herndon_2021_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "module_path = os.path.abspath(os.path.join('..', \"openpolicedata\"))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)\n",
    "\n",
    "import openpolicedata as opd\n",
    "import numpy as np\n",
    "\n",
    "pd.set_option('display.max_colwidth',100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "\n",
    "def section_num_to_title(sect_num):\n",
    "    # https://stackoverflow.com/questions/46604114/python-requests-ssl-error-certificate-verify-failed\n",
    "    # https://stackoverflow.com/questions/28667684/python-requests-getting-sslerror\n",
    "    # https://search.censys.io/hosts/198.246.135.19?resource=hosts&sort=RELEVANCE&per_page=25&virtual_hosts=EXCLUDE&q=law.lis.virginia.gov&at_time=2022-04-11T13%3A44%3A46.372Z\n",
    "    path_to_cert = \"C:\\\\Users\\\\matth\\\\repos\\\\CASH\\\\combo.pem\"\n",
    "    headers = {'User-Agent': \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.4896.75 Safari/537.36 Edg/100.0.1185.39\"}\n",
    "    d = requests.get(f\"https://law.lis.virginia.gov/api/CoVSectionsGetSectionDetailsJson/{sect_num}/\", verify=path_to_cert, headers=headers)\n",
    "\n",
    "    d.text\n",
    "    h = d.text\n",
    "    h = json.loads(h)\n",
    "    return h['ChapterList'][0]['SectionTitle']\n",
    "\n",
    "def to_percent(x):\n",
    "    return np.round(x/x.sum()*100,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6802"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "src = opd.Source(\"Virginia\")\n",
    "data = src.load_from_url(year=2021, jurisdiction_filter=\"Herndon Police Department\")\n",
    "df = data.table\n",
    "\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WHITE                        4948\n",
      "BLACK OR AFRICAN AMERICAN    1150\n",
      "ASIAN/PACIFIC ISLANDER        584\n",
      "AMERICAN INDIAN               106\n",
      "UNKNOWN                        14\n",
      "Name: race, dtype: int64\n",
      "NON-HISPANIC    4696\n",
      "HISPANIC        1874\n",
      "UNKNOWN          232\n",
      "Name: ethnicity, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"race\"].value_counts())\n",
    "print(df[\"ethnicity\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"month\"] = df[\"incident_date\"].dt.to_period(\"M\")\n",
    "df[\"month\"].unique()\n",
    "\n",
    "df[\"race_eth\"] = df[\"race\"]\n",
    "df.loc[df[\"ethnicity\"] == \"HISPANIC\", \"race_eth\"] = \"HISPANIC\"\n",
    "df.loc[df[\"ethnicity\"] == \"UNKNOWN\", \"race_eth\"] = \"UNKNOWN\"\n",
    "\n",
    "df[\"age_range\"] = pd.cut(df[\"age\"].astype(int),[-1,25,35,45,55,65, 200],labels=[\"16-25\",\"26-35\",\"36-45\",\"46-55\",\"56-65\",\"65+\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OTHER VIRGINIA JURISDICTION RESIDENT</th>\n",
       "      <td>1245</td>\n",
