Keyon_Final.ipynb

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   "id": "cutting-ukraine",
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    "# Import imports\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas\n",
    "import plotly.express as px\n",
    "from shapely.geometry import Point, Polygon"
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    "**INTRO**\n",
    "\n",
    "The datasets I will be using will be of the Pittsburgh local food gardens, and Smart Trash can locations. The goal of my datasets will be to score and determine a level of cleanliness and signs of a close knit community. The latter of which was determined as number 5 on Forbes top 10 things to look for in a neighborhood. I will be combining these two data sets to come up with an ultimate score (https://www.forbes.com/sites/learnvest/2017/08/02/10-things-you-should-look-for-when-choosing-a-new-neighborhood/?sh=65171b6b5fbc).\n",
    "\n",
    "Additionally, according to research Smart Trash cans are great for the city and neighborhood. They cut on municiple costs, by only sending garbage trucks when they are needed. They also make sure there are no 'overflowing' trash cans. Trash cans that overflow cause more litter than just the overflow. When people see that a can is overflowing, they tend to litter more. That is why I will convert the locations of trash cans to neighborhoods, and develop a metric that looks at trash cans per population density. More information about the benefits of smart trash cans are found at https://www.smartcitiesdive.com/news/pittsburghs-smart-garbage-cans-help-streamline-public-works/559156/#:~:text=The%20smart%20cans%20have%20sensors,most%20efficient%20routes%20for%20collection"
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  {
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   "id": "representative-marina",
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    "# import the data sources we will be playing with\n",
    "grow = pd.read_csv('https://data.wprdc.org/dataset/0d8c9708-3f01-48c4-adff-04d29825c9a2/resource/dbdfcb3e-8fa5-4468-9b05-f69562798f7a/download/growpghgardens201712.csv')\n",
    "trash = pd.read_csv('https://data.wprdc.org/datastore/dump/75b83ac9-8069-4cf1-bcc3-b9e6b04487d9')\n",
    "grow = grow.drop(['url', 'country', 'state', 'grower_id'], axis = 1) # clean up data by dropping some columns\n",
    "grow = grow[grow.city == 'Pittsburgh'] # limit to pittsburgh city\n",
    "\n",
    "\n"
   ]
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  {
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   "id": "later-bailey",
   "metadata": {},
   "source": [
    "I will be working on the food garden data first. The two main things will be to get pittsburgh divided into neighborhoods since the food garden data doesn't have that information. So I will need to combine a neighborhood map with the food garden locations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "sonic-individual",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the neighborhood map\n",
    "neighborhoods = geopandas.read_file(\"Neighborhoods/Neighborhoods_.shp\") # read in the shapefile\n",
    "crs = {'init': 'EPSG:4326'} #this is a coordinate reference system, but not really sure how it works\n",
    "# need to add geometry so we can drop points on the map, and potentially add a column later\n",
    "geometry = [Point(xy) for xy in zip(grow[\"longitude\"], grow['latitude'])]\n",
    "\n",
    "#geo_df = geopandas.GeoDataFrame(grow, crs = crs, geometry=geometry)\n",
    "geo_df = geopandas.GeoDataFrame(grow, geometry=geometry) # i don't think we need crs anymore, should be built in\n",
    "\n",
    "\n"
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   "source": [
    "# attempt to merge the neighborhood data into the trash can data and map on plotly\n",
    "\n",
    "\n",
    "# convert plain df to geopandas df\n",
    "\n",
    "geo_df_geopanda = geopandas.GeoDataFrame(geo_df, geometry=geopandas.points_from_xy(geo_df.longitude, geo_df.latitude))\n",
    "geo_df_geopanda = geo_df_geopanda.set_crs(\"EPSG:4326\")   # don't think I need this anymore\n",
    "geo_df = geo_df.set_crs(\"EPSG:4326\")\n",
    "\n",
    "point_in_neigh = geopandas.sjoin(geo_df, neighborhoods, how='left', op='within')\n",
    "                \n",
    "# going to try and make map of this data now\n",
    "# plotly version of the data, so can zoom and get neighborhood maps, etc\n",
    "px.set_mapbox_access_token('pk.eyJ1Ijoia2V5b25jYWwiLCJhIjoiY2tuYWM4b25rMWc5djJvbGdyYmFuN3c4MiJ9.CxmqbsOMXQHFuVwsUEFoTg')\n",
    "fig = px.scatter_mapbox(point_in_neigh, lat=\"latitude\", lon=\"longitude\", zoom=11, hover_name='hood')                  \n",
    "fig.show()\n",
    "\n",
    "\n",
    "\n"
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  },
  {
   "cell_type": "markdown",
   "id": "multiple-philip",
   "metadata": {},
   "source": [
    "Now that the food garden map is complete, I can start on the smart trash can information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "textile-battle",
   "metadata": {},
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          "East Liberty",
          "Beechview",
          "Point Breeze",
          "Central Business District",
          "East Liberty",
          "Bluff",
          "Beechview",
          "Carrick",
          "Shadyside",
          "Bloomfield",
          "Brookline",
          "Garfield",
          "Shadyside",
          "Shadyside",
          "Shadyside",
          "Brookline",
          "South Oakland",
          "Brighton Heights",
          "Shadyside",
          "Highland Park",
          "Highland Park",
          "Morningside",
          "Central Business District",
          "Brighton Heights",
          "Bluff",
          "South Side Flats",
          "South Side Slopes",
          "Brighton Heights",
          "Allentown",
          "South Side Flats",
