thecounted_visual.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "Visualization of statistics that support the claims of Black Lives Matter movement, data from 2015 and 2016.\n",
    "\n",
    "Data source: https://www.theguardian.com/us-news/ng-interactive/2015/jun/01/about-the-counted\n",
    "\n",
    "Idea from BuzzFeed article: https://www.buzzfeednews.com/article/peteraldhous/race-and-police-shootings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports\n",
    "Libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.137862Z",
     "start_time": "2019-06-16T21:36:36.522494Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from bokeh.io import output_notebook, show, export_png\n",
    "from bokeh.plotting import figure, output_file\n",
    "from bokeh.models import HoverTool, ColumnDataSource,NumeralTickFormatter\n",
    "from bokeh.palettes import Spectral4, PuBu4\n",
    "from bokeh.transform import dodge\n",
    "from bokeh.layouts import gridplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.161887Z",
     "start_time": "2019-06-16T21:36:37.139558Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>raceethnicity</th>\n",
       "      <th>armed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Black</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>White</td>\n",
       "      <td>Firearm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hispanic/Latino</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Asian/Pacific Islander</td>\n",
       "      <td>Firearm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            raceethnicity    armed\n",
       "0                   Black       No\n",
       "1                   White  Firearm\n",
       "2                   White       No\n",
       "3         Hispanic/Latino       No\n",
       "4  Asian/Pacific Islander  Firearm"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selectcolumns=['raceethnicity','armed']\n",
    "df1 = pd.read_csv('the-counted-2015.csv',usecols=selectcolumns)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.179435Z",
     "start_time": "2019-06-16T21:36:37.164865Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        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>raceethnicity</th>\n",
       "      <th>armed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Black</td>\n",
       "      <td>Firearm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>White</td>\n",
       "      <td>Firearm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>White</td>\n",
       "      <td>Knife</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>White</td>\n",
       "      <td>Knife</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>White</td>\n",
       "      <td>Firearm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  raceethnicity    armed\n",
       "0         Black  Firearm\n",
       "1         White  Firearm\n",
       "2         White    Knife\n",
       "3         White    Knife\n",
       "4         White  Firearm"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv('the-counted-2016.csv',usecols=selectcolumns)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.186498Z",
     "start_time": "2019-06-16T21:36:37.181638Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2239, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.concat([df1,df2])\n",
    "df.shape # df contains \"The Counted\" data from both 2015 and 2016"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Source for ethnicities percentage in 2015: https://www.statista.com/statistics/270272/percentage-of-us-population-by-ethnicities/\n",
    "\n",
    "Source for population total: https://en.wikipedia.org/wiki/Demography_of_the_United_States#Vital_statistics_from_1935"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.193815Z",
     "start_time": "2019-06-16T21:36:37.187862Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'White': 61.72, 'Latino': 17.66, 'Black': 12.38, 'Others': 8.23}\n",
      "{'White': 198905661, 'Latino': 56913059, 'Black': 39897150, 'Others': 26522903}\n"
     ]
    }
   ],
   "source": [
    "ethndic={\"White\": 61.72,\n",
    "         \"Latino\": 17.66,\n",
    "         \"Black\": 12.38,\n",
    "         \"Others\": (5.28+2.05+0.73+0.17)\n",
    "        }\n",
    "#print(type(ethndic))\n",
    "print(ethndic)\n",
    "population=(321442000 + 323100000)/2 # average between 2015 and 2016 data\n",
