final_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pqsKy--8l_vR"
   },
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b1XKeeQhwSuV"
   },
   "source": [
    "# EDA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "f5Ln262W0G_Z"
   },
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import io\n",
    "import datetime\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": 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       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "id": "IsHo9o_-ElNC",
    "outputId": "d6c9b945-1963-436b-b8dc-0caa9ca8e879"
   },
   "source": [
    "There are two ways to import the respective files for this notebook (first csv file is too large to push onto Github).\n",
    "\n",
    "*Note:* The first dataset (`gun-violence-data_01-2013_03-2018.csv`) cell will take awhile for the notebook to import this csv file.\n",
    "\n",
    "**Method 1**: Upload the datasets onto the same DataHub and work directly with datasets **(recommended)**\n",
    "\n",
    "**Method 2**: Follow widget to upload file from your local workspace **(works only on Google Colab)**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1st Dataset: (Main) Gun Violence Dataset\n",
    "# https://www.kaggle.com/datasets/jameslko/gun-violence-data?resource=download\n",
    "\n",
    "# Method 1 (recommended):\n",
    "\n",
    "df = pd.read_csv(\"gun-violence-data_01-2013_03-2018.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": 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       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "id": "IsHo9o_-ElNC",
    "outputId": "d6c9b945-1963-436b-b8dc-0caa9ca8e879"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-685ad3d9-9ca7-4e1c-9fcc-0e3f58994e10\" name=\"files[]\" multiple disabled\n",
       "        style=\"border:none\" />\n",
       "     <output id=\"result-685ad3d9-9ca7-4e1c-9fcc-0e3f58994e10\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving gun-violence-data_01-2013_03-2018.csv to gun-violence-data_01-2013_03-2018.csv\n"
     ]
    }
   ],
   "source": [
    "# Method 2:\n",
    "\n",
    "# from google.colab import files\n",
    "# # Run on Google Colab to select the csv file in your local computer (should be 'gun-violence-data_01-2013_03-2018.csv')\n",
    "# uploaded = files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 956
    },
    "id": "_Sr8IHIWzrh4",
    "outputId": "1095072f-db5c-4e0d-d0b7-75f9a109adc6",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-f2a97540-2d26-4938-90fe-a96f09affe36\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>incident_id</th>\n",
       "      <th>date</th>\n",
       "      <th>state</th>\n",
       "      <th>city_or_county</th>\n",
       "      <th>address</th>\n",
       "      <th>n_killed</th>\n",
       "      <th>n_injured</th>\n",
       "      <th>incident_url</th>\n",
       "      <th>source_url</th>\n",
       "      <th>incident_url_fields_missing</th>\n",
       "      <th>...</th>\n",
       "      <th>participant_age</th>\n",
       "      <th>participant_age_group</th>\n",
       "      <th>participant_gender</th>\n",
       "      <th>participant_name</th>\n",
       "      <th>participant_relationship</th>\n",
       "      <th>participant_status</th>\n",
       "      <th>participant_type</th>\n",
       "      <th>sources</th>\n",
       "      <th>state_house_district</th>\n",
       "      <th>state_senate_district</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>461105</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Mckeesport</td>\n",
       "      <td>1506 Versailles Avenue and Coursin Street</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/461105</td>\n",
       "      <td>http://www.post-gazette.com/local/south/2013/0...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::20</td>\n",
       "      <td>0::Adult 18+||1::Adult 18+||2::Adult 18+||3::A...</td>\n",
       "      <td>0::Male||1::Male||3::Male||4::Female</td>\n",
       "      <td>0::Julian Sims</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0::Arrested||1::Injured||2::Injured||3::Injure...</td>\n",
       "      <td>0::Victim||1::Victim||2::Victim||3::Victim||4:...</td>\n",
       "      <td>http://pittsburgh.cbslocal.com/2013/01/01/4-pe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>460726</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>California</td>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>13500 block of Cerise Avenue</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/460726</td>\n",
       "      <td>http://www.dailybulletin.com/article/zz/201301...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::20</td>\n",
       "      <td>0::Adult 18+||1::Adult 18+||2::Adult 18+||3::A...</td>\n",
       "      <td>0::Male</td>\n",
       "      <td>0::Bernard Gillis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0::Killed||1::Injured||2::Injured||3::Injured</td>\n",
       "      <td>0::Victim||1::Victim||2::Victim||3::Victim||4:...</td>\n",
       "      <td>http://losangeles.cbslocal.com/2013/01/01/man-...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>478855</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>Lorain</td>\n",
       "      <td>1776 East 28th Street</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/478855</td>\n",
       "      <td>http://chronicle.northcoastnow.com/2013/02/14/...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::25||1::31||2::33||3::34||4::33</td>\n",
       "      <td>0::Adult 18+||1::Adult 18+||2::Adult 18+||3::A...</td>\n",
       "      <td>0::Male||1::Male||2::Male||3::Male||4::Male</td>\n",
       "      <td>0::Damien Bell||1::Desmen Noble||2::Herman Sea...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0::Injured, Unharmed, Arrested||1::Unharmed, A...</td>\n",
       "      <td>0::Subject-Suspect||1::Subject-Suspect||2::Vic...</td>\n",
       "      <td>http://www.morningjournal.com/general-news/201...</td>\n",
       "      <td>56.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>478925</td>\n",
       "      <td>2013-01-05</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Aurora</td>\n",
       "      <td>16000 block of East Ithaca Place</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/478925</td>\n",
       "      <td>http://www.dailydemocrat.com/20130106/aurora-s...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::29||1::33||2::56||3::33</td>\n",
       "      <td>0::Adult 18+||1::Adult 18+||2::Adult 18+||3::A...</td>\n",
       "      <td>0::Female||1::Male||2::Male||3::Male</td>\n",
       "      <td>0::Stacie Philbrook||1::Christopher Ratliffe||...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0::Killed||1::Killed||2::Killed||3::Killed</td>\n",
       "      <td>0::Victim||1::Victim||2::Victim||3::Subject-Su...</td>\n",
       "      <td>http://denver.cbslocal.com/2013/01/06/officer-...</td>\n",
       "      <td>40.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>478959</td>\n",
       "      <td>2013-01-07</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Greensboro</td>\n",
       "      <td>307 Mourning Dove Terrace</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/478959</td>\n",
       "      <td>http://www.journalnow.com/news/local/article_d...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::18||1::46||2::14||3::47</td>\n",
       "      <td>0::Adult 18+||1::Adult 18+||2::Teen 12-17||3::...</td>\n",
       "      <td>0::Female||1::Male||2::Male||3::Female</td>\n",
       "      <td>0::Danielle Imani Jameison||1::Maurice Eugene ...</td>\n",
       "      <td>3::Family</td>\n",
       "      <td>0::Injured||1::Injured||2::Killed||3::Killed</td>\n",
       "      <td>0::Victim||1::Victim||2::Victim||3::Subject-Su...</td>\n",
       "      <td>http://myfox8.com/2013/01/08/update-mother-sho...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>27.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>239672</th>\n",
       "      <td>1083142</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>Rayne</td>\n",
       "      <td>North Riceland Road and Highway 90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/108...</td>\n",
       "      <td>http://www.klfy.com/news/local/rayne-woman-cha...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::25</td>\n",
       "      <td>0::Adult 18+</td>\n",
       "      <td>0::Female</td>\n",
       "      <td>0::Jhkeya Tezeno</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0::Unharmed, Arrested</td>\n",
       "      <td>0::Subject-Suspect</td>\n",
       "      <td>http://www.klfy.com/news/local/rayne-woman-cha...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239673</th>\n",
       "      <td>1083139</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>Natchitoches</td>\n",
       "      <td>247 Keyser Ave</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/108...</td>\n",
       "      <td>http://www.ksla.com/story/37854648/man-wanted-...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>1::21</td>\n",
       "      <td>0::Adult 18+||1::Adult 18+</td>\n",
       "      <td>0::Male||1::Male</td>\n",
       "      <td>0::Jamal Haskett||1::Jaquarious Tyjuan Ardison</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0::Killed||1::Unharmed, Arrested</td>\n",
       "      <td>0::Victim||1::Subject-Suspect</td>\n",
       "      <td>http://www.ksla.com/story/37854648/man-wanted-...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239674</th>\n",
       "      <td>1083151</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>Gretna</td>\n",
       "      <td>1300 block of Cook Street</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/108...</td>\n",
       "      <td>http://www.nola.com/crime/index.ssf/2018/04/sh...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::21</td>\n",
       "      <td>0::Adult 18+</td>\n",
       "      <td>0::Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0::Injured</td>\n",
       "      <td>0::Victim</td>\n",
       "      <td>http://www.nola.com/crime/index.ssf/2018/04/sh...</td>\n",
       "      <td>85.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239675</th>\n",
       "      <td>1082514</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Houston</td>\n",
       "      <td>12630 Ashford Point Dr</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/108...</td>\n",
       "      <td>https://www.chron.com/news/houston-texas/houst...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::42</td>\n",
       "      <td>0::Adult 18+</td>\n",
       "      <td>0::Male</td>\n",
       "      <td>0::Leroy Ellis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0::Killed</td>\n",
       "      <td>0::Victim</td>\n",
       "      <td>http://www.khou.com/article/news/hpd-investiga...</td>\n",
       "      <td>149.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239676</th>\n",
       "      <td>1081940</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>Maine</td>\n",
       "      <td>Norridgewock</td>\n",
       "      <td>434 Skowhegan Rd</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/108...</td>\n",
       "      <td>https://www.centralmaine.com/2018/03/31/police...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::58||1::62</td>\n",
       "      <td>0::Adult 18+||1::Adult 18+</td>\n",
       "      <td>0::Female||1::Male</td>\n",
       "      <td>0::Marie Lancaster Hale||1::William Hale</td>\n",
       "      <td>1::Significant others - current or former</td>\n",
       "      <td>0::Killed||1::Killed</td>\n",
       "      <td>0::Victim||1::Subject-Suspect</td>\n",
       "      <td>https://www.centralmaine.com/2018/03/31/police...</td>\n",
       "      <td>111.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>239677 rows × 29 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f2a97540-2d26-4938-90fe-a96f09affe36')\"\n",
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      "text/plain": [
       "        incident_id        date           state city_or_county  \\\n",
       "0            461105  2013-01-01    Pennsylvania     Mckeesport   \n",
       "1            460726  2013-01-01      California      Hawthorne   \n",
       "2            478855  2013-01-01            Ohio         Lorain   \n",
       "3            478925  2013-01-05        Colorado         Aurora   \n",
       "4            478959  2013-01-07  North Carolina     Greensboro   \n",
       "...             ...         ...             ...            ...   \n",
       "239672      1083142  2018-03-31       Louisiana          Rayne   \n",
       "239673      1083139  2018-03-31       Louisiana   Natchitoches   \n",
       "239674      1083151  2018-03-31       Louisiana         Gretna   \n",
       "239675      1082514  2018-03-31           Texas        Houston   \n",
       "239676      1081940  2018-03-31           Maine   Norridgewock   \n",
       "\n",
       "                                          address  n_killed  n_injured  \\\n",
       "0       1506 Versailles Avenue and Coursin Street         0          4   \n",
       "1                    13500 block of Cerise Avenue         1          3   \n",
       "2                           1776 East 28th Street         1          3   \n",
       "3                16000 block of East Ithaca Place         4          0   \n",
       "4                       307 Mourning Dove Terrace         2          2   \n",
       "...                                           ...       ...        ...   \n",
       "239672         North Riceland Road and Highway 90         0          0   \n",
       "239673                             247 Keyser Ave         1          0   \n",
       "239674                  1300 block of Cook Street         0          1   \n",
       "239675                     12630 Ashford Point Dr         1          0   \n",
       "239676                           434 Skowhegan Rd         2          0   \n",
       "\n",
       "                                             incident_url  \\\n",
       "0       http://www.gunviolencearchive.org/incident/461105   \n",
       "1       http://www.gunviolencearchive.org/incident/460726   \n",
       "2       http://www.gunviolencearchive.org/incident/478855   \n",
       "3       http://www.gunviolencearchive.org/incident/478925   \n",
       "4       http://www.gunviolencearchive.org/incident/478959   \n",
       "...                                                   ...   \n",
       "239672  http://www.gunviolencearchive.org/incident/108...   \n",
       "239673  http://www.gunviolencearchive.org/incident/108...   \n",
       "239674  http://www.gunviolencearchive.org/incident/108...   \n",
       "239675  http://www.gunviolencearchive.org/incident/108...   \n",
       "239676  http://www.gunviolencearchive.org/incident/108...   \n",
       "\n",
       "                                               source_url  \\\n",
       "0       http://www.post-gazette.com/local/south/2013/0...   \n",
       "1       http://www.dailybulletin.com/article/zz/201301...   \n",
       "2       http://chronicle.northcoastnow.com/2013/02/14/...   \n",
       "3       http://www.dailydemocrat.com/20130106/aurora-s...   \n",
       "4       http://www.journalnow.com/news/local/article_d...   \n",
       "...                                                   ...   \n",
       "239672  http://www.klfy.com/news/local/rayne-woman-cha...   \n",
       "239673  http://www.ksla.com/story/37854648/man-wanted-...   \n",
       "239674  http://www.nola.com/crime/index.ssf/2018/04/sh...   \n",
       "239675  https://www.chron.com/news/houston-texas/houst...   \n",
       "239676  https://www.centralmaine.com/2018/03/31/police...   \n",
       "\n",
       "        incident_url_fields_missing  ...                    participant_age  \\\n",
       "0                             False  ...                              0::20   \n",
       "1                             False  ...                              0::20   \n",
       "2                             False  ...  0::25||1::31||2::33||3::34||4::33   \n",
       "3                             False  ...         0::29||1::33||2::56||3::33   \n",
       "4                             False  ...         0::18||1::46||2::14||3::47   \n",
       "...                             ...  ...                                ...   \n",
       "239672                        False  ...                              0::25   \n",
       "239673                        False  ...                              1::21   \n",
       "239674                        False  ...                              0::21   \n",
       "239675                        False  ...                              0::42   \n",
       "239676                        False  ...                       0::58||1::62   \n",
       "\n",
       "                                    participant_age_group  \\\n",
       "0       0::Adult 18+||1::Adult 18+||2::Adult 18+||3::A...   \n",
       "1       0::Adult 18+||1::Adult 18+||2::Adult 18+||3::A...   \n",
       "2       0::Adult 18+||1::Adult 18+||2::Adult 18+||3::A...   \n",
       "3       0::Adult 18+||1::Adult 18+||2::Adult 18+||3::A...   \n",
       "4       0::Adult 18+||1::Adult 18+||2::Teen 12-17||3::...   \n",
       "...                                                   ...   \n",
       "239672                                       0::Adult 18+   \n",
       "239673                         0::Adult 18+||1::Adult 18+   \n",
       "239674                                       0::Adult 18+   \n",
       "239675                                       0::Adult 18+   \n",
       "239676                         0::Adult 18+||1::Adult 18+   \n",
       "\n",
       "                                 participant_gender  \\\n",
       "0              0::Male||1::Male||3::Male||4::Female   \n",
       "1                                           0::Male   \n",
       "2       0::Male||1::Male||2::Male||3::Male||4::Male   \n",
       "3              0::Female||1::Male||2::Male||3::Male   \n",
       "4            0::Female||1::Male||2::Male||3::Female   \n",
       "...                                             ...   \n",
       "239672                                    0::Female   \n",
       "239673                             0::Male||1::Male   \n",
       "239674                                      0::Male   \n",
       "239675                                      0::Male   \n",
       "239676                           0::Female||1::Male   \n",
       "\n",
       "                                         participant_name  \\\n",
       "0                                          0::Julian Sims   \n",
       "1                                       0::Bernard Gillis   \n",
       "2       0::Damien Bell||1::Desmen Noble||2::Herman Sea...   \n",
       "3       0::Stacie Philbrook||1::Christopher Ratliffe||...   \n",
       "4       0::Danielle Imani Jameison||1::Maurice Eugene ...   \n",
       "...                                                   ...   \n",
       "239672                                   0::Jhkeya Tezeno   \n",
       "239673     0::Jamal Haskett||1::Jaquarious Tyjuan Ardison   \n",
       "239674                                                NaN   \n",
       "239675                                     0::Leroy Ellis   \n",
       "239676           0::Marie Lancaster Hale||1::William Hale   \n",
       "\n",
       "                         participant_relationship  \\\n",
       "0                                             NaN   \n",
       "1                                             NaN   \n",
       "2                                             NaN   \n",
       "3                                             NaN   \n",
       "4                                       3::Family   \n",
       "...                                           ...   \n",
       "239672                                        NaN   \n",
       "239673                                        NaN   \n",
       "239674                                        NaN   \n",
       "239675                                        NaN   \n",
       "239676  1::Significant others - current or former   \n",
       "\n",
       "                                       participant_status  \\\n",
       "0       0::Arrested||1::Injured||2::Injured||3::Injure...   \n",
       "1           0::Killed||1::Injured||2::Injured||3::Injured   \n",
       "2       0::Injured, Unharmed, Arrested||1::Unharmed, A...   \n",
       "3              0::Killed||1::Killed||2::Killed||3::Killed   \n",
       "4            0::Injured||1::Injured||2::Killed||3::Killed   \n",
       "...                                                   ...   \n",
       "239672                              0::Unharmed, Arrested   \n",
       "239673                   0::Killed||1::Unharmed, Arrested   \n",
       "239674                                         0::Injured   \n",
       "239675                                          0::Killed   \n",
       "239676                               0::Killed||1::Killed   \n",
       "\n",
       "                                         participant_type  \\\n",
       "0       0::Victim||1::Victim||2::Victim||3::Victim||4:...   \n",
       "1       0::Victim||1::Victim||2::Victim||3::Victim||4:...   \n",
       "2       0::Subject-Suspect||1::Subject-Suspect||2::Vic...   \n",
       "3       0::Victim||1::Victim||2::Victim||3::Subject-Su...   \n",
       "4       0::Victim||1::Victim||2::Victim||3::Subject-Su...   \n",
       "...                                                   ...   \n",
       "239672                                 0::Subject-Suspect   \n",
       "239673                      0::Victim||1::Subject-Suspect   \n",
       "239674                                          0::Victim   \n",
       "239675                                          0::Victim   \n",
       "239676                      0::Victim||1::Subject-Suspect   \n",
       "\n",
       "                                                  sources  \\\n",
       "0       http://pittsburgh.cbslocal.com/2013/01/01/4-pe...   \n",
       "1       http://losangeles.cbslocal.com/2013/01/01/man-...   \n",
       "2       http://www.morningjournal.com/general-news/201...   \n",
       "3       http://denver.cbslocal.com/2013/01/06/officer-...   \n",
       "4       http://myfox8.com/2013/01/08/update-mother-sho...   \n",
       "...                                                   ...   \n",
       "239672  http://www.klfy.com/news/local/rayne-woman-cha...   \n",
       "239673  http://www.ksla.com/story/37854648/man-wanted-...   \n",
       "239674  http://www.nola.com/crime/index.ssf/2018/04/sh...   \n",
       "239675  http://www.khou.com/article/news/hpd-investiga...   \n",
       "239676  https://www.centralmaine.com/2018/03/31/police...   \n",
       "\n",
       "       state_house_district state_senate_district  \n",
       "0                       NaN                   NaN  \n",
       "1                      62.0                  35.0  \n",
       "2                      56.0                  13.0  \n",
       "3                      40.0                  28.0  \n",
       "4                      62.0                  27.0  \n",
       "...                     ...                   ...  \n",
       "239672                  NaN                   NaN  \n",
       "239673                 23.0                  31.0  \n",
       "239674                 85.0                   7.0  \n",
       "239675                149.0                  17.0  \n",
       "239676                111.0                   3.0  \n",
       "\n",
       "[239677 rows x 29 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell if using Google Colab to upload files\n",
    "\n",
    "# df = pd.read_csv(io.BytesIO(uploaded['gun-violence-data_01-2013_03-2018.csv']))\n",
    "# df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2nd Dataset: Population of Individual US Cities by Year\n",
    "# https://www.census.gov/data/datasets/time-series/demo/popest/2010s-total-cities-and-towns.html#ds (1st table)\n",
    "\n",
    "# Method 1:\n",
    "\n",
    "df_2 = pd.read_csv(\"city_population_total_2010_2019.csv\")\n",
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": 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       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "id": "zwRB-n__gKj0",
    "outputId": "3970ae88-1650-4bfa-bbb4-09f353ca20db"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-1c352fd8-a4b7-441b-9f99-50a9021e58aa\" name=\"files[]\" multiple disabled\n",
       "        style=\"border:none\" />\n",
       "     <output id=\"result-1c352fd8-a4b7-441b-9f99-50a9021e58aa\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving city_population_total_2010_2019.csv to city_population_total_2010_2019.csv\n"
     ]
    }
   ],
   "source": [
    "# Method 2:\n",
    "\n",
    "# # Run on Google Colab to select the csv file in your local computer (should be 'city_population_total_2010_2019.csv')\n",
    "# uploaded_2 = files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 748
    },
    "id": "_e4Pb3sogRv8",
    "outputId": "bffc12a0-a784-4d29-f14a-609bc2e702db",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-ae0b1340-bea8-4606-b4e6-54f1ba65b757\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>Rank</th>\n",
       "      <th>Geographic Area</th>\n",
       "      <th>Census</th>\n",
       "      <th>Estimates Base</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>New York city, New York</td>\n",
       "      <td>8,175,133</td>\n",
       "      <td>8,175,031</td>\n",
       "      <td>8,190,209</td>\n",
       "      <td>8,272,948</td>\n",
       "      <td>8,346,693</td>\n",
       "      <td>8,396,091</td>\n",
       "      <td>8,433,806</td>\n",
       "      <td>8,463,049</td>\n",
       "      <td>8,469,153</td>\n",
       "      <td>8,437,478</td>\n",
       "      <td>8,390,081</td>\n",
       "      <td>8,336,817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Los Angeles city, California</td>\n",
       "      <td>3,792,621</td>\n",
       "      <td>3,793,139</td>\n",
       "      <td>3,795,512</td>\n",
       "      <td>3,820,876</td>\n",
       "      <td>3,851,202</td>\n",
       "      <td>3,881,622</td>\n",
       "      <td>3,909,901</td>\n",
       "      <td>3,938,568</td>\n",
       "      <td>3,963,226</td>\n",
       "      <td>3,975,788</td>\n",
       "      <td>3,977,596</td>\n",
       "      <td>3,979,576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Chicago city, Illinois</td>\n",
       "      <td>2,695,598</td>\n",
       "      <td>2,695,652</td>\n",
       "      <td>2,697,477</td>\n",
       "      <td>2,708,114</td>\n",
       "      <td>2,719,141</td>\n",
       "      <td>2,725,731</td>\n",
       "      <td>2,727,066</td>\n",
       "      <td>2,724,344</td>\n",
       "      <td>2,716,723</td>\n",
       "      <td>2,711,069</td>\n",
       "      <td>2,701,423</td>\n",
       "      <td>2,693,976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Houston city, Texas</td>\n",
       "      <td>2,099,451</td>\n",
       "      <td>2,095,517</td>\n",
       "      <td>2,100,280</td>\n",
       "      <td>2,126,032</td>\n",
       "      <td>2,161,593</td>\n",
       "      <td>2,199,391</td>\n",
       "      <td>2,241,826</td>\n",
       "      <td>2,286,908</td>\n",
       "      <td>2,309,544</td>\n",
       "      <td>2,316,750</td>\n",
       "      <td>2,318,573</td>\n",
       "      <td>2,320,268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Phoenix city, Arizona</td>\n",
       "      <td>1,445,632</td>\n",
       "      <td>1,446,691</td>\n",
       "      <td>1,449,038</td>\n",
       "      <td>1,469,796</td>\n",
       "      <td>1,499,274</td>\n",
       "      <td>1,526,491</td>\n",
       "      <td>1,555,445</td>\n",
       "      <td>1,583,690</td>\n",
       "      <td>1,612,199</td>\n",
       "      <td>1,633,560</td>\n",
       "      <td>1,654,675</td>\n",
       "      <td>1,680,992</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",
       "      <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>783</th>\n",
       "      <td>784</td>\n",
       "      <td>Lakewood city, Ohio</td>\n",
       "      <td>52,131</td>\n",
       "      <td>52,131</td>\n",
       "      <td>52,026</td>\n",
       "      <td>51,662</td>\n",
       "      <td>51,400</td>\n",
       "      <td>51,257</td>\n",
       "      <td>51,088</td>\n",
       "      <td>50,811</td>\n",
       "      <td>50,528</td>\n",
       "      <td>50,211</td>\n",
       "      <td>50,012</td>\n",
       "      <td>49,678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>785</td>\n",
       "      <td>Troy city, New York</td>\n",
       "      <td>50,129</td>\n",
       "      <td>50,162</td>\n",
       "      <td>50,081</td>\n",
       "      <td>50,020</td>\n",
       "      <td>49,848</td>\n",
       "      <td>49,921</td>\n",
       "      <td>49,968</td>\n",
       "      <td>49,776</td>\n",
       "      <td>49,597</td>\n",
       "      <td>49,433</td>\n",
       "      <td>49,354</td>\n",
       "      <td>49,154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785</th>\n",
       "      <td>786</td>\n",
       "      <td>Saginaw city, Michigan</td>\n",
       "      <td>51,508</td>\n",
       "      <td>51,469</td>\n",
       "      <td>51,363</td>\n",
       "      <td>51,012</td>\n",
       "      <td>50,728</td>\n",
       "      <td>50,272</td>\n",
       "      <td>49,798</td>\n",
       "      <td>49,230</td>\n",
       "      <td>48,902</td>\n",
       "      <td>48,696</td>\n",
       "      <td>48,310</td>\n",
       "      <td>48,115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>787</td>\n",
       "      <td>Niagara Falls city, New York</td>\n",
       "      <td>50,193</td>\n",
       "      <td>50,031</td>\n",
       "      <td>49,996</td>\n",
       "      <td>49,760</td>\n",
       "      <td>49,526</td>\n",
       "      <td>49,328</td>\n",
       "      <td>49,083</td>\n",
       "      <td>48,769</td>\n",
       "      <td>48,526</td>\n",
       "      <td>48,249</td>\n",
       "      <td>48,012</td>\n",
       "      <td>47,720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>788</td>\n",
       "      <td>Charleston city, West Virginia</td>\n",
       "      <td>51,400</td>\n",
       "      <td>51,340</td>\n",
       "      <td>51,274</td>\n",
       "      <td>50,992</td>\n",
       "      <td>50,858</td>\n",
       "      <td>50,657</td>\n",
       "      <td>50,133</td>\n",
       "      <td>49,457</td>\n",
       "      <td>48,789</td>\n",
       "      <td>48,040</td>\n",
       "      <td>47,172</td>\n",
       "      <td>46,536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>788 rows × 14 columns</p>\n",
       "</div>\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
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       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
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       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "  "
      ],
      "text/plain": [
       "     Rank                 Geographic Area     Census Estimates Base  \\\n",
       "0       1         New York city, New York  8,175,133      8,175,031   \n",
       "1       2    Los Angeles city, California  3,792,621      3,793,139   \n",
       "2       3          Chicago city, Illinois  2,695,598      2,695,652   \n",
       "3       4             Houston city, Texas  2,099,451      2,095,517   \n",
       "4       5           Phoenix city, Arizona  1,445,632      1,446,691   \n",
       "..    ...                             ...        ...            ...   \n",
       "783   784             Lakewood city, Ohio     52,131         52,131   \n",
       "784   785             Troy city, New York     50,129         50,162   \n",