          "South Side Slopes",
          "South Side Flats",
          "South Side Flats",
          "South Side Flats",
          "South Side Flats",
          "West End",
          "Brookline",
          "Knoxville",
          "Carrick",
          "Carrick",
          "South Side Flats",
          "Carrick",
          "Carrick",
          "Carrick",
          "South Side Flats",
          "Brookline",
          "Carrick",
          "Carrick",
          "Carrick",
          "South Side Flats",
          "Brookline",
          "South Side Flats",
          "Central Business District",
          "Arlington",
          "South Side Flats",
          "Allentown",
          "South Side Flats",
          "South Side Flats",
          "Brookline",
          "Carrick",
          "Carrick",
          "Carrick",
          "Allentown",
          "South Side Slopes",
          "South Side Flats",
          "South Side Flats",
          "Brookline",
          "Brookline",
          "South Side Slopes",
          "South Side Flats",
          "Beltzhoover",
          "South Side Flats",
          "Allentown",
          "South Side Flats",
          "South Side Flats",
          "South Side Flats",
          "Knoxville",
          "Carrick",
          "Carrick",
          "South Side Slopes",
          "Carrick",
          "Allentown",
          "South Side Flats",
          "South Side Flats",
          "Knoxville",
          "Beltzhoover",
          "South Side Flats",
          "South Side Flats",
          "Brookline",
          "South Side Flats",
          "South Side Flats",
          "South Side Slopes",
          "Allentown",
          "South Side Flats",
          "Carrick",
          "Brighton Heights",
          "Allegheny West",
          "Bloomfield",
          "Shadyside",
          "Bloomfield",
          "Garfield",
          "Shadyside",
          "Manchester",
          "Allegheny West",
          "Allegheny West",
          "Brighton Heights",
          "Brighton Heights",
          "Allegheny West",
          "Allegheny West",
          "East Allegheny",
          "Allegheny West",
          "East Allegheny",
          "Troy Hill",
          "Troy Hill",
          "East Allegheny",
          "East Allegheny",
          "Troy Hill",
          "East Allegheny",
          "East Allegheny",
          "East Allegheny",
          "Troy Hill",
          "East Allegheny",
          "Brighton Heights",
          "Brighton Heights",
          "Brighton Heights",
          "East Allegheny",
          "Knoxville",
          "South Side Flats",
          "South Side Flats",
          "Brookline",
          "Carrick",
          "South Side Flats",
          "South Side Slopes",
          "South Side Flats",
          "South Side Slopes",
          "South Side Flats",
          "South Side Flats",
          "Knoxville",
          "South Side Flats",
          "Carrick",
          "South Side Slopes",
          "Highland Park",
          "Bloomfield",
          "Shadyside",
          "Allegheny Center",
          "Bluff",
          "Allegheny West",
          "Brookline",
          "Bloomfield",
          "Highland Park",
          "Shadyside",
          "Shadyside",
          "Shadyside",
          "Morningside",
          "Central Oakland",
          "East Liberty",
          "Bluff",
          "Bloomfield",
          "Shadyside",
          "Greenfield",
          "East Liberty",
          "Shadyside",
          "Shadyside",
          "Bluff",
          "Highland Park",
          "Bluff",
          "Bluff",
          "Knoxville",
          "Point Breeze",
          "Shadyside",
          "Bloomfield",
          "Shadyside",
          "Larimer",
          "Bluff",
          "East Liberty",
          "Shadyside",
          "Shadyside",
          "Brighton Heights",
          "South Shore",
          "Mount Washington",
          "Sheraden",
          "Crafton Heights",
          "Elliott",
          "Sheraden",
          "Sheraden",
          "Beechview",
          "Crafton Heights",
          "Crafton Heights",
          "Westwood",
          "Beechview",
          "Beechview",
          "Greenfield",
          "Morningside",
          "Elliott",
          "Central Oakland",
          "Elliott",
          "Mount Washington",
          "Beechview",
          "Beechview",
          "Mount Washington",
          "Mount Washington",
          "West End",
          "Sheraden",
          "Crafton Heights",
          "Mount Washington",
          "Elliott",
          "Crafton Heights",
          "Windgap",
          "Sheraden",
          "Mount Washington",
          "West End",
          "Mount Washington",
          "Crafton Heights",
          "Elliott",
          "Sheraden",
          "Central Oakland",
          "Central Oakland",
          "Sheraden",
          "Point Breeze",
          "Central Oakland",
          "Mount Washington",
          "West End",
          "West End",
          "Mount Washington",
          "West End",
          "Crafton Heights",
          "Elliott",
          "Crafton Heights",
          "East Carnegie",
          "Mount Washington",
          "Sheraden",
          "Crafton Heights",
          "Sheraden",
          "Mount Washington",
          "Sheraden",
          "Mount Washington",
          "Elliott",
          "Beechview",
          "Sheraden",
          "Central Oakland",
          "Sheraden",
          "Carrick",
          "Point Breeze",
          "Elliott",
          "Crawford-Roberts",
          "Point Breeze",
          "Central Oakland",
          "Knoxville",
          "Highland Park",
          "Elliott",
          "Shadyside",
          "Shadyside",
          "Central Oakland",
          "Carrick",
          "East Liberty",
          "Shadyside",
          "Shadyside",
          "Bedford Dwellings",
          "Perry North",
          "North Shore",
          "Strip District",
          "Brighton Heights",
