police_dataviz.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
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       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import plotly.plotly as py\n",
    "import plotly.figure_factory as ff\n",
    "from plotly.graph_objs import *\n",
    "from plotly.offline import init_notebook_mode, iplot\n",
    "import matplotlib.pyplot as plt\n",
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "kbp = pd.read_csv(\"data/killedbypolice.csv\")\n",
    "mpv = pd.read_csv(\"data/MPVDataset.csv\")\n",
    "wsp = pd.read_csv(\"https://raw.githubusercontent.com/washingtonpost/data-police-shootings/master/fatal-police-shootings-data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpv = mpv[[\"Victim's name\", \"Victim's age\", \"Victim's race\", \"Victim's gender\", \"Date of Incident (month/day/year)\", \"City\", \"State\", \"Cause of death\", \"Body Camera (Source: WaPo)\"]]\n",
    "mpv = mpv[mpv[\"Victim's age\"] != \"Unknown\"]\n",
    "mpv[\"Victim's age\"] = [entry if type(entry)==float else entry[0:2] for entry in mpv[\"Victim's age\"]]\n",
    "mpv[\"Victim's age\"] = pd.to_numeric(mpv[\"Victim's age\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a bar chart of months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a column for month and year in Washington Post data\n",
    "dateswsp = list(wsp[\"date\"])\n",
    "monthwsp = [date[date.find(\"-\")+1:date.find(\"-\")+3] for date in dateswsp]\n",
    "yearwsp = [int(date[0:date.find(\"-\")]) for date in dateswsp]\n",
    "months = {\"01\": \"January\",\n",
    "         \"02\": \"February\",\n",
    "         \"03\": \"March\",\n",
    "         \"04\": \"April\",\n",
    "         \"05\": \"May\",\n",
    "         \"06\": \"June\",\n",
    "         \"07\": \"July\",\n",
    "         \"08\": \"August\",\n",
    "         \"09\": \"September\",\n",
    "         \"10\": \"October\",\n",
    "         \"11\": \"November\",\n",
    "         \"12\": \"December\"}\n",
    "monthwsp = [month.replace(month, months[month]) for month in monthwsp]\n",
    "wsp[\"month\"] = np.array(monthwsp)\n",
    "wsp[\"year\"] = np.array(yearwsp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "groupings = wsp.groupby([\"year\", \"month\"]).count()\n",
    "months2015 = groupings.loc[2015][\"id\"]\n",
    "months2016 = groupings.loc[2016][\"id\"]\n",
    "months2017 = groupings.loc[2017][\"id\"]\n",
    "months2018 = groupings.loc[2018][\"id\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "April         84\n",
       "August        94\n",
       "December      89\n",
       "February      77\n",
       "January       76\n",
       "July         104\n",
       "June          65\n",
       "March         92\n",
       "May           71\n",
       "November      77\n",
       "October       84\n",
       "September     82\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "months = [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"]\n",
    "kill2015 = []\n",
    "kill2016 = []\n",
    "kill2017 = []\n",
    "kill2018 = []\n",
    "for month in months:\n",
    "    kill2015.append(months2015.loc[month])\n",
    "    kill2016.append(months2016.loc[month])\n",
    "    kill2017.append(months2017.loc[month])\n",
    "    try:\n",
    "        kill2018.append(months2018.loc[month])\n",
    "    except:\n",
    "        continue\n",
    "kill2018+=[0]*(12 - len(kill2018))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "trace_2015 = Bar(x=months,\n",
    "                y=kill2015,\n",
    "                name='2015',\n",
    "                marker=dict(color='#ffcdd2'))\n",
    "\n",
    "trace_2016 = Bar(x=months,\n",
    "                y=kill2016,\n",
    "                name='2016',\n",
    "                marker=dict(color='#A2D5F2'))\n",
    "\n",
    "trace_2017 = Bar(x=months,\n",
    "                y=kill2017,\n",
    "                name='2017',\n",
    "                marker=dict(color='#59606D'))\n",
    "trace_2018 = Bar(x=months,\n",
    "                y=kill2018,\n",
    "                name='2018',\n",
    "                marker=dict(color='#111111'))\n",
    "\n",
    "data = [trace_2015, trace_2016, trace_2017, trace_2018]\n",
    "layout = Layout(title=\"Fatal Police Shootings per Month\",\n",
    "                xaxis=dict(title='Month'),\n",
    "                yaxis=dict(title='Deaths'))\n",
    "fig = Figure(data=data, layout=layout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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        {
         "marker": {
          "color": "#ffcdd2"
         },
         "name": "2015",
         "type": "bar",
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          "January",
          "February",
          "March",
          "April",
          "May",
          "June",
          "July",
          "August",
          "September",
          "October",
          "November",
          "December"
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         "name": "2016",
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          "January",
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        {
         "marker": {
          "color": "#59606D"
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         "name": "2017",
         "type": "bar",
         "x": [
          "January",
          "February",
          "March",
          "April",
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        {
         "marker": {
          "color": "#111111"
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         "name": "2018",
         "type": "bar",
         "x": [
          "January",
          "February",
          "March",
          "April",
          "May",
          "June",
          "July",
          "August",
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       "layout": {
