tables that summarize the findings.\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# moved to final NB 6/25\n", + "\n", + "new_summary = f\"\"\"\n", + "\n", + "# Bus Procurement Cost Analysis\n", + "\n", + "## Summary\n", + "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", + "\n", + "Data was compiled from three data sources:\n", + "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", + "2. TIRCP project data (state-funded, California only)\n", + "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", + "\n", + "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", + "The resulting dataset only contained projects that were solely used to procure buses. \n", + "\n", + "{len(merged_data)} projects were determined to contain solely bus purchases. \n", + "These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", + "\n", + "\n", + "Breakdown of each data souce:\n", + "{pivot_source.to_markdown(index=False)}\n", + "\n", + "\n", + "**ZEB buses include:**\n", + "- zero-emission (not specified) \n", + "- electric (not specified)\n", + "- battery electric \n", + "- fuel cell electric\n", + "\n", + "**Non-ZEB buses include:**\n", + "- CNG \n", + "- ethanol \n", + "- ow emission (hybrid, propane) \n", + "- diesel \n", + "- gas\n", + "\n", + "Below are charts and tables that summarize the findings.\n", + "\n", + "\n", + "\n", + "\n", + "\"\"\"\n", + "from IPython.display import Markdown, display\n", + "\n", + "display(Markdown(new_summary))" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "7472ba04-7def-46ef-814b-bf63c1016f3b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "**ZEB Summary**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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prop_typebus_counttotal_costcost_per_bus
0BEB163.01672324891025966
1FCEB102.01209513351185797
2electric (not specified)44.0566780001288136
3zero-emission bus (not specified)143.0128156513896199
4Grand Total452.04730183371046500
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" + ], + "text/plain": [ + " prop_type bus_count total_cost cost_per_bus\n", + "0 BEB 163.0 167232489 1025966\n", + "1 FCEB 102.0 120951335 1185797\n", + "2 electric (not specified) 44.0 56678000 1288136\n", + "3 zero-emission bus (not specified) 143.0 128156513 896199\n", + "4 Grand Total 452.0 473018337 1046500" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Non-ZEB Summary**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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prop_typebus_counttotal_costcost_per_bus
0CNG252.0176039140698568
1ethanol9.01006750111861
2low emission (hybrid)145.091824361633271
3low emission (propane)44.08403969190999
4mix (zero and low emission)125.036775430294203
5Grand Total575.0314049650546173
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" + ], + "text/plain": [ + " prop_type bus_count total_cost cost_per_bus\n", + "0 CNG 252.0 176039140 698568\n", + "1 ethanol 9.0 1006750 111861\n", + "2 low emission (hybrid) 145.0 91824361 633271\n", + "3 low emission (propane) 44.0 8403969 190999\n", + "4 mix (zero and low emission) 125.0 36775430 294203\n", + "5 Grand Total 575.0 314049650 546173" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "The remaining buses did not specify a propulsion type" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# moved to final NB 6/25\n", - "\n", - "new_summary = f\"\"\"\n", - "\n", - "# Bus Procurement Cost Analysis\n", - "\n", - "## Summary\n", - "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", - "\n", - "Data was compiled from three data sources:\n", - "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", - "2. TIRCP project data (state-funded, California only)\n", - "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", - "\n", - "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", - "The resulting dataset only contained projects that were solely used to procure buses. \n", - "\n", - "{len(merged_data)} projects were determined to contain solely bus purchases. \n", - "These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", - "\n", - "\n", - "Breakdown of each data souce:\n", - "{pivot_source.to_markdown(index=False)}\n", - "\n", - "\n", - "**ZEB buses include:**\n", - "- zero-emission (not specified) \n", - "- electric (not specified)\n", - "- battery electric \n", - "- fuel cell electric\n", - "\n", - "**Non-ZEB buses include:**\n", - "- CNG \n", - "- ethanol \n", - "- ow emission (hybrid, propane) \n", - "- diesel \n", - "- gas\n", - "\n", - "Below are charts and tables that summarize the findings.\n", - "\n", - "\n", - "\n", - "\n", - "\"\"\"\n", - "from IPython.display import Markdown, display\n", - "\n", - "display(Markdown(new_summary))" + "display(\n", + " Markdown(\"**ZEB Summary**\"),\n", + " pivot_zeb_prop,\n", + " \n", + " Markdown(\"**Non-ZEB Summary**\"),\n", + " pivot_non_zeb_prop,\n", + " \n", + " Markdown(\"The remaining buses did not specify a propulsion type\")\n", + ")" ] }, { "cell_type": "code", - "execution_count": null, - "id": "7472ba04-7def-46ef-814b-bf63c1016f3b", + "execution_count": 46, + "id": "91d0361d-b165-4607-b22e-66ae4234863d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/markdown": [ + "**Max new_cost_per_bus**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
71Transit Joint Powers Authority for Merced County32233242.01611662
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" + ], + "text/plain": [ + " transit_agency total_agg_cost \\\n", + "71 Transit Joint Powers Authority for Merced County 3223324 \n", + "\n", + " total_bus_count new_cost_per_bus \n", + "71 2.0 1611662 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Min new_cost_per_bus**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
45Oregon Department of Transportation on behalf ...1812505.036250
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
61South Carolina Department of Transportation on...15423904160.096399
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" + ], + "text/plain": [ + " transit_agency total_agg_cost \\\n", + "61 South Carolina Department of Transportation on... 15423904 \n", + "\n", + " total_bus_count new_cost_per_bus \n", + "61 160.0 96399 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Min total_bus_count**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
9City of Beloit6531841.0653184
16City of San Luis Obispo8592701.0859270
49SLO TRANSIT (SAN LUIS OBISPO, CA)8472141.0847214
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" + ], + "text/plain": [ + " transit_agency total_agg_cost total_bus_count \\\n", + "9 City of Beloit 653184 1.0 \n", + "16 City of San Luis Obispo 859270 1.0 \n", + "49 SLO TRANSIT (SAN LUIS OBISPO, CA) 847214 1.0 \n", + "\n", + " new_cost_per_bus \n", + "9 653184 \n", + "16 859270 \n", + "49 847214 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Max total_agg_cost**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
24Dallas Area Rapid Transit (DART)10300000090.01144444
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" + ], + "text/plain": [ + " transit_agency total_agg_cost total_bus_count \\\n", + "24 Dallas Area Rapid Transit (DART) 103000000 90.0 \n", + "\n", + " new_cost_per_bus \n", + "24 1144444 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Min total_agg_cost**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
45Oregon Department of Transportation on behalf ...1812505.036250
\n", + "
" + ], + "text/plain": [ + " transit_agency total_agg_cost \\\n", + "45 Oregon Department of Transportation on behalf ... 181250 \n", + "\n", + " total_bus_count new_cost_per_bus \n", + "45 5.0 36250 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# moved to final NB 6/25\n", - "display(\n", - " Markdown(\"**ZEB Summary**\"),\n", - " pivot_zeb_prop,\n", - " \n", - " Markdown(\"**Non-ZEB Summary**\"),\n", - " pivot_non_zeb_prop,\n", - " \n", - " Markdown(\"The remaining buses did not specify a propulsion type\")\n", - ")" + "#min max values for all projects\n", + "bus_min_max_summary(data=agg_agency,col1=\"new_cost_per_bus\")\n", + "bus_min_max_summary(data=agg_agency,col1=\"total_bus_count\")\n", + "bus_min_max_summary(data=agg_agency,col1=\"total_agg_cost\")\n" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "91d0361d-b165-4607-b22e-66ae4234863d", + "execution_count": 47, + "id": "6896d09d-a8e8-4351-bf69-6538d031bf93", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "**Max new_cost_per_bus**" + "**Which Agneices had the highest and lowest cost per bus?**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Max cost_per_bus**" ], "text/plain": [ "" @@ -2210,29 +5335,31 @@ " \n", " \n", " transit_agency\n", - " total_agg_cost\n", - " total_bus_count\n", - " new_cost_per_bus\n", + " prop_type\n", + " total_cost\n", + " bus_count\n", + " cost_per_bus\n", " \n", " \n", " \n", " \n", - " 71\n", - " Transit Joint Powers Authority for Merced County\n", - " 3223324\n", + " 76\n", + " University of California - San Diego\n", + " BEB\n", + " 4134000\n", " 2.0\n", - " 1611662\n", + " 2067000\n", " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency total_agg_cost \\\n", - "71 Transit Joint Powers Authority for Merced County 3223324 \n", + " transit_agency prop_type total_cost bus_count \\\n", + "76 University of California - San Diego BEB 4134000 2.0 \n", "\n", - " total_bus_count new_cost_per_bus \n", - "71 2.0 1611662 " + " cost_per_bus \n", + "76 2067000 " ] }, "metadata": {}, @@ -2241,7 +5368,7 @@ { "data": { "text/markdown": [ - "**Min new_cost_per_bus**" + "**Min cost_per_bus**" ], "text/plain": [ "" @@ -2272,29 +5399,66 @@ " \n", " \n", " transit_agency\n", - " total_agg_cost\n", - " total_bus_count\n", - " new_cost_per_bus\n", + " prop_type\n", + " total_cost\n", + " bus_count\n", + " cost_per_bus\n", " \n", " \n", " \n", " \n", " 45\n", - " Oregon Department of Transportation on behalf ...\n", - " 181250\n", - " 5.0\n", - " 36250\n", + " City of Wasco\n", + " zero-emission bus (not specified)\n", + " 1543000\n", + " 3.0\n", + " 514333\n", " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency total_agg_cost \\\n", - "45 Oregon Department of Transportation on behalf ... 181250 \n", + " transit_agency prop_type total_cost bus_count \\\n", + "45 City of Wasco zero-emission bus (not specified) 1543000 3.0 \n", "\n", - " total_bus_count new_cost_per_bus \n", - "45 5.0 36250 " + " cost_per_bus \n", + "45 514333 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# moved to final NB 6/25\n", + "## min max values of just ZEB projects\n", + "# YES I CAN!!\n", + "new_cols =[\n", + " \"transit_agency\",\n", + " \"prop_type\",\n", + " \"total_cost\",\n", + " \"bus_count\",\n", + " \"cost_per_bus\"]\n", + "\n", + "display(Markdown(\"**Which Agneices had the highest and lowest cost per bus?**\")),\n", + "bus_min_max_summary(data=zeb_projects, col1=\"cost_per_bus\", col_list=new_cols)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "743b25a2-8693-44f7-98fe-384e910620a7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "**Which agency procured the most and least amount of ZEBs?**" + ], + "text/plain": [ + "" ] }, "metadata": {}, @@ -2303,7 +5467,7 @@ { "data": { "text/markdown": [ - "**Max total_bus_count**" + "**Max bus_count**" ], "text/plain": [ "" @@ -2334,29 +5498,31 @@ " \n", " \n", " transit_agency\n", - " total_agg_cost\n", - " total_bus_count\n", - " new_cost_per_bus\n", + " prop_type\n", + " total_cost\n", + " bus_count\n", + " cost_per_bus\n", " \n", " \n", " \n", " \n", - " 61\n", - " South Carolina Department of Transportation on...\n", - " 15423904\n", - " 160.0\n", - " 96399\n", + " 44\n", + " City of Los Angeles (LA DOT)\n", + " zero-emission bus (not specified)\n", + " 102790000\n", + " 112.0\n", + " 917767\n", " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency total_agg_cost \\\n", - "61 South Carolina Department of Transportation on... 15423904 \n", + " transit_agency prop_type \\\n", + "44 City of Los Angeles (LA DOT) zero-emission bus (not specified) \n", "\n", - " total_bus_count new_cost_per_bus \n", - "61 160.0 96399 " + " total_cost bus_count cost_per_bus \n", + "44 102790000 112.0 917767 " ] }, "metadata": {}, @@ -2365,7 +5531,7 @@ { "data": { "text/markdown": [ - "**Min total_bus_count**" + "**Min bus_count**" ], "text/plain": [ "" @@ -2396,47 +5562,68 @@ " \n", " \n", " transit_agency\n", - " total_agg_cost\n", - " total_bus_count\n", - " new_cost_per_bus\n", + " prop_type\n", + " total_cost\n", + " bus_count\n", + " cost_per_bus\n", " \n", " \n", " \n", " \n", - " 9\n", - " City of Beloit\n", - " 653184\n", + " 70\n", + " SLO TRANSIT (SAN LUIS OBISPO, CA)\n", + " BEB\n", + " 847214\n", " 1.0\n", - " 653184\n", + " 847214\n", " \n", " \n", - " 16\n", + " 82\n", " City of San Luis Obispo\n", + " BEB\n", " 859270\n", " 1.0\n", " 859270\n", " \n", - " \n", - " 49\n", - " SLO TRANSIT (SAN LUIS OBISPO, CA)\n", - " 847214\n", - " 1.0\n", - " 847214\n", - " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency total_agg_cost total_bus_count \\\n", - "9 City of Beloit 653184 1.0 \n", - "16 City of San Luis Obispo 859270 1.0 \n", - "49 SLO TRANSIT (SAN LUIS OBISPO, CA) 847214 1.0 \n", + " transit_agency prop_type total_cost bus_count \\\n", + "70 SLO TRANSIT (SAN LUIS OBISPO, CA) BEB 847214 1.0 \n", + "82 City of San Luis Obispo BEB 859270 1.0 \n", "\n", - " new_cost_per_bus \n", - "9 653184 \n", - "16 859270 \n", - "49 847214 " + " cost_per_bus \n", + "70 847214 \n", + "82 859270 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# moved to final NB 6/25\n", + "display(Markdown(\n", + " \"**Which agency procured the most and least amount of ZEBs?**\"\n", + "))\n", + "bus_min_max_summary(data=zeb_projects, col1=\"bus_count\", col_list=new_cols)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "45a95018-0ac8-450d-97d2-aa394e94779a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "**Which Agency had the most and least total ZEB cost?**" + ], + "text/plain": [ + "" ] }, "metadata": {}, @@ -2445,7 +5632,7 @@ { "data": { "text/markdown": [ - "**Max total_agg_cost**" + "**Max total_cost**" ], "text/plain": [ "" @@ -2476,29 +5663,31 @@ " \n", " \n", " transit_agency\n", - " total_agg_cost\n", - " total_bus_count\n", - " new_cost_per_bus\n", + " prop_type\n", + " total_cost\n", + " bus_count\n", + " cost_per_bus\n", " \n", " \n", " \n", " \n", - " 24\n", - " Dallas Area Rapid Transit (DART)\n", - " 103000000\n", - " 90.0\n", - " 1144444\n", + " 44\n", + " City of Los Angeles (LA DOT)\n", + " zero-emission bus (not specified)\n", + " 102790000\n", + " 112.0\n", + " 917767\n", " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency total_agg_cost total_bus_count \\\n", - "24 Dallas Area Rapid Transit (DART) 103000000 90.0 \n", + " transit_agency prop_type \\\n", + "44 City of Los Angeles (LA DOT) zero-emission bus (not specified) \n", "\n", - " new_cost_per_bus \n", - "24 1144444 " + " total_cost bus_count cost_per_bus \n", + "44 102790000 112.0 917767 " ] }, "metadata": {}, @@ -2507,7 +5696,7 @@ { "data": { "text/markdown": [ - "**Min total_agg_cost**" + "**Min total_cost**" ], "text/plain": [ "" @@ -2538,84 +5727,37 @@ " \n", " \n", " transit_agency\n", - " total_agg_cost\n", - " total_bus_count\n", - " new_cost_per_bus\n", + " prop_type\n", + " total_cost\n", + " bus_count\n", + " cost_per_bus\n", " \n", " \n", " \n", " \n", - " 45\n", - " Oregon Department of Transportation on behalf ...\n", - " 181250\n", - " 5.0\n", - " 36250\n", + " 70\n", + " SLO TRANSIT (SAN LUIS OBISPO, CA)\n", + " BEB\n", + " 847214\n", + " 1.0\n", + " 847214\n", " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency total_agg_cost \\\n", - "45 Oregon Department of Transportation on behalf ... 181250 \n", + " transit_agency prop_type total_cost bus_count \\\n", + "70 SLO TRANSIT (SAN LUIS OBISPO, CA) BEB 847214 1.0 \n", "\n", - " total_bus_count new_cost_per_bus \n", - "45 5.0 36250 " + " cost_per_bus \n", + "70 847214 " ] }, "metadata": {}, "output_type": "display_data" } ], - "source": [ - "#min max values for all projects\n", - "bus_min_max_summary(data=agg_agency,col1=\"new_cost_per_bus\")\n", - "bus_min_max_summary(data=agg_agency,col1=\"total_bus_count\")\n", - "bus_min_max_summary(data=agg_agency,col1=\"total_agg_cost\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6896d09d-a8e8-4351-bf69-6538d031bf93", - "metadata": {}, - "outputs": [], - "source": [ - "# moved to final NB 6/25\n", - "## min max values of just ZEB projects\n", - "# YES I CAN!!\n", - "new_cols =[\n", - " \"transit_agency\",\n", - " \"prop_type\",\n", - " \"total_cost\",\n", - " \"bus_count\",\n", - " \"cost_per_bus\"]\n", - "\n", - "display(Markdown(\"**Which Agneices had the highest and lowest cost per bus?**\")),\n", - "bus_min_max_summary(data=zeb_projects, col1=\"cost_per_bus\", col_list=new_cols)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "743b25a2-8693-44f7-98fe-384e910620a7", - "metadata": {}, - "outputs": [], - "source": [ - "# moved to final NB 6/25\n", - "display(Markdown(\n", - " \"**Which agency procured the most and least amount of ZEBs?**\"\n", - "))\n", - "bus_min_max_summary(data=zeb_projects, col1=\"bus_count\", col_list=new_cols)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "45a95018-0ac8-450d-97d2-aa394e94779a", - "metadata": {}, - "outputs": [], "source": [ "# moved to final NB 6/25\n", "display(Markdown(\n", @@ -2634,10 +5776,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "id": "e39c89a1-a726-44f9-808b-bcf936c77254", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/markdown": [ + "\n", + "**Conclusion**\n", + "\n", + "Based on these findings, The average cost of a ZEB, throughout the US, is ~$1,000,000, roughly twice the price of a conventional, non-ZEB.\n", + "The variance in cost depends mainly on the options the Trasnit\n", + "Agencies chooses. Highly optioned/customized buses contribute to high cost.\n", + "Unfortunately, analyzing the cost of configuable options is outside the scope of data provided. \n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# moved to final NB 6/25\n", "conclusion = f\"\"\"\n", From 7be2f2c8ecfab26131d088b6f9e39b4d2b0491de Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Wed, 26 Jun 2024 18:38:59 +0000 Subject: [PATCH 30/36] moved min max function to NB. reorganized the charts and disabled the overall charts --- bus_procurement_cost/bus_cost_utils.py | 24 +- .../cost_per_bus_analysis.ipynb | 1272 +++++++++-------- 2 files changed, 672 insertions(+), 624 deletions(-) diff --git a/bus_procurement_cost/bus_cost_utils.py b/bus_procurement_cost/bus_cost_utils.py index e15586fbd..0249d6494 100644 --- a/bus_procurement_cost/bus_cost_utils.py +++ b/bus_procurement_cost/bus_cost_utils.py @@ -235,20 +235,20 @@ def col_row_updater(df: pd.DataFrame, col1: str, val1, col2: str, new_val): return -def bus_min_max_summary(data:pd.DataFrame, col1:str, col_list=["transit_agency", - "total_agg_cost", - "total_bus_count", - "new_cost_per_bus"]): - """ - function to display min/max of specific column in aggregated bus df. +#def bus_min_max_summary(data:pd.DataFrame, col1:str, col_list=["transit_agency", +# "total_agg_cost", +# "total_bus_count", +# "new_cost_per_bus"]): +# """ +# function to display min/max of specific column in aggregated bus df. - """ +# """ - return display(Markdown(f"**Max {col1}**"), - data[data[col1] == data[col1].max()][col_list], - Markdown(f"**Min {col1}**"), - data[data[col1] == data[col1].min()][col_list] - ) +# return display(Markdown(f"**Max {col1}**"), +# data[data[col1] == data[col1].max()][col_list], +# Markdown(f"**Min {col1}**"), +# data[data[col1] == data[col1].min()][col_list] +# ) def outlier_flag(col): """ diff --git a/bus_procurement_cost/cost_per_bus_analysis.ipynb b/bus_procurement_cost/cost_per_bus_analysis.ipynb index 0829a5c22..bb46cdde6 100644 --- a/bus_procurement_cost/cost_per_bus_analysis.ipynb +++ b/bus_procurement_cost/cost_per_bus_analysis.ipynb @@ -117,6 +117,29 @@ " return df_agg" ] }, + { + "cell_type": "code", + "execution_count": 81, + "id": "2a2dc407-20cc-45de-84b1-bb5991dad8ac", + "metadata": {}, + "outputs": [], + "source": [ + "def bus_min_max_summary(data:pd.DataFrame, col1:str, col_list=[\"transit_agency\",\n", + " \"total_agg_cost\",\n", + " \"total_bus_count\",\n", + " \"new_cost_per_bus\"]):\n", + " \"\"\"\n", + " function to display min/max of specific column in aggregated bus df.\n", + " \n", + " \"\"\"\n", + "\n", + " return display(Markdown(f\"**Max {col1}**\"),\n", + " data[data[col1] == data[col1].max()][col_list],\n", + " Markdown(f\"**Min {col1}**\"),\n", + " data[data[col1] == data[col1].min()][col_list])\n", + " " + ] + }, { "cell_type": "code", "execution_count": 6, @@ -279,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 108, "id": "0a2163e3-dac1-4e64-a551-3dc961e44714", "metadata": {}, "outputs": [], @@ -289,15 +312,254 @@ "pivot_size = pd.pivot_table(\n", " merged_data,\n", " values = [\"bus_count\", \"total_cost\"],\n", - " index = \"bus_size_type\",\n", + " # multi-index pivot\n", + " index = [\"prop_type\",\"bus_size_type\"],\n", " aggfunc = \"sum\",\n", " margins = True,\n", " margins_name = \"Grand Total\"\n", - ").reset_index()\n", + ")\n", "\n", "pivot_size[\"cost_per_bus\"] = (pivot_size[\"total_cost\"] / pivot_size[\"bus_count\"]).astype(\"int64\")" ] }, + { + "cell_type": "code", + "execution_count": 109, + "id": "81e253c5-37f1-4a5e-9666-8ff2a6442635", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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bus_counttotal_costcost_per_bus
prop_typebus_size_type
BEBarticulated12.0187595761563298
standard/conventional (30ft-45ft)151.0148472913983264
CNGcutaway3.01162000387333
not specified209.0171977140822857
standard/conventional (30ft-45ft)40.0290000072500
FCEBnot specified29.0380709711312792
standard/conventional (30ft-45ft)73.0828803641135347
electric (not specified)articulated29.0394780001361310
not specified15.0172000001146666
ethanolnot specified9.01006750111861
low emission (hybrid)not specified145.091824361633271
low emission (propane)not specified44.08403969190999
mix (zero and low emission)not specified125.036775430294203
not specifiedcutaway149.015532500104244
not specified162.016503904101875
over-the-road14.09516000679714
zero-emission bus (not specified)not specified143.0128156513896199
Grand Total1352.0828620391612884
\n", + "
" + ], + "text/plain": [ + " bus_count \\\n", + "prop_type bus_size_type \n", + "BEB articulated 12.0 \n", + " standard/conventional (30ft-45ft) 151.0 \n", + "CNG cutaway 3.0 \n", + " not specified 209.0 \n", + " standard/conventional (30ft-45ft) 40.0 \n", + "FCEB not specified 29.0 \n", + " standard/conventional (30ft-45ft) 73.0 \n", + "electric (not specified) articulated 29.0 \n", + " not specified 15.0 \n", + "ethanol not specified 9.0 \n", + "low emission (hybrid) not specified 145.0 \n", + "low emission (propane) not specified 44.0 \n", + "mix (zero and low emission) not specified 125.0 \n", + "not specified cutaway 149.0 \n", + " not specified 162.0 \n", + " over-the-road 14.0 \n", + "zero-emission bus (not specified) not specified 143.0 \n", + "Grand Total 1352.0 \n", + "\n", + " total_cost \\\n", + "prop_type bus_size_type \n", + "BEB articulated 18759576 \n", + " standard/conventional (30ft-45ft) 148472913 \n", + "CNG cutaway 1162000 \n", + " not specified 171977140 \n", + " standard/conventional (30ft-45ft) 2900000 \n", + "FCEB not specified 38070971 \n", + " standard/conventional (30ft-45ft) 82880364 \n", + "electric (not specified) articulated 39478000 \n", + " not specified 17200000 \n", + "ethanol not specified 1006750 \n", + "low emission (hybrid) not specified 91824361 \n", + "low emission (propane) not specified 8403969 \n", + "mix (zero and low emission) not specified 36775430 \n", + "not specified cutaway 15532500 \n", + " not specified 16503904 \n", + " over-the-road 9516000 \n", + "zero-emission bus (not specified) not specified 128156513 \n", + "Grand Total 828620391 \n", + "\n", + " cost_per_bus \n", + "prop_type bus_size_type \n", + "BEB articulated 1563298 \n", + " standard/conventional (30ft-45ft) 983264 \n", + "CNG cutaway 387333 \n", + " not specified 822857 \n", + " standard/conventional (30ft-45ft) 72500 \n", + "FCEB not specified 1312792 \n", + " standard/conventional (30ft-45ft) 1135347 \n", + "electric (not specified) articulated 1361310 \n", + " not specified 1146666 \n", + "ethanol not specified 111861 \n", + "low emission (hybrid) not specified 633271 \n", + "low emission (propane) not specified 190999 \n", + "mix (zero and low emission) not specified 294203 \n", + "not specified cutaway 104244 \n", + " not specified 101875 \n", + " over-the-road 679714 \n", + "zero-emission bus (not specified) not specified 896199 \n", + "Grand Total 612884 " + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pivot_size" + ] + }, { "cell_type": "code", "execution_count": 14, @@ -320,7 +582,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 90, "id": "d8356953-e32d-47ab-b67c-fa016cad9c50", "metadata": {}, "outputs": [ @@ -428,502 +690,14 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "676cbd9a-db4b-4e86-b60b-900f14513468", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "**ZEB Summary**" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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prop_typebus_counttotal_costcost_per_bus
0BEB163.01672324891025966
1FCEB102.01209513351185797
2electric (not specified)44.0566780001288136
3zero-emission bus (not specified)143.0128156513896199
4Grand Total452.04730183371046500
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" - ], - "text/plain": [ - " prop_type bus_count total_cost cost_per_bus\n", - "0 BEB 163.0 167232489 1025966\n", - "1 FCEB 102.0 120951335 1185797\n", - "2 electric (not specified) 44.0 56678000 1288136\n", - "3 zero-emission bus (not specified) 143.0 128156513 896199\n", - "4 Grand Total 452.0 473018337 1046500" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "**Non-ZEB Summary**" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
prop_typebus_counttotal_costcost_per_bus
0CNG252.0176039140698568
1ethanol9.01006750111861
2low emission (hybrid)145.091824361633271
3low emission (propane)44.08403969190999
4mix (zero and low emission)125.036775430294203
5Grand Total575.0314049650546173
\n", - "
" - ], - "text/plain": [ - " prop_type bus_count total_cost cost_per_bus\n", - "0 CNG 252.0 176039140 698568\n", - "1 ethanol 9.0 1006750 111861\n", - "2 low emission (hybrid) 145.0 91824361 633271\n", - "3 low emission (propane) 44.0 8403969 190999\n", - "4 mix (zero and low emission) 125.0 36775430 294203\n", - "5 Grand Total 575.0 314049650 546173" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "The remaining buses did not specify a propulsion type" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#summary stuff\n", - "display(\n", - " Markdown(\"**ZEB Summary**\"),\n", - " pivot_zeb_prop,\n", - " \n", - " Markdown(\"**Non-ZEB Summary**\"),\n", - " pivot_non_zeb_prop,\n", - " \n", - " Markdown(\"The remaining buses did not specify a propulsion type\")\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "0158cda6-4cab-416a-bfe0-a6896d6da997", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "**Max new_cost_per_bus**" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
71Transit Joint Powers Authority for Merced County32233242.01611662
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" - ], - "text/plain": [ - " transit_agency total_agg_cost \\\n", - "71 Transit Joint Powers Authority for Merced County 3223324 \n", - "\n", - " total_bus_count new_cost_per_bus \n", - "71 2.0 1611662 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "**Min new_cost_per_bus**" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
45Oregon Department of Transportation on behalf ...1812505.036250
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
61South Carolina Department of Transportation on...15423904160.096399
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
9City of Beloit6531841.0653184
16City of San Luis Obispo8592701.0859270
49SLO TRANSIT (SAN LUIS OBISPO, CA)8472141.0847214
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" - ], - "text/plain": [ - " transit_agency total_agg_cost total_bus_count \\\n", - "9 City of Beloit 653184 1.0 \n", - "16 City of San Luis Obispo 859270 1.0 \n", - "49 SLO TRANSIT (SAN LUIS OBISPO, CA) 847214 1.0 \n", - "\n", - " new_cost_per_bus \n", - "9 653184 \n", - "16 859270 \n", - "49 847214 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, + "execution_count": 91, + "id": "676cbd9a-db4b-4e86-b60b-900f14513468", + "metadata": {}, + "outputs": [ { "data": { "text/markdown": [ - "**Max total_agg_cost**" + "**ZEB Cost Summary**" ], "text/plain": [ "" @@ -953,30 +727,59 @@ " \n", " \n", " \n", - " transit_agency\n", - " total_agg_cost\n", - " total_bus_count\n", - " new_cost_per_bus\n", + " prop_type\n", + " bus_count\n", + " total_cost\n", + " cost_per_bus\n", " \n", " \n", " \n", " \n", - " 24\n", - " Dallas Area Rapid Transit (DART)\n", - " 103000000\n", - " 90.0\n", - " 1144444\n", + " 0\n", + " BEB\n", + " 163.0\n", + " 167232489\n", + " 1025966\n", + " \n", + " \n", + " 1\n", + " FCEB\n", + " 102.0\n", + " 120951335\n", + " 1185797\n", + " \n", + " \n", + " 2\n", + " electric (not specified)\n", + " 44.0\n", + " 56678000\n", + " 1288136\n", + " \n", + " \n", + " 3\n", + " zero-emission bus (not specified)\n", + " 143.0\n", + " 128156513\n", + " 896199\n", + " \n", + " \n", + " 4\n", + " Grand Total\n", + " 452.0\n", + " 473018337\n", + " 1046500\n", " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency total_agg_cost total_bus_count \\\n", - "24 Dallas Area Rapid Transit (DART) 103000000 90.0 \n", - "\n", - " new_cost_per_bus \n", - "24 1144444 " + " prop_type bus_count total_cost cost_per_bus\n", + "0 BEB 163.0 167232489 1025966\n", + "1 FCEB 102.0 120951335 1185797\n", + "2 electric (not specified) 44.0 56678000 1288136\n", + "3 zero-emission bus (not specified) 143.0 128156513 896199\n", + "4 Grand Total 452.0 473018337 1046500" ] }, "metadata": {}, @@ -985,7 +788,7 @@ { "data": { "text/markdown": [ - "**Min total_agg_cost**" + "**Non-ZEB Cost Summary**" ], "text/plain": [ "" @@ -1015,30 +818,79 @@ " \n", " \n", " \n", - " transit_agency\n", - " total_agg_cost\n", - " total_bus_count\n", - " new_cost_per_bus\n", + " prop_type\n", + " bus_count\n", + " total_cost\n", + " cost_per_bus\n", " \n", " \n", " \n", " \n", - " 45\n", - " Oregon Department of Transportation on behalf ...\n", - " 181250\n", - " 5.0\n", - " 36250\n", + " 0\n", + " CNG\n", + " 252.0\n", + " 176039140\n", + " 698568\n", + " \n", + " \n", + " 1\n", + " ethanol\n", + " 9.0\n", + " 1006750\n", + " 111861\n", + " \n", + " \n", + " 2\n", + " low emission (hybrid)\n", + " 145.0\n", + " 91824361\n", + " 633271\n", + " \n", + " \n", + " 3\n", + " low emission (propane)\n", + " 44.0\n", + " 8403969\n", + " 190999\n", + " \n", + " \n", + " 4\n", + " mix (zero and low emission)\n", + " 125.0\n", + " 36775430\n", + " 294203\n", + " \n", + " \n", + " 5\n", + " Grand Total\n", + " 575.0\n", + " 314049650\n", + " 546173\n", " \n", " \n", "\n", "" ], "text/plain": [ - " transit_agency total_agg_cost \\\n", - "45 Oregon Department of Transportation on behalf ... 181250 \n", - "\n", - " total_bus_count new_cost_per_bus \n", - "45 5.0 36250 " + " prop_type bus_count total_cost cost_per_bus\n", + "0 CNG 252.0 176039140 698568\n", + "1 ethanol 9.0 1006750 111861\n", + "2 low emission (hybrid) 145.0 91824361 633271\n", + "3 low emission (propane) 44.0 8403969 190999\n", + "4 mix (zero and low emission) 125.0 36775430 294203\n", + "5 Grand Total 575.0 314049650 546173" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "The remaining buses did not specify a propulsion type" + ], + "text/plain": [ + "" ] }, "metadata": {}, @@ -1046,22 +898,70 @@ } ], "source": [ - "#min max values for all projects\n", - "bus_min_max_summary(data=agg_agency,col1=\"new_cost_per_bus\")\n", - "bus_min_max_summary(data=agg_agency,col1=\"total_bus_count\")\n", - "bus_min_max_summary(data=agg_agency,col1=\"total_agg_cost\")\n" + "#summary stuff\n", + "display(\n", + " Markdown(\"**ZEB Cost Summary**\"),\n", + " pivot_zeb_prop,\n", + " \n", + " Markdown(\"**Non-ZEB Cost Summary**\"),\n", + " pivot_non_zeb_prop,\n", + " \n", + " Markdown(\"The remaining buses did not specify a propulsion type\")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "id": "d99e56b6-2d69-4bc3-9ac2-169df1d3f6ef", + "metadata": {}, + "outputs": [], + "source": [ + "# overall summary\n", + "# commenting out for now.\n", + "#display(\n", + "# Markdown(\"## Which Agencies has the highest and lowest overall cost per bus?\"))\n", + "#bus_min_max_summary(data=agg_agency,col1=\"new_cost_per_bus\")" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "id": "74ecf466-3560-46e1-a792-e217231ce1b4", + "metadata": {}, + "outputs": [], + "source": [ + "# overall summary\n", + "# commenting out for now.\n", + "#display(\n", + "# Markdown(\"## Which Agencies has the highest and lowest overall procurement cost?\"))\n", + "#bus_min_max_summary(data=agg_agency,col1=\"total_agg_cost\")" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "id": "30c42e6d-3ca3-4715-a472-b7501e36f2fe", + "metadata": {}, + "outputs": [], + "source": [ + "# overall summary\n", + "# commenting out for now.\n", + "#display(\n", + "# Markdown(\"## Which Agencies procured the has the most and least overal number of buses?\"))\n", + "#bus_min_max_summary(data=agg_agency,col1=\"total_bus_count\")" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 95, "id": "80dd3d1c-86f8-4c68-bdd1-8249e6494f2c", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "**Which Agneices had the highest and lowest cost per bus?**" + "## Which Agneices had the highest and lowest ZEB cost per bus?" ], "text/plain": [ "" @@ -1208,20 +1108,20 @@ " \"bus_count\",\n", " \"cost_per_bus\"]\n", "\n", - "display(Markdown(\"**Which Agneices had the highest and lowest cost per bus?**\")),\n", + "display(Markdown(\"## Which Agneices had the highest and lowest ZEB cost per bus?\")),\n", "bus_min_max_summary(data=zeb_projects, col1=\"cost_per_bus\", col_list=new_cols)" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 96, "id": "75919ab8-7f14-49f2-bb4a-9765fdddc35c", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "**Which agency procured the most and least amount of ZEBs?**" + "## Which Agencies procured the most and least amount of ZEBs?" ], "text/plain": [ "" @@ -1371,21 +1271,21 @@ ], "source": [ "display(Markdown(\n", - " \"**Which agency procured the most and least amount of ZEBs?**\"\n", + " \"## Which Agencies procured the most and least amount of ZEBs?\"\n", "))\n", "bus_min_max_summary(data=zeb_projects, col1=\"bus_count\", col_list=new_cols)\n" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 97, "id": "c4f1b88e-f46c-4f69-888d-e116d2ce2ace", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "**Which Agency had the most and least total ZEB cost?**" + "## Which Agencies had the most and least total ZEB cost?" ], "text/plain": [ "" @@ -1525,48 +1425,39 @@ ], "source": [ "display(Markdown(\n", - " \"**Which Agency had the most and least total ZEB cost?**\"\n", + " \"## Which Agencies had the most and least total ZEB cost?\"\n", "))\n", "bus_min_max_summary(data=zeb_projects, col1=\"total_cost\", col_list=new_cols)" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 92, "id": "1f39733f-448e-4d3e-9981-feff4a13dbda", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "# Charts\n", - "dist_curve(\n", - " df=merged_data,\n", - " mean=cpb_mean,\n", - " std=cpb_std,\n", - " title=\"all buses, cost per bus distribution\",\n", - " xlabel=\"cost per bus, $ million(s)\",\n", - ")" + "# all buses\n", + "# dont know if i need this anymore\n", + "\n", + "#dist_curve(\n", + "# df=merged_data,\n", + "# mean=cpb_mean,\n", + "# std=cpb_std,\n", + "# title=\"all buses, cost per bus distribution\",\n", + "# xlabel=\"cost per bus, $ million(s)\",\n", + "#)" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 98, "id": "adebe10d-167c-480e-abff-313e8d8e91d4", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1583,20 +1474,20 @@ " mean=zeb_cpb_wt_avg,\n", " # need to investigate if std needs to be weighted as well?\n", " std=zeb_projects[\"cost_per_bus\"].std(),\n", - " title=\"ZEB buses, cost per bus distribution\",\n", + " title=\"ZEB cost per bus distribution\",\n", " xlabel=\"cost per bus, $ million(s)\",\n", ")" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 99, "id": "554eeee1-a3b6-47b0-912f-830885eb100b", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -1607,21 +1498,34 @@ ], "source": [ "# non-zeb cost per bus\n", - "dist_curve(\n", - " df=non_zeb_projects,\n", - " mean=non_zeb_cpb_wt_avg,\n", - " std=non_zeb_projects[\"cost_per_bus\"].std(),\n", - " title=\"non-ZEB costper bus Distribution\",\n", - " xlabel='\"cost per bus, $ million(s)\"',\n", - ")" + "\n", + "#dist_curve(\n", + "# df=non_zeb_projects,\n", + "# mean=non_zeb_cpb_wt_avg,\n", + "# std=non_zeb_projects[\"cost_per_bus\"].std(),\n", + "# title=\"non-ZEB cost per bus Distribution\",\n", + "# xlabel='\"cost per bus, $ million(s)\"',\n", + "#)" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 112, "id": "8d030948-59ea-4ea5-9db6-5d8639f6f8f5", "metadata": {}, "outputs": [ + { + "data": { + "text/markdown": [ + "## What is the breakdown of Propulsion Type and Bus Size Category?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/html": [ @@ -1643,51 +1547,135 @@ " \n", " \n", " \n", - " bus_size_type\n", + " \n", " bus_count\n", " total_cost\n", " cost_per_bus\n", " \n", + " \n", + " prop_type\n", + " bus_size_type\n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " 0\n", - " articulated\n", - " 41.0\n", - " 58237576\n", - " 1420428\n", + " BEB\n", + " articulated\n", + " 12.0\n", + " 18759576\n", + " 1563298\n", " \n", " \n", - " 1\n", - " cutaway\n", - " 152.0\n", - " 16694500\n", - " 109832\n", + " standard/conventional (30ft-45ft)\n", + " 151.0\n", + " 148472913\n", + " 983264\n", " \n", " \n", - " 2\n", - " not specified\n", - " 881.0\n", - " 509919038\n", - " 578795\n", + " CNG\n", + " cutaway\n", + " 3.0\n", + " 1162000\n", + " 387333\n", " \n", " \n", - " 3\n", - " over-the-road\n", + " not specified\n", + " 209.0\n", + " 171977140\n", + " 822857\n", + " \n", + " \n", + " standard/conventional (30ft-45ft)\n", + " 40.0\n", + " 2900000\n", + " 72500\n", + " \n", + " \n", + " FCEB\n", + " not specified\n", + " 29.0\n", + " 38070971\n", + " 1312792\n", + " \n", + " \n", + " standard/conventional (30ft-45ft)\n", + " 73.0\n", + " 82880364\n", + " 1135347\n", + " \n", + " \n", + " electric (not specified)\n", + " articulated\n", + " 29.0\n", + " 39478000\n", + " 1361310\n", + " \n", + " \n", + " not specified\n", + " 15.0\n", + " 17200000\n", + " 1146666\n", + " \n", + " \n", + " ethanol\n", + " not specified\n", + " 9.0\n", + " 1006750\n", + " 111861\n", + " \n", + " \n", + " low emission (hybrid)\n", + " not specified\n", + " 145.0\n", + " 91824361\n", + " 633271\n", + " \n", + " \n", + " low emission (propane)\n", + " not specified\n", + " 44.0\n", + " 8403969\n", + " 190999\n", + " \n", + " \n", + " mix (zero and low emission)\n", + " not specified\n", + " 125.0\n", + " 36775430\n", + " 294203\n", + " \n", + " \n", + " not specified\n", + " cutaway\n", + " 149.0\n", + " 15532500\n", + " 104244\n", + " \n", + " \n", + " not specified\n", + " 162.0\n", + " 16503904\n", + " 101875\n", + " \n", + " \n", + " over-the-road\n", " 14.0\n", " 9516000\n", " 679714\n", " \n", " \n", - " 4\n", - " standard/conventional (30ft-45ft)\n", - " 264.0\n", - " 234253277\n", - " 887323\n", + " zero-emission bus (not specified)\n", + " not specified\n", + " 143.0\n", + " 128156513\n", + " 896199\n", " \n", " \n", - " 5\n", - " Grand Total\n", + " Grand Total\n", + " \n", " 1352.0\n", " 828620391\n", " 612884\n", @@ -1697,22 +1685,79 @@ "" ], "text/plain": [ - " bus_size_type bus_count total_cost cost_per_bus\n", - "0 articulated 41.0 58237576 1420428\n", - "1 cutaway 152.0 16694500 109832\n", - "2 not specified 881.0 509919038 578795\n", - "3 over-the-road 14.0 9516000 679714\n", - "4 standard/conventional (30ft-45ft) 264.0 234253277 887323\n", - "5 Grand Total 1352.0 828620391 612884" + " bus_count \\\n", + "prop_type bus_size_type \n", + "BEB articulated 12.0 \n", + " standard/conventional (30ft-45ft) 151.0 \n", + "CNG cutaway 3.0 \n", + " not specified 209.0 \n", + " standard/conventional (30ft-45ft) 40.0 \n", + "FCEB not specified 29.0 \n", + " standard/conventional (30ft-45ft) 73.0 \n", + "electric (not specified) articulated 29.0 \n", + " not specified 15.0 \n", + "ethanol not specified 9.0 \n", + "low emission (hybrid) not specified 145.0 \n", + "low emission (propane) not specified 44.0 \n", + "mix (zero and low emission) not specified 125.0 \n", + "not specified cutaway 149.0 \n", + " not specified 162.0 \n", + " over-the-road 14.0 \n", + "zero-emission bus (not specified) not specified 143.0 \n", + "Grand Total 1352.0 \n", + "\n", + " total_cost \\\n", + "prop_type bus_size_type \n", + "BEB articulated 18759576 \n", + " standard/conventional (30ft-45ft) 148472913 \n", + "CNG cutaway 1162000 \n", + " not specified 171977140 \n", + " standard/conventional (30ft-45ft) 2900000 \n", + "FCEB not specified 38070971 \n", + " standard/conventional (30ft-45ft) 82880364 \n", + "electric (not specified) articulated 39478000 \n", + " not specified 17200000 \n", + "ethanol not specified 1006750 \n", + "low emission (hybrid) not specified 91824361 \n", + "low emission (propane) not specified 8403969 \n", + "mix (zero and low emission) not specified 36775430 \n", + "not specified cutaway 15532500 \n", + " not specified 16503904 \n", + " over-the-road 9516000 \n", + "zero-emission bus (not specified) not specified 128156513 \n", + "Grand Total 828620391 \n", + "\n", + " cost_per_bus \n", + "prop_type bus_size_type \n", + "BEB articulated 1563298 \n", + " standard/conventional (30ft-45ft) 983264 \n", + "CNG cutaway 387333 \n", + " not specified 822857 \n", + " standard/conventional (30ft-45ft) 72500 \n", + "FCEB not specified 1312792 \n", + " standard/conventional (30ft-45ft) 1135347 \n", + "electric (not specified) articulated 1361310 \n", + " not specified 1146666 \n", + "ethanol not specified 111861 \n", + "low emission (hybrid) not specified 633271 \n", + "low emission (propane) not specified 190999 \n", + "mix (zero and low emission) not specified 294203 \n", + "not specified cutaway 104244 \n", + " not specified 101875 \n", + " over-the-road 679714 \n", + "zero-emission bus (not specified) not specified 896199 \n", + "Grand Total 612884 " ] }, - "execution_count": 34, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "pivot_size" + "display(\n", + " Markdown(\"## What is the breakdown of Propulsion Type and Bus Size Category?\"),\n", + " pivot_size\n", + ")" ] }, { @@ -1841,7 +1886,7 @@ "source": [ "display(\n", "# cpb by prop type\n", - "make_chart(\"new_cost_per_bus\", \"Cost per bus by propulsion type\", x_col=\"prop_type\", data=agg_prop),\n", + "make_chart(\"new_cost_per_bus\", \"Cost per bus, by propulsion type\", x_col=\"prop_type\", data=agg_prop),\n", "\n", "# pivot table to\n", "agg_prop[[\"prop_type\",\"new_cost_per_bus\"]].sort_values(by=\"new_cost_per_bus\", ascending=False)\n", @@ -1974,7 +2019,7 @@ "source": [ "display(\n", "# bus count by prop type\n", - "make_chart(\"total_bus_count\", \"Bus count by propulsion type\", x_col=\"prop_type\", data=agg_prop),\n", + "make_chart(\"total_bus_count\", \"Bus counts, by propulsion type\", x_col=\"prop_type\", data=agg_prop),\n", "# pivot table to\n", "agg_prop[[\"prop_type\",\"total_bus_count\"]].sort_values(by=\"total_bus_count\", ascending=False)\n", ")" @@ -2076,7 +2121,7 @@ "source": [ "#bus size bar chart\n", "display(\n", - "make_chart(\"total_bus_count\", \"\"\"Amount of buses procured by bus size.\n", + "make_chart(\"total_bus_count\", \"\"\"Bus Size Count.\n", "excluding 'not specified' responses.\"\"\", x_col=\"bus_size_type\",data=agg_bus_size[agg_bus_size[\"bus_size_type\"]!=\"not specified\"]),\n", "agg_bus_size[[\"bus_size_type\",\"total_bus_count\"]]\n", ")\n" @@ -2124,7 +2169,10 @@ { "cell_type": "markdown", "id": "8025c84b-428f-4c40-b4d3-c969af58ce63", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ "-------\n", "# Start of old stuff" From d0210156ea0811db571036445ea0c7faf271c72d Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Wed, 26 Jun 2024 19:05:05 +0000 Subject: [PATCH 31/36] updating Makefile with additional commands, was able to run makefile to execute the final NB and export a html --- bus_procurement_cost/Makefile | 4 +- .../cost_per_bus_analysis.html | 1138 ++++++++- .../cost_per_bus_analysis.ipynb | 2122 ++++++++--------- 3 files changed, 2038 insertions(+), 1226 deletions(-) diff --git a/bus_procurement_cost/Makefile b/bus_procurement_cost/Makefile index 494e97e6b..040fa4dc3 100644 --- a/bus_procurement_cost/Makefile +++ b/bus_procurement_cost/Makefile @@ -6,4 +6,6 @@ all_bus_scripts: python cost_per_bus_cleaner.py python cost_per_bus_utils.py jupyter nbconvert --to notebook --execute --inplace cost_per_bus_analysis.ipynb - jupyter nbconvert --to html --no-input --no-prompt cost_per_bus_analysis.ipynb \ No newline at end of file + jupyter nbconvert --to html --no-input --no-prompt cost_per_bus_analysis.ipynb + git add cost_per_bus_analysis.ipynb + git add cost_per_bus_analysis.html \ No newline at end of file diff --git a/bus_procurement_cost/cost_per_bus_analysis.html b/bus_procurement_cost/cost_per_bus_analysis.html index b77c9e00d..60890591f 100644 --- a/bus_procurement_cost/cost_per_bus_analysis.html +++ b/bus_procurement_cost/cost_per_bus_analysis.html @@ -14575,6 +14575,36 @@ + + + + @@ -14761,7 +15462,7 @@

Cost per bus by propulsion type.

+ + + + @@ -14786,7 +15581,7 @@

Cost per bus by propulsion type.

+

What is the total bus counts compared to each bus size category?