    "# estimates by ethnicity\n",
    "ethnestim={\"White\": round((population*ethndic[\"White\"]/100)),\n",
    "         \"Latino\": round((population*ethndic[\"Latino\"]/100)),\n",
    "         \"Black\": round((population*ethndic[\"Black\"]/100)),\n",
    "         \"Others\": round((population*ethndic[\"Others\"]/100))\n",
    "        }\n",
    "print(ethnestim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.256036Z",
     "start_time": "2019-06-16T21:36:37.196179Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">armed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>raceethnicity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Arab-American</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>Firearm</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asian/Pacific Islander</th>\n",
       "      <td>45</td>\n",
       "      <td>7</td>\n",
       "      <td>Knife</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>573</td>\n",
       "      <td>8</td>\n",
       "      <td>Firearm</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic/Latino</th>\n",
       "      <td>378</td>\n",
       "      <td>8</td>\n",
       "      <td>Firearm</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>37</td>\n",
       "      <td>6</td>\n",
       "      <td>Firearm</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Firearm</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "      <td>Firearm</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>1158</td>\n",
       "      <td>8</td>\n",
       "      <td>Firearm</td>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       armed                     \n",
       "                       count unique      top freq\n",
       "raceethnicity                                    \n",
       "Arab-American              7      4  Firearm    2\n",
       "Asian/Pacific Islander    45      7    Knife   15\n",
       "Black                    573      8  Firearm  283\n",
       "Hispanic/Latino          378      8  Firearm  161\n",
       "Native American           37      6  Firearm   17\n",
       "Other                      1      1  Firearm    1\n",
       "Unknown                   40      6  Firearm   18\n",
       "White                   1158      8  Firearm  564"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by='raceethnicity').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if there are any missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.264050Z",
     "start_time": "2019-06-16T21:36:37.257701Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "raceethnicity    0\n",
       "armed            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.307864Z",
     "start_time": "2019-06-16T21:36:37.267623Z"
    }
   },
   "outputs": [],
   "source": [
    "df = df[(df.raceethnicity != 'Arab-American') & (df.raceethnicity != 'Unknown')]\n",
    "# no data available about the percentage of this ethnicity over population, so it is discarded\n",
    "df.replace(to_replace=['Asian/Pacific Islander','Native American','Other'],value='Others',inplace=True)\n",
    "# those categories all fall under Others in the population percentages found online\n",
    "df.replace(to_replace=['Hispanic/Latino'],value='Latino',inplace=True)\n",
    "# this value is renamed for consistency with population ethnicity data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.356100Z",
     "start_time": "2019-06-16T21:36:37.310637Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">armed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>raceethnicity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>573</td>\n",
       "      <td>8</td>\n",
       "      <td>Firearm</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>378</td>\n",
       "      <td>8</td>\n",
       "      <td>Firearm</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others</th>\n",
       "      <td>83</td>\n",
       "      <td>7</td>\n",
       "      <td>Firearm</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>1158</td>\n",
       "      <td>8</td>\n",
       "      <td>Firearm</td>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              armed                     \n",
       "              count unique      top freq\n",
       "raceethnicity                           \n",
       "Black           573      8  Firearm  283\n",