        "title": "Fatal Police Shootings per Month",
        "xaxis": {
         "title": "Month"
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        "yaxis": {
         "title": "Deaths"
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       "<div id=\"e8f8e7fc-d301-4854-b2d0-b5c20c4de069\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"e8f8e7fc-d301-4854-b2d0-b5c20c4de069\", [{\"type\": \"bar\", \"x\": [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"], \"y\": [76, 77, 92, 84, 71, 65, 104, 94, 82, 84, 77, 89], \"name\": \"2015\", \"marker\": {\"color\": \"#ffcdd2\"}}, {\"type\": \"bar\", \"x\": [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"], \"y\": [81, 86, 92, 73, 74, 92, 72, 82, 78, 77, 78, 78], \"name\": \"2016\", \"marker\": {\"color\": \"#A2D5F2\"}}, {\"type\": \"bar\", \"x\": [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"], \"y\": [92, 100, 76, 67, 74, 84, 94, 82, 70, 85, 84, 79], \"name\": \"2017\", \"marker\": {\"color\": \"#59606D\"}}, {\"type\": \"bar\", \"x\": [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"], \"y\": [92, 76, 105, 89, 81, 82, 89, 52, 0, 0, 0, 0], \"name\": \"2018\", \"marker\": {\"color\": \"#111111\"}}], {\"title\": \"Fatal Police Shootings per Month\", \"xaxis\": {\"title\": \"Month\"}, \"yaxis\": {\"title\": \"Deaths\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
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       "<div id=\"e8f8e7fc-d301-4854-b2d0-b5c20c4de069\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"e8f8e7fc-d301-4854-b2d0-b5c20c4de069\", [{\"type\": \"bar\", \"x\": [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"], \"y\": [76, 77, 92, 84, 71, 65, 104, 94, 82, 84, 77, 89], \"name\": \"2015\", \"marker\": {\"color\": \"#ffcdd2\"}}, {\"type\": \"bar\", \"x\": [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"], \"y\": [81, 86, 92, 73, 74, 92, 72, 82, 78, 77, 78, 78], \"name\": \"2016\", \"marker\": {\"color\": \"#A2D5F2\"}}, {\"type\": \"bar\", \"x\": [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"], \"y\": [92, 100, 76, 67, 74, 84, 94, 82, 70, 85, 84, 79], \"name\": \"2017\", \"marker\": {\"color\": \"#59606D\"}}, {\"type\": \"bar\", \"x\": [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"], \"y\": [92, 76, 105, 89, 81, 82, 89, 52, 0, 0, 0, 0], \"name\": \"2018\", \"marker\": {\"color\": \"#111111\"}}], {\"title\": \"Fatal Police Shootings per Month\", \"xaxis\": {\"title\": \"Month\"}, \"yaxis\": {\"title\": \"Deaths\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gender Breakdown by Month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "first, need the months in order (not alphabetical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthnum = []\n",
    "months = {\n",
    "    \"January\": 1,\n",
    "    \"February\": 2,\n",
    "    \"March\": 3,\n",
    "    \"April\": 4,\n",
    "    \"May\": 5,\n",
    "    \"June\": 6,\n",
    "    \"July\": 7,\n",
    "    \"August\": 8,\n",
    "    \"September\": 9,\n",
    "    \"October\": 10,\n",
    "    \"November\": 11,\n",
    "    \"December\": 12\n",
    "}\n",
    "for month in wsp.month:\n",
    "    monthnum.append(months[month])\n",
    "wsp[\"monthnum\"] = monthnum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "then, filter out the 2018 dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "wsp = wsp.loc[wsp[\"year\"]!=2018]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "gendermonth = wsp.groupby([\"gender\", \"monthnum\"]).count()[\"id\"]\n",
    "femalemonth = gendermonth[gendermonth.index.get_level_values(\"gender\") == \"F\"]\n",
    "malemonth = gendermonth[gendermonth.index.get_level_values(\"gender\") == \"M\"]\n",
    "months = [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "name": "Male",
         "type": "bar",
         "x": [
          "January",
          "February",
          "March",
          "April",
          "May",
          "June",
          "July",
          "August",
          "September",
          "October",
          "November",
          "December"
         ],
         "y": [
          237,
          248,
          247,
          215,
          210,
          232,
          262,
          249,
          224,
          228,
          232,
          232
         ]
        },
        {
         "name": "Female",
         "type": "bar",
         "x": [
          "January",
          "February",
          "March",
          "April",
          "May",
          "June",
          "July",
          "August",
          "September",
          "October",
          "November",
          "December"
         ],
         "y": [
          12,
          15,
          13,
          9,
          9,
          9,
          8,
          9,
          5,
          18,
          7,
          13
         ]
        }
       ],
       "layout": {
        "barmode": "stack",
        "title": "People of Each Gender Killed Per Month",
        "xaxis": {
         "title": "Month"
        },
        "yaxis": {
         "title": "Deaths"
        }
       }
      },
      "text/html": [
       "<div id=\"cce5ecc3-27ce-422e-8039-5a6986fbf4da\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"cce5ecc3-27ce-422e-8039-5a6986fbf4da\", [{\"type\": \"bar\", \"x\": [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"], \"y\": [237, 248, 247, 215, 210, 232, 262, 249, 224, 228, 232, 232], \"name\": \"Male\"}, {\"type\": \"bar\", \"x\": [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"], \"y\": [12, 15, 13, 9, 9, 9, 8, 9, 5, 18, 7, 13], \"name\": \"Female\"}], {\"title\": \"People of Each Gender Killed Per Month\", \"xaxis\": {\"title\": \"Month\"}, \"yaxis\": {\"title\": \"Deaths\"}, \"barmode\": \"stack\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"cce5ecc3-27ce-422e-8039-5a6986fbf4da\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"cce5ecc3-27ce-422e-8039-5a6986fbf4da\", [{\"type\": \"bar\", \"x\": [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"], \"y\": [237, 248, 247, 215, 210, 232, 262, 249, 224, 228, 232, 232], \"name\": \"Male\"}, {\"type\": \"bar\", \"x\": [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"], \"y\": [12, 15, 13, 9, 9, 9, 8, 9, 5, 18, 7, 13], \"name\": \"Female\"}], {\"title\": \"People of Each Gender Killed Per Month\", \"xaxis\": {\"title\": \"Month\"}, \"yaxis\": {\"title\": \"Deaths\"}, \"barmode\": \"stack\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace_male = Bar(x=months,\n",