@@ -14796,7 +15591,7 @@

Bus count by propulsion type. - @@ -14804,6 +15599,259 @@

Bus count by propulsion type. + + + + + + + + + + + + + + diff --git a/bus_procurement_cost/cost_per_bus_analysis.ipynb b/bus_procurement_cost/cost_per_bus_analysis.ipynb index bb46cdde6..c99a1544f 100644 --- a/bus_procurement_cost/cost_per_bus_analysis.ipynb +++ b/bus_procurement_cost/cost_per_bus_analysis.ipynb @@ -5,6 +5,12 @@ "execution_count": 1, "id": "da041e43-e8e2-4d4b-a498-10a7c0afe43f", "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:20.693089Z", + "iopub.status.busy": "2024-06-26T19:02:20.692134Z", + "iopub.status.idle": "2024-06-26T19:02:51.620857Z", + "shell.execute_reply": "2024-06-26T19:02:51.619248Z" + }, "tags": [] }, "outputs": [], @@ -26,7 +32,14 @@ "cell_type": "code", "execution_count": 2, "id": "d53376d9-d4b4-48b7-9916-5b9f633fbaf0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:51.627096Z", + "iopub.status.busy": "2024-06-26T19:02:51.626321Z", + "iopub.status.idle": "2024-06-26T19:02:52.398041Z", + "shell.execute_reply": "2024-06-26T19:02:52.396844Z" + } + }, "outputs": [], "source": [ "merged_data = pd.read_parquet(f'{GCS_PATH}cleaned_no_outliers_cpb_analysis_data_merge.parquet')" @@ -36,7 +49,14 @@ "cell_type": "code", "execution_count": 3, "id": "a45f2d3d-a600-4fe6-80cf-6b887036faab", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.404265Z", + "iopub.status.busy": "2024-06-26T19:02:52.403827Z", + "iopub.status.idle": "2024-06-26T19:02:52.411634Z", + "shell.execute_reply": "2024-06-26T19:02:52.410390Z" + } + }, "outputs": [], "source": [ "# for subsetting ZEB and nonZEB\n", @@ -60,7 +80,14 @@ "cell_type": "code", "execution_count": 4, "id": "8ac40482-ba3e-4fde-8c05-806e3725de44", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.417035Z", + "iopub.status.busy": "2024-06-26T19:02:52.416646Z", + "iopub.status.idle": "2024-06-26T19:02:52.433528Z", + "shell.execute_reply": "2024-06-26T19:02:52.431789Z" + } + }, "outputs": [], "source": [ "# means and standard deviations\n", @@ -77,7 +104,14 @@ "cell_type": "code", "execution_count": 5, "id": "d450fd60-cced-453b-b20b-62cdade0d7a6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.440101Z", + "iopub.status.busy": "2024-06-26T19:02:52.439344Z", + "iopub.status.idle": "2024-06-26T19:02:52.450789Z", + "shell.execute_reply": "2024-06-26T19:02:52.449493Z" + } + }, "outputs": [], "source": [ "def new_cpb_aggregate(df: pd.DataFrame, column=\"transit_agency\") -> pd.DataFrame:\n", @@ -119,9 +153,16 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 6, "id": "2a2dc407-20cc-45de-84b1-bb5991dad8ac", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.455864Z", + "iopub.status.busy": "2024-06-26T19:02:52.455468Z", + "iopub.status.idle": "2024-06-26T19:02:52.462727Z", + "shell.execute_reply": "2024-06-26T19:02:52.461471Z" + } + }, "outputs": [], "source": [ "def bus_min_max_summary(data:pd.DataFrame, col1:str, col_list=[\"transit_agency\",\n", @@ -142,9 +183,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "9a6a7ecf-5180-4691-84fe-23aa68cdae93", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.467820Z", + "iopub.status.busy": "2024-06-26T19:02:52.466886Z", + "iopub.status.idle": "2024-06-26T19:02:52.474804Z", + "shell.execute_reply": "2024-06-26T19:02:52.473241Z" + } + }, "outputs": [], "source": [ "def make_chart(y_col: str, title: str, data: pd.DataFrame, x_col: str):\n", @@ -164,9 +212,16 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "44d21201-223f-4e6c-b238-b72fba984544", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.479204Z", + "iopub.status.busy": "2024-06-26T19:02:52.478810Z", + "iopub.status.idle": "2024-06-26T19:02:52.489771Z", + "shell.execute_reply": "2024-06-26T19:02:52.488406Z" + } + }, "outputs": [], "source": [ "def dist_curve(\n", @@ -211,9 +266,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "067a14a5-5c77-4914-82a8-c5eeb170cb08", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.494690Z", + "iopub.status.busy": "2024-06-26T19:02:52.494199Z", + "iopub.status.idle": "2024-06-26T19:02:52.549925Z", + "shell.execute_reply": "2024-06-26T19:02:52.548448Z" + } + }, "outputs": [], "source": [ "# aggregating by big categories\n", @@ -225,9 +287,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "49a97d01-b17e-475c-b351-67426f3741d9", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.555324Z", + "iopub.status.busy": "2024-06-26T19:02:52.554911Z", + "iopub.status.idle": "2024-06-26T19:02:52.562099Z", + "shell.execute_reply": "2024-06-26T19:02:52.560778Z" + } + }, "outputs": [], "source": [ "# subsetting ZEB and nonZEB data\n", @@ -238,9 +307,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "4faaa4ad-b16c-4e6b-87c7-d12f7e7db3c6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.567091Z", + "iopub.status.busy": "2024-06-26T19:02:52.566720Z", + "iopub.status.idle": "2024-06-26T19:02:52.594068Z", + "shell.execute_reply": "2024-06-26T19:02:52.592332Z" + } + }, "outputs": [], "source": [ "#pivot table to get totals for each prop type\n", @@ -258,9 +334,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "b8535e97-e7bf-4d7e-b718-24c5758b0ccd", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.599044Z", + "iopub.status.busy": "2024-06-26T19:02:52.598633Z", + "iopub.status.idle": "2024-06-26T19:02:52.623052Z", + "shell.execute_reply": "2024-06-26T19:02:52.621621Z" + } + }, "outputs": [], "source": [ "#pivot for ZEB data\n", @@ -280,9 +363,16 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "829e38c9-3f9b-4e82-92a8-c86f81051580", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.627647Z", + "iopub.status.busy": "2024-06-26T19:02:52.627230Z", + "iopub.status.idle": "2024-06-26T19:02:52.648719Z", + "shell.execute_reply": "2024-06-26T19:02:52.647580Z" + } + }, "outputs": [], "source": [ "#pivot for non-ZEB data\n", @@ -302,9 +392,16 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 14, "id": "0a2163e3-dac1-4e64-a551-3dc961e44714", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.653834Z", + "iopub.status.busy": "2024-06-26T19:02:52.653150Z", + "iopub.status.idle": "2024-06-26T19:02:52.682380Z", + "shell.execute_reply": "2024-06-26T19:02:52.680881Z" + } + }, "outputs": [], "source": [ "# pivot for bus sizes\n", @@ -324,10 +421,187 @@ }, { "cell_type": "code", - "execution_count": 109, - "id": "81e253c5-37f1-4a5e-9666-8ff2a6442635", - "metadata": {}, + "execution_count": 15, + "id": "074acb8d-de54-43a0-b243-a070ecfbe1ce", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.688018Z", + "iopub.status.busy": "2024-06-26T19:02:52.687537Z", + "iopub.status.idle": "2024-06-26T19:02:52.712992Z", + "shell.execute_reply": "2024-06-26T19:02:52.711480Z" + } + }, + "outputs": [], + "source": [ + "#pivot for data soruces\n", + "pivot_source = pd.pivot_table(\n", + " merged_data,\n", + " values = [\"bus_count\", \"total_cost\"],\n", + " index = \"source\",\n", + " aggfunc = \"sum\",\n", + " margins = True,\n", + " margins_name = \"Grand Total\"\n", + ").reset_index()\n", + "\n", + "pivot_source[\"cost_per_bus\"] = (pivot_source[\"total_cost\"] / pivot_source[\"bus_count\"]).astype(\"int64\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "d8356953-e32d-47ab-b67c-fa016cad9c50", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.717961Z", + "iopub.status.busy": "2024-06-26T19:02:52.717560Z", + "iopub.status.idle": "2024-06-26T19:02:52.731720Z", + "shell.execute_reply": "2024-06-26T19:02:52.730497Z" + } + }, + "outputs": [], + "source": [ + "# new summary\n", + "\n", + "new_summary = f\"\"\"\n", + "\n", + "# Bus Procurement Cost Analysis\n", + "\n", + "## Summary\n", + "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", + "\n", + "Data was compiled from three data sources:\n", + "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", + "2. TIRCP project data (state-funded, California only)\n", + "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", + "\n", + "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", + "The resulting dataset only contained projects that were solely used to procure buses. \n", + "\n", + "{len(merged_data)} projects were determined to contain solely bus purchases. \n", + "These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", + "\n", + "\n", + "Breakdown of each data souce:\n", + "{pivot_source.to_markdown(index=False)}\n", + "\n", + "\n", + "**ZEB buses include:**\n", + "- zero-emission (not specified) \n", + "- electric (not specified)\n", + "- battery electric \n", + "- fuel cell electric\n", + "\n", + "**Non-ZEB buses include:**\n", + "- CNG \n", + "- ethanol \n", + "- ow emission (hybrid, propane) \n", + "- diesel \n", + "- gas\n", + "\n", + "Below are charts and tables that summarize the findings.\n", + "\n", + "\"\"\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "f64881b9-46f9-4bfe-afd0-511385e21306", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.736632Z", + "iopub.status.busy": "2024-06-26T19:02:52.736209Z", + "iopub.status.idle": "2024-06-26T19:02:52.744950Z", + "shell.execute_reply": "2024-06-26T19:02:52.743705Z" + } + }, + "outputs": [ + { + "data": { + "text/markdown": [ + "\n", + "\n", + "# Bus Procurement Cost Analysis\n", + "\n", + "## Summary\n", + "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", + "\n", + "Data was compiled from three data sources:\n", + "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", + "2. TIRCP project data (state-funded, California only)\n", + "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", + "\n", + "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", + "The resulting dataset only contained projects that were solely used to procure buses. \n", + "\n", + "88 projects were determined to contain solely bus purchases. \n", + "These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", + "\n", + "\n", + "Breakdown of each data souce:\n", + "| source | bus_count | total_cost | cost_per_bus |\n", + "|:------------|------------:|-------------:|---------------:|\n", + "| dgs | 236 | 250112853 | 1059800 |\n", + "| fta | 883 | 391257025 | 443099 |\n", + "| tircp | 233 | 187250513 | 803650 |\n", + "| Grand Total | 1352 | 828620391 | 612884 |\n", + "\n", + "\n", + "**ZEB buses include:**\n", + "- zero-emission (not specified) \n", + "- electric (not specified)\n", + "- battery electric \n", + "- fuel cell electric\n", + "\n", + "**Non-ZEB buses include:**\n", + "- CNG \n", + "- ethanol \n", + "- ow emission (hybrid, propane) \n", + "- diesel \n", + "- gas\n", + "\n", + "Below are charts and tables that summarize the findings.\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(\n", + " Markdown(new_summary)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "676cbd9a-db4b-4e86-b60b-900f14513468", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.752216Z", + "iopub.status.busy": "2024-06-26T19:02:52.751305Z", + "iopub.status.idle": "2024-06-26T19:02:52.792781Z", + "shell.execute_reply": "2024-06-26T19:02:52.791667Z" + } + }, "outputs": [ + { + "data": { + "text/markdown": [ + "**ZEB Cost Summary**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/html": [ @@ -349,289 +623,269 @@ " \n", " \n", " \n", - " \n", + " prop_type\n", " bus_count\n", " total_cost\n", " cost_per_bus\n", " \n", - " \n", - " prop_type\n", - " bus_size_type\n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " BEB\n", - " articulated\n", - " 12.0\n", - " 18759576\n", - " 1563298\n", + " 0\n", + " BEB\n", + " 163.0\n", + " 167232489\n", + " 1025966\n", " \n", " \n", - " standard/conventional (30ft-45ft)\n", - " 151.0\n", - " 148472913\n", - " 983264\n", + " 1\n", + " FCEB\n", + " 102.0\n", + " 120951335\n", + " 1185797\n", " \n", " \n", - " CNG\n", - " cutaway\n", - " 3.0\n", - " 1162000\n", - " 387333\n", - " \n", - " \n", - " not specified\n", - " 209.0\n", - " 171977140\n", - " 822857\n", - " \n", - " \n", - " standard/conventional (30ft-45ft)\n", - " 40.0\n", - " 2900000\n", - " 72500\n", + " 2\n", + " electric (not specified)\n", + " 44.0\n", + " 56678000\n", + " 1288136\n", " \n", " \n", - " FCEB\n", - " not specified\n", - " 29.0\n", - " 38070971\n", - " 1312792\n", + " 3\n", + " zero-emission bus (not specified)\n", + " 143.0\n", + " 128156513\n", + " 896199\n", " \n", " \n", - " standard/conventional (30ft-45ft)\n", - " 73.0\n", - " 82880364\n", - " 1135347\n", + " 4\n", + " Grand Total\n", + " 452.0\n", + " 473018337\n", + " 1046500\n", " \n", - " \n", - " electric (not specified)\n", - " articulated\n", - " 29.0\n", - " 39478000\n", - " 1361310\n", + " \n", + "\n", + "" + ], + "text/plain": [ + " prop_type bus_count total_cost cost_per_bus\n", + "0 BEB 163.0 167232489 1025966\n", + "1 FCEB 102.0 120951335 1185797\n", + "2 electric (not specified) 44.0 56678000 1288136\n", + "3 zero-emission bus (not specified) 143.0 128156513 896199\n", + "4 Grand Total 452.0 473018337 1046500" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Non-ZEB Cost Summary**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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prop_typebus_counttotal_costcost_per_bus
not specified15.01720000011466660CNG252.0176039140698568
ethanolnot specified1ethanol9.01006750111861
low emission (hybrid)not specified2low emission (hybrid)145.091824361633271
low emission (propane)not specified3low emission (propane)44.08403969190999
mix (zero and low emission)not specified4mix (zero and low emission)125.036775430294203
not specifiedcutaway149.015532500104244
not specified162.016503904101875
over-the-road14.09516000679714
zero-emission bus (not specified)not specified143.0128156513896199
Grand Total1352.08286203916128845Grand Total575.0314049650546173
\n", "
" ], "text/plain": [ - " bus_count \\\n", - "prop_type bus_size_type \n", - "BEB articulated 12.0 \n", - " standard/conventional (30ft-45ft) 151.0 \n", - "CNG cutaway 3.0 \n", - " not specified 209.0 \n", - " standard/conventional (30ft-45ft) 40.0 \n", - "FCEB not specified 29.0 \n", - " standard/conventional (30ft-45ft) 73.0 \n", - "electric (not specified) articulated 29.0 \n", - " not specified 15.0 \n", - "ethanol not specified 9.0 \n", - "low emission (hybrid) not specified 145.0 \n", - "low emission (propane) not specified 44.0 \n", - "mix (zero and low emission) not specified 125.0 \n", - "not specified cutaway 149.0 \n", - " not specified 162.0 \n", - " over-the-road 14.0 \n", - "zero-emission bus (not specified) not specified 143.0 \n", - "Grand Total 1352.0 \n", - "\n", - " total_cost \\\n", - "prop_type bus_size_type \n", - "BEB articulated 18759576 \n", - " standard/conventional (30ft-45ft) 148472913 \n", - "CNG cutaway 1162000 \n", - " not specified 171977140 \n", - " standard/conventional (30ft-45ft) 2900000 \n", - "FCEB not specified 38070971 \n", - " standard/conventional (30ft-45ft) 82880364 \n", - "electric (not specified) articulated 39478000 \n", - " not specified 17200000 \n", - "ethanol not specified 1006750 \n", - "low emission (hybrid) not specified 91824361 \n", - "low emission (propane) not specified 8403969 \n", - "mix (zero and low emission) not specified 36775430 \n", - "not specified cutaway 15532500 \n", - " not specified 16503904 \n", - " over-the-road 9516000 \n", - "zero-emission bus (not specified) not specified 128156513 \n", - "Grand Total 828620391 \n", - "\n", - " cost_per_bus \n", - "prop_type bus_size_type \n", - "BEB articulated 1563298 \n", - " standard/conventional (30ft-45ft) 983264 \n", - "CNG cutaway 387333 \n", - " not specified 822857 \n", - " standard/conventional (30ft-45ft) 72500 \n", - "FCEB not specified 1312792 \n", - " standard/conventional (30ft-45ft) 1135347 \n", - "electric (not specified) articulated 1361310 \n", - " not specified 1146666 \n", - "ethanol not specified 111861 \n", - "low emission (hybrid) not specified 633271 \n", - "low emission (propane) not specified 190999 \n", - "mix (zero and low emission) not specified 294203 \n", - "not specified cutaway 104244 \n", - " not specified 101875 \n", - " over-the-road 679714 \n", - "zero-emission bus (not specified) not specified 896199 \n", - "Grand Total 612884 " + " prop_type bus_count total_cost cost_per_bus\n", + "0 CNG 252.0 176039140 698568\n", + "1 ethanol 9.0 1006750 111861\n", + "2 low emission (hybrid) 145.0 91824361 633271\n", + "3 low emission (propane) 44.0 8403969 190999\n", + "4 mix (zero and low emission) 125.0 36775430 294203\n", + "5 Grand Total 575.0 314049650 546173" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "The remaining buses did not specify a propulsion type" + ], + "text/plain": [ + "" ] }, - "execution_count": 109, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "pivot_size" + "#summary stuff\n", + "display(\n", + " Markdown(\"**ZEB Cost Summary**\"),\n", + " pivot_zeb_prop,\n", + " \n", + " Markdown(\"**Non-ZEB Cost Summary**\"),\n", + " pivot_non_zeb_prop,\n", + " \n", + " Markdown(\"The remaining buses did not specify a propulsion type\")\n", + ")" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "074acb8d-de54-43a0-b243-a070ecfbe1ce", - "metadata": {}, - "outputs": [], - "source": [ - "#pivot for data soruces\n", - "pivot_source = pd.pivot_table(\n", - " merged_data,\n", - " values = [\"bus_count\", \"total_cost\"],\n", - " index = \"source\",\n", - " aggfunc = \"sum\",\n", - " margins = True,\n", - " margins_name = \"Grand Total\"\n", - ").reset_index()\n", - "\n", - "pivot_source[\"cost_per_bus\"] = (pivot_source[\"total_cost\"] / pivot_source[\"bus_count\"]).astype(\"int64\")" + "execution_count": 19, + "id": "d99e56b6-2d69-4bc3-9ac2-169df1d3f6ef", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.798790Z", + "iopub.status.busy": "2024-06-26T19:02:52.797936Z", + "iopub.status.idle": "2024-06-26T19:02:52.803324Z", + "shell.execute_reply": "2024-06-26T19:02:52.802211Z" + } + }, + "outputs": [], + "source": [ + "# overall summary\n", + "# commenting out for now.\n", + "#display(\n", + "# Markdown(\"## Which Agencies has the highest and lowest overall cost per bus?\"))\n", + "#bus_min_max_summary(data=agg_agency,col1=\"new_cost_per_bus\")" ] }, { "cell_type": "code", - "execution_count": 90, - "id": "d8356953-e32d-47ab-b67c-fa016cad9c50", - "metadata": {}, + "execution_count": 20, + "id": "74ecf466-3560-46e1-a792-e217231ce1b4", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.809873Z", + "iopub.status.busy": "2024-06-26T19:02:52.808811Z", + "iopub.status.idle": "2024-06-26T19:02:52.815120Z", + "shell.execute_reply": "2024-06-26T19:02:52.814166Z" + } + }, + "outputs": [], + "source": [ + "# overall summary\n", + "# commenting out for now.\n", + "#display(\n", + "# Markdown(\"## Which Agencies has the highest and lowest overall procurement cost?\"))\n", + "#bus_min_max_summary(data=agg_agency,col1=\"total_agg_cost\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "30c42e6d-3ca3-4715-a472-b7501e36f2fe", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.822730Z", + "iopub.status.busy": "2024-06-26T19:02:52.820468Z", + "iopub.status.idle": "2024-06-26T19:02:52.829935Z", + "shell.execute_reply": "2024-06-26T19:02:52.828210Z" + } + }, + "outputs": [], + "source": [ + "# overall summary\n", + "# commenting out for now.\n", + "#display(\n", + "# Markdown(\"## Which Agencies procured the has the most and least overal number of buses?\"))\n", + "#bus_min_max_summary(data=agg_agency,col1=\"total_bus_count\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "80dd3d1c-86f8-4c68-bdd1-8249e6494f2c", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.835466Z", + "iopub.status.busy": "2024-06-26T19:02:52.835000Z", + "iopub.status.idle": "2024-06-26T19:02:52.870304Z", + "shell.execute_reply": "2024-06-26T19:02:52.868683Z" + } + }, "outputs": [ { "data": { "text/markdown": [ - "\n", - "\n", - "# Bus Procurement Cost Analysis\n", - "\n", - "## Summary\n", - "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", - "\n", - "Data was compiled from three data sources:\n", - "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", - "2. TIRCP project data (state-funded, California only)\n", - "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", - "\n", - "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", - "The resulting dataset only contained projects that were solely used to procure buses. \n", - "\n", - "88 projects were determined to contain solely bus purchases. \n", - "These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", - "\n", - "\n", - "Breakdown of each data souce:\n", - "| source | bus_count | total_cost | cost_per_bus |\n", - "|:------------|------------:|-------------:|---------------:|\n", - "| dgs | 236 | 250112853 | 1059800 |\n", - "| fta | 883 | 391257025 | 443099 |\n", - "| tircp | 233 | 187250513 | 803650 |\n", - "| Grand Total | 1352 | 828620391 | 612884 |\n", - "\n", - "\n", - "**ZEB buses include:**\n", - "- zero-emission (not specified) \n", - "- electric (not specified)\n", - "- battery electric \n", - "- fuel cell electric\n", - "\n", - "**Non-ZEB buses include:**\n", - "- CNG \n", - "- ethanol \n", - "- ow emission (hybrid, propane) \n", - "- diesel \n", - "- gas\n", - "\n", - "Below are charts and tables that summarize the findings.\n", - "\n" + "## Which Agneices had the highest and lowest ZEB cost per bus?" ], "text/plain": [ "" @@ -639,65 +893,11 @@ }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "# new summary\n", - "\n", - "new_summary = f\"\"\"\n", - "\n", - "# Bus Procurement Cost Analysis\n", - "\n", - "## Summary\n", - "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", - "\n", - "Data was compiled from three data sources:\n", - "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", - "2. TIRCP project data (state-funded, California only)\n", - "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", - "\n", - "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", - "The resulting dataset only contained projects that were solely used to procure buses. \n", - "\n", - "{len(merged_data)} projects were determined to contain solely bus purchases. \n", - "These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", - "\n", - "\n", - "Breakdown of each data souce:\n", - "{pivot_source.to_markdown(index=False)}\n", - "\n", - "\n", - "**ZEB buses include:**\n", - "- zero-emission (not specified) \n", - "- electric (not specified)\n", - "- battery electric \n", - "- fuel cell electric\n", - "\n", - "**Non-ZEB buses include:**\n", - "- CNG \n", - "- ethanol \n", - "- ow emission (hybrid, propane) \n", - "- diesel \n", - "- gas\n", - "\n", - "Below are charts and tables that summarize the findings.\n", - "\n", - "\"\"\"\n", - "display(\n", - " Markdown(new_summary)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "676cbd9a-db4b-4e86-b60b-900f14513468", - "metadata": {}, - "outputs": [ + }, { "data": { "text/markdown": [ - "**ZEB Cost Summary**" + "**Max cost_per_bus**" ], "text/plain": [ "" @@ -727,59 +927,32 @@ " \n", " \n", " \n", + " transit_agency\n", " prop_type\n", - " bus_count\n", " total_cost\n", + " bus_count\n", " cost_per_bus\n", " \n", " \n", " \n", " \n", - " 0\n", + " 76\n", + " University of California - San Diego\n", " BEB\n", - " 163.0\n", - " 167232489\n", - " 1025966\n", - " \n", - " \n", - " 1\n", - " FCEB\n", - " 102.0\n", - " 120951335\n", - " 1185797\n", - " \n", - " \n", - " 2\n", - " electric (not specified)\n", - " 44.0\n", - " 56678000\n", - " 1288136\n", - " \n", - " \n", - " 3\n", - " zero-emission bus (not specified)\n", - " 143.0\n", - " 128156513\n", - " 896199\n", - " \n", - " \n", - " 4\n", - " Grand Total\n", - " 452.0\n", - " 473018337\n", - " 1046500\n", + " 4134000\n", + " 2.0\n", + " 2067000\n", " \n", " \n", "\n", "" ], "text/plain": [ - " prop_type bus_count total_cost cost_per_bus\n", - "0 BEB 163.0 167232489 1025966\n", - "1 FCEB 102.0 120951335 1185797\n", - "2 electric (not specified) 44.0 56678000 1288136\n", - "3 zero-emission bus (not specified) 143.0 128156513 896199\n", - "4 Grand Total 452.0 473018337 1046500" + " transit_agency prop_type total_cost bus_count \\\n", + "76 University of California - San Diego BEB 4134000 2.0 \n", + "\n", + " cost_per_bus \n", + "76 2067000 " ] }, "metadata": {}, @@ -788,7 +961,7 @@ { "data": { "text/markdown": [ - "**Non-ZEB Cost Summary**" + "**Min cost_per_bus**" ], "text/plain": [ "" @@ -818,270 +991,21 @@ " \n", " \n", " \n", + " transit_agency\n", " prop_type\n", - " bus_count\n", " total_cost\n", + " bus_count\n", " cost_per_bus\n", " \n", " \n", " \n", " \n", - " 0\n", - " CNG\n", - " 252.0\n", - " 176039140\n", - " 698568\n", - " \n", - " \n", - " 1\n", - " ethanol\n", - " 9.0\n", - " 1006750\n", - " 111861\n", - " \n", - " \n", - " 2\n", - " low emission (hybrid)\n", - " 145.0\n", - " 91824361\n", - " 633271\n", - " \n", - " \n", - " 3\n", - " low emission (propane)\n", - " 44.0\n", - " 8403969\n", - " 190999\n", - " \n", - " \n", - " 4\n", - " mix (zero and low emission)\n", - " 125.0\n", - " 36775430\n", - " 294203\n", - " \n", - " \n", - " 5\n", - " Grand Total\n", - " 575.0\n", - " 314049650\n", - " 546173\n", - " \n", - " \n", - "\n", - "" - ], - "text/plain": [ - " prop_type bus_count total_cost cost_per_bus\n", - "0 CNG 252.0 176039140 698568\n", - "1 ethanol 9.0 1006750 111861\n", - "2 low emission (hybrid) 145.0 91824361 633271\n", - "3 low emission (propane) 44.0 8403969 190999\n", - "4 mix (zero and low emission) 125.0 36775430 294203\n", - "5 Grand Total 575.0 314049650 546173" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "The remaining buses did not specify a propulsion type" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#summary stuff\n", - "display(\n", - " Markdown(\"**ZEB Cost Summary**\"),\n", - " pivot_zeb_prop,\n", - " \n", - " Markdown(\"**Non-ZEB Cost Summary**\"),\n", - " pivot_non_zeb_prop,\n", - " \n", - " Markdown(\"The remaining buses did not specify a propulsion type\")\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "id": "d99e56b6-2d69-4bc3-9ac2-169df1d3f6ef", - "metadata": {}, - "outputs": [], - "source": [ - "# overall summary\n", - "# commenting out for now.\n", - "#display(\n", - "# Markdown(\"## Which Agencies has the highest and lowest overall cost per bus?\"))\n", - "#bus_min_max_summary(data=agg_agency,col1=\"new_cost_per_bus\")" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "id": "74ecf466-3560-46e1-a792-e217231ce1b4", - "metadata": {}, - "outputs": [], - "source": [ - "# overall summary\n", - "# commenting out for now.\n", - "#display(\n", - "# Markdown(\"## Which Agencies has the highest and lowest overall procurement cost?\"))\n", - "#bus_min_max_summary(data=agg_agency,col1=\"total_agg_cost\")" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "id": "30c42e6d-3ca3-4715-a472-b7501e36f2fe", - "metadata": {}, - "outputs": [], - "source": [ - "# overall summary\n", - "# commenting out for now.\n", - "#display(\n", - "# Markdown(\"## Which Agencies procured the has the most and least overal number of buses?\"))\n", - "#bus_min_max_summary(data=agg_agency,col1=\"total_bus_count\")" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "id": "80dd3d1c-86f8-4c68-bdd1-8249e6494f2c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "## Which Agneices had the highest and lowest ZEB cost per bus?" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "**Max cost_per_bus**" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencyprop_typetotal_costbus_countcost_per_bus
76University of California - San DiegoBEB41340002.02067000
\n", - "
" - ], - "text/plain": [ - " transit_agency prop_type total_cost bus_count \\\n", - "76 University of California - San Diego BEB 4134000 2.0 \n", - "\n", - " cost_per_bus \n", - "76 2067000 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "**Min cost_per_bus**" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencyprop_typetotal_costbus_countcost_per_bus
45City of Wascozero-emission bus (not specified)15430003.051433345City of Wascozero-emission bus (not specified)15430003.0514333
\n", @@ -1114,9 +1038,16 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 23, "id": "75919ab8-7f14-49f2-bb4a-9765fdddc35c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.875693Z", + "iopub.status.busy": "2024-06-26T19:02:52.874979Z", + "iopub.status.idle": "2024-06-26T19:02:52.908082Z", + "shell.execute_reply": "2024-06-26T19:02:52.906765Z" + } + }, "outputs": [ { "data": { @@ -1278,9 +1209,16 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 24, "id": "c4f1b88e-f46c-4f69-888d-e116d2ce2ace", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.913086Z", + "iopub.status.busy": "2024-06-26T19:02:52.912659Z", + "iopub.status.idle": "2024-06-26T19:02:52.944936Z", + "shell.execute_reply": "2024-06-26T19:02:52.943703Z" + } + }, "outputs": [ { "data": { @@ -1432,9 +1370,16 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 25, "id": "1f39733f-448e-4d3e-9981-feff4a13dbda", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.951078Z", + "iopub.status.busy": "2024-06-26T19:02:52.950656Z", + "iopub.status.idle": "2024-06-26T19:02:52.955965Z", + "shell.execute_reply": "2024-06-26T19:02:52.954708Z" + } + }, "outputs": [], "source": [ "# all buses\n", @@ -1451,10 +1396,29 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 26, "id": "adebe10d-167c-480e-abff-313e8d8e91d4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:52.960681Z", + "iopub.status.busy": "2024-06-26T19:02:52.960272Z", + "iopub.status.idle": "2024-06-26T19:02:53.585506Z", + "shell.execute_reply": "2024-06-26T19:02:53.584074Z" + } + }, "outputs": [ + { + "data": { + "text/markdown": [ + "## What is the distribution of ZEB cost?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { 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", @@ -1468,307 +1432,73 @@ ], "source": [ "# ZEB cost per bus \n", + "display(Markdown(\n", + " \"## What is the distribution of ZEB cost?\"\n", + "))\n", "dist_curve(\n", " df=zeb_projects,\n", " #using the accounting, weighted average approach to mean (total cost/total number of buses)\n", " mean=zeb_cpb_wt_avg,\n", - " # need to investigate if std needs to be weighted as well?\n", - " std=zeb_projects[\"cost_per_bus\"].std(),\n", - " title=\"ZEB cost per bus distribution\",\n", - " xlabel=\"cost per bus, $ million(s)\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "id": "554eeee1-a3b6-47b0-912f-830885eb100b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# non-zeb cost per bus\n", - "\n", - "#dist_curve(\n", - "# df=non_zeb_projects,\n", - "# mean=non_zeb_cpb_wt_avg,\n", - "# std=non_zeb_projects[\"cost_per_bus\"].std(),\n", - "# title=\"non-ZEB cost per bus Distribution\",\n", - "# xlabel='\"cost per bus, $ million(s)\"',\n", - "#)" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "id": "8d030948-59ea-4ea5-9db6-5d8639f6f8f5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "## What is the breakdown of Propulsion Type and Bus Size Category?" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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bus_counttotal_costcost_per_bus
prop_typebus_size_type
BEBarticulated12.0187595761563298
standard/conventional (30ft-45ft)151.0148472913983264
CNGcutaway3.01162000387333
not specified209.0171977140822857
standard/conventional (30ft-45ft)40.0290000072500
FCEBnot specified29.0380709711312792
standard/conventional (30ft-45ft)73.0828803641135347
electric (not specified)articulated29.0394780001361310
not specified15.0172000001146666
ethanolnot specified9.01006750111861
low emission (hybrid)not specified145.091824361633271
low emission (propane)not specified44.08403969190999
mix (zero and low emission)not specified125.036775430294203
not specifiedcutaway149.015532500104244
not specified162.016503904101875
over-the-road14.09516000679714
zero-emission bus (not specified)not specified143.0128156513896199
Grand Total1352.0828620391612884
\n", - "
" - ], - "text/plain": [ - " bus_count \\\n", - "prop_type bus_size_type \n", - "BEB articulated 12.0 \n", - " standard/conventional (30ft-45ft) 151.0 \n", - "CNG cutaway 3.0 \n", - " not specified 209.0 \n", - " standard/conventional (30ft-45ft) 40.0 \n", - "FCEB not specified 29.0 \n", - " standard/conventional (30ft-45ft) 73.0 \n", - "electric (not specified) articulated 29.0 \n", - " not specified 15.0 \n", - "ethanol not specified 9.0 \n", - "low emission (hybrid) not specified 145.0 \n", - "low emission (propane) not specified 44.0 \n", - "mix (zero and low emission) not specified 125.0 \n", - "not specified cutaway 149.0 \n", - " not specified 162.0 \n", - " over-the-road 14.0 \n", - "zero-emission bus (not specified) not specified 143.0 \n", - "Grand Total 1352.0 \n", - "\n", - " total_cost \\\n", - "prop_type bus_size_type \n", - "BEB articulated 18759576 \n", - " standard/conventional (30ft-45ft) 148472913 \n", - "CNG cutaway 1162000 \n", - " not specified 171977140 \n", - " standard/conventional (30ft-45ft) 2900000 \n", - "FCEB not specified 38070971 \n", - " standard/conventional (30ft-45ft) 82880364 \n", - "electric (not specified) articulated 39478000 \n", - " not specified 17200000 \n", - "ethanol not specified 1006750 \n", - "low emission (hybrid) not specified 91824361 \n", - "low emission (propane) not specified 8403969 \n", - "mix (zero and low emission) not specified 36775430 \n", - "not specified cutaway 15532500 \n", - " not specified 16503904 \n", - " over-the-road 9516000 \n", - "zero-emission bus (not specified) not specified 128156513 \n", - "Grand Total 828620391 \n", - "\n", - " cost_per_bus \n", - "prop_type bus_size_type \n", - "BEB articulated 1563298 \n", - " standard/conventional (30ft-45ft) 983264 \n", - "CNG cutaway 387333 \n", - " not specified 822857 \n", - " standard/conventional (30ft-45ft) 72500 \n", - "FCEB not specified 1312792 \n", - " standard/conventional (30ft-45ft) 1135347 \n", - "electric (not specified) articulated 1361310 \n", - " not specified 1146666 \n", - "ethanol not specified 111861 \n", - "low emission (hybrid) not specified 633271 \n", - "low emission (propane) not specified 190999 \n", - "mix (zero and low emission) not specified 294203 \n", - "not specified cutaway 104244 \n", - " not specified 101875 \n", - " over-the-road 679714 \n", - "zero-emission bus (not specified) not specified 896199 \n", - "Grand Total 612884 " - ] - }, - "metadata": {}, - "output_type": "display_data" + " # need to investigate if std needs to be weighted as well?\n", + " std=zeb_projects[\"cost_per_bus\"].std(),\n", + " title=\"ZEB cost per bus distribution\",\n", + " xlabel=\"cost per bus, $ million(s)\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "554eeee1-a3b6-47b0-912f-830885eb100b", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:53.592208Z", + "iopub.status.busy": "2024-06-26T19:02:53.589893Z", + "iopub.status.idle": "2024-06-26T19:02:53.597405Z", + "shell.execute_reply": "2024-06-26T19:02:53.596198Z" } - ], + }, + "outputs": [], "source": [ - "display(\n", - " Markdown(\"## What is the breakdown of Propulsion Type and Bus Size Category?\"),\n", - " pivot_size\n", - ")" + "# non-zeb cost per bus\n", + "\n", + "#dist_curve(\n", + "# df=non_zeb_projects,\n", + "# mean=non_zeb_cpb_wt_avg,\n", + "# std=non_zeb_projects[\"cost_per_bus\"].std(),\n", + "# title=\"non-ZEB cost per bus Distribution\",\n", + "# xlabel='\"cost per bus, $ million(s)\"',\n", + "#)" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 28, "id": "5117c222-74a3-424c-9b13-1592a3f14eba", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:53.603106Z", + "iopub.status.busy": "2024-06-26T19:02:53.602478Z", + "iopub.status.idle": "2024-06-26T19:02:53.914326Z", + "shell.execute_reply": "2024-06-26T19:02:53.913029Z" + } + }, "outputs": [ { "data": { - "image/png": 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", + "text/markdown": [ + "## Which is the Cost per bus compared against all propulsion types?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", "text/plain": [ "
" ] @@ -1884,6 +1614,9 @@ } ], "source": [ + "display(Markdown(\n", + " \"## Which is the Cost per bus compared against all propulsion types?\"\n", + "))\n", "display(\n", "# cpb by prop type\n", "make_chart(\"new_cost_per_bus\", \"Cost per bus, by propulsion type\", x_col=\"prop_type\", data=agg_prop),\n", @@ -1895,13 +1628,32 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 29, "id": "65566782-7cc4-4ce0-987e-2d055f60ec57", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:53.918840Z", + "iopub.status.busy": "2024-06-26T19:02:53.918445Z", + "iopub.status.idle": "2024-06-26T19:02:54.193344Z", + "shell.execute_reply": "2024-06-26T19:02:54.192083Z" + } + }, "outputs": [ { "data": { - "image/png": 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", + "text/markdown": [ + "## What is the total bus counts compared to each propulsion type?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", "text/plain": [ "
" ] @@ -2017,6 +1769,10 @@ } ], "source": [ + "display(Markdown(\n", + " \"## What is the total bus counts compared to each propulsion type?\"\n", + "))\n", + "\n", "display(\n", "# bus count by prop type\n", "make_chart(\"total_bus_count\", \"Bus counts, by propulsion type\", x_col=\"prop_type\", data=agg_prop),\n", @@ -2027,13 +1783,32 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 30, "id": "7b56f81a-cf52-4309-ac8d-01d0389f9d4b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-26T19:02:54.198207Z", + "iopub.status.busy": "2024-06-26T19:02:54.197817Z", + "iopub.status.idle": "2024-06-26T19:02:54.440948Z", + "shell.execute_reply": "2024-06-26T19:02:54.439648Z" + } + }, "outputs": [ { "data": { - "image/png": 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", + "text/markdown": [ + "## What is the total bus counts compared to each bus size category?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", 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" ] @@ -2109,265 +1884,9 @@ " bus_size_type total_bus_count\n", "0 articulated 41.0\n", "1 cutaway 152.0\n", - "2 not specified 881.0\n", - "3 over-the-road 14.0\n", - "4 standard/conventional (30ft-45ft) 264.0" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#bus size bar chart\n", - "display(\n", - "make_chart(\"total_bus_count\", \"\"\"Bus Size Count.\n", - "excluding 'not specified' responses.\"\"\", x_col=\"bus_size_type\",data=agg_bus_size[agg_bus_size[\"bus_size_type\"]!=\"not specified\"]),\n", - "agg_bus_size[[\"bus_size_type\",\"total_bus_count\"]]\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "63f90c48-e28d-4d88-8b90-891a3e3e3681", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "\n", - "**Conclusion**\n", - "\n", - "Based on these findings, The average cost of a ZEB, throughout the US, is ~$1,000,000, roughly twice the price of a conventional, non-ZEB.\n", - "The variance in cost depends mainly on the options the Trasnit\n", - "Agencies chooses. Highly optioned/customized buses contribute to high cost.\n", - "Unfortunately, analyzing the cost of configuable options is outside the scope of data provided. \n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "conclusion = f\"\"\"\n", - "**Conclusion**\n", - "\n", - "Based on these findings, The average cost of a ZEB, throughout the US, is ~$1,000,000, roughly twice the price of a conventional, non-ZEB.\n", - "The variance in cost depends mainly on the options the Trasnit\n", - "Agencies chooses. Highly optioned/customized buses contribute to high cost.\n", - "Unfortunately, analyzing the cost of configuable options is outside the scope of data provided. \n", - "\"\"\"\n", - "display(\n", - " Markdown(conclusion)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "8025c84b-428f-4c40-b4d3-c969af58ce63", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "-------\n", - "# Start of old stuff" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "c51fe7dd-22e2-4686-b1a5-57b2f5ad8602", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "\n", - "## Summary\n", - "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", - "\n", - "Data was compiled from three data sources:\n", - "\n", - "1. 130 projects from FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", - "2. 124 projects TIRCP project data (state-funded, California only)\n", - "3. 35 projects DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc..\n", - "\n", - "The compiled dataset includes **289** total transit related projects. However, the initial dataset included projects that encompassed bus procurement and other components such as charging installation and facility construction, as well as non-bus related projects (ferries, trains). The dataset was filtered to exclude projects that were not bus related, indicated 0 buses procured, and projects that contained construction/installation work. **87** projects remained that specified the number of buses to procure and explicitly described procuring buses (bus only projects). \n", - "\n", - "Number of bus only contracts from each dataset \n", - "- FTA: **43**\n", - "- TIRCP: **9**\n", - "- DGS: **35**\n", - "\n", - "\n", - "The remaining bus only projects were categorized into different propulsion types and bus sizes, a “cost per bus” value was calculated, and outliers removed.\n", - "\n", - "A overall summary is provided below:\n", - "- Total projects: **298**\n", - "- Number of projects with mix bus procurement and other components, also non-bus projects: **204** \n", - "- Number of bus only projects: **87**\n", - "- Total dollars awarded to bus only projects: **`$831,843,715.00`**\n", - "- Total number of buses: **1353.0**\n", - "- Most common propulsion type procured for bus only projects: **BEB** at **30** projects\n", - "- Number of ZEB buses* procured: **452.0**\n", - "- Number of non-ZEB buses** procured: **575.0**\n", - "- Overall average cost per bus (ZEB & non-ZEB) is `$792,635.34` (std `$396,712.61`)\n", - "- ZEB average cost per bus is `$1,056,659.30` (std `$253,737.82`)\n", - "- Non-ZEB average cost per bus is `$528,106.49` (std `$315,932.20`) \n", - "\n", - "`*`ZEB buses include: zero-emission (not specified), electric (not specified), battery electric, fuel cell electric\n", - "\n", - "`**`Non-ZEB buses include: CNG, ethanol, low emission (hybrid, propane), diesel, gas.\n", - "\n", - "\n", - "Below are key charts that visualize more findings:\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Markdown(summary)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "4e553e15-dc1d-47d3-9818-7dec893c5294", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/markdown": [ - "\n", - "## All buses (ZEB and non-ZEB) cost/bus distribution curve.\n", - "This chart shows the cost per bus distribution of all bus only projects.\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "KeyError", - "evalue": "'cost_per_bus'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/core/indexes/base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3801\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3802\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/_libs/index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/_libs/index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'cost_per_bus'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[25], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# all bus distribution\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display(Markdown(all_bus_desc))\n\u001b[0;32m----> 4\u001b[0m \u001b[43mdist_curve\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mno_outliers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mmean\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcpb_mean\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mstd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcpb_std\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mtitle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mall buses, cost per bus distribution\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43mxlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcost per bus, $ million(s)\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[7], line 11\u001b[0m, in \u001b[0;36mdist_curve\u001b[0;34m(df, mean, std, title, xlabel)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdist_curve\u001b[39m(\n\u001b[1;32m 2\u001b[0m df: pd\u001b[38;5;241m.\u001b[39mDataFrame,\n\u001b[1;32m 3\u001b[0m mean: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 6\u001b[0m xlabel: \u001b[38;5;28mstr\u001b[39m,\n\u001b[1;32m 7\u001b[0m ):\n\u001b[1;32m 8\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;124;03m function to make distribution curve. uses the \"cpb\" column of the df.\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m sns\u001b[38;5;241m.\u001b[39mhistplot(\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcost_per_bus\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m, kde\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskyblue\u001b[39m\u001b[38;5;124m\"\u001b[39m, bins\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m)\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# mean line\u001b[39;00m\n\u001b[1;32m 13\u001b[0m plt\u001b[38;5;241m.\u001b[39maxvline(\n\u001b[1;32m 14\u001b[0m mean, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mred\u001b[39m\u001b[38;5;124m\"\u001b[39m, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdashed\u001b[39m\u001b[38;5;124m\"\u001b[39m, linewidth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMean: $\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmean\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m,.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 15\u001b[0m )\n", - "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/core/frame.py:3807\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3805\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 3806\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3807\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3808\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 3809\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n", - "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/core/indexes/base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3802\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m 3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3804\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3805\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3806\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3807\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3808\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3809\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", - "\u001b[0;31mKeyError\u001b[0m: 'cost_per_bus'" - ] - } - ], - "source": [ - "# all bus distribution\n", - "display(Markdown(all_bus_desc))\n", - "\n", - "dist_curve(\n", - " df=no_outliers,\n", - " mean=cpb_mean,\n", - " std=cpb_std,\n", - " title=\"all buses, cost per bus distribution\",\n", - " xlabel=\"cost per bus, $ million(s)\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dda584ca-76fa-4e88-9b1c-f70cc438dce6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# ZEB dist curve\n", - "display(Markdown(zeb_desc))\n", - "\n", - "dist_curve(\n", - " df=zeb_no_outliers,\n", - " mean=zeb_only_mean,\n", - " std=zeb_only_std,\n", - " title=\"ZEB only cost/bus Distribution\",\n", - " xlabel=\"cost per bus, $ million(s)\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "679d8261-85d6-4d68-9905-e4b048ebc61a", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# non_zeb distribution\n", - "display(Markdown(non_zeb_desc))\n", - "\n", - "dist_curve(\n", - " non_zeb_no_outliers,\n", - " non_zeb_only_mean,\n", - " non_zeb_only_std,\n", - " title=\"non-ZEB only cost/bus Distribution\",\n", - " xlabel='\"cost per bus, $ million(s)\"',\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "31c592b0-e37e-4da4-8726-36b0a1d3e6f5", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/markdown": [ - "\n", - "## Cost per bus by propulsion type. \n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "2 not specified 881.0\n", + "3 over-the-road 14.0\n", + "4 standard/conventional (30ft-45ft) 264.0" ] }, "metadata": {}, @@ -2375,24 +1894,35 @@ } ], "source": [ - "# COST PER BUS BY PROP TYPE\n", - "display(Markdown(cpb_prop_type_desc))\n", - "make_chart(\"cpb\", \"Cost per bus by propulsion type\", x_col=\"prop_type\", data=prop_agg)" + "#bus size bar chart\n", + "display(Markdown(\n", + " \"## What is the total bus counts compared to each bus size category?