       "Latino          378      8  Firearm  161\n",
       "Others           83      7  Firearm   32\n",
       "White          1158      8  Firearm  564"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by='raceethnicity').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.361658Z",
     "start_time": "2019-06-16T21:36:37.357890Z"
    }
   },
   "outputs": [],
   "source": [
    "def givepercent (dtf,ethnicity):\n",
    "    # Function to compute percentages by ethnicity\n",
    "    return round(((dtf.raceethnicity == ethnicity).sum()/(dtf.shape[0])*100),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.375421Z",
     "start_time": "2019-06-16T21:36:37.364292Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'White': 1158, 'Latino': 378, 'Black': 573, 'Others': 83}\n",
      "{'White': 52.83, 'Latino': 17.24, 'Black': 26.14, 'Others': 3.79}\n"
     ]
    }
   ],
   "source": [
    "killed={\"White\":(df.raceethnicity == 'White').sum(),\n",
    "         \"Latino\": (df.raceethnicity == 'Latino').sum(),\n",
    "         \"Black\": (df.raceethnicity == 'Black').sum(),\n",
    "         \"Others\": (df.raceethnicity == 'Others').sum()\n",
    "        }\n",
    "print(killed)\n",
    "killedperc={\"White\": givepercent(df,'White'), \n",
    "         \"Latino\": givepercent(df,'Latino'),\n",
    "         \"Black\": givepercent(df,'Black'),\n",
    "         \"Others\": givepercent(df,'Others')\n",
    "        }\n",
    "print(killedperc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.443094Z",
     "start_time": "2019-06-16T21:36:37.377768Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">raceethnicity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>armed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Disputed</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>Black</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Firearm</th>\n",
       "      <td>1040</td>\n",
       "      <td>4</td>\n",
       "      <td>White</td>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Knife</th>\n",
       "      <td>299</td>\n",
       "      <td>4</td>\n",
       "      <td>White</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>400</td>\n",
       "      <td>4</td>\n",
       "      <td>White</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-lethal firearm</th>\n",
       "      <td>92</td>\n",
       "      <td>4</td>\n",
       "      <td>White</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others</th>\n",
       "      <td>143</td>\n",
       "      <td>4</td>\n",
       "      <td>White</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>123</td>\n",
       "      <td>4</td>\n",
       "      <td>White</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vehicle</th>\n",
       "      <td>79</td>\n",
       "      <td>4</td>\n",
       "      <td>White</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   raceethnicity                   \n",
       "                           count unique    top freq\n",
       "armed                                              \n",
       "Disputed                      16      3  Black   12\n",
       "Firearm                     1040      4  White  564\n",
       "Knife                        299      4  White  156\n",
       "No                           400      4  White  201\n",
       "Non-lethal firearm            92      4  White   57\n",
       "Others                       143      4  White   75\n",
       "Unknown                      123      4  White   65\n",
       "Vehicle                       79      4  White   37"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by='armed').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The analysis is limited to the value *No*, but could consider *Disputed* and *Non-lethal firearm*, which constitute other 108 data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.493063Z",
     "start_time": "2019-06-16T21:36:37.445115Z"
    }
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">armed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>raceethnicity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>201</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              armed                \n",
       "              count unique top freq\n",
       "raceethnicity                      \n",
       "Black           121      1  No  121\n",