       "785   786          Saginaw city, Michigan     51,508         51,469   \n",
       "786   787    Niagara Falls city, New York     50,193         50,031   \n",
       "787   788  Charleston city, West Virginia     51,400         51,340   \n",
       "\n",
       "          2010       2011       2012       2013       2014       2015  \\\n",
       "0    8,190,209  8,272,948  8,346,693  8,396,091  8,433,806  8,463,049   \n",
       "1    3,795,512  3,820,876  3,851,202  3,881,622  3,909,901  3,938,568   \n",
       "2    2,697,477  2,708,114  2,719,141  2,725,731  2,727,066  2,724,344   \n",
       "3    2,100,280  2,126,032  2,161,593  2,199,391  2,241,826  2,286,908   \n",
       "4    1,449,038  1,469,796  1,499,274  1,526,491  1,555,445  1,583,690   \n",
       "..         ...        ...        ...        ...        ...        ...   \n",
       "783     52,026     51,662     51,400     51,257     51,088     50,811   \n",
       "784     50,081     50,020     49,848     49,921     49,968     49,776   \n",
       "785     51,363     51,012     50,728     50,272     49,798     49,230   \n",
       "786     49,996     49,760     49,526     49,328     49,083     48,769   \n",
       "787     51,274     50,992     50,858     50,657     50,133     49,457   \n",
       "\n",
       "          2016       2017       2018       2019  \n",
       "0    8,469,153  8,437,478  8,390,081  8,336,817  \n",
       "1    3,963,226  3,975,788  3,977,596  3,979,576  \n",
       "2    2,716,723  2,711,069  2,701,423  2,693,976  \n",
       "3    2,309,544  2,316,750  2,318,573  2,320,268  \n",
       "4    1,612,199  1,633,560  1,654,675  1,680,992  \n",
       "..         ...        ...        ...        ...  \n",
       "783     50,528     50,211     50,012     49,678  \n",
       "784     49,597     49,433     49,354     49,154  \n",
       "785     48,902     48,696     48,310     48,115  \n",
       "786     48,526     48,249     48,012     47,720  \n",
       "787     48,789     48,040     47,172     46,536  \n",
       "\n",
       "[788 rows x 14 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell if using Google Colab to upload files\n",
    "\n",
    "# df_2 = pd.read_csv(io.BytesIO(uploaded_2['city_population_total_2010_2019.csv']))\n",
    "# df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3rd Dataset: Dataset of Individual US State Populations by Year\n",
    "# https://www.census.gov/data/tables/time-series/demo/popest/2010s-state-total.html (1st table)\n",
    "\n",
    "# Method 1:\n",
    "\n",
    "df_3 = pd.read_csv(\"state_population_total_2010_2019.csv\")\n",
    "df_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": 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       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "id": "53r1vVv7oK_X",
    "outputId": "d9082697-d43b-4701-d7f6-fb73ca306cdf"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-da4939f7-8791-43bc-a09a-3f1fc7f641a1\" name=\"files[]\" multiple disabled\n",
       "        style=\"border:none\" />\n",
       "     <output id=\"result-da4939f7-8791-43bc-a09a-3f1fc7f641a1\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving state_population_total_2010_2019.csv to state_population_total_2010_2019.csv\n"
     ]
    }
   ],
   "source": [
    "# Method 2:\n",
    "\n",
    "# # Run on Google Colab to select the csv file in your local computer (should be 'state_population_total_2010_2019.csv')\n",
    "# uploaded_3 = files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "Xf_ebmGqnoQb",
    "outputId": "34efd130-257f-447f-c951-bd5054dfde52",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-b34bcff9-97b7-4fea-87f0-ee53f295b4cc\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>Geographic Area</th>\n",
       "      <th>Census</th>\n",
       "      <th>Estimates Base</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>4779736</td>\n",
       "      <td>4780125</td>\n",
       "      <td>4785437</td>\n",
       "      <td>4799069</td>\n",
       "      <td>4815588</td>\n",
       "      <td>4830081</td>\n",
       "      <td>4841799</td>\n",
       "      <td>4852347</td>\n",
       "      <td>4863525</td>\n",
       "      <td>4874486</td>\n",
       "      <td>4887681</td>\n",
       "      <td>4903185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>710231</td>\n",
       "      <td>710249</td>\n",
       "      <td>713910</td>\n",
       "      <td>722128</td>\n",
       "      <td>730443</td>\n",
       "      <td>737068</td>\n",
       "      <td>736283</td>\n",
       "      <td>737498</td>\n",
       "      <td>741456</td>\n",
       "      <td>739700</td>\n",
       "      <td>735139</td>\n",
       "      <td>731545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>6392017</td>\n",
       "      <td>6392288</td>\n",
       "      <td>6407172</td>\n",
       "      <td>6472643</td>\n",
       "      <td>6554978</td>\n",
       "      <td>6632764</td>\n",
       "      <td>6730413</td>\n",
       "      <td>6829676</td>\n",
       "      <td>6941072</td>\n",
       "      <td>7044008</td>\n",
       "      <td>7158024</td>\n",
       "      <td>7278717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2915918</td>\n",
       "      <td>2916031</td>\n",
       "      <td>2921964</td>\n",
       "      <td>2940667</td>\n",
       "      <td>2952164</td>\n",
       "      <td>2959400</td>\n",
       "      <td>2967392</td>\n",
       "      <td>2978048</td>\n",
       "      <td>2989918</td>\n",
       "      <td>3001345</td>\n",
       "      <td>3009733</td>\n",
       "      <td>3017804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>37253956</td>\n",
       "      <td>37254519</td>\n",
       "      <td>37319502</td>\n",
       "      <td>37638369</td>\n",
       "      <td>37948800</td>\n",
       "      <td>38260787</td>\n",
       "      <td>38596972</td>\n",
       "      <td>38918045</td>\n",
       "      <td>39167117</td>\n",
       "      <td>39358497</td>\n",
       "      <td>39461588</td>\n",
       "      <td>39512223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>5029196</td>\n",
       "      <td>5029319</td>\n",
       "      <td>5047349</td>\n",
       "      <td>5121108</td>\n",
       "      <td>5192647</td>\n",
       "      <td>5269035</td>\n",
       "      <td>5350101</td>\n",
       "      <td>5450623</td>\n",
       "      <td>5539215</td>\n",
       "      <td>5611885</td>\n",
       "      <td>5691287</td>\n",
       "      <td>5758736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>3574097</td>\n",
       "      <td>3574147</td>\n",
       "      <td>3579114</td>\n",
       "      <td>3588283</td>\n",
       "      <td>3594547</td>\n",
       "      <td>3594841</td>\n",
       "      <td>3594524</td>\n",
       "      <td>3587122</td>\n",
       "      <td>3578141</td>\n",
       "      <td>3573297</td>\n",
       "      <td>3571520</td>\n",
       "      <td>3565287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>897934</td>\n",
       "      <td>897937</td>\n",
       "      <td>899593</td>\n",
       "      <td>907381</td>\n",
       "      <td>915179</td>\n",
       "      <td>923576</td>\n",
       "      <td>932487</td>\n",
       "      <td>941252</td>\n",
       "      <td>948921</td>\n",
       "      <td>956823</td>\n",
       "      <td>965479</td>\n",
       "      <td>973764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>601723</td>\n",
       "      <td>601767</td>\n",
       "      <td>605226</td>\n",
       "      <td>619800</td>\n",
       "      <td>634924</td>\n",
       "      <td>650581</td>\n",
       "      <td>662328</td>\n",
       "      <td>675400</td>\n",
       "      <td>685815</td>\n",
       "      <td>694906</td>\n",
       "      <td>701547</td>\n",
       "      <td>705749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Florida</td>\n",
       "      <td>18801310</td>\n",
       "      <td>18804564</td>\n",
       "      <td>18845537</td>\n",
       "      <td>19053237</td>\n",
       "      <td>19297822</td>\n",
       "      <td>19545621</td>\n",
       "      <td>19845911</td>\n",
       "      <td>20209042</td>\n",
       "      <td>20613477</td>\n",
       "      <td>20963613</td>\n",
       "      <td>21244317</td>\n",
       "      <td>21477737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>9687653</td>\n",
       "      <td>9688729</td>\n",
       "      <td>9711881</td>\n",
       "      <td>9802431</td>\n",
       "      <td>9901430</td>\n",
       "      <td>9972479</td>\n",
       "      <td>10067278</td>\n",
       "      <td>10178447</td>\n",
       "      <td>10301890</td>\n",
       "      <td>10410330</td>\n",
       "      <td>10511131</td>\n",
       "      <td>10617423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>1360301</td>\n",
       "      <td>1360307</td>\n",
       "      <td>1363963</td>\n",
       "      <td>1379329</td>\n",
       "      <td>1394804</td>\n",
       "      <td>1408243</td>\n",
       "      <td>1414538</td>\n",
       "      <td>1422052</td>\n",
       "      <td>1427559</td>\n",
       "      <td>1424393</td>\n",
       "      <td>1420593</td>\n",
       "      <td>1415872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>1567582</td>\n",
       "      <td>1567657</td>\n",
       "      <td>1570746</td>\n",
       "      <td>1583910</td>\n",
       "      <td>1595324</td>\n",
       "      <td>1611206</td>\n",
       "      <td>1631112</td>\n",
       "      <td>1651059</td>\n",
       "      <td>1682380</td>\n",
       "      <td>1717715</td>\n",
       "      <td>1750536</td>\n",
       "      <td>1787065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>12830632</td>\n",
       "      <td>12831572</td>\n",
       "      <td>12840503</td>\n",
       "      <td>12867454</td>\n",
       "      <td>12882510</td>\n",
       "      <td>12895129</td>\n",
       "      <td>12884493</td>\n",
       "      <td>12858913</td>\n",
       "      <td>12820527</td>\n",
       "      <td>12778828</td>\n",
       "      <td>12723071</td>\n",
       "      <td>12671821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>6483802</td>\n",
       "      <td>6484051</td>\n",
       "      <td>6490432</td>\n",
       "      <td>6516528</td>\n",
       "      <td>6537703</td>\n",
       "      <td>6568713</td>\n",
       "      <td>6593644</td>\n",
       "      <td>6608422</td>\n",
       "      <td>6634304</td>\n",
       "      <td>6658078</td>\n",
       "      <td>6695497</td>\n",
       "      <td>6732219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>3046355</td>\n",
       "      <td>3046871</td>\n",
       "      <td>3050745</td>\n",
       "      <td>3066336</td>\n",
       "      <td>3076190</td>\n",
       "      <td>3092997</td>\n",
       "      <td>3109350</td>\n",
       "      <td>3120960</td>\n",
       "      <td>3131371</td>\n",
       "      <td>3141550</td>\n",
       "      <td>3148618</td>\n",
       "      <td>3155070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>2853118</td>\n",
       "      <td>2853123</td>\n",
       "      <td>2858190</td>\n",
       "      <td>2869225</td>\n",
       "      <td>2885257</td>\n",
       "      <td>2893212</td>\n",
       "      <td>2900475</td>\n",
       "      <td>2909011</td>\n",
       "      <td>2910844</td>\n",
       "      <td>2908718</td>\n",
       "      <td>2911359</td>\n",
       "      <td>2913314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>4339367</td>\n",
       "      <td>4339333</td>\n",
       "      <td>4348181</td>\n",
       "      <td>4369821</td>\n",
       "      <td>4386346</td>\n",
       "      <td>4404659</td>\n",
       "      <td>4414349</td>\n",
       "      <td>4425976</td>\n",
       "      <td>4438182</td>\n",
       "      <td>4452268</td>\n",
       "      <td>4461153</td>\n",
       "      <td>4467673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>4533372</td>\n",
       "      <td>4533487</td>\n",
       "      <td>4544532</td>\n",
       "      <td>4575625</td>\n",
       "      <td>4600972</td>\n",
       "      <td>4624527</td>\n",
       "      <td>4644013</td>\n",
       "      <td>4664628</td>\n",
       "      <td>4678135</td>\n",
       "      <td>4670560</td>\n",
       "      <td>4659690</td>\n",
       "      <td>4648794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Maine</td>\n",
       "      <td>1328361</td>\n",
       "      <td>1328358</td>\n",
       "      <td>1327629</td>\n",
       "      <td>1328284</td>\n",
       "      <td>1327729</td>\n",
       "      <td>1328009</td>\n",
       "      <td>1330513</td>\n",
       "      <td>1328262</td>\n",
       "      <td>1331317</td>\n",
       "      <td>1334612</td>\n",
       "      <td>1339057</td>\n",
       "      <td>1344212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>5773552</td>\n",
       "      <td>5773794</td>\n",
       "      <td>5788645</td>\n",
       "      <td>5839419</td>\n",
       "      <td>5886992</td>\n",
       "      <td>5923188</td>\n",
       "      <td>5957283</td>\n",
       "      <td>5985562</td>\n",
       "      <td>6003323</td>\n",
       "      <td>6023868</td>\n",
       "      <td>6035802</td>\n",
       "      <td>6045680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>6547629</td>\n",
       "      <td>6547785</td>\n",
       "      <td>6566307</td>\n",
       "      <td>6613583</td>\n",
       "      <td>6663005</td>\n",
       "      <td>6713315</td>\n",
       "      <td>6762596</td>\n",
       "      <td>6794228</td>\n",
       "      <td>6823608</td>\n",
       "      <td>6859789</td>\n",
       "      <td>6882635</td>\n",
       "      <td>6892503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>9883640</td>\n",
       "      <td>9884116</td>\n",
       "      <td>9877510</td>\n",
       "      <td>9882412</td>\n",
       "      <td>9897145</td>\n",
       "      <td>9913065</td>\n",
       "      <td>9929848</td>\n",
       "      <td>9931715</td>\n",
       "      <td>9950571</td>\n",
       "      <td>9973114</td>\n",
       "      <td>9984072</td>\n",
       "      <td>9986857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>5303925</td>\n",
       "      <td>5303927</td>\n",
       "      <td>5310828</td>\n",
       "      <td>5346143</td>\n",
       "      <td>5376643</td>\n",
       "      <td>5413479</td>\n",
       "      <td>5451079</td>\n",
       "      <td>5482032</td>\n",
       "      <td>5522744</td>\n",
       "      <td>5566230</td>\n",
       "      <td>5606249</td>\n",
       "      <td>5639632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>2967297</td>\n",
       "      <td>2968130</td>\n",
       "      <td>2970548</td>\n",
       "      <td>2978731</td>\n",
       "      <td>2983816</td>\n",
       "      <td>2988711</td>\n",
       "      <td>2990468</td>\n",
       "      <td>2988471</td>\n",
       "      <td>2987938</td>\n",
       "      <td>2988510</td>\n",
       "      <td>2981020</td>\n",
       "      <td>2976149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>5988927</td>\n",
       "      <td>5988950</td>\n",
       "      <td>5995974</td>\n",
       "      <td>6010275</td>\n",
       "      <td>6024367</td>\n",
       "      <td>6040715</td>\n",
       "      <td>6056202</td>\n",
       "      <td>6071732</td>\n",
       "      <td>6087135</td>\n",
       "      <td>6106670</td>\n",
       "      <td>6121623</td>\n",
       "      <td>6137428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Montana</td>\n",
       "      <td>989415</td>\n",
       "      <td>989407</td>\n",
       "      <td>990697</td>\n",
       "      <td>997316</td>\n",
       "      <td>1003783</td>\n",
       "      <td>1013569</td>\n",
       "      <td>1021869</td>\n",
       "      <td>1030475</td>\n",
       "      <td>1040859</td>\n",
       "      <td>1052482</td>\n",
       "      <td>1060665</td>\n",
       "      <td>1068778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Nebraska</td>\n",
       "      <td>1826341</td>\n",
       "      <td>1826305</td>\n",
       "      <td>1829542</td>\n",
       "      <td>1840672</td>\n",
       "      <td>1853303</td>\n",
       "      <td>1865279</td>\n",
       "      <td>1879321</td>\n",
       "      <td>1891277</td>\n",
       "      <td>1905616</td>\n",
       "      <td>1915947</td>\n",
       "      <td>1925614</td>\n",
       "      <td>1934408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2700551</td>\n",
       "      <td>2700677</td>\n",
       "      <td>2702405</td>\n",
       "      <td>2712730</td>\n",
       "      <td>2743996</td>\n",
       "      <td>2775970</td>\n",
       "      <td>2817628</td>\n",
       "      <td>2866939</td>\n",
       "      <td>2917563</td>\n",
       "      <td>2969905</td>\n",
       "      <td>3027341</td>\n",
       "      <td>3080156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>1316470</td>\n",
       "      <td>1316462</td>\n",
       "      <td>1316762</td>\n",
       "      <td>1320202</td>\n",
       "      <td>1324232</td>\n",
       "      <td>1326622</td>\n",
       "      <td>1333341</td>\n",
       "      <td>1336350</td>\n",
       "      <td>1342307</td>\n",
       "      <td>1348787</td>\n",
       "      <td>1353465</td>\n",
       "      <td>1359711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>8791894</td>\n",
       "      <td>8791978</td>\n",
       "      <td>8799446</td>\n",
       "      <td>8828117</td>\n",
       "      <td>8844942</td>\n",
       "      <td>8856972</td>\n",
       "      <td>8864525</td>\n",
       "      <td>8867949</td>\n",
       "      <td>8870827</td>\n",
       "      <td>8885525</td>\n",
       "      <td>8886025</td>\n",
       "      <td>8882190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>2059179</td>\n",
       "      <td>2059199</td>\n",
       "      <td>2064552</td>\n",
       "      <td>2080450</td>\n",
       "      <td>2087309</td>\n",
       "      <td>2092273</td>\n",
       "      <td>2089568</td>\n",
       "      <td>2089291</td>\n",
       "      <td>2091630</td>\n",
       "      <td>2091784</td>\n",
       "      <td>2092741</td>\n",
       "      <td>2096829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>New York</td>\n",
       "      <td>19378102</td>\n",
       "      <td>19378144</td>\n",
       "      <td>19399878</td>\n",
       "      <td>19499241</td>\n",
       "      <td>19572932</td>\n",
       "      <td>19624447</td>\n",
       "      <td>19651049</td>\n",
       "      <td>19654666</td>\n",
       "      <td>19633428</td>\n",
       "      <td>19589572</td>\n",
       "      <td>19530351</td>\n",
       "      <td>19453561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>9535483</td>\n",
       "      <td>9535751</td>\n",
       "      <td>9574323</td>\n",
       "      <td>9657592</td>\n",
       "      <td>9749476</td>\n",
       "      <td>9843336</td>\n",
       "      <td>9932887</td>\n",
       "      <td>10031646</td>\n",
       "      <td>10154788</td>\n",
       "      <td>10268233</td>\n",
       "      <td>10381615</td>\n",
       "      <td>10488084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>672591</td>\n",
       "      <td>672576</td>\n",
       "      <td>674715</td>\n",
       "      <td>685225</td>\n",
       "      <td>701176</td>\n",
       "      <td>722036</td>\n",
       "      <td>737401</td>\n",
       "      <td>754066</td>\n",
       "      <td>754434</td>\n",
       "      <td>754942</td>\n",
       "      <td>758080</td>\n",
       "      <td>762062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>11536504</td>\n",
       "      <td>11536751</td>\n",
       "      <td>11539336</td>\n",
       "      <td>11544663</td>\n",
       "      <td>11548923</td>\n",
       "      <td>11576684</td>\n",
       "      <td>11602700</td>\n",
       "      <td>11617527</td>\n",
       "      <td>11634370</td>\n",
       "      <td>11659650</td>\n",
       "      <td>11676341</td>\n",
       "      <td>11689100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>3751351</td>\n",
       "      <td>3751582</td>\n",
       "      <td>3759944</td>\n",
       "      <td>3788379</td>\n",
       "      <td>3818814</td>\n",
       "      <td>3853214</td>\n",
       "      <td>3878187</td>\n",
       "      <td>3909500</td>\n",
       "      <td>3926331</td>\n",
       "      <td>3931316</td>\n",
       "      <td>3940235</td>\n",
       "      <td>3956971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>3831074</td>\n",
       "      <td>3831079</td>\n",
       "      <td>3837491</td>\n",
       "      <td>3872036</td>\n",
       "      <td>3899001</td>\n",
       "      <td>3922468</td>\n",
       "      <td>3963244</td>\n",
       "      <td>4015792</td>\n",
       "      <td>4089976</td>\n",
       "      <td>4143625</td>\n",
       "      <td>4181886</td>\n",
       "      <td>4217737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>12702379</td>\n",
       "      <td>12702868</td>\n",
       "      <td>12711160</td>\n",
       "      <td>12745815</td>\n",
       "      <td>12767118</td>\n",
       "      <td>12776309</td>\n",
       "      <td>12788313</td>\n",
       "      <td>12784826</td>\n",
       "      <td>12782275</td>\n",
       "      <td>12787641</td>\n",
       "      <td>12800922</td>\n",
       "      <td>12801989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>1052567</td>\n",
       "      <td>1052964</td>\n",
       "      <td>1053959</td>\n",
       "      <td>1053649</td>\n",
       "      <td>1054621</td>\n",
       "      <td>1055081</td>\n",
       "      <td>1055936</td>\n",
       "      <td>1056065</td>\n",
       "      <td>1056770</td>\n",
       "      <td>1055673</td>\n",
       "      <td>1058287</td>\n",
       "      <td>1059361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>4625364</td>\n",
       "      <td>4625366</td>\n",
       "      <td>4635649</td>\n",
       "      <td>4671994</td>\n",
       "      <td>4717354</td>\n",
       "      <td>4764080</td>\n",
       "      <td>4823617</td>\n",
       "      <td>4891938</td>\n",
       "      <td>4957968</td>\n",
       "      <td>5021268</td>\n",
       "      <td>5084156</td>\n",
       "      <td>5148714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>South Dakota</td>\n",
       "      <td>814180</td>\n",
       "      <td>814198</td>\n",
       "      <td>816166</td>\n",
       "      <td>823579</td>\n",
       "      <td>833566</td>\n",
       "      <td>842316</td>\n",
       "      <td>849129</td>\n",
       "      <td>853988</td>\n",
       "      <td>862996</td>\n",
       "      <td>872868</td>\n",
       "      <td>878698</td>\n",
       "      <td>884659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>6346105</td>\n",
       "      <td>6346276</td>\n",
       "      <td>6355311</td>\n",
       "      <td>6399291</td>\n",
       "      <td>6453898</td>\n",
       "      <td>6494340</td>\n",
       "      <td>6541223</td>\n",
       "      <td>6591170</td>\n",
       "      <td>6646010</td>\n",
       "      <td>6708799</td>\n",
       "      <td>6771631</td>\n",
       "      <td>6829174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Texas</td>\n",
       "      <td>25145561</td>\n",
       "      <td>25146091</td>\n",
       "      <td>25241971</td>\n",
       "      <td>25645629</td>\n",
       "      <td>26084481</td>\n",
       "      <td>26480266</td>\n",
       "      <td>26964333</td>\n",
       "      <td>27470056</td>\n",
       "      <td>27914410</td>\n",
       "      <td>28295273</td>\n",
       "      <td>28628666</td>\n",
       "      <td>28995881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Utah</td>\n",
       "      <td>2763885</td>\n",
       "      <td>2763891</td>\n",
       "      <td>2775332</td>\n",
       "      <td>2814384</td>\n",
       "      <td>2853375</td>\n",
       "      <td>2897640</td>\n",
       "      <td>2936879</td>\n",
       "      <td>2981835</td>\n",
       "      <td>3041868</td>\n",
       "      <td>3101042</td>\n",
       "      <td>3153550</td>\n",
       "      <td>3205958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>625741</td>\n",
       "      <td>625737</td>\n",
       "      <td>625879</td>\n",
       "      <td>627049</td>\n",
       "      <td>626090</td>\n",
       "      <td>626210</td>\n",
       "      <td>625214</td>\n",
       "      <td>625216</td>\n",
       "      <td>623657</td>\n",
       "      <td>624344</td>\n",
       "      <td>624358</td>\n",
       "      <td>623989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>8001024</td>\n",
       "      <td>8001049</td>\n",
       "      <td>8023699</td>\n",
       "      <td>8101155</td>\n",
       "      <td>8185080</td>\n",
       "      <td>8252427</td>\n",
       "      <td>8310993</td>\n",
       "      <td>8361808</td>\n",
       "      <td>8410106</td>\n",
       "      <td>8463587</td>\n",
       "      <td>8501286</td>\n",
       "      <td>8535519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Washington</td>\n",
       "      <td>6724540</td>\n",
       "      <td>6724540</td>\n",
       "      <td>6742830</td>\n",
       "      <td>6826627</td>\n",
       "      <td>6897058</td>\n",
       "      <td>6963985</td>\n",
       "      <td>7054655</td>\n",
       "      <td>7163657</td>\n",
       "      <td>7294771</td>\n",
       "      <td>7423362</td>\n",
       "      <td>7523869</td>\n",
       "      <td>7614893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>1852994</td>\n",
       "      <td>1853018</td>\n",
       "      <td>1854239</td>\n",
       "      <td>1856301</td>\n",
       "      <td>1856872</td>\n",
       "      <td>1853914</td>\n",
       "      <td>1849489</td>\n",
       "      <td>1842050</td>\n",
       "      <td>1831023</td>\n",
       "      <td>1817004</td>\n",
       "      <td>1804291</td>\n",
       "      <td>1792147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>5686986</td>\n",
       "      <td>5687285</td>\n",
       "      <td>5690475</td>\n",
       "      <td>5705288</td>\n",
       "      <td>5719960</td>\n",
       "      <td>5736754</td>\n",
       "      <td>5751525</td>\n",
       "      <td>5760940</td>\n",
       "      <td>5772628</td>\n",
       "      <td>5790186</td>\n",
       "      <td>5807406</td>\n",
       "      <td>5822434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>563626</td>\n",
       "      <td>563775</td>\n",
       "      <td>564487</td>\n",
       "      <td>567299</td>\n",
       "      <td>576305</td>\n",
       "      <td>582122</td>\n",
       "      <td>582531</td>\n",
       "      <td>585613</td>\n",
       "      <td>584215</td>\n",
       "      <td>578931</td>\n",
       "      <td>577601</td>\n",
       "      <td>578759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
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      "text/plain": [
       "         Geographic Area    Census  Estimates Base      2010      2011  \\\n",
       "0                Alabama   4779736         4780125   4785437   4799069   \n",
       "1                 Alaska    710231          710249    713910    722128   \n",
       "2                Arizona   6392017         6392288   6407172   6472643   \n",
       "3               Arkansas   2915918         2916031   2921964   2940667   \n",
       "4             California  37253956        37254519  37319502  37638369   \n",
       "5               Colorado   5029196         5029319   5047349   5121108   \n",
       "6            Connecticut   3574097         3574147   3579114   3588283   \n",
       "7               Delaware    897934          897937    899593    907381   \n",
       "8   District of Columbia    601723          601767    605226    619800   \n",
       "9                Florida  18801310        18804564  18845537  19053237   \n",
       "10               Georgia   9687653         9688729   9711881   9802431   \n",
       "11                Hawaii   1360301         1360307   1363963   1379329   \n",
       "12                 Idaho   1567582         1567657   1570746   1583910   \n",
       "13              Illinois  12830632        12831572  12840503  12867454   \n",
       "14               Indiana   6483802         6484051   6490432   6516528   \n",
       "15                  Iowa   3046355         3046871   3050745   3066336   \n",
       "16                Kansas   2853118         2853123   2858190   2869225   \n",
       "17              Kentucky   4339367         4339333   4348181   4369821   \n",
       "18             Louisiana   4533372         4533487   4544532   4575625   \n",
       "19                 Maine   1328361         1328358   1327629   1328284   \n",
       "20              Maryland   5773552         5773794   5788645   5839419   \n",
       "21         Massachusetts   6547629         6547785   6566307   6613583   \n",
       "22              Michigan   9883640         9884116   9877510   9882412   \n",
       "23             Minnesota   5303925         5303927   5310828   5346143   \n",
       "24           Mississippi   2967297         2968130   2970548   2978731   \n",
       "25              Missouri   5988927         5988950   5995974   6010275   \n",
       "26               Montana    989415          989407    990697    997316   \n",
       "27              Nebraska   1826341         1826305   1829542   1840672   \n",
       "28                Nevada   2700551         2700677   2702405   2712730   \n",
       "29         New Hampshire   1316470         1316462   1316762   1320202   \n",
       "30            New Jersey   8791894         8791978   8799446   8828117   \n",
       "31            New Mexico   2059179         2059199   2064552   2080450   \n",
       "32              New York  19378102        19378144  19399878  19499241   \n",
       "33        North Carolina   9535483         9535751   9574323   9657592   \n",
       "34          North Dakota    672591          672576    674715    685225   \n",
       "35                  Ohio  11536504        11536751  11539336  11544663   \n",
       "36              Oklahoma   3751351         3751582   3759944   3788379   \n",
       "37                Oregon   3831074         3831079   3837491   3872036   \n",
       "38          Pennsylvania  12702379        12702868  12711160  12745815   \n",
       "39          Rhode Island   1052567         1052964   1053959   1053649   \n",
       "40        South Carolina   4625364         4625366   4635649   4671994   \n",
       "41          South Dakota    814180          814198    816166    823579   \n",
       "42             Tennessee   6346105         6346276   6355311   6399291   \n",
       "43                 Texas  25145561        25146091  25241971  25645629   \n",
       "44                  Utah   2763885         2763891   2775332   2814384   \n",
       "45               Vermont    625741          625737    625879    627049   \n",
       "46              Virginia   8001024         8001049   8023699   8101155   \n",
       "47            Washington   6724540         6724540   6742830   6826627   \n",
       "48         West Virginia   1852994         1853018   1854239   1856301   \n",
       "49             Wisconsin   5686986         5687285   5690475   5705288   \n",
       "50               Wyoming    563626          563775    564487    567299   \n",
       "\n",
       "        2012      2013      2014      2015      2016      2017      2018  \\\n",
       "0    4815588   4830081   4841799   4852347   4863525   4874486   4887681   \n",
       "1     730443    737068    736283    737498    741456    739700    735139   \n",
       "2    6554978   6632764   6730413   6829676   6941072   7044008   7158024   \n",
       "3    2952164   2959400   2967392   2978048   2989918   3001345   3009733   \n",
       "4   37948800  38260787  38596972  38918045  39167117  39358497  39461588   \n",