\"\n", + "))\n", + "\n", + "display(\n", + "make_chart(\"total_bus_count\", \"\"\"Bus Size Count.\n", + "excluding 'not specified' responses.\"\"\", x_col=\"bus_size_type\",data=agg_bus_size[agg_bus_size[\"bus_size_type\"]!=\"not specified\"]),\n", + "agg_bus_size[[\"bus_size_type\",\"total_bus_count\"]]\n", + ")\n" ] }, { "cell_type": "code", - "execution_count": 27, - "id": "7462b55c-29ef-4909-a7dd-27e1c84157d0", + "execution_count": 31, + "id": "8d030948-59ea-4ea5-9db6-5d8639f6f8f5", "metadata": { - "tags": [] + "execution": { + "iopub.execute_input": "2024-06-26T19:02:54.446159Z", + "iopub.status.busy": "2024-06-26T19:02:54.445566Z", + "iopub.status.idle": "2024-06-26T19:02:54.464993Z", + "shell.execute_reply": "2024-06-26T19:02:54.463729Z" + } }, "outputs": [ { "data": { "text/markdown": [ - "\n", - "## Bus count by propulsion type. \n" + "## What is the breakdown of Propulsion Type and Bus Size Category?" ], "text/plain": [ "" @@ -2403,9 +1933,225 @@ }, { "data": { - "image/png": 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z5s2YN2+e9DpE7yXfQDAizZDd0HMDAwNRo0YNsWjRoixDnh8/fiw6deokjIyMhKWlpRg0aJC4cuWK2tDzJ0+eCB8fH+Hk5CSMjY2Fubm5qFu3rti0aVOOMl25ckV06NBBWFhYCAMDA1G5cmUxceJEtX0uXLggmjdvLkxMTISRkZFo3LixOHnyZJbnOn/+vKhbt67Q09MTZcqUEXPmzHnv0PPWrVtnefy7Q6KFEOKvv/4S5cqVE7q6uh8dhu7t7S2MjY3FrVu3hJeXlzAyMhI2NjZi8uTJasOj3zZ06FABQKxbt+79v6R3ABA+Pj5izZo1omLFikJfX1+4ubllyfZmOPiboc1vS0tLE/7+/sLR0VEULVpU2NvbiwkTJojk5GS1/d78rvbt2ydcXV2Fvr6+cHJyEps3b87ynDn9/b/7e16yZInw8PAQxYsXF/r6+qJ8+fJizJgxIj4+Xtonu+cR4vVQcycnJ1G0aFFhY2MjhgwZIp4/f662T6NGjbId2u7t7S0cHByytL/r+vXrwsPDQxgaGgoAWYahp6SkCEtLS2Fubi5evXqV7evk5n2xdOlSUatWLWFoaChMTU2Fi4uLGDt2rHj48OFHsxKphCiknodERDk0cuRI/P3334iJiYGRkVGOHqNSqeDj46N2WaSglC1bFtWqVeO6aR+Qnp4OOzs7tGnTBn///XeW7b1798aWLVuk/nBEBYl9dohIoyQnJ2PNmjXo1KlTjgsd0jzbt2/H48eP0atXL7mjELHPDhFphtjYWBw8eBBbtmzB06dPMWLECLkjUR6cPn0aly5dwrRp0+Dm5oZGjRrJHYmIxQ4RaYawsDD06NED1tbWmD9/PmrUqCF3JMqDRYsWYc2aNahRo4bawrhEcmKfHSIiIlI09tkhIiIiRWOxQ0RERIrGPjt4PWX5w4cPYWpqmq/r4hAREVHBEULgxYsXsLOzg47O+8/fsNjB62nf36wlRERERNolKioKpUuXfu92FjuANPV6VFSUtCwAERERabaEhATY29u/dwmVN1js4P+v3mtmZsZih4iISMt8rAsKOygTERGRorHYISIiIkVjsUNERESKxj47RESUYxkZGUhLS5M7Bn0mihYtCl1d3U9+HhY7RET0UUIIxMTEIC4uTu4o9JmxsLCAra3tJ82Dx2KHiIg+6k2hY21tDSMjI07ASgVOCIGkpCTExsYCAEqWLJnn52KxQ0REH5SRkSEVOsWLF5c7Dn1GDA0NAQCxsbGwtrbO8yUtdlAmIqIPetNHx8jISOYk9Dl68777lL5iLHaIiChHeOmK5JAf7zsWO0RERKRoLHaIiIjyQe/evdG+ffsc7evp6Ynvv/++QPPQ/8cOykRElGczQp4U2muNdyuR68d4enqiRo0amDt3boE+hnIvICAA33//faFMZ8AzO0RERKRoLHaIiEiRevfujaCgIMybNw8qlQoqlQp3795FUFAQ6tSpA319fZQsWRLjx49Henr6Bx+TkZGBfv36wdHREYaGhqhcuTLmzZv3SfnS09MxbNgwmJubo0SJEpg4cSKEENJ2lUqF7du3qz3GwsICAQEBAIDU1FQMGzYMJUuWhIGBARwcHDB9+vQcvXZcXBwGDRoEGxsbGBgYoFq1ati5c6e0fevWrahatSr09fVRtmxZzJ49W+3xH8t29+5dqFQq/PPPP2jcuDGMjIxQvXp1BAcHAwCOHDmCPn36ID4+Xvo9T5kyJUfZ84KXsYiISJHmzZuHmzdvolq1apg6dSqA13MGtWrVCr1798aqVatw/fp1DBgwAAYGBpgyZUq2j7GyskJmZiZKly6NzZs3o3jx4jh58iQGDhyIkiVL4ttvv81TvpUrV6Jfv344c+YMzp07h4EDB6JMmTIYMGBAjh4/f/58/Pfff9i0aRPKlCmDqKgoREVFffRxmZmZaNmyJV68eIE1a9agfPnyCAsLk+awOX/+PL799ltMmTIFXbp0wcmTJzF06FAUL14cvXv3ztUx/vjjj/jtt99QsWJF/Pjjj+jWrRsiIiJQv359zJ07F5MmTcKNGzcAACYmJrl67txgsZNHhXGdOi/Xp4mI6DVzc3Po6enByMgItra2AF5/+Nrb22PBggVQqVRwcnLCw4cPMW7cOEyaNCnbxwCArq4u/P39pfuOjo4IDg7Gpk2b8lzs2Nvb4/fff4dKpULlypVx+fJl/P777zkudiIjI1GxYkU0bNgQKpUKDg4OOXrcwYMHcebMGVy7dg2VKlUCAJQrV07aPmfOHDRt2hQTJ04EAFSqVAlhYWH43//+l+tiZ/To0WjdujUAwN/fH1WrVkVERAScnJxgbm4OlUql9nsuKLyMRUREn41r167B3d1dbe6WBg0aIDExEffv3//gYxcuXIhatWrBysoKJiYmWLp0KSIjI/OcpV69emo53N3dER4ejoyMjBw9vnfv3rh48SIqV66M4cOHY//+/Tl63MWLF1G6dGmp0HnXtWvX0KBBA7W2Bg0a5CrbG66urtLPb5Z7eLP8Q2FisUNERPQRGzZswOjRo9GvXz/s378fFy9eRJ8+fZCamlpgr6lSqdT68ADqswjXrFkTd+7cwbRp0/Dq1St8++23+Oabbz76vG+WYCjIbG8ULVpU7THA68tohY2XsYiISLH09PTUzkZUqVIFW7duhRBC+vA9ceIETE1NUbp06Wwf82af+vXrY+jQoVLbrVu3Pinb6dOn1e6fOnUKFStWlPrOWFlZITo6WtoeHh6OpKQktceYmZmhS5cu6NKlC7755hu0aNECz549Q7Fixd77uq6urrh//z5u3ryZ7dmdKlWq4MSJE2ptJ06cQKVKlXKV7WOy+z0XFJ7ZISIixSpbtixOnz6Nu3fv4smTJxg6dCiioqLg6+uL69ev499//8XkyZPh5+cHHR2dbB+TmZmJihUr4ty5c9i3bx9u3ryJiRMn4uzZs5+ULTIyEn5+frhx4wbWr1+PP/74AyNGjJC2N2nSBAsWLEBISAjOnTuHwYMHq50pmTNnDtavX4/r16/j5s2b2Lx5M2xtbWFhYfHB123UqBE8PDzQqVMnHDhwAHfu3MGePXuwd+9eAMCoUaMQGBiIadOm4ebNm1i5ciUWLFiA0aNH5zhbTpQtWxaJiYkIDAzEkydPcl0s5QaLHSIiUqzRo0dDV1cXzs7OsLKyQlpaGnbv3o0zZ86gevXqGDx4MPr164effvrpvY+JjIzEoEGD0LFjR3Tp0gV169bF06dP1c7y5EWvXr3w6tUr1KlTBz4+PhgxYgQGDhwobZ89ezbs7e3x5Zdfonv37hg9erTaYqympqaYNWsWateujS+++AJ3797F7t27paLtQ7Zu3YovvvgC3bp1g7OzM8aOHSudZalZsyY2bdqEDRs2oFq1apg0aRKmTp2q1jn5Y9lyon79+hg8eDC6dOkCKysrzJo1K1ePzw2VePei22coISEB5ubmiI+Ph5mZWY4ew9FYRPS5SE5Oxp07d+Do6AgDAwO549Bn5kPvv5x+fvPMDhERESkaix0iIqJ8FBkZCRMTk/fePmW4ek6sXbv2va9dtWrVAn1tTcXRWERERPnIzs4OFy9e/OD2gtS2bVvUrVs322257USsFCx2iIiI8lGRIkVQoUIF2V7f1NQUpqamsr2+JuJlLCIiIlI0FjtERJQjcsx8S5Qf7ztexiIiog/S09ODjo4OHj58CCsrK+jp6amt6URUEIQQSE1NxePHj6GjowM9Pb08PxeLHSIi+iAdHR04OjoiOjoaDx8+lDsOfWaMjIxQpkyZHE2W+D4sdoiI6KP09PRQpkwZpKenF9p6RkS6urooUqTIJ59JZLFDREQ5olKpULRo0c92+DJpL3ZQJiIiIkVjsUNERESKxmKHiIiIFI3FDhERESkaix0iIiJSNBY7REREpGiyFjuLFi2Cq6srzMzMYGZmBnd3d+zZs0fanpycDB8fHxQvXhwmJibo1KkTHj16pPYckZGRaN26NYyMjGBtbY0xY8YgPT29sA+FiIiINJSsxU7p0qUxY8YMnD9/HufOnUOTJk3Qrl07XL16FQAwcuRI7NixA5s3b0ZQUBAePnyIjh07So/PyMhA69atkZqaipMnT2LlypUICAjApEmT5DokIiIi0jAqIYSQO8TbihUrhv/973/45ptvYGVlhXXr1uGbb74BAFy/fh1VqlRBcHAw6tWrhz179uDrr7/Gw4cPYWNjAwBYvHgxxo0bh8ePH+d4HY2EhASYm5sjPj4eZmZmOXrMjJAneTvAXBjvVqLAX4OIiEhb5fTzW2P67GRkZGDDhg14+fIl3N3dcf78eaSlpaFZs2bSPk5OTihTpgyCg4MBAMHBwXBxcZEKHQBo3rw5EhISpLND2UlJSUFCQoLajYiIiJRJ9mLn8uXLMDExgb6+PgYPHoxt27bB2dkZMTEx0NPTg4WFhdr+NjY2iImJAQDExMSoFTpvtr/Z9j7Tp0+Hubm5dLO3t8/fgyIiIiKNIXuxU7lyZVy8eBGnT5/GkCFD4O3tjbCwsAJ9zQkTJiA+Pl66RUVFFejrERERkXxkXwhUT08PFSpUAADUqlULZ8+exbx589ClSxekpqYiLi5O7ezOo0ePYGtrCwCwtbXFmTNn1J7vzWitN/tkR19fH/r6+vl8JERERKSJZD+z867MzEykpKSgVq1aKFq0KAIDA6VtN27cQGRkJNzd3QEA7u7uuHz5MmJjY6V9Dhw4ADMzMzg7Oxd6diIiItI8sp7ZmTBhAlq2bIkyZcrgxYsXWLduHY4cOYJ9+/bB3Nwc/fr1g5+fH4oVKwYzMzP4+vrC3d0d9erVAwB4eXnB2dkZPXv2xKxZsxATE4OffvoJPj4+PHNDREREAGQudmJjY9GrVy9ER0fD3Nwcrq6u2LdvH7766isAwO+//w4dHR106tQJKSkpaN68Of7880/p8bq6uti5cyeGDBkCd3d3GBsbw9vbG1OnTpXrkIiIiEjDaNw8O3LgPDtERETaR+vm2SEiIiIqCCx2iIiISNFY7BAREZGisdghIiIiRWOxQ0RERIrGYoeIiIgUjcUOERERKRqLHSIiIlI0FjtERESkaCx2iIiISNFY7BAREZGisdghIiIiRWOxQ0RERIrGYoeIiIgUjcUOERERKRqLHSIiIlI0FjtERESkaCx2iIiISNFY7BAREZGisdghIiIiRWOxQ0RERIrGYoeIiIgUjcUOERERKRqLHSIiIlI0FjtERESkaCx2iIiISNFY7BAREZGisdghIiIiRWOxQ0RERIrGYoeIiIgUjcUOERERKRqLHSIiIlI0FjtERESkaCx2iIiISNFY7BAREZGisdghIiIiRWOxQ0RERIrGYoeIiIgUjcUOERERKRqLHSIiIlI0FjtERESkaCx2iIiISNFY7BAREZGiyVrsTJ8+HV988QVMTU1hbW2N9u3b48aNG2r7eHp6QqVSqd0GDx6stk9kZCRat24NIyMjWFtbY8yYMUhPTy/MQyEiIiINVUTOFw8KCoKPjw+++OILpKen44cffoCXlxfCwsJgbGws7TdgwABMnTpVum9kZCT9nJGRgdatW8PW1hYnT55EdHQ0evXqhaJFi+LXX38t1OMhIiIizSNrsbN37161+wEBAbC2tsb58+fh4eEhtRsZGcHW1jbb59i/fz/CwsJw8OBB2NjYoEaNGpg2bRrGjRuHKVOmQE9Pr0CPQdvNCHlS4K8x3q1Egb8GERHR+2hUn534+HgAQLFixdTa165dixIlSqBatWqYMGECkpKSpG3BwcFwcXGBjY2N1Na8eXMkJCTg6tWr2b5OSkoKEhIS1G5ERESkTLKe2XlbZmYmvv/+ezRo0ADVqlWT2rt37w4HBwfY2dnh0qVLGDduHG7cuIF//vkHABATE6NW6ACQ7sfExGT7WtOnT4e/v38BHQkRERFpEo0pdnx8fHDlyhUcP35crX3gwIHSzy4uLihZsiSaNm2KW7duoXz58nl6rQkTJsDPz0+6n5CQAHt7+7wFJyIiIo2mEZexhg0bhp07d+Lw4cMoXbr0B/etW7cuACAiIgIAYGtri0ePHqnt8+b++/r56Ovrw8zMTO1GREREyiRrsSOEwLBhw7Bt2zYcOnQIjo6OH33MxYsXAQAlS5YEALi7u+Py5cuIjY2V9jlw4ADMzMzg7OxcILmJiIhIe8h6GcvHxwfr1q3Dv//+C1NTU6mPjbm5OQwNDXHr1i2sW7cOrVq1QvHixXHp0iWMHDkSHh4ecHV1BQB4eXnB2dkZPXv2xKxZsxATE4OffvoJPj4+0NfXl/PwiIiISAPIemZn0aJFiI+Ph6enJ0qWLCndNm7cCADQ09PDwYMH4eXlBScnJ4waNQqdOnXCjh07pOfQ1dXFzp07oaurC3d3d3z33Xfo1auX2rw8RERE9PmS9cyOEOKD2+3t7REUFPTR53FwcMDu3bvzKxYREREpiEZ0UCYiIiIqKCx2iIiISNFY7BAREZGisdghIiIiRWOxQ0RERIrGYoeIiIgUjcUOERERKRqLHSIiIlI0FjtERESkaCx2iIiISNFY7BAREZGisdghIiIiRWOxQ0RERIrGYoeIiIgUjcUOERERKRqLHSIiIlI0FjtERESkaCx2iIiISNFY7BAREZGisdghIiIiRWOxQ0RERIrGYoeIiIgUjcUOERERKRqLHSIiIlI0FjtERESkaCx2iIiISNHyVOxMnToVSUlJWdpfvXqFqVOnfnIoIiIiovySp2LH398fiYmJWdqTkpLg7+//yaGIiIiI8kueih0hBFQqVZb20NBQFCtW7JNDEREREeWXIrnZ2dLSEiqVCiqVCpUqVVIreDIyMpCYmIjBgwfne0giIiKivMpVsTN37lwIIdC3b1/4+/vD3Nxc2qanp4eyZcvC3d0930MSERER5VWuih1vb28AgKOjI+rXr4+iRYsWSCgiIiKi/JKrYueNRo0aITMzEzdv3kRsbCwyMzPVtnt4eORLOCIiIqJPladi59SpU+jevTvu3bsHIYTaNpVKhYyMjHwJR0RERPSp8lTsDB48GLVr18auXbtQsmTJbEdmEREREWmCPBU74eHh2LJlCypUqJDfeYiIiIjyVZ7m2albty4iIiLyOwsRERFRvsvTmR1fX1+MGjUKMTExcHFxyTIqy9XVNV/CEREREX2qPBU7nTp1AgD07dtXalOpVNLMyuygTERERJoiT8XOnTt38jsHERERUYHIU7Hj4OCQ3zmIiIiICkSeip1Vq1Z9cHuvXr3yFIaIiIgov+Wp2BkxYoTa/bS0NCQlJUFPTw9GRkYsdoiIiEhj5Gno+fPnz9VuiYmJuHHjBho2bIj169fn+HmmT5+OL774AqamprC2tkb79u1x48YNtX2Sk5Ph4+OD4sWLw8TEBJ06dcKjR4/U9omMjETr1q1hZGQEa2trjBkzBunp6Xk5NCIiIlKYPBU72alYsSJmzJiR5azPhwQFBcHHxwenTp3CgQMHkJaWBi8vL7x8+VLaZ+TIkdixYwc2b96MoKAgPHz4EB07dpS2Z2RkoHXr1khNTcXJkyexcuVKBAQEYNKkSfl1aERERKTF8nQZ671PVqQIHj58mOP99+7dq3Y/ICAA1tbWOH/+PDw8PBAfH4+///4b69atQ5MmTQAAK1asQJUqVXDq1CnUq1cP+/fvR1hYGA4ePAgbGxvUqFED06ZNw7hx4zBlyhTo6enl5yESERGRlslTsfPff/+p3RdCIDo6GgsWLECDBg3yHCY+Ph4AUKxYMQDA+fPnkZaWhmbNmkn7ODk5oUyZMggODka9evUQHBwMFxcX2NjYSPs0b94cQ4YMwdWrV+Hm5pbldVJSUpCSkiLdT0hIyHNmIiIi0mx5Knbat2+vdl+lUsHKygpNmjTB7Nmz8xQkMzMT33//PRo0aIBq1aoBAGJiYqCnpwcLCwu1fW1sbBATEyPt83ah82b7m23ZmT59Ovz9/fOUk4iIiLRLnoqdzMzM/M4BHx8fXLlyBcePH8/3537XhAkT4OfnJ91PSEiAvb19gb8uERERFb5P7rMjhADw+uxOXg0bNgw7d+7E0aNHUbp0aand1tYWqampiIuLUzu78+jRI9ja2kr7nDlzRu353ozWerPPu/T19aGvr5/nvERERKQ98jwaa9WqVXBxcYGhoSEMDQ3h6uqK1atX5+o5hBAYNmwYtm3bhkOHDsHR0VFte61atVC0aFEEBgZKbTdu3EBkZCTc3d0BAO7u7rh8+TJiY2OlfQ4cOAAzMzM4Ozvn9fCIiIhIIfJ0ZmfOnDmYOHEihg0bJnVIPn78OAYPHownT55g5MiROXoeHx8frFu3Dv/++y9MTU2lPjbm5uYwNDSEubk5+vXrBz8/PxQrVgxmZmbw9fWFu7s76tWrBwDw8vKCs7MzevbsiVmzZiEmJgY//fQTfHx8ePaGiIiI8lbs/PHHH1i0aJHaTMlt27ZF1apVMWXKlBwXO4sWLQIAeHp6qrWvWLECvXv3BgD8/vvv0NHRQadOnZCSkoLmzZvjzz//lPbV1dXFzp07MWTIELi7u8PY2Bje3t6YOnVqXg6NiIiIFCZPxU50dDTq16+fpb1+/fqIjo7O8fO86e/zIQYGBli4cCEWLlz43n0cHBywe/fuHL8uERERfT7y1GenQoUK2LRpU5b2jRs3omLFip8cioiIiCi/5OnMjr+/P7p06YKjR49KfXZOnDiBwMDAbIsgIiIiIrnk6cxOp06dcPr0aZQoUQLbt2/H9u3bUaJECZw5cwYdOnTI74xEREREeZbneXZq1aqFNWvW5GcWIiIionyXpzM7u3fvxr59+7K079u3D3v27PnkUERERET5JU/Fzvjx45GRkZGlXQiB8ePHf3IoIiIiovySp2InPDw829mJnZycEBER8cmhiIiIiPJLnoodc3Nz3L59O0t7REQEjI2NPzkUERERUX7JU7HTrl07fP/997h165bUFhERgVGjRqFt27b5Fo6IiIjoU+Wp2Jk1axaMjY3h5OQER0dHODo6okqVKihevDh+++23/M5IRERElGd5Gnpubm6OkydP4sCBAwgNDZVWPffw8MjvfERERESfJM/z7KhUKnh5ecHLy+u9+7i4uGD37t2wt7fP68sQERERfZI8XcbKqbt37yItLa0gX4KIiIjogwq02CEiIiKSG4sdIiIiUjQWO0RERKRoLHaIiIhI0VjsEBERkaLlW7ETFxeXpW3JkiWwsbHJr5cgIiIiyrU8FTszZ87Exo0bpfvffvstihcvjlKlSiE0NFRq7969O9fKIiIiIlnlqdhZvHixNFHggQMHcODAAezZswctW7bEmDFj8jUgERER0afI0wzKMTExUrGzc+dOfPvtt/Dy8kLZsmVRt27dfA1IRERE9CnydGbH0tISUVFRAIC9e/eiWbNmAAAhBDIyMvIvHREREdEnytOZnY4dO6J79+6oWLEinj59ipYtWwIAQkJCUKFChXwNSERERPQp8lTs/P777yhbtiyioqIwa9YsmJiYAACio6MxdOjQfA1IRERE9CnyVOwULVoUo0ePztI+cuTITw5ERERElJ/yVOysWrXqg9t79eqVpzBERERE+S1Pxc6IESPU7qelpSEpKQl6enowMjJisUNEREQaI0+jsZ4/f652S0xMxI0bN9CwYUOsX78+vzMSERER5Vm+LRdRsWJFzJgxI8tZHyIiIiI55etCoEWKFMHDhw/z8ymJiIiIPkme+uz8999/aveFEIiOjsaCBQvQoEGDfAlGRERElB/yVOy0b99e7b5KpYKVlRWaNGmC2bNn50cuIiIionyRp2InMzMzv3MQ5dmMkCcF/hrj3UoU+GsQEVHB+OQ+O0IICCHyIwsRERFRvstzsfP333+jWrVqMDAwgIGBAapVq4Zly5blZzYiIiKiT5any1iTJk3CnDlz4OvrC3d3dwBAcHAwRo4cicjISEydOjVfQxIRERHlVZ6KnUWLFuGvv/5Ct27dpLa2bdvC1dUVvr6+LHaIiIhIY+TpMlZaWhpq166dpb1WrVpIT0//5FBERERE+SVPxU7Pnj2xaNGiLO1Lly5Fjx49PjkUERERUX7J8WUsPz8/6WeVSoVly5Zh//79qFevHgDg9OnTiIyM5CKgREREpFFyXOyEhISo3a9VqxYA4NatWwCAEiVKoESJErh69Wo+xiMiIiL6NDkudg4fPpzrJ79//z7s7Oygo5OvS3ARERER5ViBViHOzs64e/fue7cfPXoUbdq0gZ2dHVQqFbZv3662vXfv3lCpVGq3Fi1aqO3z7Nkz9OjRA2ZmZrCwsEC/fv2QmJhYAEdDRERE2qhAi52Pzaz88uVLVK9eHQsXLnzvPi1atEB0dLR0W79+vdr2Hj164OrVqzhw4AB27tyJo0ePYuDAgfmSn4iIiLRfnubZyS8tW7ZEy5YtP7iPvr4+bG1ts9127do17N27F2fPnpWGwv/xxx9o1aoVfvvtN9jZ2eV7ZiIiItIuGt+Z5siRI7C2tkblypUxZMgQPH36VNoWHBwMCwsLtTl/mjVrBh0dHZw+ffq9z5mSkoKEhAS1GxERESmTRhc7LVq0wKpVqxAYGIiZM2ciKCgILVu2REZGBgAgJiYG1tbWao8pUqQIihUrhpiYmPc+7/Tp02Fubi7d7O3tC/Q4iIiISD4FehlLpVJ90uO7du0q/ezi4gJXV1eUL18eR44cQdOmTfP8vBMmTFCbNyghIYEFDxERkULJ2kE5t8qVK4cSJUogIiICAGBra4vY2Fi1fdLT0/Hs2bP39vMBXvcDMjMzU7sRERGRMhVosRMWFgYHB4d8e7779+/j6dOnKFmyJADA3d0dcXFxOH/+vLTPoUOHkJmZibp16+bb6xIREZH2yvFlrI4dO+b4Sf/55x8A+OilocTEROksDQDcuXMHFy9eRLFixVCsWDH4+/ujU6dOsLW1xa1btzB27FhUqFABzZs3BwBUqVIFLVq0wIABA7B48WKkpaVh2LBh6Nq1K0diEREREYBcFDvm5ub5/uLnzp1D48aNpftv+tF4e3tj0aJFuHTpElauXIm4uDjY2dnBy8sL06ZNg76+vvSYtWvXYtiwYWjatCl0dHTQqVMnzJ8/P9+zEhERkXbKcbGzYsWKfH9xT0/PD/br2bdv30efo1ixYli3bl1+xiKSxYyQJwX+GuPdShT4axARaRqNHnpORERE9KnyPPR8y5Yt2LRpEyIjI5Gamqq27cKFC58cjIi0U0GfoeLZKSLKrTyd2Zk/fz769OkDGxsbhISEoE6dOihevDhu37790eUfiIiIiApTnoqdP//8E0uXLsUff/wBPT09jB07FgcOHMDw4cMRHx+f3xmJiIiI8ixPxU5kZCTq168PADA0NMSLFy8AAD179syyKjkRERGRnPJU7Nja2uLZs2cAgDJlyuDUqVMAXs+Tk9+zJhMRERF9ijwVO02aNMF///0HAOjTpw9GjhyJr776Cl26dEGHDh3yNSARERHRp8jTaKylS5ciMzMTAODj44PixYvj5MmTaNu2LQYNGpSvAYmIiIg+RZ6Knfv376stBdG1a1d07doVQghERUWhTJky+RaQiIiI6FPk6TKWo6MjHj9+nKX92bNncHR0/ORQRERERPklT8WOEAIqlSpLe2JiIgwMDD45FBEREVF+ydVlrDcLdapUKkycOBFGRkbStoyMDJw+fRo1atTI14BEREREnyJXxU5ISAiA12d2Ll++DD09PWmbnp4eqlevjtGjR+dvQiIiIqJPkKti5/DhwwBeDzefN28ezMzMCiQUERERUX7J02isFStWSD/fv38fAFC6dOn8SURERESUj/LUQTkzMxNTp06Fubk5HBwc4ODgAAsLC0ybNk2af4eIiIhIE+TpzM6PP/6Iv//+GzNmzECDBg0AAMePH8eUKVOQnJyMX375JV9DEhEREeVVnoqdlStXYtmyZWjbtq3U5urqilKlSmHo0KEsdoiIiEhj5Oky1rNnz+Dk5JSl3cnJSVoglIiIiEgT5KnYqV69OhYsWJClfcGCBahevfonhyIiIiLKL3m6jDVr1iy0bt0aBw8ehLu7OwAgODgYUVFR2L17d74GJCIqbDNCnhT4a4x3K1Hgr0FEr+V5baybN2+iQ4cOiIuLQ1xcHDp27IgbN27AwcEhvzMSERER5Vmezuw4OjoiOjo6S0fkp0+fwt7eHhkZGfkSjoiIiOhT5Xkh0OxwIVAiIiLSNHleCHTSpElcCJSIiIg0HhcCJSIiIkXjQqBERESkaJ+8ECgRERGRJstTB2UiIiIibcFih4iIiBSNxQ4REREpGosdIiIiUjQWO0RERKRoLHaIiIhI0VjsEBERkaKx2CEiIiJFY7FDREREisZih4iIiBSNxQ4REREpGosdIiIiUjQWO0RERKRoLHaIiIhI0VjsEBERkaLJWuwcPXoUbdq0gZ2dHVQqFbZv3662XQiBSZMmoWTJkjA0NESzZs0QHh6uts+zZ8/Qo0cPmJmZwcLCAv369UNiYmIhHgURERFpMlmLnZcvX6J69epYuHBhtttnzZqF+fPnY/HixTh9+jSMjY3RvHlzJCcnS/v06NEDV69exYEDB7Bz504cPXoUAwcOLKxDICIiIg1XRM4Xb9myJVq2bJntNiEE5s6di59++gnt2rUDAKxatQo2NjbYvn07unbtimvXrmHv3r04e/YsateuDQD4448/0KpVK/z222+ws7MrtGMhItI0M0KeFPhrjHcrUeCvQfSpNLbPzp07dxATE4NmzZpJbebm5qhbty6Cg4MBAMHBwbCwsJAKHQBo1qwZdHR0cPr06fc+d0pKChISEtRuREREpEwaW+zExMQAAGxsbNTabWxspG0xMTGwtrZW216kSBEUK1ZM2ic706dPh7m5uXSzt7fP5/RERESkKTS22ClIEyZMQHx8vHSLioqSOxIREREVEI0tdmxtbQEAjx49Umt/9OiRtM3W1haxsbFq29PT0/Hs2TNpn+zo6+vDzMxM7UZERETKpLHFjqOjI2xtbREYGCi1JSQk4PTp03B3dwcAuLu7Iy4uDufPn5f2OXToEDIzM1G3bt1Cz0xERESaR9bRWImJiYiIiJDu37lzBxcvXkSxYsVQpkwZfP/99/j5559RsWJFODo6YuLEibCzs0P79u0BAFWqVEGLFi0wYMAALF68GGlpaRg2bBi6du3KkVhEREQEQOZi59y5c2jcuLF038/PDwDg7e2NgIAAjB07Fi9fvsTAgQMRFxeHhg0bYu/evTAwMJAes3btWgwbNgxNmzaFjo4OOnXqhPnz5xf6sRAREZFmkrXY8fT0hBDivdtVKhWmTp2KqVOnvnefYsWKYd26dQURj4iIiBRAY/vsEBEREeUHFjtERESkaCx2iIiISNFY7BAREZGisdghIiIiRWOxQ0RERIrGYoeIiIgUjcUOERERKRqLHSIiIlI0FjtERESkaCx2iIiISNFY7BAREZGisdghIiIiRWOxQ0RERIrGYoeIiIgUjcUOERERKRqLHSIiIlI0FjtERESkaCx2iIiISNFY7BAREZGisdghIiIiRWOxQ0RERIrGYoeIiIgUjcUOERERKRqLHSIiIlI0FjtERESkaCx2iIiISNFY7BAREZGisdghIiIiRWOxQ0RERIrGYoeIiIgUjcUOERERKRqLHSIiIlI0FjtERESkaCx2iIiISNFY7BAREZGisdghIiIiRWOxQ0RERIrGYoeIiIgUjcUOERERKRqLHSIiIlI0FjtERESkaCx2iIiISNE0vtiZMmUKVCqV2s3JyUnanpycDB8fHxQvXhwmJibo1KkTHj16JGNiIiIi0iQaX+wAQNWqVREdHS3djh8/Lm0bOXIkduzYgc2bNyMoKAgPHz5Ex44dZUxLREREmqSI3AFyokiRIrC1tc3SHh8fj7///hvr1q1DkyZNAAArVqxAlSpVcOrUKdSrV6+woxIREZGG0YozO+Hh4bCzs0O5cuXQo0cPREZGAgDOnz+PtLQ0NGvWTNrXyckJZcqUQXBwsFxxiYiISINo/JmdunXrIiAgAJUrV0Z0dDT8/f3x5Zdf4sqVK4iJiYGenh4sLCzUHmNjY4OYmJj3PmdKSgpSUlKk+wkJCQUVn4iIiGSm8cVOy5YtpZ9dXV1Rt25dODg4YNOmTTA0NMzTc06fPh3+/v75FZGIiIg0mFZcxnqbhYUFKlWqhIiICNja2iI1NRVxcXFq+zx69CjbPj5vTJgwAfHx8dItKiqqgFMTERGRXLSu2ElMTMStW7dQsmRJ1KpVC0WLFkVgYKC0/caNG4iMjIS7u/t7n0NfXx9mZmZqNyIiIlImjb+MNXr0aLRp0wYODg54+PAhJk+eDF1dXXTr1g3m5ubo168f/Pz8UKxYMZiZmcHX1xfu7u4ciUVEREQAtKDYuX//Prp164anT5/CysoKDRs2xKlTp2BlZQUA+P3336Gjo4NOnTohJSUFzZs3x59//ilzaiIiItIUGl/sbNiw4YPbDQwMsHDhQixcuLCQEhEREZE20fhih4iIPl8zQp4U+GuMdytR4K+hlOPQVlrXQZmIiIgoN1jsEBERkaKx2CEiIiJFY7FDREREisZih4iIiBSNxQ4REREpGosdIiIiUjQWO0RERKRoLHaIiIhI0VjsEBERkaKx2CEiIiJFY7FDREREisZih4iIiBSNxQ4REREpGosdIiIiUjQWO0RERKRoLHaIiIhI0VjsEBERkaKx2CEiIiJFY7FDREREisZih4iIiBSNxQ4REREpGosdIiIiUjQWO0RERKRoLHaIiIhI0VjsEBERkaKx2CEiIiJFY7FDREREisZih4iIiBSNxQ4REREpGosdIiIiUjQWO0RERKRoLHaIiIhI0VjsEBERkaKx2CEiIiJFY7FDREREisZih4iIiBStiNwBiIiISDvMCHlSoM8/3q1EgTwvz+wQERGRorHYISIiIkVjsUNERESKxmKHiIiIFE0xxc7ChQtRtmxZGBgYoG7dujhz5ozckYiIiEgDKKLY2bhxI/z8/DB58mRcuHAB1atXR/PmzREbGyt3NCIiIpKZIoqdOXPmYMCAAejTpw+cnZ2xePFiGBkZYfny5XJHIyIiIplpfbGTmpqK8+fPo1mzZlKbjo4OmjVrhuDgYBmTERERkSbQ+kkFnzx5goyMDNjY2Ki129jY4Pr169k+JiUlBSkpKdL9+Ph4AEBCQkKOXzc58UUe0uZOQoJegb+GEo5DCccA8DhySgnHAPA4ckoJxwDwOHIqt8fw5nNbCPHhHYWWe/DggQAgTp48qdY+ZswYUadOnWwfM3nyZAGAN95444033nhTwC0qKuqDtYLWn9kpUaIEdHV18ejRI7X2R48ewdbWNtvHTJgwAX5+ftL9zMxMPHv2DMWLF4dKpSqQnAkJCbC3t0dUVBTMzMwK5DUKmhKOAVDGcSjhGAAehyZRwjEAyjgOJRwDUDjHIYTAixcvYGdn98H9tL7Y0dPTQ61atRAYGIj27dsDeF28BAYGYtiwYdk+Rl9fH/r6+mptFhYWBZz0NTMzM61+8wLKOAZAGcehhGMAeByaRAnHACjjOJRwDEDBH4e5uflH99H6YgcA/Pz84O3tjdq1a6NOnTqYO3cuXr58iT59+sgdjYiIiGSmiGKnS5cuePz4MSZNmoSYmBjUqFEDe/fuzdJpmYiIiD4/iih2AGDYsGHvvWylCfT19TF58uQsl8+0iRKOAVDGcSjhGAAehyZRwjEAyjgOJRwDoFnHoRLiY+O1iIiIiLSX1k8qSERERPQhLHaIiIhI0VjsEBERkaKx2CEiIiJFU8xoLE2Qm7W1lDBRFBER5a+UlBSNGL2kNByNlY90dHRyvNxERkZGAachpYiLi8O2bdtw7Ngx3Lt3D0lJSbCysoKbmxuaN2+O+vXryx0xR5RyHNeuXcOGDRveexydOnXih1UhyczMRFBQULb/Fs2aNYO9vb3cET9qz5490vspKioKmZmZMDY2hpubG7y8vNCnT5+PLoUgt0uXLuV4X1dX1wJM8n4sdvJRUFCQ9PPdu3cxfvx49O7dG+7u7gCA4OBgrFy5EtOnT4e3t7dcMXMkLi4O69evx5AhQwAAPXr0wKtXr6Tturq6+OuvvwptmY3P0cOHDzFp0iSsXbsWdnZ2qFOnDuzs7GBoaIhnz57hypUrOH/+PBwcHDB58mR06dJF7sjZUspxXLhwAWPHjsXx48fRoEGDbI/j2LFjSEhIwNixY/H9999rZdGTmpqK1NRUmJiYyB3lvV69eoXZs2dj0aJFePbsGWrUqJHl3+Lhw4fw8vLCpEmTUK9ePbkjZ7Ft2zaMGzcOL168QKtWrd77fgoODkbv3r0xbdo0WFlZyR07W2++6L+vnHizTaVSyfdF/1NXHafsNWnSRKxbty5L+9q1a0WjRo0KP1AuzZo1S3Tv3l26b2JiIjp16iR69+4tevfuLSpXriwmT54sX8BcOHTokPjtt9/E8ePHhRBCLF68WNjb24sSJUqI/v37i6SkJJkTZs/a2lqMGTNGXL169b37JCUliXXr1ol69eqJ//3vf4WYLueUchxly5YVCxcuFM+fP//gfidPnhRdunQRv/zyS+EE+wTLly8Xw4YNE2vWrBFCCDF+/Hihp6cndHR0RLNmzcSTJ09kTpi90qVLi86dO4tdu3aJ1NTUbPe5e/eu+PXXX4WDg4NYunRpISf8uHr16omdO3eKjIyMD+53//59MW7cODFnzpxCSpZ7d+/ezfFNLix2CoihoaG4efNmlvYbN24IQ0NDGRLlTp06dcSBAwek+yYmJuLWrVvS/X/++UfUqFFDjmi5snTpUqGrqysqVKgg9PX1xa+//iqMjY3F4MGDxdChQ4WZmZkYN26c3DGzldsPGk39YFLKcbzvQzW/9i9sP//8szA0NBTNmjUTxYoVE4MHDxa2trZixowZYtasWaJ06dJi8ODBcsfMVlhYWI73TU1NFREREQWYhrQBi50CUqlSJTFmzJgs7WPGjBGVKlWSIVHulChRQkRGRkr3a9WqJaKioqT7t27dEsbGxnJEy5WqVauK+fPnCyGE2LNnjyhSpIgICAiQtm/atEmUL19ernhEsqlQoYJ09vns2bNCR0dHbNmyRdq+e/duUaZMGbnifZZSUlLE9evXRVpamtxRPklERIQYNmyYaNq0qWjatKnw9fWVveBkn50Csnv3bnTq1AkVKlRA3bp1AQBnzpxBeHg4tm7dilatWsmc8MOMjIxw5swZVKtWLdvtly9fRt26dZGUlFTIyXLHyMgI165dg4ODAwBAT08PoaGhqFKlCgAgMjISFStWREpKipwxs/Xff//leN+2bdsWYJJPo5TjeFdgYCACAwMRGxuLzMxMtW3Lly+XKVXO6evrIyIiQurEq6+vj0uXLqFy5coAgAcPHsDR0RGpqalyxsyWNnSIzY2kpCT4+vpi5cqVAICbN2+iXLly8PX1RalSpTB+/HiZE+bcvn370LZtW9SoUQMNGjQAAJw4cQKhoaHYsWMHvvrqK1lyceh5AWnVqhVu3ryJRYsW4fr16wCANm3aYPDgwVoxQqBcuXK4cOHCe4udc+fOwdHRsZBT5V5ycjIMDQ2l+/r6+mqdRvX19ZGeni5HtI9q37692v13OwC+PfJPk0f3KeU43ubv74+pU6eidu3aKFmyZI5HYWqStLQ0tf8Lenp6KFq0qHS/SJEiGvvvUaNGDbVOrx+iqcfwtgkTJiA0NBRHjhxBixYtpPZmzZphypQpWlXsjB8/HiNHjsSMGTOytI8bN062YoeXsShbP/30k7C3txcxMTFZtkVHRwt7e3vx448/ypAsd3R0dERERISIj48XcXFxwtTUVISGhor4+HgRHx8vbt68KXR0dOSO+VEHDhwQNWvWFHv37pWy7927V9SuXVvs379f7ng5ppTjsLW1FatWrZI7xidRqVTi8OHDIjQ0VISGhgpjY2Oxa9cu6X5gYKDG/t94u8Prtm3bRPny5cXixYul7IsXLxYVK1YU27ZtkztqjpQpU0YEBwcLIdT7R4aHhwtTU1M5o+Wavr7+e/ur6uvry5DoNRY7Bejo0aOiR48ewt3dXdy/f18IIcSqVavEsWPHZE72cQkJCaJKlSrC1NRUDB06VMydO1fMnTtXDBkyRJiamgonJyeRkJAgd8yPUqlUQkdHR7q9776mq1q1arbvm6NHjwonJycZEuWNUo6jWLFisvdB+FRv3vsqlSrLTZv+b3zxxRdi165dWdp37dolatasKUOi3DM0NJQKnLeLnYsXLwozMzM5o+Va6dKlxaZNm7K0b9y4Udjb28uQ6DVexiogW7duRc+ePdGjRw9cuHBB6hMSHx+PX3/9Fbt375Y54YeZmprixIkTmDBhAtavX4+4uDgAgIWFBbp3745ff/0Vpqam8obMgcOHD8sdIV/cunUr2zmNzM3Ncffu3ULPk1dKOY7+/ftj3bp1mDhxotxR8uzOnTtyR8gXly9fzvaSuqOjI8LCwmRIlHu1a9fGrl274OvrC+D/X9pdtmyZNE+bthgwYAAGDhyI27dvSxOFnjhxAjNnzoSfn59sudhBuYC4ublh5MiR6NWrF0xNTREaGopy5cohJCQELVu2RExMjNwRc0wIgcePHwMArKystLJ/grbz8PCAgYEBVq9eDRsbGwDAo0eP0KtXLyQnJ6tNaKnJlHIcI0aMwKpVq+Dq6gpXV1e1vi4AMGfOHJmSfX5q1qyJatWqYdmyZdDT0wPwemLE/v3748qVK7hw4YLMCT/u+PHjaNmyJb777jsEBARg0KBBCAsLw8mTJxEUFIRatWrJHTHHhBCYO3cuZs+ejYcPHwIA7OzsMGbMGAwfPly2zw8WOwXEyMgIYWFhKFu2rFqxc/v2bTg7OyM5OVnuiJ+FTZs2oX379tIfwfv378POzg46Oq/XwE1KSsKCBQswduxYOWN+VEREBDp06ICbN29KHdyjoqJQsWJFbN++HRUqVJA5Yc4o5TgaN2783m0qlQqHDh0qxDR5M2vWLPj6+kod+E+cOIHatWtLnZZfvHiBcePG4c8//5Qz5kedOXMGbdq0gRBCGnl16dIlqFQq7NixA3Xq1JE5Yc7cunULM2bMQGhoKBITE1GzZk2MGzcOLi4uckfLsxcvXgCARlwFYLFTQMqVK4elS5eiWbNmasXOqlWrMGPGDI0/vdq4ceOPVuAqlQqBgYGFlChvdHV1ER0dDWtrawCvF2C9ePEiypUrB+D1WQU7OzutGLEhhMCBAwek0X1VqlRBs2bNtO5Mm1KOQ9sp6f/Gy5cvsXbtWrX3VPfu3WFsbCxzMtIU7LNTQAYMGIARI0Zg+fLlUKlUePjwIYKDgzF69GituM5fo0aN92578eIF1q1bp5Fz07zr3Vpem2t7lUoFLy8veHl5yR3lk7w5Dg8PD+jr62t9kXP//n0AQOnSpWVOkjtK+r9hbGyMgQMHyh3jk2RmZiIiIiLbeZs8PDxkSpV7jx49wujRo6U5qN59X8lVPLPYKSDjx49HZmYmmjZtiqSkJOkP++jRo6VOaJrs999/z9KWnp6OhQsX4pdffkGpUqUwbdo0GZJ9PubPn4+BAwfCwMAA8+fP/+C+w4cPL6RUnyYzMxO//PILFi9ejEePHkmTp02cOBFly5ZFv3795I6YI5mZmfj5558xe/ZsJCYmAnh9qn7UqFH48ccfpcukVDhWr16NJUuW4Pbt2wgODoaDgwN+//13lCtXDu3atZM73kedOnUK3bt3x71797IUB7IunpkHvXv3RmRkJCZOnKhZc1AV+vivz0xKSoq4evWqOH36tHjx4oXccfJszZo1oly5cqJkyZJi4cKFWjOduUqlEo8ePZLuv7vGV0xMjMYOry1btqy0TlTZsmXfe3N0dJQ5ac75+/uLcuXKiTVr1qgNt92wYYOoV6+ezOlybvz48cLKykr8+eef0twuCxcuFFZWVuKHH36QO16OaPP/jbf9+eefokSJEuLnn38WBgYG0jGsWLFCeHp6ypwuZ6pXry46d+4swsLCxPPnz0VcXJzaTZuYmJiIkJAQuWNkwTM7BUxPTw/Ozs5yx8izvXv3Yvz48bhz5w5Gjx4NPz8/rbsOvm/fPpibmwN4/Y08MDAQV65cAQBpSL0mentosFKGCa9atQpLly5F06ZNMXjwYKm9evXqUn8LbbBy5UosW7ZMbXkLV1dXlCpVCkOHDsUvv/wiY7qcW7ZsGUxMTAC8PnMbEBCAEiVKAPj/nUs13R9//IG//voL7du3V5u1t3bt2hg9erSMyXIuPDwcW7Zs0ZoO+h9ib2+vkZdEWezko44dOyIgIABmZmbo2LHjB/f9559/CilV3pw5cwbjxo3DqVOnMHjwYBw8eFD6I6htvL291e4PGjRI7b7GnGZ9j7S0NDg5OWHnzp3Sml7a6sGDB9n+Qc/MzERaWpoMifLm2bNncHJyytLu5OSEZ8+eyZAo98qUKYO//vpLum9ra4vVq1dn2UfT3blzB25ublna9fX18fLlSxkS5V7dunURERGhiGJn7ty5GD9+PJYsWYKyZcvKHUfCYicfmZubSx+cZmZmGv8h+iH16tWDoaEhBg8eDEdHR6xbty7b/TS9r8i7Hf20UdGiRRUzVYGzszOOHTsmLcz6xpYtW7L9wNJU1atXx4IFC7L0pVqwYAGqV68uU6rc0aZJHD/E0dERFy9ezPKe2rt3r9Z8OfD19cWoUaMQExMDFxeXLPM2acNipm906dIFSUlJKF++PIyMjLIci1xfBljs5KMOHTrAwMAAABAQECBvmE9UpkwZqFQqbN++/b37qFQqjS923nj69CmKFy8O4PW8Ln/99ReSk5PRpk0bfPnllzKn+zgfHx/MnDkTy5YtQ5Ei2vvfdtKkSfD29saDBw+QmZmJf/75Bzdu3MCqVauwc+dOuePl2KxZs9C6dWscPHhQmuE2ODgYUVFRGj87utL4+fnBx8cHycnJEELgzJkzWL9+PaZPn45ly5bJHS9HOnXqBADo27ev1Pb2Qqfa1EF57ty5ckfIFufZyUe6urqIiYmBlZVVljksSB6XL19GmzZtpInrNmzYgBYtWuDly5fQ0dHBy5cvsWXLliwrc2uaDh06IDAwECYmJnBxccnSb0rTL4u+7dixY5g6dara5GmTJk3SuiH1Dx8+xMKFC9Xmdhk6dCjs7OxkTpYzhw4dwrBhw3Dq1CmYmZmpbYuPj0f9+vWxaNEirRj2vHbtWkyZMgW3bt0C8HrGXn9/f60Z3Xfv3r0Pbn/3rBXlHoudfGRra4u//voLbdq0gY6ODh49egQrKyu5Y+VJTv4QLl68WOPPirRs2RJFihTB+PHjsXr1auzcuRPNmzeX+ir4+vri/PnzOHXqlMxJP6xPnz4f3L5ixYpCSkJK0bZtWzRu3BgjR47Mdvv8+fNx+PBhbNu2rZCT5V1SUhISExP5JVNDJCcnIzU1Va3t3c+TwsJiJx9NmTIFU6dOzVFfHU0/LamUP4QlSpTAoUOH4OrqisTERJiZmeHs2bPSWjPXr19HvXr1NHpUFmmGS5cuoVq1atDR0cGlS5c+uK829LFwcHD4YL+W69evw8vLC5GRkYWc7PMVFhaGyMjILAXC26P+NN3Lly8xbtw4bNq0CU+fPs2ynZMKKsCUKVPQtWtXREREoG3btlixYkW2Kzxrg9DQUMycOfO92728vPDbb78VYqK8efbsGWxtbQEAJiYmMDY2hqWlpbTd0tJSa4bYAkBsbCxu3LgBAKhcubJWfIMtVqwYbt68iRIlSsDS0vKDXwY0eSRTjRo1EBMTA2tra9SoUUPqU/Eubelj8ejRoyydR99WpEgRaQFgTVOzZk0EBgbC0tISbm5uH3xPacNCoLdv30aHDh1w+fJltffVm+PShvfTG2PHjsXhw4exaNEi9OzZEwsXLsSDBw+wZMkStakBChuLnXzm5OQEJycnTJ48GZ07d4aRkZHckfJEm/8QvuvdP4TaOEouISEBPj4+2LBhg/SHT1dXF126dMHChQuleYQ00e+//y4tBKipnRdz4s6dO9JlaSXMe1SqVClcuXLlvcOdL126hJIlSxZyqpxp166dtGCppve3y4kRI0bA0dERgYGBcHR0xJkzZ/D06VOMGjVKK75Uvm3Hjh1YtWoVPD090adPH3z55ZeoUKECHBwcsHbtWvTo0UOWXCx2CsjkyZPljvBJtPkP4bt69+4t/WFMTk7G4MGDpQ6+2rC+F/B6rbWQkBDs3LlTbfTPiBEjMGjQIGzYsEHmhO8XGhqKb775Bvr6+nB0dET9+vW1ckTZ251EldBhtFWrVpg4cSJatGghjSJ949WrV5g8eTK+/vprmdJ9mKWlpbQkR58+fVC6dGmtXqIjODgYhw4dQokSJaCjowMdHR00bNgQ06dPx/DhwxESEiJ3xBx79uyZtJismZmZdLa2YcOGGDJkiGy52GcnHynp1Kqvry+OHDmCs2fPZvuHsE6dOmjcuPFH12yS28c69r6h6R18jY2NsW/fPjRs2FCt/dixY9LoMk1VtGhR3L9/HzY2NooZpbhy5UqUKFECrVu3BvD61P3SpUvh7OyM9evXa0Ux9OjRI9SsWRO6uroYNmwYKleuDOB1X52FCxciIyMDFy5cgI2NjcxJsypSpAgePnwIa2trRbynLC0tceHCBTg6OqJ8+fJYtmwZGjdujFu3bsHFxQVJSUlyR8wxV1dX/PHHH2jUqBGaNWuGGjVq4LfffsP8+fMxa9YsaeHcwqZ9X680mJJOrf7000/4559/UKlSpff+Ifzxxx9lTvlxml7E5FTx4sWzvVRlbm6u1gdJE5UtWxbz58+Hl5cXhBAIDg5+b2ZtGOYMAL/++isWLVoE4PW38gULFmDu3LnYuXMnRo4cqRVTAdjY2ODkyZMYMmQIJkyYoNZPpHnz5li4cKFGFjrA66HlW7duRatWrSCEwP3799878aY2zAJdrVo1hIaGwtHREXXr1sWsWbOgp6eHpUuXSmdJtEWfPn0QGhqKRo0aYfz48WjTpg0WLFiAtLQ0zJkzR75ghb8cF2mLu3fvipYtWwodHR2hUqmESqUSOjo6omXLluL27dtyx/usLFmyRDRr1kxER0dLbdHR0cLLy0ssXrxYxmQft23bNmFjYyO9f968l969acOik28YGhqKe/fuCSGEGDt2rOjZs6cQQogrV66IEiVKyBktx27duiUyMzOFEEI8e/ZMnDlzRpw+fVo8e/ZM5mQft2TJEqGnpyd0dHTee9Om99TevXvF1q1bhRBChIeHi8qVKwuVSiVKlCghAgMDZU73ae7evSu2bt0qQkNDZc3By1gF5OzZs8jMzETdunXV2k+fPg1dXV3Url1bpmS59/z5c0REREAIgYoVK2r8mQSlePdSaHh4OFJSUqRvqpGRkdDX10fFihU1/rIoAGno/40bN957yUGTO1q/zdraGvv27YObmxvc3Nzg5+eHnj174tatW6hevToSExPljvhR717+6dKlC+bPn6+xZ3Pe9eLFC9y7dw+urq44ePCgNEP6u7Rl+Y53PXv27KOjFynneBmrgPj4+GDs2LFZip0HDx5g5syZOH36tEzJcs/S0hJffPGF3DE+O9p+KfRdJiYmOHz4MBwdHbWyg/LbvvrqK/Tv3x9ubm64efMmWrVqBQC4evWqRi1++CHvfs/dvXs3pk+fLlOa3DM1NUW1atWwYsUKNGjQQOpCoO2ioqIAvF49XFsFBgYiMDAQsbGxWdYnXL58uSyZtPsvjgYLCwtDzZo1s7S7ubkhLCxMhkSkbbR9RN8bCQkJ0qypbm5uH+xsKdfsqrm1cOFC/PTTT4iKisLWrVulswrnz59Ht27dZE73efH29pY7widLT0+Hv78/5s+fL50VNDExga+vLyZPnvzBaUA0jb+/P6ZOnYratWujZMmSGnNmisVOAdHX18ejR4+ydC6Ljo7W+m+1VPi8vb3Rr18/renA+zZLS0vpcomFhUW2f/yEli14aGFhgQULFmRp9/f3lyFN3qhUKq2dg0opE1W+4evri3/++QezZs1Sm1piypQpePr0qdQZXhssXrwYAQEB6Nmzp9xR1PBTt4B4eXlhwoQJ+Pfff6V+CHFxcfjhhx/w1VdfyZyOtE18fDyaNWsGBwcH9OnTB97e3ihVqpTcsXLk0KFDKFasmPSztnygfsjevXthYmIiTQWwcOFC/PXXX3B2dsbChQu1ol+bEOKDc1C9oYkjy96eqPL333/X+vfUunXrsGHDBrRs2VJqc3V1hb29Pbp166ZVxU5qairq168vd4ws2EG5gDx48AAeHh54+vQp3NzcAAAXL16EjY0NDhw4oNXXY0kejx8/xurVq7Fy5UqEhYWhWbNm6NevH9q1a6dVp7mVwMXFBTNnzkSrVq1w+fJlfPHFF/Dz88Phw4fh5OSkFVMeKGUOKiWwtrZGUFBQlnXKrl27Bg8PD62ZrR4Axo0bBxMTE0ycOFHuKGpY7BSgly9fYu3atQgNDYWhoSFcXV3RrVs3fjDRJ7tw4QJWrFiBZcuWwcTEBN999x2GDh2KihUryh3tg1asWAETExN07txZrX3z5s1ISkrSmv4XJiYmuHLlCsqWLYspU6bgypUr2LJlCy5cuIBWrVohJiZG7oifjd27d0NXVxfNmzdXa9+/fz8yMjLUzpZoqqlTp+L69etYsWKFdKYtJSUF/fr1Q8WKFTW+/56fn5/0c2ZmJlauXAlXV1e4urpm+byTa64dXsYqQMbGxhg4cKDcMUhhoqOjceDAARw4cAC6urrS2QVnZ2fMmjXrvSvVa4Lp06djyZIlWdqtra0xcOBArSl29PT0pI7WBw8eRK9evQC87kuSkJAgZ7TPzvjx47NdYDIzMxPjx4/XimInJCQEgYGBKF26tDRUPjQ0FKmpqWjatCk6duwo7auJlxXfXc6iRo0aAIArV67IkCZ7LHYK0OrVq7FkyRLcvn0bwcHBcHBwwO+//45y5cqhXbt2cscjLZKWlob//vsPK1aswP79++Hq6orvv/8e3bt3l0Ywbdu2DX379tXoYicyMhKOjo5Z2h0cHBAZGSlDorxp2LAh/Pz80KBBA5w5cwYbN24EANy8eROlS5eWOd3nJTw8HM7OzlnanZycEBERIUOi3LOwsECnTp3U2rSpq8Phw4fljvBRLHYKyKJFizBp0iR8//33+Pnnn6VRJpaWlpg7dy6LHcqVkiVLIjMzE926dcOZM2ekb05va9y4MSwsLAo9W25YW1vj0qVLWeaiCQ0Nfe+kcJpowYIFGDp0KLZs2YJFixZJncX37NmDFi1ayJzu82Jubo7bt29neU9FRERk6WytqZTUL6pv376YN2+e1IH8jZcvX8LX11e2eXa4XEQBqVKliti2bZsQQggTExNx69YtIYQQly9fFsWLF5cxGWmjVatWiVevXskd45ONHTtWODg4iEOHDon09HSRnp4uAgMDhYODgxg1apTc8UgLDRw4ULi4uIiIiAipLTw8XLi6uop+/frJmCz3YmNjxbFjx8SxY8dEbGys3HHyREdHRzx69ChL++PHj4Wurq4MiV7jmZ0CcufOHWkU1tv09fU1eoVq0kyaNmdFXk2bNg13795F06ZNpfmmMjMz0atXL/z6668yp/uwtydH/Fi/HG2ZHFEJZs2ahRYtWsDJyUm6hHj//n18+eWX+O2332ROlzNvznqsWrVKmnFYV1cXvXr1wh9//AEjIyOZE35cQkIChBAQQuDFixcwMDCQtmVkZGD37t2yrkzPYqeAODo64uLFi3BwcFBr37t3b5bhhUQf8/LlS8yYMeO9U7Dfvn1bpmS5o6enh40bN2LatGnSKEUXF5cs/080kRInR1QCc3NznDx5EgcOHFAb+apNE3D6+fkhKCgIO3bsQIMGDQAAx48fx/DhwzFq1CitmGfnzf8JlUqFSpUqZdmuUqlknXSTxU4B8fPzg4+PD5KTkyGEwJkzZ7B+/XpMnz4dy5YtkzseaZn+/fsjKCgIPXv21Kgp2POqbNmyEEKgfPnyWjOj+NuTI2pDh8zPiUqlgpeXFzw8PKCvr691/z+2bt2KLVu2wNPTU2pr1aoVDA0N8e2332pFsXP48GEIIdCkSRNs3bpV+r8CvP6S4+DgADs7O/kCynYB7TOwZs0aUaFCBaFSqYRKpRKlSpUSy5YtkzsWaSFzc3Nx/PhxuWN8spcvX4q+ffsKXV1doaurK/VlGzZsmJg+fbrM6UgbZWRkiKlTpwo7Ozu199RPP/2kNX9vDQ0NRVhYWJb2K1euCCMjIxkS5d3du3fF0aNHRY8ePUS9evXE/fv3hRCv+x0eO3ZMtlw68pVZytejRw+Eh4cjMTERMTExuH//Pvr16yd3LNJClpaWat+UtNWECRMQGhqKI0eOqF3Tb9asmTR8W1skJyfjzJkz2LlzJ/777z+1GxWen3/+GQEBAZg1axb09PSk9mrVqmnNWXR3d3dMnjwZycnJUturV6/g7+8vrZWlLc6dO4fmzZvD0NAQISEhSElJAfB6yRtZ++XJVmZ9Jh49eiSOHj0qjh49qrW960l+q1evFt988414+fKl3FE+SZkyZURwcLAQQn2UYnh4uDA1NZUzWq7s2bNHWFlZSWdt377p6OjIHe+zUr58eXHw4EEhhPp76tq1a8LCwkLOaDl26dIlYWdnJ4oXLy6aNGkimjRpIooXLy5KlSolrly5Ine8XKlRo4ZYuXKlEEL93+PChQvCxsZGtlzacbFcC7148QJDhw7F+vXr1XrXd+nSBQsXLpQWByV6Hzc3N7W+BxEREbCxsUHZsmWzTMF+4cKFwo6XJ48fP852RMbLly+1qp+Fr68vOnfujEmTJsHGxkbuOJ+1Bw8eoEKFClnaMzMzkZaWJkOi3HNxcUF4eDjWrl2L69evAwC6deuGHj16wNDQUOZ0uXPjxo1sO4ebm5sjLi6u8AP9HxY7BaR///4ICQnBrl27pNOQwcHBGDFiBAYNGoQNGzbInJA0Xfv27eWOkO9q166NXbt2wdfXFwCkAmfZsmVadbr+0aNH8PPzY6GjAZydnXHs2LEsI/q2bNmS7fQfmiYtLQ1OTk7YuXMnBgwYIHecT2Zra4uIiIgskzweP34c5cqVkycUWOwUmJ07d2Lfvn1o2LCh1Na8eXP89ddfnGGVckTTF//Li19//RUtW7ZEWFgY0tPTMW/ePISFheHkyZMICgqSO16OffPNNzhy5AjKly8vd5TP3qRJk+Dt7Y0HDx4gMzMT//zzD27cuIFVq1Zh586dcsf7qKJFi6r11dF2AwYMwIgRI7B8+XKoVCo8fPgQwcHBGD16tKwroXPV8wJSpkwZ7Nq1Cy4uLmrtly5dQqtWrXD//n2ZkpG2EP83Z4vS3Lp1CzNmzEBoaCgSExNRs2ZNjBs3Lsv/FU2WlJSEzp07w8rKCi4uLlkuKw4fPlymZJ+nY8eOYerUqWrvqUmTJsHLy0vuaDny66+/4ubNm1i2bJnWTMXwPkII/Prrr5g+fbq0WK6+vj5Gjx6NadOmyZaLxU4BWbp0KTZv3ozVq1fD1tYWABATEwNvb2907NgRgwYNkjkhaTpnZ2dMmjQJHTt2VBtl8q7w8HDMmTMHDg4OGD9+fCEm/Hz9/fffGDx4MAwMDFC8eHG1olSlUmnNJI+kGTp06IDAwECYmJjAxcUly5pemrjS+cekpqYiIiICiYmJcHZ2homJiax5WOwUEDc3N0RERCAlJQVlypQB8HrFZ319fVSsWFFtX23pXEqFKzAwEOPGjcPt27fx1VdfoXbt2rCzs4OBgQGeP3+OsLAwHD9+HFevXsWwYcPwww8/aEXH94yMDGzbtg3Xrl0D8Lqoa9eunVZ9o7W1tcXw4cMxfvx46OhwBg9NcO7cObX3VK1atWROlHN9+vT54HYlLRQqFxY7BSQ302IrsW8G5Z/jx49j48aNOHbsGO7du4dXr16hRIkScHNzQ/PmzdGjRw9YWlrKHTNHrl69irZt2yImJgaVK1cGANy8eRNWVlbYsWMHqlWrJnPCnClWrBjOnj3LPjsa4P79++jWrRtOnDgBCwsLAEBcXBzq16+PDRs2SOtl0eeNxQ4RFRp3d3dYWVlh5cqVUoH2/Plz9O7dG48fP8bJkydlTpgzI0eOhJWVFX744Qe5o3z2WrRogbi4OKxcuVIqoG/cuIE+ffrAzMwMe/fulTlhzsXGxuLGjRsAgMqVK8u6cKbSsNgpIFFRUVCpVNK3ijNnzmDdunVwdnbGwIEDZU5HJA9DQ0OcO3cOVatWVWu/cuUKvvjiC7x69UqmZLkzfPhwrFq1CtWrV4erq2uWDspz5syRKdnnx9DQECdPnswyzPz8+fP48ssvpU6ymiwhIQE+Pj7YsGGDtIgs52XLX7zYXEC6d+8uLRYYExODZs2a4cyZM/jxxx8xdepUmdMRyaNSpUp49OhRlvbY2NhsJ4bTVJcvX4abmxt0dHRw5coVhISESLeLFy/KHe+zYm9vn+3kgRkZGfIuPJkLAwYMwOnTp7Fz507ExcUhLi4OO3fuxLlz5ziYJb/IMW3z58DCwkJcv35dCCHEvHnzRP369YUQQuzbt084OjrKGY1INrt27RJVq1YVmzdvFlFRUSIqKkps3rxZuLi4iF27don4+HjpRpQT27dvF3Xq1BFnz56V2s6ePSvq1asntm3bJl+wXDAyMsp2kcyjR49q3UKgmoqXsQqIiYkJrly5grJly6Jt27Zo0KABxo0bh8jISFSuXFlrTtcT5ae3Ry69Ga795k/Q2/dVKpV0Ol+TRURE4NatW/Dw8IChoaFi50bSZJaWlkhKSkJ6ero0ou/Nz+8O4X727JkcET+K87IVPO0Z66llqlatisWLF6N169Y4cOCANJnSw4cPUbx4cZnTEcnjzaVdbff06VN8++23OHz4MFQqFcLDw1GuXDn069cPlpaWmD17ttwRPxtz586VO8In++mnn+Dn55dlXrYxY8bIOuuwkvDMTgE5cuQIOnTogISEBHh7e2P58uUAgB9++AHXr1/XykmiSF6ZmZmIiIhAbGystLjsG9ktvEcFp1evXoiNjcWyZctQpUoVhIaGoly5cti3bx/8/Pxw9epVuSOSFuG8bAWPZ3YKiKenJ548eYKEhAS1OVAGDhwIIyMjGZORNjp16hS6d++Oe/fu4d3vJ9pyyQcA9u7dCxMTE2nNuIULF+Kvv/6Cs7MzFi5cqDXzBe3fvx/79u3LModLxYoVce/ePZlSfZ4uXLiAokWLSpeA/v33X6xYsQLOzs6YMmXKB2cf1xRKXPRX0/DMDpEWqFGjBipVqgR/f3+ULFkyS78QbRma6uLigpkzZ6JVq1a4fPkyateujVGjRuHw4cNwcnLSmpliTU1NceHCBVSsWBGmpqbSmZ1z586hefPmePr0qdwRPxtffPEFxo8fj06dOuH27dtwdnZGx44dcfbsWbRu3VoRl7no07HYIdICxsbGCA0N1arh2dl5u+P+lClTcOXKFWzZsgUXLlxAq1atEBMTI3fEHGnVqhVq1aqFadOmwdTUFJcuXYKDgwO6du2KzMxMbNmyRe6Inw1zc3NcuHAB5cuXx8yZM3Ho0CHs27cPJ06cQNeuXREVFSV3xGyxM3vh4jw7RFqgbt26iIiIkDvGJ9PT05MmeTt48KC0KnWxYsWQkJAgZ7RcmTVrFpYuXYqWLVsiNTUVY8eORbVq1XD06FHMnDlT7nifFSGE1Ift4MGDaNWqFYDX8+88efJEzmgfVLVqVWzYsAGpqakf3C88PBxDhgzBjBkzCimZMrHPDpEW8PX1xahRoxATEwMXF5csM/a6urrKlCx3GjZsCD8/PzRo0ABnzpzBxo0bAbxeH0ub1jCqVq0abt68iQULFsDU1BSJiYno2LEjfHx8ULJkSbnjfVZq166Nn3/+Gc2aNUNQUBAWLVoEALhz5w5sbGxkTvd+f/zxB8aNG4ehQ4fmaKHfIUOGyB1Zq/EyVgFZtWoVunTpAn19fbX21NRUbNiwAb169ZIpGWmj7FbWVqlUWjUnDfB6hMnQoUMRFRWF4cOHo1+/fgBerzWVkZGB+fPny5yQtM2lS5fQo0cPREZGws/PT1pY2dfXF0+fPsW6detkTvhhSlroV5Ox2Ckgurq6iI6OzrKQ29OnT2Ftba01H06kGT42wsfBwaGQkhBph+TkZOjq6mY5C0qfJ17GKiDv63x2//59rRk5Q5qDxQxR7hgYGMgdgTQIi5185ubmBpVKBZVKhaZNm0rTlwOvF6a7c+cOWrRoIWNC0la3bt3C3Llzce3aNQCAs7MzRowYgfLly8ucjIhIs7HYyWdvJoe6ePEimjdvDhMTE2mbnp4eypYti06dOsmUjrTVvn370LZtW9SoUQMNGjQAAJw4cQJVq1bFjh078NVXX8mckIhIc7HPTgFZuXIlunTpwlOplC/edFZ8d/jp+PHjsX//fk4hX8iWL1+Oxo0bw9HRUe4oRJQDnGengHh7e8PAwADnz5/HmjVrsGbNGoSEhMgdi7TUtWvXpJFLb+vbty/CwsJkSJQ3ffv2xYsXL7K0v3z5En379pUhUd5Mnz4dFSpUQJkyZdCzZ08sW7ZMEfMgaaOpU6dKcze97dWrV5g6daoMiUgT8cxOAYmNjUXXrl1x5MgRWFhYAADi4uLQuHFjbNiwAVZWVvIGJK1ib2+POXPmoHPnzmrtmzZtwujRoxEZGSlTstx53yjFJ0+ewNbWFunp6TIly70HDx7gyJEjOHr0KIKCghAeHo6SJUvC09MTa9askTveZ0MpI1+50G/BYp+dAuLr64sXL17g6tWrqFKlCgAgLCwM3t7eGD58ONavXy9zQtImAwYMwMCBA3H79m3Ur18fwOs+OzNnzoSfn5/M6T4uISEBQggIIfDixQu1y7sZGRnYvXt3lg8rTVeqVCn06NEDHTp0wLFjx7B+/XqsXbsWGzZsYLFTiN438jU0NBTFihWTIVHuKWWhX03GMzsFxNzcHAcPHsQXX3yh1n7mzBl4eXkhLi5OnmCklYQQmDt3LmbPno2HDx8CAOzs7DBmzBgMHz5c49fY0dHR+WBGlUoFf39//Pjjj4WYKu/279+PI0eO4MiRIwgJCUGVKlXQqFEjeHp6wsPDg5PAFQJLS0uoVCrEx8fDzMxM7f2VkZGBxMREDB48GAsXLpQxZc4oZaFfTcZip4CYmpri2LFjqFGjhlp7SEgIGjVqpFXrAJFmedPnxdTUVOYkORcUFAQhBJo0aYKtW7eqfePW09ODg4MD7OzsZEyYOzo6OrCyssKoUaMwcOBA6VI1FZ6VK1dCCIG+ffti7ty5agXBm5Gv7u7uMibMOaUs9KvJWOwUkHbt2iEuLg7r16+X/og/ePBAmvp727ZtMickKnz37t2Dvb19tstfaJO5c+fi6NGjOHr0KPT19aWzOp6enqhUqZLc8T4rQUFBqF+/vlbPlNykSROMHTuWc7AVIBY7BSQqKgpt27bF1atXYW9vL7VVq1YN//33n1YtekjyqFmzJgIDA2FpaSlNVvk+2jT0PC4uDn///bc0OWLVqlXRt29frT1Vf/nyZQQFBeHQoUPYuXMnrK2tcf/+fbljfVYyMjKwfft2tfdU27ZtoaurK3OynNm2bRt++uknjBkzRqsX+tVkLHYKkBACBw8exPXr1wEAVapUQbNmzWRORdrC398fY8aMgZGREfz9/T+475vFDzXduXPn0Lx5cxgaGqJOnToAgLNnz+LVq1fYv38/atasKXPCnBNCICQkBEeOHMHhw4dx/PhxvHjxAi4uLpxmohBFRESgVatWePDgASpXrgwAuHHjBuzt7bFr1y6tmGFcKQv9ajIWO0RUaL788ktUqFABf/31l7SUSnp6Ovr374/bt2/j6NGjMifMmTZt2uDEiRNISEhA9erV4enpiUaNGsHDw4P9dwpZq1atIITA2rVrpb5gT58+xXfffQcdHR3s2rVL5oQfx4V+Cx6LnQIUGBiIwMDAbOdNWL58uUypSBtFRUVBpVJJlz/PnDmDdevWwdnZGQMHDpQ5Xc4ZGhoiJCQETk5Oau1hYWGoXbt2tpPDaaIxY8agUaNG+PLLL7X28ptSGBsb49SpU3BxcVFrDw0NRYMGDZCYmChTMtIknGengPj7+2Pq1KmoXbt2tkMJiXKje/fuGDhwIHr27ImYmBg0a9YM1apVw9q1axETE4NJkybJHTFHzMzMEBkZmaXYiYqK0qrRZf/73//kjkD/R19fP9tZuRMTE6GnpydDorzhQr8FTFCBsLW1FatWrZI7BimEhYWFuH79uhBCiHnz5on69esLIYTYt2+fcHR0lDNarvj6+orSpUuLDRs2iMjISBEZGSnWr18vSpcuLUaMGCF3vFw5cuSI+Prrr0X58uVF+fLlRZs2bcTRo0fljvXZ6dmzp6hatao4deqUyMzMFJmZmSI4OFhUq1ZNeHt7yx0vR/bu3Sv09PREnTp1xMiRI8XIkSNFnTp1hL6+vti/f7/c8RSBxU4BKVasmIiIiJA7BimEsbGxuHPnjhBCiDZt2ogZM2YIIYS4d++eMDAwkDFZ7qSkpIjhw4cLPT09oaOjI3R0dIS+vr74/vvvRXJystzxcmz16tWiSJEi4ttvvxXz5s0T8+bNE99++60oWrSoWLt2rdzxPivPnz8Xbdu2FSqVSujp6Unvrfbt24u4uDi54+VIjRo1xLhx47K0jxs3Tri5ucmQSHnYZ6eAjBs3DiYmJpg4caLcUUgB6tati8aNG6N169bw8vLCqVOnUL16dZw6dQrffPON1g11TkpKwq1btwAA5cuXh5GRkcyJcqdKlSoYOHAgRo4cqdY+Z84c/PXXX9KlCCo84eHhaiNftWmCPgMDA1y+fBkVK1ZUa7958yZcXV2RnJwsUzLlYJ+dApKcnIylS5fi4MGDcHV1zTJvwpw5c2RKRtpo5syZ6NChA/73v//B29sb1atXBwD8999/0hBubWJkZJSlQ6k2uX37Ntq0aZOlvW3btvjhhx9kSEQVK1bMUixoCysrK1y8eDFL/osXL2rdmnGaisVOAbl06ZK0VMSVK1fUtrGzMuWWp6cnnjx5goSEBLV1lwYOHKhVZ0VevnyJGTNmvHeU4u3bt2VKljv29vYIDAzMcvbg4MGD0iSiVDgyMjIQEBDw3vfUoUOHZEqWc9q+0K82YLFTQA4fPix3BFKQV69eQQghFTr37t3Dtm3bUKVKFTRv3lzmdDnXv39/BAUFoWfPnlo9SnHUqFEYPnw4Ll68qPbhFBAQgHnz5smc7vMyYsQIBAQEoHXr1qhWrZpWvqcmTpwIU1NTzJ49GxMmTADweqHfKVOmYPjw4TKnUwb22SHSAl5eXujYsSMGDx6MuLg4ODk5oWjRonjy5AnmzJmDIUOGyB0xRywsLLBr1y40aNBA7iifbNu2bZg9e7bUP6dKlSoYM2YM2rVrJ3Oyz0uJEiWwatUqtGrVSu4o+UIbF/rVBtq9Gh/RZ+LChQv48ssvAQBbtmyBjY0N7t27h1WrVmH+/Pkyp8s5S0tLtRXPtVmHDh1w/PhxPH36FE+fPsXx48dZ6MhAT09Pqzojf4ypqSkLnQLAMztEWsDIyAjXr19HmTJl8O2336Jq1aqYPHkyoqKiULlyZa2ZeXjNmjX4999/sXLlSq3qa0Saa/bs2bh9+zYWLFigVZewlLrQr6Zinx0iLVChQgVs374dHTp0wL59+6Qhz7GxsTAzM5M5Xc7Nnj0bt27dgo2NDcqWLZtllKIm/1G3tLTM8Yfps2fPCjgNvXH8+HEcPnwYe/bsQdWqVbO8p/755x+Zkn1Yu3btoK+vDwBo3769vGE+Ayx2iLTApEmT0L17d4wcORJNmzaFu7s7AGD//v1wc3OTOV3OafMf9blz58odgbJhYWGBDh06yB0j1yZPnpztz1QweBmLSEvExMQgOjoa1atXh47O6+52Z86cgZmZWZa1pohIeyhloV9NxmKHiAqUEEKr+lIQFbYvv/xSbaHfSpUqoVq1aggPD4evr6/WLPSryVjsEGmojh07IiAgAGZmZujYseMH99XUfgnA69WbJ02ahI4dO35wFerw8HDMmTMHDg4OGD9+fCEmJG3TokULTJkyBfXq1fvgfi9evMCff/4JExMT+Pj4FFK63LO0tMSpU6dQuXJlzJ8/Hxs3bsSJEyewf/9+DB48WGsm29Rk7LNDpKHMzc2lMyLm5uYyp8m7P/74A+PGjcPQoUPx1VdfoXbt2rCzs4OBgQGeP3+OsLAwHD9+HFevXsWwYcO0Zs4gkk/nzp3RqVMnmJubo02bNu99T+3evRutW7fG//73P7kjf1BaWprUWfngwYNo27YtAMDJyQnR0dFyRlMMntkhokJx/PhxbNy4EceOHcO9e/fw6tUrlChRAm5ubmjevDl69OihthQG0YekpKRg8+bN2LhxI44fP474+HgAr5fjcXZ2RvPmzdGvXz9UqVJF5qQfp7SFfjURix0iItJ68fHxePXqFYoXL55l+LmmO3LkCDp06ICEhAR4e3tj+fLlAIAffvgB169f1+jL1NqCxQ6RFnj69CkmTZqEw4cPZ7vYIed1KXgf6zf1Nn44UW5lZGRkWej37t27MDIy4srn+YB9doi0QM+ePREREYF+/frBxsaGo5tk8Ha/KSEEtm3bBnNzc9SuXRsAcP78ecTFxeWqKCIClLPQrybjmR0iLWBqaorjx4+jevXqckchAOPGjcOzZ8+wePFi6OrqAnj9zXzo0KEwMzPT+A6xpFmUstCvJuNCoERawMnJCa9evZI7Bv2f5cuXY/To0VKhAwC6urrw8/OT+lsQ5ZRSFvrVZCx2iLTAn3/+iR9//BFBQUF4+vQpEhIS1G5UuNLT03H9+vUs7devX8/Sn4roY5KSkqSVzvfv34+OHTtCR0cH9erVw71792ROpwzss0OkBSwsLJCQkIAmTZqotb+ZnTgjI0OmZLlz4cIFFC1aFC4uLgCAf//9FytWrICzszOmTJnywUkHNUmfPn3Qr18/3Lp1C3Xq1AEAnD59GjNmzECfPn1kTvd5OXv2LDIzM1G3bl219tOnT0NXV1fqU6XJlLLQryZjsUOkBXr06IGiRYti3bp1Wt1BedCgQRg/fjxcXFxw+/ZtdO3aFR06dMDmzZuRlJSkNYtt/vbbb7C1tcXs2bOlSd9KliyJMWPGYNSoUTKn+7z4+Phg7NixWYqdBw8eYObMmTh9+rRMyXJOKQv9ajJ2UCbSAkZGRggJCUHlypXljvJJzM3NceHCBZQvXx4zZ87EoUOHsG/fPpw4cQJdu3ZFVFSU3BFz7c1lRH4Dl4eJiQkuXbqEcuXKqbXfuXMHrq6uePHihUzJcocL/RYsntkh0gK1a9dGVFSU1hc7QgipT8vBgwfx9ddfAwDs7e3x5MkTOaPlGYsceenr6+PRo0dZip3o6GgUKaI9H3G2trawtbVVa3tziZQ+Hc/sEGmBzZs3Y8qUKRgzZgxcXFyyzBDr6uoqU7LcadKkCezt7dGsWTP069cPYWFhqFChAoKCguDt7Y27d+/KHTFHHj16hNGjRyMwMBCxsbF498+otvShUoJu3bohOjoa//77rzQXUlxcHNq3bw9ra2ts2rRJ5oTZU8pCv9pCe8peos9Yly5dAAB9+/aV2lQqldZ1UJ47dy569OiB7du348cff0SFChUAvB5uW79+fZnT5Vzv3r0RGRmJiRMnomTJklrbh0oJfvvtN3h4eMDBwUHq33Lx4kXY2Nhg9erVMqd7P6Us9KsteGaHSAt8bPipg4NDISUpGMnJydDV1dWaNY1MTU1x7Ngx1KhRQ+4oBODly5dYu3YtQkNDYWhoCFdXV3Tr1k1r3k9U8Hhmh0gLaHsx8zEGBgZyR8gVe3v7LJeuSD7GxsYYOHCg3DFIg/HMDpGWWL16NRYvXow7d+4gODgYDg4OmDt3LhwdHdGuXTu54+WIjo7OBy/5aMvluP3792P27NlYsmQJypYtK3ecz85///2Hli1bomjRovjvv/8+uG/btm0LKVXecaHfgsczO0RaYNGiRZg0aRK+//57/PLLL1JRYGFhgblz52pNsbNt2za1+2lpaQgJCcHKlSvh7+8vU6rc69KlC5KSklC+fHkYGRlluVzCD6eC1b59e8TExMDa2hrt27d/737a0p+NC/0WPJ7ZIdICzs7O+PXXX9G+fXuYmpoiNDQU5cqVw5UrV+Dp6am1w7bfWLduHTZu3Ih///1X7ig5snLlyg9u9/b2LqQkpARc6Lfg8cwOkRa4c+dOtjOp6uvr4+XLlzIkyl/16tXTqj4XLGY0Q1paGlq0aIHFixejYsWKcsfJMy70W/C4ECiRFnB0dMTFixeztO/duxdVqlQp/ED56NWrV5g/fz5KlSold5Q8SU5O5sKsMilatCguXbokd4xPxoV+Cx7P7BBpAT8/P/j4+CA5ORlCCJw5cwbr16/H9OnTsWzZMrnj5ZilpaVafwQhBF68eAEjIyOsWbNGxmS58/LlS4wbNw6bNm3C06dPs2zXhn4iSvHdd9/h77//xowZM+SOkmdKWehXk7HYIdIC/fv3h6GhIX766SckJSWhe/fusLOzw7x589C1a1e54+XYuwt96ujowMrKCnXr1oWlpaU8ofJg7NixOHz4MBYtWoSePXti4cKFePDgAZYsWaLVH7raKD09HcuXL8fBgwdRq1YtGBsbq22fM2eOTMlyTikL/WoydlAm0jJJSUlITEyEtbW13FE+W2XKlMGqVavg6ekJMzMzXLhwARUqVMDq1auxfv167N69W+6In43GjRt/cPvhw4cLKUneKWWhX03GMztEWsbIyAhGRkZyx8iz58+f4++//8a1a9cAvB5p1qdPHxQrVkzmZDn37NkzaeFJMzMzaah5w4YNMWTIEDmjfXa0oZj5GKUs9KvJ2EGZiArN0aNHUbZsWcyfPx/Pnz/H8+fPMX/+fDg6OuLo0aNyx8uxcuXK4c6dOwBej6R5s9jkjh07YGFhIWOyz0/fvn3x4sWLLO0vX75UW0tOk/n6+mLEiBEICAjA+fPncenSJbUbfTpexiKiQuPi4gJ3d3csWrQIurq6AF535h06dChOnjyJy5cvy5wwZ37//Xfo6upi+PDhOHjwINq0aQMhBNLS0jBnzhyMGDFC7oifDV1dXURHR2e5rPvkyRPY2toiPT1dpmQ5p6OT9byDNi70q8lY7BBRoTE0NMTFixeznK6/ceMGatSoobVzjdy7dw/nz59HhQoV4OrqKnecz0JCQgKEELC0tER4eDisrKykbRkZGdixYwfGjx+Phw8fypgyZ5S+0K8mYJ8dIi2QnJysdYtlZqdmzZq4du1almLn2rVrWj17rIODAz+QCpmFhQVUKhVUKhUqVaqUZbtKpdKaJUj43il4LHaItICFhQXq1KmDRo0awdPTE/Xr14ehoaHcsXLk7T4Hw4cPx4gRIxAREYF69eoBAE6dOoWFCxdyyDblyuHDhyGEQJMmTbB161a1Du56enpwcHCAnZ2djAlzRwkL/WoyXsYi0gLHjx/H0aNHceTIEZw8eRLp6emoXbu2VPx89dVXckd8rzcrnX/sTw37JlBe3Lt3D2XKlNHquWneXej3ypUrKFeuHAICArBy5UpFjDiTG4sdIi2Tnp6Os2fPYsmSJVi7di0yMzM1ukj4WH+Et/F0PuXWihUrYGJigs6dO6u1b968GUlJSVqxjpnSF/rVBLyMRaQlbt68iSNHjki3lJQUfP311/D09JQ72gexgKGCNH36dCxZsiRLu7W1NQYOHKgVxY7SF/rVBCx2iLRAqVKl8OrVK3h6esLT0xPjxo2Dq6urVp+613YZGRnYvn27NDli1apV0bZtW2lIPRWOyMhIODo6Zml3cHBAZGSkDIly781Cv+9+MVDCQr+agsUOkRawsrLC9evXERMTg5iYGDx69AivXr3S6pmUtVlERARat26N+/fvSyPLpk+fDnt7e+zatQvly5eXOeHnw9raGpcuXULZsmXV2kNDQ1G8eHF5QuWSUhb61WTss0OkJeLi4nD06FEEBQUhKCgIYWFhqFGjBho3boxffvlF7niflVatWkEIgbVr10qjgJ4+fYrvvvsOOjo62LVrl8wJPx/jxo3Dxo0bsWLFCnh4eAAAgoKC0LdvX3zzzTf47bffZE6YM2vXrsWUKVNw69YtAICdnR38/f3Rr18/mZMpA4sdIi3z9OlTHDlyBP/++y/Wr1+v8R2UlcjY2BinTp2Ci4uLWntoaCgaNGiAxMREmZJ9flJTU9GzZ09s3rwZRYq8vliRmZmJXr16YfHixdDT05M5Ye5wod+CwctYRFrgn3/+kTomh4WFoVixYmjYsCFmz56NRo0ayR0v11JTUxEbG4vMzEy19jJlysiUKHf09fWzXY8pMTFR6z5ctZ2enh42btyIadOmITQ0FIaGhnBxcdHajvHavtCvpuKZHSItYG1tDQ8PD3h6eqJRo0ZZzihoi/DwcPTt2xcnT55Ua9e2NYB69eqFCxcu4O+//0adOnUAAKdPn8aAAQNQq1YtBAQEyBvwM5Samoo7d+6gfPny0hkeojdY7BBRoWnQoAGKFCmC8ePHo2TJkllGk2nLkhFxcXHw9vbGjh07ULRoUQCv5z9q27YtAgICYG5uLnPCz0dSUhJ8fX2xcuVKAK+naChXrhx8fX1RqlQpjB8/XuaEpAlY7BBpiXeHOjs7O6Ndu3ZaNdTZ2NgY58+fh5OTk9xR8kwIgaioKFhZWeHBgwfSv0eVKlVQoUIFmdN9fkaMGIETJ05g7ty5aNGiBS5duoRy5crh33//xZQpUxASEiJ3RNIAPNdHpAUiIiLQqlUrPHjwQKuHOjs7O2v9bLBCCFSoUAFXr15FxYoVWeDIbPv27di4cSPq1aundqawatWq0sgmTaeUhX41mY7cAYjo44YPH47y5csjKioKFy5cwIULF6TJ1IYPHy53vBybOXMmxo4diyNHjuDp06dISEhQu2kDHR0dVKxYEU+fPpU7CgF4/PhxtiOXXr58qTWTblpYWMDDwwMTJ05EYGAgXr16JXckxeFlLCItoJShzjo6r79fvfshpG0dlHfs2IFZs2Zh0aJFqFatmtxxPmseHh7o3LkzfH19YWpqikuXLsHR0RG+vr4IDw/H3r175Y74Udq80K+2YLFDpAWKFSuGnTt3on79+mrtJ06cQJs2bfDs2TOZkuVOUFDQB7dryzB6S0tLJCUlIT09HXp6ejA0NFTbri3/Hkpw/PhxtGzZEt999x0CAgIwaNAghIWF4eTJkwgKCkKtWrXkjpgr2rbQr7Zgnx0iLfD1119j4MCBWYY6Dx48GG3btpU5Xc5pSzHzMXPnzpU7Av2fhg0b4uLFi5gxYwZcXFywf/9+1KxZE8HBwVo1RYO2LvSrLXhmh0gLKGmoc1xcHP7++2+1BTT79u2rVcdAlJ/eXei3UaNGXOg3n7HYIdIi4eHhuH79OgDtHOp87tw5NG/eHIaGhtIZqrNnz+LVq1fSN3JtcevWLaxYsQK3bt3CvHnzYG1tjT179qBMmTKoWrWq3PEULTed2c3MzAowSf6oUaMGrl+/jpo1a0oFT8OGDTmTcj5isUNEhebLL79EhQoV8Ndff0mz3Kanp6N///64ffs2jh49KnPCnAkKCkLLli3RoEEDHD16FNeuXUO5cuUwY8YMnDt3Dlu2bJE7oqLp6Oh89KyHtnV650K/BYvFDpGG8vPzy/G+c+bMKcAk+cfQ0BAhISFZJhUMCwtD7dq1kZSUJFOy3HF3d0fnzp3h5+cHU1NThIaGoly5cjhz5gw6duyI+/fvyx1R0T7W0f1t2tZPjAv9Fgx2UCbSUDmd+VWbruubmZkhMjIyS7ETFRUFU1NTmVLl3uXLl7Fu3bos7dbW1lo/aaI20LYC5mOUttCvJmKxQ6ShDh8+LHeEfNelSxf069cPv/32mzSM/sSJExgzZgy6desmc7qcs7C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bus_counttotal_costcost_per_bus
prop_typebus_size_type
BEBarticulated12.0187595761563298
standard/conventional (30ft-45ft)151.0148472913983264
CNGcutaway3.01162000387333
not specified209.0171977140822857
standard/conventional (30ft-45ft)40.0290000072500
FCEBnot specified29.0380709711312792
standard/conventional (30ft-45ft)73.0828803641135347
electric (not specified)articulated29.0394780001361310
not specified15.0172000001146666
ethanolnot specified9.01006750111861
low emission (hybrid)not specified145.091824361633271
low emission (propane)not specified44.08403969190999
mix (zero and low emission)not specified125.036775430294203
not specifiedcutaway149.015532500104244
not specified162.016503904101875
over-the-road14.09516000679714
zero-emission bus (not specified)not specified143.0128156513896199
Grand Total1352.0828620391612884
\n", + "
" + ], "text/plain": [ - "
" + " bus_count \\\n", + "prop_type bus_size_type \n", + "BEB articulated 12.0 \n", + " standard/conventional (30ft-45ft) 151.0 \n", + "CNG cutaway 3.0 \n", + " not specified 209.0 \n", + " standard/conventional (30ft-45ft) 40.0 \n", + "FCEB not specified 29.0 \n", + " standard/conventional (30ft-45ft) 73.0 \n", + "electric (not specified) articulated 29.0 \n", + " not specified 15.0 \n", + "ethanol not specified 9.0 \n", + "low emission (hybrid) not specified 145.0 \n", + "low emission (propane) not specified 44.0 \n", + "mix (zero and low emission) not specified 125.0 \n", + "not specified cutaway 149.0 \n", + " not specified 162.0 \n", + " over-the-road 14.0 \n", + "zero-emission bus (not specified) not specified 143.0 \n", + "Grand Total 1352.0 \n", + "\n", + " total_cost \\\n", + "prop_type bus_size_type \n", + "BEB articulated 18759576 \n", + " standard/conventional (30ft-45ft) 148472913 \n", + "CNG cutaway 1162000 \n", + " not specified 171977140 \n", + " standard/conventional (30ft-45ft) 2900000 \n", + "FCEB not specified 38070971 \n", + " standard/conventional (30ft-45ft) 82880364 \n", + "electric (not specified) articulated 39478000 \n", + " not specified 17200000 \n", + "ethanol not specified 1006750 \n", + "low emission (hybrid) not specified 91824361 \n", + "low emission (propane) not specified 8403969 \n", + "mix (zero and low emission) not specified 36775430 \n", + "not specified cutaway 15532500 \n", + " not specified 16503904 \n", + " over-the-road 9516000 \n", + "zero-emission bus (not specified) not specified 128156513 \n", + "Grand Total 828620391 \n", + "\n", + " cost_per_bus \n", + "prop_type bus_size_type \n", + "BEB articulated 1563298 \n", + " standard/conventional (30ft-45ft) 983264 \n", + "CNG cutaway 387333 \n", + " not specified 822857 \n", + " standard/conventional (30ft-45ft) 72500 \n", + "FCEB not specified 1312792 \n", + " standard/conventional (30ft-45ft) 1135347 \n", + "electric (not specified) articulated 1361310 \n", + " not specified 1146666 \n", + "ethanol not specified 111861 \n", + "low emission (hybrid) not specified 633271 \n", + "low emission (propane) not specified 190999 \n", + "mix (zero and low emission) not specified 294203 \n", + "not specified cutaway 104244 \n", + " not specified 101875 \n", + " over-the-road 679714 \n", + "zero-emission bus (not specified) not specified 896199 \n", + "Grand Total 612884 " ] }, "metadata": {}, @@ -2413,29 +2159,30 @@ } ], "source": [ - "# bus count BY PROP TYPE\n", - "display(Markdown(bus_count_prop_type_desc))\n", - "make_chart(\n", - " \"total_bus_count\", \n", - " \"Bus count by propulsion type\",\n", - " x_col=\"prop_type\",\n", - " data=prop_agg\n", + "display(\n", + " Markdown(\"## What is the breakdown of Propulsion Type and Bus Size Category?\"),\n", + " pivot_size\n", ")" ] }, { "cell_type": "code", - "execution_count": 28, - "id": "4f092539-c4c6-4579-aa02-fbee65414ec3", + "execution_count": 32, + "id": "63f90c48-e28d-4d88-8b90-891a3e3e3681", "metadata": { - "tags": [] + "execution": { + "iopub.execute_input": "2024-06-26T19:02:54.469661Z", + "iopub.status.busy": "2024-06-26T19:02:54.469255Z", + "iopub.status.idle": "2024-06-26T19:02:54.476818Z", + "shell.execute_reply": "2024-06-26T19:02:54.475612Z" + } }, "outputs": [ { "data": { "text/markdown": [ "\n", - "**Conclusion**\n", + "## **Conclusion**\n", "\n", "Based on these findings, The average cost of a ZEB, throughout the US, is ~$1,000,000, roughly twice the price of a conventional, non-ZEB.\n", "The variance in cost depends mainly on the options the Trasnit\n", @@ -2451,13 +2198,23 @@ } ], "source": [ - "display(Markdown(conclusion))" + "conclusion = f\"\"\"\n", + "## **Conclusion**\n", + "\n", + "Based on these findings, The average cost of a ZEB, throughout the US, is ~$1,000,000, roughly twice the price of a conventional, non-ZEB.\n", + "The variance in cost depends mainly on the options the Trasnit\n", + "Agencies chooses. Highly optioned/customized buses contribute to high cost.\n", + "Unfortunately, analyzing the cost of configuable options is outside the scope of data provided. \n", + "\"\"\"\n", + "display(\n", + " Markdown(conclusion)\n", + ")" ] }, { "cell_type": "code", "execution_count": null, - "id": "c6ce4e1b-c1a2-40d1-84c1-0a20a4400eb3", + "id": "8f8c2bae-652c-4532-8b46-2f4fa7003d65", "metadata": {}, "outputs": [], "source": [] @@ -2480,7 +2237,10 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" - } + }, + "toc-autonumbering": false, + "toc-showcode": true, + "toc-showmarkdowntxt": true }, "nbformat": 4, "nbformat_minor": 5 From 13ac4ac65d161e919caaf7fc2fcf73736a47d81a Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Wed, 26 Jun 2024 22:50:34 +0000 Subject: [PATCH 32/36] full run of Makefile. analysis nb now shows mainly ZEB metrics --- bus_procurement_cost/Makefile | 3 - ...ipts.py => OLD_cost_per_bus_nb_scripts.py} | 0 .../cost_per_bus_analysis.html | 161 +++--- .../cost_per_bus_analysis.ipynb | 495 ++++++++---------- 4 files changed, 279 insertions(+), 380 deletions(-) rename bus_procurement_cost/{cost_per_bus_nb_scripts.py => OLD_cost_per_bus_nb_scripts.py} (100%) diff --git a/bus_procurement_cost/Makefile b/bus_procurement_cost/Makefile index 040fa4dc3..23cf662b3 100644 --- a/bus_procurement_cost/Makefile +++ b/bus_procurement_cost/Makefile @@ -4,8 +4,5 @@ all_bus_scripts: python tircp_data_cleaner.py python dgs_data_cleaner.py python cost_per_bus_cleaner.py - python cost_per_bus_utils.py jupyter nbconvert --to notebook --execute --inplace cost_per_bus_analysis.ipynb jupyter nbconvert --to html --no-input --no-prompt cost_per_bus_analysis.ipynb - git add cost_per_bus_analysis.ipynb - git add cost_per_bus_analysis.html \ No newline at end of file diff --git a/bus_procurement_cost/cost_per_bus_nb_scripts.py b/bus_procurement_cost/OLD_cost_per_bus_nb_scripts.py similarity index 100% rename from bus_procurement_cost/cost_per_bus_nb_scripts.py rename to bus_procurement_cost/OLD_cost_per_bus_nb_scripts.py diff --git a/bus_procurement_cost/cost_per_bus_analysis.html b/bus_procurement_cost/cost_per_bus_analysis.html index 60890591f..cf6a8338e 100644 --- a/bus_procurement_cost/cost_per_bus_analysis.html +++ b/bus_procurement_cost/cost_per_bus_analysis.html @@ -14618,36 +14618,65 @@ @@ -14874,7 +14914,7 @@