       "      <td>49.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESIDENT OF TOWN/CITY/COUNTY OF STOP</th>\n",
       "      <td>960</td>\n",
       "      <td>38.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUT OF STATE RESIDENT</th>\n",
       "      <td>229</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNKNOWN</th>\n",
       "      <td>69</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Count  Percent\n",
       "OTHER VIRGINIA JURISDICTION RESIDENT   1245     49.7\n",
       "RESIDENT OF TOWN/CITY/COUNTY OF STOP    960     38.4\n",
       "OUT OF STATE RESIDENT                   229      9.1\n",
       "UNKNOWN                                  69      2.8"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = df[\"residency\"].value_counts()\n",
    "perc = np.round(counts/counts.sum()*100,1)\n",
    "sum_res = pd.DataFrame({'Count':counts,'Percent':perc})\n",
    "sum_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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></th>\n",
       "      <th>WHITE</th>\n",
       "      <th>HISPANIC</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>ASIAN/PACIFIC ISLANDER</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>% Population</th>\n",
       "      <td>33.7</td>\n",
       "      <td>36.9</td>\n",
       "      <td>7.2</td>\n",
       "      <td>18.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Total</th>\n",
       "      <th>% Stops of Residents</th>\n",
       "      <td>43.3</td>\n",
       "      <td>31.2</td>\n",
       "      <td>15.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>% Stops of Non-Residents</th>\n",
       "      <td>43.4</td>\n",
       "      <td>26.9</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>5842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">WARNING</th>\n",
       "      <th>% Stops of Residents</th>\n",
       "      <td>34.9</td>\n",
       "      <td>34.9</td>\n",
       "      <td>18.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>% Stops of Non-Residents</th>\n",
       "      <td>38.1</td>\n",
       "      <td>28.5</td>\n",
       "      <td>18.8</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">CITATION/SUMMONS</th>\n",
       "      <th>% Stops of Residents</th>\n",
       "      <td>49.3</td>\n",
       "      <td>27.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>% Stops of Non-Residents</th>\n",
       "      <td>47.5</td>\n",
       "      <td>25.3</td>\n",
       "      <td>14.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>3270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ARREST</th>\n",
       "      <th>% Stops of Residents</th>\n",
       "      <td>33.3</td>\n",
       "      <td>66.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>% Stops of Non-Residents</th>\n",
       "      <td>34.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">NO ENFORCEMENT ACTION</th>\n",
       "      <th>% Stops of Residents</th>\n",