       "5    5192647   5269035   5350101   5450623   5539215   5611885   5691287   \n",
       "6    3594547   3594841   3594524   3587122   3578141   3573297   3571520   \n",
       "7     915179    923576    932487    941252    948921    956823    965479   \n",
       "8     634924    650581    662328    675400    685815    694906    701547   \n",
       "9   19297822  19545621  19845911  20209042  20613477  20963613  21244317   \n",
       "10   9901430   9972479  10067278  10178447  10301890  10410330  10511131   \n",
       "11   1394804   1408243   1414538   1422052   1427559   1424393   1420593   \n",
       "12   1595324   1611206   1631112   1651059   1682380   1717715   1750536   \n",
       "13  12882510  12895129  12884493  12858913  12820527  12778828  12723071   \n",
       "14   6537703   6568713   6593644   6608422   6634304   6658078   6695497   \n",
       "15   3076190   3092997   3109350   3120960   3131371   3141550   3148618   \n",
       "16   2885257   2893212   2900475   2909011   2910844   2908718   2911359   \n",
       "17   4386346   4404659   4414349   4425976   4438182   4452268   4461153   \n",
       "18   4600972   4624527   4644013   4664628   4678135   4670560   4659690   \n",
       "19   1327729   1328009   1330513   1328262   1331317   1334612   1339057   \n",
       "20   5886992   5923188   5957283   5985562   6003323   6023868   6035802   \n",
       "21   6663005   6713315   6762596   6794228   6823608   6859789   6882635   \n",
       "22   9897145   9913065   9929848   9931715   9950571   9973114   9984072   \n",
       "23   5376643   5413479   5451079   5482032   5522744   5566230   5606249   \n",
       "24   2983816   2988711   2990468   2988471   2987938   2988510   2981020   \n",
       "25   6024367   6040715   6056202   6071732   6087135   6106670   6121623   \n",
       "26   1003783   1013569   1021869   1030475   1040859   1052482   1060665   \n",
       "27   1853303   1865279   1879321   1891277   1905616   1915947   1925614   \n",
       "28   2743996   2775970   2817628   2866939   2917563   2969905   3027341   \n",
       "29   1324232   1326622   1333341   1336350   1342307   1348787   1353465   \n",
       "30   8844942   8856972   8864525   8867949   8870827   8885525   8886025   \n",
       "31   2087309   2092273   2089568   2089291   2091630   2091784   2092741   \n",
       "32  19572932  19624447  19651049  19654666  19633428  19589572  19530351   \n",
       "33   9749476   9843336   9932887  10031646  10154788  10268233  10381615   \n",
       "34    701176    722036    737401    754066    754434    754942    758080   \n",
       "35  11548923  11576684  11602700  11617527  11634370  11659650  11676341   \n",
       "36   3818814   3853214   3878187   3909500   3926331   3931316   3940235   \n",
       "37   3899001   3922468   3963244   4015792   4089976   4143625   4181886   \n",
       "38  12767118  12776309  12788313  12784826  12782275  12787641  12800922   \n",
       "39   1054621   1055081   1055936   1056065   1056770   1055673   1058287   \n",
       "40   4717354   4764080   4823617   4891938   4957968   5021268   5084156   \n",
       "41    833566    842316    849129    853988    862996    872868    878698   \n",
       "42   6453898   6494340   6541223   6591170   6646010   6708799   6771631   \n",
       "43  26084481  26480266  26964333  27470056  27914410  28295273  28628666   \n",
       "44   2853375   2897640   2936879   2981835   3041868   3101042   3153550   \n",
       "45    626090    626210    625214    625216    623657    624344    624358   \n",
       "46   8185080   8252427   8310993   8361808   8410106   8463587   8501286   \n",
       "47   6897058   6963985   7054655   7163657   7294771   7423362   7523869   \n",
       "48   1856872   1853914   1849489   1842050   1831023   1817004   1804291   \n",
       "49   5719960   5736754   5751525   5760940   5772628   5790186   5807406   \n",
       "50    576305    582122    582531    585613    584215    578931    577601   \n",
       "\n",
       "        2019  \n",
       "0    4903185  \n",
       "1     731545  \n",
       "2    7278717  \n",
       "3    3017804  \n",
       "4   39512223  \n",
       "5    5758736  \n",
       "6    3565287  \n",
       "7     973764  \n",
       "8     705749  \n",
       "9   21477737  \n",
       "10  10617423  \n",
       "11   1415872  \n",
       "12   1787065  \n",
       "13  12671821  \n",
       "14   6732219  \n",
       "15   3155070  \n",
       "16   2913314  \n",
       "17   4467673  \n",
       "18   4648794  \n",
       "19   1344212  \n",
       "20   6045680  \n",
       "21   6892503  \n",
       "22   9986857  \n",
       "23   5639632  \n",
       "24   2976149  \n",
       "25   6137428  \n",
       "26   1068778  \n",
       "27   1934408  \n",
       "28   3080156  \n",
       "29   1359711  \n",
       "30   8882190  \n",
       "31   2096829  \n",
       "32  19453561  \n",
       "33  10488084  \n",
       "34    762062  \n",
       "35  11689100  \n",
       "36   3956971  \n",
       "37   4217737  \n",
       "38  12801989  \n",
       "39   1059361  \n",
       "40   5148714  \n",
       "41    884659  \n",
       "42   6829174  \n",
       "43  28995881  \n",
       "44   3205958  \n",
       "45    623989  \n",
       "46   8535519  \n",
       "47   7614893  \n",
       "48   1792147  \n",
       "49   5822434  \n",
       "50    578759  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell if using Google Colab to upload files\n",
    "\n",
    "# df_3 = pd.read_csv(io.BytesIO(uploaded_3['state_population_total_2010_2019.csv']))\n",
    "# df_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "This dataset does not require you to follow either of the 2 methods above\n",
    "since this dataset is being pulled from another Github repository\n",
    "'''\n",
    "\n",
    "# 4th Dataset: Dataset of land area of each US State\n",
    "# (to calculate state population density for later)\n",
    "# https://github.com/jakevdp/data-USstates/blob/master/state-areas.csv\n",
    "\n",
    "df_4 = pd.read_csv('https://raw.githubusercontent.com/jakevdp/data-USstates/master/state-areas.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "WNoKJy8B2Mvm",
    "outputId": "33581ad6-cf94-447f-f901-02df23592ca0",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
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      "text/plain": [
       "                   state  area (sq. mi)\n",
       "0                Alabama          52423\n",
       "1                 Alaska         656425\n",
       "2                Arizona         114006\n",
       "3               Arkansas          53182\n",
       "4             California         163707\n",
       "5               Colorado         104100\n",
       "6            Connecticut           5544\n",
       "7               Delaware           1954\n",
       "8                Florida          65758\n",
       "9                Georgia          59441\n",
       "10                Hawaii          10932\n",
       "11                 Idaho          83574\n",
       "12              Illinois          57918\n",
       "13               Indiana          36420\n",
       "14                  Iowa          56276\n",
       "15                Kansas          82282\n",
       "16              Kentucky          40411\n",
       "17             Louisiana          51843\n",
       "18                 Maine          35387\n",
       "19              Maryland          12407\n",
       "20         Massachusetts          10555\n",
       "21              Michigan          96810\n",
       "22             Minnesota          86943\n",
       "23           Mississippi          48434\n",
       "24              Missouri          69709\n",
       "25               Montana         147046\n",
       "26              Nebraska          77358\n",
       "27                Nevada         110567\n",
       "28         New Hampshire           9351\n",
       "29            New Jersey           8722\n",
       "30            New Mexico         121593\n",
       "31              New York          54475\n",
       "32        North Carolina          53821\n",
       "33          North Dakota          70704\n",
       "34                  Ohio          44828\n",
       "35              Oklahoma          69903\n",
       "36                Oregon          98386\n",
       "37          Pennsylvania          46058\n",
       "38          Rhode Island           1545\n",
       "39        South Carolina          32007\n",
       "40          South Dakota          77121\n",
       "41             Tennessee          42146\n",
       "42                 Texas         268601\n",
       "43                  Utah          84904\n",
       "44               Vermont           9615\n",
       "45              Virginia          42769\n",
       "46            Washington          71303\n",
       "47         West Virginia          24231\n",
       "48             Wisconsin          65503\n",
       "49               Wyoming          97818\n",
       "50  District of Columbia             68\n",
       "51           Puerto Rico           3515"
      ]
     },
     "execution_count": 8,
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   "source": [
    "df_4"
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  {
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   "metadata": {},
   "source": [
    "## Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "VzEeOZGc6uVM"
   },
   "outputs": [],
   "source": [
    "# Cleaning #1: Adding 'year' column to help keep track of gun violence over year\n",
    "\n",
    "df['datetime'] = pd.to_datetime(df[\"date\"])\n",
    "df['year'] = pd.DatetimeIndex(df['datetime']).year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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    "id": "9BKyBVBFhUCs",
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       "      <td>4</td>\n",
       "      <td>2099451</td>\n",
       "      <td>2095517</td>\n",
       "      <td>2100280</td>\n",
       "      <td>2126032</td>\n",
       "      <td>2161593</td>\n",
       "      <td>2199391</td>\n",
       "      <td>2241826</td>\n",
       "      <td>2286908</td>\n",
       "      <td>2309544</td>\n",
       "      <td>2316750</td>\n",
       "      <td>2318573</td>\n",
       "      <td>2320268</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1445632</td>\n",
       "      <td>1446691</td>\n",
       "      <td>1449038</td>\n",
       "      <td>1469796</td>\n",
       "      <td>1499274</td>\n",
       "      <td>1526491</td>\n",
       "      <td>1555445</td>\n",
       "      <td>1583690</td>\n",
       "      <td>1612199</td>\n",
       "      <td>1633560</td>\n",
       "      <td>1654675</td>\n",
       "      <td>1680992</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>Arizona</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",
       "      <td>...</td>\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>783</th>\n",
       "      <td>784</td>\n",
       "      <td>52131</td>\n",
       "      <td>52131</td>\n",
       "      <td>52026</td>\n",
       "      <td>51662</td>\n",
       "      <td>51400</td>\n",
       "      <td>51257</td>\n",
       "      <td>51088</td>\n",
       "      <td>50811</td>\n",
       "      <td>50528</td>\n",
       "      <td>50211</td>\n",
       "      <td>50012</td>\n",
       "      <td>49678</td>\n",
       "      <td>Lakewood</td>\n",
       "      <td>Ohio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>785</td>\n",
       "      <td>50129</td>\n",
       "      <td>50162</td>\n",
       "      <td>50081</td>\n",
       "      <td>50020</td>\n",
       "      <td>49848</td>\n",
       "      <td>49921</td>\n",
       "      <td>49968</td>\n",
       "      <td>49776</td>\n",
       "      <td>49597</td>\n",
       "      <td>49433</td>\n",
       "      <td>49354</td>\n",
       "      <td>49154</td>\n",
       "      <td>Troy</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785</th>\n",
       "      <td>786</td>\n",
       "      <td>51508</td>\n",
       "      <td>51469</td>\n",
       "      <td>51363</td>\n",
       "      <td>51012</td>\n",
       "      <td>50728</td>\n",
       "      <td>50272</td>\n",
       "      <td>49798</td>\n",
       "      <td>49230</td>\n",
       "      <td>48902</td>\n",
       "      <td>48696</td>\n",
       "      <td>48310</td>\n",
       "      <td>48115</td>\n",
       "      <td>Saginaw</td>\n",
       "      <td>Michigan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>787</td>\n",
       "      <td>50193</td>\n",
       "      <td>50031</td>\n",
       "      <td>49996</td>\n",
       "      <td>49760</td>\n",
       "      <td>49526</td>\n",
       "      <td>49328</td>\n",
       "      <td>49083</td>\n",
       "      <td>48769</td>\n",
       "      <td>48526</td>\n",
       "      <td>48249</td>\n",
       "      <td>48012</td>\n",
       "      <td>47720</td>\n",
       "      <td>Niagara Falls</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>788</td>\n",
       "      <td>51400</td>\n",
       "      <td>51340</td>\n",
       "      <td>51274</td>\n",
       "      <td>50992</td>\n",
       "      <td>50858</td>\n",
       "      <td>50657</td>\n",
       "      <td>50133</td>\n",
       "      <td>49457</td>\n",
       "      <td>48789</td>\n",
       "      <td>48040</td>\n",
       "      <td>47172</td>\n",
       "      <td>46536</td>\n",
       "      <td>Charleston</td>\n",
       "      <td>West Virginia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>788 rows × 15 columns</p>\n",
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      ],
      "text/plain": [
       "     Rank   Census Estimates Base     2010     2011     2012     2013  \\\n",
       "0       1  8175133        8175031  8190209  8272948  8346693  8396091   \n",
       "1       2  3792621        3793139  3795512  3820876  3851202  3881622   \n",
       "2       3  2695598        2695652  2697477  2708114  2719141  2725731   \n",
       "3       4  2099451        2095517  2100280  2126032  2161593  2199391   \n",
       "4       5  1445632        1446691  1449038  1469796  1499274  1526491   \n",
       "..    ...      ...            ...      ...      ...      ...      ...   \n",
       "783   784    52131          52131    52026    51662    51400    51257   \n",
       "784   785    50129          50162    50081    50020    49848    49921   \n",
       "785   786    51508          51469    51363    51012    50728    50272   \n",
       "786   787    50193          50031    49996    49760    49526    49328   \n",
       "787   788    51400          51340    51274    50992    50858    50657   \n",
       "\n",
       "        2014     2015     2016     2017     2018     2019 city_or_county  \\\n",
       "0    8433806  8463049  8469153  8437478  8390081  8336817       New York   \n",
       "1    3909901  3938568  3963226  3975788  3977596  3979576    Los Angeles   \n",
       "2    2727066  2724344  2716723  2711069  2701423  2693976        Chicago   \n",
       "3    2241826  2286908  2309544  2316750  2318573  2320268        Houston   \n",
       "4    1555445  1583690  1612199  1633560  1654675  1680992        Phoenix   \n",
       "..       ...      ...      ...      ...      ...      ...            ...   \n",
       "783    51088    50811    50528    50211    50012    49678       Lakewood   \n",
       "784    49968    49776    49597    49433    49354    49154           Troy   \n",
       "785    49798    49230    48902    48696    48310    48115        Saginaw   \n",
       "786    49083    48769    48526    48249    48012    47720  Niagara Falls   \n",
       "787    50133    49457    48789    48040    47172    46536     Charleston   \n",
       "\n",
       "             state  \n",
       "0         New York  \n",
       "1       California  \n",
       "2         Illinois  \n",
       "3            Texas  \n",
       "4          Arizona  \n",
       "..             ...  \n",
       "783           Ohio  \n",
       "784       New York  \n",
       "785       Michigan  \n",
       "786       New York  \n",
       "787  West Virginia  \n",
       "\n",
       "[788 rows x 15 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaning #2: Cleaning up geography name and replacing with 'city' and 'state' columns for city population\n",
    "\n",
    "city = []\n",
    "state = []\n",
    "for i in df_2['Geographic Area'].values:\n",
    "  area = i.split(\" city, \")\n",
    "  city.append(area[0])\n",
    "  state.append(area[1])\n",
    "  \n",
    "# Cleaning #2.5: Removing commas from population values\n",
    "\n",
    "df_2.replace(',','', regex=True, inplace=True)\n",
    "\n",
    "df_2['city_or_county'] = city\n",
    "df_2['state'] = state\n",
    "city_pop_df = df_2.drop(columns='Geographic Area')\n",
    "city_pop_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "znZoChRZpESF",
    "outputId": "8c1d8c2e-52c4-4a7e-9ecd-00e43634f741"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>Census</th>\n",
       "      <th>Estimates Base</th>\n",
       "      <th>2010</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>4779736</td>\n",
       "      <td>4780125</td>\n",
       "      <td>4785437</td>\n",
       "      <td>4799069</td>\n",
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       "      <td>4841799</td>\n",
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       "      <td>4903185</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>710231</td>\n",
       "      <td>710249</td>\n",
       "      <td>713910</td>\n",
       "      <td>722128</td>\n",
       "      <td>730443</td>\n",
       "      <td>737068</td>\n",
       "      <td>736283</td>\n",
       "      <td>737498</td>\n",
       "      <td>741456</td>\n",
       "      <td>739700</td>\n",
       "      <td>735139</td>\n",
       "      <td>731545</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>6392017</td>\n",
       "      <td>6392288</td>\n",
       "      <td>6407172</td>\n",
       "      <td>6472643</td>\n",
       "      <td>6554978</td>\n",
       "      <td>6632764</td>\n",
       "      <td>6730413</td>\n",
       "      <td>6829676</td>\n",
       "      <td>6941072</td>\n",
       "      <td>7044008</td>\n",
       "      <td>7158024</td>\n",
       "      <td>7278717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2915918</td>\n",
       "      <td>2916031</td>\n",
       "      <td>2921964</td>\n",
       "      <td>2940667</td>\n",
       "      <td>2952164</td>\n",
       "      <td>2959400</td>\n",
       "      <td>2967392</td>\n",
       "      <td>2978048</td>\n",
       "      <td>2989918</td>\n",
       "      <td>3001345</td>\n",
       "      <td>3009733</td>\n",
       "      <td>3017804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>37253956</td>\n",
       "      <td>37254519</td>\n",
       "      <td>37319502</td>\n",
       "      <td>37638369</td>\n",
       "      <td>37948800</td>\n",
       "      <td>38260787</td>\n",
       "      <td>38596972</td>\n",
       "      <td>38918045</td>\n",
       "      <td>39167117</td>\n",
       "      <td>39358497</td>\n",
       "      <td>39461588</td>\n",
       "      <td>39512223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>5029196</td>\n",
       "      <td>5029319</td>\n",
       "      <td>5047349</td>\n",
       "      <td>5121108</td>\n",
       "      <td>5192647</td>\n",
       "      <td>5269035</td>\n",
       "      <td>5350101</td>\n",
       "      <td>5450623</td>\n",
       "      <td>5539215</td>\n",
       "      <td>5611885</td>\n",
       "      <td>5691287</td>\n",
       "      <td>5758736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>3574097</td>\n",
       "      <td>3574147</td>\n",
       "      <td>3579114</td>\n",
       "      <td>3588283</td>\n",
       "      <td>3594547</td>\n",
       "      <td>3594841</td>\n",
       "      <td>3594524</td>\n",
       "      <td>3587122</td>\n",
       "      <td>3578141</td>\n",
       "      <td>3573297</td>\n",
       "      <td>3571520</td>\n",
       "      <td>3565287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>897934</td>\n",
       "      <td>897937</td>\n",
       "      <td>899593</td>\n",
       "      <td>907381</td>\n",
       "      <td>915179</td>\n",
       "      <td>923576</td>\n",
       "      <td>932487</td>\n",
       "      <td>941252</td>\n",
       "      <td>948921</td>\n",
       "      <td>956823</td>\n",
       "      <td>965479</td>\n",
       "      <td>973764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>601723</td>\n",
       "      <td>601767</td>\n",
       "      <td>605226</td>\n",
       "      <td>619800</td>\n",
       "      <td>634924</td>\n",
       "      <td>650581</td>\n",
       "      <td>662328</td>\n",
       "      <td>675400</td>\n",
       "      <td>685815</td>\n",
       "      <td>694906</td>\n",
       "      <td>701547</td>\n",
       "      <td>705749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Florida</td>\n",
       "      <td>18801310</td>\n",
       "      <td>18804564</td>\n",
       "      <td>18845537</td>\n",
       "      <td>19053237</td>\n",
       "      <td>19297822</td>\n",
       "      <td>19545621</td>\n",
       "      <td>19845911</td>\n",
       "      <td>20209042</td>\n",
       "      <td>20613477</td>\n",
       "      <td>20963613</td>\n",
       "      <td>21244317</td>\n",
       "      <td>21477737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>9687653</td>\n",
       "      <td>9688729</td>\n",
       "      <td>9711881</td>\n",
       "      <td>9802431</td>\n",
       "      <td>9901430</td>\n",
       "      <td>9972479</td>\n",
       "      <td>10067278</td>\n",
       "      <td>10178447</td>\n",
       "      <td>10301890</td>\n",
       "      <td>10410330</td>\n",
       "      <td>10511131</td>\n",
       "      <td>10617423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>1360301</td>\n",
       "      <td>1360307</td>\n",
       "      <td>1363963</td>\n",
       "      <td>1379329</td>\n",
       "      <td>1394804</td>\n",
       "      <td>1408243</td>\n",
       "      <td>1414538</td>\n",
       "      <td>1422052</td>\n",
       "      <td>1427559</td>\n",
       "      <td>1424393</td>\n",
       "      <td>1420593</td>\n",
       "      <td>1415872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>1567582</td>\n",
       "      <td>1567657</td>\n",
       "      <td>1570746</td>\n",
       "      <td>1583910</td>\n",
       "      <td>1595324</td>\n",
       "      <td>1611206</td>\n",
       "      <td>1631112</td>\n",
       "      <td>1651059</td>\n",
       "      <td>1682380</td>\n",
       "      <td>1717715</td>\n",
       "      <td>1750536</td>\n",
       "      <td>1787065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>12830632</td>\n",
       "      <td>12831572</td>\n",
       "      <td>12840503</td>\n",
       "      <td>12867454</td>\n",
       "      <td>12882510</td>\n",
       "      <td>12895129</td>\n",
       "      <td>12884493</td>\n",
       "      <td>12858913</td>\n",
       "      <td>12820527</td>\n",
       "      <td>12778828</td>\n",
       "      <td>12723071</td>\n",
       "      <td>12671821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>6483802</td>\n",
       "      <td>6484051</td>\n",
       "      <td>6490432</td>\n",
       "      <td>6516528</td>\n",
       "      <td>6537703</td>\n",
       "      <td>6568713</td>\n",
       "      <td>6593644</td>\n",
       "      <td>6608422</td>\n",
       "      <td>6634304</td>\n",
       "      <td>6658078</td>\n",
       "      <td>6695497</td>\n",
       "      <td>6732219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>3046355</td>\n",
       "      <td>3046871</td>\n",
       "      <td>3050745</td>\n",
       "      <td>3066336</td>\n",
       "      <td>3076190</td>\n",
       "      <td>3092997</td>\n",
       "      <td>3109350</td>\n",
       "      <td>3120960</td>\n",
       "      <td>3131371</td>\n",
       "      <td>3141550</td>\n",
       "      <td>3148618</td>\n",
       "      <td>3155070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>2853118</td>\n",
       "      <td>2853123</td>\n",
       "      <td>2858190</td>\n",
       "      <td>2869225</td>\n",
       "      <td>2885257</td>\n",
       "      <td>2893212</td>\n",
       "      <td>2900475</td>\n",
       "      <td>2909011</td>\n",
       "      <td>2910844</td>\n",
       "      <td>2908718</td>\n",
       "      <td>2911359</td>\n",
       "      <td>2913314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>4339367</td>\n",
       "      <td>4339333</td>\n",
       "      <td>4348181</td>\n",
       "      <td>4369821</td>\n",
       "      <td>4386346</td>\n",
       "      <td>4404659</td>\n",
       "      <td>4414349</td>\n",
       "      <td>4425976</td>\n",
       "      <td>4438182</td>\n",
       "      <td>4452268</td>\n",
       "      <td>4461153</td>\n",
       "      <td>4467673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>4533372</td>\n",
       "      <td>4533487</td>\n",
       "      <td>4544532</td>\n",
       "      <td>4575625</td>\n",
       "      <td>4600972</td>\n",
       "      <td>4624527</td>\n",
       "      <td>4644013</td>\n",
       "      <td>4664628</td>\n",
       "      <td>4678135</td>\n",
       "      <td>4670560</td>\n",
       "      <td>4659690</td>\n",
       "      <td>4648794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Maine</td>\n",
       "      <td>1328361</td>\n",
       "      <td>1328358</td>\n",
       "      <td>1327629</td>\n",
       "      <td>1328284</td>\n",
       "      <td>1327729</td>\n",
       "      <td>1328009</td>\n",
       "      <td>1330513</td>\n",
       "      <td>1328262</td>\n",
       "      <td>1331317</td>\n",
       "      <td>1334612</td>\n",
       "      <td>1339057</td>\n",
       "      <td>1344212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>5773552</td>\n",
       "      <td>5773794</td>\n",
       "      <td>5788645</td>\n",
       "      <td>5839419</td>\n",
       "      <td>5886992</td>\n",
       "      <td>5923188</td>\n",
       "      <td>5957283</td>\n",
       "      <td>5985562</td>\n",
       "      <td>6003323</td>\n",
       "      <td>6023868</td>\n",
       "      <td>6035802</td>\n",
       "      <td>6045680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>6547629</td>\n",
       "      <td>6547785</td>\n",
       "      <td>6566307</td>\n",
       "      <td>6613583</td>\n",
       "      <td>6663005</td>\n",
       "      <td>6713315</td>\n",
       "      <td>6762596</td>\n",
       "      <td>6794228</td>\n",
       "      <td>6823608</td>\n",
       "      <td>6859789</td>\n",
       "      <td>6882635</td>\n",
       "      <td>6892503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>9883640</td>\n",
       "      <td>9884116</td>\n",
       "      <td>9877510</td>\n",
       "      <td>9882412</td>\n",
       "      <td>9897145</td>\n",
       "      <td>9913065</td>\n",
       "      <td>9929848</td>\n",
       "      <td>9931715</td>\n",
       "      <td>9950571</td>\n",
       "      <td>9973114</td>\n",
       "      <td>9984072</td>\n",
       "      <td>9986857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>5303925</td>\n",
       "      <td>5303927</td>\n",
       "      <td>5310828</td>\n",
       "      <td>5346143</td>\n",
       "      <td>5376643</td>\n",
       "      <td>5413479</td>\n",
       "      <td>5451079</td>\n",
       "      <td>5482032</td>\n",
       "      <td>5522744</td>\n",
       "      <td>5566230</td>\n",
       "      <td>5606249</td>\n",
       "      <td>5639632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>2967297</td>\n",
       "      <td>2968130</td>\n",
       "      <td>2970548</td>\n",
       "      <td>2978731</td>\n",
       "      <td>2983816</td>\n",
       "      <td>2988711</td>\n",
       "      <td>2990468</td>\n",
       "      <td>2988471</td>\n",
       "      <td>2987938</td>\n",
       "      <td>2988510</td>\n",
       "      <td>2981020</td>\n",
       "      <td>2976149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>5988927</td>\n",
       "      <td>5988950</td>\n",
       "      <td>5995974</td>\n",
       "      <td>6010275</td>\n",
       "      <td>6024367</td>\n",
       "      <td>6040715</td>\n",
       "      <td>6056202</td>\n",
       "      <td>6071732</td>\n",
       "      <td>6087135</td>\n",
       "      <td>6106670</td>\n",
       "      <td>6121623</td>\n",
       "      <td>6137428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Montana</td>\n",
       "      <td>989415</td>\n",
       "      <td>989407</td>\n",
       "      <td>990697</td>\n",
       "      <td>997316</td>\n",
       "      <td>1003783</td>\n",
       "      <td>1013569</td>\n",
       "      <td>1021869</td>\n",
       "      <td>1030475</td>\n",
       "      <td>1040859</td>\n",
       "      <td>1052482</td>\n",
       "      <td>1060665</td>\n",
       "      <td>1068778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Nebraska</td>\n",
       "      <td>1826341</td>\n",
       "      <td>1826305</td>\n",
       "      <td>1829542</td>\n",
       "      <td>1840672</td>\n",
       "      <td>1853303</td>\n",
       "      <td>1865279</td>\n",
       "      <td>1879321</td>\n",
       "      <td>1891277</td>\n",
       "      <td>1905616</td>\n",
       "      <td>1915947</td>\n",
       "      <td>1925614</td>\n",
       "      <td>1934408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2700551</td>\n",
       "      <td>2700677</td>\n",
       "      <td>2702405</td>\n",
       "      <td>2712730</td>\n",
       "      <td>2743996</td>\n",
       "      <td>2775970</td>\n",
       "      <td>2817628</td>\n",
       "      <td>2866939</td>\n",
       "      <td>2917563</td>\n",
       "      <td>2969905</td>\n",
       "      <td>3027341</td>\n",
       "      <td>3080156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>1316470</td>\n",
       "      <td>1316462</td>\n",
       "      <td>1316762</td>\n",
       "      <td>1320202</td>\n",
       "      <td>1324232</td>\n",
       "      <td>1326622</td>\n",
       "      <td>1333341</td>\n",
       "      <td>1336350</td>\n",
       "      <td>1342307</td>\n",
       "      <td>1348787</td>\n",
       "      <td>1353465</td>\n",
       "      <td>1359711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>8791894</td>\n",
       "      <td>8791978</td>\n",
       "      <td>8799446</td>\n",
       "      <td>8828117</td>\n",
       "      <td>8844942</td>\n",
       "      <td>8856972</td>\n",
       "      <td>8864525</td>\n",
       "      <td>8867949</td>\n",
       "      <td>8870827</td>\n",
       "      <td>8885525</td>\n",
       "      <td>8886025</td>\n",
       "      <td>8882190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>2059179</td>\n",
       "      <td>2059199</td>\n",
       "      <td>2064552</td>\n",
       "      <td>2080450</td>\n",
       "      <td>2087309</td>\n",
       "      <td>2092273</td>\n",
       "      <td>2089568</td>\n",
       "      <td>2089291</td>\n",
       "      <td>2091630</td>\n",
       "      <td>2091784</td>\n",
       "      <td>2092741</td>\n",
       "      <td>2096829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>New York</td>\n",
       "      <td>19378102</td>\n",
       "      <td>19378144</td>\n",
       "      <td>19399878</td>\n",
       "      <td>19499241</td>\n",
       "      <td>19572932</td>\n",
       "      <td>19624447</td>\n",
       "      <td>19651049</td>\n",
       "      <td>19654666</td>\n",
       "      <td>19633428</td>\n",
       "      <td>19589572</td>\n",
       "      <td>19530351</td>\n",