Bus Procurement Cost Analysis -

Which Agneices had the highest and lowest ZEB cost per bus?

+

Which agencies had the highest and lowest ZEB cost per bus?

@@ -15008,7 +15048,7 @@

Which Agnei @@ -15150,7 +15190,7 @@

Which Agenci @@ -15323,7 +15363,7 @@

What is the distribution of ZEB c @@ -15685,7 +15725,7 @@

What i @@ -15739,25 +15779,6 @@

What is 148472913 983264 - - CNG - cutaway - 3.0 - 1162000 - 387333 - - - not specified - 209.0 - 171977140 - 822857 - - - standard/conventional (30ft-45ft) - 40.0 - 2900000 - 72500 - FCEB not specified @@ -15784,53 +15805,6 @@

What is 17200000 1146666 - - ethanol - not specified - 9.0 - 1006750 - 111861 - - - low emission (hybrid) - not specified - 145.0 - 91824361 - 633271 - - - low emission (propane) - not specified - 44.0 - 8403969 - 190999 - - - mix (zero and low emission) - not specified - 125.0 - 36775430 - 294203 - - - not specified - cutaway - 149.0 - 15532500 - 104244 - - - not specified - 162.0 - 16503904 - 101875 - - - over-the-road - 14.0 - 9516000 - 679714 - zero-emission bus (not specified) not specified @@ -15838,13 +15812,6 @@