    "                 y=list(malemonth),\n",
    "                name='Male')\n",
    "trace_female = Bar(x=months,\n",
    "                 y=list(femalemonth),\n",
    "                name='Female')\n",
    "data = [trace_male, trace_female]\n",
    "layout = Layout(title=\"People of Each Gender Killed Per Month\",\n",
    "                xaxis=dict(title='Month'),\n",
    "                yaxis=dict(title='Deaths'),\n",
    "               barmode='stack')\n",
    "fig = Figure(data=data, layout=layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Armed Breakdown by Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "newarmed = []\n",
    "for arm in wsp.armed:\n",
    "    if arm == 'unarmed':\n",
    "        arm = 'unarmed'\n",
    "    else:\n",
    "        arm = 'armed'\n",
    "    newarmed.append(arm)\n",
    "wsp.armed = newarmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "agecat = []\n",
    "for age in wsp.age:\n",
    "    if age<=18:\n",
    "        agecat.append(\"Under 18\")\n",
    "    elif age>18 and age<=38:\n",
    "        agecat.append(\"Gen Y\")\n",
    "    elif age>38 and age<=53:\n",
    "        agecat.append(\"Gen X\")\n",
    "    else:\n",
    "        agecat.append(\"Baby Boomer\")\n",
    "wsp[\"age_categorical\"] = agecat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "armed    age_categorical\n",
       "armed    Baby Boomer         397\n",
       "         Gen X               713\n",
       "         Gen Y              1519\n",
       "         Under 18            103\n",
       "unarmed  Baby Boomer          15\n",
       "         Gen X                33\n",
       "         Gen Y               149\n",
       "         Under 18             16\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wsp.groupby([\"armed\", \"age_categorical\"]).count()[\"id\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "labels": [
          "Baby Boomer",
          "Gen X",
          "Gen Y",
          "Under 18"
         ],
         "type": "pie",
         "values": [
          472,
          800,
          1677,
          111
         ]
        }
       ],
       "layout": {}
      },
      "text/html": [
       "<div id=\"8b96fdfa-fbeb-42a8-9976-fe8d9d7ca3f0\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"8b96fdfa-fbeb-42a8-9976-fe8d9d7ca3f0\", [{\"type\": \"pie\", \"labels\": [\"Baby Boomer\", \"Gen X\", \"Gen Y\", \"Under 18\"], \"values\": [472, 800, 1677, 111]}], {}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"8b96fdfa-fbeb-42a8-9976-fe8d9d7ca3f0\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"8b96fdfa-fbeb-42a8-9976-fe8d9d7ca3f0\", [{\"type\": \"pie\", \"labels\": [\"Baby Boomer\", \"Gen X\", \"Gen Y\", \"Under 18\"], \"values\": [472, 800, 1677, 111]}], {}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [\"Baby Boomer\", \"Gen X\", \"Gen Y\", \"Under 18\"]\n",
    "values = [472, 800, 1677, 111]\n",
    "trace = Pie(labels=labels, values=values)\n",
    "iplot([trace])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "labels": [
          "Baby Boomer",
          "Gen X",
          "Gen Y",
          "Under 18"
         ],
         "type": "pie",
         "values": [
          17,
          38,
          158,
          16
         ]
        }
       ],
       "layout": {}
      },
      "text/html": [
       "<div id=\"32ca347d-a2f6-4648-b9dc-3349901bf8c7\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"32ca347d-a2f6-4648-b9dc-3349901bf8c7\", [{\"type\": \"pie\", \"labels\": [\"Baby Boomer\", \"Gen X\", \"Gen Y\", \"Under 18\"], \"values\": [17, 38, 158, 16]}], {}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"32ca347d-a2f6-4648-b9dc-3349901bf8c7\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"32ca347d-a2f6-4648-b9dc-3349901bf8c7\", [{\"type\": \"pie\", \"labels\": [\"Baby Boomer\", \"Gen X\", \"Gen Y\", \"Under 18\"], \"values\": [17, 38, 158, 16]}], {}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [\"Baby Boomer\", \"Gen X\", \"Gen Y\", \"Under 18\"]\n",
    "values = [17, 38, 158, 16]\n",
    "trace = Pie(labels=labels, values=values)\n",
    "iplot([trace])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "armed    age_categorical\n",
       "armed    Baby Boomer         397\n",
       "         Gen X               713\n",
       "         Gen Y              1519\n",
       "         Under 18            103\n",
       "unarmed  Baby Boomer          15\n",
       "         Gen X                33\n",
       "         Gen Y               149\n",
       "         Under 18             16\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wsp.groupby([\"armed\", \"age_categorical\"]).count()[\"id\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = [\"armed\", \"unarmed\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "domain": {
          "x": [
           0,
           0.48
          ],
          "y": [
           0,
           0.49
          ]
         },
         "hoverinfo": "label+percent+name",
         "labels": [
          "armed",
          "unarmed"
         ],
         "marker": {
          "colors": [
           "#2f4f4f",
           "#d3d3d3"
          ]
         },
         "name": "Baby Boomer",
         "textinfo": "none",
         "type": "pie",
         "values": [
          472,