          "North Shore",
          "North Shore",
          "North Shore",
          "North Shore",
          "North Shore",
          "Strip District",
          "Strip District",
          "Mount Washington",
          "Strip District",
          "North Shore",
          "Strip District",
          "Strip District",
          "Sheraden",
          "Strip District",
          "Mount Washington",
          "Allegheny West",
          "North Shore",
          "North Shore",
          "Shadyside",
          "Upper Hill",
          "North Shore",
          "Strip District",
          "Polish Hill",
          "Crawford-Roberts",
          "Squirrel Hill South",
          "North Shore",
          "Strip District",
          "Greenfield",
          "North Shore",
          "North Shore",
          "Strip District",
          "Strip District",
          "Crawford-Roberts",
          "Polish Hill",
          "North Shore",
          "North Shore",
          "North Shore",
          "Polish Hill",
          "Central Lawrenceville",
          "North Shore",
          "North Shore",
          "Strip District",
          "North Shore",
          "Duquesne Heights",
          "Crawford-Roberts",
          "North Shore",
          "Strip District",
          "North Shore",
          "Polish Hill",
          "Strip District",
          "Polish Hill",
          "North Shore",
          "Mount Washington",
          "North Shore",
          "North Shore",
          "Strip District",
          "Polish Hill",
          "Crawford-Roberts",
          "North Shore",
          "Bedford Dwellings",
          "Polish Hill",
          "Carrick",
          "North Shore",
          "Chateau",
          "North Shore",
          "Shadyside",
          "Strip District",
          "North Shore",
          "Strip District",
          "Polish Hill",
          "North Shore",
          "Strip District",
          "North Shore",
          "Strip District",
          "North Shore",
          "North Oakland",
          "Perry North",
          "Chateau",
          "North Oakland",
          "Greenfield",
          "North Shore",
          "Central Oakland",
          "North Shore",
          "Middle Hill",
          "Lower Lawrenceville",
          "North Shore",
          "Strip District",
          "Strip District",
          "Polish Hill",
          "North Shore",
          "Strip District",
          "Carrick",
          "Central Oakland",
          "North Shore",
          "North Shore",
          "Polish Hill",
          "North Shore",
          "North Shore",
          "Central Oakland",
          "Strip District",
          "Carrick",
          "Strip District",
          "Strip District",
          "Strip District",
          "Central Oakland",
          "Crawford-Roberts",
          "Strip District",
          "Strip District",
          "Bluff",
          "Polish Hill",
          "Strip District",
          "Strip District",
          "Strip District",
          "Mount Washington",
          "Strip District",
          "Strip District",
          "Strip District",
          "Strip District",
          "Perry North",
          "North Oakland",
          "Allegheny West",
          "Strip District",
          "Strip District",
          "Strip District",
          "North Shore",
          "North Shore",
          "Bluff",
          "North Shore",
          "Strip District",
          "Squirrel Hill South",
          "Polish Hill",
          "North Shore",
          "Mount Washington",
          "Strip District",
          "Crawford-Roberts",
          "North Shore",
          "Middle Hill",
          "Morningside",
          "Central Northside",
          "North Shore",
          "Strip District",
          "Strip District",
          "Chateau",
          "Central Oakland",
          "North Shore",
          "North Shore",
          "Strip District",
          "Strip District",
          "North Shore",
          "Strip District",
          "North Shore",
          "North Shore",
          "Strip District",
          "North Shore",
          "North Oakland",
          "Carrick",
          "North Shore",
          "Carrick",
          "South Oakland",
          "Upper Hill",
          "South Side Flats",
          "Mount Washington",
          "Banksville",
          "South Side Flats",
          "Terrace Village",
          "East Liberty",
          "Point Breeze",
          "Perry North",
          "North Oakland",
          "East Liberty",
          "Hazelwood",
          "South Side Flats",
          "Hazelwood",
          "Crawford-Roberts",
          "Terrace Village",
          "Upper Hill",
          "South Side Slopes",
          "South Side Flats",
          "Point Breeze",
          "Shadyside",
          "Middle Hill",
          "South Side Flats",
          "East Liberty",
          "Squirrel Hill South",
          "Bluff",
          "Bloomfield",
          "Mount Washington",
          "Bedford Dwellings",
          "West Oakland",
          "South Oakland",
          "Hazelwood",
          "Squirrel Hill North",
          "Upper Hill",
          "East Liberty",
          "Lincoln Place",
          "Bloomfield",
          "South Side Flats",
          "Central Oakland",
          "Middle Hill",
          "East Liberty",
          "Central Lawrenceville",
          "Squirrel Hill North",
          "Shadyside",
          "Bluff",
          "Greenfield",
          "Hazelwood",
          "Mount Washington",
          "Hazelwood",
          "West Oakland",
          "Middle Hill",
          "Hazelwood",
          "North Oakland",
          "Middle Hill",
          "Hazelwood",
          "Hazelwood",
          "Middle Hill",
          "Point Breeze",
          "Middle Hill",
          "Bluff",
          "East Liberty",
          "Hazelwood",
          "Hazelwood",
          "Middle Hill",
          "Perry North",
          "Perry North",
          "Perry North",