What is 128156513 896199 - - Grand Total - - 1352.0 - 828620391 - 612884 - diff --git a/bus_procurement_cost/cost_per_bus_analysis.ipynb b/bus_procurement_cost/cost_per_bus_analysis.ipynb index c99a1544f..b0f11ee15 100644 --- a/bus_procurement_cost/cost_per_bus_analysis.ipynb +++ b/bus_procurement_cost/cost_per_bus_analysis.ipynb @@ -6,10 +6,10 @@ "id": "da041e43-e8e2-4d4b-a498-10a7c0afe43f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:20.693089Z", - "iopub.status.busy": "2024-06-26T19:02:20.692134Z", - "iopub.status.idle": "2024-06-26T19:02:51.620857Z", - "shell.execute_reply": "2024-06-26T19:02:51.619248Z" + "iopub.execute_input": "2024-06-26T22:31:31.913513Z", + "iopub.status.busy": "2024-06-26T22:31:31.912578Z", + "iopub.status.idle": "2024-06-26T22:31:50.667778Z", + "shell.execute_reply": "2024-06-26T22:31:50.666257Z" }, "tags": [] }, @@ -21,7 +21,6 @@ "import seaborn as sns\n", "import shared_utils\n", "from bus_cost_utils import *\n", - "from cost_per_bus_nb_scripts import *\n", "from IPython.display import Markdown, display\n", "from matplotlib.ticker import ScalarFormatter\n", "from scipy.stats import zscore\n", @@ -34,10 +33,10 @@ "id": "d53376d9-d4b4-48b7-9916-5b9f633fbaf0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:51.627096Z", - "iopub.status.busy": "2024-06-26T19:02:51.626321Z", - "iopub.status.idle": "2024-06-26T19:02:52.398041Z", - "shell.execute_reply": "2024-06-26T19:02:52.396844Z" + "iopub.execute_input": "2024-06-26T22:31:50.673481Z", + "iopub.status.busy": "2024-06-26T22:31:50.672715Z", + "iopub.status.idle": "2024-06-26T22:31:51.546067Z", + "shell.execute_reply": "2024-06-26T22:31:51.544853Z" } }, "outputs": [], @@ -51,10 +50,10 @@ "id": "a45f2d3d-a600-4fe6-80cf-6b887036faab", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.404265Z", - "iopub.status.busy": "2024-06-26T19:02:52.403827Z", - "iopub.status.idle": "2024-06-26T19:02:52.411634Z", - "shell.execute_reply": "2024-06-26T19:02:52.410390Z" + "iopub.execute_input": "2024-06-26T22:31:51.551406Z", + "iopub.status.busy": "2024-06-26T22:31:51.550898Z", + "iopub.status.idle": "2024-06-26T22:31:51.556803Z", + "shell.execute_reply": "2024-06-26T22:31:51.555317Z" } }, "outputs": [], @@ -82,10 +81,10 @@ "id": "8ac40482-ba3e-4fde-8c05-806e3725de44", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.417035Z", - "iopub.status.busy": "2024-06-26T19:02:52.416646Z", - "iopub.status.idle": "2024-06-26T19:02:52.433528Z", - "shell.execute_reply": "2024-06-26T19:02:52.431789Z" + "iopub.execute_input": "2024-06-26T22:31:51.561945Z", + "iopub.status.busy": "2024-06-26T22:31:51.561550Z", + "iopub.status.idle": "2024-06-26T22:31:51.575229Z", + "shell.execute_reply": "2024-06-26T22:31:51.573936Z" } }, "outputs": [], @@ -106,10 +105,10 @@ "id": "d450fd60-cced-453b-b20b-62cdade0d7a6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.440101Z", - "iopub.status.busy": "2024-06-26T19:02:52.439344Z", - "iopub.status.idle": "2024-06-26T19:02:52.450789Z", - "shell.execute_reply": "2024-06-26T19:02:52.449493Z" + "iopub.execute_input": "2024-06-26T22:31:51.580134Z", + "iopub.status.busy": "2024-06-26T22:31:51.579769Z", + "iopub.status.idle": "2024-06-26T22:31:51.587220Z", + "shell.execute_reply": "2024-06-26T22:31:51.586024Z" } }, "outputs": [], @@ -157,10 +156,10 @@ "id": "2a2dc407-20cc-45de-84b1-bb5991dad8ac", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.455864Z", - "iopub.status.busy": "2024-06-26T19:02:52.455468Z", - "iopub.status.idle": "2024-06-26T19:02:52.462727Z", - "shell.execute_reply": "2024-06-26T19:02:52.461471Z" + "iopub.execute_input": "2024-06-26T22:31:51.591838Z", + "iopub.status.busy": "2024-06-26T22:31:51.591138Z", + "iopub.status.idle": "2024-06-26T22:31:51.599353Z", + "shell.execute_reply": "2024-06-26T22:31:51.597716Z" } }, "outputs": [], @@ -187,10 +186,10 @@ "id": "9a6a7ecf-5180-4691-84fe-23aa68cdae93", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.467820Z", - "iopub.status.busy": "2024-06-26T19:02:52.466886Z", - "iopub.status.idle": "2024-06-26T19:02:52.474804Z", - "shell.execute_reply": "2024-06-26T19:02:52.473241Z" + "iopub.execute_input": "2024-06-26T22:31:51.605065Z", + "iopub.status.busy": "2024-06-26T22:31:51.604337Z", + "iopub.status.idle": "2024-06-26T22:31:51.612060Z", + "shell.execute_reply": "2024-06-26T22:31:51.610827Z" } }, "outputs": [], @@ -216,10 +215,10 @@ "id": "44d21201-223f-4e6c-b238-b72fba984544", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.479204Z", - "iopub.status.busy": "2024-06-26T19:02:52.478810Z", - "iopub.status.idle": "2024-06-26T19:02:52.489771Z", - "shell.execute_reply": "2024-06-26T19:02:52.488406Z" + "iopub.execute_input": "2024-06-26T22:31:51.617891Z", + "iopub.status.busy": "2024-06-26T22:31:51.617172Z", + "iopub.status.idle": "2024-06-26T22:31:51.626056Z", + "shell.execute_reply": "2024-06-26T22:31:51.625225Z" } }, "outputs": [], @@ -270,10 +269,10 @@ "id": "067a14a5-5c77-4914-82a8-c5eeb170cb08", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.494690Z", - "iopub.status.busy": "2024-06-26T19:02:52.494199Z", - "iopub.status.idle": "2024-06-26T19:02:52.549925Z", - "shell.execute_reply": "2024-06-26T19:02:52.548448Z" + "iopub.execute_input": "2024-06-26T22:31:51.630715Z", + "iopub.status.busy": "2024-06-26T22:31:51.630048Z", + "iopub.status.idle": "2024-06-26T22:31:51.681613Z", + "shell.execute_reply": "2024-06-26T22:31:51.680496Z" } }, "outputs": [], @@ -291,10 +290,10 @@ "id": "49a97d01-b17e-475c-b351-67426f3741d9", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.555324Z", - "iopub.status.busy": "2024-06-26T19:02:52.554911Z", - "iopub.status.idle": "2024-06-26T19:02:52.562099Z", - "shell.execute_reply": "2024-06-26T19:02:52.560778Z" + "iopub.execute_input": "2024-06-26T22:31:51.686475Z", + "iopub.status.busy": "2024-06-26T22:31:51.686087Z", + "iopub.status.idle": "2024-06-26T22:31:51.693353Z", + "shell.execute_reply": "2024-06-26T22:31:51.692185Z" } }, "outputs": [], @@ -311,10 +310,10 @@ "id": "4faaa4ad-b16c-4e6b-87c7-d12f7e7db3c6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.567091Z", - "iopub.status.busy": "2024-06-26T19:02:52.566720Z", - "iopub.status.idle": "2024-06-26T19:02:52.594068Z", - "shell.execute_reply": "2024-06-26T19:02:52.592332Z" + "iopub.execute_input": "2024-06-26T22:31:51.697847Z", + "iopub.status.busy": "2024-06-26T22:31:51.697459Z", + "iopub.status.idle": "2024-06-26T22:31:51.721570Z", + "shell.execute_reply": "2024-06-26T22:31:51.720310Z" } }, "outputs": [], @@ -338,10 +337,10 @@ "id": "b8535e97-e7bf-4d7e-b718-24c5758b0ccd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.599044Z", - "iopub.status.busy": "2024-06-26T19:02:52.598633Z", - "iopub.status.idle": "2024-06-26T19:02:52.623052Z", - "shell.execute_reply": "2024-06-26T19:02:52.621621Z" + "iopub.execute_input": "2024-06-26T22:31:51.726520Z", + "iopub.status.busy": "2024-06-26T22:31:51.726137Z", + "iopub.status.idle": "2024-06-26T22:31:51.747856Z", + "shell.execute_reply": "2024-06-26T22:31:51.746760Z" } }, "outputs": [], @@ -367,10 +366,10 @@ "id": "829e38c9-3f9b-4e82-92a8-c86f81051580", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.627647Z", - "iopub.status.busy": "2024-06-26T19:02:52.627230Z", - "iopub.status.idle": "2024-06-26T19:02:52.648719Z", - "shell.execute_reply": "2024-06-26T19:02:52.647580Z" + "iopub.execute_input": "2024-06-26T22:31:51.752532Z", + "iopub.status.busy": "2024-06-26T22:31:51.752121Z", + "iopub.status.idle": "2024-06-26T22:31:51.775786Z", + "shell.execute_reply": "2024-06-26T22:31:51.774495Z" } }, "outputs": [], @@ -396,10 +395,10 @@ "id": "0a2163e3-dac1-4e64-a551-3dc961e44714", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.653834Z", - "iopub.status.busy": "2024-06-26T19:02:52.653150Z", - "iopub.status.idle": "2024-06-26T19:02:52.682380Z", - "shell.execute_reply": "2024-06-26T19:02:52.680881Z" + "iopub.execute_input": "2024-06-26T22:31:51.781580Z", + "iopub.status.busy": "2024-06-26T22:31:51.781106Z", + "iopub.status.idle": "2024-06-26T22:31:51.811715Z", + "shell.execute_reply": "2024-06-26T22:31:51.810339Z" } }, "outputs": [], @@ -425,10 +424,10 @@ "id": "074acb8d-de54-43a0-b243-a070ecfbe1ce", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.688018Z", - "iopub.status.busy": "2024-06-26T19:02:52.687537Z", - "iopub.status.idle": "2024-06-26T19:02:52.712992Z", - "shell.execute_reply": "2024-06-26T19:02:52.711480Z" + "iopub.execute_input": "2024-06-26T22:31:51.817262Z", + "iopub.status.busy": "2024-06-26T22:31:51.816880Z", + "iopub.status.idle": "2024-06-26T22:31:51.839897Z", + "shell.execute_reply": "2024-06-26T22:31:51.838748Z" } }, "outputs": [], @@ -452,11 +451,12 @@ "id": "d8356953-e32d-47ab-b67c-fa016cad9c50", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.717961Z", - "iopub.status.busy": "2024-06-26T19:02:52.717560Z", - "iopub.status.idle": "2024-06-26T19:02:52.731720Z", - "shell.execute_reply": "2024-06-26T19:02:52.730497Z" - } + "iopub.execute_input": "2024-06-26T22:31:51.845001Z", + "iopub.status.busy": "2024-06-26T22:31:51.844645Z", + "iopub.status.idle": "2024-06-26T22:31:51.855316Z", + "shell.execute_reply": "2024-06-26T22:31:51.853886Z" + }, + "tags": [] }, "outputs": [], "source": [ @@ -467,34 +467,35 @@ "# Bus Procurement Cost Analysis\n", "\n", "## Summary\n", - "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", + "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type.\n", "\n", "Data was compiled from three data sources:\n", "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", - "2. TIRCP project data (state-funded, California only)\n", + "2. TIRCP project data (state-funded, California only data)\n", "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", "\n", - "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", - "The resulting dataset only contained projects that were solely used to procure buses. \n", + "The initial dataset included nearly 300 projects. It was reduced to {len(merged_data)} projects after applying criteria to exclude non-bus related work. \n", + "Projects involving the construction of new facilities, training programs, or the procurement of non-bus items such as trains and ferries were excluded. \n", + "The final dataset comprised only projects focused on bus procurement. \n", + "\n", "\n", - "{len(merged_data)} projects were determined to contain solely bus purchases. \n", "These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", "\n", "\n", - "Breakdown of each data souce:\n", - "{pivot_source.to_markdown(index=False)}\n", + "Breakdown of each data souce showing the total buses and cost for each source:\n", + "{pivot_source.to_html(index= False)}\n", "\n", "\n", - "**ZEB buses include:**\n", + "**ZEB projects are categorized into the following propulsion types:**\n", "- zero-emission (not specified) \n", "- electric (not specified)\n", "- battery electric \n", "- fuel cell electric\n", "\n", - "**Non-ZEB buses include:**\n", - "- CNG \n", + "**Non-ZEB projects include the following propulsion types:**\n", + "- compressed natural gass (CNG) \n", "- ethanol \n", - "- ow emission (hybrid, propane) \n", + "- low-emission (hybrid, propane) \n", "- diesel \n", "- gas\n", "\n", @@ -509,11 +510,12 @@ "id": "f64881b9-46f9-4bfe-afd0-511385e21306", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.736632Z", - "iopub.status.busy": "2024-06-26T19:02:52.736209Z", - "iopub.status.idle": "2024-06-26T19:02:52.744950Z", - "shell.execute_reply": "2024-06-26T19:02:52.743705Z" - } + "iopub.execute_input": "2024-06-26T22:31:51.860259Z", + "iopub.status.busy": "2024-06-26T22:31:51.859879Z", + "iopub.status.idle": "2024-06-26T22:31:51.871304Z", + "shell.execute_reply": "2024-06-26T22:31:51.869818Z" + }, + "tags": [] }, "outputs": [ { @@ -524,39 +526,70 @@ "# Bus Procurement Cost Analysis\n", "\n", "## Summary\n", - "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", + "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type.\n", "\n", "Data was compiled from three data sources:\n", "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", - "2. TIRCP project data (state-funded, California only)\n", + "2. TIRCP project data (state-funded, California only data)\n", "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", "\n", - "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", - "The resulting dataset only contained projects that were solely used to procure buses. \n", + "The initial dataset included nearly 300 projects. It was reduced to 88 projects after applying criteria to exclude non-bus related work. \n", + "Projects involving the construction of new facilities, training programs, or the procurement of non-bus items such as trains and ferries were excluded. \n", + "The final dataset comprised only projects focused on bus procurement. \n", + "\n", "\n", - "88 projects were determined to contain solely bus purchases. \n", "These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", "\n", "\n", - "Breakdown of each data souce:\n", - "| source | bus_count | total_cost | cost_per_bus |\n", - "|:------------|------------:|-------------:|---------------:|\n", - "| dgs | 236 | 250112853 | 1059800 |\n", - "| fta | 883 | 391257025 | 443099 |\n", - "| tircp | 233 | 187250513 | 803650 |\n", - "| Grand Total | 1352 | 828620391 | 612884 |\n", + "Breakdown of each data souce showing the total buses and cost for each source:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
sourcebus_counttotal_costcost_per_bus
dgs236.02501128531059800
fta883.0391257025443099
tircp233.0187250513803650
Grand Total1352.0828620391612884
\n", "\n", "\n", - "**ZEB buses include:**\n", + "**ZEB projects are categorized into the following propulsion types:**\n", "- zero-emission (not specified) \n", "- electric (not specified)\n", "- battery electric \n", "- fuel cell electric\n", "\n", - "**Non-ZEB buses include:**\n", - "- CNG \n", + "**Non-ZEB projects include the following propulsion types:**\n", + "- compressed natural gass (CNG) \n", "- ethanol \n", - "- ow emission (hybrid, propane) \n", + "- low-emission (hybrid, propane) \n", "- diesel \n", "- gas\n", "\n", @@ -577,16 +610,24 @@ ")" ] }, + { + "cell_type": "markdown", + "id": "41f7d57c-e324-4a59-bd0a-c3282a2baa4a", + "metadata": {}, + "source": [ + "---" + ] + }, { "cell_type": "code", "execution_count": 18, "id": "676cbd9a-db4b-4e86-b60b-900f14513468", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.752216Z", - "iopub.status.busy": "2024-06-26T19:02:52.751305Z", - "iopub.status.idle": "2024-06-26T19:02:52.792781Z", - "shell.execute_reply": "2024-06-26T19:02:52.791667Z" + "iopub.execute_input": "2024-06-26T22:31:51.876290Z", + "iopub.status.busy": "2024-06-26T22:31:51.875917Z", + "iopub.status.idle": "2024-06-26T22:31:51.905559Z", + "shell.execute_reply": "2024-06-26T22:31:51.904501Z" } }, "outputs": [ @@ -684,7 +725,7 @@ { "data": { "text/markdown": [ - "**Non-ZEB Cost Summary**" + "**Non-ZEB Cost Summary** *" ], "text/plain": [ "" @@ -783,7 +824,7 @@ { "data": { "text/markdown": [ - "The remaining buses did not specify a propulsion type" + "*The remaining buses did not specify a propulsion type" ], "text/plain": [ "" @@ -799,10 +840,10 @@ " Markdown(\"**ZEB Cost Summary**\"),\n", " pivot_zeb_prop,\n", " \n", - " Markdown(\"**Non-ZEB Cost Summary**\"),\n", + " Markdown(\"**Non-ZEB Cost Summary** *\"),\n", " pivot_non_zeb_prop,\n", " \n", - " Markdown(\"The remaining buses did not specify a propulsion type\")\n", + " Markdown(\"*The remaining buses did not specify a propulsion type\")\n", ")" ] }, @@ -812,10 +853,10 @@ "id": "d99e56b6-2d69-4bc3-9ac2-169df1d3f6ef", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.798790Z", - "iopub.status.busy": "2024-06-26T19:02:52.797936Z", - "iopub.status.idle": "2024-06-26T19:02:52.803324Z", - "shell.execute_reply": "2024-06-26T19:02:52.802211Z" + "iopub.execute_input": "2024-06-26T22:31:51.910126Z", + "iopub.status.busy": "2024-06-26T22:31:51.909770Z", + "iopub.status.idle": "2024-06-26T22:31:51.914788Z", + "shell.execute_reply": "2024-06-26T22:31:51.913645Z" } }, "outputs": [], @@ -833,10 +874,10 @@ "id": "74ecf466-3560-46e1-a792-e217231ce1b4", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.809873Z", - "iopub.status.busy": "2024-06-26T19:02:52.808811Z", - "iopub.status.idle": "2024-06-26T19:02:52.815120Z", - "shell.execute_reply": "2024-06-26T19:02:52.814166Z" + "iopub.execute_input": "2024-06-26T22:31:51.919184Z", + "iopub.status.busy": "2024-06-26T22:31:51.918740Z", + "iopub.status.idle": "2024-06-26T22:31:51.923858Z", + "shell.execute_reply": "2024-06-26T22:31:51.922344Z" } }, "outputs": [], @@ -854,10 +895,10 @@ "id": "30c42e6d-3ca3-4715-a472-b7501e36f2fe", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.822730Z", - "iopub.status.busy": "2024-06-26T19:02:52.820468Z", - "iopub.status.idle": "2024-06-26T19:02:52.829935Z", - "shell.execute_reply": "2024-06-26T19:02:52.828210Z" + "iopub.execute_input": "2024-06-26T22:31:51.928563Z", + "iopub.status.busy": "2024-06-26T22:31:51.927511Z", + "iopub.status.idle": "2024-06-26T22:31:51.932809Z", + "shell.execute_reply": "2024-06-26T22:31:51.931719Z" } }, "outputs": [], @@ -875,17 +916,17 @@ "id": "80dd3d1c-86f8-4c68-bdd1-8249e6494f2c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.835466Z", - "iopub.status.busy": "2024-06-26T19:02:52.835000Z", - "iopub.status.idle": "2024-06-26T19:02:52.870304Z", - "shell.execute_reply": "2024-06-26T19:02:52.868683Z" + "iopub.execute_input": "2024-06-26T22:31:51.937326Z", + "iopub.status.busy": "2024-06-26T22:31:51.936964Z", + "iopub.status.idle": "2024-06-26T22:31:51.968595Z", + "shell.execute_reply": "2024-06-26T22:31:51.967461Z" } }, "outputs": [ { "data": { "text/markdown": [ - "## Which Agneices had the highest and lowest ZEB cost per bus?" + "## Which agencies had the highest and lowest ZEB cost per bus?" ], "text/plain": [ "" @@ -1032,7 +1073,7 @@ " \"bus_count\",\n", " \"cost_per_bus\"]\n", "\n", - "display(Markdown(\"## Which Agneices had the highest and lowest ZEB cost per bus?\")),\n", + "display(Markdown(\"## Which agencies had the highest and lowest ZEB cost per bus?\")),\n", "bus_min_max_summary(data=zeb_projects, col1=\"cost_per_bus\", col_list=new_cols)" ] }, @@ -1042,17 +1083,17 @@ "id": "75919ab8-7f14-49f2-bb4a-9765fdddc35c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.875693Z", - "iopub.status.busy": "2024-06-26T19:02:52.874979Z", - "iopub.status.idle": "2024-06-26T19:02:52.908082Z", - "shell.execute_reply": "2024-06-26T19:02:52.906765Z" + "iopub.execute_input": "2024-06-26T22:31:51.973568Z", + "iopub.status.busy": "2024-06-26T22:31:51.973122Z", + "iopub.status.idle": "2024-06-26T22:31:52.005396Z", + "shell.execute_reply": "2024-06-26T22:31:52.004151Z" } }, "outputs": [ { "data": { "text/markdown": [ - "## Which Agencies procured the most and least amount of ZEBs?" + "## Which agencies procured the most and least amount of ZEBs?" ], "text/plain": [ "" @@ -1202,7 +1243,7 @@ ], "source": [ "display(Markdown(\n", - " \"## Which Agencies procured the most and least amount of ZEBs?\"\n", + " \"## Which agencies procured the most and least amount of ZEBs?\"\n", "))\n", "bus_min_max_summary(data=zeb_projects, col1=\"bus_count\", col_list=new_cols)\n" ] @@ -1213,17 +1254,17 @@ "id": "c4f1b88e-f46c-4f69-888d-e116d2ce2ace", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.913086Z", - "iopub.status.busy": "2024-06-26T19:02:52.912659Z", - "iopub.status.idle": "2024-06-26T19:02:52.944936Z", - "shell.execute_reply": "2024-06-26T19:02:52.943703Z" + "iopub.execute_input": "2024-06-26T22:31:52.010755Z", + "iopub.status.busy": "2024-06-26T22:31:52.010329Z", + "iopub.status.idle": "2024-06-26T22:31:52.042705Z", + "shell.execute_reply": "2024-06-26T22:31:52.041052Z" } }, "outputs": [ { "data": { "text/markdown": [ - "## Which Agencies had the most and least total ZEB cost?" + "## Which agencies had the most and least total ZEB cost?" ], "text/plain": [ "" @@ -1363,7 +1404,7 @@ ], "source": [ "display(Markdown(\n", - " \"## Which Agencies had the most and least total ZEB cost?\"\n", + " \"## Which agencies had the most and least total ZEB cost?\"\n", "))\n", "bus_min_max_summary(data=zeb_projects, col1=\"total_cost\", col_list=new_cols)" ] @@ -1374,10 +1415,10 @@ "id": "1f39733f-448e-4d3e-9981-feff4a13dbda", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.951078Z", - "iopub.status.busy": "2024-06-26T19:02:52.950656Z", - "iopub.status.idle": "2024-06-26T19:02:52.955965Z", - "shell.execute_reply": "2024-06-26T19:02:52.954708Z" + "iopub.execute_input": "2024-06-26T22:31:52.047293Z", + "iopub.status.busy": "2024-06-26T22:31:52.046911Z", + "iopub.status.idle": "2024-06-26T22:31:52.051593Z", + "shell.execute_reply": "2024-06-26T22:31:52.050435Z" } }, "outputs": [], @@ -1400,10 +1441,10 @@ "id": "adebe10d-167c-480e-abff-313e8d8e91d4", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:52.960681Z", - "iopub.status.busy": "2024-06-26T19:02:52.960272Z", - "iopub.status.idle": "2024-06-26T19:02:53.585506Z", - "shell.execute_reply": "2024-06-26T19:02:53.584074Z" + "iopub.execute_input": "2024-06-26T22:31:52.056350Z", + "iopub.status.busy": "2024-06-26T22:31:52.055512Z", + "iopub.status.idle": "2024-06-26T22:31:52.641659Z", + "shell.execute_reply": "2024-06-26T22:31:52.640458Z" } }, "outputs": [ @@ -1452,10 +1493,10 @@ "id": "554eeee1-a3b6-47b0-912f-830885eb100b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:53.592208Z", - "iopub.status.busy": "2024-06-26T19:02:53.589893Z", - "iopub.status.idle": "2024-06-26T19:02:53.597405Z", - "shell.execute_reply": "2024-06-26T19:02:53.596198Z" + "iopub.execute_input": "2024-06-26T22:31:52.646395Z", + "iopub.status.busy": "2024-06-26T22:31:52.645558Z", + "iopub.status.idle": "2024-06-26T22:31:52.650275Z", + "shell.execute_reply": "2024-06-26T22:31:52.649176Z" } }, "outputs": [], @@ -1477,17 +1518,17 @@ "id": "5117c222-74a3-424c-9b13-1592a3f14eba", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:53.603106Z", - "iopub.status.busy": "2024-06-26T19:02:53.602478Z", - "iopub.status.idle": "2024-06-26T19:02:53.914326Z", - "shell.execute_reply": "2024-06-26T19:02:53.913029Z" + "iopub.execute_input": "2024-06-26T22:31:52.654731Z", + "iopub.status.busy": "2024-06-26T22:31:52.654282Z", + "iopub.status.idle": "2024-06-26T22:31:52.933449Z", + "shell.execute_reply": "2024-06-26T22:31:52.931966Z" } }, "outputs": [ { "data": { "text/markdown": [ - "## Which is the Cost per bus compared against all propulsion types?" + "## Which is the cost per bus compared against all propulsion types?" ], "text/plain": [ "" @@ -1615,7 +1656,7 @@ ], "source": [ "display(Markdown(\n", - " \"## Which is the Cost per bus compared against all propulsion types?\"\n", + " \"## Which is the cost per bus compared against all propulsion types?\"\n", "))\n", "display(\n", "# cpb by prop type\n", @@ -1632,10 +1673,10 @@ "id": "65566782-7cc4-4ce0-987e-2d055f60ec57", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:53.918840Z", - "iopub.status.busy": "2024-06-26T19:02:53.918445Z", - "iopub.status.idle": "2024-06-26T19:02:54.193344Z", - "shell.execute_reply": "2024-06-26T19:02:54.192083Z" + "iopub.execute_input": "2024-06-26T22:31:52.937528Z", + "iopub.status.busy": "2024-06-26T22:31:52.937140Z", + "iopub.status.idle": "2024-06-26T22:31:53.198711Z", + "shell.execute_reply": "2024-06-26T22:31:53.197611Z" } }, "outputs": [ @@ -1787,10 +1828,10 @@ "id": "7b56f81a-cf52-4309-ac8d-01d0389f9d4b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:54.198207Z", - "iopub.status.busy": "2024-06-26T19:02:54.197817Z", - "iopub.status.idle": "2024-06-26T19:02:54.440948Z", - "shell.execute_reply": "2024-06-26T19:02:54.439648Z" + "iopub.execute_input": "2024-06-26T22:31:53.203230Z", + "iopub.status.busy": "2024-06-26T22:31:53.202863Z", + "iopub.status.idle": "2024-06-26T22:31:53.424602Z", + "shell.execute_reply": "2024-06-26T22:31:53.423340Z" } }, "outputs": [ @@ -1912,17 +1953,17 @@ "id": "8d030948-59ea-4ea5-9db6-5d8639f6f8f5", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:54.446159Z", - "iopub.status.busy": "2024-06-26T19:02:54.445566Z", - "iopub.status.idle": "2024-06-26T19:02:54.464993Z", - "shell.execute_reply": "2024-06-26T19:02:54.463729Z" + "iopub.execute_input": "2024-06-26T22:31:53.428991Z", + "iopub.status.busy": "2024-06-26T22:31:53.428624Z", + "iopub.status.idle": "2024-06-26T22:31:53.445762Z", + "shell.execute_reply": "2024-06-26T22:31:53.444499Z" } }, "outputs": [ { "data": { "text/markdown": [ - "## What is the breakdown of Propulsion Type and Bus Size Category?" + "## What is the breakdown of ZEB Propulsion Type and Bus Size Category?" ], "text/plain": [ "" @@ -1980,25 +2021,6 @@ " 983264\n", " \n", " \n", - " CNG\n", - " cutaway\n", - " 3.0\n", - " 1162000\n", - " 387333\n", - " \n", - " \n", - " not specified\n", - " 209.0\n", - " 171977140\n", - " 822857\n", - " \n", - " \n", - " standard/conventional (30ft-45ft)\n", - " 40.0\n", - " 2900000\n", - " 72500\n", - " \n", - " \n", " FCEB\n", " not specified\n", " 29.0\n", @@ -2025,66 +2047,12 @@ " 1146666\n", " \n", " \n", - " ethanol\n", - " not specified\n", - " 9.0\n", - " 1006750\n", - " 111861\n", - " \n", - " \n", - " low emission (hybrid)\n", - " not specified\n", - " 145.0\n", - " 91824361\n", - " 633271\n", - " \n", - " \n", - " low emission (propane)\n", - " not specified\n", - " 44.0\n", - " 8403969\n", - " 190999\n", - " \n", - " \n", - " mix (zero and low emission)\n", - " not specified\n", - " 125.0\n", - " 36775430\n", - " 294203\n", - " \n", - " \n", - " not specified\n", - " cutaway\n", - " 149.0\n", - " 15532500\n", - " 104244\n", - " \n", - " \n", - " not specified\n", - " 162.0\n", - " 16503904\n", - " 101875\n", - " \n", - " \n", - " over-the-road\n", - " 14.0\n", - " 9516000\n", - " 679714\n", - " \n", - " \n", " zero-emission bus (not specified)\n", " not specified\n", " 143.0\n", " 128156513\n", " 896199\n", " \n", - " \n", - " Grand Total\n", - " \n", - " 1352.0\n", - " 828620391\n", - " 612884\n", - " \n", " \n", "\n", "" @@ -2094,64 +2062,31 @@ "prop_type bus_size_type \n", "BEB articulated 12.0 \n", " standard/conventional (30ft-45ft) 151.0 \n", - "CNG cutaway 3.0 \n", - " not specified 209.0 \n", - " standard/conventional (30ft-45ft) 40.0 \n", "FCEB not specified 29.0 \n", " standard/conventional (30ft-45ft) 73.0 \n", "electric (not specified) articulated 29.0 \n", " not specified 15.0 \n", - "ethanol not specified 9.0 \n", - "low emission (hybrid) not specified 145.0 \n", - "low emission (propane) not specified 44.0 \n", - "mix (zero and low emission) not specified 125.0 \n", - "not specified cutaway 149.0 \n", - " not specified 162.0 \n", - " over-the-road 14.0 \n", "zero-emission bus (not specified) not specified 143.0 \n", - "Grand Total 1352.0 \n", "\n", " total_cost \\\n", "prop_type bus_size_type \n", "BEB articulated 18759576 \n", " standard/conventional (30ft-45ft) 148472913 \n", - "CNG cutaway 1162000 \n", - " not specified 171977140 \n", - " standard/conventional (30ft-45ft) 2900000 \n", "FCEB not specified 38070971 \n", " standard/conventional (30ft-45ft) 82880364 \n", "electric (not specified) articulated 39478000 \n", " not specified 17200000 \n", - "ethanol not specified 1006750 \n", - "low emission (hybrid) not specified 91824361 \n", - "low emission (propane) not specified 8403969 \n", - "mix (zero and low emission) not specified 36775430 \n", - "not specified cutaway 15532500 \n", - " not specified 16503904 \n", - " over-the-road 9516000 \n", "zero-emission bus (not specified) not specified 128156513 \n", - "Grand Total 828620391 \n", "\n", " cost_per_bus \n", "prop_type bus_size_type \n", "BEB articulated 1563298 \n", " standard/conventional (30ft-45ft) 983264 \n", - "CNG cutaway 387333 \n", - " not specified 822857 \n", - " standard/conventional (30ft-45ft) 72500 \n", "FCEB not specified 1312792 \n", " standard/conventional (30ft-45ft) 1135347 \n", "electric (not specified) articulated 1361310 \n", " not specified 1146666 \n", - "ethanol not specified 111861 \n", - "low emission (hybrid) not specified 633271 \n", - "low emission (propane) not specified 190999 \n", - "mix (zero and low emission) not specified 294203 \n", - "not specified cutaway 104244 \n", - " not specified 101875 \n", - " over-the-road 679714 \n", - "zero-emission bus (not specified) not specified 896199 \n", - "Grand Total 612884 " + "zero-emission bus (not specified) not specified 896199 " ] }, "metadata": {}, @@ -2160,8 +2095,8 @@ ], "source": [ "display(\n", - " Markdown(\"## What is the breakdown of Propulsion Type and Bus Size Category?\"),\n", - " pivot_size\n", + " Markdown(\"## What is the breakdown of ZEB Propulsion Type and Bus Size Category?\"),\n", + " pivot_size.loc[zeb_list]\n", ")" ] }, @@ -2171,10 +2106,10 @@ "id": "63f90c48-e28d-4d88-8b90-891a3e3e3681", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T19:02:54.469661Z", - "iopub.status.busy": "2024-06-26T19:02:54.469255Z", - "iopub.status.idle": "2024-06-26T19:02:54.476818Z", - "shell.execute_reply": "2024-06-26T19:02:54.475612Z" + "iopub.execute_input": "2024-06-26T22:31:53.451014Z", + "iopub.status.busy": "2024-06-26T22:31:53.450492Z", + "iopub.status.idle": "2024-06-26T22:31:53.463354Z", + "shell.execute_reply": "2024-06-26T22:31:53.462038Z" } }, "outputs": [ From a433c2c368a7a70276a35d8e5fcbcc05acadb5da Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Wed, 26 Jun 2024 23:32:47 +0000 Subject: [PATCH 33/36] updated output file name for TIRCP cleaner to be consistent with other files. --- .../cost_per_bus_analysis.ipynb | 256 +++++++++--------- bus_procurement_cost/cost_per_bus_cleaner.py | 2 +- bus_procurement_cost/tircp_data_cleaner.py | 2 +- 3 files changed, 130 insertions(+), 130 deletions(-) diff --git a/bus_procurement_cost/cost_per_bus_analysis.ipynb b/bus_procurement_cost/cost_per_bus_analysis.ipynb index b0f11ee15..009886a33 100644 --- a/bus_procurement_cost/cost_per_bus_analysis.ipynb +++ b/bus_procurement_cost/cost_per_bus_analysis.ipynb @@ -6,10 +6,10 @@ "id": "da041e43-e8e2-4d4b-a498-10a7c0afe43f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:31.913513Z", - "iopub.status.busy": "2024-06-26T22:31:31.912578Z", - "iopub.status.idle": "2024-06-26T22:31:50.667778Z", - "shell.execute_reply": "2024-06-26T22:31:50.666257Z" + "iopub.execute_input": "2024-06-26T23:31:22.076309Z", + "iopub.status.busy": "2024-06-26T23:31:22.075847Z", + "iopub.status.idle": "2024-06-26T23:31:41.268659Z", + "shell.execute_reply": "2024-06-26T23:31:41.267222Z" }, "tags": [] }, @@ -33,10 +33,10 @@ "id": "d53376d9-d4b4-48b7-9916-5b9f633fbaf0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:50.673481Z", - "iopub.status.busy": "2024-06-26T22:31:50.672715Z", - "iopub.status.idle": "2024-06-26T22:31:51.546067Z", - "shell.execute_reply": "2024-06-26T22:31:51.544853Z" + "iopub.execute_input": "2024-06-26T23:31:41.275269Z", + "iopub.status.busy": "2024-06-26T23:31:41.273892Z", + "iopub.status.idle": "2024-06-26T23:31:42.396698Z", + "shell.execute_reply": "2024-06-26T23:31:42.395233Z" } }, "outputs": [], @@ -50,10 +50,10 @@ "id": "a45f2d3d-a600-4fe6-80cf-6b887036faab", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:51.551406Z", - "iopub.status.busy": "2024-06-26T22:31:51.550898Z", - "iopub.status.idle": "2024-06-26T22:31:51.556803Z", - "shell.execute_reply": "2024-06-26T22:31:51.555317Z" + "iopub.execute_input": "2024-06-26T23:31:42.402301Z", + "iopub.status.busy": "2024-06-26T23:31:42.401073Z", + "iopub.status.idle": "2024-06-26T23:31:42.406919Z", + "shell.execute_reply": "2024-06-26T23:31:42.406184Z" } }, "outputs": [], @@ -81,10 +81,10 @@ "id": "8ac40482-ba3e-4fde-8c05-806e3725de44", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:51.561945Z", - "iopub.status.busy": "2024-06-26T22:31:51.561550Z", - "iopub.status.idle": "2024-06-26T22:31:51.575229Z", - "shell.execute_reply": "2024-06-26T22:31:51.573936Z" + "iopub.execute_input": "2024-06-26T23:31:42.411899Z", + "iopub.status.busy": "2024-06-26T23:31:42.410719Z", + "iopub.status.idle": "2024-06-26T23:31:42.424045Z", + "shell.execute_reply": "2024-06-26T23:31:42.422703Z" } }, "outputs": [], @@ -105,10 +105,10 @@ "id": "d450fd60-cced-453b-b20b-62cdade0d7a6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:51.580134Z", - "iopub.status.busy": "2024-06-26T22:31:51.579769Z", - "iopub.status.idle": "2024-06-26T22:31:51.587220Z", - "shell.execute_reply": "2024-06-26T22:31:51.586024Z" + "iopub.execute_input": "2024-06-26T23:31:42.429985Z", + "iopub.status.busy": "2024-06-26T23:31:42.428735Z", + "iopub.status.idle": "2024-06-26T23:31:42.438675Z", + "shell.execute_reply": "2024-06-26T23:31:42.437203Z" } }, "outputs": [], @@ -156,10 +156,10 @@ "id": "2a2dc407-20cc-45de-84b1-bb5991dad8ac", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:51.591838Z", - "iopub.status.busy": "2024-06-26T22:31:51.591138Z", - "iopub.status.idle": "2024-06-26T22:31:51.599353Z", - "shell.execute_reply": "2024-06-26T22:31:51.597716Z" + "iopub.execute_input": "2024-06-26T23:31:42.443616Z", + "iopub.status.busy": "2024-06-26T23:31:42.443245Z", + "iopub.status.idle": "2024-06-26T23:31:42.449251Z", + "shell.execute_reply": "2024-06-26T23:31:42.448129Z" } }, "outputs": [], @@ -186,10 +186,10 @@ "id": "9a6a7ecf-5180-4691-84fe-23aa68cdae93", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:51.605065Z", - "iopub.status.busy": "2024-06-26T22:31:51.604337Z", - "iopub.status.idle": "2024-06-26T22:31:51.612060Z", - "shell.execute_reply": "2024-06-26T22:31:51.610827Z" + "iopub.execute_input": "2024-06-26T23:31:42.454688Z", + "iopub.status.busy": "2024-06-26T23:31:42.453665Z", + "iopub.status.idle": "2024-06-26T23:31:42.462034Z", + "shell.execute_reply": "2024-06-26T23:31:42.460971Z" } }, "outputs": [], @@ -215,10 +215,10 @@ "id": "44d21201-223f-4e6c-b238-b72fba984544", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:51.617891Z", - "iopub.status.busy": "2024-06-26T22:31:51.617172Z", - "iopub.status.idle": "2024-06-26T22:31:51.626056Z", - "shell.execute_reply": "2024-06-26T22:31:51.625225Z" + "iopub.execute_input": "2024-06-26T23:31:42.466539Z", + "iopub.status.busy": "2024-06-26T23:31:42.466167Z", + "iopub.status.idle": "2024-06-26T23:31:42.475338Z", + "shell.execute_reply": "2024-06-26T23:31:42.474135Z" } }, "outputs": [], @@ -269,10 +269,10 @@ "id": "067a14a5-5c77-4914-82a8-c5eeb170cb08", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:51.630715Z", - "iopub.status.busy": "2024-06-26T22:31:51.630048Z", - "iopub.status.idle": "2024-06-26T22:31:51.681613Z", - "shell.execute_reply": "2024-06-26T22:31:51.680496Z" + "iopub.execute_input": "2024-06-26T23:31:42.480883Z", + "iopub.status.busy": "2024-06-26T23:31:42.480520Z", + "iopub.status.idle": "2024-06-26T23:31:42.540136Z", + "shell.execute_reply": "2024-06-26T23:31:42.538977Z" } }, "outputs": [], @@ -290,10 +290,10 @@ "id": "49a97d01-b17e-475c-b351-67426f3741d9", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:51.686475Z", - "iopub.status.busy": "2024-06-26T22:31:51.686087Z", - "iopub.status.idle": "2024-06-26T22:31:51.693353Z", - "shell.execute_reply": "2024-06-26T22:31:51.692185Z" + "iopub.execute_input": "2024-06-26T23:31:42.545384Z", + "iopub.status.busy": "2024-06-26T23:31:42.544807Z", + "iopub.status.idle": "2024-06-26T23:31:42.552797Z", + "shell.execute_reply": "2024-06-26T23:31:42.551294Z" } }, "outputs": [], @@ -310,10 +310,10 @@ "id": "4faaa4ad-b16c-4e6b-87c7-d12f7e7db3c6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:51.697847Z", - "iopub.status.busy": "2024-06-26T22:31:51.697459Z", - "iopub.status.idle": "2024-06-26T22:31:51.721570Z", - "shell.execute_reply": "2024-06-26T22:31:51.720310Z" + "iopub.execute_input": "2024-06-26T23:31:42.558015Z", + "iopub.status.busy": "2024-06-26T23:31:42.557625Z", + "iopub.status.idle": "2024-06-26T23:31:42.583902Z", + "shell.execute_reply": "2024-06-26T23:31:42.581389Z" } }, "outputs": [], @@ -337,10 +337,10 @@ "id": "b8535e97-e7bf-4d7e-b718-24c5758b0ccd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:51.726520Z", - "iopub.status.busy": "2024-06-26T22:31:51.726137Z", - "iopub.status.idle": "2024-06-26T22:31:51.747856Z", - "shell.execute_reply": "2024-06-26T22:31:51.746760Z" + "iopub.execute_input": "2024-06-26T23:31:42.593655Z", + "iopub.status.busy": "2024-06-26T23:31:42.593253Z", + "iopub.status.idle": "2024-06-26T23:31:42.617829Z", + "shell.execute_reply": "2024-06-26T23:31:42.616633Z" } }, "outputs": [], @@ -366,10 +366,10 @@ "id": "829e38c9-3f9b-4e82-92a8-c86f81051580", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:51.752532Z", - "iopub.status.busy": "2024-06-26T22:31:51.752121Z", - "iopub.status.idle": "2024-06-26T22:31:51.775786Z", - "shell.execute_reply": "2024-06-26T22:31:51.774495Z" + "iopub.execute_input": "2024-06-26T23:31:42.623085Z", + "iopub.status.busy": "2024-06-26T23:31:42.622309Z", + "iopub.status.idle": "2024-06-26T23:31:42.647068Z", + "shell.execute_reply": "2024-06-26T23:31:42.645629Z" } }, "outputs": [], @@ -395,10 +395,10 @@ "id": "0a2163e3-dac1-4e64-a551-3dc961e44714", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:51.781580Z", - "iopub.status.busy": "2024-06-26T22:31:51.781106Z", - "iopub.status.idle": "2024-06-26T22:31:51.811715Z", - "shell.execute_reply": "2024-06-26T22:31:51.810339Z" + "iopub.execute_input": "2024-06-26T23:31:42.652492Z", + "iopub.status.busy": "2024-06-26T23:31:42.651667Z", + "iopub.status.idle": "2024-06-26T23:31:42.676873Z", + "shell.execute_reply": "2024-06-26T23:31:42.675765Z" } }, "outputs": [], @@ -424,10 +424,10 @@ "id": "074acb8d-de54-43a0-b243-a070ecfbe1ce", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:51.817262Z", - "iopub.status.busy": "2024-06-26T22:31:51.816880Z", - "iopub.status.idle": "2024-06-26T22:31:51.839897Z", - "shell.execute_reply": "2024-06-26T22:31:51.838748Z" + "iopub.execute_input": "2024-06-26T23:31:42.682116Z", + "iopub.status.busy": "2024-06-26T23:31:42.681322Z", + "iopub.status.idle": "2024-06-26T23:31:42.706549Z", + "shell.execute_reply": "2024-06-26T23:31:42.705488Z" } }, "outputs": [], @@ -451,10 +451,10 @@ "id": "d8356953-e32d-47ab-b67c-fa016cad9c50", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:51.845001Z", - "iopub.status.busy": "2024-06-26T22:31:51.844645Z", - "iopub.status.idle": "2024-06-26T22:31:51.855316Z", - "shell.execute_reply": "2024-06-26T22:31:51.853886Z" + "iopub.execute_input": "2024-06-26T23:31:42.711308Z", + "iopub.status.busy": "2024-06-26T23:31:42.710956Z", + "iopub.status.idle": "2024-06-26T23:31:42.721845Z", + "shell.execute_reply": "2024-06-26T23:31:42.720467Z" }, "tags": [] }, @@ -510,10 +510,10 @@ "id": "f64881b9-46f9-4bfe-afd0-511385e21306", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:51.860259Z", - "iopub.status.busy": "2024-06-26T22:31:51.859879Z", - "iopub.status.idle": "2024-06-26T22:31:51.871304Z", - "shell.execute_reply": "2024-06-26T22:31:51.869818Z" + "iopub.execute_input": "2024-06-26T23:31:42.726916Z", + "iopub.status.busy": "2024-06-26T23:31:42.726027Z", + "iopub.status.idle": "2024-06-26T23:31:42.736211Z", + "shell.execute_reply": "2024-06-26T23:31:42.734904Z" }, "tags": [] }, @@ -624,10 +624,10 @@ "id": "676cbd9a-db4b-4e86-b60b-900f14513468", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:51.876290Z", - "iopub.status.busy": "2024-06-26T22:31:51.875917Z", - "iopub.status.idle": "2024-06-26T22:31:51.905559Z", - "shell.execute_reply": "2024-06-26T22:31:51.904501Z" + "iopub.execute_input": "2024-06-26T23:31:42.742030Z", + "iopub.status.busy": "2024-06-26T23:31:42.741621Z", + "iopub.status.idle": "2024-06-26T23:31:42.771146Z", + "shell.execute_reply": "2024-06-26T23:31:42.769929Z" } }, "outputs": [ @@ -853,10 +853,10 @@ "id": "d99e56b6-2d69-4bc3-9ac2-169df1d3f6ef", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:51.910126Z", - "iopub.status.busy": "2024-06-26T22:31:51.909770Z", - "iopub.status.idle": "2024-06-26T22:31:51.914788Z", - "shell.execute_reply": "2024-06-26T22:31:51.913645Z" + "iopub.execute_input": "2024-06-26T23:31:42.778967Z", + "iopub.status.busy": "2024-06-26T23:31:42.778555Z", + "iopub.status.idle": "2024-06-26T23:31:42.784844Z", + "shell.execute_reply": "2024-06-26T23:31:42.783462Z" } }, "outputs": [], @@ -874,10 +874,10 @@ "id": "74ecf466-3560-46e1-a792-e217231ce1b4", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:51.919184Z", - "iopub.status.busy": "2024-06-26T22:31:51.918740Z", - "iopub.status.idle": "2024-06-26T22:31:51.923858Z", - "shell.execute_reply": "2024-06-26T22:31:51.922344Z" + "iopub.execute_input": "2024-06-26T23:31:42.790110Z", + "iopub.status.busy": "2024-06-26T23:31:42.789738Z", + "iopub.status.idle": "2024-06-26T23:31:42.795592Z", + "shell.execute_reply": "2024-06-26T23:31:42.794219Z" } }, "outputs": [], @@ -895,10 +895,10 @@ "id": "30c42e6d-3ca3-4715-a472-b7501e36f2fe", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:51.928563Z", - "iopub.status.busy": "2024-06-26T22:31:51.927511Z", - "iopub.status.idle": "2024-06-26T22:31:51.932809Z", - "shell.execute_reply": "2024-06-26T22:31:51.931719Z" + "iopub.execute_input": "2024-06-26T23:31:42.801382Z", + "iopub.status.busy": "2024-06-26T23:31:42.800899Z", + "iopub.status.idle": "2024-06-26T23:31:42.808096Z", + "shell.execute_reply": "2024-06-26T23:31:42.807160Z" } }, "outputs": [], @@ -916,10 +916,10 @@ "id": "80dd3d1c-86f8-4c68-bdd1-8249e6494f2c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:51.937326Z", - "iopub.status.busy": "2024-06-26T22:31:51.936964Z", - "iopub.status.idle": "2024-06-26T22:31:51.968595Z", - "shell.execute_reply": "2024-06-26T22:31:51.967461Z" + "iopub.execute_input": "2024-06-26T23:31:42.813451Z", + "iopub.status.busy": "2024-06-26T23:31:42.812643Z", + "iopub.status.idle": "2024-06-26T23:31:42.848098Z", + "shell.execute_reply": "2024-06-26T23:31:42.846224Z" } }, "outputs": [ @@ -1083,10 +1083,10 @@ "id": "75919ab8-7f14-49f2-bb4a-9765fdddc35c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:51.973568Z", - "iopub.status.busy": "2024-06-26T22:31:51.973122Z", - "iopub.status.idle": "2024-06-26T22:31:52.005396Z", - "shell.execute_reply": "2024-06-26T22:31:52.004151Z" + "iopub.execute_input": "2024-06-26T23:31:42.853097Z", + "iopub.status.busy": "2024-06-26T23:31:42.852703Z", + "iopub.status.idle": "2024-06-26T23:31:42.882586Z", + "shell.execute_reply": "2024-06-26T23:31:42.881399Z" } }, "outputs": [ @@ -1254,10 +1254,10 @@ "id": "c4f1b88e-f46c-4f69-888d-e116d2ce2ace", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:52.010755Z", - "iopub.status.busy": "2024-06-26T22:31:52.010329Z", - "iopub.status.idle": "2024-06-26T22:31:52.042705Z", - "shell.execute_reply": "2024-06-26T22:31:52.041052Z" + "iopub.execute_input": "2024-06-26T23:31:42.887562Z", + "iopub.status.busy": "2024-06-26T23:31:42.887195Z", + "iopub.status.idle": "2024-06-26T23:31:42.918246Z", + "shell.execute_reply": "2024-06-26T23:31:42.917119Z" } }, "outputs": [ @@ -1415,10 +1415,10 @@ "id": "1f39733f-448e-4d3e-9981-feff4a13dbda", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:52.047293Z", - "iopub.status.busy": "2024-06-26T22:31:52.046911Z", - "iopub.status.idle": "2024-06-26T22:31:52.051593Z", - "shell.execute_reply": "2024-06-26T22:31:52.050435Z" + "iopub.execute_input": "2024-06-26T23:31:42.922970Z", + "iopub.status.busy": "2024-06-26T23:31:42.922280Z", + "iopub.status.idle": "2024-06-26T23:31:42.926968Z", + "shell.execute_reply": "2024-06-26T23:31:42.925773Z" } }, "outputs": [], @@ -1441,10 +1441,10 @@ "id": "adebe10d-167c-480e-abff-313e8d8e91d4", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:52.056350Z", - "iopub.status.busy": "2024-06-26T22:31:52.055512Z", - "iopub.status.idle": "2024-06-26T22:31:52.641659Z", - "shell.execute_reply": "2024-06-26T22:31:52.640458Z" + "iopub.execute_input": "2024-06-26T23:31:42.931493Z", + "iopub.status.busy": "2024-06-26T23:31:42.931123Z", + "iopub.status.idle": "2024-06-26T23:31:43.310794Z", + "shell.execute_reply": "2024-06-26T23:31:43.309493Z" } }, "outputs": [ @@ -1493,10 +1493,10 @@ "id": "554eeee1-a3b6-47b0-912f-830885eb100b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:52.646395Z", - "iopub.status.busy": "2024-06-26T22:31:52.645558Z", - "iopub.status.idle": "2024-06-26T22:31:52.650275Z", - "shell.execute_reply": "2024-06-26T22:31:52.649176Z" + "iopub.execute_input": "2024-06-26T23:31:43.315967Z", + "iopub.status.busy": "2024-06-26T23:31:43.315568Z", + "iopub.status.idle": "2024-06-26T23:31:43.320371Z", + "shell.execute_reply": "2024-06-26T23:31:43.319221Z" } }, "outputs": [], @@ -1518,10 +1518,10 @@ "id": "5117c222-74a3-424c-9b13-1592a3f14eba", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:52.654731Z", - "iopub.status.busy": "2024-06-26T22:31:52.654282Z", - "iopub.status.idle": "2024-06-26T22:31:52.933449Z", - "shell.execute_reply": "2024-06-26T22:31:52.931966Z" + "iopub.execute_input": "2024-06-26T23:31:43.324770Z", + "iopub.status.busy": "2024-06-26T23:31:43.324392Z", + "iopub.status.idle": "2024-06-26T23:31:43.795786Z", + "shell.execute_reply": "2024-06-26T23:31:43.794491Z" } }, "outputs": [ @@ -1673,10 +1673,10 @@ "id": "65566782-7cc4-4ce0-987e-2d055f60ec57", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:52.937528Z", - "iopub.status.busy": "2024-06-26T22:31:52.937140Z", - "iopub.status.idle": "2024-06-26T22:31:53.198711Z", - "shell.execute_reply": "2024-06-26T22:31:53.197611Z" + "iopub.execute_input": "2024-06-26T23:31:43.801067Z", + "iopub.status.busy": "2024-06-26T23:31:43.800688Z", + "iopub.status.idle": "2024-06-26T23:31:44.075389Z", + "shell.execute_reply": "2024-06-26T23:31:44.073175Z" } }, "outputs": [ @@ -1828,10 +1828,10 @@ "id": "7b56f81a-cf52-4309-ac8d-01d0389f9d4b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:53.203230Z", - "iopub.status.busy": "2024-06-26T22:31:53.202863Z", - "iopub.status.idle": "2024-06-26T22:31:53.424602Z", - "shell.execute_reply": "2024-06-26T22:31:53.423340Z" + "iopub.execute_input": "2024-06-26T23:31:44.080934Z", + "iopub.status.busy": "2024-06-26T23:31:44.080469Z", + "iopub.status.idle": "2024-06-26T23:31:44.315206Z", + "shell.execute_reply": "2024-06-26T23:31:44.313686Z" } }, "outputs": [ @@ -1953,10 +1953,10 @@ "id": "8d030948-59ea-4ea5-9db6-5d8639f6f8f5", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:53.428991Z", - "iopub.status.busy": "2024-06-26T22:31:53.428624Z", - "iopub.status.idle": "2024-06-26T22:31:53.445762Z", - "shell.execute_reply": "2024-06-26T22:31:53.444499Z" + "iopub.execute_input": "2024-06-26T23:31:44.319904Z", + "iopub.status.busy": "2024-06-26T23:31:44.319501Z", + "iopub.status.idle": "2024-06-26T23:31:44.340895Z", + "shell.execute_reply": "2024-06-26T23:31:44.339535Z" } }, "outputs": [ @@ -2106,10 +2106,10 @@ "id": "63f90c48-e28d-4d88-8b90-891a3e3e3681", "metadata": { "execution": { - "iopub.execute_input": "2024-06-26T22:31:53.451014Z", - "iopub.status.busy": "2024-06-26T22:31:53.450492Z", - "iopub.status.idle": "2024-06-26T22:31:53.463354Z", - "shell.execute_reply": "2024-06-26T22:31:53.462038Z" + "iopub.execute_input": "2024-06-26T23:31:44.346496Z", + "iopub.status.busy": "2024-06-26T23:31:44.345382Z", + "iopub.status.idle": "2024-06-26T23:31:44.355400Z", + "shell.execute_reply": "2024-06-26T23:31:44.354301Z" } }, "outputs": [ diff --git a/bus_procurement_cost/cost_per_bus_cleaner.py b/bus_procurement_cost/cost_per_bus_cleaner.py index 29a201f61..637249926 100644 --- a/bus_procurement_cost/cost_per_bus_cleaner.py +++ b/bus_procurement_cost/cost_per_bus_cleaner.py @@ -31,7 +31,7 @@ def prepare_all_data() ->pd.DataFrame: # reading in data # bus only projects for each datase fta = pd.read_parquet(f"{GCS_PATH}clean_fta_bus_only.parquet") - tircp = pd.read_parquet(f"{GCS_PATH}clean_tircp_bus_only_clean.parquet") + tircp = pd.read_parquet(f"{GCS_PATH}clean_tircp_bus_only.parquet") dgs = pd.read_parquet(f"{GCS_PATH}clean_dgs_bus_only_w_options.parquet") # adding new column to identify source diff --git a/bus_procurement_cost/tircp_data_cleaner.py b/bus_procurement_cost/tircp_data_cleaner.py index cf74902cd..0199742b7 100644 --- a/bus_procurement_cost/tircp_data_cleaner.py +++ b/bus_procurement_cost/tircp_data_cleaner.py @@ -126,4 +126,4 @@ def tircp_agg_bus_only(df: pd.DataFrame) -> pd.DataFrame: # export both df's as parquets to GCS df1.to_parquet(f'{GCS_PATH}clean_tircp_all_project.parquet') - df2.to_parquet(f'{GCS_PATH}clean_tircp_bus_only_clean.parquet') \ No newline at end of file + df2.to_parquet(f'{GCS_PATH}clean_tircp_bus_only.parquet') \ No newline at end of file From 492cbc1e6169cea654ce8411f467a1f9f1d725ad Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Fri, 28 Jun 2024 17:09:47 +0000 Subject: [PATCH 34/36] update readme --- bus_procurement_cost/README.md | 43 ++++++++++++++++++++++------------ 1 file changed, 28 insertions(+), 15 deletions(-) diff --git a/bus_procurement_cost/README.md b/bus_procurement_cost/README.md index 3969a34d9..1953c0f6b 100644 --- a/bus_procurement_cost/README.md +++ b/bus_procurement_cost/README.md @@ -7,10 +7,11 @@ * (upcoming )Washington and/or Georgia Contract list (via Rebel) ## GH issue -Research Request - Bus Procurement Costs & Awards #897 +* [Research Request - Bus Procurement Costs & Awards #897](https://github.com/cal-itp/data-analyses/issues/897) +* [Research Task - Refactor: Bus Procurement Cost #1142](https://github.com/cal-itp/data-analyses/issues/1142) ## Research Question -Identify federal awards to fund bus purchases and how much agencies pay for them. +Analyze bus procurement projects to see how much transit agencies pay for them. ## Methodology - Examine each dataset to: @@ -49,39 +50,51 @@ Identify federal awards to fund bus purchases and how much agencies pay for them ## Script Explanation +- **bus_cost_utils.py** + * contains all the shared functions and variable used throughout the cleaner scripts +

+ Executing `make all_bus_scripts` will run the following scripts

+ - **fta_data_cleaner.py:** * Reads in and cleans FTA data * outputs 2 files: - * cleaned, all projects: `fta_all_projects_clean.parquet` - * cleaned, bus only projects:`fta_bus_cost_clean.parquet` + * cleaned, all projects: `clean_fta_all_projects.parquet` + * cleaned, bus only projects:`clean_fta_bus_only.parquet`

+ - **tircp_data_cleaner.py** * Reads in and cleans tircp data * outputs 2 files: - * cleaned, all projects: `clean_tircp_project.parquet` - * cleaned, bus only projects:`clean_tircp_project_bus_only.parquet` + * cleaned, all projects: `clean_tircp_all_project.parquet` + * cleaned, bus only projects:`clean_tircp_bus_only.parquet`

+ - **dgs_data_cleaner.py** * Reads in and cleans DGS data * outputs 2 files: - * cleaned, bus only projects: `dgs_agg_clean.parquet` - * cleaned, bus only projects with options:`dgs_agg_w_options_clean.parquet` + * cleaned, bus only projects: `clean_dgs_all_projects.parquet` + * cleaned, bus only projects with options:`clean_dgs_bus_only_w_options.parquet`

+ - **cost_per_bus_cleaner.py** * Reads in and merges all the bus only datasets - * updates columns names -

-- **cost_per_bus_utils.py** - * stores variables for summary section (total projects, total buses, etc) - * stores chart functions to be used in notebook - * stores the summary and conclusion text. + * updates columns names + * calculates `cost_per_bus`, z-score and idetifies outliers. + * outputs 2 files: + * cleaned projects: `cleaned_cpb_analysis_data_merge.parquet` + * cleaned, removed outliers: `cleaned_no_outliers_cpb_analysis_data_merge.parquet`

+ - **nbconvert --to notebook** * runs all cells in the `cost_per_bus_analysis.ipynb` * overwrites the nb in place

+ - **nbconvert --to html** * converts the nb to HTML - * hides the code cells and prompts \ No newline at end of file + * hides the code cells and prompts +

+ +output files are saved to GCS at: `calitp-analytics-data/data-analyses/bus_procurement_cost` From 95787fb837e91b73a8ad949d8de28963b124c080 Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Fri, 28 Jun 2024 17:21:56 +0000 Subject: [PATCH 35/36] removed old, initial exploratory notebooks --- .../OLD_FTA_bus_grant_analysis.ipynb | 1281 ---------- bus_procurement_cost/OLD_cost_per_bus.ipynb | 388 --- .../OLD_cost_per_bus_nb_scripts.py | 455 ---- .../OLD_dgs_usage_report_bus_analysis.ipynb | 813 ------ .../OLD_tircp_bus_analysis.ipynb | 2195 ----------------- 5 files changed, 5132 deletions(-) delete mode 100644 bus_procurement_cost/OLD_FTA_bus_grant_analysis.ipynb delete mode 100644 bus_procurement_cost/OLD_cost_per_bus.ipynb delete mode 100644 bus_procurement_cost/OLD_cost_per_bus_nb_scripts.py delete mode 100644 bus_procurement_cost/OLD_dgs_usage_report_bus_analysis.ipynb delete mode 100644 bus_procurement_cost/OLD_tircp_bus_analysis.ipynb diff --git a/bus_procurement_cost/OLD_FTA_bus_grant_analysis.ipynb b/bus_procurement_cost/OLD_FTA_bus_grant_analysis.ipynb deleted file mode 100644 index d99820354..000000000 --- a/bus_procurement_cost/OLD_FTA_bus_grant_analysis.ipynb +++ /dev/null @@ -1,1281 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "46b3c4b1-8668-49b7-a330-9bd1f092f7b9", - "metadata": { - "tags": [] - }, - "source": [ - "## FY23 FTA Bus and Low- and No-Emission Grant Awards Analysis\n", - "\n", - "GH issue: \n", - "* Research Request - Bus Procurement Costs & Awards #897\n", - "\n", - "Data source(s): \n", - "1. https://www.transit.dot.gov/funding/grants/fy23-fta-bus-and-low-and-no-emission-grant-awards\n", - "2. https://storymaps.arcgis.com/stories/022abf31cedd438b808ec2b827b6faff\n", - "\n", - "Definitions: \n", - "* Grants for Buses and Bus Facilities Program:\n", - " * 49 U.S.C. 5339(b)) makes federal resources available to states and direct recipients to replace, rehabilitate and purchase buses and related equipment and to construct bus-related facilities, including technological changes or innovations to modify low or no emission vehicles or facilities. Funding is provided through formula allocations and competitive grants. \n", - "