          18
         ]
        },
        {
         "domain": {
          "x": [
           0.52,
           1
          ],
          "y": [
           0,
           0.49
          ]
         },
         "hoverinfo": "label+percent+name",
         "labels": [
          "armed",
          "unarmed"
         ],
         "marker": {
          "colors": [
           "#2f4f4f",
           "#d3d3d3"
          ]
         },
         "name": "Gen X",
         "textinfo": "none",
         "type": "pie",
         "values": [
          801,
          38
         ]
        },
        {
         "domain": {
          "x": [
           0,
           0.48
          ],
          "y": [
           0.51,
           1
          ]
         },
         "hoverinfo": "label+percent+name",
         "labels": [
          "armed",
          "unarmed"
         ],
         "marker": {
          "colors": [
           "#2f4f4f",
           "#d3d3d3"
          ]
         },
         "name": "Gen Y",
         "textinfo": "none",
         "type": "pie",
         "values": [
          1679,
          158
         ]
        },
        {
         "domain": {
          "x": [
           0.52,
           1
          ],
          "y": [
           0.51,
           1
          ]
         },
         "hoverinfo": "label+percent+name",
         "labels": [
          "armed",
          "unarmed"
         ],
         "marker": {
          "colors": [
           "#2f4f4f",
           "#d3d3d3"
          ]
         },
         "name": "Under 18",
         "textinfo": "none",
         "type": "pie",
         "values": [
          111,
          16
         ]
        }
       ],
       "layout": {
        "annotations": [
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "Gen Y",
          "x": 0.07,
          "y": 1
         },
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "Under 18",
          "x": 0.6,
          "y": 1
         },
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "Baby Boomer",
          "x": -0.03,
          "y": 0.4
         },
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "Gen X",
          "x": 0.6,
          "y": 0.4
         }
        ],
        "title": "Percentage of Armed and Unarmed People per Age Group"
       }
      },
      "text/html": [
       "<div id=\"f551967d-da36-40ca-bf26-b8f8ac96c1ba\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"f551967d-da36-40ca-bf26-b8f8ac96c1ba\", [{\"labels\": [\"armed\", \"unarmed\"], \"values\": [472, 18], \"type\": \"pie\", \"name\": \"Baby Boomer\", \"marker\": {\"colors\": [\"#2f4f4f\", \"#d3d3d3\"]}, \"domain\": {\"x\": [0, 0.48], \"y\": [0, 0.49]}, \"hoverinfo\": \"label+percent+name\", \"textinfo\": \"none\"}, {\"labels\": [\"armed\", \"unarmed\"], \"values\": [801, 38], \"marker\": {\"colors\": [\"#2f4f4f\", \"#d3d3d3\"]}, \"type\": \"pie\", \"name\": \"Gen X\", \"domain\": {\"x\": [0.52, 1], \"y\": [0, 0.49]}, \"hoverinfo\": \"label+percent+name\", \"textinfo\": \"none\"}, {\"labels\": [\"armed\", \"unarmed\"], \"values\": [1679, 158], \"marker\": {\"colors\": [\"#2f4f4f\", \"#d3d3d3\"]}, \"type\": \"pie\", \"name\": \"Gen Y\", \"domain\": {\"x\": [0, 0.48], \"y\": [0.51, 1]}, \"hoverinfo\": \"label+percent+name\", \"textinfo\": \"none\"}, {\"labels\": [\"armed\", \"unarmed\"], \"values\": [111, 16], \"marker\": {\"colors\": [\"#2f4f4f\", \"#d3d3d3\"]}, \"type\": \"pie\", \"name\": \"Under 18\", \"domain\": {\"x\": [0.52, 1], \"y\": [0.51, 1]}, \"hoverinfo\": \"label+percent+name\", \"textinfo\": \"none\"}], {\"title\": \"Percentage of Armed and Unarmed People per Age Group\", \"annotations\": [{\"font\": {\"size\": 16}, \"showarrow\": false, \"text\": \"Gen Y\", \"x\": 0.07, \"y\": 1.0}, {\"font\": {\"size\": 16}, \"text\": \"Under 18\", \"showarrow\": false, \"x\": 0.6, \"y\": 1.0}, {\"font\": {\"size\": 16}, \"text\": \"Baby Boomer\", \"showarrow\": false, \"x\": -0.03, \"y\": 0.4}, {\"font\": {\"size\": 16}, \"text\": \"Gen X\", \"showarrow\": false, \"x\": 0.6, \"y\": 0.4}]}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"f551967d-da36-40ca-bf26-b8f8ac96c1ba\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"f551967d-da36-40ca-bf26-b8f8ac96c1ba\", [{\"labels\": [\"armed\", \"unarmed\"], \"values\": [472, 18], \"type\": \"pie\", \"name\": \"Baby Boomer\", \"marker\": {\"colors\": [\"#2f4f4f\", \"#d3d3d3\"]}, \"domain\": {\"x\": [0, 0.48], \"y\": [0, 0.49]}, \"hoverinfo\": \"label+percent+name\", \"textinfo\": \"none\"}, {\"labels\": [\"armed\", \"unarmed\"], \"values\": [801, 38], \"marker\": {\"colors\": [\"#2f4f4f\", \"#d3d3d3\"]}, \"type\": \"pie\", \"name\": \"Gen X\", \"domain\": {\"x\": [0.52, 1], \"y\": [0, 0.49]}, \"hoverinfo\": \"label+percent+name\", \"textinfo\": \"none\"}, {\"labels\": [\"armed\", \"unarmed\"], \"values\": [1679, 158], \"marker\": {\"colors\": [\"#2f4f4f\", \"#d3d3d3\"]}, \"type\": \"pie\", \"name\": \"Gen Y\", \"domain\": {\"x\": [0, 0.48], \"y\": [0.51, 1]}, \"hoverinfo\": \"label+percent+name\", \"textinfo\": \"none\"}, {\"labels\": [\"armed\", \"unarmed\"], \"values\": [111, 16], \"marker\": {\"colors\": [\"#2f4f4f\", \"#d3d3d3\"]}, \"type\": \"pie\", \"name\": \"Under 18\", \"domain\": {\"x\": [0.52, 1], \"y\": [0.51, 1]}, \"hoverinfo\": \"label+percent+name\", \"textinfo\": \"none\"}], {\"title\": \"Percentage of Armed and Unarmed People per Age Group\", \"annotations\": [{\"font\": {\"size\": 16}, \"showarrow\": false, \"text\": \"Gen Y\", \"x\": 0.07, \"y\": 1.0}, {\"font\": {\"size\": 16}, \"text\": \"Under 18\", \"showarrow\": false, \"x\": 0.6, \"y\": 1.0}, {\"font\": {\"size\": 16}, \"text\": \"Baby Boomer\", \"showarrow\": false, \"x\": -0.03, \"y\": 0.4}, {\"font\": {\"size\": 16}, \"text\": \"Gen X\", \"showarrow\": false, \"x\": 0.6, \"y\": 0.4}]}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = {\n",
    "    'data': [\n",
    "        {\n",
    "            'labels': labels,\n",