          "Middle Hill",
          "Allentown",
          "California-Kirkbride",
          "Squirrel Hill South",
          "Middle Hill",
          "Sheraden",
          "Mount Washington",
          "Mount Washington",
          "Mount Washington",
          "Carrick",
          "Homewood South",
          "Central Oakland",
          "West Oakland",
          "Shadyside",
          "Homewood South",
          "Crawford-Roberts",
          "Terrace Village",
          "South Side Flats",
          "Hazelwood",
          "Hazelwood",
          "Central Oakland",
          "East Liberty",
          "Mount Washington",
          "Shadyside",
          "Strip District",
          "Hazelwood",
          "Central Oakland",
          "Central Oakland",
          "Shadyside",
          "Lincoln Place",
          "Hazelwood",
          "Bluff",
          "West Oakland",
          "East Liberty",
          "Central Business District",
          "North Oakland",
          "Terrace Village",
          "Central Oakland",
          "South Oakland",
          "Upper Hill",
          "Squirrel Hill South",
          "Terrace Village",
          "Brookline",
          "Squirrel Hill South",
          "Bluff",
          "Regent Square",
          "North Oakland",
          "Central Oakland",
          "Carrick",
          "Bluff",
          "Bluff",
          "North Oakland",
          "Homewood South",
          "Regent Square",
          "Hazelwood",
          "South Side Flats",
          "Middle Hill",
          "Greenfield",
          "Squirrel Hill South",
          "Central Oakland",
          "Upper Hill",
          "Greenfield",
          "Middle Hill",
          "Middle Hill",
          "Squirrel Hill North",
          "East Liberty",
          "Terrace Village",
          "Lincoln Place",
          "North Oakland",
          "West Oakland",
          "Hazelwood",
          "Squirrel Hill North",
          "Bluff",
          "Middle Hill",
          "Crawford-Roberts",
          "Middle Hill",
          "Upper Hill",
          "Squirrel Hill South",
          "Greenfield",
          "Squirrel Hill South",
          "Central Business District",
          "Central Business District",
          "Crawford-Roberts",
          "Crawford-Roberts",
          "Homewood South",
          "Middle Hill",
          "Bluff",
          "Crawford-Roberts",
          "Upper Hill",
          "Homewood South",
          "Larimer",
          "South Oakland",
          "Homewood South",
          "Greenfield",
          "East Liberty",
          "Carrick",
          "East Liberty",
          "Crawford-Roberts",
          "Homewood South",
          "South Side Flats",
          "North Oakland",
          "Hazelwood",
          "South Side Flats",
          "Allentown",
          "Squirrel Hill South",
          "Squirrel Hill North",
          "South Side Flats",
          "Larimer",
          "Carrick",
          "Point Breeze",
          "Squirrel Hill South",
          "Carrick",
          "Carrick",
          "North Oakland",
          "Squirrel Hill North",
          "South Side Flats",
          "Central Oakland",
          "Squirrel Hill South",
          "Squirrel Hill North",
          "Squirrel Hill South",
          "Squirrel Hill South",
          "Squirrel Hill South",
          "Squirrel Hill North",
          "Middle Hill",
          "Carrick",
          "Squirrel Hill South",
          "Carrick",
          "Squirrel Hill South",
          "Squirrel Hill North",
          "South Side Flats",
          "Carrick",
          "Squirrel Hill South",
          "Central Oakland",
          "East Liberty",
          "Point Breeze",
          "Squirrel Hill North",
          "Squirrel Hill South",
          "Squirrel Hill South",
          "Squirrel Hill South",
          "Squirrel Hill South",
          "Allentown",
          "Squirrel Hill South",
          "Sheraden",
          "Perry North",
          "Squirrel Hill South",
          "Banksville",
          "North Shore",
          "St. Clair",
          "Allentown",
          "Carrick",
          "Carrick",
          "Allentown",
          "Middle Hill",
          "Perry North",
          "South Side Flats",
          "South Side Flats",
          "South Side Flats",
          "South Side Flats",
          "Squirrel Hill South",
          "Spring Hill-City View",
          "Squirrel Hill South",
          "South Side Flats",
          "Crawford-Roberts",
          "Crawford-Roberts",
          "Middle Hill",
          "Middle Hill",
          "Crawford-Roberts",
          "North Oakland",
          "California-Kirkbride",
          "North Oakland",
          "Middle Hill",
          "North Oakland",
          "South Side Flats",
          "Squirrel Hill South",
          "Middle Hill",
          "Crawford-Roberts",
          "Squirrel Hill South",
          "Squirrel Hill North",
          "South Side Flats",
          "South Side Flats",
          "Middle Hill",
          "South Side Flats",
          "South Side Flats",
          "South Side Flats",
          "Carrick",
          "Duquesne Heights",
          "Squirrel Hill South",
          "Squirrel Hill South",
          "Middle Hill",
          "Hazelwood",
          "Squirrel Hill South",
          "Squirrel Hill South",
          "Squirrel Hill South",
          "Squirrel Hill North",
          "Middle Hill",
          "Squirrel Hill South",
          "Middle Hill",
          "Squirrel Hill South",
          "Hazelwood",
          "Squirrel Hill South",
          "Squirrel Hill South",
          "Westwood",
          "Homewood North",
          "Squirrel Hill South",
          "Squirrel Hill South",
          "South Side Flats",
          "Squirrel Hill South",
          "Hazelwood",
          "Squirrel Hill South",
          "Squirrel Hill South",
          "Oakwood",
          "South Side Flats",
          "Squirrel Hill South",
