Bohman-final-part1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Project, Part 1\n",
    "Washington Post Police Shootings Database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the Readme (https://github.com/washingtonpost/data-police-shootings):\n",
    "\n",
    "In 2015, The Post began tracking more than a dozen details about each killing — including the race of the deceased, the circumstances of the shooting, whether the person was armed and whether the person was experiencing a mental-health crisis — by culling local news reports, law enforcement websites and social media, and by monitoring independent databases such as Killed by Police and Fatal Encounters. The Post conducted additional reporting in many cases.\n",
    "\n",
    "The Post is documenting only those shootings in which a police officer, in the line of duty, shoots and kills a civilian — the circumstances that most closely parallel the 2014 killing of Michael Brown in Ferguson, Mo., which began the protest movement culminating in Black Lives Matter and an increased focus on police accountability nationwide. The Post is not tracking deaths of people in police custody, fatal shootings by off-duty officers or non-shooting deaths.\n",
    "\n",
    "The FBI and the Centers for Disease Control and Prevention log fatal shootings by police, but officials acknowledge that their data is incomplete. Since 2015, The Post has documented more than twice as many fatal shootings by police as recorded on average annually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>date</th>\n",
       "      <th>manner_of_death</th>\n",
       "      <th>armed</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>race</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>signs_of_mental_illness</th>\n",
       "      <th>threat_level</th>\n",
       "      <th>flee</th>\n",
       "      <th>body_camera</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>is_geocoding_exact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Tim Elliot</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>shot</td>\n",
       "      <td>gun</td>\n",
       "      <td>53.0</td>\n",
       "      <td>M</td>\n",
       "      <td>A</td>\n",
       "      <td>Shelton</td>\n",
       "      <td>WA</td>\n",
       "      <td>True</td>\n",
       "      <td>attack</td>\n",
       "      <td>Not fleeing</td>\n",
       "      <td>False</td>\n",
       "      <td>-123.122</td>\n",
       "      <td>47.247</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>Lewis Lee Lembke</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>shot</td>\n",
       "      <td>gun</td>\n",
       "      <td>47.0</td>\n",
       "      <td>M</td>\n",
       "      <td>W</td>\n",
       "      <td>Aloha</td>\n",
       "      <td>OR</td>\n",
       "      <td>False</td>\n",
       "      <td>attack</td>\n",
       "      <td>Not fleeing</td>\n",
       "      <td>False</td>\n",
       "      <td>-122.892</td>\n",
       "      <td>45.487</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>John Paul Quintero</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>shot and Tasered</td>\n",
       "      <td>unarmed</td>\n",
       "      <td>23.0</td>\n",
       "      <td>M</td>\n",
       "      <td>H</td>\n",
       "      <td>Wichita</td>\n",
       "      <td>KS</td>\n",
       "      <td>False</td>\n",
       "      <td>other</td>\n",
       "      <td>Not fleeing</td>\n",
       "      <td>False</td>\n",
       "      <td>-97.281</td>\n",
       "      <td>37.695</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>Matthew Hoffman</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>shot</td>\n",
       "      <td>toy weapon</td>\n",
       "      <td>32.0</td>\n",
       "      <td>M</td>\n",
       "      <td>W</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>True</td>\n",
       "      <td>attack</td>\n",
       "      <td>Not fleeing</td>\n",
       "      <td>False</td>\n",
       "      <td>-122.422</td>\n",
       "      <td>37.763</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>Michael Rodriguez</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>shot</td>\n",
       "      <td>nail gun</td>\n",
       "      <td>39.0</td>\n",
       "      <td>M</td>\n",
       "      <td>H</td>\n",
       "      <td>Evans</td>\n",
       "      <td>CO</td>\n",
       "      <td>False</td>\n",
       "      <td>attack</td>\n",
       "      <td>Not fleeing</td>\n",
       "      <td>False</td>\n",
       "      <td>-104.692</td>\n",
       "      <td>40.384</td>\n",
       "      <td>True</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5758</th>\n",
       "      <td>6296</td>\n",
       "      <td>Stephen Campos</td>\n",
       "      <td>2020-11-07</td>\n",
       "      <td>shot</td>\n",
       "      <td>gun</td>\n",
       "      <td>43.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lilburn</td>\n",
       "      <td>GA</td>\n",
       "      <td>False</td>\n",
       "      <td>attack</td>\n",
       "      <td>Not fleeing</td>\n",
       "      <td>False</td>\n",
       "      <td>-84.077</td>\n",
       "      <td>33.846</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5759</th>\n",
       "      <td>6297</td>\n",
       "      <td>Jesse James Kale Brown</td>\n",
       "      <td>2020-11-07</td>\n",
       "      <td>shot and Tasered</td>\n",
       "      <td>knife</td>\n",
       "      <td>34.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Missoula</td>\n",
       "      <td>MT</td>\n",
       "      <td>False</td>\n",
       "      <td>attack</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>-114.035</td>\n",
       "      <td>46.874</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5760</th>\n",
       "      <td>6292</td>\n",
       "      <td>Rodriguez Duandre Pam</td>\n",
       "      <td>2020-11-08</td>\n",
       "      <td>shot</td>\n",
       "      <td>gun</td>\n",
       "      <td>33.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Evansville</td>\n",
       "      <td>IN</td>\n",