\n", - "* Low or No Emission Vehicle Program:\n", - " * 5339(c) provides funding to state and local governmental authorities for the purchase or lease of zero-emission and low-emission transit buses as well as acquisition, construction, and leasing of required supporting facilities.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "1ad111b5-3933-45c4-aa3c-f12ea701e882", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import shared_utils\n", - "\n", - "# set_option to increase max rows displayed to 200, to see entire df in 1 go/\n", - "pd.set_option(\"display.max_rows\", 300)" - ] - }, - { - "cell_type": "markdown", - "id": "467fc38e-6313-44c7-9797-456ecd752e57", - "metadata": { - "tags": [] - }, - "source": [ - "## Reading in raw data from gcs" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a42530b9-806e-498b-8f82-9f123ddb087c", - "metadata": {}, - "outputs": [], - "source": [ - "gcs_path = \"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/\"\n", - "file = \"data-analyses_bus_procurement_cost_fta_press_release_data_csv.csv\"\n", - "\n", - "fta = pd.read_csv(gcs_path+file)" - ] - }, - { - "cell_type": "markdown", - "id": "7f37c5d6-664c-48fd-9c32-cf7dcd3318ae", - "metadata": { - "tags": [] - }, - "source": [ - "## Data Cleaning\n", - "1. snake-case column names\n", - "2. remove currency formatting from funding column (with $ and , )\n", - "3. seperate text from # of bus col (split at '(')\n", - " a. trim spaces in new col\n", - " b. get rid of () characters in new col\n", - "4. trim spaces in other columns\n", - "5. exnamine column values and replace/update as needed\n", - "6. create new columns for bus size type and prop type\n" - ] - }, - { - "cell_type": "markdown", - "id": "cb70936a-8d54-4ae5-b9cc-ef64ea04c8b5", - "metadata": { - "tags": [] - }, - "source": [ - "### Dataframe cleaning" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "7efb6ebf-474a-4c58-8052-a7427e881649", - "metadata": {}, - "outputs": [], - "source": [ - "def snake_case(df):\n", - " '''\n", - " snake case dataframe columns and stip of extra spaces\n", - " '''\n", - " df.columns = df.columns.str.lower().str.replace(\" \", \"_\").str.strip()\n", - "\n", - "\n", - "def fund_cleaner(df, column):\n", - " '''\n", - " function to clean the funding column and make column int64\n", - " '''\n", - " df[column] = df[column].str.replace(\"$\", \"\").str.replace(\",\", \"\").str.strip().astype('int64')\n", - "\n", - " \n", - "\n", - "def value_replacer(df, col1, col1_val, col2, col2_new_val):\n", - " '''\n", - " function that replaces the value at a speicific row on a specific column.\n", - " in this case, filters the df by a speific col/val, then replaces the value at new col/val\n", - " '''\n", - " df.loc[df[col1] == col1_val , col2] = col2_new_val\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f55008ae-1de5-4cd3-89f2-87cd4eac17cd", - "metadata": {}, - "outputs": [], - "source": [ - "# snake case function to Df\n", - "snake_case(fta)" - ] - }, - { - "cell_type": "markdown", - "id": "d012306e-86ff-4351-86b3-c3a8dc3145fd", - "metadata": { - "tags": [] - }, - "source": [ - "### Column Cleaning" - ] - }, - { - "cell_type": "markdown", - "id": "6fb7a5e0-6649-468b-9f0e-4b36281e0db0", - "metadata": { - "tags": [] - }, - "source": [ - "#### propulsion_type rename to propulstion category" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9e22e6fa-857d-44c3-beec-0f83f71a6b1c", - "metadata": {}, - "outputs": [], - "source": [ - "# rename col to propulsion category\n", - "fta = fta.rename(columns={\"propulsion_type\": \"propulsion_category\"})\n", - "\n", - "# make values in prop_cat col lower case and remove spaces\n", - "fta[\"propulsion_category\"] = fta[\"propulsion_category\"].str.lower()\n", - "fta[\"propulsion_category\"] = fta[\"propulsion_category\"].str.replace(\" \", \"\")" - ] - }, - { - "cell_type": "markdown", - "id": "354cb76f-f71f-4a30-8c85-aece5ac3f0d3", - "metadata": { - "tags": [] - }, - "source": [ - "#### funding" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "ff8721be-5cbd-430f-b947-4110c397de23", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_773/192928216.py:12: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n", - " df[column] = df[column].str.replace(\"$\", \"\").str.replace(\",\", \"\").str.strip().astype('int64')\n" - ] - } - ], - "source": [ - "fund_cleaner(fta, \"funding\")" - ] - }, - { - "cell_type": "markdown", - "id": "7077380f-1f92-4108-bac1-77db8f79568d", - "metadata": { - "tags": [] - }, - "source": [ - "#### split `approx_#_of_buses` to `bus_count` and `prop_type`" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "1fb94754-e795-4e78-8a91-2732566a1792", - "metadata": {}, - "outputs": [], - "source": [ - "# test of removing the spaces first in # of bus colum values, THEN split by (\n", - "fta[\"approx_#_of_buses\"] = fta[\"approx_#_of_buses\"].str.replace(\" \", \"\")\n", - "\n", - "# spliting the # of buses column into 2, using the ( char as the delimiter\n", - "# also fills `none` values with `needs manual check`\n", - "fta[[\"bus_count\", \"prop_type\"]] = fta[\"approx_#_of_buses\"].str.split(\n", - " pat=\"(\", n=1, expand=True\n", - ")\n", - "fta[[\"bus_count\", \"prop_type\"]] = fta[[\"bus_count\", \"prop_type\"]].fillna(\n", - " \"needs manual check\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "7a6e2c90-9da1-4cfb-8032-397baa74579a", - "metadata": { - "tags": [] - }, - "source": [ - "#### bus_count" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c979c629-1a2f-4804-9f16-cdd27092f964", - "metadata": {}, - "outputs": [], - "source": [ - "# running function on rows that need specific value changes\n", - "value_replacer(fta,'bus_count','56estimated-cutawayvans', 'bus_count', 56)\n", - "value_replacer(fta,'bus_count','12batteryelectric','bus_count', 12)\n", - "value_replacer(fta,'prop_type','PM-awardwillnotfund68buses)', 'prop_type', 'estimated-cutaway vans (PM- award will not fund 68 buses)')\n", - "value_replacer(fta,'project_sponsor','City of Charlotte - Charlotte Area Transit System','bus_count',31)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "ef56c2ba-a2db-46c4-82ac-c8d95b6e11ad", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dtype('int64')" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#checl data type for bus _count\n", - "fta['bus_count'] = fta['bus_count'].astype('int64')\n", - "\n", - "#check work\n", - "fta['bus_count'].dtype" - ] - }, - { - "cell_type": "markdown", - "id": "58fd7f90-2935-4e58-92c6-4253758ad3c1", - "metadata": { - "tags": [] - }, - "source": [ - "#### project_type" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "ca77c135-1168-489e-802e-402c614bbb77", - "metadata": {}, - "outputs": [], - "source": [ - "# using str.lower() on project type\n", - "fta[\"project_type\"] = fta[\"project_type\"].str.lower().str.replace(\" \", \"\")\n", - "# using str.lower() on project type\n", - "# fta[\"project_type\"] = fta[\"project_type\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "2014394c-33d9-44e9-b162-ab21988d9e8d", - "metadata": {}, - "outputs": [], - "source": [ - "# some values still need to get adjusted. will use a short dictionary to fix\n", - "new_type = {\n", - " \"\\tbus/facility\": \"bus/facility\",\n", - " \"bus/facilitiy\": \"bus/facility\",\n", - " \"facilities\": \"facility\",\n", - "}\n", - "# using replace() with the dictionary to replace keys in project type col\n", - "fta.replace({\"project_type\": new_type}, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "id": "dd2125a9-1117-4baa-b704-1aad30249e6d", - "metadata": { - "tags": [] - }, - "source": [ - "#### `prop_type`" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "09750ef1-1eba-4561-b5ad-b05f2a3b5875", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_773/4065559258.py:3: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n", - " fta[\"prop_type\"] = fta[\"prop_type\"].str.replace(\")\", \"\").str.strip()\n" - ] - } - ], - "source": [ - "# clearning the bus desc/prop_type col.\n", - "# removing the )\n", - "fta[\"prop_type\"] = fta[\"prop_type\"].str.replace(\")\", \"\").str.strip()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "bba62d67-35ab-4334-898b-f7581b40c574", - "metadata": {}, - "outputs": [], - "source": [ - "# creating a dictionary to add spaces back to the values\n", - "spaces = {\n", - " \"beb\": \"BEB\",\n", - " \"estimated-CNGbuses\": \"estimated-CNG buses\",\n", - " \"cngbuses\": \"CNG buses\",\n", - " \"BEBs\": \"BEB\",\n", - " \"Electric\\n16(Hybrid\": \"15 electic, 16 hybrid\",\n", - " \"FuelCellElectric\": \"fuel cell electric\",\n", - " \"FuelCell\": \"fuel cell\",\n", - " \"lowemissionCNG\": \"low emission CNG\",\n", - " \"cng\": \"CNG\",\n", - " \"BEBsparatransitbuses\": \"BEBs paratransit buses\",\n", - " \"hybridelectric\": \"hybrid electric\",\n", - " \"zeroemissionbuses\": \"zero emission buses\",\n", - " \"dieselelectrichybrids\": \"diesel electric hybrids\",\n", - " \"hydrogenfuelcell\": \"hydrogen fuel cell\",\n", - " \"2BEBsand4HydrogenFuelCellBuses\": \"2 BEBs and 4 hydrogen fuel cell buses\",\n", - " \"4fuelcell/3CNG\": \"4 fuel cell / 3 CNG\",\n", - " \"hybridelectricbuses\": \"hybrid electric buses\",\n", - " \"CNGfueled\": \"CNG fueled\",\n", - " \"zeroemissionelectric\": \"zero emission electric\",\n", - " \"hybridelectrics\": \"hybrid electrics\",\n", - " \"dieselandgas\": \"diesel and gas\",\n", - " \"diesel-electrichybrids\": \"diesel-electric hybrids\",\n", - " \"propanebuses\": \"propane buses\",\n", - " \"1:CNGbus;2cutawayCNGbuses\": \"1:CNGbus ;2 cutaway CNG buses\",\n", - " \"zeroemission\": \"zero emission\",\n", - " \"propanedpoweredvehicles\": \"propaned powered vehicles\",\n", - "}\n", - "\n", - "# using new dictionary to replace values in the bus desc col\n", - "fta.replace({\"prop_type\": spaces}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "9bd26f13-146d-43cc-9a23-23032ac2b173", - "metadata": {}, - "outputs": [], - "source": [ - "# dict to validate prop_type values\n", - "prop_type_dict = {\n", - " \"15 electic, 16 hybrid\": \"mix (zero and low emission buses)\",\n", - " \"1:CNGbus ;2 cutaway CNG buses\": \"mix (zero and low emission buses)\",\n", - " \"2 BEBs and 4 hydrogen fuel cell buses\": \"mix (BEB and FCEB)\",\n", - " \"4 fuel cell / 3 CNG\": \"mix (zero and low emission buses)\",\n", - " \"BEBs paratransit buses\": \"BEB\",\n", - " \"CNG buses\": \"CNG\",\n", - " \"CNG fueled\": \"CNG\",\n", - " \"Electric\": \"electric (not specified)\",\n", - " \"battery electric\": \"BEB\",\n", - " \"diesel and gas\": \"mix (low emission)\",\n", - " \"diesel electric hybrids\": \"low emission (hybrid)\",\n", - " \"diesel-electric\": \"low emission (hybrid)\",\n", - " \"diesel-electric hybrids\": \"low emission (hybrid)\",\n", - " \"electric\": \"electric (not specified)\",\n", - " \"estimated-CNG buses\": \"CNG\",\n", - " \"estimated-cutaway vans (PM- award will not fund 68 buses\": \"mix (zero and low emission buses)\",\n", - " \"fuel cell\": \"FCEB\",\n", - " \"fuel cell electric\": \"FCEB\",\n", - " \"hybrid\": \"low emission (hybrid)\",\n", - " \"hybrid electric\": \"low emission (hybrid)\",\n", - " \"hybrid electric buses\": \"low emission (hybrid)\",\n", - " \"hybrid electrics\": \"low emission (hybrid)\",\n", - " \"hydrogen fuel cell\": \"FCEB\",\n", - " \"low emission CNG\": \"CNG\",\n", - " \"propane\": \"low emission (propane)\",\n", - " \"propane buses\": \"low emission (propane)\",\n", - " \"propaned powered vehicles\": \"low emission (propane)\",\n", - " \"zero emission\": \"zero-emission bus (not specified)\",\n", - " \"zero emission buses\": \"zero-emission bus (not specified)\",\n", - " \"zero emission electric\": \"zero-emission bus (not specified)\",\n", - " \"zero-emission\": \"zero-emission bus (not specified)\",\n", - "}\n", - "\n", - "# repalcing values in prop type with prop type dictionary\n", - "fta.replace({\"prop_type\": prop_type_dict}, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "id": "dc3aacbb-0dcb-4bb0-9ea7-6c2fe1d75b49", - "metadata": {}, - "source": [ - "### fix `prop_type == needs manual check`\n", - "\n", - "- subset a df of only prop type == needs manual check\n", - "- create list of keywords to check prop type\n", - "- create function to replace `needs manualc check` values with list values\n", - "- then... do something with both dataframes? \n", - " * remove rows with `needs manual check`\n", - " * then append subset df to initial df?\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "580e9c87-9aae-4221-bc29-251e4e1469be", - "metadata": {}, - "outputs": [], - "source": [ - "# subdf of just `needs manual check` prop_types\n", - "manual_check = fta[fta[\"prop_type\"] == \"needs manual check\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "9ea7203e-c44f-4b02-a6da-31e7dea885c9", - "metadata": {}, - "outputs": [], - "source": [ - "# function to match keywords to list\n", - "def prop_type_finder(description):\n", - " for keyword in manual_checker_list:\n", - " if keyword in description:\n", - " return keyword\n", - " return \"no bus procurement\"" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "3e66c103-bec0-4925-9a71-b446f6931c33", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_773/3261224827.py:40: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " manual_check[\"prop_type\"] = manual_check[\"description\"].apply(prop_type_finder)\n" - ] - } - ], - "source": [ - "manual_checker_list = [\n", - " \"propane-powered\",\n", - " \"hybrid diesel-electric buses\",\n", - " \"propane fueled buses\",\n", - " \"cutaway vehicles\",\n", - " \"diesel-electric hybrid\",\n", - " \"low or no emission buses\",\n", - " \"electric buses\",\n", - " \"hybrid-electric vehicles\",\n", - " \"electric commuter\",\n", - " \"Electric Buses\",\n", - " \"battery electric\",\n", - " \"Batery Electric\",\n", - " \"battery-electric\",\n", - " \"fuel-cell\",\n", - " \"fuel cell\",\n", - " \"Fuel Cell\",\n", - " \"zero emission\",\n", - " \"Zero Emission\",\n", - " \"zero-emission electric buses\",\n", - " \"zero-emission buses\",\n", - " \"zero‐emission\",\n", - " \"zero-emission\",\n", - " \"zeroemission\",\n", - " \"CNG\",\n", - " \"cng\",\n", - " \"County Mass Transit District will receive funding to buy buses\",\n", - " \"Colorado will receive funding to buy vans to replace older ones\",\n", - " \"ethanol-fueled buses\",\n", - " \"will receive funding to buy vans to replace\",\n", - " \"funding to replace the oldest buses\",\n", - " \"to buy buses and charging equipment\",\n", - " \"counties by buying buses\",\n", - " \"receive funding to buy cutaway paratransit buses\",\n", - " \"new replacement vehicles\",\n", - "]\n", - "\n", - "# creates a new column called 'prop_type' by applying function to description column. \n", - "# the function will check the values against the description col against the list, then return the keyword the row matched too\n", - "manual_check[\"prop_type\"] = manual_check[\"description\"].apply(prop_type_finder)" - ] - }, - { - "cell_type": "markdown", - "id": "eb4126ed-30a6-4d36-9aa4-4edcdd3d3606", - "metadata": {}, - "source": [ - "### use dictionary to change manual_check prop_type values to match validated values" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "347a856d-84d4-420e-b9ca-5a57c784a9ec", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_773/3041575545.py:25: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " manual_check.replace({\"prop_type\": manual_check_dict}, inplace=True)\n" - ] - } - ], - "source": [ - "manual_check_dict= {'zero emission': 'zero-emission bus (not specified)',\n", - " 'electric buses':'electric (not specified)',\n", - " 'zero-emission': 'zero-emission bus (not specified)',\n", - " 'low or no emission buses' : 'mix (zero and low emission buses)',\n", - " 'zero-emission buses': 'zero-emission bus (not specified)',\n", - " 'new replacement vehicles':'not specified',\n", - " 'receive funding to buy cutaway paratransit buses': 'not specified',\n", - " 'counties by buying buses': 'not specified',\n", - " 'battery-electric' : 'BEB',\n", - " 'to buy buses and charging equipment':'not specified',\n", - " 'propane-powered': 'low emission (propane)',\n", - " 'funding to replace the oldest buses':'not specified',\n", - " 'diesel-electric hybrid': 'low emission (hybrid)',\n", - " 'hybrid diesel-electric buses': 'low emission (hybrid)',\n", - " 'cutaway vehicles':'not specified',\n", - " 'propane fueled buses': 'low emission (propane)',\n", - " 'County Mass Transit District will receive funding to buy buses':'not specified',\n", - " 'ethanol-fueled buses': 'low emission (ethanol)',\n", - " 'will receive funding to buy vans to replace': 'not specified',\n", - " 'Colorado will receive funding to buy vans to replace older ones': 'not specified',\n", - " 'hybrid-electric vehicles': 'low emission (hybrid)'\n", - "}\n", - "\n", - "# replace prop_type values using manual_check_dict\n", - "manual_check.replace({\"prop_type\": manual_check_dict}, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "id": "d0ab5f47-a5b9-42a4-8c8d-c7c7be793ad8", - "metadata": {}, - "source": [ - "### deleting rows from iniail df that have prop_type == 'needs manual check'" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "cf13bebf-ae89-477b-9ee7-f9a5d8d922e9", - "metadata": {}, - "outputs": [], - "source": [ - "# filters df for rows that do not equal `needs manual check`\n", - "# expect rows to drop from 130 to 72?\n", - "fta = fta[fta['prop_type'] != 'needs manual check']" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "11fdb527-0c74-4c4b-a9dd-54679e8cb744", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_773/3394426208.py:2: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " fta = fta.append(manual_check, ignore_index=True)\n" - ] - } - ], - "source": [ - "### appending rows from manual_check to initial df\n", - "fta = fta.append(manual_check, ignore_index=True)" - ] - }, - { - "cell_type": "markdown", - "id": "8a498a0b-4552-4c38-90cd-6e1c4045d40d", - "metadata": { - "tags": [] - }, - "source": [ - "### Need new column for `bus size type` via list and function\n", - "cutaway, 40ft etc" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "6053159a-62b1-4866-a13e-47fabf4576b4", - "metadata": {}, - "outputs": [], - "source": [ - "bus_size = [\n", - " \"standard\",\n", - " \"40 foot\",\n", - " \"40-foot\",\n", - " \"40ft\",\n", - " \"articulated\",\n", - " \"cutaway\",\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "3500afae-6358-435f-9d25-1854b9f16634", - "metadata": {}, - "outputs": [], - "source": [ - "# Function to match keywords\n", - "def find_bus_size_type(description):\n", - " for keyword in bus_size:\n", - " if keyword in description.lower():\n", - " return keyword\n", - " return \"not specified\"" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "b7a0ae00-3a85-4e95-ae94-7ffeb55c3a8d", - "metadata": {}, - "outputs": [], - "source": [ - "# new column called bus size type based on description column\n", - "fta[\"bus_size_type\"] = fta[\"description\"].apply(find_bus_size_type)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "75ec2282-9c0b-4640-9741-f3363c233b74", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([100, 90, 20, 40, 35, 16, 30, 31, 23, 7, 25, 13, 4,\n", - " 17, 39, 12, 37, 14, 50, 8, 6, 11, 56, 10, 9, 5,\n", - " 15, 2, 3, 1, 0, 69, 18, 160, 134, 42])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#fix bus_count col\n", - "fta['bus_count'].unique()" - ] - }, - { - "cell_type": "markdown", - "id": "9a83f537-ccff-44c2-8b41-82b2c88b9ccd", - "metadata": { - "tags": [] - }, - "source": [ - "## Exporting cleaned data to GCS" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "609a3659-8bf2-4412-aabc-d6ea956c3bbe", - "metadata": {}, - "outputs": [], - "source": [ - "# saving to GCS as csv\n", - "\n", - "clean_file = 'fta_bus_cost_clean.parquet'\n", - "\n", - "fta.to_parquet(gcs_path+clean_file)" - ] - }, - { - "cell_type": "markdown", - "id": "8a04ee87-fba7-46df-ac67-44956fa82c7c", - "metadata": { - "tags": [] - }, - "source": [ - "## Reading in cleaned data from GCS" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0886453f-e9d5-46d8-9987-3365137137b2", - "metadata": {}, - "outputs": [], - "source": [ - "bus_cost = pd.read_csv(\n", - " \"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/fta_bus_cost_clean.csv\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8a4e9841-79f5-41e1-9c53-3def3d802bd9", - "metadata": {}, - "outputs": [], - "source": [ - "# confirming cleaned data shows as expected.\n", - "display(bus_cost.shape, type(bus_cost), bus_cost.columns)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f95f7ae2-2ddf-40b7-aa76-af00e83854d1", - "metadata": {}, - "outputs": [], - "source": [ - "bus_cost[\"prop_type\"].sort_values(ascending=True).unique()" - ] - }, - { - "cell_type": "markdown", - "id": "1ede25fd-850a-4be6-bac0-a1bffa05b776", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "## DEPRECATED - Data Analysis\n", - "actual data analysis and summary stats exist in the `cost_per_bus_analysis.ipynb` notebook" - ] - }, - { - "cell_type": "markdown", - "id": "42ca0dfd-2ea1-4194-b431-0e4853d21879", - "metadata": { - "tags": [] - }, - "source": [ - "### Cost per Bus, per Transit Agency dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "26485b67-dc48-4102-a835-96460649d8ff", - "metadata": {}, - "outputs": [], - "source": [ - "only_bus = bus_cost[bus_cost[\"bus_count\"] > 0]\n", - "only_bus.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a8f05625-7da1-4aea-ac71-c177d02ca008", - "metadata": {}, - "outputs": [], - "source": [ - "cost_per_bus = (\n", - " only_bus.groupby(\"project_sponsor\")\n", - " .agg({\"funding\": \"sum\", \"bus_count\": \"sum\"})\n", - " .reset_index()\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c3ab90a5-5e7f-4ab6-8ecc-a7598e185f5b", - "metadata": {}, - "outputs": [], - "source": [ - "cost_per_bus[\"cost_per_bus\"] = (\n", - " cost_per_bus[\"funding\"] / cost_per_bus[\"bus_count\"]\n", - ").astype(\"int64\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ce71b957-ea77-40c8-95de-d6f160462f58", - "metadata": {}, - "outputs": [], - "source": [ - "cost_per_bus.dtypes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b6f9ca72-4977-4536-9e6b-828af63c23b1", - "metadata": {}, - "outputs": [], - "source": [ - "cost_per_bus" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f02f391a-920c-458a-95e0-fd7875e6a653", - "metadata": {}, - "outputs": [], - "source": [ - "## export cost_per_bus df to gcs\n", - "cost_per_bus.to_csv(\n", - " \"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/fta_cost_per_bus.csv\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "79e18bf0-3dff-4263-9b47-8f2e4ee3d2e8", - "metadata": { - "tags": [] - }, - "source": [ - "### Cost per bus, stats analysis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ec98703-78f8-4c1c-b5c3-5a4834b1b44f", - "metadata": {}, - "outputs": [], - "source": [ - "# read in fta cost per bus csv\n", - "cost_per_bus = pd.read_csv(\n", - " \"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/fta_cost_per_bus.csv\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6aebe6c0-eeb6-4da3-88ce-79f1fe7f5f6f", - "metadata": {}, - "outputs": [], - "source": [ - "display(cost_per_bus.shape, cost_per_bus.head())" - ] - }, - { - "cell_type": "markdown", - "id": "299d75c2-2f1a-4a7d-a1ee-87271a1f9e4b", - "metadata": { - "tags": [] - }, - "source": [ - "### Initial Summary Stats" - ] - }, - { - "cell_type": "markdown", - "id": "c4f9c488-8306-4eb3-bac0-40c75ac1dfed", - "metadata": { - "tags": [] - }, - "source": [ - "### Summary Stats" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "76e7e2f6-47c8-4423-a9b3-2f02ee06fb9f", - "metadata": {}, - "outputs": [], - "source": [ - "# top level alanysis\n", - "\n", - "bus_cost.agg({\"project_title\": \"count\", \"funding\": \"sum\", \"bus_count\": \"sum\"})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a1c0bdc5-9b63-40bd-94b7-2aa04b9bc70a", - "metadata": {}, - "outputs": [], - "source": [ - "# start of agg. by project_type\n", - "\n", - "bus_cost.groupby(\"project_type\").agg(\n", - " {\"project_type\": \"count\", \"funding\": \"sum\", \"bus_count\": \"sum\"}\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f036cf52-fcc7-4f43-b11a-79a75515efc3", - "metadata": {}, - "outputs": [], - "source": [ - "# agg by program\n", - "\n", - "bus_cost.groupby(\"bus/low-no_program\").agg(\n", - " {\"project_type\": \"count\", \"funding\": \"sum\", \"bus_count\": \"sum\"}\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "228da8c2-9b22-4d1c-9259-4417e9374309", - "metadata": {}, - "outputs": [], - "source": [ - "# agg by state, by funding\n", - "bus_cost.groupby(\"state\").agg(\n", - " {\"project_type\": \"count\", \"funding\": \"sum\", \"bus_count\": \"sum\"}\n", - ").sort_values(by=\"funding\", ascending=False)" - ] - }, - { - "cell_type": "markdown", - "id": "e85e540e-0396-49b0-9f2e-64e5236e63e8", - "metadata": { - "tags": [] - }, - "source": [ - "### Projects with bus purchases" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8bad1e5d-9d05-49ef-ab21-81b98d80ef75", - "metadata": {}, - "outputs": [], - "source": [ - "# df of only projects with a bus count\n", - "only_bus = bus_cost[bus_cost[\"bus_count\"] > 0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6c5013e3-ed56-4ea6-b191-af9a8e797b4b", - "metadata": {}, - "outputs": [], - "source": [ - "display(only_bus.shape, only_bus.columns)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e93b7fe9-49e8-475d-aa89-c085f6792978", - "metadata": {}, - "outputs": [], - "source": [ - "# agg by propulsion type\n", - "only_bus[\"propulsion_type\"].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dc7cc8c9-f81a-43c1-bb62-e8e2467358ce", - "metadata": {}, - "outputs": [], - "source": [ - "only_bus.project_type.value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d86ee60d-e3e8-4dea-880f-731f6ac64376", - "metadata": {}, - "outputs": [], - "source": [ - "# of the rows with bus_count >1, what are the project types?\n", - "bus_agg = only_bus.groupby(\"project_type\").agg(\n", - " {\"project_type\": \"count\", \"funding\": \"sum\", \"bus_count\": \"sum\"}\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f7afb2df-374e-4ee2-b99c-6959d2111a37", - "metadata": {}, - "outputs": [], - "source": [ - "# new column that calculates `cost per bus`\n", - "bus_agg[\"cost_per_bus\"] = (bus_agg[\"funding\"] / bus_agg[\"bus_count\"]).astype(\"int64\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f8b1c63c-9763-4d24-9613-433aefd8e4bf", - "metadata": {}, - "outputs": [], - "source": [ - "bus_agg" - ] - }, - { - "cell_type": "markdown", - "id": "211cb7b6-8fb1-4d52-890a-7106afb981a0", - "metadata": { - "tags": [] - }, - "source": [ - "### Projects with no buses" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e018dd24-4c96-4d1b-ad48-d67d706d165b", - "metadata": {}, - "outputs": [], - "source": [ - "no_bus = bus_cost[bus_cost[\"bus_count\"] < 1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c9ce24e3-5366-4cb5-9f92-4d24b50493e4", - "metadata": {}, - "outputs": [], - "source": [ - "no_bus[\"project_type\"].value_counts()" - ] - }, - { - "cell_type": "markdown", - "id": "ac02fbbb-2a88-486f-8001-fd8156c50bfb", - "metadata": { - "tags": [] - }, - "source": [ - "### Overall Summary" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6ad1b9f5-a5c7-4fee-abb2-cb74dee4ab40", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "project_count = bus_cost.project_title.count()\n", - "fund_sum = bus_cost.funding.sum()\n", - "bus_count_sum = bus_cost.bus_count.sum()\n", - "overall_cost_per_bus = (fund_sum) / (bus_count_sum)\n", - "bus_program_count = bus_cost[\"bus/low-no_program\"].value_counts()\n", - "\n", - "projects_with_bus = only_bus.project_title.count()\n", - "projects_with_bus_funds = only_bus.funding.sum()\n", - "cost_per_bus = (only_bus.funding.sum()) / (bus_count_sum)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7e932b1e-f5e5-4215-91b5-4c922e78062a", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "summary = f\"\"\"\n", - "Top Level observation:\n", - "- {project_count} projects awarded\n", - "- ${fund_sum:,.2f} dollars awarded\n", - "- {bus_count_sum} buses to be purchased\n", - "- ${overall_cost_per_bus:,.2f} overall cost per bus\n", - "\n", - "Projects have some mix of buses, facilities and equipment. Making it difficult to disaggregate actual bus cost.\n", - "\n", - "Of the {project_count} projects awarded, {projects_with_bus} projects inlcuded buses. The remainder were facilities, chargers and equipment\n", - "\n", - "Projects with buses purchases:\n", - "- {projects_with_bus} projects\n", - "- ${projects_with_bus_funds:,.2f} awarded to purchases buses\n", - "- ${cost_per_bus:,.2f} cost per bus\n", - "\"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dfb95588-9083-4298-8822-460c2dad9941", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "print(summary)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2429adb2-fdfe-4a0f-a7c5-8ed2035f6fad", - "metadata": {}, - "outputs": [], - "source": [ - "# Assuming your DataFrame is named df\n", - "cost_per_bus_values = cost_per_bus[\"cost_per_bus\"]\n", - "\n", - "# Calculate mean and standard deviation\n", - "mean_value = cost_per_bus_values.mean()\n", - "std_deviation = cost_per_bus_values.std()\n", - "\n", - "# Plot histogram\n", - "plt.hist(cost_per_bus_values, bins=30, color=\"skyblue\", edgecolor=\"black\", alpha=0.7)\n", - "\n", - "# Add vertical lines for mean and standard deviation\n", - "plt.axvline(mean_value, color=\"red\", linestyle=\"dashed\", linewidth=2, label=\"Mean\")\n", - "plt.axvline(\n", - " mean_value + std_deviation,\n", - " color=\"green\",\n", - " linestyle=\"dashed\",\n", - " linewidth=2,\n", - " label=\"Mean + 1 Std Dev\",\n", - ")\n", - "plt.axvline(\n", - " mean_value - std_deviation,\n", - " color=\"green\",\n", - " linestyle=\"dashed\",\n", - " linewidth=2,\n", - " label=\"Mean - 1 Std Dev\",\n", - ")\n", - "\n", - "# Set labels and title\n", - "plt.xlabel(\"cost_per_bus\")\n", - "plt.ylabel(\"Frequency\")\n", - "plt.title(\"Histogram of cost_per_bus with Mean and Std Dev Lines\")\n", - "plt.legend()\n", - "\n", - "# Show the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c3b5a48f-804e-4525-98fc-39d3fa845f6f", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import matplotlib.ticker as mticker\n", - "import pandas as pd\n", - "\n", - "# Assuming your DataFrame is named df\n", - "cost_per_bus_values = cost_per_bus[\"cost_per_bus\"]\n", - "\n", - "# Calculate mean and standard deviation\n", - "mean_value = cost_per_bus_values.mean()\n", - "std_deviation = cost_per_bus_values.std()\n", - "\n", - "# Plot histogram\n", - "plt.hist(cost_per_bus_values, bins=20, color=\"skyblue\", edgecolor=\"black\", alpha=0.7)\n", - "\n", - "# Add vertical lines for mean and standard deviation\n", - "plt.axvline(mean_value, color=\"red\", linestyle=\"dashed\", linewidth=2, label=\"Mean\")\n", - "plt.axvline(\n", - " mean_value + std_deviation,\n", - " color=\"green\",\n", - " linestyle=\"dashed\",\n", - " linewidth=2,\n", - " label=\"Mean + 1 Std Dev\",\n", - ")\n", - "plt.axvline(\n", - " mean_value - std_deviation,\n", - " color=\"green\",\n", - " linestyle=\"dashed\",\n", - " linewidth=2,\n", - " label=\"Mean - 1 Std Dev\",\n", - ")\n", - "\n", - "# Set labels and title\n", - "plt.xlabel(\"Cost per Bus (USD)\")\n", - "plt.ylabel(\"Frequency\")\n", - "plt.title(\"Histogram of Cost per Bus with Mean and Std Dev Lines\")\n", - "plt.legend()\n", - "\n", - "# Format x-axis ticks as USD\n", - "plt.gca().xaxis.set_major_formatter(mticker.StrMethodFormatter(\"${x:,.0f}\"))\n", - "\n", - "# Show the plot\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/bus_procurement_cost/OLD_cost_per_bus.ipynb b/bus_procurement_cost/OLD_cost_per_bus.ipynb deleted file mode 100644 index 8443f10df..000000000 --- a/bus_procurement_cost/OLD_cost_per_bus.ipynb +++ /dev/null @@ -1,388 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "411091ad-2d69-485a-9b08-c009d5566940", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from fta_data_cleaner import gcs_path\n", - "from cost_per_bus_utils import *" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "81da1fc0-17e9-4623-a7cf-d163f6ca1eee", - "metadata": {}, - "outputs": [], - "source": [ - "pd.set_option('display.max_rows', None)\n", - "pd.set_option('display.max_columns', None)\n", - "pd.set_option('display.max_colwidth', None)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "4548178c-1d1b-4128-8cc8-42a3837892ea", - "metadata": {}, - "outputs": [], - "source": [ - "all_bus = pd.read_parquet(\n", - " \"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/cpb_analysis_data_merge.parquet\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b9aded29-aa16-4bcb-8b1e-4a6c95ca952c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "89" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(all_bus)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "287c09d4-0df4-4be0-880b-94bb863b2122", - "metadata": {}, - "outputs": [], - "source": [ - "all_bus[\"source\"].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b800c7b2-d467-4291-adbb-940a8240ad47", - "metadata": {}, - "outputs": [], - "source": [ - "# need initial dataset numbers?\n", - "# ## projects from FTA, ## projects from TICP, ## projects from dgs\n", - "# of which, only ## were bus only projects \n", - "\n", - "all_fta = \"fta_all_projects_clean.parquet\"\n", - "all_tircp = \"clean_tircp_project.parquet\"\n", - "all_dgs = \"dgs_agg_clean.parquet\"\n", - "\n", - "#data going into notebook\n", - "fta_bus_data = \"fta_bus_cost_clean.parquet\"\n", - "tircp_bus_data = \"clean_tircp_project_bus_only.parquet\"\n", - "dgs_bus_data = \"dgs_agg_w_options_clean.parquet\" #cost of bus + options.\n", - "\n", - "fta1 = pd.read_parquet(f\"{gcs_path}{all_fta}\")\n", - "tircp1 = pd.read_parquet(f\"{gcs_path}{all_tircp}\")\n", - "dgs1 = pd.read_parquet(f\"{gcs_path}{all_dgs}\")\n", - "\n", - "fta2 = pd.read_parquet(f\"{gcs_path}{fta_bus_data}\")\n", - "tircp2 = pd.read_parquet(f\"{gcs_path}{tircp_bus_data}\")\n", - "dgs2 = pd.read_parquet(f\"{gcs_path}{dgs_bus_data}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "703a1753-a828-438b-92c3-2c945dadf785", - "metadata": {}, - "outputs": [], - "source": [ - "display(\n", - " len(fta1),\n", - " len(fta2),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "83e34c14-1e08-4aa4-8d72-2c3c454516d2", - "metadata": {}, - "outputs": [], - "source": [ - "display(\n", - " len(tircp1),\n", - " len(tircp2)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "80dac111-63c3-462e-851a-b0d424ba1019", - "metadata": {}, - "outputs": [], - "source": [ - "len(fta1)+len(tircp1)+len(dgs1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a974fda7-b6ab-4806-a4ef-3077bfcc1beb", - "metadata": {}, - "outputs": [], - "source": [ - "display(\n", - " len(dgs1),\n", - " len(dgs2)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1ec995ef-9ea4-47c4-8191-4c52c6d7b946", - "metadata": {}, - "outputs": [], - "source": [ - "display(\n", - " dgs1[\"source\"].value_counts(),\n", - " dgs2[\"source\"].value_counts()\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a4f560b-4fdf-4fae-b7fe-e20e019082cf", - "metadata": {}, - "outputs": [], - "source": [ - "display(\n", - " dgs1[dgs1[\"source\"] == \"17c\"].sort_values(by = \"ordering_agency_name\"),\n", - " dgs2[dgs2[\"source\"] == \"17c\"].sort_values(by = \"ordering_agency_name\")\n", - ")\n", - "# dgs2 had duplicate agencies \n", - "# overall, use dgs1 (dgs_agg_clean.parquet) to get count of dgs projects" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c23d567a-e71f-48c0-b5e7-005cc18819f1", - "metadata": {}, - "outputs": [], - "source": [ - "#so final count of total projects is \n", - "# fta all projects, tircp all projects, dgs agg clean\n", - "\n", - "\n", - "all_fta = pd.read_parquet(\"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/fta_all_projects_clean.parquet\")\n", - "all_tircp = pd.read_parquet(\"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/clean_tircp_project.parquet\")\n", - "all_dgs = pd.read_parquet(\"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/dgs_agg_clean.parquet\")\n", - "\n", - "count_all_fta = len(pd.read_parquet(\"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/fta_all_projects_clean.parquet\"))\n", - "count_all_tircp = len(pd.read_parquet(\"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/clean_tircp_project.parquet\"))\n", - "count_all_dgs = len(pd.read_parquet(\"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/dgs_agg_clean.parquet\"))\n", - "\n", - "count_all_projects = count_all_fta+count_all_tircp+count_all_dgs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "718a5c84-1c78-40a5-9651-24034fa22fd0", - "metadata": {}, - "outputs": [], - "source": [ - "count_of_all_projects" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "f846fe68-f87b-4eda-b098-f5d07b21215a", - "metadata": {}, - "outputs": [], - "source": [ - "def all_project_counter(fta_file: str, tircp_file:str, dgs_file: str) -> int:\n", - " \"\"\"\n", - " function to count all the projects from fta, tircp and dgs files.\n", - " use to find the total number of projects and the total number of bus only projects\n", - " \"\"\"\n", - " gcs_path = \"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/\"\n", - "\n", - " \n", - " all_fta = len(pd.read_parquet(f\"{gcs_path}{fta_file}\"))\n", - " all_tircp = len(pd.read_parquet(f\"{gcs_path}{tircp_file}\"))\n", - " all_dgs = len(pd.read_parquet(f\"{gcs_path}{dgs_file}\"))\n", - " \n", - " count_all_projects = all_fta+all_tircp+all_dgs\n", - " \n", - " return count_all_projects" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "32adf818-b4ed-40b4-ac52-298e97c1842e", - "metadata": {}, - "outputs": [], - "source": [ - "all_project_count = all_project_counter(\n", - " fta_file = \"fta_all_projects_clean.parquet\",\n", - " tircp_file = \"clean_tircp_project.parquet\",\n", - " dgs_file = \"dgs_agg_clean.parquet\"\n", - ")\n", - "\n", - "bus_only_project_count = all_project_counter(\n", - " fta_file = \"fta_bus_cost_clean.parquet\",\n", - " tircp_file = \"clean_tircp_project_bus_only.parquet\",\n", - " dgs_file = \"dgs_agg_clean.parquet\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "9f8aedf5-c69e-4be7-ad4d-bfa796c3d5f6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "289" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "87" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(all_project_count,\n", - " bus_only_project_count\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7a2b20d7-4e9a-413e-9c73-077027f89465", - "metadata": {}, - "outputs": [], - "source": [ - "all_fta = \"fta_all_projects_clean.parquet\"\n", - "all_tircp = \"clean_tircp_project.parquet\"\n", - "all_dgs = \"dgs_agg_clean.parquet\"" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "c39e1bb0-306f-4a21-8d17-e3775a804be7", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "bus_only_count_fta = len(pd.read_parquet(\"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/fta_bus_cost_clean.parquet\"))\n", - "bus_only_count_tircp = len(pd.read_parquet(\"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/clean_tircp_project_bus_only.parquet\"))\n", - "bus_only_count_dgs = len(pd.read_parquet(\"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/dgs_agg_clean.parquet\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "6b1aa66f-da1a-4155-b2d7-b4424d1bc7c7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "43" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "9" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "35" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(\n", - " bus_only_fta,\n", - " bus_only_tircp,\n", - " bus_only_dgs\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "32068f95-df74-40e4-a4af-dc7719f22fc7", - "metadata": {}, - "source": [ - "## FIX NUMBER OF BUSES" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "02065103-933e-4ab5-a43a-2954213ebdc1", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/bus_procurement_cost/OLD_cost_per_bus_nb_scripts.py b/bus_procurement_cost/OLD_cost_per_bus_nb_scripts.py deleted file mode 100644 index 1380d2635..000000000 --- a/bus_procurement_cost/OLD_cost_per_bus_nb_scripts.py +++ /dev/null @@ -1,455 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import seaborn as sns -import shared_utils -from matplotlib.ticker import ScalarFormatter -from scipy.stats import zscore - -def overall_cpb(df: pd.DataFrame) -> pd.DataFrame: - """ - function to calculate cpb on overall dataframe. - """ - # copy of df - df1 = df.copy() - - # add new column for cost per bus (cpb) - df1['cpb'] = (df1['total_cost'] / df1['bus_count']).astype("int64") - - return df1 - -def get_zscore(df: pd.DataFrame) -> pd.DataFrame: - """ - seperate function to calculate zscore. - """ - # add new column for z-score - df1 = df.copy() - - df1["zscore_cost_per_bus"] = zscore(df1["cpb"]) - - return df1 - -def remove_outliers(df: pd.DataFrame, zscore_col: int) -> pd.DataFrame: - """ - function to remove zscore outliers from data. - keeps rows with ascore -3>x<3 - - """ - df1 = df[ - (df[zscore_col] >= -3) & (df[zscore_col] <= 3) - ] - return df1 - -def cpb_zscore_outliers(df: pd.DataFrame) -> pd.DataFrame: - """ - function that calculated cost per bus col, z-score col, then removes outliers(remove rows with zscore >3) - """ - df = overall_cpb(df) - df = get_zscore(df) - df1 = remove_outliers(df, "zscore_cost_per_bus") - - return df1 - - -def cpb_aggregate(df: pd.DataFrame, column: str) -> pd.DataFrame: - """ - function to aggregate compiled data by different categories (transit agency, propulsion type, size type). - aggregate on columns: - "project_title" - "ppno" - "total_cost" - "bus_count" - - Then, cost per bus is calculated AFTER the aggregation. - """ - df_agg = ( - df.groupby(column) - .agg( - total_project_count=("project_title", "count"), - total_project_count_ppno=("ppno", "count"), - total_agg_cost=("total_cost", "sum"), - total_bus_count=("bus_count", "sum"), - ) - .reset_index() - ) - df_agg["cpb"] = (df_agg["total_agg_cost"] / df_agg["total_bus_count"]).astype("int64") - return df_agg - -def zeb_only_df(df: pd.DataFrame) -> pd.DataFrame: - """ - filters df to only show rows that are zero-emission buses (ZEB). - """ - zeb_list =[ - 'BEB', - #'CNG', - 'FCEB', - 'electric (not specified)', - #'ethanol', - #'low emission (hybrid)', - #'low emission (propane)', - #'mix (diesel and gas)', - #'mix (zero and low emission)', - #'not specified', - 'zero-emission bus (not specified)' - ] - df1 = df.copy() - - df1 = df1[df1["prop_type"].isin(zeb_list)] - - return df1 - -def non_zeb_only_df(df: pd.DataFrame) -> pd.DataFrame: - non_zeb_list =[ - 'CNG', - 'ethanol', - 'low emission (hybrid)', - 'low emission (propane)', - 'mix (diesel and gas)', - 'mix (zero and low emission)', - ] - - df1 = df.copy() - - df1 = df1[df1["prop_type"].isin(non_zeb_list)] - - return df1 - -def dist_curve( - df: pd.DataFrame, - mean: str, - std: str, - title: str, - xlabel: str, -): - """ - function to make distribution curve. uses the "cpb" column of the df. - """ - sns.histplot(df["cpb"], kde=True, color="skyblue", bins=20) - # mean line - plt.axvline( - mean, color="red", linestyle="dashed", linewidth=2, label=f"Mean: ${mean:,.2f}" - ) - # mean+1std - plt.axvline( - mean + std, - color="green", - linestyle="dashed", - linewidth=2, - label=f"Standard Deviation: ${std:,.2f}", - ) - plt.axvline(mean - std, color="green", linestyle="dashed", linewidth=2) - plt.axvline(mean + (std * 2), color="green", linestyle="dashed", linewidth=2) - plt.axvline(mean + (std * 3), color="green", linestyle="dashed", linewidth=2) - - plt.title(title + " with Mean and Standard Deviation") - plt.xlabel(xlabel) - plt.ylabel("Frequency") - - # Turn off scientific notation on x-axis? - plt.gca().xaxis.set_major_formatter(ScalarFormatter(useMathText=False)) - - plt.legend() - plt.show() - - return - -def make_chart(y_col: str, title: str, data: pd.DataFrame, x_col: str): - """ - function to create chart. sorts values by y_col ascending.""" - - data.sort_values(by=y_col, ascending=False).head(10).plot( - x=x_col, y=y_col, kind="bar", color="skyblue" - ) - plt.title(title) - plt.xlabel(x_col) - plt.ylabel(y_col) - - plt.ticklabel_format(style="plain", axis="y") - plt.show() - -### VARIABLES - -#INITIAL DF AGG VARIABLES -gcs_path = "gs://calitp-analytics-data/data-analyses/bus_procurement_cost/old/" - -# initial, overall df -all_bus = pd.read_parquet( - "gs://calitp-analytics-data/data-analyses/bus_procurement_cost/old/cpb_analysis_data_merge.parquet" -) - -# count of all projects from each source -def all_project_counter(fta_file: str, tircp_file:str, dgs_file: str) -> int: - """ - function to count all the projects from fta, tircp and dgs files. - use to find the total number of projects and the total number of bus only projects - """ - gcs_path = "gs://calitp-analytics-data/data-analyses/bus_procurement_cost/old/" - - - all_fta = len(pd.read_parquet(f"{gcs_path}{fta_file}")) - all_tircp = len(pd.read_parquet(f"{gcs_path}{tircp_file}")) - all_dgs = len(pd.read_parquet(f"{gcs_path}{dgs_file}")) - - count_all_projects = all_fta+all_tircp+all_dgs - - return count_all_projects - -all_project_count = all_project_counter( - fta_file = "fta_all_projects_clean.parquet", - tircp_file = "clean_tircp_project.parquet", - dgs_file = "dgs_agg_clean.parquet" -) - -bus_only_project_count = all_project_counter( - fta_file = "fta_bus_cost_clean.parquet", - tircp_file = "clean_tircp_project_bus_only.parquet", - dgs_file = "dgs_agg_clean.parquet" -) - -#count of all bus only projects per dataset -bus_only_count_fta = len(pd.read_parquet( - f"{gcs_path}fta_bus_cost_clean.parquet")) -bus_only_count_tircp = len(pd.read_parquet( - f"{gcs_path}clean_tircp_project_bus_only.parquet")) -bus_only_count_dgs = len(pd.read_parquet( - f"{gcs_path}dgs_agg_clean.parquet")) - -#count of all projects per dataset -count_all_fta = len(pd.read_parquet(f"{gcs_path}fta_all_projects_clean.parquet")) -count_all_tircp = len(pd.read_parquet(f"{gcs_path}clean_tircp_project.parquet")) -count_all_dgs = len(pd.read_parquet(f"{gcs_path}dgs_agg_clean.parquet")) - -# Variables -all_bus_only_projects = len(all_bus) -total_bus_count = sum(all_bus.bus_count) -total_funding = sum(all_bus.total_cost) - - - -## ALL BUS - -# initial df with cpb col -#all_bus_cpb = overall_cpb(all_bus) - -# get zscore -#cpb_zscore = get_zscore(all_bus_cpb) - -# initial df with cpb/zscore, remove outliers -no_outliers = cpb_zscore_outliers(all_bus) - - -# aggregate by transit agency -agency_agg = cpb_aggregate(no_outliers, "transit_agency") - -# aggregate by prop type -prop_agg = cpb_aggregate(no_outliers, "prop_type") - -# aggregate by bus size -size_agg = cpb_aggregate(no_outliers, "bus_size_type") - -min_bus_cost = no_outliers.cpb.min() -max_bus_cost = no_outliers.cpb.max() -max_bus_count = no_outliers.bus_count.max() - - -#how many zeb and non-zeb bus -zeb_list =[ - "BEB", - "FCEB", - "electric (not specified)", - "zero-emission bus (not specified)", -] - -non_zeb_list =[ - "CNG", - "ethanol", - "low emission (hybrid)", - "low emission (propane)", - "mix (zero and low emission)", -] -just_zeb_count = prop_agg[prop_agg["prop_type"].isin(zeb_list)]["total_bus_count"].sum() -just_non_zeb_count = prop_agg[prop_agg["prop_type"].isin(non_zeb_list)]["total_bus_count"].sum() - - -# VARIABLES -cpb_mean = no_outliers.cpb.mean() -cpb_std = no_outliers.cpb.std() - -# agency with highest bus count -agency_with_most_bus = no_outliers.loc[ - no_outliers["bus_count"].idxmax(), "transit_agency" -] - -# propulsion type max count and name -prop_type_name_max_freq = no_outliers["prop_type"].value_counts().idxmax() -prop_type_max = no_outliers["prop_type"].value_counts().max() - -# prop type min count and anme -prop_type_name_min_freq = no_outliers["prop_type"].value_counts().idxmin() -prop_type_min = no_outliers["prop_type"].value_counts().min() - -# how many buses do they have? already answered -agency_with_highest_funds = no_outliers.loc[ - all_bus["total_cost"].idxmax(), "transit_agency" -] - -# what is the highest amount? already answered -agency_max_cpb = no_outliers.loc[no_outliers["cpb"].idxmax(), "transit_agency"] -agency_min_cpb = no_outliers.loc[no_outliers["cpb"].idxmin(), "transit_agency"] -prop_type_max_cpb = no_outliers.loc[no_outliers["cpb"].idxmax(), "prop_type"] -prop_type_min_cpb = no_outliers.loc[no_outliers["cpb"].idxmin(), "prop_type"] - -## ZEB ONLY VARIABLES - -# zeb only df -zeb_only = zeb_only_df(all_bus) - -# calc cpb -#zeb_cpb = overall_cpb(zeb_only) - -# get cpb, zscore, remove outliers -zeb_no_outliers = cpb_zscore_outliers(zeb_only) - -# remove outliers -#zeb_no_outliers = remove_outliers(zeb_zscore, "zscore_cost_per_bus") - -# aggregate by transit agency -zeb_agency_agg = cpb_aggregate(zeb_no_outliers, "transit_agency") - -# aggregate by prop type -zeb_prop_agg = cpb_aggregate(zeb_no_outliers, "prop_type") - -# aggregate by bus size -zeb_size_agg = cpb_aggregate(zeb_no_outliers, "bus_size_type") - -# VARIABLES -zeb_count = len(zeb_no_outliers.prop_type) - -# zeb only, no outliers cpb curve -zeb_only_mean = zeb_no_outliers.cpb.mean() -zeb_only_std = zeb_no_outliers.cpb.std() - -## NON-ZEB VARIABLES - -# no zeb df -non_zeb_only = non_zeb_only_df(all_bus) - -# calc cpb -#non_zeb_cpb = overall_cpb(non_zeb_only) - -# get zscore -#non_zeb_zscore = get_zscore(non_zeb_cpb) - -# get cpb, zscore, remove outliers -non_zeb_no_outliers = cpb_zscore_outliers(non_zeb_only) - -# aggregate by transit agency -non_zeb_agency_agg = cpb_aggregate(non_zeb_no_outliers, "transit_agency") - -# aggregate by prop type -non_zeb_prop_agg = cpb_aggregate(non_zeb_no_outliers, "prop_type") - -# aggregate by bus size -non_zeb_size_agg = cpb_aggregate(non_zeb_no_outliers, "bus_size_type") - -# VARIABLES -non_zeb_count = len(non_zeb_no_outliers.prop_type) - -# non-zeb cpb mean and std dev -non_zeb_only_mean = non_zeb_no_outliers.cpb.mean() -non_zeb_only_std = non_zeb_no_outliers.cpb.std() - -# start summary narative -summary = f""" -## Summary -This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes. - -Data was compiled from three data sources: - -1. {count_all_fta} projects from FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data) -2. {count_all_tircp} projects TIRCP project data (state-funded, California only) -3. {count_all_dgs} projects DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. - -The compiled dataset includes **{all_project_count}** total transit related projects. However, the initial dataset included projects that encompassed bus procurement and other components such as charging installation and facility construction, as well as non-bus related projects (ferries, trains). The dataset was filtered to exclude projects that were not bus related, indicated 0 buses procured, and projects that contained construction/installation work. **{bus_only_project_count}** projects remained that specified the number of buses to procure and explicitly described procuring buses (bus only projects). - -Number of bus only contracts from each dataset -- FTA: **{bus_only_count_fta}** -- TIRCP: **{bus_only_count_tircp}** -- DGS: **{bus_only_count_dgs}** - - -The remaining bus only projects were categorized into different propulsion types and bus sizes, a “cost per bus” value was calculated, and outliers removed. - -A overall summary is provided below: -- Total projects: **298** -- Number of projects with mix bus procurement and other components, also non-bus projects: **204** -- Number of bus only projects: **{bus_only_project_count}** -- Total dollars awarded to bus only projects: **`${total_funding:,.2f}`** -- Total number of buses: **{total_bus_count}** -- Most common propulsion type procured for bus only projects: **{prop_type_name_max_freq}** at **{prop_type_max}** projects -- Number of ZEB buses* procured: **{just_zeb_count}** -- Number of non-ZEB buses** procured: **{just_non_zeb_count}** -- Overall average cost per bus (ZEB & non-ZEB) is `${cpb_mean:,.2f}` (std `${cpb_std:,.2f}`) -- ZEB average cost per bus is `${zeb_only_mean:,.2f}` (std `${zeb_only_std:,.2f}`) -- Non-ZEB average cost per bus is `${non_zeb_only_mean:,.2f}` (std `${non_zeb_only_std:,.2f}`) - -`*`ZEB buses include: zero-emission (not specified), electric (not specified), battery electric, fuel cell electric - -`**`Non-ZEB buses include: CNG, ethanol, low emission (hybrid, propane), diesel, gas. - - -Below are key charts that visualize more findings: - - -""" - -all_bus_desc = """ -## All buses (ZEB and non-ZEB) cost/bus distribution curve. -This chart shows the cost per bus distribution of all bus only projects. -""" - -# ZEB only, cpb distribution -zeb_desc = """ -## ZEB only cost/bus Distribution Chart. -Chart of projects with zero-emission, electric, battery electric, hydrogen fuel cell bus procurements. -""" - -# non-ZEB -non_zeb_desc = """ -## non-ZEB cost/bus Distribution. -Chart of projects with non-ZEB bus procurement (hybrids, diesel, cng) -This distrubtion is wider than the ZEB projects.""" - -#highest cpb agency -highest_cpb_desc = """ -## Highest Cost per Bus by Transit Agency -SFMTA is the agency with the highest cost per bus of all agencies in the analysis -""" - -# Highest awarded agency -highest_award = """ -## Most funds Awarded by Transit Agency -LA Metro was awarded almost double the next agency. Followed by SFMTA""" - -# most buses -most_bus = """ -## Highest Bus Count by Agency. -LA Metro plans to procure the most buses.""" - -#prop_type cpb -cpb_prop_type_desc = """ -## Cost per bus by propulsion type. -""" - -#prop_type bus coutn -bus_count_prop_type_desc = """ -## Bus count by propulsion type. -""" - -conclusion = """ -## Conclusion -Based on the findings so far in bus only projects, there is evidence that bus procurement cost vary widely amongst transit agencies all over the country. Non-ZEB bus cost variation was wide. Whereas ZEB cost variation was much tighter. However ZEBs do have a higher cost per bus than non-ZEB. - -Most of the bus only projects were for non-ZEBs. This can be explained by looking into the initial project list. Lots of projects that procured ZEBs also included the installation of chargers and related charging infrastructure. Indicating that transit agencies are still adopting and preparing for ZEBs and need to make the initial investment in the equipment. - -""" \ No newline at end of file diff --git a/bus_procurement_cost/OLD_dgs_usage_report_bus_analysis.ipynb b/bus_procurement_cost/OLD_dgs_usage_report_bus_analysis.ipynb deleted file mode 100644 index cb847b29b..000000000 --- a/bus_procurement_cost/OLD_dgs_usage_report_bus_analysis.ipynb +++ /dev/null @@ -1,813 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "d4cd3d09-86ee-439c-bb3d-081b369f48bd", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "from matplotlib.ticker import ScalarFormatter\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import shared_utils\n", - "from scipy.stats import zscore\n", - "\n", - "# set_option to increase max rows displayed to 200, to see entire df in 1 go/\n", - "pd.set_option(\"display.max_rows\", 200)\n", - "\n", - "# function to display df info\n", - "def df_peek(df):\n", - " display(type(df), df.shape, df.dtypes)" - ] - }, - { - "cell_type": "markdown", - "id": "a56fb4a4-2d26-4623-89d4-d4d73e6dc9c0", - "metadata": { - "tags": [] - }, - "source": [ - "## Read in Raw Data\n", - "17C and 17B via excels" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "3fc06efc-e6aa-42dd-945c-1719d27ba1a1", - "metadata": {}, - "outputs": [], - "source": [ - "GCS_FILE_PATH ='gs://calitp-analytics-data/data-analyses/bus_procurement_cost/'\n", - "file_17c = '17c compiled-Proterra Compiled Contract Usage Report .xlsx'\n", - "file_17b = '17b compiled.xlsx'\n", - "sheet_17c = 'Proterra '\n", - "sheet_17b = 'Usage Report Template'\n", - "\n", - "def get_data(path, file, sheet):\n", - " df = pd.read_excel(path + file, sheet_name=sheet)\n", - " \n", - " return df\n", - "\n", - "dgs_17c = get_data(GCS_FILE_PATH, file_17c, sheet_17c)\n", - "dgs_17b = get_data(GCS_FILE_PATH, file_17b, sheet_17b)\n" - ] - }, - { - "cell_type": "markdown", - "id": "6c03e147-68df-4c9e-b150-da4ef7f35652", - "metadata": {}, - "source": [ - "## Merge data frames" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f657e2e7-b43f-46aa-870c-8c3ab0bc8404", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'dgs_17c' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[3], line 23\u001b[0m\n\u001b[1;32m 1\u001b[0m merge_col\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSupplier Contract Usage ID\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOrdering Agency Name\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mState (S) or Local (L) agency\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mExtended Contract Price Paid\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 21\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msource\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m---> 23\u001b[0m dgs_17bc \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mmerge(dgs_17b, \u001b[43mdgs_17c\u001b[49m, how\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mouter\u001b[39m\u001b[38;5;124m'\u001b[39m, on\u001b[38;5;241m=\u001b[39m merge_col)\u001b[38;5;241m.