          "South Side Flats",
          "Squirrel Hill South",
          "Westwood",
          "South Side Flats",
          "South Side Flats",
          "Westwood",
          "Duquesne Heights",
          "Middle Hill",
          "Middle Hill",
          "Shadyside",
          "Crawford-Roberts",
          "Squirrel Hill North",
          "Middle Hill",
          "Squirrel Hill North",
          "Squirrel Hill North",
          "South Side Flats",
          "Greenfield",
          "Homewood North",
          "North Shore",
          "Central Oakland",
          "Squirrel Hill South",
          "Squirrel Hill North",
          "Squirrel Hill North",
          "Sheraden",
          "West End",
          "Squirrel Hill South",
          "Central Oakland",
          "Central Oakland",
          "Squirrel Hill South",
          "Central Oakland",
          "Squirrel Hill North",
          "Squirrel Hill South",
          "Middle Hill",
          "South Side Flats",
          "Westwood",
          "West End",
          "West End",
          "Homewood South",
          "Homewood North",
          "Point Breeze North",
          "Brookline",
          "Larimer",
          "Bloomfield",
          "Highland Park",
          "Homewood North",
          "Friendship",
          "Homewood South",
          "Larimer",
          "Larimer",
          "East Liberty",
          "Brookline",
          "Homewood South",
          "East Liberty",
          "East Liberty",
          "Brookline",
          "Bloomfield",
          "Larimer",
          "Lincoln-Lemington-Belmar",
          "Homewood South",
          "Homewood South",
          "Larimer",
          "Homewood West",
          "Larimer",
          "Central Lawrenceville",
          "Shadyside",
          "Larimer",
          "East Liberty",
          "East Liberty",
          "Bloomfield",
          "Larimer",
          "East Liberty",
          "Bloomfield",
          "East Hills",
          "Lower Lawrenceville",
          "East Liberty",
          "Shadyside",
          "Bloomfield",
          "Bloomfield",
          "Lincoln-Lemington-Belmar",
          "Brookline",
          "East Liberty",
          "Lower Lawrenceville",
          "Shadyside",
          "Shadyside",
          "Brookline",
          "East Liberty",
          "Homewood South",
          "Larimer",
          "East Liberty",
          "Friendship",
          "Homewood South",
          "East Liberty",
          "Bloomfield",
          "Lower Lawrenceville",
          "Bloomfield",
          "Friendship",
          "Homewood West",
          "Point Breeze North",
          "Brookline",
          "Larimer",
          "Bluff",
          "Homewood South",
          "Homewood South",
          "Bloomfield",
          "Bluff",
          "Bluff",
          "Homewood North",
          "Bluff",
          "East Liberty",
          "Homewood South",
          "Homewood South",
          "Lower Lawrenceville",
          "Lincoln-Lemington-Belmar",
          "Lincoln-Lemington-Belmar",
          "Central Lawrenceville",
          "Homewood South",
          "Bloomfield",
          "East Liberty",
          "Lower Lawrenceville",
          "Lower Lawrenceville",
          "Highland Park",
          "Homewood North",
          "East Hills",
          "Homewood North",
          "East Liberty",
          "East Liberty",
          "Bloomfield",
          "East Liberty",
          "Friendship",
          "Larimer",
          "Highland Park",
          "Bloomfield",
          "Friendship",
          "Homewood South",
          "Lower Lawrenceville",
          "Lincoln-Lemington-Belmar",
          "Shadyside",
          "Friendship",
          "Shadyside",
          "Friendship",
          "North Shore",
          "Friendship",
          "Lincoln-Lemington-Belmar",
          "East Liberty",
          "Homewood South",
          "Bloomfield",
          "Lincoln-Lemington-Belmar",
          "Bloomfield",
          "Friendship",
          "Bloomfield",
          "East Liberty",
          "East Liberty",
          "Larimer",
          "Shadyside",
          "East Liberty",
          "East Liberty",
          "Middle Hill",
          "Bluff",
          "Central Oakland",
          "Bluff",
          "South Oakland",
          "South Oakland",
          "Central Oakland",
          "Central Oakland",
          "Brighton Heights",
          "Bluff",
          "Bluff",
          "Middle Hill",
          "Crawford-Roberts",
          "West Oakland",
          "Bluff",
          "South Side Flats",
          "Central Oakland",
          "Bluff",
          "South Oakland",
          "West Oakland",
          "Central Oakland",
          "Crawford-Roberts",
          "Central Lawrenceville",
          "West Oakland",
          "North Shore",
          "Central Oakland",
          "Strip District",
          "Bloomfield",
          "Mount Washington",
          "Homewood South",
          "Upper Lawrenceville",
          "Bloomfield",
          "Central Oakland",
          "Stanton Heights",
          "Central Oakland",
          "Central Oakland",
          "Central Oakland",
          "Central Oakland",
          "North Oakland",
          "Central Oakland",
          "Central Lawrenceville",
          "Central Oakland",
          "West Oakland",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Carrick",
          "Central Lawrenceville",
          "Bloomfield",
          "Spring Hill-City View",
          "Allentown",
          "Bloomfield",
          "Central Lawrenceville",
          "Bloomfield",
          "Bloomfield",
          "Greenfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Central Lawrenceville",
          "Central Lawrenceville",
          "Friendship",
          "Shadyside",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "East Liberty",
          "Shadyside",
          "Homewood West",
          "Homewood South",
          "Bloomfield",