    "            'values': [472, 18],\n",
    "            'type': 'pie',\n",
    "            'name': 'Baby Boomer',\n",
    "            'marker': {'colors': ['#2f4f4f', '#d3d3d3']},\n",
    "            'domain': {'x': [0, .48], 'y': [0, .49]},\n",
    "            'hoverinfo':'label+percent+name',\n",
    "            'textinfo':'none'\n",
    "        },\n",
    "        {\n",
    "            'labels': labels,\n",
    "            'values': [801, 38],\n",
    "            'marker': {'colors': ['#2f4f4f', '#d3d3d3']},\n",
    "            'type': 'pie',\n",
    "            'name': 'Gen X',\n",
    "            'domain': {'x': [.52, 1], 'y': [0, .49]},\n",
    "            'hoverinfo':'label+percent+name',\n",
    "            'textinfo':'none'\n",
    "\n",
    "        },\n",
    "        {\n",
    "            'labels': labels,\n",
    "            'values': [1679, 158],\n",
    "            'marker': {'colors': ['#2f4f4f', '#d3d3d3']},\n",
    "            'type': 'pie',\n",
    "            'name': 'Gen Y',\n",
    "            'domain': {'x': [0, .48], 'y': [.51, 1]},\n",
    "            'hoverinfo':'label+percent+name',\n",
    "            'textinfo':'none'\n",
    "        },\n",
    "        {\n",
    "            'labels': labels,\n",
    "            'values': [111, 16],\n",
    "            'marker': {'colors': ['#2f4f4f', '#d3d3d3']},\n",
    "            'type': 'pie',\n",
    "            'name':'Under 18',\n",
    "            'domain': {'x': [.52, 1], 'y': [.51, 1]},\n",
    "            'hoverinfo':'label+percent+name',\n",
    "            'textinfo': 'none'\n",
    "        }\n",
    "    ],\n",
    "    'layout': {'title': 'Percentage of Armed and Unarmed People per Age Group',\n",
    "            \"annotations\": [{\n",
    "                \"font\": {\"size\": 16},\n",
    "                \"showarrow\": False,\n",
    "                \"text\": \"Gen Y\",\n",
    "                \"x\": 0.07,\n",
    "                \"y\": 1.0\n",
    "            },\n",
    "            {\n",
    "                \"font\": {\"size\": 16},\n",
    "                \"text\": \"Under 18\",\n",
    "                \"showarrow\": False,\n",
    "                \"x\": 0.6,\n",
    "                \"y\": 1.0\n",
    "            },\n",
    "            {\n",
    "                \"font\": {\"size\": 16},\n",
    "                \"text\": \"Baby Boomer\",\n",
    "                \"showarrow\": False,\n",
    "                \"x\": -0.03,\n",
    "                \"y\": 0.4\n",
    "            },\n",
    "            {\n",
    "                \"font\": {\"size\": 16},\n",
    "                \"text\": \"Gen X\",\n",
    "                \"showarrow\": False,\n",
    "                \"x\": 0.6,\n",
    "                \"y\": 0.4\n",
    "            }]\n",
    "    }\n",
    "}\n",
    "\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a bar chart of ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "under18 = mpv.loc[mpv[\"Victim's age\"] <= 18]\n",
    "geny = mpv.loc[(mpv[\"Victim's age\"] > 18) & (mpv[\"Victim's age\"] <= 38)]\n",
    "genx = mpv.loc[(mpv[\"Victim's age\"] > 38) & (mpv[\"Victim's age\"] <= 53)]\n",
    "babyboomers = mpv.loc[mpv[\"Victim's age\"] > 53]\n",
    "\n",
    "under18counts = under18.groupby(\"Victim's race\").count()[\"Victim's name\"]\n",
    "under18counts.drop(\"Unknown race\")\n",
    "otherunder18 = under18counts.loc[\"Asian\"]+under18counts.loc[\"Pacific Islander\"]+under18counts.loc[\"Native American\"]\n",
    "\n",
    "genycounts = geny.groupby(\"Victim's race\").count()[\"Victim's name\"]\n",
    "genycounts.drop(\"Unknown race\")\n",
    "othergeny = genycounts.loc[\"Asian\"]+genycounts.loc[\"Pacific Islander\"]+genycounts.loc[\"Native American\"]\n",
    "\n",
    "genxcounts = genx.groupby(\"Victim's race\").count()[\"Victim's name\"]\n",
    "genxcounts.drop(\"Unknown race\")\n",
    "othergenx = genxcounts.loc[\"Asian\"]+genxcounts.loc[\"Pacific Islander\"]+genxcounts.loc[\"Native American\"]\n",
    "\n",
    "boomercounts = babyboomers.groupby(\"Victim's race\").count()[\"Victim's name\"]\n",
    "boomercounts.drop(\"Unknown race\")\n",
    "otherboomers = boomercounts.loc[\"Asian\"]+boomercounts.loc[\"Pacific Islander\"]+boomercounts.loc[\"Native American\"]\n",
    "\n",
    "races = [\"Black\", \"White\", \"Hispanic\", \"Other\"]\n",
    "agesunder18 = [under18counts.loc[\"Black\"], under18counts.loc[\"White\"], under18counts.loc[\"Hispanic\"], otherunder18]\n",
    "genxers = [genxcounts.loc[\"Black\"], genxcounts.loc[\"White\"], genxcounts.loc[\"Hispanic\"], othergenx]\n",
    "genyers = [genycounts.loc[\"Black\"], genycounts.loc[\"White\"], genycounts.loc[\"Hispanic\"], othergeny]\n",
    "boomers = [boomercounts.loc[\"Black\"], boomercounts.loc[\"White\"], boomercounts.loc[\"Hispanic\"], otherboomers]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "name": "Under 18",
         "type": "bar",
         "x": [
          "Black",
          "White",
          "Hispanic",
          "Other"
         ],
         "y": [
          106,
          72,
          59,
          10
         ]
        },
        {
         "name": "Gen Y (18-38)",
         "type": "bar",
         "x": [
          "Black",
          "White",
          "Hispanic",
          "Other"
         ],
         "y": [
          1105,
          1376,
          707,
          150
         ]
        },
        {
         "name": "Gen X (39-53)",
         "type": "bar",
         "x": [
          "Black",
          "White",
          "Hispanic",
          "Other"
         ],
         "y": [
          319,
          885,
          227,
          43
         ]
        },
        {
         "name": "Baby Boomers (54 and up)",
         "type": "bar",
         "x": [
          "Black",
          "White",
          "Hispanic",
          "Other"