       "      <td>19453561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>9535483</td>\n",
       "      <td>9535751</td>\n",
       "      <td>9574323</td>\n",
       "      <td>9657592</td>\n",
       "      <td>9749476</td>\n",
       "      <td>9843336</td>\n",
       "      <td>9932887</td>\n",
       "      <td>10031646</td>\n",
       "      <td>10154788</td>\n",
       "      <td>10268233</td>\n",
       "      <td>10381615</td>\n",
       "      <td>10488084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>672591</td>\n",
       "      <td>672576</td>\n",
       "      <td>674715</td>\n",
       "      <td>685225</td>\n",
       "      <td>701176</td>\n",
       "      <td>722036</td>\n",
       "      <td>737401</td>\n",
       "      <td>754066</td>\n",
       "      <td>754434</td>\n",
       "      <td>754942</td>\n",
       "      <td>758080</td>\n",
       "      <td>762062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>11536504</td>\n",
       "      <td>11536751</td>\n",
       "      <td>11539336</td>\n",
       "      <td>11544663</td>\n",
       "      <td>11548923</td>\n",
       "      <td>11576684</td>\n",
       "      <td>11602700</td>\n",
       "      <td>11617527</td>\n",
       "      <td>11634370</td>\n",
       "      <td>11659650</td>\n",
       "      <td>11676341</td>\n",
       "      <td>11689100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>3751351</td>\n",
       "      <td>3751582</td>\n",
       "      <td>3759944</td>\n",
       "      <td>3788379</td>\n",
       "      <td>3818814</td>\n",
       "      <td>3853214</td>\n",
       "      <td>3878187</td>\n",
       "      <td>3909500</td>\n",
       "      <td>3926331</td>\n",
       "      <td>3931316</td>\n",
       "      <td>3940235</td>\n",
       "      <td>3956971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>3831074</td>\n",
       "      <td>3831079</td>\n",
       "      <td>3837491</td>\n",
       "      <td>3872036</td>\n",
       "      <td>3899001</td>\n",
       "      <td>3922468</td>\n",
       "      <td>3963244</td>\n",
       "      <td>4015792</td>\n",
       "      <td>4089976</td>\n",
       "      <td>4143625</td>\n",
       "      <td>4181886</td>\n",
       "      <td>4217737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>12702379</td>\n",
       "      <td>12702868</td>\n",
       "      <td>12711160</td>\n",
       "      <td>12745815</td>\n",
       "      <td>12767118</td>\n",
       "      <td>12776309</td>\n",
       "      <td>12788313</td>\n",
       "      <td>12784826</td>\n",
       "      <td>12782275</td>\n",
       "      <td>12787641</td>\n",
       "      <td>12800922</td>\n",
       "      <td>12801989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>1052567</td>\n",
       "      <td>1052964</td>\n",
       "      <td>1053959</td>\n",
       "      <td>1053649</td>\n",
       "      <td>1054621</td>\n",
       "      <td>1055081</td>\n",
       "      <td>1055936</td>\n",
       "      <td>1056065</td>\n",
       "      <td>1056770</td>\n",
       "      <td>1055673</td>\n",
       "      <td>1058287</td>\n",
       "      <td>1059361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>4625364</td>\n",
       "      <td>4625366</td>\n",
       "      <td>4635649</td>\n",
       "      <td>4671994</td>\n",
       "      <td>4717354</td>\n",
       "      <td>4764080</td>\n",
       "      <td>4823617</td>\n",
       "      <td>4891938</td>\n",
       "      <td>4957968</td>\n",
       "      <td>5021268</td>\n",
       "      <td>5084156</td>\n",
       "      <td>5148714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>South Dakota</td>\n",
       "      <td>814180</td>\n",
       "      <td>814198</td>\n",
       "      <td>816166</td>\n",
       "      <td>823579</td>\n",
       "      <td>833566</td>\n",
       "      <td>842316</td>\n",
       "      <td>849129</td>\n",
       "      <td>853988</td>\n",
       "      <td>862996</td>\n",
       "      <td>872868</td>\n",
       "      <td>878698</td>\n",
       "      <td>884659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>6346105</td>\n",
       "      <td>6346276</td>\n",
       "      <td>6355311</td>\n",
       "      <td>6399291</td>\n",
       "      <td>6453898</td>\n",
       "      <td>6494340</td>\n",
       "      <td>6541223</td>\n",
       "      <td>6591170</td>\n",
       "      <td>6646010</td>\n",
       "      <td>6708799</td>\n",
       "      <td>6771631</td>\n",
       "      <td>6829174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Texas</td>\n",
       "      <td>25145561</td>\n",
       "      <td>25146091</td>\n",
       "      <td>25241971</td>\n",
       "      <td>25645629</td>\n",
       "      <td>26084481</td>\n",
       "      <td>26480266</td>\n",
       "      <td>26964333</td>\n",
       "      <td>27470056</td>\n",
       "      <td>27914410</td>\n",
       "      <td>28295273</td>\n",
       "      <td>28628666</td>\n",
       "      <td>28995881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Utah</td>\n",
       "      <td>2763885</td>\n",
       "      <td>2763891</td>\n",
       "      <td>2775332</td>\n",
       "      <td>2814384</td>\n",
       "      <td>2853375</td>\n",
       "      <td>2897640</td>\n",
       "      <td>2936879</td>\n",
       "      <td>2981835</td>\n",
       "      <td>3041868</td>\n",
       "      <td>3101042</td>\n",
       "      <td>3153550</td>\n",
       "      <td>3205958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>625741</td>\n",
       "      <td>625737</td>\n",
       "      <td>625879</td>\n",
       "      <td>627049</td>\n",
       "      <td>626090</td>\n",
       "      <td>626210</td>\n",
       "      <td>625214</td>\n",
       "      <td>625216</td>\n",
       "      <td>623657</td>\n",
       "      <td>624344</td>\n",
       "      <td>624358</td>\n",
       "      <td>623989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>8001024</td>\n",
       "      <td>8001049</td>\n",
       "      <td>8023699</td>\n",
       "      <td>8101155</td>\n",
       "      <td>8185080</td>\n",
       "      <td>8252427</td>\n",
       "      <td>8310993</td>\n",
       "      <td>8361808</td>\n",
       "      <td>8410106</td>\n",
       "      <td>8463587</td>\n",
       "      <td>8501286</td>\n",
       "      <td>8535519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Washington</td>\n",
       "      <td>6724540</td>\n",
       "      <td>6724540</td>\n",
       "      <td>6742830</td>\n",
       "      <td>6826627</td>\n",
       "      <td>6897058</td>\n",
       "      <td>6963985</td>\n",
       "      <td>7054655</td>\n",
       "      <td>7163657</td>\n",
       "      <td>7294771</td>\n",
       "      <td>7423362</td>\n",
       "      <td>7523869</td>\n",
       "      <td>7614893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>1852994</td>\n",
       "      <td>1853018</td>\n",
       "      <td>1854239</td>\n",
       "      <td>1856301</td>\n",
       "      <td>1856872</td>\n",
       "      <td>1853914</td>\n",
       "      <td>1849489</td>\n",
       "      <td>1842050</td>\n",
       "      <td>1831023</td>\n",
       "      <td>1817004</td>\n",
       "      <td>1804291</td>\n",
       "      <td>1792147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>5686986</td>\n",
       "      <td>5687285</td>\n",
       "      <td>5690475</td>\n",
       "      <td>5705288</td>\n",
       "      <td>5719960</td>\n",
       "      <td>5736754</td>\n",
       "      <td>5751525</td>\n",
       "      <td>5760940</td>\n",
       "      <td>5772628</td>\n",
       "      <td>5790186</td>\n",
       "      <td>5807406</td>\n",
       "      <td>5822434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>563626</td>\n",
       "      <td>563775</td>\n",
       "      <td>564487</td>\n",
       "      <td>567299</td>\n",
       "      <td>576305</td>\n",
       "      <td>582122</td>\n",
       "      <td>582531</td>\n",
       "      <td>585613</td>\n",
       "      <td>584215</td>\n",
       "      <td>578931</td>\n",
       "      <td>577601</td>\n",
       "      <td>578759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                   state    Census  Estimates Base      2010      2011  \\\n",
       "0                Alabama   4779736         4780125   4785437   4799069   \n",
       "1                 Alaska    710231          710249    713910    722128   \n",
       "2                Arizona   6392017         6392288   6407172   6472643   \n",
       "3               Arkansas   2915918         2916031   2921964   2940667   \n",
       "4             California  37253956        37254519  37319502  37638369   \n",
       "5               Colorado   5029196         5029319   5047349   5121108   \n",
       "6            Connecticut   3574097         3574147   3579114   3588283   \n",
       "7               Delaware    897934          897937    899593    907381   \n",
       "8   District of Columbia    601723          601767    605226    619800   \n",
       "9                Florida  18801310        18804564  18845537  19053237   \n",
       "10               Georgia   9687653         9688729   9711881   9802431   \n",
       "11                Hawaii   1360301         1360307   1363963   1379329   \n",
       "12                 Idaho   1567582         1567657   1570746   1583910   \n",
       "13              Illinois  12830632        12831572  12840503  12867454   \n",
       "14               Indiana   6483802         6484051   6490432   6516528   \n",
       "15                  Iowa   3046355         3046871   3050745   3066336   \n",
       "16                Kansas   2853118         2853123   2858190   2869225   \n",
       "17              Kentucky   4339367         4339333   4348181   4369821   \n",
       "18             Louisiana   4533372         4533487   4544532   4575625   \n",
       "19                 Maine   1328361         1328358   1327629   1328284   \n",
       "20              Maryland   5773552         5773794   5788645   5839419   \n",
       "21         Massachusetts   6547629         6547785   6566307   6613583   \n",
       "22              Michigan   9883640         9884116   9877510   9882412   \n",
       "23             Minnesota   5303925         5303927   5310828   5346143   \n",
       "24           Mississippi   2967297         2968130   2970548   2978731   \n",
       "25              Missouri   5988927         5988950   5995974   6010275   \n",
       "26               Montana    989415          989407    990697    997316   \n",
       "27              Nebraska   1826341         1826305   1829542   1840672   \n",
       "28                Nevada   2700551         2700677   2702405   2712730   \n",
       "29         New Hampshire   1316470         1316462   1316762   1320202   \n",
       "30            New Jersey   8791894         8791978   8799446   8828117   \n",
       "31            New Mexico   2059179         2059199   2064552   2080450   \n",
       "32              New York  19378102        19378144  19399878  19499241   \n",
       "33        North Carolina   9535483         9535751   9574323   9657592   \n",
       "34          North Dakota    672591          672576    674715    685225   \n",
       "35                  Ohio  11536504        11536751  11539336  11544663   \n",
       "36              Oklahoma   3751351         3751582   3759944   3788379   \n",
       "37                Oregon   3831074         3831079   3837491   3872036   \n",
       "38          Pennsylvania  12702379        12702868  12711160  12745815   \n",
       "39          Rhode Island   1052567         1052964   1053959   1053649   \n",
       "40        South Carolina   4625364         4625366   4635649   4671994   \n",
       "41          South Dakota    814180          814198    816166    823579   \n",
       "42             Tennessee   6346105         6346276   6355311   6399291   \n",
       "43                 Texas  25145561        25146091  25241971  25645629   \n",
       "44                  Utah   2763885         2763891   2775332   2814384   \n",
       "45               Vermont    625741          625737    625879    627049   \n",
       "46              Virginia   8001024         8001049   8023699   8101155   \n",
       "47            Washington   6724540         6724540   6742830   6826627   \n",
       "48         West Virginia   1852994         1853018   1854239   1856301   \n",
       "49             Wisconsin   5686986         5687285   5690475   5705288   \n",
       "50               Wyoming    563626          563775    564487    567299   \n",
       "\n",
       "        2012      2013      2014      2015      2016      2017      2018  \\\n",
       "0    4815588   4830081   4841799   4852347   4863525   4874486   4887681   \n",
       "1     730443    737068    736283    737498    741456    739700    735139   \n",
       "2    6554978   6632764   6730413   6829676   6941072   7044008   7158024   \n",
       "3    2952164   2959400   2967392   2978048   2989918   3001345   3009733   \n",
       "4   37948800  38260787  38596972  38918045  39167117  39358497  39461588   \n",
       "5    5192647   5269035   5350101   5450623   5539215   5611885   5691287   \n",
       "6    3594547   3594841   3594524   3587122   3578141   3573297   3571520   \n",
       "7     915179    923576    932487    941252    948921    956823    965479   \n",
       "8     634924    650581    662328    675400    685815    694906    701547   \n",
       "9   19297822  19545621  19845911  20209042  20613477  20963613  21244317   \n",
       "10   9901430   9972479  10067278  10178447  10301890  10410330  10511131   \n",
       "11   1394804   1408243   1414538   1422052   1427559   1424393   1420593   \n",
       "12   1595324   1611206   1631112   1651059   1682380   1717715   1750536   \n",
       "13  12882510  12895129  12884493  12858913  12820527  12778828  12723071   \n",
       "14   6537703   6568713   6593644   6608422   6634304   6658078   6695497   \n",
       "15   3076190   3092997   3109350   3120960   3131371   3141550   3148618   \n",
       "16   2885257   2893212   2900475   2909011   2910844   2908718   2911359   \n",
       "17   4386346   4404659   4414349   4425976   4438182   4452268   4461153   \n",
       "18   4600972   4624527   4644013   4664628   4678135   4670560   4659690   \n",
       "19   1327729   1328009   1330513   1328262   1331317   1334612   1339057   \n",
       "20   5886992   5923188   5957283   5985562   6003323   6023868   6035802   \n",
       "21   6663005   6713315   6762596   6794228   6823608   6859789   6882635   \n",
       "22   9897145   9913065   9929848   9931715   9950571   9973114   9984072   \n",
       "23   5376643   5413479   5451079   5482032   5522744   5566230   5606249   \n",
       "24   2983816   2988711   2990468   2988471   2987938   2988510   2981020   \n",
       "25   6024367   6040715   6056202   6071732   6087135   6106670   6121623   \n",
       "26   1003783   1013569   1021869   1030475   1040859   1052482   1060665   \n",
       "27   1853303   1865279   1879321   1891277   1905616   1915947   1925614   \n",
       "28   2743996   2775970   2817628   2866939   2917563   2969905   3027341   \n",
       "29   1324232   1326622   1333341   1336350   1342307   1348787   1353465   \n",
       "30   8844942   8856972   8864525   8867949   8870827   8885525   8886025   \n",
       "31   2087309   2092273   2089568   2089291   2091630   2091784   2092741   \n",
       "32  19572932  19624447  19651049  19654666  19633428  19589572  19530351   \n",
       "33   9749476   9843336   9932887  10031646  10154788  10268233  10381615   \n",
       "34    701176    722036    737401    754066    754434    754942    758080   \n",
       "35  11548923  11576684  11602700  11617527  11634370  11659650  11676341   \n",
       "36   3818814   3853214   3878187   3909500   3926331   3931316   3940235   \n",
       "37   3899001   3922468   3963244   4015792   4089976   4143625   4181886   \n",
       "38  12767118  12776309  12788313  12784826  12782275  12787641  12800922   \n",
       "39   1054621   1055081   1055936   1056065   1056770   1055673   1058287   \n",
       "40   4717354   4764080   4823617   4891938   4957968   5021268   5084156   \n",
       "41    833566    842316    849129    853988    862996    872868    878698   \n",
       "42   6453898   6494340   6541223   6591170   6646010   6708799   6771631   \n",
       "43  26084481  26480266  26964333  27470056  27914410  28295273  28628666   \n",
       "44   2853375   2897640   2936879   2981835   3041868   3101042   3153550   \n",
       "45    626090    626210    625214    625216    623657    624344    624358   \n",
       "46   8185080   8252427   8310993   8361808   8410106   8463587   8501286   \n",
       "47   6897058   6963985   7054655   7163657   7294771   7423362   7523869   \n",
       "48   1856872   1853914   1849489   1842050   1831023   1817004   1804291   \n",
       "49   5719960   5736754   5751525   5760940   5772628   5790186   5807406   \n",
       "50    576305    582122    582531    585613    584215    578931    577601   \n",
       "\n",
       "        2019  \n",
       "0    4903185  \n",
       "1     731545  \n",
       "2    7278717  \n",
       "3    3017804  \n",
       "4   39512223  \n",
       "5    5758736  \n",
       "6    3565287  \n",
       "7     973764  \n",
       "8     705749  \n",
       "9   21477737  \n",
       "10  10617423  \n",
       "11   1415872  \n",
       "12   1787065  \n",
       "13  12671821  \n",
       "14   6732219  \n",
       "15   3155070  \n",
       "16   2913314  \n",
       "17   4467673  \n",
       "18   4648794  \n",
       "19   1344212  \n",
       "20   6045680  \n",
       "21   6892503  \n",
       "22   9986857  \n",
       "23   5639632  \n",
       "24   2976149  \n",
       "25   6137428  \n",
       "26   1068778  \n",
       "27   1934408  \n",
       "28   3080156  \n",
       "29   1359711  \n",
       "30   8882190  \n",
       "31   2096829  \n",
       "32  19453561  \n",
       "33  10488084  \n",
       "34    762062  \n",
       "35  11689100  \n",
       "36   3956971  \n",
       "37   4217737  \n",
       "38  12801989  \n",
       "39   1059361  \n",
       "40   5148714  \n",
       "41    884659  \n",
       "42   6829174  \n",
       "43  28995881  \n",
       "44   3205958  \n",
       "45    623989  \n",
       "46   8535519  \n",
       "47   7614893  \n",
       "48   1792147  \n",
       "49   5822434  \n",
       "50    578759  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaning #3: Cleaning up geography name and replacing with 'state' columns for state population\n",
    "\n",
    "state_pop_df = df_3.rename(columns={'Geographic Area': 'state'})\n",
    "state_pop_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "7dioQQKiB73X"
   },
   "outputs": [],
   "source": [
    "# Cleaning #4: Renaming 'city_or_county' values that have 'New York (Manhattan)' to 'New York'\n",
    "# (for accurate comparsion of different cities' population vs gun violence incidents later)\n",
    "\n",
    "df['city_or_county'] = df['city_or_county'].replace('New York (Manhattan)', 'New York')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "OYrJWvUsqPvj"
   },
   "outputs": [],
   "source": [
    "# Cleaning #5: Merging both population df's (state & city) with gun_violence df\n",
    "# and then filtering down to 'city_pop' and 'state_pop column based on year/city and year/state, respectively\n",
    "\n",
    "# @Team: This cell takes a bit of time to run; if you know a more efficient way\n",
    "# to add population of city by year, please do so\n",
    "\n",
    "# Merge with State population first\n",
    "# This'll refer to state population by the years as (2013_x, 2014_x, etc.)\n",
    "clean_df = df.merge(state_pop_df, how='left', on=['state'], sort=False)\n",
    "\n",
    "# Merge with City population next\n",
    "# This'll refer to city population by the years as (2013_y, 2014_y, etc.)\n",
    "clean_df = clean_df.merge(city_pop_df, how='left', on=['city_or_county', 'state'], sort=False)\n",
    "\n",
    "yr_idx = zip(clean_df['year'], clean_df.index)\n",
    "pop_by_year_state = []\n",
    "pop_by_year_city = []\n",
    "for yr, idx in yr_idx:\n",
    "    state = str(yr) + '_x'\n",
    "    city = str(yr) + '_y'\n",
    "    state_pop = clean_df.iloc[idx][state]\n",
    "    city_pop = clean_df.iloc[idx][city]\n",
    "    pop_by_year_state.append(state_pop)\n",
    "    pop_by_year_city.append(city_pop)\n",
    "clean_df['state_pop'] = pop_by_year_state\n",
    "clean_df['city_pop'] = pop_by_year_city\n",
    "clean_df = clean_df.drop(columns=['Census_x', 'Estimates Base_x', '2010_x', '2011_x', '2012_x',\n",
    "                                  '2013_x','2014_x', '2015_x', '2016_x', '2017_x', '2018_x', '2019_x',\n",
    "                                  'Rank', 'Census_y', 'Estimates Base_y', '2010_y', '2011_y', '2012_y',\n",
    "                                  '2013_y','2014_y', '2015_y', '2016_y', '2017_y', '2018_y', '2019_y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "ru3iMMgg84ZO"
   },
   "outputs": [],
   "source": [
    "# Cleaning #6: Merging land area of states and calculating\n",
    "# each state's population densities, then adding as 'pop_density' column\n",
    "\n",
    "clean_df = clean_df.merge(df_4, how='left', on='state')\n",
    "clean_df['pop_density'] = clean_df['state_pop']/clean_df['area (sq. mi)']"
   ]
  },
  {
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    "outputId": "39bd1aa1-6c96-47c8-8748-ea9451c21839",
    "scrolled": false
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   "outputs": [
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       "      <th></th>\n",
       "      <th>incident_id</th>\n",
       "      <th>date</th>\n",
       "      <th>state</th>\n",
       "      <th>city_or_county</th>\n",
       "      <th>address</th>\n",
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       "      <th>state_house_district</th>\n",
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       "      <th>0</th>\n",
       "      <td>461105</td>\n",
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       "      <td>http://www.post-gazette.com/local/south/2013/0...</td>\n",
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       "      <td>...</td>\n",
       "      <td>0::Victim||1::Victim||2::Victim||3::Victim||4:...</td>\n",
       "      <td>http://pittsburgh.cbslocal.com/2013/01/01/4-pe...</td>\n",
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       "      <td>2013-01-01</td>\n",
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       "      <td>277.396088</td>\n",
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       "      <th>1</th>\n",
       "      <td>460726</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>California</td>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>13500 block of Cerise Avenue</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/460726</td>\n",
       "      <td>http://www.dailybulletin.com/article/zz/201301...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::Victim||1::Victim||2::Victim||3::Victim||4:...</td>\n",
       "      <td>http://losangeles.cbslocal.com/2013/01/01/man-...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2013</td>\n",
       "      <td>38260787</td>\n",
       "      <td>85863</td>\n",
       "      <td>163707</td>\n",
       "      <td>233.715034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>478855</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>Lorain</td>\n",
       "      <td>1776 East 28th Street</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/478855</td>\n",
       "      <td>http://chronicle.northcoastnow.com/2013/02/14/...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::Subject-Suspect||1::Subject-Suspect||2::Vic...</td>\n",
       "      <td>http://www.morningjournal.com/general-news/201...</td>\n",
       "      <td>56.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2013</td>\n",
       "      <td>11576684</td>\n",
       "      <td>63735</td>\n",
       "      <td>44828</td>\n",
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       "      <th>3</th>\n",
       "      <td>478925</td>\n",
       "      <td>2013-01-05</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Aurora</td>\n",
       "      <td>16000 block of East Ithaca Place</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/478925</td>\n",
       "      <td>http://www.dailydemocrat.com/20130106/aurora-s...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::Victim||1::Victim||2::Victim||3::Subject-Su...</td>\n",
       "      <td>http://denver.cbslocal.com/2013/01/06/officer-...</td>\n",
       "      <td>40.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2013-01-05</td>\n",
       "      <td>2013</td>\n",
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       "      <td>50.615130</td>\n",
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       "      <th>4</th>\n",
       "      <td>478959</td>\n",
       "      <td>2013-01-07</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Greensboro</td>\n",
       "      <td>307 Mourning Dove Terrace</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/478959</td>\n",
       "      <td>http://www.journalnow.com/news/local/article_d...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::Victim||1::Victim||2::Victim||3::Subject-Su...</td>\n",
       "      <td>http://myfox8.com/2013/01/08/update-mother-sho...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2013-01-07</td>\n",
       "      <td>2013</td>\n",
       "      <td>9843336</td>\n",
       "      <td>279244</td>\n",
       "      <td>53821</td>\n",
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       "      <th>239672</th>\n",
       "      <td>1083142</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>Rayne</td>\n",
       "      <td>North Riceland Road and Highway 90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/108...</td>\n",
       "      <td>http://www.klfy.com/news/local/rayne-woman-cha...</td>\n",
       "      <td>False</td>\n",
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       "      <td>0::Subject-Suspect</td>\n",
       "      <td>http://www.klfy.com/news/local/rayne-woman-cha...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>2018</td>\n",
       "      <td>4659690</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51843</td>\n",
       "      <td>89.880794</td>\n",
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       "      <th>239673</th>\n",
       "      <td>1083139</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>Natchitoches</td>\n",
       "      <td>247 Keyser Ave</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/108...</td>\n",
       "      <td>http://www.ksla.com/story/37854648/man-wanted-...</td>\n",
       "      <td>False</td>\n",
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       "      <td>0::Victim||1::Subject-Suspect</td>\n",
       "      <td>http://www.ksla.com/story/37854648/man-wanted-...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>2018</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>51843</td>\n",
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       "      <td>1083151</td>\n",
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       "      <td>0</td>\n",
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       "      <td>False</td>\n",
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       "      <td>85.0</td>\n",
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       "      <td>1082514</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::Victim</td>\n",
       "      <td>http://www.khou.com/article/news/hpd-investiga...</td>\n",
       "      <td>149.0</td>\n",
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       "      <td>2318573</td>\n",
       "      <td>268601</td>\n",
       "      <td>106.584361</td>\n",
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       "      <td>Norridgewock</td>\n",
       "      <td>434 Skowhegan Rd</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/108...</td>\n",
       "      <td>https://www.centralmaine.com/2018/03/31/police...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::Victim||1::Subject-Suspect</td>\n",
       "      <td>https://www.centralmaine.com/2018/03/31/police...</td>\n",
       "      <td>111.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>2018</td>\n",
       "      <td>1339057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35387</td>\n",
       "      <td>37.840365</td>\n",
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      "text/plain": [
       "        incident_id        date           state city_or_county  \\\n",
       "0            461105  2013-01-01    Pennsylvania     Mckeesport   \n",
       "1            460726  2013-01-01      California      Hawthorne   \n",
       "2            478855  2013-01-01            Ohio         Lorain   \n",
       "3            478925  2013-01-05        Colorado         Aurora   \n",
       "4            478959  2013-01-07  North Carolina     Greensboro   \n",
       "...             ...         ...             ...            ...   \n",
       "239672      1083142  2018-03-31       Louisiana          Rayne   \n",
       "239673      1083139  2018-03-31       Louisiana   Natchitoches   \n",
       "239674      1083151  2018-03-31       Louisiana         Gretna   \n",
       "239675      1082514  2018-03-31           Texas        Houston   \n",
       "239676      1081940  2018-03-31           Maine   Norridgewock   \n",
       "\n",
       "                                          address  n_killed  n_injured  \\\n",
       "0       1506 Versailles Avenue and Coursin Street         0          4   \n",
       "1                    13500 block of Cerise Avenue         1          3   \n",
       "2                           1776 East 28th Street         1          3   \n",
       "3                16000 block of East Ithaca Place         4          0   \n",
       "4                       307 Mourning Dove Terrace         2          2   \n",
       "...                                           ...       ...        ...   \n",
       "239672         North Riceland Road and Highway 90         0          0   \n",
       "239673                             247 Keyser Ave         1          0   \n",
       "239674                  1300 block of Cook Street         0          1   \n",
       "239675                     12630 Ashford Point Dr         1          0   \n",
       "239676                           434 Skowhegan Rd         2          0   \n",
       "\n",
       "                                             incident_url  \\\n",
       "0       http://www.gunviolencearchive.org/incident/461105   \n",
       "1       http://www.gunviolencearchive.org/incident/460726   \n",
       "2       http://www.gunviolencearchive.org/incident/478855   \n",
       "3       http://www.gunviolencearchive.org/incident/478925   \n",
       "4       http://www.gunviolencearchive.org/incident/478959   \n",
       "...                                                   ...   \n",
       "239672  http://www.gunviolencearchive.org/incident/108...   \n",
       "239673  http://www.gunviolencearchive.org/incident/108...   \n",
       "239674  http://www.gunviolencearchive.org/incident/108...   \n",
       "239675  http://www.gunviolencearchive.org/incident/108...   \n",
       "239676  http://www.gunviolencearchive.org/incident/108...   \n",
       "\n",
       "                                               source_url  \\\n",
       "0       http://www.post-gazette.com/local/south/2013/0...   \n",
       "1       http://www.dailybulletin.com/article/zz/201301...   \n",