          "East Liberty",
          "East Liberty",
          "East Liberty",
          "East Liberty",
          "Bloomfield",
          "Friendship",
          "Central Lawrenceville",
          "Lower Lawrenceville",
          "Homewood West",
          "Strip District",
          "Lower Lawrenceville",
          "Highland Park",
          "East Liberty",
          "East Liberty",
          "East Liberty",
          "Lower Lawrenceville",
          "Highland Park",
          "East Liberty",
          "East Liberty",
          "East Liberty",
          "East Liberty",
          "Garfield",
          "East Liberty",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Bloomfield",
          "Shadyside",
          "Bloomfield",
          "Garfield",
          "Garfield",
          "Friendship",
          "Garfield",
          "Carrick",
          "East Liberty",
          "Garfield",
          "East Liberty",
          "Carrick",
          "East Liberty",
          "Morningside",
          "Morningside",
          "East Liberty",
          "East Liberty",
          "East Liberty",
          "Bloomfield",
          "East Liberty",
          "Upper Lawrenceville",
          "Central Lawrenceville",
          "Upper Lawrenceville",
          "Lower Lawrenceville",
          "Upper Lawrenceville",
          "Upper Lawrenceville",
          "Morningside",
          "Morningside",
          "Central Lawrenceville",
          "Central Lawrenceville",
          "Lower Lawrenceville",
          "Stanton Heights",
          "Upper Lawrenceville",
          "Central Lawrenceville",
          "Lower Lawrenceville",
          "Central Lawrenceville",
          "Lower Lawrenceville",
          "Central Lawrenceville",
          "Lower Lawrenceville",
          "Central Lawrenceville",
          "Central Lawrenceville",
          "Lower Lawrenceville",
          "Upper Lawrenceville",
          "Central Lawrenceville",
          "Central Lawrenceville",
          "Lower Lawrenceville",
          "Central Lawrenceville",
          "Lower Lawrenceville",
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     "metadata": {},
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   ],
   "source": [
    "# time to work on trash can data. \n",
    "# gonna try convert it to geo pandas, and maybe helps get the neighborhood name\n",
    "trash_df = trash # copy just in case \n",
    "\n",
    "trash_geo = geopandas.GeoDataFrame(trash_df, geometry=geopandas.points_from_xy(trash_df.x, trash_df.y))\n",
    "trash_geo.set_crs(\"EPSG:4326\")\n",
    "\n",
    "px.set_mapbox_access_token('pk.eyJ1Ijoia2V5b25jYWwiLCJhIjoiY2tuYWM4b25rMWc5djJvbGdyYmFuN3c4MiJ9.CxmqbsOMXQHFuVwsUEFoTg')\n",
    "fig = px.scatter_mapbox(trash_geo, lat=\"y\", lon=\"x\", zoom=11, hover_name=\"neighborhood\")                  \n",
    "fig.show()\n",
    "\n",
    "# this cell works to get the trash cans on the map, and show the neighborhood on hover"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "searching-pixel",
   "metadata": {},
   "source": [
    "Now for the ultimate goal. Combine the two data sets and come up with a metric that describes my goal. Each neighborhood will end up with a score that is determined by how many smart trash cans they have, and if they have a local food garden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "portable-gabriel",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper Lawrenceville</th>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West End</th>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Oakland</th>\n",
       "      <td>9</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Westwood</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Windgap</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     smart_trash  food_garden\n",
       "hood                                         \n",
       "Allegheny Center               8          0.0\n",
       "Allegheny West                15          0.0\n",
       "Allentown                     12          0.0\n",
       "Arlington                      2          0.0\n",
       "Banksville                     2          0.0\n",
       "...                          ...          ...\n",
       "Upper Lawrenceville           16          0.0\n",
       "West End                       9          1.0\n",
       "West Oakland                   9          3.0\n",
       "Westwood                       5          0.0\n",
       "Windgap                        1          0.0\n",
       "\n",
       "[79 rows x 2 columns]"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# want to merge the two data sets so can do metric of trash_grow / 100 people\n",
    "grow_neigh = point_in_neigh\n",
    "trash_df = trash_geo\n",
    "\n",
    "trash_df=trash_df.rename(columns={'neighborhood':'hood'})   # rename columns so they match for the aggregate\n",
    "\n",
    "# want to know how many trash and food are in each neighborhood. So going to just do a count of neighborhoods in eac\n",
    "grow_neigh_group= grow_neigh.dissolve(by='hood', aggfunc='count')\n",
    "trash_df_group = trash_df.dissolve(by='hood', aggfunc='count')\n",
    "\n",
    "# now need to merge the data\n",
    "merged_df = trash_df_group.merge(grow_neigh_group, on='hood', how='left')   # trash has more entries so merge on left\n",
    "merged_df = merged_df[['container_id', 'page_numbe']]   # since just a count, we only need these two columns (maybe add geometry later)\n",
    "merged_df = merged_df.rename(columns={'container_id':'smart_trash', 'page_numbe': 'food_garden'})  # rename columns to make clear\n",
    "merged_df['food_garden'] = merged_df['food_garden'].fillna(0)   # get rid of the NaN by replacing with 0\n",
    "\n",
    "\n",
    "merged_df\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "automated-decrease",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hood</th>\n",
       "      <th>smart_trash</th>\n",