         ],
         "y": [
          86,
          478,
          57,
          18
         ]
        }
       ],
       "layout": {
        "barmode": "stack"
       }
      },
      "text/html": [
       "<div id=\"fe4463d1-23f2-4d00-a25e-0bb63a832201\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"fe4463d1-23f2-4d00-a25e-0bb63a832201\", [{\"type\": \"bar\", \"x\": [\"Black\", \"White\", \"Hispanic\", \"Other\"], \"y\": [106, 72, 59, 10], \"name\": \"Under 18\"}, {\"type\": \"bar\", \"x\": [\"Black\", \"White\", \"Hispanic\", \"Other\"], \"y\": [1105, 1376, 707, 150], \"name\": \"Gen Y (18-38)\"}, {\"type\": \"bar\", \"x\": [\"Black\", \"White\", \"Hispanic\", \"Other\"], \"y\": [319, 885, 227, 43], \"name\": \"Gen X (39-53)\"}, {\"type\": \"bar\", \"x\": [\"Black\", \"White\", \"Hispanic\", \"Other\"], \"y\": [86, 478, 57, 18], \"name\": \"Baby Boomers (54 and up)\"}], {\"barmode\": \"stack\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"fe4463d1-23f2-4d00-a25e-0bb63a832201\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"fe4463d1-23f2-4d00-a25e-0bb63a832201\", [{\"type\": \"bar\", \"x\": [\"Black\", \"White\", \"Hispanic\", \"Other\"], \"y\": [106, 72, 59, 10], \"name\": \"Under 18\"}, {\"type\": \"bar\", \"x\": [\"Black\", \"White\", \"Hispanic\", \"Other\"], \"y\": [1105, 1376, 707, 150], \"name\": \"Gen Y (18-38)\"}, {\"type\": \"bar\", \"x\": [\"Black\", \"White\", \"Hispanic\", \"Other\"], \"y\": [319, 885, 227, 43], \"name\": \"Gen X (39-53)\"}, {\"type\": \"bar\", \"x\": [\"Black\", \"White\", \"Hispanic\", \"Other\"], \"y\": [86, 478, 57, 18], \"name\": \"Baby Boomers (54 and up)\"}], {\"barmode\": \"stack\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace_under18 = Bar(\n",
    "    x=races,\n",
    "    y=agesunder18,\n",
    "    name='Under 18'\n",
    ")\n",
    "trace_geny = Bar(\n",
    "    x=races,\n",
    "    y=genyers,\n",
    "    name='Gen Y (18-38)'\n",
    ")\n",
    "trace_genx = Bar(\n",
    "    x=races,\n",
    "    y=genxers,\n",
    "    name='Gen X (39-53)'\n",
    ")\n",
    "trace_genboomer = Bar(\n",
    "    x=races,\n",
    "    y=boomers,\n",
    "    name='Baby Boomers (54 and up)'\n",
    ")\n",
    "\n",
    "data = [trace_under18, trace_geny, trace_genx, trace_genboomer]\n",
    "layout = Layout(\n",
    "    barmode='stack'\n",
    ")\n",
    "\n",
    "fig = Figure(data=data, layout=layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a Waffle Chart of ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pywaffle import Waffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "allages = mpv.groupby(\"Victim's race\").count()[\"Victim's name\"]\n",
    "allages.drop(\"Unknown race\")\n",
    "otherall = allages.loc[\"Asian\"]+allages.loc[\"Pacific Islander\"]+allages.loc[\"Native American\"]\n",
    "allagesy = [allages.loc[\"Black\"], allages.loc[\"White\"], allages.loc[\"Hispanic\"], otherall]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(\n",
    "    {\n",
    "        'labels': ['Under 18', 'Gen Y', 'Gen X', 'Baby Boomers'],\n",
    "        'Black': [int(agesunder18[0]/allagesy[0]*100), int(genyers[0]/allagesy[0]*100), int(genxers[0]/allagesy[0]*100), int(boomers[0]/allagesy[0]*100)],\n",
    "        'White': [int(agesunder18[1]/allagesy[1]*100), int(genyers[1]/allagesy[1]*100), int(genxers[1]/allagesy[1]*100), int(boomers[1]/allagesy[0]*100)],\n",
    "        'Hispanic': [int(agesunder18[2]/allagesy[2]*100), int(genyers[2]/allagesy[2]*100), int(genxers[2]/allagesy[2]*100), int(boomers[2]/allagesy[2]*100)],\n",
    "        'Other': [int(agesunder18[3]/allagesy[3]*100), int(genyers[3]/allagesy[3]*100), int(genxers[3]/allagesy[3]*100), int(boomers[3]/allagesy[3]*100)]\n",
    "    },\n",
    ").set_index('labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/AG/anaconda3/lib/python3.6/site-packages/matplotlib/figure.py:2267: UserWarning:\n",
      "\n",
      "This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Waffle size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Waffle size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Waffle size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Waffle size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for race in races:\n",
    "    fig = plt.figure(\n",
    "        FigureClass=Waffle,\n",
    "        rows=5,\n",
    "        colors=(\"#6980bc\", \"#bba394\", \"#eeaaee\", \"#d03ba2\"),\n",
    "        figsize=(9,5),\n",
    "        values={\"Under 18\": int(data[race].loc[\"Under 18\"]), \"Gen Y (18-38)\": int(data[race].loc[\"Gen Y\"]), \"Gen X (39-53)\": int(data[race].loc[\"Gen X\"]), \"Baby Boomers (54 and up)\": int(data[race].loc[\"Baby Boomers\"])},\n",
    "        legend={'loc': 'upper left', 'bbox_to_anchor': (1, 1)},\n",
    "        title={'label': 'Percentages of {0} People Killed'.format(race), 'loc': 'center', 'size':20},\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Color Map of Killings by State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpv[\"year\"] = np.array([ str(\"20\"+date.split(\"/\")[2]) for date in mpv[\"Date of Incident (month/day/year)\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpvpast = mpv.loc[mpv[\"year\"] != \"2018\"]\n",
    "mpvstate = mpvpast.groupby([\"year\", \"State\"]).count()[\"Victim's name\"]\n",
    "\n",
    "# make lists of data by year\n",
    "mpv2013 = mpvstate[mpvstate.index.get_level_values(\"year\") == \"2013\"]\n",
    "mpv2014 = mpvstate[mpvstate.index.get_level_values(\"year\") == \"2014\"]\n",
    "mpv2015 = mpvstate[mpvstate.index.get_level_values(\"year\") == \"2015\"]\n",
    "mpv2016 = mpvstate[mpvstate.index.get_level_values(\"year\") == \"2016\"]\n",
    "mpv2017 = mpvstate[mpvstate.index.get_level_values(\"year\") == \"2017\"]\n",
    "yeardata = [list(mpv2013), list(mpv2014), list(mpv2015), list(mpv2016), list(mpv2017)]\n",