       "      <td>43.8</td>\n",
       "      <td>37.5</td>\n",
       "      <td>18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>% Stops of Non-Residents</th>\n",
       "      <td>39.8</td>\n",
       "      <td>29.4</td>\n",
       "      <td>15.9</td>\n",
       "      <td>5.8</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                WHITE  HISPANIC  \\\n",
       "                      % Population               33.7      36.9   \n",
       "Total                 % Stops of Residents       43.3      31.2   \n",
       "                      % Stops of Non-Residents   43.4      26.9   \n",
       "WARNING               % Stops of Residents       34.9      34.9   \n",
       "                      % Stops of Non-Residents   38.1      28.5   \n",
       "CITATION/SUMMONS      % Stops of Residents       49.3      27.7   \n",
       "                      % Stops of Non-Residents   47.5      25.3   \n",
       "ARREST                % Stops of Residents       33.3      66.7   \n",
       "                      % Stops of Non-Residents   34.0      50.0   \n",
       "NO ENFORCEMENT ACTION % Stops of Residents       43.8      37.5   \n",
       "                      % Stops of Non-Residents   39.8      29.4   \n",
       "\n",
       "                                                BLACK OR AFRICAN AMERICAN  \\\n",
       "                      % Population                                    7.2   \n",
       "Total                 % Stops of Residents                           15.6   \n",
       "                      % Stops of Non-Residents                       16.0   \n",
       "WARNING               % Stops of Residents                           18.8   \n",
       "                      % Stops of Non-Residents                       18.8   \n",
       "CITATION/SUMMONS      % Stops of Residents                           13.8   \n",
       "                      % Stops of Non-Residents                       14.1   \n",
       "ARREST                % Stops of Residents                            0.0   \n",
       "                      % Stops of Non-Residents                        8.0   \n",
       "NO ENFORCEMENT ACTION % Stops of Residents                           18.8   \n",
       "                      % Stops of Non-Residents                       15.9   \n",
       "\n",
       "                                                ASIAN/PACIFIC ISLANDER  Total  \n",
       "                      % Population                                18.5      0  \n",
       "Total                 % Stops of Residents                         7.6    960  \n",
       "                      % Stops of Non-Residents                     8.4   5842  \n",
       "WARNING               % Stops of Residents                         8.0    373  \n",
       "                      % Stops of Non-Residents                     9.3   2213  \n",