       "      <th>food_garden</th>\n",
       "      <th>area</th>\n",
       "      <th>pop</th>\n",
       "      <th>percent_pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Allegheny Center</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.4</td>\n",
       "      <td>933.0</td>\n",
       "      <td>0.003156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allegheny West</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.2</td>\n",
       "      <td>462.0</td>\n",
       "      <td>0.001563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allentown</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>188.8</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>0.008457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arlington</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.8</td>\n",
       "      <td>1869.0</td>\n",
       "      <td>0.006322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Banksville</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>620.2</td>\n",
       "      <td>4144.0</td>\n",
       "      <td>0.014018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Upper Lawrenceville</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>259.2</td>\n",
       "      <td>2669.0</td>\n",
       "      <td>0.009028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>West End</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.000859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>West Oakland</td>\n",
       "      <td>9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>137.6</td>\n",
       "      <td>2604.0</td>\n",
       "      <td>0.008808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Westwood</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>442.9</td>\n",
       "      <td>3066.0</td>\n",
       "      <td>0.010371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Windgap</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>265.6</td>\n",
       "      <td>1369.0</td>\n",
       "      <td>0.004631</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   hood  smart_trash  food_garden   area     pop  percent_pop\n",
       "0      Allegheny Center            8          0.0  134.4   933.0     0.003156\n",
       "1        Allegheny West           15          0.0   90.2   462.0     0.001563\n",
       "2             Allentown           12          0.0  188.8  2500.0     0.008457\n",
       "3             Arlington            2          0.0  300.8  1869.0     0.006322\n",
       "4            Banksville            2          0.0  620.2  4144.0     0.014018\n",
       "..                  ...          ...          ...    ...     ...          ...\n",
       "74  Upper Lawrenceville           16          0.0  259.2  2669.0     0.009028\n",
       "75             West End            9          1.0  128.0   254.0     0.000859\n",
       "76         West Oakland            9          3.0  137.6  2604.0     0.008808\n",
       "77             Westwood            5          0.0  442.9  3066.0     0.010371\n",
       "78              Windgap            1          0.0  265.6  1369.0     0.004631\n",
       "\n",
       "[79 rows x 6 columns]"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now that we just have the breakdown of smart_trash and food_garden by neighborhood lets get population\n",
    "#pop_df = pd.read_csv('https://data.wprdc.org/dataset/3cb2b9c2-66e2-4ffa-b0a1-35fb2c6f7230/resource/eb1f8714-e0eb-4614-acfe-dfe0ebab942f/download/school-enrollment-by-detailed-level-of-school-for-the-population-3-years-and-over.csv')\n",
    "pop_df = pd.read_csv('https://data.wprdc.org/dataset/40188e1c-6d2e-4f20-9391-607bd3054949/resource/8ac5c768-09ba-43a8-829f-ce4a417d20fb/download/population-density.csv')\n",
    "pop_df = pop_df[['Neighborhood', 'Land Area (acres)', 'Pop. 2010']]   # only want the population\n",
    "pop_df = pop_df.rename(columns = {'Neighborhood':'hood', 'Land Area (acres)':'area', 'Pop. 2010':'pop'})  #rename the columns\n",
    "merged_final = merged_df.merge(pop_df, how='left', on='hood')\n",
    "merged_final.replace(',','', regex=True, inplace=True)\n",
    "merged_final['area'] = merged_final['area'].astype(float)\n",
    "merged_final['pop'] = merged_final['pop'].astype(float)\n",
    "merged_final['percent_pop'] = merged_final['pop'] / merged_final['pop'].sum()\n",
    "merged_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "knowing-blood",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>smart_trash</th>\n",
       "      <th>food_garden</th>\n",
       "      <th>area</th>\n",
       "      <th>pop</th>\n",
       "      <th>percent_pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.025316</td>\n",
       "      <td>1.303797</td>\n",
       "      <td>396.905063</td>\n",
       "      <td>3742.101266</td>\n",
       "      <td>0.012658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18.124905</td>\n",
       "      <td>0.773842</td>\n",
       "      <td>309.291995</td>\n",
       "      <td>3290.050384</td>\n",
       "      <td>0.011129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>181.400000</td>\n",
       "      <td>1365.000000</td>\n",
       "      <td>0.004617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>284.800000</td>\n",
       "      <td>2714.000000</td>\n",
       "      <td>0.009181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>532.150000</td>\n",
       "      <td>4742.000000</td>\n",
       "      <td>0.016041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1709.400000</td>\n",
       "      <td>15110.000000</td>\n",
       "      <td>0.051112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       smart_trash  food_garden         area           pop  percent_pop\n",
       "count    79.000000    79.000000    79.000000     79.000000    79.000000\n",
       "mean     16.025316     1.303797   396.905063   3742.101266     0.012658\n",