\u001b[39mfillna(\u001b[38;5;241m0\u001b[39m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'dgs_17c' is not defined" - ] - } - ], - "source": [ - "merge_col=['Supplier Contract Usage ID',\n", - " 'Ordering Agency Name',\n", - " 'State (S) or Local (L) agency',\n", - " 'Purchasing Authority Number (for State departments)',\n", - " 'Agency Billing Code',\n", - " 'Purchase Order Number',\n", - " 'Purchase Order Date',\n", - " 'Delivery Date',\n", - " 'Contract Line Item Number (CLIN) (RFP ID)',\n", - " 'UNSPSC Code\\n(Version 10)',\n", - " 'Manufacturer Part Number (OEM #)',\n", - " 'Manufacturer (OEM)',\n", - " 'Item Description',\n", - " 'Unit of Measure',\n", - " 'Quantity in \\nUnit of Measure\\n',\n", - " 'Quantity',\n", - " 'List Price/MSRP',\n", - " 'Index Date / Catalog Version',\n", - " 'Contract Unit Price',\n", - " 'Extended Contract Price Paid',\n", - " 'source']\n", - "\n", - "dgs_17bc = pd.merge(dgs_17b, dgs_17c, how='outer', on= merge_col).fillna(0)" - ] - }, - { - "cell_type": "markdown", - "id": "7bab257b-5715-4dfb-9b08-dddb19c05d23", - "metadata": {}, - "source": [ - "## Data Cleaning and QC" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "30d97c40-8b3e-4d7a-955e-8ae4301c8602", - "metadata": {}, - "outputs": [], - "source": [ - "#snake case columns\n", - "def snake_case(df):\n", - " df.columns = df.columns.str.lower().str.replace(' ', '_').str.strip()\n", - " \n", - "snake_case(dgs_17bc)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cb5aba84-349f-4ae0-9d22-2d4024b1e41f", - "metadata": {}, - "outputs": [], - "source": [ - "# check financial columns to be `int64`\n", - "money =['contract_unit_price',\n", - " 'extended_contract_price_paid',\n", - " 'total_with_options_per_unit',\n", - " 'grand_total']\n", - "\n", - "# loop that takes money list to convert to int64 dtype\n", - "for column in money:\n", - " dgs_17bc[column] = dgs_17bc[column].astype('int64')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7e8378dd-f0bb-42b0-8582-a99be893dc0c", - "metadata": {}, - "outputs": [], - "source": [ - "# drop unnessary columns?\n", - "drops =['supplier_contract_usage_id',\n", - " 'state_(s)_or_local_(l)_agency',\n", - " 'purchasing_authority_number____________________(for_state_departments)',\n", - " 'agency_billing_code',\n", - " 'unspsc_code\\n(version_10)',\n", - " 'unit_of_measure',\n", - " 'epp_(y/n)_x',\n", - " 'epp_(y/n)_y',\n", - " 'list_price/msrp',\n", - " 'index_date_/_catalog_version',\n", - " 'core/_noncore',\n", - " 'group_id/_segment_id']\n", - "\n", - "dgs_17bc.drop(columns=drops, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b106dfff-eb1c-46ab-8805-ab362da92366", - "metadata": {}, - "outputs": [], - "source": [ - "# new column for total cost\n", - "# 17b >> `grand total` = total_with_options * quanity\n", - "# 17c >> `extended contract price paid` = contract unit price * quanity\n", - "\n", - "# what im trying to do: create a new column called \"total_cost\". for each row, if `totals_with_options_per_unit` is >=0, then multiply `totals_with_options_per_unit` by `quanity'\n", - "# if 0, then multiple `contract_unit_price` by `quanity`.\n", - "\n", - "# df['total_cost'] = df['quanity'] * df['total_with_options'] or df['contract unit price']???\n", - "\n", - "def calculate_total_cost(row):\n", - " if row['total_with_options_per_unit'] > 0:\n", - " return row['total_with_options_per_unit'] * row['quantity']\n", - " else:\n", - " return row['contract_unit_price'] * row['quantity']\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a5356705-c2bb-4945-95b5-03f709219287", - "metadata": {}, - "outputs": [], - "source": [ - "# new colum for total cost\n", - "\n", - "dgs_17bc['total_cost'] = dgs_17bc.apply(calculate_total_cost, axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5ab1ba22-5200-44d7-b40f-400aa8efcf76", - "metadata": {}, - "outputs": [], - "source": [ - "# comparing totals columns to new `total_cost` column to see if logic works\n", - "# 17b = Grand total\n", - "# 17c = Extended Contract Price Paid\n", - "keep_col=['ordering_agency_name',\n", - " 'purchase_order_number',\n", - " 'item_description',\n", - " 'source',\n", - " 'grand_total',\n", - " 'extended_contract_price_paid',\n", - " 'total_cost']\n", - "\n", - "col_compare = dgs_17bc[keep_col]\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "df7343dc-e164-40e3-a512-32edd09d53d4", - "metadata": {}, - "outputs": [], - "source": [ - "# new column for prop_type\n", - "\n", - "prop_list = ['Battery Electric Bus',\n", - " 'battery electric bus',\n", - " 'Fuel Cell Electric Bus',\n", - " 'fuel cell electric bus',\n", - " 'Hydrogen Electic Bus',\n", - " 'hydrogen electric bus',\n", - " 'battery electric',\n", - " ]\n", - "\n", - "# function to match keywords to list\n", - "def prop_type_finder(description):\n", - " for keyword in prop_list:\n", - " if keyword in description:\n", - " return keyword\n", - " return \"not specified\"\n", - "\n", - "# add new col `prop_type`, fill it with values based on project_description using prop_type_finder function\n", - "dgs_17bc[\"prop_type\"] = dgs_17bc[\"item_description\"].apply(prop_type_finder)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6048c764-8cc0-4e57-9f35-93626eb5167d", - "metadata": {}, - "outputs": [], - "source": [ - "# new column for bus size type\n", - "\n", - "size_list =['35 Foot',\n", - " '40 Foot',\n", - " '60 foot',\n", - " '40 foot',\n", - " '35 foot',\n", - " ]\n", - "\n", - "def bus_size_finder(description):\n", - " for keyword in size_list:\n", - " if keyword in description:\n", - " return keyword\n", - " return \"not specified\"\n", - "\n", - "dgs_17bc[\"bus_size_type\"] = dgs_17bc[\"item_description\"].apply(bus_size_finder)" - ] - }, - { - "cell_type": "markdown", - "id": "b2ed8168-1b24-444f-9f53-ba6a6d911ed9", - "metadata": { - "tags": [] - }, - "source": [ - "## Aggregate by Agency\n", - "need a df that:\n", - "1. each row is an agency\n", - "2. total quantity of buses only (not manuals, equipment, part, warranty)\n", - "3. aggregates total cost for the agency (bus, manals, etc)\n", - "4. keep the prop_type of the bus (should be no 'not specified')\n", - "5. keep the bus_size_type for the bus (no 'not specified')" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "62793376-d7c4-4d59-8249-56c0b18a123a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(30, 6)" - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# agency bus count\n", - "# filtered df by item desc containing 'bus' or 'Bus'\n", - "agg_agency_bus_count = dgs_17bc[(dgs_17bc['item_description'].str.contains('bus')) | (dgs_17bc['item_description'].str.contains('Bus'))]\n", - "\n", - "agg_agency_bus_count = agg_agency_bus_count[['ordering_agency_name',\n", - " 'item_description',\n", - " 'quantity',\n", - " 'source',\n", - " 'total_cost',\n", - " 'prop_type',\n", - " 'bus_size_type']]\n", - "\n", - "#i think this is it.. the numbers are matching up\n", - "agg_agency_bus_count = agg_agency_bus_count.groupby('ordering_agency_name').agg({\n", - " 'quantity':'sum',\n", - " 'total_cost':'sum',\n", - " 'prop_type':'max',\n", - " 'bus_size_type':'max',\n", - " 'source':'max',\n", - "}).reset_index()\n", - "\n", - "\n", - "# looks good. manualy double checked agsint pivot tables in excel. GOOD TO GO\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "8b842838-6faa-45d1-b4e1-4b757a4a3045", - "metadata": {}, - "source": [ - "## Export Cleaned data\n", - "save out as parquet" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "4a8c202e-d6bd-463c-bcb6-7a4fe704d0da", - "metadata": {}, - "outputs": [], - "source": [ - "agg_agency_bus_count.to_parquet('gs://calitp-analytics-data/data-analyses/bus_procurement_cost/dgs_agg_clean.parquet')" - ] - }, - { - "cell_type": "markdown", - "id": "85153b50-0f1f-433b-b320-5d6d70a8bc71", - "metadata": {}, - "source": [ - "## Test to read in parquet" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "9456bbc8-506b-4331-ae17-ab648ce959b6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(30, 6)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "url= 'gs://calitp-analytics-data/data-analyses/bus_procurement_cost/dgs_agg_clean.parquet'\n", - "dgs = pd.read_parquet(url)\n", - "\n", - "dgs.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "cf1a46bc-4b08-445e-8ea0-4c729f96b0d4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ordering_agency_namequantitytotal_costprop_typebus_size_typesource
0Alameda County Transit Authority2022846640hydrogen electric bus40 foot17b
1CITY OF PORTERVILLE (PORTERVILLE, CA)32781891battery electric bus35 foot17b
2CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...43623536Battery Electric Bus40 Foot17b
3City of Roseville106990000Battery Electric Bus35 Foot17c
4City of San Luis Obispo1689000Battery Electric Bus35 Foot17c
5City of Santa Rosa(Santa Rosa CityBus)53495000Battery Electric Bus40 Foot17c
6City of Visalia - Visalia City Coach(Visalia T...42756000Battery Electric Bus35 Foot17c
7Foothill Transit, West Covina, CA3337642044Hydrogen Electic Bus40 Foot17b
8GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA)55406355Hydrogen Electic Bus40 Foot17b
9Golden Empire Transit55458305Hydrogen Electic Bus40 Foot17b
10Lane Transit (Oregon)3027894999battery electric bus40 foot17b
11Napa Valley Transportation Authority21398000Battery Electric Bus40 Foot17c
12ORANGE COUNTY TRANSPORTATION AUTHORITY (ORANGE...109319520Battery Electric Bus40 Foot17b
13SLO TRANSIT (SAN LUIS OBISPO, CA)1847214battery electric bus35 foot17b
14SUNLINE TRANSIT AGENCY (THOUSAND PALMS)55755155Fuel Cell Electric Bus40 Foot17b
15SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA)55771865Hydrogen Electic Bus40 Foot17b
16Sacramento County Airport System53495000Battery Electric Bus40 Foot17c
17San Diego Metro1218759576battery electric60 foot17b
18Santa Maria Area Transit21378000Battery Electric Bus35 Foot17c
19Santa Maria Regional Transit54552010Battery Electric Bus35 Foot17c
20Santa Rosa City Bus42796000Battery Electric Bus40 Foot17c
21Sonoma County Transit108990000Battery Electric Bus40 Foot17c
22The Bus, City of Merced54786285battery electric bus40 foot17b
23Transit Joint Powers Authority for Merced County32077000Battery Electric Bus40 Foot17c
24UC DAVIS (UNITRANS) (DAVIS, CA)109321926battery electric bus40 foot17b
25UCLA FLEET & TRANSIT22008826battery electric bus40 foot17b
26University of California - San Diego64134000Battery Electric Bus35 Foot17c
27University of California, Irvine53995000Battery Electric Bus40 Foot17c
28VACAVILLE PUBLIC TRANSPORTATION (CITY COACH) (...1010175590battery electric bus35 foot17b
29VICTOR VALLEY TRANSIT AUTHORITY (VVTA)54508160Battery Electric Bus40 Foot17b
\n", - "
" - ], - "text/plain": [ - " ordering_agency_name quantity total_cost \\\n", - "0 Alameda County Transit Authority 20 22846640 \n", - "1 CITY OF PORTERVILLE (PORTERVILLE, CA) 3 2781891 \n", - "2 CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ... 4 3623536 \n", - "3 City of Roseville 10 6990000 \n", - "4 City of San Luis Obispo 1 689000 \n", - "5 City of Santa Rosa(Santa Rosa CityBus) 5 3495000 \n", - "6 City of Visalia - Visalia City Coach(Visalia T... 4 2756000 \n", - "7 Foothill Transit, West Covina, CA 33 37642044 \n", - "8 GOLDEN EMPIRE TRANSIT (BAKERSFIELD, CA) 5 5406355 \n", - "9 Golden Empire Transit 5 5458305 \n", - "10 Lane Transit (Oregon) 30 27894999 \n", - "11 Napa Valley Transportation Authority 2 1398000 \n", - "12 ORANGE COUNTY TRANSPORTATION AUTHORITY (ORANGE... 10 9319520 \n", - "13 SLO TRANSIT (SAN LUIS OBISPO, CA) 1 847214 \n", - "14 SUNLINE TRANSIT AGENCY (THOUSAND PALMS) 5 5755155 \n", - "15 SUNLINE TRANSIT AGENCY (THOUSAND PALMS, CA) 5 5771865 \n", - "16 Sacramento County Airport System 5 3495000 \n", - "17 San Diego Metro 12 18759576 \n", - "18 Santa Maria Area Transit 2 1378000 \n", - "19 Santa Maria Regional Transit 5 4552010 \n", - "20 Santa Rosa City Bus 4 2796000 \n", - "21 Sonoma County Transit 10 8990000 \n", - "22 The Bus, City of Merced 5 4786285 \n", - "23 Transit Joint Powers Authority for Merced County 3 2077000 \n", - "24 UC DAVIS (UNITRANS) (DAVIS, CA) 10 9321926 \n", - "25 UCLA FLEET & TRANSIT 2 2008826 \n", - "26 University of California - San Diego 6 4134000 \n", - "27 University of California, Irvine 5 3995000 \n", - "28 VACAVILLE PUBLIC TRANSPORTATION (CITY COACH) (... 10 10175590 \n", - "29 VICTOR VALLEY TRANSIT AUTHORITY (VVTA) 5 4508160 \n", - "\n", - " prop_type bus_size_type source \n", - "0 hydrogen electric bus 40 foot 17b \n", - "1 battery electric bus 35 foot 17b \n", - "2 Battery Electric Bus 40 Foot 17b \n", - "3 Battery Electric Bus 35 Foot 17c \n", - "4 Battery Electric Bus 35 Foot 17c \n", - "5 Battery Electric Bus 40 Foot 17c \n", - "6 Battery Electric Bus 35 Foot 17c \n", - "7 Hydrogen Electic Bus 40 Foot 17b \n", - "8 Hydrogen Electic Bus 40 Foot 17b \n", - "9 Hydrogen Electic Bus 40 Foot 17b \n", - "10 battery electric bus 40 foot 17b \n", - "11 Battery Electric Bus 40 Foot 17c \n", - "12 Battery Electric Bus 40 Foot 17b \n", - "13 battery electric bus 35 foot 17b \n", - "14 Fuel Cell Electric Bus 40 Foot 17b \n", - "15 Hydrogen Electic Bus 40 Foot 17b \n", - "16 Battery Electric Bus 40 Foot 17c \n", - "17 battery electric 60 foot 17b \n", - "18 Battery Electric Bus 35 Foot 17c \n", - "19 Battery Electric Bus 35 Foot 17c \n", - "20 Battery Electric Bus 40 Foot 17c \n", - "21 Battery Electric Bus 40 Foot 17c \n", - "22 battery electric bus 40 foot 17b \n", - "23 Battery Electric Bus 40 Foot 17c \n", - "24 battery electric bus 40 foot 17b \n", - "25 battery electric bus 40 foot 17b \n", - "26 Battery Electric Bus 35 Foot 17c \n", - "27 Battery Electric Bus 40 Foot 17c \n", - "28 battery electric bus 35 foot 17b \n", - "29 Battery Electric Bus 40 Foot 17b " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dgs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d7a7889-13e0-4b5a-b77c-6808619c19b1", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/bus_procurement_cost/OLD_tircp_bus_analysis.ipynb b/bus_procurement_cost/OLD_tircp_bus_analysis.ipynb deleted file mode 100644 index 3798e4c64..000000000 --- a/bus_procurement_cost/OLD_tircp_bus_analysis.ipynb +++ /dev/null @@ -1,2195 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "7e2bb160-f2fa-49a0-9cbf-b0796599f1d1", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import shared_utils\n", - "from dgs_data_cleaner import project_type_checker\n", - "\n", - "# set_option to increase max rows displayed to 200, to see entire df in 1 go/\n", - "pd.set_option(\"display.max_rows\", 200)\n", - "pd.set_option('display.max_colwidth', None)" - ] - }, - { - "cell_type": "markdown", - "id": "9f3cd0cb-98d8-43a4-be8d-b7b41f80dd75", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "## AGREEMENT ALLOCATIONS SHEET DATA" - ] - }, - { - "cell_type": "markdown", - "id": "52794293-2f66-47a7-b580-6170fc72ca94", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "### Agreement Allocations - Read in Raw data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "70250817-0eee-4ab0-95b4-b43050b46f8c", - "metadata": {}, - "outputs": [], - "source": [ - "url = \"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/TIRCP Tracking Sheets 2_1-10-2024.xlsx\"\n", - "sheet_name = \"Agreement Allocations\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f80fccec-2313-4a82-9529-5943ba26e401", - "metadata": {}, - "outputs": [], - "source": [ - "tircp = pd.read_excel(url, sheet_name)" - ] - }, - { - "cell_type": "markdown", - "id": "79002d63-c2a1-4218-81a2-31e6a10d981e", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "### Agreement Allocations -Data Cleaning and QC" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "78289f7c-54f3-48af-8881-101f50853694", - "metadata": {}, - "outputs": [], - "source": [ - "# reducing initialdf to first 11 columns.\n", - "tircp = tircp.iloc[:, :12]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6526421b-36f3-4326-bfce-4679ffcf2b52", - "metadata": {}, - "outputs": [], - "source": [ - "# dictionary for column name update\n", - "new_col = [\n", - " \"award_year\",\n", - " \"project_#\",\n", - " \"grant_recipient\",\n", - " \"implementing_agency\",\n", - " \"ppno\",\n", - " \"project_id\",\n", - " \"ea\",\n", - " \"components\",\n", - " \"#_of_buses\",\n", - " \"phase\",\n", - " \"allocation_amount\",\n", - " \"expended_amount\",\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c973346f-9232-4d21-8145-6fbe331094de", - "metadata": {}, - "outputs": [], - "source": [ - "tircp.columns = new_col\n", - "tircp.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "14061d68-8a66-42de-baad-c45e0b4664f5", - "metadata": {}, - "outputs": [], - "source": [ - "tircp = tircp.drop(\"expended_amount\", axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4ad96350-0b11-4733-aa04-d1053681b24b", - "metadata": {}, - "outputs": [], - "source": [ - "# fill NaN with zero?\n", - "# see if you can sum the bus column\n", - "tircp.agg({\"#_of_buses\": \"sum\"})\n", - "# nope this is correct" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f2e30eb4-6adf-48b8-827b-548d806b101a", - "metadata": {}, - "outputs": [], - "source": [ - "display(tircp.shape, list(tircp.columns), tircp.head())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9eb613a5-7d1f-489c-8d9f-1226f25a61bf", - "metadata": {}, - "outputs": [], - "source": [ - "tircp.grant_recipient.nunique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "33b665bf-20a0-416f-8b6b-fe835670560d", - "metadata": {}, - "outputs": [], - "source": [ - "# use strip to help combine names\n", - "tircp[\"grant_recipient\"] = tircp[\"grant_recipient\"].str.strip()\n", - "\n", - "tircp.grant_recipient.nunique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ad999ca5-3dda-4bed-a161-7835b7317e3b", - "metadata": {}, - "outputs": [], - "source": [ - "# see list of unique names\n", - "# may be able to consolidate a few\n", - "tircp.grant_recipient.sort_values().unique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "57c1b6ab-64ec-4af4-958f-3fd69a9fcf94", - "metadata": {}, - "outputs": [], - "source": [ - "new_dict = {\n", - " \"Antelope Valley Transit Authority\": \"Antelope Valley Transit Authority (AVTA)\",\n", - " \"Bay Area Rapid Transit District\": \"Bay Area Rapid Transit (BART)\",\n", - " \"Capitol Corridor Joint Powers Authority\": \"Capitol Corridor Joint Powers Authority (CCJPA)\",\n", - " \"Los Angeles County Metropolitan Transportation (LA Metro)\": \"Los Angeles County Metropolitan Transportation Authority (LA Metro)\",\n", - " \"Los Angeles County Metropolitan Transportation Authority\": \"Los Angeles County Metropolitan Transportation Authority (LA Metro)\",\n", - " \"Sacramento Regional Transit (SacRT)\": \"Sacramento Regional Transit District (SacRT)\",\n", - " \"Sacramento Regional Transit District\": \"Sacramento Regional Transit District (SacRT)\",\n", - " \"San Diego Metropolitan Transit System (SDMTS)\": \"San Diego Metropolitan Transit System (MTS)\",\n", - " \"San Francisco Bay Area Water Emergency Transportation Authority\": \"San Francisco Bay Area Water Emergency Transportation Authority (WETA)\",\n", - " \"San Francisco Municipal Transportation Agency\": \"San Francisco Municipal Transportation Authority (SFMTA)\",\n", - " \"Santa Barbara County Association of Governments\\n(SBCAG)\": \"Santa Barbara County Association of Governments (SBCAG)\",\n", - " \"Santa Clara Valley Transportation Authority\": \"Santa Clara Valley Transportation Authority (VTA)\",\n", - " \"Transportation Agency for Monterey County\": \"Transportation Agency for Monterey County (TAMC)\",\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bf769e25-97fe-41eb-9ff5-c1bbda4200ad", - "metadata": {}, - "outputs": [], - "source": [ - "# replace the values in grant_recipient using dict\n", - "# df.replace({'bus_desc': new_dict}, inplace=True)\n", - "tircp = tircp.replace({\"grant_recipient\": new_dict})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "97e5ec8e-464a-451f-add3-5965a38d2e3a", - "metadata": {}, - "outputs": [], - "source": [ - "# see that some rows were consolidated\n", - "display(tircp.grant_recipient.nunique())" - ] - }, - { - "cell_type": "markdown", - "id": "3a197b7b-29a6-46b8-adf5-e31b60c694e0", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "### Agreement Allocations-Export Cleaned data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5150ed0b-2fcd-4825-b12c-1fdb6bef7e64", - "metadata": {}, - "outputs": [], - "source": [ - "tircp.to_csv(\n", - " \"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/tircp_allocations_clean.csv\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "17549ca7-4ac2-4067-95ae-79e5547500f3", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "### Agreement Allocations-Read in Cleaned data from GCS" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7e27a6f9-c57f-4e13-ae94-6d13b2ef87ba", - "metadata": {}, - "outputs": [], - "source": [ - "tircp = pd.read_csv(\n", - " \"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/tircp_allocations_clean.csv\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6b29bc28-2f4e-4d6f-8ffc-2df929f7e27f", - "metadata": {}, - "outputs": [], - "source": [ - "display(tircp.shape, tircp.columns, tircp.head())" - ] - }, - { - "cell_type": "markdown", - "id": "eab85396-0dfd-43ef-b938-310745c9d518", - "metadata": { - "tags": [] - }, - "source": [ - "### Agreement Allocations-Cost per Bus, per agency" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "024b16e0-45c3-4497-b9b9-6470b3981479", - "metadata": {}, - "outputs": [], - "source": [ - "# filer to project with bus count values\n", - "# caveat: some rows in \"component\" column state some variation of \"purchased buses\", but did not specify the amount of buses.\n", - "# only rows stating the specificy number of buses purchased are included\n", - "only_bus = tircp[tircp[\"#_of_buses\"] > 0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "02fab0b3-0c75-4220-8129-3402fb9b3007", - "metadata": {}, - "outputs": [], - "source": [ - "display(only_bus.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ca5e0397-b297-4236-a9c6-43d61d1021aa", - "metadata": {}, - "outputs": [], - "source": [ - "# aggregate # of buses and allocation by transit agency\n", - "bus_cost = (\n", - " only_bus.groupby(\"grant_recipient\")\n", - " .agg({\"#_of_buses\": \"sum\", \"allocation_amount\": \"sum\"})\n", - " .reset_index()\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ddd7f056-4e8e-43be-9391-6b6c0574fc58", - "metadata": {}, - "outputs": [], - "source": [ - "bus_cost" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a699f202-6fdc-4261-8ed5-d0fc328aa7a1", - "metadata": {}, - "outputs": [], - "source": [ - "bus_cost[\"cost_per_bus\"] = (\n", - " (bus_cost[\"allocation_amount\"]) / (bus_cost[\"#_of_buses\"])\n", - ").astype(\"int64\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "29bff029-a90d-4815-aa21-7405051d3063", - "metadata": {}, - "outputs": [], - "source": [ - "display(bus_cost.dtypes, bus_cost)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d988e8f0-f655-4477-a1e0-a56f61df9e8b", - "metadata": {}, - "outputs": [], - "source": [ - "# exporting cost per bus\n", - "bus_cost.to_csv(\n", - " \"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/tircp_allocation_cost_per_bus.csv\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "1c2e30d8-2d49-4dcb-9c75-1a8b264bb011", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "### Agreement Allocations - Stat analysis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "02b1eb6a-559d-41a4-aa7f-982399dfe7f5", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4bda679f-ea66-4651-a389-80da221864d3", - "metadata": {}, - "outputs": [], - "source": [ - "bus_cost" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3f7b6bd6-f0e1-476b-ac36-b695f88536d6", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(\n", - "plt.hist(bus_cost['cost_per_bus'],density=True)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "1de41d29-4183-41a3-b6af-58e1d676f788", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "## PROJECT TRACKING SHEET DATA" - ] - }, - { - "cell_type": "markdown", - "id": "b29ced72-1fd8-40aa-95c0-178a4db4a36b", - "metadata": { - "tags": [] - }, - "source": [ - "### project tracking - read raw data\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "af4ead17-a1fc-4f42-9fbf-227ae511b930", - "metadata": {}, - "outputs": [], - "source": [ - "gcs_path = 'gs://calitp-analytics-data/data-analyses/bus_procurement_cost/'\n", - "file_name = 'TIRCP Tracking Sheets 2_1-10-2024.xlsx'\n", - "sheet_name = 'Project Tracking'\n", - "\n", - "def get_data(path, file, sheet):\n", - " df = pd.read_excel(path+file, sheet_name=sheet)\n", - " \n", - " return df\n", - "\n", - "project = get_data(gcs_path, file_name, sheet_name)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "767d535b-84ce-4884-8145-390c7d38b879", - "metadata": {}, - "outputs": [], - "source": [ - "display(\n", - " project.shape,\n", - " project.columns,\n", - " project.dtypes,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "2e9e142b-92aa-47c5-b97a-d83852dbc4a5", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "## Project Tracking- data cleaning" - ] - }, - { - "cell_type": "markdown", - "id": "88b2f5ce-af56-4e9c-b854-9c31e0c6a853", - "metadata": { - "tags": [] - }, - "source": [ - "### data frame cleaning" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e6b9047c-eda3-4631-9a1c-3338abf6c281", - "metadata": {}, - "outputs": [], - "source": [ - "# only keep first couple of columns\n", - "# tircp = tircp.iloc[:, :12]\n", - "project = project.iloc[:, :20]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "75c5cbb1-4f8c-4c7b-838b-1c0f139b8a0a", - "metadata": {}, - "outputs": [], - "source": [ - "list(project.columns)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6692bddd-e46d-415f-abeb-287578f15b74", - "metadata": {}, - "outputs": [], - "source": [ - "# drop specific columns\n", - "drop_col = [\n", - " \"Master Agreement Expiration Date\",\n", - " \"Project Manager\",\n", - " \"Regional Coordinator\",\n", - " \"Technical Assistance-CALITP (Y/N)\",\n", - " \"Technical Assistance-Fleet (Y/N)\",\n", - " \"Technical Assistance-Network Integration (Y/N)\",\n", - " \"Technical Assistance-Priority Population (Y/N)\",\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d2e692da-16fc-4f97-946f-a1e6fb3414e8", - "metadata": {}, - "outputs": [], - "source": [ - "project.drop(columns=drop_col, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e7b312e7-3676-415b-bc3c-8bf2d95694ee", - "metadata": {}, - "outputs": [], - "source": [ - "len(project.columns)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a39fe55-6664-49ce-979e-e158ae27f795", - "metadata": {}, - "outputs": [], - "source": [ - "# replace space with _ & lower everything\n", - "project.columns = project.columns.str.replace(\" \", \"_\")\n", - "project.columns = project.columns.str.lower()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4937d3c4-f5df-4a42-84ea-161ec61e637b", - "metadata": {}, - "outputs": [], - "source": [ - "# check work\n", - "project.columns" - ] - }, - { - "cell_type": "markdown", - "id": "4372f730-9137-4e33-a45e-6563abdf4085", - "metadata": { - "tags": [] - }, - "source": [ - "### check columns\n", - "check values of all columns to see if:\n", - "-any duplicates values\n", - "-invalid int/str values\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "83152dd0-e2a4-4892-9019-ceb28ce00f5f", - "metadata": {}, - "outputs": [], - "source": [ - "project.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b5f5a0d4-8616-44cf-82ab-08dcc0bc58c7", - "metadata": {}, - "outputs": [], - "source": [ - "# function to check column information\n", - "def col_checker(col):\n", - " display(\n", - " f\"Displaying column: {col}\",\n", - " len(project[col]),\n", - " list(project[col].sort_values(ascending=True).unique()),\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "24b1a759-280a-4a2b-9900-327cdf073c59", - "metadata": {}, - "outputs": [], - "source": [ - "# col is OK, all numbers\n", - "col_checker(\"tircp_award_amount_($)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f912f6d4-4b1e-4868-9fe9-4e4c85124e9a", - "metadata": {}, - "outputs": [], - "source": [ - "# col is good, everything is a number\n", - "col_checker(\"total_project_cost\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a584257f-455a-4939-9897-085e1c7f95f1", - "metadata": {}, - "outputs": [], - "source": [ - "# col is OK\n", - "col_checker(\"master_agreement_number\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d39123d-1e8b-458e-8b18-8113666de00f", - "metadata": {}, - "outputs": [], - "source": [ - "# col is OK\n", - "col_checker(\"bus_count\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "022bba0f-73c7-4ae2-9986-ae234dd5517b", - "metadata": {}, - "outputs": [], - "source": [ - "# column is OK\n", - "col_checker(\"project_description\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d77f19e7-87cb-4ae0-acd0-829addb935b6", - "metadata": {}, - "outputs": [], - "source": [ - "project[project[\"district\"] == \"VAR\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b1526686-6a92-4567-92df-0cf27c25ff01", - "metadata": {}, - "outputs": [], - "source": [ - "# Project title OK,\n", - "col_checker(\"project_title\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9dc1361f-6d1e-4067-8cf3-738f4fe9ad85", - "metadata": {}, - "outputs": [], - "source": [ - "# award year OK\n", - "col_checker(\"award_year\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e50bb102-bdab-45f2-af8e-ccca23f25247", - "metadata": {}, - "outputs": [], - "source": [ - "# project num OK\n", - "col_checker(\"project_#\")" - ] - }, - { - "cell_type": "markdown", - "id": "92513e2d-8c76-4150-b4d0-f2034f3ce85c", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "94f27bed-11ce-480d-bcf5-5c2ed07b03a8", - "metadata": {}, - "outputs": [], - "source": [ - "# DROP COL\n", - "# Col is OK\n", - "col_checker(\"allocated_amount\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "55860953-f885-4e1d-ae02-f4d8d6be46bf", - "metadata": {}, - "outputs": [], - "source": [ - "# NEEDS CLEANING grant_recipient need to clean\n", - "col_checker(\"grant_recipient\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c74990d4-bda0-4615-9494-832f7b44f3f3", - "metadata": {}, - "outputs": [], - "source": [ - "# may need to clean, there are rows that say '3, 4'\n", - "col_checker(\"county\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "00cc4687-19d7-4af3-81f9-37e66cd6cb33", - "metadata": {}, - "outputs": [], - "source": [ - "# Move to cleaning, check what is 'VAR'. various?\n", - "# may be ok just check to make sure\n", - "project.district.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e71ef09e-c7e5-4cdb-8e48-1be0ad1fa087", - "metadata": {}, - "outputs": [], - "source": [ - "# couldnt run col_checker, guessing because some PPNO numbers are inconsistent\n", - "# may need to clean, there is a ppno of CP052/CP053\n", - "project.ppno.unique()" - ] - }, - { - "cell_type": "markdown", - "id": "3f4e1191-979e-4a31-bce3-296fabc586dc", - "metadata": { - "tags": [] - }, - "source": [ - "### dropping allocated amount column" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "209b153b-f42f-4a57-97c1-087e403fec55", - "metadata": {}, - "outputs": [], - "source": [ - "# dropping allocated amount column\n", - "project.drop(columns=[\"allocated_amount\"], inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "16a7479b-707a-4621-906c-022598a64179", - "metadata": {}, - "outputs": [], - "source": [ - "# checking work\n", - "project.columns" - ] - }, - { - "cell_type": "markdown", - "id": "7e8652ea-a95f-4009-b592-1414da775c61", - "metadata": { - "tags": [] - }, - "source": [ - "### Clean `grant_recipient` column" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "77b1e0be-20ee-454e-8e8b-a6c0e0951d44", - "metadata": {}, - "outputs": [], - "source": [ - "list(project.grant_recipient.sort_values(ascending=True).unique())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e98893df-a0f5-4fbd-af6a-8fb58602a556", - "metadata": {}, - "outputs": [], - "source": [ - "agency_dict = {\n", - " \"Antelope Valley Transit Authority \": \"Antelope Valley Transit Authority (AVTA)\",\n", - " \"Humboldt Transit Authority\": \"Humboldt Transit Authority (HTA)\",\n", - " \"Orange County Transportation Authority\": \"Orange County Transportation Authority (OCTA)\",\n", - " \"Capitol Corridor Joint Powers Authority\": \"Capitol Corridor Joint Powers Authority (CCJPA)\",\n", - " \"Los Angeles County Metropolitan Transportation Authority\": \"Los Angeles County Metropolitan Transportation Authority (LA Metro)\",\n", - " \"Monterey-Salinas Transit\": \"Monterey-Salinas Transit District (MST)\",\n", - " \"Sacramento Regional Transit (SacRT)\": \"Sacramento Regional Transit District (SacRT)\",\n", - " \"Sacramento Regional Transit District\": \"Sacramento Regional Transit District (SacRT)\",\n", - " \"Sacramento Regional Transit District (SacRT) \": \"Sacramento Regional Transit District (SacRT)\",\n", - " \"San Diego Association of Governments\": \"San Diego Association of Governments (SANDAG)\",\n", - " \"Santa Clara Valley Transportation Authority (SCVTA)\": \"Santa Clara Valley Transportation Authority (VTA)\",\n", - " \"Southern California Regional Rail Authority (SCRRA)\": \"Southern California Regional Rail Authority (SCRRA - Metrolink)\",\n", - " \"Southern California Regional Rail Authority\": \"Southern California Regional Rail Authority (SCRRA - Metrolink)\",\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "41eedd51-e052-4462-bf8f-e14bea1df7cc", - "metadata": {}, - "outputs": [], - "source": [ - "# df.replace({'bus_desc': new_dict}, inplace=True)\n", - "project.replace({\"grant_recipient\": agency_dict}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "195fb532-8154-4ca1-ae6a-81b16d6f031e", - "metadata": {}, - "outputs": [], - "source": [ - "# check work. looks good\n", - "list(project[\"grant_recipient\"].sort_values().unique())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4acd9422-7cc0-4082-a87a-628187d1736f", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "15660296-1348-4463-a962-ac22234c2f7e", - "metadata": { - "tags": [] - }, - "source": [ - "### Cleaning `county` column" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "13635af7-3952-42f4-a272-c7a462ea1358", - "metadata": {}, - "outputs": [], - "source": [ - "col_checker(\"county\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4e3f6614-dc19-4011-a878-fcc0e8570b41", - "metadata": {}, - "outputs": [], - "source": [ - "#checking specific row with '3,4' as county\n", - "project[project[\"county\"] == \"3, 4\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1de28ea8-d063-4797-8111-2cf073e71e71", - "metadata": {}, - "outputs": [], - "source": [ - "# change county value from '3, 4' to 'VAR' like the other rows.\n", - "project.at[3, \"county\"] = \"VAR\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "23170f02-1d66-4bb3-917c-9ca2ceaba627", - "metadata": {}, - "outputs": [], - "source": [ - "# check work\n", - "project.iloc[3]" - ] - }, - { - "cell_type": "markdown", - "id": "52ae834d-72a2-4c51-8b78-a533a807d497", - "metadata": { - "tags": [] - }, - "source": [ - "### Cleaning `district`column\n", - "This is good as is, no cleaning requried. All rows with VAR district has VAR in county as well." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "39b424d0-7d59-4d7b-88f2-ffc5b80ae9c8", - "metadata": {}, - "outputs": [], - "source": [ - "#GTG\n", - "project.district.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3ad49c6e-c13a-4460-b214-8ddac6e24f29", - "metadata": {}, - "outputs": [], - "source": [ - "#GTG \n", - "project[project[\"district\"] == \"VAR\"]" - ] - }, - { - "cell_type": "markdown", - "id": "b7be5cfa-b593-4c26-849a-2d9c2f777f34", - "metadata": { - "tags": [] - }, - "source": [ - "### Clean `ppno` column\n", - "This should all be fine as is, no cleaning needed" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3611650f-aa4d-4cf8-8153-4c809c0e7240", - "metadata": {}, - "outputs": [], - "source": [ - "list(project.ppno.unique())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8c92f80b-5898-4ff7-b184-cdb804ff564a", - "metadata": {}, - "outputs": [], - "source": [ - "#GTG \n", - "project[project[\"ppno\"] == \"CP052/CP053\"]" - ] - }, - { - "cell_type": "markdown", - "id": "7f8dd43f-ebef-4a75-aa2f-63b1faf6f514", - "metadata": { - "tags": [] - }, - "source": [ - "### Skim the project description column?\n", - "double check to ensure bus count is accurate to what the description says?\n", - "\n", - "Saw that some rows mention procuring both zero and non-zero emission buses (count total buses in `bus count` and `VAR` in prop type and bus size?\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "163a339e-7564-455e-9477-b9df0914ef0c", - "metadata": {}, - "outputs": [], - "source": [ - "project[\n", - " project[\"project_title\"]\n", - " == \"ATN FAST (Family of Advanced Solutions for Transit): Revolutionizing Transit for a Global Audience\"\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d6c9992f-8a4f-403d-8bc8-f45b55575402", - "metadata": {}, - "outputs": [], - "source": [ - "# iloc check\n", - "project.iloc[73]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1527b7df-c3ca-4041-91bb-a44998da584e", - "metadata": {}, - "outputs": [], - "source": [ - "# code to update value at specific index and column\n", - "project.loc[project['ppno'] == 'CP106', 'bus_count'] = 42\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "74702a2f-0b82-47ae-81ae-f7c021394ef0", - "metadata": {}, - "outputs": [], - "source": [ - "# check work\n", - "project.iloc[73]" - ] - }, - { - "cell_type": "markdown", - "id": "1c6f0650-9ef5-46cd-a2b7-63265c793780", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "id": "5140da93-1ee3-4bd8-8cd4-01745947ff48", - "metadata": {}, - "source": [ - "## Export cleaned Project df " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "55872945-c6fa-4a98-b674-91f91d39d08f", - "metadata": {}, - "outputs": [], - "source": [ - "# exproject cleaned project df\n", - "project.to_csv(\n", - " \"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/tircp_project_clean.csv\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "a931b907-8431-4290-96dc-2e1b40b6e64f", - "metadata": { - "tags": [] - }, - "source": [ - "## Read in cleaned project data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9b12c971-94d5-4ef8-93d3-2994df1826d3", - "metadata": {}, - "outputs": [], - "source": [ - "project = pd.read_csv(\n", - " \"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/tircp_project_clean.csv\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d5c9f3d1-9437-48a3-a37f-0d74860a1499", - "metadata": {}, - "outputs": [], - "source": [ - "# ensure df is able to read in\n", - "display(project.shape, project.columns)" - ] - }, - { - "cell_type": "markdown", - "id": "51f95400-1198-46a6-a41a-fef99b3a2ffa", - "metadata": { - "tags": [] - }, - "source": [ - "### filter df for project descriptions that contain bus" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fc9ee142-13b2-4fc2-86af-d712ab5df6c4", - "metadata": {}, - "outputs": [], - "source": [ - "bus_only = project[project[\"bus_count\"] > 0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c65b773e-dd41-4c95-8e6b-2132e5d7e978", - "metadata": {}, - "outputs": [], - "source": [ - "# this looks correct\n", - "display(project.shape, bus_only.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "fdd09938-88b1-4fcd-8159-1637a57ee0f4", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "## New column for propulsion type - `prop_type`\n", - "Use on `bus_only` df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "630e1662-9a32-4a2c-8174-1a0dc59ad42e", - "metadata": {}, - "outputs": [], - "source": [ - "prop_type = [\n", - " \"electric buses\",\n", - " \"electric commuter\",\n", - " \"Electric Buses\",\n", - " \"battery electric\",\n", - " \"Batery Electric\",\n", - " \"battery-electric\",\n", - " \"fuel-cell\",\n", - " \"fuel cell\",\n", - " \"Fuel Cell\",\n", - " \"zero emission\",\n", - " \"Zero Emission\",\n", - " \"zero-emission electric buses\",\n", - " \"zero-emission buses\",\n", - " \"zero‐emission\",\n", - " \"zero-emission\",\n", - " \"zeroemission\",\n", - " \"CNG\",\n", - " \"cng\",\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9cb544f0-81e3-4644-a4f8-5b83009e3e18", - "metadata": {}, - "outputs": [], - "source": [ - "type(prop_type)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fa958cba-0e59-464b-959f-c4c18b61f8cc", - "metadata": {}, - "outputs": [], - "source": [ - "# function to match keywords to list\n", - "def prop_type_finder(description):\n", - " for keyword in prop_type:\n", - " if keyword in description:\n", - " return keyword\n", - " return \"not specified\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6c9f56c8-ee70-426c-ba62-2294b2b13fa7", - "metadata": {}, - "outputs": [], - "source": [ - "# add new col `prop_type`, fill it with values based on project_description using prop_type_finder function\n", - "bus_only[\"prop_type\"] = bus_only[\"project_description\"].apply(prop_type_finder)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c35ceca0-1049-4c1f-b24c-569c45f97f5e", - "metadata": {}, - "outputs": [], - "source": [ - "# check work\n", - "display(\n", - " bus_only.columns,\n", - " bus_only[\"prop_type\"].value_counts(),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "251b72b3-7ab4-4028-8cad-1f39b4e334c0", - "metadata": {}, - "outputs": [], - "source": [ - "# exploring the not specified rows\n", - "bus_only[bus_only[\"prop_type\"] == \"not specified\"]\n", - "# coach-style buses, this row does not specify if buses are zero or non-zero emission bus. GOOD TO GO" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bd6eda27-aa67-4a3c-b863-531fbb667da3", - "metadata": {}, - "outputs": [], - "source": [ - "# what is in CNG rows?\n", - "bus_only[bus_only[\"prop_type\"] == \"CNG\"]\n", - "# was 4 rows, then adjusted prop list to have cng at the bottom. now showing 1 row thats actually CNG" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8f056155-1a0f-4d7f-85af-ee6d96069eab", - "metadata": {}, - "outputs": [], - "source": [ - "# consolidate values\n", - "list(bus_only[\"prop_type\"].sort_values(ascending=True).unique())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8964ac4d-d7c8-457c-98b0-577d6e5e30ef", - "metadata": {}, - "outputs": [], - "source": [ - "prop_dict = {\n", - " \"battery electric\": \"BEB\",\n", - " \"battery-electric\": \"BEB\",\n", - " \"electric buses\": \"electric (not specified)\",\n", - " \"electric commuter\": \"electric (not specified)\",\n", - " \"fuel cell\": \"FCEB\",\n", - " \"fuel-cell\": \"FCEB\",\n", - " \"zero-emission buses\": \"zero-emission bus (not specified)\",\n", - " \"zero emission\": \"zero-emission bus (not specified)\",\n", - " \"zero-emission\": \"zero-emission bus (not specified)\",\n", - " \"zero‐emission\": \"zero-emission bus (not specified)\",\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1204ed35-8e16-4a14-9c41-8c22b24a1503", - "metadata": {}, - "outputs": [], - "source": [ - "# replacing prop_type values with dictionary\n", - "bus_only.replace({\"prop_type\": prop_dict}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d2da1c1b-c91b-42e5-b3ff-583f3fd60676", - "metadata": {}, - "outputs": [], - "source": [ - "# check work\n", - "display(bus_only.prop_type.value_counts(), bus_only.head())\n", - "\n", - "# looks good" - ] - }, - { - "cell_type": "markdown", - "id": "392dd768-88e4-42bb-a26f-ab95e225b271", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "## New column for bus size type - `bus_size_type`\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "278079a5-9ebb-4ac2-9363-ecfc0b6a818f", - "metadata": {}, - "outputs": [], - "source": [ - "bus_size = [\n", - " \"standard\",\n", - " \"30-foot\",\n", - " \"40 foot\",\n", - " \"40-foot\",\n", - " \"45-foot\",\n", - " \"45 foot\",\n", - " \"40ft\",\n", - " \"60-foot\",\n", - " \"articulated\",\n", - " \"cutaway\",\n", - " \"coach-style\",\n", - " \"over-the-road\",\n", - " \"feeder bus\",\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9988791c-8ec6-45c4-b7b6-4fa8ccbfe823", - "metadata": {}, - "outputs": [], - "source": [ - "type(bus_size)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "71b25adf-440a-4f52-9d18-f908f57d5aab", - "metadata": {}, - "outputs": [], - "source": [ - "# re writing prop type funct for bus size\n", - "def bus_size_finder(description):\n", - " for keyword in bus_size:\n", - " if keyword in description:\n", - " return keyword\n", - " return \"not specified\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "45ab8fdc-e752-4584-8bb8-af4426861a71", - "metadata": {}, - "outputs": [], - "source": [ - "# creating new column, filling the column using the function applied to project_desctiotion\n", - "bus_only[\"bus_size_type\"] = bus_only[\"project_description\"].apply(bus_size_finder)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c0ce4822-7356-4e52-94cb-04269eb82db9", - "metadata": {}, - "outputs": [], - "source": [ - "# checking work\n", - "display(bus_only.columns, bus_only.bus_size_type.value_counts())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a17a1dfa-d70a-4076-b9c1-618a43398262", - "metadata": {}, - "outputs": [], - "source": [ - "list(bus_only['bus_size_type'].sort_values().unique())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ad0bda67-e3b2-4bdf-9bfb-0afa7b19bc0d", - "metadata": {}, - "outputs": [], - "source": [ - "# expected that not a lot of rows specify a size type.\n", - "# will still take a random peek into some\n", - "\n", - "bus_only[bus_only[\"bus_size_type\"] == \"not specified\"].sample(5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ab06f71f-3f64-4b12-8164-fe3524c97d64", - "metadata": {}, - "outputs": [], - "source": [ - "# consolidate\n", - "size_dict={'40 foot': 'conventional (40-ft like)' ,\n", - " '40-foot': 'conventional (40-ft like)',\n", - " '45-foot': 'conventional (40-ft like)',\n", - " 'coach-style':'over-the-road',\n", - " 'feeder bus': 'conventional (40-ft like)',\n", - " }" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "be465cfd-fb25-4862-a709-c3dd675a8fbf", - "metadata": {}, - "outputs": [], - "source": [ - "type(size_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d66297eb-2ced-4148-9722-167797349280", - "metadata": {}, - "outputs": [], - "source": [ - "# .replace() with size_dict to replace values in bus size col\n", - "bus_only.replace({\"bus_size_type\": size_dict}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "055fbee5-b73b-4779-801b-1c0d23e081af", - "metadata": {}, - "outputs": [], - "source": [ - "# check work\n", - "bus_only.bus_size_type.value_counts()" - ] - }, - { - "cell_type": "markdown", - "id": "1b95f567-13fc-4184-8b09-f512f702f3f0", - "metadata": { - "tags": [] - }, - "source": [ - "## export project- bus only df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cecea030-b37a-4170-9997-656e8bd0c080", - "metadata": {}, - "outputs": [], - "source": [ - "bus_only.to_parquet(\n", - " \"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/tircp_project_bus_only.parquet\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "1ea926da-8d76-481c-a5fb-ef39606f45ca", - "metadata": { - "tags": [] - }, - "source": [ - "## Read in project bus only data\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "abaf10b2-0dc3-432d-845a-8dacb2af806f", - "metadata": {}, - "outputs": [], - "source": [ - "bus_checker = pd.read_parquet(\n", - " \"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/tircp_project_bus_only.parquet\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ab4b7cf3-183e-4ea1-baca-e3f5d0bd9dd6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(35, 14)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bus_checker.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "9000c886-c46c-436d-a0b5-ec55bd2a4cd2", - "metadata": {}, - "outputs": [], - "source": [ - "bus_checker = bus_checker.assign(\n", - " project_type = bus_checker['project_description'].apply(project_type_checker)\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "bcdeb08c-50bf-4a63-b2c8-086cdfa96ffe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "includes bus and non-bus components 24\n", - "bus only 11\n", - "Name: project_type, dtype: int64" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bus_checker[\"project_type\"].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "fb93fbc9-ab8d-4239-a55e-3ec2b3000541", - "metadata": {}, - "outputs": [], - "source": [ - "# just_bus rows are all good. \n", - "just_bus = bus_checker[bus_checker['project_type'] == \"bus only\"]\n", - "\n", - "# bus_non_bus rows are all good\n", - "bus_non_bus = bus_checker[bus_checker['project_type'] == \"includes bus and non-bus components\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "b88e65bc-86d2-419e-93d6-e3f066173b2b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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project_descriptionproject_typebus_countprop_type
0Purchase 13 60-foot articulated BRT buses and 16 45-foot electric commuter busesbus only13.0electric (not specified)
5Purchase five 40-foot CNG buses for BRT Route linking SARTC to Metrolink/Amtrakbus only40.0CNG
16Purchase 20 zero-emission buses to extend Route 486 to the Pamona Metrolink station and increase frequenciesbus only20.0zero-emission bus (not specified)
34Acquire 112 zero-emission buses to replace existing propane vehicles and add new vehicles, in order to increase frequency of all existing DASH routes to 15-minute service and add 4 new routes, serving communities throughout the City of Los Angeles as recommended in the comprehensive Transit Service Analysis.bus only112.0zero-emission bus (not specified)
51Purchase 7 new coach-style buses to support a new intercity service that connects Redding to Sacramento. Purchase 7 new coach-style buses to support a new intercity service that connects Redding to Sacramento Shata Regional Transportation Agency Bus System to Sacramento International Airportbus only14.0not specified
68Purchase 7 zero emission buses to enhance and extend Route 14 from Playa Vista to Inglewood, bringing new transit opportunities to disadvantaged communities, while also integrating light rail and bus services.bus only7.0zero-emission bus (not specified)
70Purchase 7 electric buses to expand services on Line 4X (between Torrance and Downton LA), on an extended line 10 (serving the Metro Green Line Crenshaw station and the Inglewood Stadium and Entertainment District, an extended line 9 (newly serving the Kaiser Permanente South Bay Medical Center), and the acquisition of the western portion of LA Metro’s Route 130 between the Blue Line Artesia Station and the South Bay Galleria Mall.bus only7.0electric (not specified)
71Purchases 3 zero-emission electric buses to increase fleet size and extend bus service levels on 2 fixed routes in Merced county. The proposed project allows for an expansion of service frequency on one existing inter-community route connecting rural communities to the city of Merced. The route currently operates on limited frequency and is not enough to keep up with existing demand. The project also expands local service on one local route to provide better bus service to a developed residential area currently with limited access to service.bus only3.0electric (not specified)
81Purchase of 3 zero-emission buses that will support Wasco's local Dia-a-Ride shuttle services to expand service to affordable housing projects and expand overall service availability by 50%.bus only3.0zero-emission bus (not specified)
95Supports the phased development of an east-west Cross Valley Corridor by purchasing 14 zero-emission feeder buses in multiple cities in and along the corridor (as well as 16 micro-transit vehicles to be operated in selected cities) that will provide comprehensive access to the future rail system for all these communities and will connect to the California High Speed Rail system.bus only14.0zero-emission bus (not specified)
103The Project implements a new transit service using electric minibuses to serve underserved communities and includes purchasing 5 zero‐emission buses. Also establishes an all‐day and late‐night micro‐transit service in the Downtown area of Culver City and includes procuring 5 vehicles to operate the servicebus only5.0zero-emission bus (not specified)
\n", - "