    "# add in 0 for Rhode Island in 2015\n",
    "yeardata[2].insert(39, 0)\n",
    "\n",
    "# make list of states for each year\n",
    "#statecode=list(mpvstate[mpvstate.index.get_level_values(\"year\")==\"2017\"].index.get_level_values(\"State\"))\n",
    "states2013 = mpv2013.index.get_level_values(\"State\")\n",
    "states2014 = mpv2014.index.get_level_values(\"State\")\n",
    "states2015 = mpv2015.index.get_level_values(\"State\")\n",
    "states2016 = mpv2016.index.get_level_values(\"State\")\n",
    "states2017 = mpv2017.index.get_level_values(\"State\")\n",
    "statecode = [list(states2013), list(states2014), list(states2015), list(states2016), list(states2017)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "scl = [[0.0, '#ffe5e5'],[0.1, '#ffb2b2'], [0.2, '#ff7f7f'],[0.3, '#ff4c4c'],\n",
    "       [0.4, '#ff0000'], [0.5, '#b20000'], [0.7, '#7f0000'], [1.0, '#4c0000']\n",
    "      ]\n",
    "\n",
    "data = [{\n",
    "        'type':'choropleth',\n",
    "        'colorscale': scl,\n",
    "        'autocolorscale': False,\n",
    "        'locations': statecode[0],\n",
    "        'z': yeardata[0],\n",
    "        'locationmode': 'USA-states',\n",
    "        'marker': dict(\n",
    "            line = dict (\n",
    "                color = 'rgb(255,255,255)',\n",
    "                width = 2\n",
    "            )\n",
    "        ),\n",
    "        'colorbar': {\n",
    "            'title': \"Deaths\"\n",
    "        }\n",
    "}]\n",
    "\n",
    "layout = dict(\n",
    "        title = 'US Police Killings',\n",
    "        geo = dict(\n",
    "            scope='usa',\n",
    "            projection=dict( type='albers usa' ),\n",
    "            showlakes = True,\n",
    "            lakecolor = 'rgb(255, 255, 255)'\n",
    "        )\n",
    ")\n",
    "\n",
    "layout['updatemenus'] = [\n",
    "    {\n",
    "        'buttons': [\n",
    "            {\n",
    "                'args': [None, {'frame': {'duration': 1500, 'redraw': False},\n",
    "                         'fromcurrent': True, 'transition': {'duration': 300, 'easing': 'quadratic-in-out'}}],\n",
    "                'label': 'Play',\n",
    "                'method': 'animate'\n",
    "            },\n",
    "            {\n",
    "                'args': [[None], {'frame': {'duration': 0, 'redraw': False}, 'mode': 'immediate',\n",
    "                'transition': {'duration': 0}}],\n",
    "                'label': 'Pause',\n",
    "                'method': 'animate'\n",
    "            }\n",
    "        ],\n",
    "        'direction': 'left',\n",
    "        'pad': {'r': 10, 't': 87},\n",
    "        'showactive': False,\n",
    "        'type': 'buttons',\n",
    "        'x': 0.1,\n",
    "        'xanchor': 'right',\n",
    "        'y': 0,\n",
    "        'yanchor': 'top'\n",
    "    }\n",
    "]\n",
    "layout['sliders'] = [{\n",
    "    'active': 0,\n",
    "    'yanchor': 'top',\n",
    "    'xanchor': 'left',\n",
    "    'currentvalue': {\n",
    "        'font': {'size': 20},\n",
    "        'prefix': 'Year: ',\n",
    "        'visible': True,\n",
    "        'xanchor': 'right'\n",
    "    },\n",
    "    'transition': {'duration': 300, 'easing': 'cubic-in-out'},\n",
    "    'pad': {'b': 10, 't': 50},\n",
    "    'len': 0.9,\n",
    "    'x': 0.1,\n",
    "    'y': 0,\n",
    "    'steps': []\n",
    "}]\n",
    "years=[\"2013\", \"2014\", \"2015\", \"2016\", \"2017\"]\n",
    "for year in years:\n",
    "    # creating and appending each slider step\n",
    "    slider_step = {'args': [\n",
    "        [year],\n",
    "        {'frame': {'duration': 1500, 'redraw': False},\n",
    "         'mode': 'immediate',\n",
    "       'transition': {'duration': 300}}\n",
    "     ],\n",
    "     'label': year,\n",
    "     'method': 'animate'}\n",
    "    layout['sliders'][0]['steps'].append(slider_step)\n",
    "\n",
    "frames = []\n",
    "for i in range(len(years)):\n",
    "    # creating and appending data to frame\n",
    "    frame = {\n",
    "        'data': [],\n",
    "        'name': years[i]\n",
    "    }\n",
    "    data_dict = {\n",
    "        'type':'choropleth',\n",
    "        'colorscale': scl,\n",
    "        'autocolorscale': False,\n",
    "        'locations': statecode[i],\n",
    "        'z': yeardata[i],\n",
    "        'locationmode': 'USA-states',\n",
    "        'marker': dict(\n",
    "            line = dict (\n",
    "                color = 'rgb(255,255,255)',\n",
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     "metadata": {},
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   "source": [
    "fig = {\n",
    "    'data': data,\n",
    "    'layout': layout,\n",
    "    'frames': frames\n",
    "}\n",
    "iplot(fig)"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Color Map of Killings by State per Capita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "statepop = pd.read_csv(\"us-population.csv\", thousands=\",\")\n",
    "us_state_abbrev = {\n",
    "    'Alabama': 'AL',\n",
    "    'Alaska': 'AK',\n",
    "    'Arizona': 'AZ',\n",
    "    'Arkansas': 'AR',\n",
    "    'California': 'CA',\n",
    "    'Colorado': 'CO',\n",
    "    'Connecticut': 'CT',\n",
    "    'District of Columbia': 'DC',\n",
    "    'Delaware': 'DE',\n",
    "    'Florida': 'FL',\n",
    "    'Georgia': 'GA',\n",
    "    'Hawaii': 'HI',\n",
    "    'Idaho': 'ID',\n",
    "    'Illinois': 'IL',\n",
    "    'Indiana': 'IN',\n",
    "    'Iowa': 'IA',\n",
    "    'Kansas': 'KS',\n",
    "    'Kentucky': 'KY',\n",
    "    'Louisiana': 'LA',\n",
    "    'Maine': 'ME',\n",
    "    'Maryland': 'MD',\n",
    "    'Massachusetts': 'MA',\n",
    "    'Michigan': 'MI',\n",
    "    'Minnesota': 'MN',\n",
    "    'Mississippi': 'MS',\n",
    "    'Missouri': 'MO',\n",
    "    'Montana': 'MT',\n",
    "    'Nebraska': 'NE',\n",