       "2       http://chronicle.northcoastnow.com/2013/02/14/...   \n",
       "3       http://www.dailydemocrat.com/20130106/aurora-s...   \n",
       "4       http://www.journalnow.com/news/local/article_d...   \n",
       "...                                                   ...   \n",
       "239672  http://www.klfy.com/news/local/rayne-woman-cha...   \n",
       "239673  http://www.ksla.com/story/37854648/man-wanted-...   \n",
       "239674  http://www.nola.com/crime/index.ssf/2018/04/sh...   \n",
       "239675  https://www.chron.com/news/houston-texas/houst...   \n",
       "239676  https://www.centralmaine.com/2018/03/31/police...   \n",
       "\n",
       "        incident_url_fields_missing  ...  \\\n",
       "0                             False  ...   \n",
       "1                             False  ...   \n",
       "2                             False  ...   \n",
       "3                             False  ...   \n",
       "4                             False  ...   \n",
       "...                             ...  ...   \n",
       "239672                        False  ...   \n",
       "239673                        False  ...   \n",
       "239674                        False  ...   \n",
       "239675                        False  ...   \n",
       "239676                        False  ...   \n",
       "\n",
       "                                         participant_type  \\\n",
       "0       0::Victim||1::Victim||2::Victim||3::Victim||4:...   \n",
       "1       0::Victim||1::Victim||2::Victim||3::Victim||4:...   \n",
       "2       0::Subject-Suspect||1::Subject-Suspect||2::Vic...   \n",
       "3       0::Victim||1::Victim||2::Victim||3::Subject-Su...   \n",
       "4       0::Victim||1::Victim||2::Victim||3::Subject-Su...   \n",
       "...                                                   ...   \n",
       "239672                                 0::Subject-Suspect   \n",
       "239673                      0::Victim||1::Subject-Suspect   \n",
       "239674                                          0::Victim   \n",
       "239675                                          0::Victim   \n",
       "239676                      0::Victim||1::Subject-Suspect   \n",
       "\n",
       "                                                  sources  \\\n",
       "0       http://pittsburgh.cbslocal.com/2013/01/01/4-pe...   \n",
       "1       http://losangeles.cbslocal.com/2013/01/01/man-...   \n",
       "2       http://www.morningjournal.com/general-news/201...   \n",
       "3       http://denver.cbslocal.com/2013/01/06/officer-...   \n",
       "4       http://myfox8.com/2013/01/08/update-mother-sho...   \n",
       "...                                                   ...   \n",
       "239672  http://www.klfy.com/news/local/rayne-woman-cha...   \n",
       "239673  http://www.ksla.com/story/37854648/man-wanted-...   \n",
       "239674  http://www.nola.com/crime/index.ssf/2018/04/sh...   \n",
       "239675  http://www.khou.com/article/news/hpd-investiga...   \n",
       "239676  https://www.centralmaine.com/2018/03/31/police...   \n",
       "\n",
       "       state_house_district state_senate_district   datetime  year  state_pop  \\\n",
       "0                       NaN                   NaN 2013-01-01  2013   12776309   \n",
       "1                      62.0                  35.0 2013-01-01  2013   38260787   \n",
       "2                      56.0                  13.0 2013-01-01  2013   11576684   \n",
       "3                      40.0                  28.0 2013-01-05  2013    5269035   \n",
       "4                      62.0                  27.0 2013-01-07  2013    9843336   \n",
       "...                     ...                   ...        ...   ...        ...   \n",
       "239672                  NaN                   NaN 2018-03-31  2018    4659690   \n",
       "239673                 23.0                  31.0 2018-03-31  2018    4659690   \n",
       "239674                 85.0                   7.0 2018-03-31  2018    4659690   \n",
       "239675                149.0                  17.0 2018-03-31  2018   28628666   \n",
       "239676                111.0                   3.0 2018-03-31  2018    1339057   \n",
       "\n",
       "        city_pop area (sq. mi) pop_density  \n",
       "0            NaN         46058  277.396088  \n",
       "1          85863        163707  233.715034  \n",
       "2          63735         44828  258.246721  \n",
       "3         345613        104100   50.615130  \n",
       "4         279244         53821  182.890247  \n",
       "...          ...           ...         ...  \n",
       "239672       NaN         51843   89.880794  \n",
       "239673       NaN         51843   89.880794  \n",
       "239674       NaN         51843   89.880794  \n",
       "239675   2318573        268601  106.584361  \n",
       "239676       NaN         35387   37.840365  \n",
       "\n",
       "[239677 rows x 35 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (Almost) Final Result of cleaned dataframe\n",
    "\n",
    "clean_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6FxwfLdQWzDr"
   },
   "source": [
    "# Cleaning \n",
    "The Kaggle gun violence dataset contains 29 columns, of which we will be looking at 5: date, state, city or county, number of killed, and number of injured. The census population dataset (along with the city census population dataset) contains 13 columns, of which we will be looking at 11: state and the population in 2010-2019. Finally, we found a dataset with state areas in square miles on Github. In order to clean and merge the datasets so that we can look at the combined gun violence per state/city population/density, we had to clean the dataset in multiple steps.\n",
    "\n",
    "In order to get population data, we have to merge the gun violence and both the state and city census datasets. Merging was difficult because we have to look at merging not only the state and city, but also the year in which the incidents occurred and what the population was at the time (as it changes year by year). Further, the city population dataset had city names in a format that included the state but did not have a separate column for it. We had to use regex to separate this, and then we could merge with the state census dataset. Then, with this combined table, we were able to merge it using Pandas with the gun violence dataset by state and city, using a for loop to get the total population for the matching year for each incident. We merge on incidents instead of by cities/states because of the repeated values in all the data frames. To get the population density for each state, we divided the state population by the area in square miles. The finalized cleaned dataframe included this density as a column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6FxwfLdQWzDr"
   },
   "source": [
    "For our visualizations, we take the gun violence data and convert the dates (in hyphenated format) to Python Datetime so that we can easily obtain only the year. Because our cities of interest are Camden in New Jersey and Philadelphia in Pennsylvania (as they are very similar in geographic location), we created a function that filtered our cleaned dataframe to exclude any data outside of the range of latitude and longitude boundaries for Camden and Philadelphia, then graphed with Matplotlib the number of incidents reported in the dataset and the number of victims (victims being those killed and those injured) over the years. We also filtered the city population dataset (already sorted in descending order from most populated to least populated) to include the 5 most populated cities and 5 least populated cities in order to plot visualization 3 comparing gun violence for these cities. Finally, we plotted bar graphs after grouping the cleaned dataframe by state and obtaining the top 5 states with gun violence incidents, as well as obtaining their associated population densities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 991
    },
    "id": "sYIb0E4bEfvI",
    "outputId": "eb1c151a-9435-4843-afba-d399fcd78d8c"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>incident_id</th>\n",
       "      <th>date</th>\n",
       "      <th>state</th>\n",
       "      <th>city_or_county</th>\n",
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       "      <th>incident_url</th>\n",
       "      <th>source_url</th>\n",
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       "      <th>...</th>\n",
       "      <th>participant_type</th>\n",
       "      <th>sources</th>\n",
       "      <th>state_house_district</th>\n",
       "      <th>state_senate_district</th>\n",
       "      <th>datetime</th>\n",
       "      <th>year</th>\n",
       "      <th>state_pop</th>\n",
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       "      <th>area (sq. mi)</th>\n",
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       "      <th>288</th>\n",
       "      <td>92423</td>\n",
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       "      <td>Pennsylvania</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>34th Street and Haverford Avenue</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/92423</td>\n",
       "      <td>http://www.philly.com/philly/blogs/dncrime/Man...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::Victim||1::Subject-Suspect</td>\n",
       "      <td>http://www.philly.com/philly/blogs/dncrime/Man...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>12788313</td>\n",
       "      <td>1565460</td>\n",
       "      <td>46058</td>\n",
       "      <td>277.656715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>92747</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>Camden</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/92747</td>\n",
       "      <td>http://www.courierpostonline.com/article/20140...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::Victim||1::Subject-Suspect</td>\n",
       "      <td>http://www.courierpostonline.com/article/20140...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>8864525</td>\n",
       "      <td>75673</td>\n",
       "      <td>8722</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>92349</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>7th Street</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/92349</td>\n",
       "      <td>http://www.philly.com/philly/blogs/dncrime/S-P...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::Victim||1::Victim||2::Subject-Suspect</td>\n",
       "      <td>http://www.philly.com/philly/blogs/dncrime/S-P...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>2014</td>\n",
       "      <td>12788313</td>\n",
       "      <td>1565460</td>\n",
       "      <td>46058</td>\n",
       "      <td>277.656715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>220357</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>34th and Haverford Avenue</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/220357</td>\n",
       "      <td>http://blog.phillypolice.com/2014/02/wanted-su...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::Victim||1::Subject-Suspect</td>\n",
       "      <td>http://blog.phillypolice.com/2014/02/wanted-su...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>2014</td>\n",
       "      <td>12788313</td>\n",
       "      <td>1565460</td>\n",
       "      <td>46058</td>\n",
       "      <td>277.656715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>92727</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>Camden</td>\n",
       "      <td>1600 block of Louis Street</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/92727</td>\n",
       "      <td>http://www.nj.com/camden/index.ssf/2014/01/cam...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::Victim</td>\n",
       "      <td>http://www.nj.com/camden/index.ssf/2014/01/cam...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>2014</td>\n",
       "      <td>8864525</td>\n",
       "      <td>75673</td>\n",
       "      <td>8722</td>\n",
       "      <td>1016.340862</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>235382</th>\n",
       "      <td>1062115</td>\n",
       "      <td>2018-03-04</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>N 6th St and Spring Garden St</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/106...</td>\n",
       "      <td>http://6abc.com/1-killed-1-injured-in-spring-g...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::Victim||1::Victim</td>\n",
       "      <td>http://philadelphia.cbslocal.com/2018/03/04/po...</td>\n",
       "      <td>181.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-03-04</td>\n",
       "      <td>2018</td>\n",
       "      <td>12800922</td>\n",
       "      <td>1583592</td>\n",
       "      <td>46058</td>\n",
       "      <td>277.930479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235559</th>\n",
       "      <td>1063484</td>\n",
       "      <td>2018-03-05</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>Camden</td>\n",
       "      <td>400 block of Mechanic St</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/106...</td>\n",
       "      <td>http://www.nj.com/camden/index.ssf/2018/03/man...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::Victim</td>\n",
       "      <td>https://www.courierpostonline.com/story/news/c...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2018-03-05</td>\n",
       "      <td>2018</td>\n",
       "      <td>8886025</td>\n",
       "      <td>73725</td>\n",
       "      <td>8722</td>\n",
       "      <td>1018.805893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236316</th>\n",
       "      <td>1066841</td>\n",
       "      <td>2018-03-10</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>200 block of Lombard St</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/106...</td>\n",
       "      <td>http://6abc.com/parking-lot-attendant-shot-dur...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::Victim</td>\n",
       "      <td>http://6abc.com/parking-lot-attendant-shot-dur...</td>\n",
       "      <td>175.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-03-10</td>\n",
       "      <td>2018</td>\n",
       "      <td>12800922</td>\n",
       "      <td>1583592</td>\n",
       "      <td>46058</td>\n",
       "      <td>277.930479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237129</th>\n",
       "      <td>1070055</td>\n",
       "      <td>2018-03-15</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>700 block of S Columbus Blvd</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/107...</td>\n",
       "      <td>http://6abc.com/2-men-run-from-south-philly-tr...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::Subject-Suspect||1::Subject-Suspect</td>\n",
       "      <td>http://6abc.com/2-men-run-from-south-philly-tr...</td>\n",
       "      <td>175.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-03-15</td>\n",
       "      <td>2018</td>\n",
       "      <td>12800922</td>\n",
       "      <td>1583592</td>\n",
       "      <td>46058</td>\n",
       "      <td>277.930479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239385</th>\n",
       "      <td>1080442</td>\n",
       "      <td>2018-03-29</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>Camden</td>\n",
       "      <td>Kaighn Ave and Haddon Ave</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/108...</td>\n",
       "      <td>https://www.courierpostonline.com/story/news/c...</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0::Victim||1::Victim||2::Victim||3::Victim</td>\n",
       "      <td>http://philadelphia.cbslocal.com/2018/03/29/ca...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2018-03-29</td>\n",
       "      <td>2018</td>\n",
       "      <td>8886025</td>\n",
       "      <td>73725</td>\n",
       "      <td>8722</td>\n",
       "      <td>1018.805893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>609 rows × 35 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c633446b-d681-4b7c-952f-f90ad12aba32')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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      "text/plain": [
       "        incident_id        date         state city_or_county  \\\n",
       "288           92423  2014-01-01  Pennsylvania   Philadelphia   \n",
       "297           92747  2014-01-01    New Jersey         Camden   \n",
       "532           92349  2014-01-02  Pennsylvania   Philadelphia   \n",
       "557          220357  2014-01-02  Pennsylvania   Philadelphia   \n",
       "604           92727  2014-01-02    New Jersey         Camden   \n",
       "...             ...         ...           ...            ...   \n",
       "235382      1062115  2018-03-04  Pennsylvania   Philadelphia   \n",
       "235559      1063484  2018-03-05    New Jersey         Camden   \n",
       "236316      1066841  2018-03-10  Pennsylvania   Philadelphia   \n",
       "237129      1070055  2018-03-15  Pennsylvania   Philadelphia   \n",
       "239385      1080442  2018-03-29    New Jersey         Camden   \n",
       "\n",
       "                                 address  n_killed  n_injured  \\\n",
       "288     34th Street and Haverford Avenue         0          1   \n",
       "297                                  NaN         0          1   \n",
       "532                           7th Street         0          1   \n",
       "557            34th and Haverford Avenue         0          1   \n",
       "604           1600 block of Louis Street         1          0   \n",
       "...                                  ...       ...        ...   \n",
       "235382     N 6th St and Spring Garden St         1          1   \n",
       "235559          400 block of Mechanic St         1          0   \n",
       "236316           200 block of Lombard St         0          1   \n",
       "237129      700 block of S Columbus Blvd         0          0   \n",
       "239385         Kaighn Ave and Haddon Ave         0          4   \n",
       "\n",
       "                                             incident_url  \\\n",
       "288      http://www.gunviolencearchive.org/incident/92423   \n",
       "297      http://www.gunviolencearchive.org/incident/92747   \n",
       "532      http://www.gunviolencearchive.org/incident/92349   \n",
       "557     http://www.gunviolencearchive.org/incident/220357   \n",
       "604      http://www.gunviolencearchive.org/incident/92727   \n",
       "...                                                   ...   \n",
       "235382  http://www.gunviolencearchive.org/incident/106...   \n",
       "235559  http://www.gunviolencearchive.org/incident/106...   \n",
       "236316  http://www.gunviolencearchive.org/incident/106...   \n",
       "237129  http://www.gunviolencearchive.org/incident/107...   \n",
       "239385  http://www.gunviolencearchive.org/incident/108...   \n",
       "\n",
       "                                               source_url  \\\n",
       "288     http://www.philly.com/philly/blogs/dncrime/Man...   \n",
       "297     http://www.courierpostonline.com/article/20140...   \n",
       "532     http://www.philly.com/philly/blogs/dncrime/S-P...   \n",
       "557     http://blog.phillypolice.com/2014/02/wanted-su...   \n",
       "604     http://www.nj.com/camden/index.ssf/2014/01/cam...   \n",
       "...                                                   ...   \n",
       "235382  http://6abc.com/1-killed-1-injured-in-spring-g...   \n",
       "235559  http://www.nj.com/camden/index.ssf/2018/03/man...   \n",
       "236316  http://6abc.com/parking-lot-attendant-shot-dur...   \n",
       "237129  http://6abc.com/2-men-run-from-south-philly-tr...   \n",
       "239385  https://www.courierpostonline.com/story/news/c...   \n",
       "\n",
       "        incident_url_fields_missing  ...  \\\n",
       "288                           False  ...   \n",
       "297                           False  ...   \n",
       "532                           False  ...   \n",
       "557                           False  ...   \n",
       "604                           False  ...   \n",
       "...                             ...  ...   \n",
       "235382                        False  ...   \n",
       "235559                        False  ...   \n",
       "236316                        False  ...   \n",
       "237129                        False  ...   \n",
       "239385                        False  ...   \n",
       "\n",
       "                                  participant_type  \\\n",
       "288                  0::Victim||1::Subject-Suspect   \n",
       "297                  0::Victim||1::Subject-Suspect   \n",
       "532       0::Victim||1::Victim||2::Subject-Suspect   \n",
       "557                  0::Victim||1::Subject-Suspect   \n",
       "604                                      0::Victim   \n",
       "...                                            ...   \n",
       "235382                        0::Victim||1::Victim   \n",
       "235559                                   0::Victim   \n",
       "236316                                   0::Victim   \n",
       "237129      0::Subject-Suspect||1::Subject-Suspect   \n",
       "239385  0::Victim||1::Victim||2::Victim||3::Victim   \n",
       "\n",
       "                                                  sources  \\\n",
       "288     http://www.philly.com/philly/blogs/dncrime/Man...   \n",
       "297     http://www.courierpostonline.com/article/20140...   \n",
       "532     http://www.philly.com/philly/blogs/dncrime/S-P...   \n",
       "557     http://blog.phillypolice.com/2014/02/wanted-su...   \n",
       "604     http://www.nj.com/camden/index.ssf/2014/01/cam...   \n",
       "...                                                   ...   \n",
       "235382  http://philadelphia.cbslocal.com/2018/03/04/po...   \n",
       "235559  https://www.courierpostonline.com/story/news/c...   \n",
       "236316  http://6abc.com/parking-lot-attendant-shot-dur...   \n",
       "237129  http://6abc.com/2-men-run-from-south-philly-tr...   \n",
       "239385  http://philadelphia.cbslocal.com/2018/03/29/ca...   \n",
       "\n",
       "       state_house_district state_senate_district   datetime  year  state_pop  \\\n",
       "288                     NaN                   NaN 2014-01-01  2014   12788313   \n",
       "297                     5.0                   5.0 2014-01-01  2014    8864525   \n",
       "532                     NaN                   NaN 2014-01-02  2014   12788313   \n",
       "557                     NaN                   NaN 2014-01-02  2014   12788313   \n",
       "604                     5.0                   5.0 2014-01-02  2014    8864525   \n",
       "...                     ...                   ...        ...   ...        ...   \n",
       "235382                181.0                   1.0 2018-03-04  2018   12800922   \n",
       "235559                  5.0                   5.0 2018-03-05  2018    8886025   \n",
       "236316                175.0                   1.0 2018-03-10  2018   12800922   \n",
       "237129                175.0                   1.0 2018-03-15  2018   12800922   \n",
       "239385                  5.0                   5.0 2018-03-29  2018    8886025   \n",
       "\n",
       "        city_pop area (sq. mi)  pop_density  \n",
       "288      1565460         46058   277.656715  \n",
       "297        75673          8722  1016.340862  \n",
       "532      1565460         46058   277.656715  \n",
       "557      1565460         46058   277.656715  \n",
       "604        75673          8722  1016.340862  \n",
       "...          ...           ...          ...  \n",
       "235382   1583592         46058   277.930479  \n",
       "235559     73725          8722  1018.805893  \n",
       "236316   1583592         46058   277.930479  \n",
       "237129   1583592         46058   277.930479  \n",
       "239385     73725          8722  1018.805893  \n",
       "\n",
       "[609 rows x 35 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defining new dataframe ('phil_camden') limiting to gun violence incident\n",
    "# in Philadelphia, PA and Camden, NJ for our visualizations for 1st question\n",
    "\n",
    "# The longitude and latitudes were chosen based on South Philadelphia, PA, and Camden, NJ\n",
    "\n",
    "def get_boundaries(df):\n",
    "    boundaried = df[(df['latitude'] <= 39.9661) & (df['latitude'] >= 39.8906)]\n",
    "    boundaried = boundaried[(boundaried['longitude'] <= -75.0649) & (boundaried['longitude'] >= -75.1952)]\n",
    "    return boundaried\n",
    "\n",
    "phil_camden = get_boundaries(clean_df)\n",
    "phil_camden"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization #1 (Numerical):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "N2qgb4POFz3Y",
    "outputId": "4b0bd3fe-e761-4bd5-f52e-06b6804a47bb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparing number of Gun Violence Incidents & Victims\n",
    "# Between Philadelphia, PA, and Camden, NJ, over time\n",
    "\n",
    "phil_camden_sum = phil_camden.groupby(['city_or_county','year']).sum()\n",
    "phil_camden_sum['num_incidents'] = phil_camden.groupby(['city_or_county', 'year']).count()['incident_id'].values\n",
    "phil_camden_sum['num_victims'] = phil_camden_sum['n_killed'].values + phil_camden_sum['n_injured'].values\n",
    "\n",
    "phil = phil_camden_sum.loc['Philadelphia']\n",
    "camden = phil_camden_sum.loc['Camden']\n",
    "years = list(phil.index)\n",
    "num_victims_phil = phil['num_victims'].values\n",
    "num_incidents_phil = phil['num_incidents'].values\n",
    "num_victims_camden = camden['num_victims'].values\n",
    "num_incidents_camden = camden['num_incidents'].values\n",
    "\n",
    "fig, [ax1, ax2] = plt.subplots(1, 2, figsize=(14, 4))\n",
    "ax1.plot(years, num_incidents_phil, label='Philadelphia')\n",
    "ax1.plot(years, num_incidents_camden, label='Camden')\n",
    "ax1.set_title('Number of Incidents in Philadelphia & Camden over Time')\n",
    "ax1.set_xlabel('Year')\n",
    "ax1.set_ylabel('Number of Incidents')\n",
    "ax1.set_xticks(years)\n",
    "ax1.legend()\n",
    "\n",
    "ax2.plot(years, num_victims_phil, label='Philadelphia')\n",
    "ax2.plot(years, num_victims_camden, label='Camden')\n",
    "ax2.set_title('Number of Victims in Philadelphia & Camden over Time')\n",
    "ax2.set_xlabel('Year')\n",
    "ax2.set_ylabel('Number of Victims')\n",
    "ax2.set_xticks(years)\n",
    "ax2.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VzxYBwAH0PMo"
   },
   "source": [
    "After edging our research area down to number of victimes and incidents in both Philadelphia (treatment group) and Camden (control group), we were able to find that there has been similar trend in a shape of fall, then rise, and then falling movement in Philadelphia. This demonstrates that after the in-statement of Act of 192 in Pennsylvania, there has been, ironically, a remarkable decrease in number of incidents and victims from gun violence, within Philadelphia. This was a natural outcome as number of victims will be evidently less as the number of incidents goes down. This demonstrates that the enactment of Act of 192 had a positive relation to decreasing the frequency of gun mis-usage. The decrease in number of incident is remarkable after 2014 as it dropped from around 140 to 60 from 2014 to 2015. In the case of Camden, which did not receive the treatment from the Act of 192, also demonstrated decreasing trend in the number of gun-violence incidents from around 110 to 60 over 1-year period. However, the number of victims did not show any changes during that period.\n",
    "\n",
    "Overall, both Camden and Philadelphia have demonstrated decreasing number of gun-violence cases and victims over the 4-year period from our data sets. However, the amount of decrease has varied for both cities as the number of cases and victims have slightly increased in Philadelphia from 2015 to 2016, while the numbers of continuously gone down in Camden. We assume that the increasing number and strictness within gun-control policy has caused overall number of cases and victims to go down throughout the entire nation, but as one can see, the Act of 192 seemed to had some correlation to the bounce back of number of gun-violence cases in Philadelphia. The difference between Philadelphia and Camden was the presence of Act of 192, and we believe our assumption is reasonable since the Act of 192 gave an opportunity for gun advocacy organizations to challenge legislative power on gun-control in Pennsylvania. This may have potentially let to out-break in spread of guns in the wrong hands.\n",
    "\n",
    "This is related to our question regarding whether there are less casualities over time. In this case, the data from Philadelphia and Camden seems to suggest this. However, we need further analysis to determine why and if there is a causation (whether due to policy or another variable)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "id": "2SUYPff7XAxf",
    "outputId": "54afad20-5ac2-4b35-fae1-f87bd851393f"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>num_incidents</th>\n",
       "      <th>num_victims</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_or_county</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Camden</th>\n",
       "      <th>2014</th>\n",
       "      <td>105</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>55</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>48</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Philadelphia</th>\n",
       "      <th>2014</th>\n",
       "      <td>137</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>56</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>82</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>59</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0da31b96-7939-4480-98e5-447d1d320f2b')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
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       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-0da31b96-7939-4480-98e5-447d1d320f2b button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-0da31b96-7939-4480-98e5-447d1d320f2b');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                     num_incidents  num_victims\n",
       "city_or_county year                            \n",
       "Camden         2014            105           55\n",
       "               2015             55           56\n",
       "               2016             48           46\n",
       "               2017             31           32\n",
       "               2018              6            9\n",
       "Philadelphia   2014            137          132\n",
       "               2015             56           54\n",
       "               2016             82           84\n",
       "               2017             59           70\n",
       "               2018             14           17"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phil_camden_sum.loc[[\"Camden\", \"Philadelphia\"]][['num_incidents', 'num_victims']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization #2 (Categorical):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "53ZZ2LIeIaiy",
    "outputId": "0d1ab456-00e4-46a4-ae99-5b7a4a893a9e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total number of Gun Violence Incidents between Philadelphia & Camden.\n",
    "\n",
    "total_incidents_phil = phil['num_incidents'].sum()\n",
    "total_incidents_camden = camden['num_incidents'].sum()\n",
    "\n",
    "plt.bar(['Philadelphia', 'Camden'], [total_incidents_phil, total_incidents_camden])\n",
    "plt.xlabel('City')\n",
    "plt.ylabel('Number of incidents')\n",
    "plt.title('Total Number of Incidents in Philadelphia and Camden')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vWF_ZBny4ta7"