       "CITATION/SUMMONS      % Stops of Residents                         7.7    556  \n",
       "                      % Stops of Non-Residents                     8.2   3270  \n",
       "ARREST                % Stops of Residents                         0.0     15  \n",
       "                      % Stops of Non-Residents                     4.0     50  \n",
       "NO ENFORCEMENT ACTION % Stops of Residents                         0.0     16  \n",
       "                      % Stops of Non-Residents                     5.8    309  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop = pd.Series([33.7, 36.9, 7.2, 18.5], index=[\"WHITE\", \"HISPANIC\", \"BLACK OR AFRICAN AMERICAN\", \"ASIAN/PACIFIC ISLANDER\"],name=\"% Population\")\n",
    "\n",
    "stops_df = pd.DataFrame([pop])\n",
    "index = [(\"\",pop.name)]\n",
    "\n",
    "actions = df[\"action_taken\"].unique()\n",
    "\n",
    "for i in range(len(actions)+1):\n",
    "    if i == 0:\n",
    "        tf = [True for x in range(len(df))]\n",
    "        name = \"Total\"\n",
    "    else:\n",
    "        tf = df[\"action_taken\"]==actions[i-1]\n",
    "        name = actions[i-1]\n",
    "\n",
    "    dfResCur = df[(df[\"residency\"]==\"RESIDENT OF TOWN/CITY/COUNTY OF STOP\") & tf]\n",
    "    dfNonRes = df[(df[\"residency\"]!=\"RESIDENT OF TOWN/CITY/COUNTY OF STOP\") & tf]\n",
    "    stops = dfResCur[\"race_eth\"].value_counts()\n",
    "    total = stops.sum()\n",
    "    stops_nonRes = dfNonRes[\"race_eth\"].value_counts()\n",
    "    total_nonRes = stops_nonRes.sum()\n",
    "    stops = to_percent(stops)\n",
    "    stops[\"Total\"] = int(total)\n",
    "    stops.name = \"% Stops of Residents\"\n",
    "    stops_nonRes = to_percent(stops_nonRes)\n",
    "    stops_nonRes[\"Total\"] = int(total_nonRes)\n",
    "    stops_nonRes.name = \"% Stops of Non-Residents\"\n",
    "\n",
    "    index.append((name, stops.name))\n",
    "    index.append((name, stops_nonRes.name))\n",
    "\n",
    "    stops_df = pd.concat([stops_df,stops.to_frame().transpose(),stops_nonRes.to_frame().transpose()])\n",
    "\n",
    "stops_df.index = pd.MultiIndex.from_tuples(index)\n",
    "\n",
    "stops_df.fillna(0).convert_dtypes().drop(columns=[\"UNKNOWN\",\"AMERICAN INDIAN\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TRAFFIC VIOLATION      6265\n",
       "EQUIPMENT VIOLATION     413\n",
       "OTHER                    88\n",
       "TERRY STOP               22\n",
       "CALLS FOR SERVICE        14\n",
       "Name: reason_for_stop, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Totals for reason for stop\n",
    "df[\"reason_for_stop\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>month</th>\n",
       "      <th>2021-01</th>\n",
       "      <th>2021-02</th>\n",
       "      <th>2021-03</th>\n",
       "      <th>2021-04</th>\n",