       "Latino           67      1  No   67\n",
       "Others           11      1  No   11\n",
       "White           201      1  No  201"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfunarmed = df[(df.armed == 'No')]\n",
    "dfunarmed.groupby(by='raceethnicity').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.505365Z",
     "start_time": "2019-06-16T21:36:37.495409Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'White': 201, 'Latino': 67, 'Black': 121, 'Others': 11}\n",
      "{'White': 50.25, 'Latino': 16.75, 'Black': 30.25, 'Others': 2.75}\n"
     ]
    }
   ],
   "source": [
    "unarmed={\"White\":(dfunarmed.raceethnicity == 'White').sum(),\n",
    "         \"Latino\": (dfunarmed.raceethnicity == 'Latino').sum(),\n",
    "         \"Black\": (dfunarmed.raceethnicity == 'Black').sum(),\n",
    "         \"Others\": (dfunarmed.raceethnicity == 'Others').sum()\n",
    "        }\n",
    "print(unarmed)\n",
    "unarmedperc={\"White\":givepercent(dfunarmed,'White'),\n",
    "         \"Latino\": givepercent(dfunarmed,'Latino'),\n",
    "         \"Black\": givepercent(dfunarmed,'Black'),\n",
    "         \"Others\": givepercent(dfunarmed,'Others')\n",
    "        }\n",
    "print(unarmedperc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.510630Z",
     "start_time": "2019-06-16T21:36:37.507039Z"
    }
   },
   "outputs": [],
   "source": [
    "def percent1ethn (portion,population,decimals):\n",
    "    # Function to compute the percentage of the portion killed over a given population\n",
    "    return round((portion/population*100),decimals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.519860Z",
     "start_time": "2019-06-16T21:36:37.512742Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'White': 0.000582, 'Latino': 0.000664, 'Black': 0.001436, 'Others': 0.000313}\n",
      "{'White': 17.36, 'Latino': 17.72, 'Black': 21.12, 'Others': 13.25}\n"
     ]
    }
   ],
   "source": [
    "killed1ethn={\"White\": percent1ethn(killed['White'],ethnestim['White'],6), \n",
    "         \"Latino\": percent1ethn(killed['Latino'],ethnestim['Latino'],6), \n",
    "         \"Black\": percent1ethn(killed['Black'],ethnestim['Black'],6), \n",
    "         \"Others\": percent1ethn(killed['Others'],ethnestim['Others'],6)\n",
    "        }\n",
    "print(killed1ethn)\n",
    "unarmedoverkilled={\"White\": percent1ethn(unarmed['White'],killed['White'],2), \n",
    "         \"Latino\": percent1ethn(unarmed['Latino'],killed['Latino'],2), \n",
    "         \"Black\": percent1ethn(unarmed['Black'],killed['Black'],2), \n",
    "         \"Others\": percent1ethn(unarmed['Others'],killed['Others'],2)\n",
    "        }\n",
    "print(unarmedoverkilled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hypothesis testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.528108Z",
     "start_time": "2019-06-16T21:36:37.521512Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.540072690469898e-06\n",
      "13.688442014042046\n",
      "The null hypothesis is rejected.\n"
     ]
    }
   ],
   "source": [
    "whitesample=ethnestim['White']\n",
    "blacksample=ethnestim['Black']\n",
    "wkilled=killed['White']\n",
    "bkilled=killed['Black']\n",
    "pw=wkilled/whitesample\n",
    "pb=bkilled/blacksample\n",
    "\n",
    "#happened by chance?\n",
    "#Hnull pw-pb = 0 (no difference between white and black)\n",
    "#Halt pw-pb != 0 (the two proportions are different)\n",
    "\n",
    "#Significance level = 5%\n",
    "\n",
    "# Test statistic: Z-statistic\n",
    "difference=pb-pw\n",
    "print(difference)\n",
    "standarderror=np.sqrt(((pw*(1-pw))/whitesample)+((pb*(1-pb))/blacksample))\n",
    "zstat=(difference)/standarderror\n",
    "print(zstat)\n",
    "\n",
    "# Z-score for significance level\n",
    "zscore=1.96\n",
    "if zstat > zscore:\n",
    "    print(\"The null hypothesis is rejected.\")\n",
    "else:\n",
    "    print(\"The null hypothesis is not rejected.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.537452Z",
     "start_time": "2019-06-16T21:36:37.530321Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.037594154934035034\n",
      "1.8463487953468565\n",
      "The null hypothesis is not rejected.\n"
     ]
    }
   ],
   "source": [
    "whitesample=killed['White']\n",
    "blacksample=killed['Black']\n",
    "wunarmed=unarmed['White']\n",