   },
   "source": [
    "From a bigger point-of-view, there were (overall) greater number of gun-violence incidents in Philadelphia compared to the number in Camden. This aligns with our analysis from the previous visualization that the Act of 192 potentially had correlation to a higher frequency of incidents in Philadelphia. \n",
    "\n",
    "This relates to our causal inference question on whether gun control policy increases or decreases gun violence on a very broad scale. This gives us a preliminary insight on this relationship using the two cities as an example of a treatment and control group (controlled for other factors). It seems that the total number of incidents are greater for the city of Philadelphia, but not by much. Further analysis is needed to see if the Act of 192 really decreased gun violence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "guC4qfHBXk2y",
    "outputId": "37a88b38-553a-4abb-93d9-ca8858f59892"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_incidents</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_or_county</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Camden</th>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philadelphia</th>\n",
       "      <td>348</td>\n",
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       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-c5d7e93d-1684-4817-a480-67f80f346a4a');\n",
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       "                                                     [key], {});\n",
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       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "  "
      ],
      "text/plain": [
       "                num_incidents\n",
       "city_or_county               \n",
       "Camden                    245\n",
       "Philadelphia              348"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = phil_camden_sum.groupby(level=\"city_or_county\").sum()\n",
    "total.loc[[\"Camden\", \"Philadelphia\"]][['num_incidents']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization #3 (Categorical):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 194
    },
    "id": "lwY7TBakI4cG",
    "outputId": "2c7a1c79-9ec2-48c7-f1e2-42b2c63e209e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar graph of Number of Gun Violence Incidents from\n",
    "# top 5 MOST populated to top 5 LEAST populated cities\n",
    "# (rank of city populations are pulled from 'city_pop_df' from 'Cleaning' section)\n",
    "\n",
    "most_5 = city_pop_df.iloc[:5]\n",
    "most_5_cities = most_5[['city_or_county', 'state']].values\n",
    "least_5 = city_pop_df.iloc[-5:]\n",
    "least_5_cities = least_5[['city_or_county', 'state']].values\n",
    "locations = most_5_cities.tolist()+least_5_cities.tolist()\n",
    "\n",
    "most_least_cities_df = clean_df.groupby(['city_or_county', 'state']).sum()\n",
    "most_least_cities_df['num_incidents'] = clean_df.groupby(['city_or_county', 'state']).count()['incident_id'].values\n",
    "\n",
    "num_incidents_locations = []\n",
    "for i in locations:\n",
    "    num_incidents_locations.append(most_least_cities_df.loc[(i[0], i[1])]['num_incidents'])\n",
    "\n",
    "city_state = []\n",
    "pairing = []\n",
    "for i in locations:\n",
    "    pairing.append(tuple([i[0], i[1]]))\n",
    "    pair = \", \".join(i)\n",
    "    city_state.append(pair)\n",
    "\n",
    "plt.figure(figsize=(25, 4))\n",
    "plt.bar(city_state, num_incidents_locations, color=['green', 'green', 'green', 'green', 'green', \n",
    "                                                    'blue', 'blue', 'blue', 'blue', 'blue'])\n",
    "plt.xlabel('Cities')\n",
    "plt.ylabel('Number of Incidents')\n",
    "plt.title('Comparing the Number of Gun Violence Incidents between Top 5 and Least 5 Populated Cities')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ae4-PZkl7FkT"
   },
   "source": [
    "The top 5 cities with highest population (ranked highest from left to right) are marked with green bars, while the top 5 cities with lowest population (ranked lowest from left to right) are marked with blue bars. From the visualization #3, it demonstrates a stark difference between the number of incidents between the top 5 populated cities and least 5 populated cities. This was a reasonable outcome as force of regulation is weaker for cities with greater populations. However, we discovered an interesting finding as the city of Chicago demonstrated exceptionally higher number of gun-violence case reports compared to the other 4 of the most populated cities in the United States. Chicago isn't the most populated city in US as the top 5 most populated cities are ranked from New York, Los Angelas, Chicago, Houston, and Phoenix. It was unexpected to find that Chicago (the third most populated city) was exponentially outnumbering the gun-violence incidents from cities that were more heavily populated than Chicago. Another unexpected founding was that the cities (from top 5 cities) that were less populated than New York and Los Angelas had more case reports on gun-violences. There are multiple confounding variables to discover the causal-relationship between the gun-violence and population, but reporter Lakeidra Chavis has affiliated dozens of gang affiliated residential population ratio in Chicago for the cause of gun-violence (Chavis). On the other hand, the finding that we found from observing the gun-violance incidents from the least populated cities was more on the reasonable level. The lowest 2 cities from the lowest number of populations were Lakewood and Troy. \n",
    "\n",
    "This relates to our question of whether more populated cities are associated with more instances or deadlier instances of gun violence. Before we look at correlations, we can take a look at the top 5 and least 5 populated cities as a whole, and it seems to suggest that there seems to somewhat be a difference between populated and unpopulated cities (but of course, this is on a very broad scale)."
   ]
  },
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      "text/plain": [
       "                              num_incidents\n",
       "city_or_county state                       \n",
       "New York       New York                 377\n",
       "Los Angeles    California              1066\n",
       "Chicago        Illinois               10814\n",
       "Houston        Texas                   2480\n",
       "Phoenix        Arizona                  969\n",
       "Lakewood       Ohio                      28\n",
       "Troy           New York                  90\n",
       "Saginaw        Michigan                 241\n",
       "Niagara Falls  New York                 182\n",
       "Charleston     West Virginia            271"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_least_cities_df.loc[(i for i in pairing)][['num_incidents']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization #4 (Numerical):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
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    "id": "J4ap2Q8DZIUX",
    "outputId": "6696bc8b-af70-4ca0-ab50-af9a03efe6d8"
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   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar graph of top 5 states with highest number of gun incidents\n",
    "# alongside their population density\n",
    "state_groups = clean_df.groupby('state').count()\n",
    "state_density = clean_df.groupby('state').mean()\n",
    "incidents_by_state = list(zip(state_groups.index, state_groups['incident_id'].values))\n",
    "incidents_by_state.sort(key = lambda x: x[1], reverse=True)\n",
    "\n",
    "top_5_states = list(i[0] for i in incidents_by_state[:5])\n",
    "top_5_incidents = list(i[1] for i in incidents_by_state[:5])\n",
    "top_5_pop = list(state_density.loc[i]['pop_density'] for i in top_5_states)\n",
    "\n",
    "fig, [ax1, ax2] = plt.subplots(1, 2, figsize=(12, 4))\n",
    "ax1.bar(top_5_states, top_5_incidents)\n",
    "ax1.set_title('Top 5 States with most Gun Violence Incidents')\n",
    "ax1.set_xlabel('State')\n",
    "ax1.set_ylabel('Number of Violences')\n",
    "\n",
    "ax2.bar(top_5_states, top_5_pop)\n",
    "ax2.set_title('Population Density of Top 5 States')\n",
    "ax2.set_xlabel('State')\n",
    "ax2.set_ylabel('Density');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wNMeJPDiNu8O"
   },
   "source": [
    "The visualization #4 is a variation of analysis made on visualization #3 that there is uncertainty on correlation between strictness in legislative force and gun-violence control. This is well demonstrated from the two histograms represented above. Illinois (as it was shown from visualization #3) continues to have the highest number of incidents, despite being 3rd highest population density out of the top 5 states with highest population density. Similarly, California was the second state with the highest number of gun violence. California was ranked third highest state within state population density. \n",
    "\n",
    "We wanted to relate population density to our question of how properties of a location (particularly states or cities and in this case, states) pertain to gun violence. Here, although we take a look at the top 5 gun violence incident states, we see it does not represent the most densely populated states. This preliminarily suggests that population density is not the (biggest) contributing factor to gun violence."
   ]
  },
  {
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    "outputId": "983423d7-f4e9-4d29-8fcd-cebae9b8796c"
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       "            pop_density  num_incidents\n",
       "state                                 \n",
       "Illinois     221.393517          17556\n",
       "California   238.589069          16306\n",
       "Florida      311.913044          15029\n",
       "Texas        103.138129          13577\n",
       "Ohio         259.493494          10244"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_density_and_incidents = state_density.loc[top_5_states][['pop_density']]\n",
    "state_density_and_incidents['num_incidents'] = top_5_incidents\n",
    "state_density_and_incidents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to the file size of `clean_df` being too large to upload onto Github (>100MB), our group decided to cut down on the dataframe. In the following cells, we dropped the following columns that we deemed irrelevant to our research: \n",
    "\n",
    "`'incident_url', 'source_url', 'incident_url_fields_missing', 'incident_characteristics', 'notes', 'participant_name', 'participant_relationship', 'sources'`\n",
    "\n",
    "These are the reasons why we deemed the above columns are not necessary for our research:\n",
    "\n",
    "- `'incident_url', 'source_url', 'incident_url_fields_missing', 'sources'`: removed to lack of need to access the URL's of the reported incidents\n",
    "- `'incident_characteristics', 'notes'`: will not be analyzing the intricate, minor details of the incidents for our questions\n",
    "- `'participant_name', 'participant_relationship'`: removed for the sake of privacy\n",
    "\n",
    "After dropping the columns, the dataframes were then exported to csv's."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "92arGhsFifME"
   },
   "outputs": [],
   "source": [
    "# More cleaning to `clean_df` to shrink size \n",
    "# (dropping columns that won't be useful for our project)\n",
    "# Removed 'incident_url', 'source_url', 'incident_url_fields_missing', \n",
    "# 'incident_characteristics', 'notes', 'participant_name', 'participant_relationship', 'sources'\n",
    "final_clean_df = clean_df.drop(columns=['incident_url', 'source_url', 'incident_url_fields_missing', \n",
    "                           'incident_characteristics', 'notes', 'participant_name', \n",
    "                           'participant_relationship', 'sources'])\n",
    "final_clean_df.to_csv('gun_violence.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "rKU2edD1izL-"
   },
   "outputs": [],
   "source": [
    "# Exporting Gun Violence Incidents in Philadelphia, PA & Camden, NJ\n",
    "phil_camden.to_csv('philadelphia_camden_gun_violence.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\pagebreak"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pqsKy--8l_vR"
   },
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/haroldcha/gunviolencedata/blob/master/hypothesis.ipynb)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xBvu7KuSyMpP"
   },
   "source": [
    "# Multiple Hypothesis Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.graphics.regressionplots import abline_plot\n",
    "import datetime\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random \n",
    "df = pd.read_csv('https://raw.githubusercontent.com/haroldcha/gunviolencedata/master/gun_violence.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "s9MiGlX_qhBZ"
   },
   "outputs": [
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       "      <td>2018-03-31</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>Gretna</td>\n",
       "      <td>1300 block of Cook Street</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0::Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>0::Injured</td>\n",
       "      <td>0::Victim</td>\n",
       "      <td>85.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>2018</td>\n",
       "      <td>4659690</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51843</td>\n",
       "      <td>89.880794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239675</th>\n",
       "      <td>239675</td>\n",
       "      <td>1082514</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Houston</td>\n",
       "      <td>12630 Ashford Point Dr</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0::Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>0::Killed</td>\n",
       "      <td>0::Victim</td>\n",
       "      <td>149.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>2018</td>\n",
       "      <td>28628666</td>\n",
       "      <td>2318573.0</td>\n",
       "      <td>268601</td>\n",
       "      <td>106.584361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239676</th>\n",
       "      <td>239676</td>\n",
       "      <td>1081940</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>Maine</td>\n",
       "      <td>Norridgewock</td>\n",
       "      <td>434 Skowhegan Rd</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0::Unknown||1::Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>0::Killed||1::Killed</td>\n",
       "      <td>0::Victim||1::Subject-Suspect</td>\n",
       "      <td>111.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>2018</td>\n",
       "      <td>1339057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35387</td>\n",
       "      <td>37.840365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>239677 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  incident_id        date           state city_or_county  \\\n",
       "0                0       461105  2013-01-01    Pennsylvania     Mckeesport   \n",
       "1                1       460726  2013-01-01      California      Hawthorne   \n",
       "2                2       478855  2013-01-01            Ohio         Lorain   \n",
       "3                3       478925  2013-01-05        Colorado         Aurora   \n",
       "4                4       478959  2013-01-07  North Carolina     Greensboro   \n",
       "...            ...          ...         ...             ...            ...   \n",
       "239672      239672      1083142  2018-03-31       Louisiana          Rayne   \n",
       "239673      239673      1083139  2018-03-31       Louisiana   Natchitoches   \n",
       "239674      239674      1083151  2018-03-31       Louisiana         Gretna   \n",
       "239675      239675      1082514  2018-03-31           Texas        Houston   \n",
       "239676      239676      1081940  2018-03-31           Maine   Norridgewock   \n",
       "\n",
       "                                          address  n_killed  n_injured  \\\n",
       "0       1506 Versailles Avenue and Coursin Street         0          4   \n",
       "1                    13500 block of Cerise Avenue         1          3   \n",
       "2                           1776 East 28th Street         1          3   \n",
       "3                16000 block of East Ithaca Place         4          0   \n",
       "4                       307 Mourning Dove Terrace         2          2   \n",
       "...                                           ...       ...        ...   \n",
       "239672         North Riceland Road and Highway 90         0          0   \n",
       "239673                             247 Keyser Ave         1          0   \n",
       "239674                  1300 block of Cook Street         0          1   \n",
       "239675                     12630 Ashford Point Dr         1          0   \n",
       "239676                           434 Skowhegan Rd         2          0   \n",
       "\n",
       "        congressional_district              gun_stolen  ...  \\\n",
       "0                         14.0                     NaN  ...   \n",
       "1                         43.0                     NaN  ...   \n",
       "2                          9.0  0::Unknown||1::Unknown  ...   \n",
       "3                          6.0                     NaN  ...   \n",
       "4                          6.0  0::Unknown||1::Unknown  ...   \n",
       "...                        ...                     ...  ...   \n",
       "239672                     NaN              0::Unknown  ...   \n",
       "239673                     4.0              0::Unknown  ...   \n",
       "239674                     2.0              0::Unknown  ...   \n",
       "239675                     9.0              0::Unknown  ...   \n",
       "239676                     2.0  0::Unknown||1::Unknown  ...   \n",
       "\n",
       "                                       participant_status  \\\n",
       "0       0::Arrested||1::Injured||2::Injured||3::Injure...   \n",
       "1           0::Killed||1::Injured||2::Injured||3::Injured   \n",
       "2       0::Injured, Unharmed, Arrested||1::Unharmed, A...   \n",
       "3              0::Killed||1::Killed||2::Killed||3::Killed   \n",
       "4            0::Injured||1::Injured||2::Killed||3::Killed   \n",
       "...                                                   ...   \n",
       "239672                              0::Unharmed, Arrested   \n",
       "239673                   0::Killed||1::Unharmed, Arrested   \n",
       "239674                                         0::Injured   \n",
       "239675                                          0::Killed   \n",
       "239676                               0::Killed||1::Killed   \n",
       "\n",
       "                                         participant_type  \\\n",
       "0       0::Victim||1::Victim||2::Victim||3::Victim||4:...   \n",
       "1       0::Victim||1::Victim||2::Victim||3::Victim||4:...   \n",
       "2       0::Subject-Suspect||1::Subject-Suspect||2::Vic...   \n",
       "3       0::Victim||1::Victim||2::Victim||3::Subject-Su...   \n",
       "4       0::Victim||1::Victim||2::Victim||3::Subject-Su...   \n",
       "...                                                   ...   \n",
       "239672                                 0::Subject-Suspect   \n",
       "239673                      0::Victim||1::Subject-Suspect   \n",
       "239674                                          0::Victim   \n",
       "239675                                          0::Victim   \n",
       "239676                      0::Victim||1::Subject-Suspect   \n",
       "\n",
       "       state_house_district  state_senate_district    datetime  year  \\\n",
       "0                       NaN                    NaN  2013-01-01  2013   \n",
       "1                      62.0                   35.0  2013-01-01  2013   \n",
       "2                      56.0                   13.0  2013-01-01  2013   \n",
       "3                      40.0                   28.0  2013-01-05  2013   \n",
       "4                      62.0                   27.0  2013-01-07  2013   \n",
       "...                     ...                    ...         ...   ...   \n",
       "239672                  NaN                    NaN  2018-03-31  2018   \n",
       "239673                 23.0                   31.0  2018-03-31  2018   \n",
       "239674                 85.0                    7.0  2018-03-31  2018   \n",
       "239675                149.0                   17.0  2018-03-31  2018   \n",
       "239676                111.0                    3.0  2018-03-31  2018   \n",
       "\n",
       "       state_pop   city_pop area (sq. mi) pop_density  \n",
       "0       12776309        NaN         46058  277.396088  \n",
       "1       38260787    85863.0        163707  233.715034  \n",
       "2       11576684    63735.0         44828  258.246721  \n",
       "3        5269035   345613.0        104100   50.615130  \n",
       "4        9843336   279244.0         53821  182.890247  \n",
       "...          ...        ...           ...         ...  \n",
       "239672   4659690        NaN         51843   89.880794  \n",
       "239673   4659690        NaN         51843   89.880794  \n",
       "239674   4659690        NaN         51843   89.880794  \n",
       "239675  28628666  2318573.0        268601  106.584361  \n",
       "239676   1339057        NaN         35387   37.840365  \n",
       "\n",
       "[239677 rows x 28 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reminder of cleaned dataset\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Sr96yCcr2D7P"
   },
   "outputs": [],
   "source": [
    "# Determining whether or not the differences between the two evaluating groups are significant\n",
    "# by comparing the groups' means and the individual value\n",
    "\n",
    "def test_statistic(df, column):\n",
    "    means_table = df.groupby(column).mean()\n",
    "    if 1 not in means_table.index:\n",
    "        diff = 0 - means_table.loc[0].values\n",
    "    elif 0 not in means_table.index:\n",
    "        diff = means_table.loc[1].values - 0\n",
    "    else:\n",
    "        diff = means_table.loc[1].values - means_table.loc[0].values\n",
    "    \n",
    "    return diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "3uA6ZD2m4bH4"
   },
   "outputs": [],
   "source": [
    "# Shuffling label of the group to test significance of the groups (A/B testing)\n",
    "\n",
    "def simulated_test_stat(df, column):\n",
    "    shuffled_labels = df.sample(frac=1, replace=True)[column]\n",
    "    df[\"shuffled_labels\"] = shuffled_labels.to_numpy()\n",
    "    df = df.drop(column,axis=1)\n",
    "    \n",
    "    return test_statistic(df, 'shuffled_labels')   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "CvbC35Vx8gie"
   },
   "outputs": [],
   "source": [
    "# Implementing A/B testing and bootstrapping to calculate p_value\n",
    "\n",
    "def p_value(df, column, observed_difference, comparison):\n",
    "    differences = []\n",
    "\n",
    "    repetitions = 1000\n",
    "    for i in np.arange(repetitions):\n",
    "        new_difference = simulated_test_stat(df, column)\n",
    "        differences = np.append(differences, new_difference) \n",
    "    if comparison==\"greater\":\n",
    "        #indicates that higher values of the difference favor the alternative hypothesis that the treatment incidents were higher on average\n",
    "        empirical_p = np.count_nonzero(differences >= observed_difference) / repetitions\n",
    "    else:\n",
    "        #indicates that lower values of the difference favor the alternative hypothesis that the treatment incidents were lower on average\n",
    "        empirical_p = np.count_nonzero(differences <= observed_difference) / repetitions  \n",
    "    return empirical_p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EdReM-jIEzlx",
    "tags": []
   },
   "source": [
    "## 1. Are more populated cities associated with deadlier instances of gun violence?\n",
    "\n",
    "Null hypothesis: In the U.S., the distribution of deadly instances of gun violence (according to our definition of 'deadly' above) is the same for cities that are populated and cities that are not. The difference in the sample is due to chance.\n",
    "\n",
    "Alternative hypothesis: In the U.S., the instances of gun violence are deadlier for cities that are populated vs. cities that are not. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "Y6EXEIRoyPpE"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_83/3574120211.py:2: 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",
      "  most_populous['populous?'] = 1\n",
      "/tmp/ipykernel_83/3574120211.py:4: 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",
      "  least_populous['populous?'] = 0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>populous?</th>\n",
       "      <th>num_incidents</th>\n",
       "      <th>num_victims</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_or_county</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cerritos</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Charleston</th>\n",
       "      <td>0</td>\n",
       "      <td>519</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago</th>\n",
       "      <td>1</td>\n",
       "      <td>10814</td>\n",
       "      <td>12531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coral Gables</th>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dallas</th>\n",
       "      <td>1</td>\n",
       "      <td>1179</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enid</th>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Houston</th>\n",
       "      <td>1</td>\n",
       "      <td>2501</td>\n",
       "      <td>2400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lakewood</th>\n",
       "      <td>0</td>\n",
       "      <td>113</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Los Angeles</th>\n",
       "      <td>1</td>\n",
       "      <td>1066</td>\n",
       "      <td>1189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>1</td>\n",
       "      <td>377</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niagara Falls</th>\n",
       "      <td>0</td>\n",
       "      <td>182</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philadelphia</th>\n",
       "      <td>1</td>\n",
       "      <td>2963</td>\n",
       "      <td>3227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phoenix</th>\n",
       "      <td>1</td>\n",
       "      <td>973</td>\n",
       "      <td>886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saginaw</th>\n",
       "      <td>0</td>\n",
       "      <td>242</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Antonio</th>\n",
       "      <td>1</td>\n",
       "      <td>1628</td>\n",
       "      <td>1545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Diego</th>\n",
       "      <td>1</td>\n",
       "      <td>375</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Jose</th>\n",
       "      <td>1</td>\n",
       "      <td>237</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas City</th>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Troy</th>\n",
       "      <td>0</td>\n",
       "      <td>167</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Twin Falls</th>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                populous?  num_incidents  num_victims\n",
       "city_or_county                                       \n",
       "Cerritos                0              7            6\n",
       "Charleston              0            519          350\n",
       "Chicago                 1          10814        12531\n",
       "Coral Gables            0             14           12\n",
       "Dallas                  1           1179          999\n",
       "Enid                    0             60           24\n",
       "Houston                 1           2501         2400\n",
       "Lakewood                0            113           85\n",
       "Los Angeles             1           1066         1189\n",
       "New York                1            377          327\n",
       "Niagara Falls           0            182           75\n",
       "Philadelphia            1           2963         3227\n",
       "Phoenix                 1            973          886\n",
       "Saginaw                 0            242          218\n",
       "San Antonio             1           1628         1545\n",
       "San Diego               1            375          285\n",
       "San Jose                1            237          208\n",
       "Texas City              0             24           20\n",
       "Troy                    0            167           89\n",
       "Twin Falls              0             21           19"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_populous = df.loc[df['city_or_county'].isin(['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix', 'Philadelphia', 'San Antonio', 'San Diego', 'Dallas', 'San Jose'])]\n",
    "most_populous['populous?'] = 1\n",
    "least_populous = df.loc[df['city_or_county'].isin(['Charleston', 'Niagara Falls', 'Saginaw', 'Troy', 'Lakewood', 'Enid', 'Coral Gables', 'Cerritos', 'Texas City', 'Twin Falls'])]\n",
    "least_populous['populous?'] = 0\n",
    "populous = pd.concat([most_populous, least_populous])\n",
    "populous_df = populous.groupby(['city_or_county', 'populous?']).sum()\n",
    "populous_df['num_incidents'] = populous.groupby(['city_or_county', 'populous?']).count()['incident_id'].values\n",
    "populous_df['num_victims'] = populous_df['n_killed'].values + populous_df['n_injured'].values\n",
    "populous_df = populous_df.loc[:,['num_incidents', 'num_victims']]\n",
    "populous_df = populous_df.reset_index('populous?')\n",
    "populous_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "victim_populous = populous_df.loc[:,['populous?', 'num_victims']]\n",
    "test_victim_populous = test_statistic(victim_populous, 'populous?')\n",
    "p_value(victim_populous, 'populous?', test_victim_populous, 'greater')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tCIM_5suFEa2",
    "tags": []
   },
   "source": [
    "## 2. Are more populated cities associated with more instances of gun violence?\n",
    "\n",
    "Null hypothesis: In the U.S., the distribution of instances of gun violence is the same for cities that are populated and cities that are not. The difference in the sample is due to chance.\n",
    "\n",
    "Alternative hypothesis: In the U.S., the number of instances of gun violence (according to our definition of deadly above) is higher for cities that are populated vs. cities that are not. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "BmtsMPscFINA"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.005"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incidents_populous = populous_df.loc[:,['populous?', 'num_incidents']]\n",
    "test_incidents_populous = test_statistic(incidents_populous, 'populous?')\n",
    "p_value(incidents_populous, 'populous?', test_incidents_populous, 'greater')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sDRU6kf_FI5N"
   },
   "source": [
    "## 3. Are more densely populated states associated with deadlier instances of gun violence?\n",
    "\n",
    "Null hypothesis: In the U.S., the distribution of deadly instances of gun violence (according to our definition of deadly above) is the same for cities that are densely populated and cities that are not. The difference in the sample is due to chance.\n",
    "\n",