       "std      18.124905     0.773842   309.291995   3290.050384     0.011129\n",
       "min       1.000000     1.000000    64.000000     11.000000     0.000037\n",
       "25%       4.000000     1.000000   181.400000   1365.000000     0.004617\n",
       "50%       9.000000     1.000000   284.800000   2714.000000     0.009181\n",
       "75%      19.000000     1.000000   532.150000   4742.000000     0.016041\n",
       "max      78.000000     4.000000  1709.400000  15110.000000     0.051112"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now we can add the column for the metric\n",
    "merged_final.describe()    # to help guide us for weighting garden more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "cubic-occasion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hood</th>\n",
       "      <th>smart_trash</th>\n",
       "      <th>food_garden</th>\n",
       "      <th>area</th>\n",
       "      <th>pop</th>\n",
       "      <th>percent_pop</th>\n",
       "      <th>clean_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>South Shore</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135.7</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.000047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Chateau</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245.1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>St. Clair</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>198.4</td>\n",
       "      <td>209.0</td>\n",
       "      <td>0.000707</td>\n",
       "      <td>0.000356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>East Carnegie</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>276.5</td>\n",
       "      <td>570.0</td>\n",
       "      <td>0.001928</td>\n",
       "      <td>0.000697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Swisshelm Park</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>307.2</td>\n",
       "      <td>1361.0</td>\n",
       "      <td>0.004604</td>\n",
       "      <td>0.001499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bluff</td>\n",
       "      <td>32</td>\n",
       "      <td>1.0</td>\n",
       "      <td>209.3</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>0.022326</td>\n",
       "      <td>0.394670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bloomfield</td>\n",
       "      <td>60</td>\n",
       "      <td>2.0</td>\n",
       "      <td>449.3</td>\n",
       "      <td>8442.0</td>\n",
       "      <td>0.028556</td>\n",
       "      <td>0.444902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>East Liberty</td>\n",
       "      <td>72</td>\n",
       "      <td>4.0</td>\n",
       "      <td>371.8</td>\n",
       "      <td>5869.0</td>\n",
       "      <td>0.019853</td>\n",
       "      <td>0.491247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Central Oakland</td>\n",
       "      <td>44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>179.8</td>\n",
       "      <td>6086.0</td>\n",
       "      <td>0.020587</td>\n",
       "      <td>0.503793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Shadyside</td>\n",
       "      <td>78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>589.4</td>\n",
       "      <td>13915.0</td>\n",
       "      <td>0.047070</td>\n",
       "      <td>0.622910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               hood  smart_trash  food_garden   area      pop  percent_pop  \\\n",
       "60      South Shore            1          0.0  135.7     19.0     0.000064   \n",
       "18          Chateau            4          0.0  245.1     11.0     0.000037   \n",
       "67        St. Clair            1          0.0  198.4    209.0     0.000707   \n",
       "23    East Carnegie            1          0.0  276.5    570.0     0.001928   \n",
       "70   Swisshelm Park            1          0.0  307.2   1361.0     0.004604   \n",
       "..              ...          ...          ...    ...      ...          ...   \n",
       "9             Bluff           32          1.0  209.3   6600.0     0.022326   \n",
       "8        Bloomfield           60          2.0  449.3   8442.0     0.028556   \n",
       "25     East Liberty           72          4.0  371.8   5869.0     0.019853   \n",
       "17  Central Oakland           44          0.0  179.8   6086.0     0.020587   \n",
       "57        Shadyside           78          0.0  589.4  13915.0     0.047070   \n",
       "\n",
       "    clean_score  \n",
       "60     0.000047  \n",
       "18     0.000061  \n",
       "67     0.000356  \n",
       "23     0.000697  \n",
       "70     0.001499  \n",
       "..          ...  \n",
       "9      0.394670  \n",
       "8      0.444902  \n",
       "25     0.491247  \n",
       "17     0.503793  \n",
       "57     0.622910  \n",
       "\n",
       "[79 rows x 7 columns]"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we will make every food garden worth 5 points, and every trash can worth 1 point\n",
    "# then we will figure out how many points per area size and weight by populations percent.\n",
    "# Hopefully giving us our best areas in pitt as judged by level of community and cleanliness\n",
    "merged_final['clean_score'] = ((merged_final['smart_trash'] + (merged_final['food_garden'] * 5)) / (merged_final['area']/100)) * merged_final['percent_pop']\n",
    "\n",
    "# lets see how the data looks on a bar graph\n",
    "\n",
    "\n",
    "merged_final_sort = merged_final.sort_values(by=['clean_score'], ascending=True)\n",
    "\n",
    "graph = merged_final_sort.plot.barh(x='hood', y='clean_score', figsize=(20, 20))\n",
    "merged_final_sort"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pregnant-auction",
   "metadata": {},
   "source": [
    "According to my data **Shadyside** is both a clean neighborhood, with lots of local love. In other words, it is a clean, and close-knit community."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}