    "bunarmed=unarmed['Black']\n",
    "pw=wunarmed/whitesample\n",
    "pb=bunarmed/blacksample\n",
    "\n",
    "#happened by chance?\n",
    "#Hnull pw-pb = 0 (no difference between white and black)\n",
    "#Halt pw-pb != 0 (the two proportions are different)\n",
    "\n",
    "#Significance level = 5%\n",
    "\n",
    "# Test statistic: Z-statistic\n",
    "difference=pb-pw\n",
    "print(difference)\n",
    "standarderror=np.sqrt(((pw*(1-pw))/whitesample)+((pb*(1-pb))/blacksample))\n",
    "zstat=(difference)/standarderror\n",
    "print(zstat)\n",
    "\n",
    "# Z-score for significance level\n",
    "zscore=1.96\n",
    "if zstat > zscore:\n",
    "    print(\"The null hypothesis is rejected.\")\n",
    "else:\n",
    "    print(\"The null hypothesis is not rejected.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.545730Z",
     "start_time": "2019-06-16T21:36:37.539942Z"
    }
   },
   "outputs": [],
   "source": [
    "ethnicities = list(ethndic.keys())\n",
    "populethn = list(ethndic.values())\n",
    "killed = list(killedperc.values())\n",
    "unarmed = list(unarmedperc.values())\n",
    "\n",
    "data1 = {'ethnicities' : ethnicities,\n",
    "        'populethn'   : populethn,\n",
    "        'killed'   : killed,\n",
    "        'unarmed'   : unarmed}\n",
    "\n",
    "source = ColumnDataSource(data=data1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.582715Z",
     "start_time": "2019-06-16T21:36:37.548532Z"
    }
   },
   "outputs": [],
   "source": [
    "TOOLS = \"pan,wheel_zoom,box_zoom,reset,save,box_select\"\n",
    "palette=Spectral4\n",
    "titlefontsize='16pt'\n",
    "\n",
    "cplot = figure(title=\"The Counted (data from 2015 and 2016)\", tools=TOOLS,\n",
    "               x_range=ethnicities, y_range=(0, 75))#, sizing_mode='scale_both')\n",
    "\n",
    "cplot.vbar(x=dodge('ethnicities',  0.25, range=cplot.x_range),top='populethn', source=source,\n",
    "           width=0.4,line_width=0 ,line_color=None, legend='Ethnicity % over population',\n",
    "           color=str(Spectral4[0]), name='populethn')\n",
    "\n",
    "cplot.vbar(x=dodge('ethnicities', -0.25, range=cplot.x_range), top='killed', source=source,\n",
    "           width=0.4, line_width=0 ,line_color=None, legend=\"Killed % over total killed\",\n",
    "           color=str(Spectral4[2]), name=\"killed\")\n",
    "\n",
    "cplot.vbar(x=dodge('ethnicities',  0.0, range=cplot.x_range), top='unarmed', source=source,\n",
    "           width=0.4, line_width=0 ,line_color=None, legend=\"Unarmed % over total unarmed\",\n",
    "          color=str(Spectral4[1]), name=\"unarmed\")\n",
    "\n",
    "cplot.add_tools(HoverTool(names=[\"unarmed\"],\n",
    "    tooltips=[\n",
    "    ( 'Population', '@populethn{(00.00)}%' ),\n",
    "    ( 'Killed', '@killed{(00.00)}%' ),\n",
    "    ( 'Unarmed', '@unarmed{(00.00)}%' )], # Fields beginning with @ display values from ColumnDataSource. \n",
    "    mode='vline'))\n",
    "\n",
    "#cplot.x_range.range_padding = 0.1\n",
    "cplot.xgrid.grid_line_color = None\n",
    "\n",
    "cplot.legend.location = \"top_right\"\n",
    "cplot.xaxis.axis_label = \"Ethnicity\"\n",
    "cplot.xaxis.axis_label_text_font_size='18pt'\n",
    "\n",
    "cplot.xaxis.minor_tick_line_color = None\n",
    "cplot.title.text_font_size=titlefontsize\n",
    "cplot.legend.label_text_font_size='16pt'\n",
    "cplot.xaxis.major_label_text_font_size='16pt'\n",
    "cplot.yaxis.major_label_text_font_size='16pt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.587598Z",
     "start_time": "2019-06-16T21:36:37.584319Z"
    }
   },
   "outputs": [],
   "source": [
    "perckillethn = list(killed1ethn.values())\n",
    "\n",
    "data2 = {'ethnicities' : ethnicities,\n",
    "        'perckillethn'   : perckillethn}\n",
    "\n",
    "source = ColumnDataSource(data=dict(data2, color=PuBu4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.602682Z",
     "start_time": "2019-06-16T21:36:37.589696Z"
    }
   },
   "outputs": [],
   "source": [
    "plot2 = figure(title=\"Killed % over population with same ethnicity\",\n",
    "               tools=TOOLS, x_range=ethnicities, y_range=(0, max(perckillethn)*1.2))#, sizing_mode='scale_both')\n",
    "\n",
    "plot2.vbar(x=dodge('ethnicities',  0.0, range=cplot.x_range), top='perckillethn', source=source,\n",