    "Alternative hypothesis: In the U.S., the instances of gun violence are deadlier for cities that are densely populated vs. cities that are not. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "ROIqvswTFMXa"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_83/404976281.py:2: 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",
      "  most_dense['dense?'] = 1\n",
      "/tmp/ipykernel_83/404976281.py:4: 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",
      "  least_dense['dense?'] = 0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>dense?</th>\n",
       "      <th>num_incidents</th>\n",
       "      <th>num_victims</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_or_county</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aurora</th>\n",
       "      <td>0</td>\n",
       "      <td>373</td>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boston</th>\n",
       "      <td>1</td>\n",
       "      <td>1737</td>\n",
       "      <td>762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago</th>\n",
       "      <td>1</td>\n",
       "      <td>10814</td>\n",
       "      <td>12531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jacksonville</th>\n",
       "      <td>0</td>\n",
       "      <td>2448</td>\n",
       "      <td>1643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kansas City</th>\n",
       "      <td>0</td>\n",
       "      <td>1381</td>\n",
       "      <td>1442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Long Beach</th>\n",
       "      <td>1</td>\n",
       "      <td>343</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Los Angeles</th>\n",
       "      <td>1</td>\n",
       "      <td>1066</td>\n",
       "      <td>1189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Memphis</th>\n",
       "      <td>0</td>\n",
       "      <td>2386</td>\n",
       "      <td>2313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miami</th>\n",
       "      <td>1</td>\n",
       "      <td>846</td>\n",
       "      <td>922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nashville</th>\n",
       "      <td>0</td>\n",
       "      <td>1329</td>\n",
       "      <td>1068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Orleans</th>\n",
       "      <td>0</td>\n",
       "      <td>3071</td>\n",
       "      <td>2595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>1</td>\n",
       "      <td>377</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oklahoma City</th>\n",
       "      <td>0</td>\n",
       "      <td>815</td>\n",
       "      <td>663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philadelphia</th>\n",
       "      <td>1</td>\n",
       "      <td>2963</td>\n",
       "      <td>3227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Francisco</th>\n",
       "      <td>1</td>\n",
       "      <td>421</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seattle</th>\n",
       "      <td>1</td>\n",
       "      <td>732</td>\n",
       "      <td>409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tucson</th>\n",
       "      <td>0</td>\n",
       "      <td>333</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tulsa</th>\n",
       "      <td>0</td>\n",
       "      <td>1260</td>\n",
       "      <td>845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia Beach</th>\n",
       "      <td>0</td>\n",
       "      <td>289</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                dense?  num_incidents  num_victims\n",
       "city_or_county                                    \n",
       "Aurora               0            373          273\n",
       "Boston               1           1737          762\n",
       "Chicago              1          10814        12531\n",
       "Jacksonville         0           2448         1643\n",
       "Kansas City          0           1381         1442\n",
       "Long Beach           1            343          339\n",
       "Los Angeles          1           1066         1189\n",
       "Memphis              0           2386         2313\n",
       "Miami                1            846          922\n",
       "Nashville            0           1329         1068\n",
       "New Orleans          0           3071         2595\n",
       "New York             1            377          327\n",
       "Oklahoma City        0            815          663\n",
       "Philadelphia         1           2963         3227\n",
       "San Francisco        1            421          413\n",
       "Seattle              1            732          409\n",
       "Tucson               0            333          312\n",
       "Tulsa                0           1260          845\n",
       "Virginia Beach       0            289          249"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_dense = df.loc[df['city_or_county'].isin(['New York','San Francisco','Boston','Miami','Chicago', 'Philadelphia', 'District of Columbia', 'Long Beach', 'Seattle', 'Los Angeles'])]\n",
    "most_dense['dense?'] = 1\n",
    "least_dense = df.loc[df['city_or_county'].isin(['Oklahoma City', 'Jacksonville', 'Nashville', 'Kansas City', 'Virginia Beach', 'Tulsa', 'Memphis', 'Tucson', 'New Orleans', 'Aurora'])]\n",
    "least_dense['dense?'] = 0\n",
    "dense = pd.concat([most_dense, least_dense])\n",
    "dense_df = dense.groupby(['city_or_county', 'dense?']).sum()\n",
    "dense_df['num_incidents'] = dense.groupby(['city_or_county', 'dense?']).count()['incident_id'].values\n",
    "dense_df['num_victims'] = dense_df['n_killed'].values + dense_df['n_injured'].values\n",
    "dense_df = dense_df.loc[:,['num_incidents', 'num_victims']]\n",
    "dense_df = dense_df.reset_index('dense?')\n",
    "dense_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.291"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "victims_dense = dense_df.loc[:,['dense?', 'num_victims']]\n",
    "test_victim_dense = test_statistic(victims_dense, 'dense?')\n",
    "p_value(victims_dense, 'dense?', test_victim_dense, 'greater')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J6IQUmLQFM8c"
   },
   "source": [
    "## 4. Are more densely populated states associated with more instances of gun violence?\n",
    "\n",
    "Null hypothesis: In the U.S., the distribution of instances of gun violence is the same for cities that are densely populated and cities that are not. The difference in the sample is due to chance.\n",
    "\n",
    "Alternative hypothesis: In the U.S., the number of instances of gun violence is higher for cities that are densely populated vs. cities that are not. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "paylHrOiFO2m"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.302"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incidents_dense = dense_df.loc[:,['dense?', 'num_victims']]\n",
    "test_incidents_dense = test_statistic(incidents_dense, 'dense?')\n",
    "p_value(incidents_dense, 'dense?', test_incidents_dense, 'greater')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VszSP9OjFPWZ"
   },
   "source": [
    "## 5. Are crimes getting less violent over time (number of casualties)?\n",
    "\n",
    "Null hypothesis: In the U.S., the distribution of instances of deadly gun violence is the same before as it was after. The difference in the sample is due to chance.\n",
    "\n",
    "Alternative hypothesis: In the U.S., the number of instances of deadly gun is higher before than it was after. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_83/762332063.py:2: 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",
      "  before['before?'] = 1\n",
      "/tmp/ipykernel_83/762332063.py:4: 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",
      "  after['before?'] = 0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>before?</th>\n",
       "      <th>num_victims</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>1</td>\n",
       "      <td>1296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>1</td>\n",
       "      <td>35559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1</td>\n",
       "      <td>40451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0</td>\n",
       "      <td>45646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0</td>\n",
       "      <td>46214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0</td>\n",
       "      <td>9704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      before?  num_victims\n",
       "year                      \n",
       "2013        1         1296\n",
       "2014        1        35559\n",
       "2015        1        40451\n",
       "2016        0        45646\n",
       "2017        0        46214\n",
       "2018        0         9704"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "before = df.loc[df['year'].isin([2013, 2014, 2015])]\n",
    "before['before?'] = 1\n",
    "after = df.loc[df['year'].isin([2016, 2017, 2018])]\n",
    "after['before?'] = 0\n",
    "time = pd.concat([after, before])\n",
    "time_df = time.groupby(['year', 'before?']).sum()\n",
    "time_df['num_victims'] = time_df['n_killed'].values + time_df['n_injured'].values\n",
    "time_df = time_df.loc[:,['num_victims']]\n",
    "time_df = time_df.reset_index('before?')\n",
    "time_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.65"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_time = test_statistic(time_df, 'before?')\n",
    "p_value(time_df, 'before?', test_time, 'greater')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary and Interpretation of the Result on 5 Hypothesis Based on P-Value testing\n",
    "\n",
    "For the procedure of multiple hypothesis testing, we gave 5 hypothesis regarding the count of deadly gun-violence incident, and the count of gun-violence incidents based off of the facts on the population and density of the cities. We will be comparing the individual p-values calculated above to the naive 0.05 threshold before implemention the p-value corrections following this section\n",
    "\n",
    "For our first hypothesis question regarding the correlation between population of the city and deadly gun-violence incident, our p-value was 0.01. This p-value is statistically significant, therefore rejecting the null hypothesis. This also signifies that there are some positive correlation between the populous city and the deadly gun-violence incidents. \n",
    "\n",
    "Similarly, we discovered a similar result for our second hypothesis regarding the correlation between population of the city and total count of gun-violence incidents. The p-value between the two factors was 0.005, which demonstrated statistically significant correlation between the two factors. The result was naturally predictable as the factor on deadly gun-violence incidents and total count of gun-violence incidents showed similar numbers. Therefore, our p-value indicated statistically significant relationship between total gun-violence incident and populous cities. Once again, we rejected our null hypothesis. \n",
    "\n",
    "For our third hypothesis, we studied the correlation between density of the cities against the gun-violence cases that led to death or severe injuries. The p-value that we derived from studying their correlation was 0.291, which was statistically not significant. This showed that there wasn't a strong correlation between the two factors and it failed to reject the null hypothesis. The results were expected due to multiple confounding variables involved in strictness on regulations of gun-violence.\n",
    "\n",
    "Correspondingly, our fourth hypothesis, which studied the correlation between the density of the cities against the total count of gun-violence, had a close result similar to our third hypothesis. The resulting p-value between the two factos was 0.302, which was statistically insignificant. The p-value showed that this study also failed to reject the null hypothesis for a similar reason from our third hypothesis.\n",
    "\n",
    "The fifth hypothesis studied the correlation between the total count of gun-violence and the time. We tried to discover if the total count of gun-violences have gone down over time. Different from our expectation, the p-value that resulted from our study was 0.65 which was, again, statistically insignificant. This demonstrated that our study failed to reject the null hypothesis. This was unexpected as we were able to observe that the total number of gun-violence have gone down overtime from our previous analysis on the gun-violence case reports between Philladelphia and Camden."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Corrections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Controls FWER; increases decision-discoveries, but also increases chances of FP\n",
    "def bonferroni(df, column, test, alpha):\n",
    "    \"\"\"\n",
    "    Returns decisions on p-values using the Bonferroni correction.\n",
    "    \n",
    "    Inputs:\n",
    "        p_values: array of p-values\n",
    "        alpha_total: desired family-wise error rate (FWER = P(at least one false discovery))\n",
    "    \n",
    "    Returns:\n",
    "        decisions: binary array of same length as p-values, where `decisions[i]` is 1\n",
    "        if `p_values[i]` is deemed significant, and 0 otherwise\n",
    "    \"\"\"\n",
    "    p_values = []\n",
    "\n",
    "    for i in range(10):\n",
    "        # test = test_statistic(df, column)\n",
    "        p = p_value(df, column, test, 'greater')\n",
    "        p_values.append(p)\n",
    "    \n",
    "    decisions = [(p_value <= alpha/len(p_values)) for p_value in p_values]\n",
    "    return np.sum(decisions)/len(decisions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Controls FDR; decrease FP, but also decreases decision-discoveries\n",
    "def benjamini_hochberg(df, column, test, alpha):\n",
    "    \"\"\"\n",
    "    Returns decisions on p-values using Benjamini-Hochberg.\n",
    "    \n",
    "    Inputs:\n",
    "        p_values: array of p-values\n",
    "        alpha: desired FDR (FDR = E[# false positives / # positives])\n",
    "    \n",
    "    Returns:\n",
    "        decisions: binary array of same length as p-values, where `decisions[i]` is 1\n",
    "        if `p_values[i]` is deemed significant, and 0 otherwise\n",
    "    \"\"\"\n",
    "    p_values = []\n",
    "    for i in range(10):\n",
    "        # test = test_statistic(df, column)\n",
    "        p = p_value(df, column, test, 'greater')\n",
    "        p_values.append(p)\n",
    "        \n",
    "    p_list = np.array(p_values)\n",
    "    p_list.sort()\n",
    "    k = np.arange(len(p_values)) + 1\n",
    "    pk = k*alpha/len(p_values)\n",
    "    boolean_array = list(p_list <= pk)\n",
    "    if sum(boolean_array) == 0:\n",
    "        return 0\n",
    "    p_max = np.max(p_list[boolean_array])\n",
    "    decisions = [(p_value <= p_max) for p_value in p_values]\n",
    "    return np.sum(decisions)/len(decisions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 1. Are more populated cities associated with deadlier instances of gun violence?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bonferroni_decisions_1 = bonferroni(victim_populous, 'populous?', test_victim_populous, alpha)\n",
    "bonferroni_decisions_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bh_decisions_1 = benjamini_hochberg(incidents_populous, 'populous?', test_victim_populous, alpha)\n",
    "bh_decisions_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Are more populated cities associated with more instances of gun violence?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bonferroni_decisions_2 = bonferroni(incidents_populous, 'populous?', test_incidents_populous, alpha)\n",
    "bonferroni_decisions_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bh_decisions_2 = benjamini_hochberg(incidents_populous, 'populous?', test_incidents_populous, alpha)\n",
    "bh_decisions_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Are more densely populated states associated with deadlier instances of gun violence?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bonferroni_decisions_3 = bonferroni(victims_dense, 'dense?', test_victim_dense, alpha)\n",
    "bonferroni_decisions_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bh_decisions_3 = benjamini_hochberg(victims_dense, 'dense?', test_victim_dense, alpha)\n",
    "bh_decisions_3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Are more densely populated states associated with more instances of gun violence?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bonferroni_decisions_4 = bonferroni(victims_dense, 'dense?', test_incidents_dense, alpha)\n",
    "bonferroni_decisions_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bh_decisions_4 = benjamini_hochberg(incidents_dense, 'dense?', test_incidents_dense, alpha)\n",
    "bh_decisions_4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Are crimes getting less violent over time (number of casualties)?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bonferroni_decisions_5 = bonferroni(time_df, 'before?', test_time, alpha)\n",
    "bonferroni_decisions_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bh_decisions_5 = benjamini_hochberg(time_df, 'before?', test_time, alpha)\n",
    "bh_decisions_5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary and Interpretation of the Result\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our correction in the process of multiple hypothesis, we utilized Bonferroni Correction and Benjamini-Hochberg (B-H). For each hypothesis tests that we had at the earlier part of our experiment, we derived the decisions for each tests using both Bonferroni and Benjamini-Hochberg. Instead of comparing/graphing a Binary-Decision table, we compared 10 instances of generated p-values for each hypothesis and returned the number of statistically significant p-values when comparing to their respective corrections out of 10. For example, if 3 of the 10 p-values were below $\\frac{\\alpha}{10}$, then the results should return `0.3`. Our team decided to compare between 10 p-values due to the limitations of Jupyter notebooks (either from limited space usage or the kernel breaking).\n",
    "\n",
    "The results were different from our previous part of the experiment. Before the correction, we were able to find some statistically significant relation between the factors for few of the hypothesis that we had, but we weren't able to find any statistic significance between the variables that we listed for all five of our hypothesis. Even some of the decisions that we derived from the first hypothesis (which was one of the only two hypotheses that showed some sign of statistic significance) had only 1 decision value out of 10 simulations. For this discovery, we denoted the multiple confounding variables that are behind the correlation between the two compared factors as the reason for low significance. \n",
    "\n",
    "Before we derived the results, we expected that we would have most of our simulations to have the same discovery for each individual tests. However, the aggregate result demonstrated a completely different discovery as none of the decisions were withheld with our expectations. Most of our hypotheses demonstrated 0 decisions for both Bonferroni and Benhamini experiment. \n",
    "\n",
    "For some of the limitations that we had in our analysis, as it was mentioned before, there were countless number of confounding variables that we could count into our analysis. Therefore, we were never able to discover the clear correlation between the factors for each of our hypothesis. The p-values/decisions that were made for each tests did not show any sign of significance. The way we avoided p-hacking was by adding more city variables to outweigh the confounding variables that were getting in our way of experiment.\n",
    "\n",
    "If we had more data, the additional tests that we would have conducted is generalized linear model. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\pagebreak"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pqsKy--8l_vR"
   },
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/haroldcha/gunviolencedata/blob/master/causal_inference.ipynb)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Causal Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "CmQVK4ZkCsRM"
   },
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import datetime\n",
    "import math\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8f8hNsBbCsRN"
   },
   "source": [
    "### Using difference-in-difference for causal inference\n",
    "\n",
    "Difference-in-difference is a statistical technique used to study the differential effects in natural experiments. The technique works by comparing a treatment group $T$ and a control group $C$ before and after an intervention. We first look at the slope before and after intervention of the control group. Then, we calculate the slope before and after intervention of the treatment. The difference between these two slopes, $Q$, is our \"normal\" difference between the two groups. That is our difference-in-difference estimate of the intervention effect is\n",
    "\n",
    "$$\n",
    "Q = (T_2 - T_1) - (C_2 - C_1)\n",
    "$$\n",
    "\n",
    "![difference in difference illustration](https://upload.wikimedia.org/wikipedia/commons/d/da/Illustration_of_Difference_in_Differences.png)\n",
    "\n",
    "A major challenge with difference-in-difference is that in application, our calculated $Q$ can include confounding variables that occur before and after the intervention, so our implementation requires a **parallel trend assumption**. That is to say, in order to assume the treatment and control slopes are parallel in the absense of a treatment, we must account for all relevant confounders. This is difficult if we only calculate the naive difference of differences, so we will instead use a regression analysis. \n",
    "\n",
    "Regression models yield the same result when looking at individual-level data. Consider each unit of the natural experiment to have four values:\n",
    "\n",
    "- $y$ is the value of interest\n",
    "- $T$ is a binary value indicating whether they received the treatment\n",
    "- $S$ is whether the unit was measured before or after the intervention\n",
    "- $T \\cdot S$ represents the interaction between time $\\cdot$ treatment\n",
    "\n",
    "See the following OLS model\n",
    "\n",
    "$$\n",
    "y = \\beta_0 + \\beta_1 S + \\beta_2 T + \\beta_3 T \\cdot S \\hspace{0.1cm} [+ \\beta_{i+3} X_{i}] + \\epsilon\n",
    "$$\n",
    "\n",
    "When running our regression analysis, the difference in difference estimate will be the calculated coefficient of the interaction term $T \\cdot S$. This is the same as our naive calculation $Q$ because when calculating the conditional expectation of $y$, the population means are the same as the expected values from the regression.\n",
    "\n",
    "Consider that each optimal coefficient corresponds to a difference in conditional expectations. For example,\n",
    "\n",
    "$$\n",
    "\\hat{\\beta_1} = \\hat{E}(Y | S = 1, T = 0) - \\hat{E}(Y | S = 0, T = 0)\n",
    "$$\n",
    "\n",
    "And in the first expectation term, by calculating the conditional expectation of $y$ given treatment before the intervention, we can obtain over all $y$ in that set the mean in the naive difference-in-difference estimator at the beginning of this section.\n",
    "\n",
    "$$\n",
    "\\hat{E}(Y | S = 1, T = 0) = \\frac{\\hat{E}(y I(S = 1, T = 0))}{\\hat{P}(S = 1, T = 0)}\n",
    "$$\n",
    "\n",
    "$$\n",
    "= \\frac{\\sum_{i=1}^{n}{y_{i,S=1}I(i \\in T = 0)}}{n_{T=0}} = \\bar{y}_{T=0,S=1}\n",
    "$$\n",
    "\n",
    "We can apply these same steps to see that each $\\beta_k$ corresponds to the means of units grouped by treatment and time. So by using a regression, we can easily include each confounder $X_i$ and still get an unbiased estimation of the intervention effects.\n",
    "\n",
    "### Experiment design\n",
    "\n",
    "Using difference-in-difference, we can see if our selected intervention, the 2014 Act 192, caused an increase in injuries + fatalities per incident. Our hypothesis is that if gun advocates can sue Philadelphia municipalities for gun legislation, more violent guns will be available and there will be a higher injury + death rate per gun incident.\n",
    "\n",
    "Our value of interest $y$ is the number of deaths in a given incident. Our treatment value $T$ is a binary value indicating 1 if the incident occurred in South Philadelphia, PA or 0 if in Camden, NJ. We also have a value $I$ to indicate 1 if the violent incident occurred after the intervention, and 0 if it occurred before.\n",
    "\n",
    "Since South Philadelphia and Camden are adjacent, we can exclude confounders such as weather, geography, etc. We'll be controlling for population density, median household income, unemployment rates, and the median age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "bohxQr6QCsRN"
   },
   "outputs": [],
   "source": [
    "phil_camden = pd.read_csv('https://raw.githubusercontent.com/haroldcha/gunviolencedata/master/philadelphia_camden_gun_violence.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 991
    },
    "id": "euwj8u-xCsRN",
    "outputId": "5bc05bf7-aaca-46cb-e615-977d2a6c81dd"
   },
   "outputs": [
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       "      <td>...</td>\n",
       "      <td>0::Victim</td>\n",
       "      <td>http://6abc.com/parking-lot-attendant-shot-dur...</td>\n",
       "      <td>175.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-03-10</td>\n",
       "      <td>2018</td>\n",
       "      <td>12800922</td>\n",
       "      <td>1583592.0</td>\n",
       "      <td>46058</td>\n",
       "      <td>277.930479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>237129</td>\n",
       "      <td>1070055</td>\n",
       "      <td>2018-03-15</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>700 block of S Columbus Blvd</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/107...</td>\n",
       "      <td>http://6abc.com/2-men-run-from-south-philly-tr...</td>\n",
       "      <td>...</td>\n",
       "      <td>0::Subject-Suspect||1::Subject-Suspect</td>\n",
       "      <td>http://6abc.com/2-men-run-from-south-philly-tr...</td>\n",
       "      <td>175.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-03-15</td>\n",
       "      <td>2018</td>\n",
       "      <td>12800922</td>\n",
       "      <td>1583592.0</td>\n",
       "      <td>46058</td>\n",
       "      <td>277.930479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>239385</td>\n",
       "      <td>1080442</td>\n",
       "      <td>2018-03-29</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>Camden</td>\n",
       "      <td>Kaighn Ave and Haddon Ave</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/108...</td>\n",
       "      <td>https://www.courierpostonline.com/story/news/c...</td>\n",
       "      <td>...</td>\n",
       "      <td>0::Victim||1::Victim||2::Victim||3::Victim</td>\n",
       "      <td>http://philadelphia.cbslocal.com/2018/03/29/ca...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2018-03-29</td>\n",
       "      <td>2018</td>\n",
       "      <td>8886025</td>\n",
       "      <td>73725.0</td>\n",
       "      <td>8722</td>\n",
       "      <td>1018.805893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>609 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  incident_id        date         state city_or_county  \\\n",
       "0           288        92423  2014-01-01  Pennsylvania   Philadelphia   \n",
       "1           297        92747  2014-01-01    New Jersey         Camden   \n",
       "2           532        92349  2014-01-02  Pennsylvania   Philadelphia   \n",
       "3           557       220357  2014-01-02  Pennsylvania   Philadelphia   \n",
       "4           604        92727  2014-01-02    New Jersey         Camden   \n",
       "..          ...          ...         ...           ...            ...   \n",
       "604      235382      1062115  2018-03-04  Pennsylvania   Philadelphia   \n",
       "605      235559      1063484  2018-03-05    New Jersey         Camden   \n",
       "606      236316      1066841  2018-03-10  Pennsylvania   Philadelphia   \n",
       "607      237129      1070055  2018-03-15  Pennsylvania   Philadelphia   \n",
       "608      239385      1080442  2018-03-29    New Jersey         Camden   \n",
       "\n",
       "                              address  n_killed  n_injured  \\\n",
       "0    34th Street and Haverford Avenue         0          1   \n",
       "1                                 NaN         0          1   \n",
       "2                          7th Street         0          1   \n",
       "3           34th and Haverford Avenue         0          1   \n",
       "4          1600 block of Louis Street         1          0   \n",
       "..                                ...       ...        ...   \n",
       "604     N 6th St and Spring Garden St         1          1   \n",
       "605          400 block of Mechanic St         1          0   \n",
       "606           200 block of Lombard St         0          1   \n",
       "607      700 block of S Columbus Blvd         0          0   \n",
       "608         Kaighn Ave and Haddon Ave         0          4   \n",
       "\n",
       "                                          incident_url  \\\n",
       "0     http://www.gunviolencearchive.org/incident/92423   \n",
       "1     http://www.gunviolencearchive.org/incident/92747   \n",
       "2     http://www.gunviolencearchive.org/incident/92349   \n",
       "3    http://www.gunviolencearchive.org/incident/220357   \n",
       "4     http://www.gunviolencearchive.org/incident/92727   \n",
       "..                                                 ...   \n",
       "604  http://www.gunviolencearchive.org/incident/106...   \n",
       "605  http://www.gunviolencearchive.org/incident/106...   \n",
       "606  http://www.gunviolencearchive.org/incident/106...   \n",
       "607  http://www.gunviolencearchive.org/incident/107...   \n",
       "608  http://www.gunviolencearchive.org/incident/108...   \n",
       "\n",
       "                                            source_url  ...  \\\n",
       "0    http://www.philly.com/philly/blogs/dncrime/Man...  ...   \n",
       "1    http://www.courierpostonline.com/article/20140...  ...   \n",
       "2    http://www.philly.com/philly/blogs/dncrime/S-P...  ...   \n",
       "3    http://blog.phillypolice.com/2014/02/wanted-su...  ...   \n",
       "4    http://www.nj.com/camden/index.ssf/2014/01/cam...  ...   \n",
       "..                                                 ...  ...   \n",
       "604  http://6abc.com/1-killed-1-injured-in-spring-g...  ...   \n",
       "605  http://www.nj.com/camden/index.ssf/2018/03/man...  ...   \n",
       "606  http://6abc.com/parking-lot-attendant-shot-dur...  ...   \n",
       "607  http://6abc.com/2-men-run-from-south-philly-tr...  ...   \n",
       "608  https://www.courierpostonline.com/story/news/c...  ...   \n",
       "\n",
       "                               participant_type  \\\n",
       "0                 0::Victim||1::Subject-Suspect   \n",
       "1                 0::Victim||1::Subject-Suspect   \n",
       "2      0::Victim||1::Victim||2::Subject-Suspect   \n",
       "3                 0::Victim||1::Subject-Suspect   \n",
       "4                                     0::Victim   \n",
       "..                                          ...   \n",
       "604                        0::Victim||1::Victim   \n",
       "605                                   0::Victim   \n",
       "606                                   0::Victim   \n",
       "607      0::Subject-Suspect||1::Subject-Suspect   \n",
       "608  0::Victim||1::Victim||2::Victim||3::Victim   \n",