" - ], - "text/plain": [ - " project_description \\\n", - "0 Purchase 13 60-foot articulated BRT buses and 16 45-foot electric commuter buses \n", - "5 Purchase five 40-foot CNG buses for BRT Route linking SARTC to Metrolink/Amtrak \n", - "16 Purchase 20 zero-emission buses to extend Route 486 to the Pamona Metrolink station and increase frequencies \n", - "34 Acquire 112 zero-emission buses to replace existing propane vehicles and add new vehicles, in order to increase frequency of all existing DASH routes to 15-minute service and add 4 new routes, serving communities throughout the City of Los Angeles as recommended in the comprehensive Transit Service Analysis. \n", - "51 Purchase 7 new coach-style buses to support a new intercity service that connects Redding to Sacramento. Purchase 7 new coach-style buses to support a new intercity service that connects Redding to Sacramento Shata Regional Transportation Agency Bus System to Sacramento International Airport \n", - "68 Purchase 7 zero emission buses to enhance and extend Route 14 from Playa Vista to Inglewood, bringing new transit opportunities to disadvantaged communities, while also integrating light rail and bus services. \n", - "70 Purchase 7 electric buses to expand services on Line 4X (between Torrance and Downton LA), on an extended line 10 (serving the Metro Green Line Crenshaw station and the Inglewood Stadium and Entertainment District, an extended line 9 (newly serving the Kaiser Permanente South Bay Medical Center), and the acquisition of the western portion of LA Metro’s Route 130 between the Blue Line Artesia Station and the South Bay Galleria Mall. \n", - "71 Purchases 3 zero-emission electric buses to increase fleet size and extend bus service levels on 2 fixed routes in Merced county. The proposed project allows for an expansion of service frequency on one existing inter-community route connecting rural communities to the city of Merced. The route currently operates on limited frequency and is not enough to keep up with existing demand. The project also expands local service on one local route to provide better bus service to a developed residential area currently with limited access to service. \n", - "81 Purchase of 3 zero-emission buses that will support Wasco's local Dia-a-Ride shuttle services to expand service to affordable housing projects and expand overall service availability by 50%. \n", - "95 Supports the phased development of an east-west Cross Valley Corridor by purchasing 14 zero-emission feeder buses in multiple cities in and along the corridor (as well as 16 micro-transit vehicles to be operated in selected cities) that will provide comprehensive access to the future rail system for all these communities and will connect to the California High Speed Rail system. \n", - "103 The Project implements a new transit service using electric minibuses to serve underserved communities and includes purchasing 5 zero‐emission buses. Also establishes an all‐day and late‐night micro‐transit service in the Downtown area of Culver City and includes procuring 5 vehicles to operate the service \n", - "\n", - " project_type bus_count prop_type \n", - "0 bus only 13.0 electric (not specified) \n", - "5 bus only 40.0 CNG \n", - "16 bus only 20.0 zero-emission bus (not specified) \n", - "34 bus only 112.0 zero-emission bus (not specified) \n", - "51 bus only 14.0 not specified \n", - "68 bus only 7.0 zero-emission bus (not specified) \n", - "70 bus only 7.0 electric (not specified) \n", - "71 bus only 3.0 electric (not specified) \n", - "81 bus only 3.0 zero-emission bus (not specified) \n", - "95 bus only 14.0 zero-emission bus (not specified) \n", - "103 bus only 5.0 zero-emission bus (not specified) " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "just_bus[[\"project_description\", \"project_type\", \"bus_count\", \"prop_type\"]]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "4b4db842-cf70-4c44-9e04-a2599d162df9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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project_descriptionproject_typebus_countprop_type
11Bus rapid transit infrastructure along the MLK Corridor and Crosstown Miner Corridor, including the acquisition of 12 new zero-emission electric vehiclesincludes bus and non-bus components12.0zero-emission bus (not specified)
29Deploys 40 zero-emission electric buses to double service levels on up to 8 routes, add 2 new routes; Implements a new circulator/on-demand first-mile/last-mile service; and construction of a new maintenance facility with solar canopy structures.includes bus and non-bus components40.0electric (not specified)
30Deploys 7 zero-emission battery electric buses and upgrades charging infrastructure serving AVTA local and commuter bus routes, bringing the entire AVTA system to fully electric status (the first in the nation) by 2019; Deploys 5 zero-emission battery electric buses and related infrastructure for Long Beach Transit services. Increased frequency on up to 5 local and community transit routes operated by LBT.includes bus and non-bus components7.0electric (not specified)
33Purchase of 6 zero-emission battery-electric buses and the construction of charging infrastructure to allow extension of 15-min service connecting Southwest Fresno to the northern part of Fresno and creating a new route providing access to job centers.includes bus and non-bus components6.0electric (not specified)
35Construction- Purchase 10- 40 foot battery electric busesincludes bus and non-bus components10.0electric (not specified)
52Purchases 13 electric buses and funds capital improvements including new bus stops, pedestrian crossings, and charging infrastructureincludes bus and non-bus components13.0electric (not specified)
56Purchase of 11 zero emission battery electric buses and supportive charging infrastructure to allow for expansion of the zero-emission bus fleet and implement a new zero-emission microtransit service that is fully integrated into local and regional intermodal transit networks.includes bus and non-bus components11.0electric (not specified)
60Construction of a new transit center in Clearlake and purchase 4 hydrogen fuel-cell buses with associated infrastructure. The project would expand service to out of county destinations, including the Sonoma County Airport and the Santa Rosa Bus Terminal in Downtown Santa Rosa. Hydrogen fuel cell technology is used in order to allow extended range services to be operated, contributing to increased ridership.includes bus and non-bus components4.0FCEB
61Purchase of 5 zero-emission battery- electric buses and the construction of charging infrastructure to create a zero-emission over-the-road coach commuter route between the Greater Long Beach area and the University of California, Los Angeles (UCLA).includes bus and non-bus components5.0electric (not specified)
73Creates a zero-emission transit ecosystem that offers end-to-end solutions for residents, employees and the global audience drawn by tourism/convention centers and the LA 2028 Summer Olympics events. Project components include (1) purchase of 7 zero-emission battery electric vans to implement a new service connecting John Wayne Airport to Anaheim, (2) purchase of 10 electric vehicles and associated infrastructure to expand on-demand micro transit services into new neighborhoods and service areas, (3) purchase of 15 zeroemission buses to replace existing buses and augment existing routes, including installation of photovoltaic electricity generation at two facilities, and (4) purchase of 10 additional zero-emission buses for a new east/west connector service.includes bus and non-bus components42.0BEB
74Purchase of 6 zero emission microtransit buses to augment existing microtransit services and expand the service area, purchase 6 zeroemission school buses (in partnership with the Antelope Valley School Transportation Agency), and implements associated charging infrastructure.includes bus and non-bus components12.0zero-emission bus (not specified)
78Project purchases 27 battery-electric busses for replacement and expansion, allowing for reduced headway service on two routes and a new route to connect the Glendale Transportation Center with Glendale Community College, and completing the transition to zeroemission for the Arroyo Verdugo Transit Operators, serving Glendale, La Cañada Flintridge, La Crescenta and Montrose. Also includes design and construction of a new parking deck to accommodate associated infrastructure and a photovoltaic (solar) canopy. Lastly, it provides upgrades to 400 bus stops, contactless payment options and a new smart phone application for riders.includes bus and non-bus components27.0BEB
80Purchase of 10 zero-emission electric buses and associated charging infrastructure to replace CNG and gas buses and implement service expansion between the City of Torrance and Downtown Los Angeles. Buses will be deployed in more frequent service on key routes, including services that will take advantage of bus priority lanesincludes bus and non-bus components10.0electric (not specified)
84Procure 11 hydrogen fuel cell electric buses, design and install a hydrogen fueling station to provide fuel for the buses and for private and other fleet vehicles, and design and construct an intermodal transit and housing center. The buses will serve the local Trinidad to Scotia route as well as a new intercity route to Ukiah, connecting riders to Mendocino County and south to the SMART train and the San Francisco Bay Area. The hydrogen station and transit and housing center will both be located in low-income census tracts in downtown Eureka, the Humboldt County seat and largest city.includes bus and non-bus components11.0electric (not specified)
85Purchases 261 zero emission buses and supportive infrastructure to deploy on LA Metro's Tier 1 and Tier 2 routes from Divisions 9 and 18, as well as the J (Silver) Line. Project also includes corridor improvements on high frequency bus corridors (many shared with other transit agencies), including bus-only lanes, transit signal priority, bus bulbs and boarding islands, bus shelters, and real-time passenger information.includes bus and non-bus components261.0zero-emission bus (not specified)
92Purchases eight battery-electric buses and 3 electric microtransit vans, continuing fleet conversion and allowing expansion of microtransit service into additional zones serving the City of Goleta, UC Santa Barbara, and the Goleta rail station. Funds general transit improvements including signal priority, contactless payment deployment, additional bike racks, and bus shelter improvements, and constructs facility improvements at two terminals including the construction of new ZEB infrastructure.includes bus and non-bus components8.0electric (not specified)
93Includes the purchase of 30 zero-emission buses and associated charging infrastructure and passenger amenities for Petaluma Transit, Santa Rosa CityBus and Sonoma County Transit, construction of the SMART Petaluma North commuter rail station, and improved network integration among all application partners and other transit operators in Sonoma County, including contactless payment equipment for Mendocino Transit Authorityincludes bus and non-bus components30.0zero-emission bus (not specified)
99Purchases 40 zero-emission buses and associated infrastructure and implements a set of interrelated transit improvements. Includes service optimization improvements such as transit signal priority and other corridor improvements, installation of fare payment validators, and onboard passenger amenities.includes bus and non-bus components40.0zero-emission bus (not specified)
101The Project expands frequency on two high‐performing routes and fully delivers phases 2‐4 of the City's bus charging infrastructure plan. This includes the implementation of a new vehicle charging system and utility upgrades, including construction of a charging canopy, to support fleet electrification efforts; and purchase up to 103 zero‐emission buses to replace existing CNG buses (which includes a mix of 30‐foot, 40‐foot and 60‐foot vehicles).includes bus and non-bus components103.0zero-emission bus (not specified)
102The Project constructs a new transit center, linking city bus routes with regional routes, including to regional rail service; constructs a park‐and‐ride lot at the proposed new transit center; purchases 6 zero‐emission buses to replace existing CNG buses that will allow the applicant to operate three routes with zero‐emission buses.includes bus and non-bus components6.0zero-emission bus (not specified)
105This project deploys 4 zero‐emission buses in Tribal and low‐income population regions, constructs a new transit center in Crescent City, expand existing intercity routes under a unified Redwood Coast Express brand to reduce transfers and increase ridership, and accelerate statewide efforts to develop a prototype fuel cell electric over‐the‐road coach (procuring 1 demonstration coach)includes bus and non-bus components4.0FCEB
110The Project includes a set of interrelated investments with five components, including: Purchasing 33 expansion zero‐emission buses and associated infrastructure to expand service; Replacing 10 gas‐powered paratransit vehicles with zero‐emission vehicles to support an existing paratransit bus pilot effort; Implementation of transit signal priority along a key 12‐mile corridor; Installation of bicycle lockers and fast charging stations at targeted transit stations.includes bus and non-bus components33.0zero-emission bus (not specified)
117Purchases 24 zero-emission buses to expand service frequency on the heavily traversed Highways 1 and Highway 17 corridors while also redeveloping the Watsonville Transit Station and Pacific Station to include more than 180 mixed‐use, affordable housing units and implements real time passenger information, bicycle amenities and other multimodal improvements.includes bus and non-bus components24.0zero-emission bus (not specified)
123Constructs a new transit facility that can support a growing zero‐emission vehicle fleet and purchases 15 zero‐emission buses to implement a new on‐demand local bus service in five newly formed transit service zones within Yuba and Sutter counties. Also includes 1 zero‐emission over‐the‐road coach to implement a new commuter service to Roseville.includes bus and non-bus components16.0zero-emission bus (not specified)
\n", - "
" - ], - "text/plain": [ - " project_description \\\n", - "11 Bus rapid transit infrastructure along the MLK Corridor and Crosstown Miner Corridor, including the acquisition of 12 new zero-emission electric vehicles \n", - "29 Deploys 40 zero-emission electric buses to double service levels on up to 8 routes, add 2 new routes; Implements a new circulator/on-demand first-mile/last-mile service; and construction of a new maintenance facility with solar canopy structures. \n", - "30 Deploys 7 zero-emission battery electric buses and upgrades charging infrastructure serving AVTA local and commuter bus routes, bringing the entire AVTA system to fully electric status (the first in the nation) by 2019; Deploys 5 zero-emission battery electric buses and related infrastructure for Long Beach Transit services. Increased frequency on up to 5 local and community transit routes operated by LBT. \n", - "33 Purchase of 6 zero-emission battery-electric buses and the construction of charging infrastructure to allow extension of 15-min service connecting Southwest Fresno to the northern part of Fresno and creating a new route providing access to job centers. \n", - "35 Construction- Purchase 10- 40 foot battery electric buses \n", - "52 Purchases 13 electric buses and funds capital improvements including new bus stops, pedestrian crossings, and charging infrastructure \n", - "56 Purchase of 11 zero emission battery electric buses and supportive charging infrastructure to allow for expansion of the zero-emission bus fleet and implement a new zero-emission microtransit service that is fully integrated into local and regional intermodal transit networks. \n", - "60 Construction of a new transit center in Clearlake and purchase 4 hydrogen fuel-cell buses with associated infrastructure. The project would expand service to out of county destinations, including the Sonoma County Airport and the Santa Rosa Bus Terminal in Downtown Santa Rosa. Hydrogen fuel cell technology is used in order to allow extended range services to be operated, contributing to increased ridership. \n", - "61 Purchase of 5 zero-emission battery- electric buses and the construction of charging infrastructure to create a zero-emission over-the-road coach commuter route between the Greater Long Beach area and the University of California, Los Angeles (UCLA). \n", - "73 Creates a zero-emission transit ecosystem that offers end-to-end solutions for residents, employees and the global audience drawn by tourism/convention centers and the LA 2028 Summer Olympics events. Project components include (1) purchase of 7 zero-emission battery electric vans to implement a new service connecting John Wayne Airport to Anaheim, (2) purchase of 10 electric vehicles and associated infrastructure to expand on-demand micro transit services into new neighborhoods and service areas, (3) purchase of 15 zeroemission buses to replace existing buses and augment existing routes, including installation of photovoltaic electricity generation at two facilities, and (4) purchase of 10 additional zero-emission buses for a new east/west connector service. \n", - "74 Purchase of 6 zero emission microtransit buses to augment existing microtransit services and expand the service area, purchase 6 zeroemission school buses (in partnership with the Antelope Valley School Transportation Agency), and implements associated charging infrastructure. \n", - "78 Project purchases 27 battery-electric busses for replacement and expansion, allowing for reduced headway service on two routes and a new route to connect the Glendale Transportation Center with Glendale Community College, and completing the transition to zeroemission for the Arroyo Verdugo Transit Operators, serving Glendale, La Cañada Flintridge, La Crescenta and Montrose. Also includes design and construction of a new parking deck to accommodate associated infrastructure and a photovoltaic (solar) canopy. Lastly, it provides upgrades to 400 bus stops, contactless payment options and a new smart phone application for riders. \n", - "80 Purchase of 10 zero-emission electric buses and associated charging infrastructure to replace CNG and gas buses and implement service expansion between the City of Torrance and Downtown Los Angeles. Buses will be deployed in more frequent service on key routes, including services that will take advantage of bus priority lanes \n", - "84 Procure 11 hydrogen fuel cell electric buses, design and install a hydrogen fueling station to provide fuel for the buses and for private and other fleet vehicles, and design and construct an intermodal transit and housing center. The buses will serve the local Trinidad to Scotia route as well as a new intercity route to Ukiah, connecting riders to Mendocino County and south to the SMART train and the San Francisco Bay Area. The hydrogen station and transit and housing center will both be located in low-income census tracts in downtown Eureka, the Humboldt County seat and largest city. \n", - "85 Purchases 261 zero emission buses and supportive infrastructure to deploy on LA Metro's Tier 1 and Tier 2 routes from Divisions 9 and 18, as well as the J (Silver) Line. Project also includes corridor improvements on high frequency bus corridors (many shared with other transit agencies), including bus-only lanes, transit signal priority, bus bulbs and boarding islands, bus shelters, and real-time passenger information. \n", - "92 Purchases eight battery-electric buses and 3 electric microtransit vans, continuing fleet conversion and allowing expansion of microtransit service into additional zones serving the City of Goleta, UC Santa Barbara, and the Goleta rail station. Funds general transit improvements including signal priority, contactless payment deployment, additional bike racks, and bus shelter improvements, and constructs facility improvements at two terminals including the construction of new ZEB infrastructure. \n", - "93 Includes the purchase of 30 zero-emission buses and associated charging infrastructure and passenger amenities for Petaluma Transit, Santa Rosa CityBus and Sonoma County Transit, construction of the SMART Petaluma North commuter rail station, and improved network integration among all application partners and other transit operators in Sonoma County, including contactless payment equipment for Mendocino Transit Authority \n", - "99 Purchases 40 zero-emission buses and associated infrastructure and implements a set of interrelated transit improvements. Includes service optimization improvements such as transit signal priority and other corridor improvements, installation of fare payment validators, and onboard passenger amenities. \n", - "101 The Project expands frequency on two high‐performing routes and fully delivers phases 2‐4 of the City's bus charging infrastructure plan. This includes the implementation of a new vehicle charging system and utility upgrades, including construction of a charging canopy, to support fleet electrification efforts; and purchase up to 103 zero‐emission buses to replace existing CNG buses (which includes a mix of 30‐foot, 40‐foot and 60‐foot vehicles). \n", - "102 The Project constructs a new transit center, linking city bus routes with regional routes, including to regional rail service; constructs a park‐and‐ride lot at the proposed new transit center; purchases 6 zero‐emission buses to replace existing CNG buses that will allow the applicant to operate three routes with zero‐emission buses. \n", - "105 This project deploys 4 zero‐emission buses in Tribal and low‐income population regions, constructs a new transit center in Crescent City, expand existing intercity routes under a unified Redwood Coast Express brand to reduce transfers and increase ridership, and accelerate statewide efforts to develop a prototype fuel cell electric over‐the‐road coach (procuring 1 demonstration coach) \n", - "110 The Project includes a set of interrelated investments with five components, including: Purchasing 33 expansion zero‐emission buses and associated infrastructure to expand service; Replacing 10 gas‐powered paratransit vehicles with zero‐emission vehicles to support an existing paratransit bus pilot effort; Implementation of transit signal priority along a key 12‐mile corridor; Installation of bicycle lockers and fast charging stations at targeted transit stations. \n", - "117 Purchases 24 zero-emission buses to expand service frequency on the heavily traversed Highways 1 and Highway 17 corridors while also redeveloping the Watsonville Transit Station and Pacific Station to include more than 180 mixed‐use, affordable housing units and implements real time passenger information, bicycle amenities and other multimodal improvements. \n", - "123 Constructs a new transit facility that can support a growing zero‐emission vehicle fleet and purchases 15 zero‐emission buses to implement a new on‐demand local bus service in five newly formed transit service zones within Yuba and Sutter counties. Also includes 1 zero‐emission over‐the‐road coach to implement a new commuter service to Roseville. \n", - "\n", - " project_type bus_count \\\n", - "11 includes bus and non-bus components 12.0 \n", - "29 includes bus and non-bus components 40.0 \n", - "30 includes bus and non-bus components 7.0 \n", - "33 includes bus and non-bus components 6.0 \n", - "35 includes bus and non-bus components 10.0 \n", - "52 includes bus and non-bus components 13.0 \n", - "56 includes bus and non-bus components 11.0 \n", - "60 includes bus and non-bus components 4.0 \n", - "61 includes bus and non-bus components 5.0 \n", - "73 includes bus and non-bus components 42.0 \n", - "74 includes bus and non-bus components 12.0 \n", - "78 includes bus and non-bus components 27.0 \n", - "80 includes bus and non-bus components 10.0 \n", - "84 includes bus and non-bus components 11.0 \n", - "85 includes bus and non-bus components 261.0 \n", - "92 includes bus and non-bus components 8.0 \n", - "93 includes bus and non-bus components 30.0 \n", - "99 includes bus and non-bus components 40.0 \n", - "101 includes bus and non-bus components 103.0 \n", - "102 includes bus and non-bus components 6.0 \n", - "105 includes bus and non-bus components 4.0 \n", - "110 includes bus and non-bus components 33.0 \n", - "117 includes bus and non-bus components 24.0 \n", - "123 includes bus and non-bus components 16.0 \n", - "\n", - " prop_type \n", - "11 zero-emission bus (not specified) \n", - "29 electric (not specified) \n", - "30 electric (not specified) \n", - "33 electric (not specified) \n", - "35 electric (not specified) \n", - "52 electric (not specified) \n", - "56 electric (not specified) \n", - "60 FCEB \n", - "61 electric (not specified) \n", - "73 BEB \n", - "74 zero-emission bus (not specified) \n", - "78 BEB \n", - "80 electric (not specified) \n", - "84 electric (not specified) \n", - "85 zero-emission bus (not specified) \n", - "92 electric (not specified) \n", - "93 zero-emission bus (not specified) \n", - "99 zero-emission bus (not specified) \n", - "101 zero-emission bus (not specified) \n", - "102 zero-emission bus (not specified) \n", - "105 FCEB \n", - "110 zero-emission bus (not specified) \n", - "117 zero-emission bus (not specified) \n", - "123 zero-emission bus (not specified) " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bus_non_bus[[\"project_description\", \"project_type\", \"bus_count\", \"prop_type\"]]" - ] - }, - { - "cell_type": "markdown", - "id": "c0aa374e-985b-46b3-a7ab-f3bc66e36204", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "## DEPRECATED - Data Analysis\n", - "see `cost_per_bus_analysis` notebook" - ] - }, - { - "cell_type": "markdown", - "id": "02cce57d-f82c-4f85-be53-814538b6b6c3", - "metadata": { - "tags": [] - }, - "source": [ - "### Consolidate up grant recipient name" - ] - }, - { - "cell_type": "markdown", - "id": "a5d0e920-cfd9-465f-89eb-81214b27070a", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "### aggregate up" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a0a396f0-c9ad-48bd-9767-45b5a8b53d25", - "metadata": {}, - "outputs": [], - "source": [ - "# aggregate # of buses and allocation by transit agency\n", - "# bus_cost = only_bus.groupby('grant_recipient').agg({\n", - "# '#_of_buses':\"sum\",\n", - "# 'allocation_amount':'sum'\n", - "# }).reset_index()\n", - "\n", - "bus_cost = (\n", - " bus_only.groupby(\"grant_recipient\")\n", - " .agg({\"bus_count\": \"sum\", \"tircp_award_amount_($)\": \"sum\"})\n", - " .reset_index()\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c5c8dfa3-310f-47bd-8d95-2eed37feec73", - "metadata": {}, - "outputs": [], - "source": [ - "# confirm aggregation worked\n", - "bus_cost" - ] - }, - { - "cell_type": "markdown", - "id": "0f074183-9110-41fc-820b-4483fe9b076b", - "metadata": { - "tags": [] - }, - "source": [ - "### create new cost per bus column" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "391fdd1a-585b-43e4-b70b-18c7f54a8263", - "metadata": {}, - "outputs": [], - "source": [ - "bus_cost[\"cost_per_bus\"] = (\n", - " bus_cost[\"tircp_award_amount_($)\"] / bus_cost[\"bus_count\"]\n", - ").astype(\"int64\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3219820e-0d80-4c1b-92c5-f098f09a22a9", - "metadata": {}, - "outputs": [], - "source": [ - "# confirm new column was created and values were populated\n", - "bus_cost.sort_values(\"cost_per_bus\")" - ] - }, - { - "cell_type": "markdown", - "id": "7c2df629-863e-476f-8f1c-934535a1feb0", - "metadata": {}, - "source": [ - "### Export cost per bus via project tracking sheet to gcs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b7ad9fe7-a705-4138-8f3d-138f6d0146f6", - "metadata": {}, - "outputs": [], - "source": [ - "bus_cost.to_csv(\n", - " \"gs://calitp-analytics-data/data-analyses/bus_procurement_cost/tircp_project_cost_per_bus.csv\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b6946ba9-55e8-4e53-9da9-56310e9c3661", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 83225404276a65a6a0aa3e8f6452dbaa3be94b4a Mon Sep 17 00:00:00 2001 From: csuyat-dot Date: Fri, 28 Jun 2024 20:59:20 +0000 Subject: [PATCH 36/36] left notes on refacor_notes --- .../cost_per_bus_analysis.ipynb | 2 +- bus_procurement_cost/refactor_bus_cost.ipynb | 3939 +---------------- bus_procurement_cost/refactor_notes.md | 23 +- 3 files changed, 102 insertions(+), 3862 deletions(-) diff --git a/bus_procurement_cost/cost_per_bus_analysis.ipynb b/bus_procurement_cost/cost_per_bus_analysis.ipynb index 009886a33..a47c83eda 100644 --- a/bus_procurement_cost/cost_per_bus_analysis.ipynb +++ b/bus_procurement_cost/cost_per_bus_analysis.ipynb @@ -1656,7 +1656,7 @@ ], "source": [ "display(Markdown(\n", - " \"## Which is the cost per bus compared against all propulsion types?\"\n", + " \"## What is the cost per bus compared against all propulsion types?\"\n", "))\n", "display(\n", "# cpb by prop type\n", diff --git a/bus_procurement_cost/refactor_bus_cost.ipynb b/bus_procurement_cost/refactor_bus_cost.ipynb index ed390c240..196491b0c 100644 --- a/bus_procurement_cost/refactor_bus_cost.ipynb +++ b/bus_procurement_cost/refactor_bus_cost.ipynb @@ -434,7 +434,19 @@ "execution_count": 12, "id": "0b2be581-f4e9-4f7e-bde5-01f2de183479", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'cost_per_bus_nb_scripts'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[12], line 7\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mseaborn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01msns\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mshared_utils\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcost_per_bus_nb_scripts\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdisplay\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Markdown, display\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mticker\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ScalarFormatter\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'cost_per_bus_nb_scripts'" + ] + } + ], "source": [ "## moved to analysis NB 6/25\n", "import matplotlib.pyplot as plt\n", @@ -487,7 +499,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "fb1ae513-a8bf-4eb1-9e7b-71f828ebb9ea", "metadata": { "tags": [] @@ -512,7 +524,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "4a5bc209-a660-4c18-86b0-574640391a7d", "metadata": { "tags": [] @@ -587,7 +599,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "ea9c3269-d53d-4d94-bc22-c6768cb63d91", "metadata": { "tags": [] @@ -736,7 +748,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "4e16119d-f6f3-478b-a419-7c4989557910", "metadata": { "tags": [] @@ -889,7 +901,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "359f3b7a-d691-446f-9a14-424c47fc0929", "metadata": { "tags": [] @@ -1102,7 +1114,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "19f4bd75-f614-4937-880a-1e1a6ff2eb7f", "metadata": { "tags": [] @@ -1305,7 +1317,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "c9ffd5a9-b772-4509-b84c-9a96760b3112", "metadata": {}, "outputs": [], @@ -1317,32 +1329,10 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "f8aa3674-78fe-4ba9-8f5e-697d91ff4011", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(82, 8)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Index(['transit_agency', 'total_project_count', 'total_project_count_ppno',\n", - " 'total_agg_cost', 'total_bus_count', 'new_cost_per_bus',\n", - " 'new_zscore_cost_per_bus', 'new_is_cpb_outlier?'],\n", - " dtype='object')" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# testing the improved cpb agg function\n", "# default grouby column is `transit_agency`\n", @@ -1359,101 +1349,10 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "d2bca15a-c12c-4cf5-a5a9-d591ee73a359", "metadata": {}, - 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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
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48Rogue Valley Transportation District1039375006.0656250-0.319040False
80Whatcom Transportation Authority (WTA)10964486511.08768050.231518False
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
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prop_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
0BEB031170455813164.010393640.923505False
1CNG121176039140252.06985680.122141False
2FCEB26120951335102.011857971.267835False
3electric (not specified)125667800044.012881361.508480False
4ethanol1010067509.0111861-1.257470False
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bus_size_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpb
0articulated025823757641.01420428
1cutaway3016694500152.0109832
2not specified406509919038881.0578795
3over-the-road01951600014.0679714
4standard/conventional (30ft-45ft)036234253277264.0887323
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bus_size_typetotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
0articulated025823757641.014204281.598471False
1cutaway3016694500152.0109832-1.466801False
2not specified406509919038881.0578795-0.369972False
3over-the-road01951600014.0679714-0.133939False
4standard/conventional (30ft-45ft)037237476601265.08961380.372242False
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" - ], - "text/plain": [ - " bus_size_type total_project_count \\\n", - "0 articulated 0 \n", - "1 cutaway 3 \n", - "2 not specified 40 \n", - "3 over-the-road 0 \n", - "4 standard/conventional (30ft-45ft) 0 \n", - "\n", - " total_project_count_ppno total_agg_cost total_bus_count \\\n", - "0 2 58237576 41.0 \n", - "1 0 16694500 152.0 \n", - "2 6 509919038 881.0 \n", - "3 1 9516000 14.0 \n", - "4 37 237476601 265.0 \n", - "\n", - " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", - "0 1420428 1.598471 False \n", - "1 109832 -1.466801 False \n", - "2 578795 -0.369972 False \n", - "3 679714 -0.133939 False \n", - "4 896138 0.372242 False " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# double checking the bus size agg new vs old\n", "#EVERYTHING CHECKS OUT!\n", @@ -2098,244 +1438,10 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "0391dd4d-23e1-49cb-8123-509954c796e8", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(82, 6)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "(82, 8)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countcpb
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)10100000008.01250000
1Alameda County Transit Authority012284664020.01142332
2Antelope Valley Transit Authority (AVTA)013947800029.01361310
3CITY OF PORTERVILLE (PORTERVILLE, CA)0127818913.0927297
4CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...0136235364.0905884
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)10100000008.012500001.163100False
1Alameda County Transit Authority012284664020.011423320.894336False
2Antelope Valley Transit Authority (AVTA)013947800029.013613101.440957False
3CITY OF PORTERVILLE (PORTERVILLE, CA)0127818913.09272970.357558False
4CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...0136235364.09058840.304106False
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" - ], - "text/plain": [ - " transit_agency total_project_count \\\n", - "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) 1 \n", - "1 Alameda County Transit Authority 0 \n", - "2 Antelope Valley Transit Authority (AVTA) 0 \n", - "3 CITY OF PORTERVILLE (PORTERVILLE, CA) 0 \n", - "4 CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ... 0 \n", - "\n", - " total_project_count_ppno total_agg_cost total_bus_count \\\n", - "0 0 10000000 8.0 \n", - "1 1 22846640 20.0 \n", - "2 1 39478000 29.0 \n", - "3 1 2781891 3.0 \n", - "4 1 3623536 4.0 \n", - "\n", - " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", - "0 1250000 1.163100 False \n", - "1 1142332 0.894336 False \n", - "2 1361310 1.440957 False \n", - "3 927297 0.357558 False \n", - "4 905884 0.304106 False " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#EVERYTHING CHECKS OUT!\n", "# move forward with `new_cpb_aggregate` function\n", @@ -2362,218 +1468,10 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "f21298ee-0efb-4f91-ba63-55fc2645a4d2", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['transit_agency', 'project_title', 'prop_type', 'bus_size_type',\n", - " 'description', 'new_project_type', 'total_cost', 'bus_count', 'source',\n", - " 'ppno', 'project_description', 'cost_per_bus', 'zscore_cost_per_bus',\n", - " 'is_cpb_outlier?'],\n", - " dtype='object')" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "(88, 14)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencyproject_titleprop_typebus_size_typedescriptionnew_project_typetotal_costbus_countsourceppnoproject_descriptioncost_per_buszscore_cost_per_busis_cpb_outlier?
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)Puerto Rico Initiative Minimizing Emissions Pl...electric (not specified)not specifiedThe Metropolitan Bus Authority will receive fu...bus only100000008.0ftaNoneNone12500000.917956False
1Cape Fear Public Transportation AuthorityWave Transit Low Emissions Replacement VehiclesCNGnot specifiedWave Transit will receive funding to buy compr...bus only28602505.0ftaNoneNone572050-0.529139False
2Central Oklahoma Transportation and Parking Au...COTPA, dba EMBARK Elimination of Fixed Route D...CNGnot specifiedThe Central Oklahoma Transportation and Parkin...bus only42787729.0ftaNoneNone475419-0.735399False
3Champaign-Urbana Mass Transit DistrictMTD 40-Foot Hybrid Replacement Buseslow emission (hybrid)not specifiedThe Champaign-Urbana Mass Transit District wil...bus only663539410.0ftaNoneNone663539-0.333854False
4City of BeaumontBeaumont Municipal Transit Zips to Improve Low...CNGnot specifiedBeaumont Municipal Transit will receive fundin...bus only28194605.0ftaNoneNone563892-0.546552False
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" - ], - "text/plain": [ - " transit_agency \\\n", - "0 AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA) \n", - "1 Cape Fear Public Transportation Authority \n", - "2 Central Oklahoma Transportation and Parking Au... \n", - "3 Champaign-Urbana Mass Transit District \n", - "4 City of Beaumont \n", - "\n", - " project_title \\\n", - "0 Puerto Rico Initiative Minimizing Emissions Pl... \n", - "1 Wave Transit Low Emissions Replacement Vehicles \n", - "2 COTPA, dba EMBARK Elimination of Fixed Route D... \n", - "3 MTD 40-Foot Hybrid Replacement Buses \n", - "4 Beaumont Municipal Transit Zips to Improve Low... \n", - "\n", - " prop_type bus_size_type \\\n", - "0 electric (not specified) not specified \n", - "1 CNG not specified \n", - "2 CNG not specified \n", - "3 low emission (hybrid) not specified \n", - "4 CNG not specified \n", - "\n", - " description new_project_type \\\n", - "0 The Metropolitan Bus Authority will receive fu... bus only \n", - "1 Wave Transit will receive funding to buy compr... bus only \n", - "2 The Central Oklahoma Transportation and Parkin... bus only \n", - "3 The Champaign-Urbana Mass Transit District wil... bus only \n", - "4 Beaumont Municipal Transit will receive fundin... bus only \n", - "\n", - " total_cost bus_count source ppno project_description cost_per_bus \\\n", - "0 10000000 8.0 fta None None 1250000 \n", - "1 2860250 5.0 fta None None 572050 \n", - "2 4278772 9.0 fta None None 475419 \n", - "3 6635394 10.0 fta None None 663539 \n", - "4 2819460 5.0 fta None None 563892 \n", - "\n", - " zscore_cost_per_bus is_cpb_outlier? \n", - "0 0.917956 False \n", - "1 -0.529139 False \n", - "2 -0.735399 False \n", - "3 -0.333854 False \n", - "4 -0.546552 False " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "min -1.672813\n", - "max 2.661856\n", - "Name: zscore_cost_per_bus, dtype: float64" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# read in all cleaned project data without outliers\n", "# cpb_analysis_data_merge is bus only projects. all DGS rows were Bus only projects anyways\n", @@ -2590,55 +1488,10 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "ece95fb7-cbb8-46bd-a5f9-2b68a47a4817", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "min 36250\n", - "max 1611662\n", - "Name: new_cost_per_bus, dtype: int64" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "min 1.0\n", - "max 160.0\n", - "Name: total_bus_count, dtype: float64" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "min 181250\n", - "max 103000000\n", - "Name: total_agg_cost, dtype: int64" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "min -1.939451\n", - "max 2.182513\n", - "Name: new_zscore_cost_per_bus, dtype: float64" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "## moved to final NB\n", "\n", @@ -2661,320 +1514,10 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "03940ccb-d1e6-439d-a930-13dae17537b2", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(88, 14)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "(82, 8)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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transit_agencyproject_titleprop_typebus_size_typedescriptionnew_project_typetotal_costbus_countsourceppnoproject_descriptioncost_per_buszscore_cost_per_busis_cpb_outlier?
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)Puerto Rico Initiative Minimizing Emissions Pl...electric (not specified)not specifiedThe Metropolitan Bus Authority will receive fu...bus only100000008.0ftaNoneNone12500000.917956False
1Cape Fear Public Transportation AuthorityWave Transit Low Emissions Replacement VehiclesCNGnot specifiedWave Transit will receive funding to buy compr...bus only28602505.0ftaNoneNone572050-0.529139False
2Central Oklahoma Transportation and Parking Au...COTPA, dba EMBARK Elimination of Fixed Route D...CNGnot specifiedThe Central Oklahoma Transportation and Parkin...bus only42787729.0ftaNoneNone475419-0.735399False
3Champaign-Urbana Mass Transit DistrictMTD 40-Foot Hybrid Replacement Buseslow emission (hybrid)not specifiedThe Champaign-Urbana Mass Transit District wil...bus only663539410.0ftaNoneNone663539-0.333854False
4City of BeaumontBeaumont Municipal Transit Zips to Improve Low...CNGnot specifiedBeaumont Municipal Transit will receive fundin...bus only28194605.0ftaNoneNone563892-0.546552False
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transit_agencytotal_project_counttotal_project_count_ppnototal_agg_costtotal_bus_countnew_cost_per_busnew_zscore_cost_per_busnew_is_cpb_outlier?
0AUTORIDAD METROPOLITANA DE AUTOBUSES (PRMBA)10100000008.012500001.236248False
1Alameda County Transit Authority012284664020.011423320.954542False
2Antelope Valley Transit Authority (AVTA)013947800029.013613101.527483False
3CITY OF PORTERVILLE (PORTERVILLE, CA)0127818913.09272970.391916False
4CULVER CITY TRANSPORTATION DEPARTMENT (CULVER ...0136235364.09058840.335891False
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1CNG176039140252.0698568
2FCEB120951335102.01185797
3electric (not specified)5667800044.01288136
4ethanol10067509.0111861
5low emission (hybrid)91824361145.0633271
6low emission (propane)840396944.0190999
7mix (zero and low emission)36775430125.0294203
8not specified41552404325.0127853
9zero-emission bus (not specified)128156513143.0896199
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4ethanol9.01006750111861
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6low emission (propane)44.08403969190999
7mix (zero and low emission)125.036775430294203
8not specified325.041552404127853
9zero-emission bus (not specified)143.0128156513896199
10Grand Total1352.0828620391612884
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1FCEB102.01209513351185797
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bus_size_typetotal_agg_costtotal_bus_countnew_cost_per_bus
0articulated5823757641.01420428
1cutaway16694500152.0109832
2not specified509919038881.0578795
3over-the-road951600014.0679714
4standard/conventional (30ft-45ft)234253277264.0887323
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bus_size_typebus_counttotal_costcost_per_bus
0articulated41.0582375761420428
1cutaway152.016694500109832
2not specified881.0509919038578795
3over-the-road14.09516000679714
4standard/conventional (30ft-45ft)264.0234253277887323
5Grand Total1352.0828620391612884
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prop_typebus_counttotal_costcost_per_bus
10Grand Total1352.0828620391612884
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" - ], - "text/plain": [ - " prop_type bus_count total_cost cost_per_bus\n", - "10 Grand Total 1352.0 828620391 612884" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# answers total buses sizes\n", "pivot_size = pd.pivot_table(\n", @@ -3773,145 +1669,10 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "2c933257-bdc2-4007-9571-58475118073c", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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sourcetotal_agg_costtotal_bus_countnew_cost_per_bus
0dgs250112853236.01059800
1fta391257025883.0443099
2tircp187250513233.0803650
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" - ], - "text/plain": [ - " source total_agg_cost total_bus_count new_cost_per_bus\n", - "0 dgs 250112853 236.0 1059800\n", - "1 fta 391257025 883.0 443099\n", - "2 tircp 187250513 233.0 803650" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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sourcebus_counttotal_costcost_per_bus
0dgs236.02501128531059800
1fta883.0391257025443099
2tircp233.0187250513803650
3Grand Total1352.0828620391612884
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" - ], - "text/plain": [ - " source bus_count total_cost cost_per_bus\n", - "0 dgs 236.0 250112853 1059800\n", - "1 fta 883.0 391257025 443099\n", - "2 tircp 233.0 187250513 803650\n", - "3 Grand Total 1352.0 828620391 612884" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# moved to final NB 6/25\n", "\n", @@ -3948,68 +1709,20 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "aace38a4-3f2d-460d-a258-59efa659f852", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(88, 14)" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "merged_data.shape" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "4753f3ea-00b6-4d5e-a3f0-73b3d3593acb", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "792635.3409090909" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "396712.6067531972" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "1046500.7455752213" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "546173.304347826" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# means and standard deviations\n", "# for graphs\n", @@ -4029,108 +1742,10 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "id": "2007be9d-13ec-4d0d-a642-d9a42448b924", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1056659.3043478262" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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prop_typebus_counttotal_costcost_per_bus
0BEB163.01672324891025966
1FCEB102.01209513351185797
2electric (not specified)44.0566780001288136
3zero-emission bus (not specified)143.0128156513896199
4Grand Total452.04730183371046500
\n", - "
" - ], - "text/plain": [ - " prop_type bus_count total_cost cost_per_bus\n", - "0 BEB 163.0 167232489 1025966\n", - "1 FCEB 102.0 120951335 1185797\n", - "2 electric (not specified) 44.0 56678000 1288136\n", - "3 zero-emission bus (not specified) 143.0 128156513 896199\n", - "4 Grand Total 452.0 473018337 1046500" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "1046500.7455752213" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# why is the average different when i use .mean() vs. total cost / bus cout\n", "\n", @@ -4150,21 +1765,10 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "8645cf77-b30a-4c45-b943-ac81e8b5a613", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# chart of all cost per bus in the analysis.\n", "# moved to final NB 6/26\n", @@ -4179,21 +1783,10 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "id": "cefa6800-df50-4eda-95f8-74363ef942d0", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# ZEB cost per bus \n", "# moved to final NB 6/26\n", @@ -4210,21 +1803,10 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "id": "563304d2-2d98-44e6-b3a4-fd54f63fc0d8", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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0articulated025823757641.014204281.605005False
1cutaway3016694500152.0109832-1.464878False
2not specified406509919038881.0578795-0.366399False
3over-the-road01951600014.0679714-0.130011False
4standard/conventional (30ft-45ft)036234253277264.08873230.356283False
\n", - "
" - ], - "text/plain": [ - " bus_size_type total_project_count \\\n", - "0 articulated 0 \n", - "1 cutaway 3 \n", - "2 not specified 40 \n", - "3 over-the-road 0 \n", - "4 standard/conventional (30ft-45ft) 0 \n", - "\n", - " total_project_count_ppno total_agg_cost total_bus_count \\\n", - "0 2 58237576 41.0 \n", - "1 0 16694500 152.0 \n", - "2 6 509919038 881.0 \n", - "3 1 9516000 14.0 \n", - "4 36 234253277 264.0 \n", - "\n", - " new_cost_per_bus new_zscore_cost_per_bus new_is_cpb_outlier? \n", - "0 1420428 1.605005 False \n", - "1 109832 -1.464878 False \n", - "2 578795 -0.366399 False \n", - "3 679714 -0.130011 False \n", - "4 887323 0.356283 False " - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "agg_bus_size" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "id": "5f11c857-ddbe-4871-aeca-e27fa00fbde8", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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prop_typenew_cost_per_bustotal_bus_count
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2FCEB1185797102.0
0BEB1025966163.0
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1CNG698568252.0
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7mix (zero and low emission)294203125.0
6low emission (propane)19099944.0
8not specified127853325.0
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" - ], - "text/plain": [ - " prop_type new_cost_per_bus total_bus_count\n", - "3 electric (not specified) 1288136 44.0\n", - "2 FCEB 1185797 102.0\n", - "0 BEB 1025966 163.0\n", - "9 zero-emission bus (not specified) 896199 143.0\n", - "1 CNG 698568 252.0\n", - "5 low emission (hybrid) 633271 145.0\n", - "7 mix (zero and low emission) 294203 125.0\n", - "6 low emission (propane) 190999 44.0\n", - "8 not specified 127853 325.0\n", - "4 ethanol 111861 9.0" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# multiple bar charts in one cell\n", "# moved to final NB 6/26\n", @@ -4545,68 +1868,10 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "2472461d-7663-4b66-9bde-4c2a199707a5", "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "\n", - "\n", - "# Bus Procurement Cost Analysis\n", - "\n", - "## Summary\n", - "This analysis examines the cost of buses for transit agencies across the county. Specifically, to observe the variation of bus cost for propulsion type and bus sizes.\n", - "\n", - "Data was compiled from three data sources:\n", - "1. FTA Bus and Low- and No-Emission Grant Awards press release (federally funded, nationwide data)\n", - "2. TIRCP project data (state-funded, California only)\n", - "3. DGS usage report for all procurements from California agencies purchasing from New Flyer and Portera Inc.. \n", - "\n", - "The initial dataset contained close to 300 projects, but was paired down due to projects including components other than buses. Examples include: projects that constructed new facilities, trainings or other non-bus related items like trains and sea farries were excluded.\n", - "The resulting dataset only contained projects that were solely used to procure buses. \n", - "\n", - "88 projects were determined to contain solely bus purchases. \n", - "These projects were aggregated against propulsion type and bus size type, and categorized by ZEB and non-ZEB.\n", - "\n", - "\n", - "Breakdown of each data souce:\n", - "| source | bus_count | total_cost | cost_per_bus |\n", - "|:------------|------------:|-------------:|---------------:|\n", - "| dgs | 236 | 250112853 | 1059800 |\n", - "| fta | 883 | 391257025 | 443099 |\n", - "| tircp | 233 | 187250513 | 803650 |\n", - "| Grand Total | 1352 | 828620391 | 612884 |\n", - "\n", - "\n", - "**ZEB buses include:**\n", - "- zero-emission (not specified) \n", - "- electric (not specified)\n", - "- battery electric \n", - "- fuel cell electric\n", - "\n", - "**Non-ZEB buses include:**\n", - "- CNG \n", - "- ethanol \n", - "- ow emission (hybrid, propane) \n", - "- diesel \n", - "- gas\n", - "\n", - "Below are charts and tables that summarize the findings.\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# moved to final NB 6/25\n", "\n", @@ -4659,213 +1924,10 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "7472ba04-7def-46ef-814b-bf63c1016f3b", "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "**ZEB Summary**" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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prop_typebus_counttotal_costcost_per_bus
0BEB163.01672324891025966
1FCEB102.01209513351185797
2electric (not specified)44.0566780001288136
3zero-emission bus (not specified)143.0128156513896199
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1ethanol9.01006750111861
2low emission (hybrid)145.091824361633271
3low emission (propane)44.08403969190999
4mix (zero and low emission)125.036775430294203
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transit_agencytotal_agg_costtotal_bus_countnew_cost_per_bus
71Transit Joint Powers Authority for Merced County32233242.01611662
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9City of Beloit6531841.0653184
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49SLO TRANSIT (SAN LUIS OBISPO, CA)8472141.0847214
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transit_agencyprop_typetotal_costbus_countcost_per_bus
76University of California - San DiegoBEB41340002.02067000
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transit_agencyprop_typetotal_costbus_countcost_per_bus
45City of Wascozero-emission bus (not specified)15430003.0514333
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transit_agencyprop_typetotal_costbus_countcost_per_bus
44City of Los Angeles (LA DOT)zero-emission bus (not specified)102790000112.0917767
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82City of San Luis ObispoBEB8592701.0859270
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44City of Los Angeles (LA DOT)zero-emission bus (not specified)102790000112.0917767
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transit_agencyprop_typetotal_costbus_countcost_per_bus
70SLO TRANSIT (SAN LUIS OBISPO, CA)BEB8472141.0847214
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