       "      <th>2021-05</th>\n",
       "      <th>2021-06</th>\n",
       "      <th>2021-07</th>\n",
       "      <th>2021-08</th>\n",
       "      <th>2021-09</th>\n",
       "      <th>2021-10</th>\n",
       "      <th>2021-11</th>\n",
       "      <th>2021-12</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reason_for_stop</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CALLS FOR SERVICE</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EQUIPMENT VIOLATION</th>\n",
       "      <td>75.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTHER</th>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TERRY STOP</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRAFFIC VIOLATION</th>\n",
       "      <td>564.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>735.0</td>\n",
       "      <td>587.0</td>\n",
       "      <td>532.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>481.0</td>\n",
       "      <td>537.0</td>\n",
       "      <td>478.0</td>\n",
       "      <td>503.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>6265.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "month                2021-01  2021-02  2021-03  2021-04  2021-05  2021-06  \\\n",
       "reason_for_stop                                                             \n",
       "CALLS FOR SERVICE        NaN      NaN      1.0      NaN      NaN      2.0   \n",
       "EQUIPMENT VIOLATION     75.0     58.0     52.0     28.0     19.0     16.0   \n",
       "OTHER                   14.0     11.0      NaN      2.0      8.0      9.0   \n",
       "TERRY STOP               NaN      NaN      8.0      NaN      3.0      1.0   \n",
       "TRAFFIC VIOLATION      564.0    460.0    735.0    587.0    532.0    485.0   \n",
       "\n",
       "month                2021-07  2021-08  2021-09  2021-10  2021-11  2021-12  \\\n",
       "reason_for_stop                                                             \n",
       "CALLS FOR SERVICE        2.0      1.0      2.0      4.0      1.0      1.0   \n",
       "EQUIPMENT VIOLATION     20.0     25.0     22.0     22.0     39.0     37.0   \n",
       "OTHER                    8.0      7.0      6.0      8.0      6.0      9.0   \n",
       "TERRY STOP               3.0      1.0      NaN      1.0      1.0      4.0   \n",
       "TRAFFIC VIOLATION      446.0    481.0    537.0    478.0    503.0    457.0   \n",
       "\n",
       "month                 Total  \n",
       "reason_for_stop              \n",
       "CALLS FOR SERVICE      14.0  \n",
       "EQUIPMENT VIOLATION   413.0  \n",
       "OTHER                  88.0  \n",
       "TERRY STOP             22.0  \n",