       "\n",
       "                                               sources state_house_district  \\\n",
       "0    http://www.philly.com/philly/blogs/dncrime/Man...                  NaN   \n",
       "1    http://www.courierpostonline.com/article/20140...                  5.0   \n",
       "2    http://www.philly.com/philly/blogs/dncrime/S-P...                  NaN   \n",
       "3    http://blog.phillypolice.com/2014/02/wanted-su...                  NaN   \n",
       "4    http://www.nj.com/camden/index.ssf/2014/01/cam...                  5.0   \n",
       "..                                                 ...                  ...   \n",
       "604  http://philadelphia.cbslocal.com/2018/03/04/po...                181.0   \n",
       "605  https://www.courierpostonline.com/story/news/c...                  5.0   \n",
       "606  http://6abc.com/parking-lot-attendant-shot-dur...                175.0   \n",
       "607  http://6abc.com/2-men-run-from-south-philly-tr...                175.0   \n",
       "608  http://philadelphia.cbslocal.com/2018/03/29/ca...                  5.0   \n",
       "\n",
       "    state_senate_district    datetime  year state_pop   city_pop  \\\n",
       "0                     NaN  2014-01-01  2014  12788313  1565460.0   \n",
       "1                     5.0  2014-01-01  2014   8864525    75673.0   \n",
       "2                     NaN  2014-01-02  2014  12788313  1565460.0   \n",
       "3                     NaN  2014-01-02  2014  12788313  1565460.0   \n",
       "4                     5.0  2014-01-02  2014   8864525    75673.0   \n",
       "..                    ...         ...   ...       ...        ...   \n",
       "604                   1.0  2018-03-04  2018  12800922  1583592.0   \n",
       "605                   5.0  2018-03-05  2018   8886025    73725.0   \n",
       "606                   1.0  2018-03-10  2018  12800922  1583592.0   \n",
       "607                   1.0  2018-03-15  2018  12800922  1583592.0   \n",
       "608                   5.0  2018-03-29  2018   8886025    73725.0   \n",
       "\n",
       "     area (sq. mi)  pop_density  \n",
       "0            46058   277.656715  \n",
       "1             8722  1016.340862  \n",
       "2            46058   277.656715  \n",
       "3            46058   277.656715  \n",
       "4             8722  1016.340862  \n",
       "..             ...          ...  \n",
       "604          46058   277.930479  \n",
       "605           8722  1018.805893  \n",
       "606          46058   277.930479  \n",
       "607          46058   277.930479  \n",
       "608           8722  1018.805893  \n",
       "\n",
       "[609 rows x 36 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phil_camden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 991
    },
    "id": "NBAuOI6DCsRO",
    "outputId": "26ba7182-d23d-4770-f513-43df2aecf52b"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>incident_id</th>\n",
       "      <th>date</th>\n",
       "      <th>state</th>\n",
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       "      <th>area (sq. mi)</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>288</td>\n",
       "      <td>92423</td>\n",
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       "      <td>Pennsylvania</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>34th Street and Haverford Avenue</td>\n",
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       "      <td>1</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/92423</td>\n",
       "      <td>http://www.philly.com/philly/blogs/dncrime/Man...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>2014</td>\n",
       "      <td>12788313</td>\n",
       "      <td>1565460.0</td>\n",
       "      <td>46058</td>\n",
       "      <td>277.656715</td>\n",
       "      <td>45300</td>\n",
       "      <td>0.072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>297</td>\n",
       "      <td>92747</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>Camden</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/92747</td>\n",
       "      <td>http://www.courierpostonline.com/article/20140...</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>2014-01-01</td>\n",
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       "      <td>8864525</td>\n",
       "      <td>75673.0</td>\n",
       "      <td>8722</td>\n",
       "      <td>1016.340862</td>\n",
       "      <td>26214</td>\n",
       "      <td>0.093</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>532</td>\n",
       "      <td>92349</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>7th Street</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/92349</td>\n",
       "      <td>http://www.philly.com/philly/blogs/dncrime/S-P...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>2014</td>\n",
       "      <td>12788313</td>\n",
       "      <td>1565460.0</td>\n",
       "      <td>46058</td>\n",
       "      <td>277.656715</td>\n",
       "      <td>45300</td>\n",
       "      <td>0.072</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>557</td>\n",
       "      <td>220357</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>34th and Haverford Avenue</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/220357</td>\n",
       "      <td>http://blog.phillypolice.com/2014/02/wanted-su...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>46058</td>\n",
       "      <td>277.656715</td>\n",
       "      <td>45300</td>\n",
       "      <td>0.072</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>604</td>\n",
       "      <td>92727</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>Camden</td>\n",
       "      <td>1600 block of Louis Street</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/92727</td>\n",
       "      <td>http://www.nj.com/camden/index.ssf/2014/01/cam...</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>2014-01-02</td>\n",
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       "      <td>8864525</td>\n",
       "      <td>75673.0</td>\n",
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       "      <td>1016.340862</td>\n",
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       "      <th>604</th>\n",
       "      <td>235382</td>\n",
       "      <td>1062115</td>\n",
       "      <td>2018-03-04</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>N 6th St and Spring Garden St</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/106...</td>\n",
       "      <td>http://6abc.com/1-killed-1-injured-in-spring-g...</td>\n",
       "      <td>...</td>\n",
       "      <td>181.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-03-04</td>\n",
       "      <td>2018</td>\n",
       "      <td>12800922</td>\n",
       "      <td>1583592.0</td>\n",
       "      <td>46058</td>\n",
       "      <td>277.930479</td>\n",
       "      <td>45300</td>\n",
       "      <td>0.072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>235559</td>\n",
       "      <td>1063484</td>\n",
       "      <td>2018-03-05</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>Camden</td>\n",
       "      <td>400 block of Mechanic St</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/106...</td>\n",
       "      <td>http://www.nj.com/camden/index.ssf/2018/03/man...</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2018-03-05</td>\n",
       "      <td>2018</td>\n",
       "      <td>8886025</td>\n",
       "      <td>73725.0</td>\n",
       "      <td>8722</td>\n",
       "      <td>1018.805893</td>\n",
       "      <td>26214</td>\n",
       "      <td>0.093</td>\n",
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       "      <th>606</th>\n",
       "      <td>236316</td>\n",
       "      <td>1066841</td>\n",
       "      <td>2018-03-10</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>200 block of Lombard St</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/106...</td>\n",
       "      <td>http://6abc.com/parking-lot-attendant-shot-dur...</td>\n",
       "      <td>...</td>\n",
       "      <td>175.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-03-10</td>\n",
       "      <td>2018</td>\n",
       "      <td>12800922</td>\n",
       "      <td>1583592.0</td>\n",
       "      <td>46058</td>\n",
       "      <td>277.930479</td>\n",
       "      <td>45300</td>\n",
       "      <td>0.072</td>\n",
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       "      <td>237129</td>\n",
       "      <td>1070055</td>\n",
       "      <td>2018-03-15</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>700 block of S Columbus Blvd</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/107...</td>\n",
       "      <td>http://6abc.com/2-men-run-from-south-philly-tr...</td>\n",
       "      <td>...</td>\n",
       "      <td>175.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-03-15</td>\n",
       "      <td>2018</td>\n",
       "      <td>12800922</td>\n",
       "      <td>1583592.0</td>\n",
       "      <td>46058</td>\n",
       "      <td>277.930479</td>\n",
       "      <td>45300</td>\n",
       "      <td>0.072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>239385</td>\n",
       "      <td>1080442</td>\n",
       "      <td>2018-03-29</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>Camden</td>\n",
       "      <td>Kaighn Ave and Haddon Ave</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/108...</td>\n",
       "      <td>https://www.courierpostonline.com/story/news/c...</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2018-03-29</td>\n",
       "      <td>2018</td>\n",
       "      <td>8886025</td>\n",
       "      <td>73725.0</td>\n",
       "      <td>8722</td>\n",
       "      <td>1018.805893</td>\n",
       "      <td>26214</td>\n",
       "      <td>0.093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>609 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  incident_id        date         state city_or_county  \\\n",
       "0           288        92423  2014-01-01  Pennsylvania   Philadelphia   \n",
       "1           297        92747  2014-01-01    New Jersey         Camden   \n",
       "2           532        92349  2014-01-02  Pennsylvania   Philadelphia   \n",
       "3           557       220357  2014-01-02  Pennsylvania   Philadelphia   \n",
       "4           604        92727  2014-01-02    New Jersey         Camden   \n",
       "..          ...          ...         ...           ...            ...   \n",
       "604      235382      1062115  2018-03-04  Pennsylvania   Philadelphia   \n",
       "605      235559      1063484  2018-03-05    New Jersey         Camden   \n",
       "606      236316      1066841  2018-03-10  Pennsylvania   Philadelphia   \n",
       "607      237129      1070055  2018-03-15  Pennsylvania   Philadelphia   \n",
       "608      239385      1080442  2018-03-29    New Jersey         Camden   \n",
       "\n",
       "                              address  n_killed  n_injured  \\\n",
       "0    34th Street and Haverford Avenue         0          1   \n",
       "1                                 NaN         0          1   \n",
       "2                          7th Street         0          1   \n",
       "3           34th and Haverford Avenue         0          1   \n",
       "4          1600 block of Louis Street         1          0   \n",
       "..                                ...       ...        ...   \n",
       "604     N 6th St and Spring Garden St         1          1   \n",
       "605          400 block of Mechanic St         1          0   \n",
       "606           200 block of Lombard St         0          1   \n",
       "607      700 block of S Columbus Blvd         0          0   \n",
       "608         Kaighn Ave and Haddon Ave         0          4   \n",
       "\n",
       "                                          incident_url  \\\n",
       "0     http://www.gunviolencearchive.org/incident/92423   \n",
       "1     http://www.gunviolencearchive.org/incident/92747   \n",
       "2     http://www.gunviolencearchive.org/incident/92349   \n",
       "3    http://www.gunviolencearchive.org/incident/220357   \n",
       "4     http://www.gunviolencearchive.org/incident/92727   \n",
       "..                                                 ...   \n",
       "604  http://www.gunviolencearchive.org/incident/106...   \n",
       "605  http://www.gunviolencearchive.org/incident/106...   \n",
       "606  http://www.gunviolencearchive.org/incident/106...   \n",
       "607  http://www.gunviolencearchive.org/incident/107...   \n",
       "608  http://www.gunviolencearchive.org/incident/108...   \n",
       "\n",
       "                                            source_url  ...  \\\n",
       "0    http://www.philly.com/philly/blogs/dncrime/Man...  ...   \n",
       "1    http://www.courierpostonline.com/article/20140...  ...   \n",
       "2    http://www.philly.com/philly/blogs/dncrime/S-P...  ...   \n",
       "3    http://blog.phillypolice.com/2014/02/wanted-su...  ...   \n",
       "4    http://www.nj.com/camden/index.ssf/2014/01/cam...  ...   \n",
       "..                                                 ...  ...   \n",
       "604  http://6abc.com/1-killed-1-injured-in-spring-g...  ...   \n",
       "605  http://www.nj.com/camden/index.ssf/2018/03/man...  ...   \n",
       "606  http://6abc.com/parking-lot-attendant-shot-dur...  ...   \n",
       "607  http://6abc.com/2-men-run-from-south-philly-tr...  ...   \n",
       "608  https://www.courierpostonline.com/story/news/c...  ...   \n",
       "\n",
       "     state_house_district  state_senate_district    datetime  year state_pop  \\\n",
       "0                     NaN                    NaN  2014-01-01  2014  12788313   \n",
       "1                     5.0                    5.0  2014-01-01  2014   8864525   \n",
       "2                     NaN                    NaN  2014-01-02  2014  12788313   \n",
       "3                     NaN                    NaN  2014-01-02  2014  12788313   \n",
       "4                     5.0                    5.0  2014-01-02  2014   8864525   \n",
       "..                    ...                    ...         ...   ...       ...   \n",
       "604                 181.0                    1.0  2018-03-04  2018  12800922   \n",
       "605                   5.0                    5.0  2018-03-05  2018   8886025   \n",
       "606                 175.0                    1.0  2018-03-10  2018  12800922   \n",
       "607                 175.0                    1.0  2018-03-15  2018  12800922   \n",
       "608                   5.0                    5.0  2018-03-29  2018   8886025   \n",
       "\n",
       "      city_pop area (sq. mi)  pop_density  median_income unemployment  \n",
       "0    1565460.0         46058   277.656715          45300        0.072  \n",
       "1      75673.0          8722  1016.340862          26214        0.093  \n",
       "2    1565460.0         46058   277.656715          45300        0.072  \n",
       "3    1565460.0         46058   277.656715          45300        0.072  \n",
       "4      75673.0          8722  1016.340862          26214        0.093  \n",
       "..         ...           ...          ...            ...          ...  \n",
       "604  1583592.0         46058   277.930479          45300        0.072  \n",
       "605    73725.0          8722  1018.805893          26214        0.093  \n",
       "606  1583592.0         46058   277.930479          45300        0.072  \n",
       "607  1583592.0         46058   277.930479          45300        0.072  \n",
       "608    73725.0          8722  1018.805893          26214        0.093  \n",
       "\n",
       "[609 rows x 38 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phil_camden['median_income'] = 26214\n",
    "phil_camden.loc[phil_camden['city_or_county'] == 'Philadelphia', 'median_income'] = 45300\n",
    "\n",
    "phil_camden['unemployment'] = 0.093\n",
    "phil_camden.loc[phil_camden['city_or_county'] == 'Philadelphia', 'unemployment'] = 0.072\n",
    "\n",
    "phil_camden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "W7M7RtcSCsRP"
   },
   "outputs": [],
   "source": [
    "def parse_age(age_string):\n",
    "    if type(age_string) != str:\n",
    "        return None\n",
    "    \n",
    "    ages = []\n",
    "    age_split = []\n",
    "    if '||' in age_string:\n",
    "        age_split = age_string.split('||')\n",
    "    elif '|' in age_string:\n",
    "        age_split = age_string.split('|')\n",
    "    else:\n",
    "        age_split = [age_string]\n",
    "    \n",
    "    for age in age_split:\n",
    "        if '::' in age:\n",
    "            ages.append(int(age.split('::')[-1]))\n",
    "        elif ':' in age:\n",
    "            ages.append(int(age.split(':')[-1]))\n",
    "        else:\n",
    "            return None\n",
    "\n",
    "    return np.mean(ages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "zz3xyHc3CsRP"
   },
   "outputs": [],
   "source": [
    "phil_camden['mean_participant_age'] = phil_camden['participant_age'].apply(parse_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "QJgqQTyECsRQ",
    "outputId": "97bbd04c-7926-48ff-f4ba-de6bb0336c40",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>T</th>\n",
       "      <th>I</th>\n",
       "      <th>T*I</th>\n",
       "      <th>median_income</th>\n",
       "      <th>pop_density</th>\n",
       "      <th>unemployment</th>\n",
       "      <th>mean_participant_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>45300</td>\n",
       "      <td>277.656715</td>\n",
       "      <td>0.072</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26214</td>\n",
       "      <td>1016.340862</td>\n",
       "      <td>0.093</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>45300</td>\n",
       "      <td>277.656715</td>\n",
       "      <td>0.072</td>\n",
       "      <td>19.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>45300</td>\n",
       "      <td>277.656715</td>\n",
       "      <td>0.072</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>45300</td>\n",
       "      <td>277.930479</td>\n",
       "      <td>0.072</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45300</td>\n",
       "      <td>277.930479</td>\n",
       "      <td>0.072</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45300</td>\n",
       "      <td>277.930479</td>\n",
       "      <td>0.072</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26214</td>\n",
       "      <td>1018.805893</td>\n",
       "      <td>0.093</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>45300</td>\n",
       "      <td>277.930479</td>\n",
       "      <td>0.072</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>465 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     y  T  I  T*I  median_income  pop_density  unemployment  \\\n",
       "0    1  1  0    1          45300   277.656715         0.072   \n",
       "3    1  1  0    1          45300   277.656715         0.072   \n",
       "4    1  0  0    0          26214  1016.340862         0.093   \n",
       "5    1  1  0    1          45300   277.656715         0.072   \n",
       "6    1  1  0    1          45300   277.656715         0.072   \n",
       "..  .. .. ..  ...            ...          ...           ...   \n",
       "602  1  1  1    1          45300   277.930479         0.072   \n",
       "603  1  1  1    1          45300   277.930479         0.072   \n",
       "604  2  1  1    1          45300   277.930479         0.072   \n",
       "605  1  0  1    0          26214  1018.805893         0.093   \n",
       "606  1  1  1    1          45300   277.930479         0.072   \n",
       "\n",
       "     mean_participant_age  \n",
       "0                    18.0  \n",
       "3                    18.0  \n",
       "4                    39.0  \n",
       "5                    19.0  \n",
       "6                    31.0  \n",
       "..                    ...  \n",
       "602                  59.0  \n",
       "603                  17.0  \n",
       "604                  32.0  \n",
       "605                  41.0  \n",
       "606                  51.0  \n",
       "\n",
       "[465 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "did_df = phil_camden[['year', 'city_or_county', 'n_killed', 'n_injured', 'median_income', 'pop_density', 'unemployment', 'mean_participant_age']]\n",
    "did_df = did_df.dropna()\n",
    "\n",
    "did_df['T'] = 0\n",
    "did_df.loc[did_df['city_or_county'] == 'Philadelphia', 'T'] = 1\n",
    "\n",
    "did_df['I'] = 0\n",
    "did_df.loc[did_df['year'] > 2014, 'I'] = 1\n",
    "\n",
    "did_df['y'] = did_df['n_killed'] + did_df['n_injured']\n",
    "\n",
    "did_df['T*I'] = did_df['T']\n",
    "\n",
    "did_df = did_df[['y', 'T', 'I', 'T*I', 'median_income', 'pop_density', 'unemployment', 'mean_participant_age']]\n",
    "did_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-BxdHNb-CsRQ"
   },
   "source": [
    "Using OLS, we can find the equivalent value of the difference-in-difference estimate of the policy effect. That is, we can find the mean number of deaths before and after Act 192 in the Philadelphia treatment group, and the mean number of deaths before and after Act 192 in the Camden control group, and calculate the difference between these means. This is because our averages are just expected values from our estimator.\n",
    "\n",
    "The reason we use OLS is to easily include our four confounders as well as calculating errors.\n",
    "\n",
    "To do this, we can estimate the unknown parameters in $y = \\beta_0 + \\beta_1 T + \\beta_2 I + \\beta_3 TI + \\beta_4 X_1 + \\beta_5 X_2 + \\beta_6 X_3 + \\beta_7 X_4$ where $X_i$ is the $i^{th}$ confounder in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zjllI2PGCsRR",
    "outputId": "22769a06-eed6-4126-ffd0-72c1b1ba036d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T                       80.069005\n",
       "I                        0.176325\n",
       "T*I                     80.069005\n",
       "median_income           -0.004166\n",
       "pop_density              0.108780\n",
       "unemployment            -7.103068\n",
       "mean_participant_age    -0.001602\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = did_df.loc[:, did_df.columns != 'y']\n",
    "Y = did_df['y']\n",
    "\n",
    "model = sm.OLS(Y, X)\n",
    "results = model.fit()\n",
    "results.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D7pVwQRrCsRR"
   },
   "source": [
    "We can visualize this result by plotting the mean number of injuries + deaths pre and post intervention, segregated by treatment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "IMIJg7v8CsRR",
    "outputId": "4145feff-98f0-4d3b-f3fa-c144ed937bda"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "did_df_treatment = did_df[did_df['T'] == 1]\n",
    "did_df_control = did_df[did_df['T'] == 0]\n",
    "\n",
    "mean_treatment_before = np.mean(did_df_treatment[did_df_treatment['I'] == 0]['y'])\n",
    "mean_treatment_after = np.mean(did_df_treatment[did_df_treatment['I'] == 1]['y'])\n",
    "\n",
    "mean_control_before = np.mean(did_df_control[did_df_control['I'] == 0]['y'])\n",
    "mean_control_after = np.mean(did_df_control[did_df_control['I'] == 1]['y'])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4), tight_layout=True)\n",
    "plt.plot([0, 1], [mean_treatment_before, mean_treatment_after], label='philly')\n",
    "ax.set_xticks([0, 1])\n",
    "plt.plot([0, 1], [mean_control_before, mean_control_after], label='camden')\n",
    "ax.set_xticklabels(['Before Act 192', 'After Act 192'])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4w1NQC6oD3Q0"
   },
   "source": [
    "**Simple Version of Difference-in-Difference Calculation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Gi23kJKACsRS",
    "outputId": "c7d3110d-cd70-4b57-eb97-4ca591484f2d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5126821370750135"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Difference-in-difference (before)\n",
    "mean_treatment_before - mean_control_before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dKWxh9S2DQ4n",
    "outputId": "83a2b6e1-7798-4767-97fa-b9b4b0922dc1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2494172494172493"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Difference-in-difference (after)\n",
    "mean_treatment_after - mean_control_after"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eAJqs9yKEIoj"
   },
   "source": [
    "**Difference-in-Difference Using Fit OLS Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "eXCjX3CgEH19"
   },
   "outputs": [],
   "source": [
    "def fit_OLS_model(df, target_variable, explanatory_variables, intercept = False):\n",
    "    \"\"\"\n",
    "    Fits an OLS model from data.\n",
    "    \n",
    "    Inputs:\n",
    "        df: pandas DataFrame\n",
    "        target_variable: string, name of the target variable\n",
    "        explanatory_variables: list of strings, names of the explanatory variables\n",
    "        intercept: bool, if True add intercept term\n",
    "    Outputs:\n",
    "        fitted_model: model containing OLS regression results\n",
    "    \"\"\"\n",
    "    \n",
    "    target = df[target_variable]\n",
    "    inputs = df[explanatory_variables]\n",
    "    if intercept:\n",
    "        inputs = sm.add_constant(inputs)\n",
    "    \n",
    "    fitted_model = sm.OLS(target, inputs).fit()\n",
    "    return(fitted_model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jFm84PG4DZLT",
    "outputId": "b0ae7c72-fb00-4833-c97e-773fa7c24df1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yangj\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
      "  x = pd.concat(x[::order], 1)\n"
     ]
    }
   ],
   "source": [
    "linear_model = fit_OLS_model(did_df, 'y', ['T', 'I', 'T*I', 'median_income', 'pop_density', 'unemployment', 'mean_participant_age'], intercept=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "R-NyDIbuFbFn",
    "outputId": "e6d022e8-908b-4942-a266-997ad3aae246"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.094\n",
      "Model:                            OLS   Adj. R-squared:                  0.086\n",
      "Method:                 Least Squares   F-statistic:                     11.94\n",
      "Date:                Wed, 04 May 2022   Prob (F-statistic):           3.08e-09\n",
      "Time:                        18:18:16   Log-Likelihood:                -446.12\n",
      "No. Observations:                 465   AIC:                             902.2\n",
      "Df Residuals:                     460   BIC:                             923.0\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "========================================================================================\n",
      "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------\n",
      "const                  -46.1205     24.137     -1.911      0.057     -93.553       1.312\n",
      "T                       63.3450     33.152      1.911      0.057      -1.803     128.493\n",
      "I                        0.1763      0.065      2.718      0.007       0.049       0.304\n",
      "T*I                     63.3450     33.152      1.911      0.057      -1.803     128.493\n",
      "median_income           -0.0024      0.001     -1.893      0.059      -0.005    9.21e-05\n",
      "pop_density              0.1088      0.057      1.913      0.056      -0.003       0.221\n",
      "unemployment            -5.6195      2.941     -1.911      0.057     -11.399       0.160\n",
      "mean_participant_age    -0.0016      0.003     -0.569      0.570      -0.007       0.004\n",
      "==============================================================================\n",
      "Omnibus:                      134.413   Durbin-Watson:                   1.970\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              425.322\n",
      "Skew:                           1.334   Prob(JB):                     4.39e-93\n",
      "Kurtosis:                       6.851   Cond. No.                     2.41e+20\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 1.17e-29. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "print(linear_model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qlO1Ff5fHksY"
   },
   "source": [
    "# Analysis\n",
    "\n",
    "Observing the Fit OLS model generated based on our time x treatment interaction $T*I$ against our treatment variable (Act of 192) and confounding variables (median income, population density, unemployment, and mean of participant's age) the constant coefficient demonstrated positive number of 63.345, which signifies positive relationship between our target variable against our confounding variables before and after intervention of Act 192. This also signifies that the general number of injuries and deaths from gun violence incidents have gone up over time.\n",
    "\n",
    "As indicated from the summary of the fit OLS model, the standard error for our interaction variable is fairly high, but it still is within the range of a positive interaction coefficient. The standard error is 33.152 for our $T*I$ term.\n",
    "\n",
    "Since Act 192 allowed individuals to sue cities with strict gun laws, relaxing the gun laws in our treatment region was expected to increase harm of gun violence incidents. So in line with our hypothesis, we believe **Act 192 increased the number of people who were killed or injured in each gun violence incident.**\n",
    "\n",
    "The coefficients of our confounders seemed to have minor effects with the exception of unemployment. Unemployment lead to a decrease in gun violence which is an unexpected result, but considering the limitation of using mean unemployment for our treatment and control groups, it might not be that informative of a term. In a future study, we would recommend researching more granular unemployment statistics to see if the effect of its coefficient still decreases the amount of deaths and injuries from gun violence incidents.\n",
    "\n",
    "Another limitation of our study are the limited number of confounders. While we included four confounding variables, mean participant age was the only one that had individual-level values for each unit in the study. The others were aggregate statistics corresponding to the entirety of South Philadelphia, our treatment, and Camden, our control. For future studies, we would recommend finding other confounders that could explain discrepancies such as rates of policing on a neighborhood level, net worth, educational attainment, and other individual or neighborhood-level variables."
   ]
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    "### Citations\n",
    "For Data Cleaning\n",
    "\n",
    "*   Chavis, L. (2021, August 31). The problems with Chicago's gang-centric narrative of gun violence. Injustice Watch. Retrieved April 25, 2022, from https://www.injusticewatch.org/news/police-and-prosecutors/2021/chicago-gun-violence-gang-narrative/ \n",
    "*   Pennsylvania Supreme Court affirms Commonwealth Court's decision in leach v. commonwealth declaring act 192 unconstitutional - municipal law update. Lamb McErlane PC. (2016, July 14). Retrieved April 25, 2022, from https://www.lambmcerlane.com/news/pennsylvania-supreme-court-affirms-commonwealth-courts-decision-leach-v-commonwealth-declaring-act-192-unconstitutional-municipal-law-update/\n",
    "\n",
    "\n"
   ]
  },
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   "source": [
    "For Multiple Hypothesis\n",
    "- https://inferentialthinking.com/chapters/12/1/AB_Testing.html\n",
    "- 'lab01', Data 102 https://data102.datahub.berkeley.edu/user/doud/notebooks/sp22/labs/lab01/lab01.ipynb"
   ]
  }
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