       "      <td>False</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>-87.567</td>\n",
       "      <td>37.969</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5761</th>\n",
       "      <td>6295</td>\n",
       "      <td>Terry David Fox</td>\n",
       "      <td>2020-11-08</td>\n",
       "      <td>shot</td>\n",
       "      <td>gun</td>\n",
       "      <td>58.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kingston Springs</td>\n",
       "      <td>TN</td>\n",
       "      <td>False</td>\n",
       "      <td>other</td>\n",
       "      <td>Not fleeing</td>\n",
       "      <td>False</td>\n",
       "      <td>-87.164</td>\n",
       "      <td>36.055</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5762</th>\n",
       "      <td>6293</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-11-09</td>\n",
       "      <td>shot</td>\n",
       "      <td>gun</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>MD</td>\n",
       "      <td>False</td>\n",
       "      <td>attack</td>\n",
       "      <td>Not fleeing</td>\n",
       "      <td>False</td>\n",
       "      <td>-76.671</td>\n",
       "      <td>39.308</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5763 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                    name        date   manner_of_death       armed  \\\n",
       "0        3              Tim Elliot  2015-01-02              shot         gun   \n",
       "1        4        Lewis Lee Lembke  2015-01-02              shot         gun   \n",
       "2        5      John Paul Quintero  2015-01-03  shot and Tasered     unarmed   \n",
       "3        8         Matthew Hoffman  2015-01-04              shot  toy weapon   \n",
       "4        9       Michael Rodriguez  2015-01-04              shot    nail gun   \n",
       "...    ...                     ...         ...               ...         ...   \n",
       "5758  6296          Stephen Campos  2020-11-07              shot         gun   \n",
       "5759  6297  Jesse James Kale Brown  2020-11-07  shot and Tasered       knife   \n",
       "5760  6292   Rodriguez Duandre Pam  2020-11-08              shot         gun   \n",
       "5761  6295         Terry David Fox  2020-11-08              shot         gun   \n",
       "5762  6293                     NaN  2020-11-09              shot         gun   \n",
       "\n",
       "       age gender race              city state  signs_of_mental_illness  \\\n",
       "0     53.0      M    A           Shelton    WA                     True   \n",
       "1     47.0      M    W             Aloha    OR                    False   \n",
       "2     23.0      M    H           Wichita    KS                    False   \n",
       "3     32.0      M    W     San Francisco    CA                     True   \n",
       "4     39.0      M    H             Evans    CO                    False   \n",
       "...    ...    ...  ...               ...   ...                      ...   \n",
       "5758  43.0      M  NaN           Lilburn    GA                    False   \n",
       "5759  34.0      M  NaN          Missoula    MT                    False   \n",
       "5760  33.0      M  NaN        Evansville    IN                    False   \n",
       "5761  58.0      M  NaN  Kingston Springs    TN                    False   \n",
       "5762   NaN      M  NaN         Baltimore    MD                    False   \n",
       "\n",
       "     threat_level         flee  body_camera  longitude  latitude  \\\n",
       "0          attack  Not fleeing        False   -123.122    47.247   \n",
       "1          attack  Not fleeing        False   -122.892    45.487   \n",
       "2           other  Not fleeing        False    -97.281    37.695   \n",
       "3          attack  Not fleeing        False   -122.422    37.763   \n",
       "4          attack  Not fleeing        False   -104.692    40.384   \n",
       "...           ...          ...          ...        ...       ...   \n",
       "5758       attack  Not fleeing        False    -84.077    33.846   \n",
       "5759       attack          NaN        False   -114.035    46.874   \n",
       "5760        other          NaN         True    -87.567    37.969   \n",
       "5761        other  Not fleeing        False    -87.164    36.055   \n",
       "5762       attack  Not fleeing        False    -76.671    39.308   \n",
       "\n",
       "      is_geocoding_exact  \n",
       "0                   True  \n",
       "1                   True  \n",
       "2                   True  \n",
       "3                   True  \n",
       "4                   True  \n",
       "...                  ...  \n",
       "5758                True  \n",
       "5759                True  \n",
       "5760                True  \n",
       "5761                True  \n",
       "5762                True  \n",
       "\n",
       "[5763 rows x 17 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"fatal-police-shootings-data.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26f3d105b08>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "count = df.groupby([\"manner_of_death\"])[\"id\"].count()\n",
    "count.plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26f3d125b88>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby([\"race\"])[\"id\"].count().plot(kind=\"pie\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "*What is the \"name\" of the dataset?*\n",
    "\n",
    "\"Fatal Force\"\n",
    "\n",
    "*Where did you obtain it?*\n",
    "\n",
    "From the Washington Post's github page.\n",
    "\n",
    "*Where can we obtain it? (i.e., URL)*\n",
    "\n",
    "https://github.com/washingtonpost/data-police-shootings\n",
    "\n",
    "*What is the license of the dataset? What are we allowed to do with it?*\n",
    "\n",
    "https://github.com/washingtonpost/data-police-shootings/blob/master/LICENSE\n",
    "Noncommercial adaptive use is allowed.\n",
    "\n",
    "*How big is it in file size and in items?*\n",
    "\n",
    "5763 rows x 17 columns, 627 KB (1 csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}