    "    'Nevada': 'NV',\n",
    "    'New Hampshire': 'NH',\n",
    "    'New Jersey': 'NJ',\n",
    "    'New Mexico': 'NM',\n",
    "    'New York': 'NY',\n",
    "    'North Carolina': 'NC',\n",
    "    'North Dakota': 'ND',\n",
    "    'Ohio': 'OH',\n",
    "    'Oklahoma': 'OK',\n",
    "    'Oregon': 'OR',\n",
    "    'Pennsylvania': 'PA',\n",
    "    'Rhode Island': 'RI',\n",
    "    'South Carolina': 'SC',\n",
    "    'South Dakota': 'SD',\n",
    "    'Tennessee': 'TN',\n",
    "    'Texas': 'TX',\n",
    "    'Utah': 'UT',\n",
    "    'Vermont': 'VT',\n",
    "    'Virginia': 'VA',\n",
    "    'Washington': 'WA',\n",
    "    'West Virginia': 'WV',\n",
    "    'Wisconsin': 'WI',\n",
    "    'Wyoming': 'WY',\n",
    "}\n",
    "statepop[\"States\"] = [us_state_abbrev[state.strip(\".\")] for state in statepop[\"States\"]]\n",
    "statepop.set_index(\"States\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "statepop.apply(pd.to_numeric, errors='ignore')\n",
    "statepop = statepop[[\"2013\", \"2014\", \"2015\", \"2016\", \"2017\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "population = [list(statepop[\"2013\"]), list(statepop[\"2014\"]), list(statepop[\"2015\"]), list(statepop[\"2016\"]), list(statepop[\"2017\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "scl = [[0.0, '#ffe5e5'],[0.1, '#ffb2b2'], [0.2, '#ff7f7f'],[0.3, '#ff4c4c'],\n",
    "       [0.4, '#ff0000'], [0.5, '#b20000'], [0.7, '#7f0000'], [1.0, '#4c0000']\n",
    "      ]\n",
    "\n",
    "data = [{\n",
    "        'type':'choropleth',\n",
    "        'colorscale': scl,\n",
    "        'autocolorscale': False,\n",
    "        'locations': statecode[0],\n",
    "        'z': yeardata[0],\n",
    "        'locationmode': 'USA-states',\n",
    "        'marker': dict(\n",
    "            line = dict (\n",
    "                color = 'rgb(255,255,255)',\n",
    "                width = 2\n",
    "            )\n",
    "        ),\n",
    "        'colorbar': {\n",
    "            'title': \"Deaths per 1,000,000 People\"\n",
    "        }\n",
    "}]\n",
    "\n",
    "layout = dict(\n",
    "        title = 'US Police Killings per Capita',\n",
    "        geo = dict(\n",
    "            scope='usa',\n",
    "            projection=dict( type='albers usa' ),\n",
    "            showlakes = True,\n",
    "            lakecolor = 'rgb(255, 255, 255)'\n",
    "        )\n",
    ")\n",
    "\n",
    "layout['updatemenus'] = [{\n",
    "    'buttons': [\n",
    "        {\n",
    "            'args': [None, {'frame': {'duration': 1500, 'redraw': False},\n",
    "                     'fromcurrent': True, 'transition': {'duration': 300, 'easing': 'quadratic-in-out'}}],\n",
    "            'label': 'Play',\n",
    "            'method': 'animate'\n",
    "        },\n",
    "        {\n",
    "            'args': [[None], {'frame': {'duration': 0, 'redraw': False}, 'mode': 'immediate',\n",
    "            'transition': {'duration': 0}}],\n",
    "            'label': 'Pause',\n",
    "            'method': 'animate'\n",
    "        }\n",
    "    ],\n",
    "    'direction': 'left',\n",
    "    'pad': {'r': 10, 't': 87},\n",
    "    'showactive': False,\n",
    "    'type': 'buttons',\n",
    "    'x': 0.1,\n",
    "    'xanchor': 'right',\n",
    "    'y': 0,\n",
    "    'yanchor': 'top'\n",
    "}]\n",
    "layout['sliders'] = [{\n",
    "    'active': 0,\n",
    "    'yanchor': 'top',\n",
    "    'xanchor': 'left',\n",
    "    'currentvalue': {\n",
    "        'font': {'size': 20},\n",
    "        'prefix': 'Year: ',\n",
    "        'visible': True,\n",
    "        'xanchor': 'right'\n",
    "    },\n",
    "    'transition': {'duration': 300, 'easing': 'cubic-in-out'},\n",
    "    'pad': {'b': 10, 't': 50},\n",
    "    'len': 0.9,\n",
    "    'x': 0.1,\n",
    "    'y': 0,\n",
    "    'steps': []\n",
    "}]\n",
    "years=[\"2013\", \"2014\", \"2015\", \"2016\", \"2017\"]\n",
    "for year in years:\n",
    "    # creating and appending each slider step\n",
    "    slider_step = {'args': [\n",
    "        [year],\n",
    "        {'frame': {'duration': 1500, 'redraw': False},\n",
    "         'mode': 'immediate',\n",
    "       'transition': {'duration': 300}}\n",
    "     ],\n",
    "     'label': year,\n",
    "     'method': 'animate'}\n",
    "    layout['sliders'][0]['steps'].append(slider_step)\n",
    "\n",
    "frames = []\n",
    "for i in range(len(years)):\n",
    "    # creating and appending data to frame\n",
    "    frame = {\n",
    "        'data': [],\n",
    "        'name': years[i]\n",
    "    }\n",
    "    data_dict = {\n",
    "        'type':'choropleth',\n",
    "        'colorscale': scl,\n",
    "        'autocolorscale': False,\n",
    "        'locations': statecode[i],\n",
    "        'z': [int(x / y * 1000000) for x, y in zip(yeardata[i], population[i])],\n",
    "        'locationmode': 'USA-states',\n",
    "        'marker': dict(\n",
    "            line = dict (\n",
    "                color = 'rgb(255,255,255)',\n",
    "                width = 2\n",
    "            ) ),\n",
    "        'colorbar': dict(\n",
    "            title = \"Deaths per 1,000,000 People\") \n",
    "    }\n",
    "    frame['data'].append(data_dict)\n",
    "    frames.append(frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
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          [
           0.3,
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         "locationmode": "USA-states",
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            ],
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            ],
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           },
           {
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             {
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