    "           width=0.4, line_width=0 ,line_color=None, legend=\"\",\n",
    "          color='color', name=\"perckillethn\")\n",
    "\n",
    "plot2.add_tools(HoverTool(names=[\"perckillethn\"],\n",
    "    tooltips=[\n",
    "    ( 'Killed', '@perckillethn{(0.00000)}%' )],\n",
    "    #( 'Unarmed', '@unarmed{(00.00)}%' )], # Fields beginning with @ display values from ColumnDataSource. \n",
    "    mode='vline'))\n",
    "\n",
    "#plot2.x_range.range_padding = 0.1\n",
    "plot2.xgrid.grid_line_color = None\n",
    "\n",
    "plot2.xaxis.axis_label = \"Ethnicity\"\n",
    "plot2.xaxis.axis_label_text_font_size='18pt'\n",
    "\n",
    "plot2.xaxis.minor_tick_line_color = None\n",
    "plot2.title.text_font_size=titlefontsize\n",
    "plot2.xaxis.major_label_text_font_size='16pt'\n",
    "plot2.yaxis.major_label_text_font_size='16pt'\n",
    "plot2.yaxis[0].formatter = NumeralTickFormatter(format=\"0.0000\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.612438Z",
     "start_time": "2019-06-16T21:36:37.607968Z"
    }
   },
   "outputs": [],
   "source": [
    "percunarmethn = list(unarmedoverkilled.values())\n",
    "\n",
    "data3 = {'ethnicities' : ethnicities,\n",
    "        'percunarmethn'   : percunarmethn}\n",
    "\n",
    "source = ColumnDataSource(data=dict(data3, color=PuBu4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:37.632142Z",
     "start_time": "2019-06-16T21:36:37.617465Z"
    }
   },
   "outputs": [],
   "source": [
    "plot3 = figure(title=\"Unarmed % over killed with same ethnicity\",\n",
    "               tools=TOOLS, x_range=ethnicities, y_range=(0, max(percunarmethn)*1.2))#, sizing_mode='scale_both')\n",
    "\n",
    "plot3.vbar(x=dodge('ethnicities',  0.0, range=cplot.x_range), top='percunarmethn', source=source,\n",
    "           width=0.4, line_width=0 ,line_color=None, legend=\"\",\n",
    "          color='color', name=\"percunarmethn\")\n",
    "\n",
    "plot3.add_tools(HoverTool(names=[\"percunarmethn\"],\n",
    "    tooltips=[\n",
    "    ( 'Unarmed', '@percunarmethn{(00.00)}%' )],\n",
    "    #( 'Unarmed', '@unarmed{(00.00)}%' )], # Fields beginning with @ display values from ColumnDataSource. \n",
    "    mode='vline'))\n",
    "\n",
    "#plot3.x_range.range_padding = 0.1\n",
    "plot3.xgrid.grid_line_color = None\n",
    "\n",
    "plot3.xaxis.axis_label = \"Ethnicity\"\n",
    "plot3.xaxis.axis_label_text_font_size='18pt'\n",
    "\n",
    "plot3.xaxis.minor_tick_line_color = None\n",
    "plot3.title.text_font_size=titlefontsize\n",
    "plot3.xaxis.major_label_text_font_size='16pt'\n",
    "plot3.yaxis.major_label_text_font_size='16pt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T21:36:40.076170Z",
     "start_time": "2019-06-16T21:36:37.634045Z"
    }
   },
   "outputs": [
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       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1182\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"1182\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1182' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1182\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1182\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"1182\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1182' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1182\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"5276a924-46f1-49d3-a9f8-1bb89b4346ea\" data-root-id=\"1216\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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    "output_file(\"thecounted.html\", title=\"The Counted Visualization\")\n",
    "\n",
    "output_notebook()\n",
    "gplot=gridplot([cplot, plot2, plot3], sizing_mode='stretch_both', ncols=3)#, plot_width=800, plot_height=600)\n",
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    "Hover on the bar charts to read the percentage values."
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   "source": [
    "# Conclusions\n",
    "The plot shows that if the people shot by police were proportional to the population distribution, the orange and green bar charts should have been almost the same height as the blue ones. Although this is true for Latino ethnicity, it is not for the Black one: this is the second most represented among killed and among those killed who were unarmed."
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