       "TRAFFIC VIOLATION    6265.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vxQYBwLfyDQh20CYmhFNHC7yBIKTRzxEU5pWCQPB59MyAlkhECA4LICjUSUmhi8LcUvJzisnPKSYwxElwuBNnoL1JA60J6vVzeNdJNny0n8PfnSI4IoBRN/Qql06lfZcwrr67P5n7clj//j7WLt5NyqcHGXp1HAkjOjbrhR5GRS06CPhzBtppGxPCqaPePYLo4EbbzfR4lKK8UoJCnNjMD7ZBiAiBIU4CQ5y4St0U5pZSlF9KcUEpdqeNkPAAAkOd2Jr5kmCjcqrqXfmncWT3KUIiAhh9Yzx9x3SucoOs40WRTPnZQNJ3nuDrpftYNX8nm5cfYNi1ccQPjmn2y78Ny3kTBMB6TGLbjiGcyirg1NFCItsHERjS8FvqxfmlqOp59eSwGTNmsG3bNm699Vbuv//+Bl12SkoKR44c4aqrrmqQ5TmcdsLb2QltE0hxQSmFuaXknigi72QxQaFOcyK+BVFV0necZMPH1oOIQiMDGP39eBJHd67x36hLn3ZMS2hL2rfZrF+6lxX/3s7mZQe55No4evRvb/bMmtl5FQQA7A6bFQiOFpJzrJDwKCU4rOHSHasqhXmlOJz2c2YLVVVUFZut+fcUMjMz+eqrrzhw4ECN53G5XDgcNfvzp6SksHHjxgYLAmVstjOHilwl1t5BYX4phXklOAPtBIc3/4nk1kpVObT9BBs+2k/mvtOEtglk7PTeXDyqU52O7YsIcf3b0yMpij2bjrL+/X18/I9viYmLYPiUi+jSp+Umj7zQ1WgNJiJpIvKtiKSIyEZvWTsRWSEiu73vbf3qPyIie0Rkl4hc0eCdtttoExNCQJCD3OwiCk4XN9iyXSVuXCVu65LUs1Y+aWlpXHzxxdx9990MGjSI3/3udwwdOpT+/fszd+5cX72pU6cyePBgEhMTmTdvHgBut5tZs2aRlJREv379ePbZZwFrBTt8+HD69+/Pddddx8mTJwEYN24cc+bMYdiwYfTu3Zs1a9ZU2edJkyZx9OhRkpOTWbNmzTmX+ctf/pJLL72Uv/71r5Uua/HixSQlJTFgwADGjh1LSUkJjz32GG+99RbJycm89dZbnDhxgqlTp9K/f3+GDx/O1q1bAXj88ce5+eabmTBhAvHx8bz00ks1+s5FBGegg4j2wbSPDSW0TSBut3L6eCHZh/PIP1WM23Ue5Bu/AKgqB1KzeeePm/jgb1vIO1nMpTN6c/PvRtBvXJd6n9wVmxA/NIYZj1/C+P/rQ/6pYpb+JYX3nv0fmftyGuhTGLVStkV7rheQBrQ/q+yPwMPe4YeBP3iH+wJbgEAgDtgL2M+1/N69e+vZtm/fXqHsbB63R08dzdestBzNPVGoHo+n2nmqk3OsQI8eOK1ud8Vl7d+/X0VE161bp8uXL9c777xTPR6Put1uvfrqq/Xzzz9XVdXs7GxVVS0oKNDExEQ9fvy4bty4US+77DLfsk6ePKmqqv369dPVq1erquqvf/1rve+++1RV9dJLL9UHHnhAVVU/+ugjnThxYpV93r9/vyYmJvrGz7XMn/zkJ+f8/ElJSZqenl6uj6+88orOnj3bV+eee+7Rxx9/XFVVV65cqQMGDFBV1blz52r//v21oKBAjx07pl26dNHDhw+fs72qeDweLcov0ZOZ1t83Ky1HTx3N1+LC0gb5O9fk99WaeDwe3b/lmC566ht9/kcr9bVHvtTUL9LVVeJu1HZLS1ya8ulB/feDX+jzP1qpH76wRY8dym3UNi8kwEatwTr8XK/6HMuYArzmHX4NmOpXvlBVi1V1P7AHGFaPdqokNiGifTBBYU4KTpeQe6KoLEDVicftoajARdA5TlB2796d4cOH88knn/DJJ58wcOBABg0axM6dO9m9ezcAzz33HAMGDGD48OEcOnSI3bt3c9FFF7Fv3z5++tOfsmzZMiIiIsjJyeHUqVNceumlAMycOZMvvvjC19b1118PwODBg0lLS6vRZ6humT/4wQ/OOf+oUaOYNWsWL730Em63u9I6a9eu5eabbwZgwoQJZGdnk5NjbcVNmTKF4OBg2rdvz/jx4/nmm29q1O+zlZ1IbhMTQrvOoYREBFBS5OZUVgEnMvIpOF2Cx9P8Nzqe71SV/VuOsfj3G/no71spzCtl/P/14abfDidxTCx2Z+Me7nQ47QyY2JX/+90ILplyEUd2n+KtJ7/hk39v41RWy01Zrh7lxJF8tn95hC/e+o5d6zNxlVb+/9LS1fScgAKfiIgCL6rqPCBGVTMAVDVDRDp468YCX/vNm+4taxQiQni7IGw2oeB0CepRIqKC63TlQVF+KagSHF71CeHQUCt1sqryyCOP8KMf/ajc9NWrV/Ppp5+ybt06QkJCGDduHEVFRbRt25YtW7awfPlyXnjhBRYtWuQ7JFSVwEDraV52ux2Xy1Xrz3Ou/lfln//8J+vXr+ejjz4iOTmZlJSUCnUqC7Rlh87OPoTWEMfzHU47YW3thEYGUuQ9kZx3soj8U+ZEcl1ZK//jbPhoP8cP5RHRPojxN/chYXjzXMIZEORgyJU9SBobS8qKg2z57BB7Nh3l4hEdGXJ1XLOnbSnKLyUr7TRZ+3LI3H+arP2nKSm0/idtDuHbVemsXeSkz8hOJI7uTJuYkGbtb23UNAiMUtUj3hX9ChHZeY66lf3XV1hriMhdwF0A0dHRrF69utz0yMhIcnNza9g9wAGOECgucJFdkktAeO1WQKpKcQ7YHFBYXACVnGbIy8vD4/GQm5vLmDFjeOKJJ7j22msJCwvjyJEjOJ1OMjMzCQ8Px+12s2nTJr7++msKCgpIS0vD6XQyadIkOnbsyE9+8hNsNhuRkZEsX76ckSNH8q9//YsRI0aQm5uL2+0mPz+f3Nxc8vLyUNUqvw//ftV0mVXZt28fffv2pW/fvixdupSdO3ficDg4ceKEb77hw4fz8ssvM2fOHNasWUO7du0QEYqLi/noo4+45557yM/PZ9WqVfzqV7+q3d+xBpxhYHeBq0gpzCuhMK8EmwMcgWALqNnfvaioqMJvrjVQVXLT4dg2pegUBIRB7CVCZPdijpZ+x9E13zV3F6EN9LxKObZd2bEugx3rMmjbC6L7Co6gxr9IQD1K8WkoyIbC40phNhSf9k4UCIyE0M7QPkoIaW99h/lHhZN7S0n59CApKw4SGgPtegnhsbT4S2FrFARU9Yj3/aiILME6vJMlIp28ewGdgLLHgqUDXf1m7wIcqWSZ84B5YOUOGjduXLnpO3bsIDy8ljnqw6Ewr4Tc7CJc+XbadAiu8XX+JYUuijwFhLcLrjLnSVhYGDabjfDwcKZOncqBAweYNGmSb9r8+fO57rrreO211xg1ahQJCQkMHz6ckJAQcnJyuPXWW/F4rBOcf/jDHwgPD+c///kPP/7xjykoKOCiiy7ilVdeITw8HLvdTmhoKOHh4RQXF1t7PFV8H/79Amq0zKr85je/Yffu3agqEydOZOTIkVx88cX89a9/ZcyYMTzyyCM89dRT3HrrrYwaNYqQkBD+85//EB4eTmBgIMOHD2f69OkcPHiQxx57jN69e9fo+68rj9tDUV4phXmllOR7sBUJQWEBBFdzR3JQUBADBw5s1L61JOpR9v7vGBs/3k/24XzaxIQwelZ34ofGtNx7YSZbqdw3fLSfnesyOX3AxoAJXRh4ebcGvTS8KL+UzH05ZO0/bb2nnaa0yDq0ExTqpONFEXSMi6TjRRF06BFxzvuT8nOK2fHlEbatOcKhL4sJiQyg76jO9B3dudn3ZqpSbQI5EQkFbKqa6x1eAfwWmAhkq+rTIvIw0E5VHxKRROBNrEDRGVgJxKtqlQfMKksgt2PHDi6++OI6fajiglJyjhdhtwttYkJqlJ4g52gBpcVuomLDWnzkbqkef/xxwsLCePDBB5u8bVWlpMhNYW6Jbzc9MNhBcHgAzqCKdyTX5/d1PvF4lL2bj7Lx4zROHMmnbccQhlzVg15DYs6rG/NOZRXwzQf72L3xKIEhDgZO6kb/8V3PeRl3ZTwe5cSRPDL3nTm0U3buQQSiuoTRMS6SGO+KP7JDcJ0OaXo8ysHUbFLXHOZAajYCdO/XnqSxsXTt267BvvumSiAXAyzxfhEO4E1VXSYiG4BFInI7cBC4EUBVt4nIImA74AJmnysANIbAECdtOgg5Rws5mVlAmw7B5zxm7HZ5KC50ERIRYALAeUpECAx2EBjswF3q8R4mKqW4sAC7w0ZQmJPAEEeryV/j8Sh7Nmax8eM0TmYW0LZTKJNuT6Tn4A7n1cq/TJuYECbdkcTAK3L55v19fP3ePrZ8ls6QK7uTOLrqE9iFeSVk7bO28DP3n+Zo2mlKi63VUXC4k5i4SPqM6EjHuEiiu4c3WBYCm03o0b89Pfq35/TxQravPcL2rzJI23qc8KggEsd05uKRnQmJaLh7nOqqxaaSbogttdISNzlHC1CFNh1CqtxqyDtVTEFOMVGdwxr9aoj6WL58OXPmzClXFhcXx5IlS2q9rCeffJLFixeXK7vxxht59NFH69XHlkQ9at2RnFfq+8d3BNgJCnWw/9BeEhP7NnMPG5aqcjKzgP1bjrFzXSansgpo1znU2vIf1OGC2sDJ3JfD1+/t5fB3pwhrF2jlJRrWkRMZ+WTtzyHTu+LPOVYIWMfl23cJo2NcBDEXWYd2ItrXbSu/rtwuD/u3HCf1i8Mc3nUSm124KDmapLGxdO7dpk59aYg9gQs6CAC4St3kHC3E41Yr31Bw+UivqmQfzsMRYKdNh/PnjL5RO26Xh+KCUoryXbhK3KQd2kv61x56D42h56AOBIc3/xZZXXg8Sta+HPZvOc6+LcfIOWqt9Dr0iGDg5d3oOTD6glr5+1NV0nee5Oul+ziadhoRKFudBUcE0DEugo7eFX5094hmeTBVVU5m5rNtzRF2rsuguMBFm5gQksbGkjC8Y62ew2CCQA25XR5yjhbgKvVY9xX4fclF+aWcPl5IZHRwo+QhMloeV6mb1G+3s/2DAk5m5CM2oUuftsQPieGi5PYt/nfgKnFzaOdJ9qccI+3b4xTmlmKzCbEJbYgbEE3cgPaEtW2ZJyEbg6qStvU4GXtyaN/NOqYfHhV0XqQbcZW42bP5KNu+OEzmvtPYnTbiB3cg8dJYYnpEVPsZTBCoBY/bQ86xQkqL3YS3C/Jt+Z3MKsBd6iEq1jw4pjXZsWMHffr04cSRfL7bkMWejVmcPl6EzSF0T4wifmgMPfq1r/WJx8ZSmFfCgW+z2ZdyjEM7TuAq8RAQZKd7UhRxA6LplhRFYPB5lwrM8HM8PZfUL47w3fpMSovdtO8aRuKYWHoPi6nyXIUJArXk8Vj5aEoKXYS2CSQwxMGJI/mEtgkkNDKwQdsyWrazf1+qytG0XHZvtAJCfk4JjgAbcf3bEz80hm59o5r8fFHOsQL2bznO/i3HydhzClUIbRNI3ID2xA1oT2zvts3yYB6jcZUUufjumyxSvzhMdnoeziA7CcM6kjg2lvZdwsrVbYgg0Kp+QTabEBltHQ7KP1XsO35aVcrozMxMpk+fTs+ePenbty9XXXUV33135maaZ599lqCgIF/KBLDuGL7mmmsqLGvcuHFs3LixXFlBQQE33XQT/fr1IykpidGjR5OXl1dh3h49etCvXz+Sk5NJTk7mq6++AmDbtm1MmDCB3r17Ex8fz+9+9zvf3byvvvoq0dHRJCcn06dPnyrvTs7KyuKaa65hwIABvs8IVrK84OBgX5vJycm8/vrr5frTv39/Lr30Ug4cOMDq1asZMWJEuWW7XC5iYmLIyMhg1qxZvP3224D1/OiHH36Y+Ph4kpKSGDZsGP/9738r/az33ntvpf1uaCJCTFwEo2+M55bfj2LqAwNJGN6JQztO8vE/vuXlh9ay8vUdHNp+Ao+7cZLZqSpZaaf5euleFvx2PfN//TVfvr2H4oJSBl/ZgxsfGcLM34/k0hkJVlAyAeCCFBDkIGlsLD94dCjT5gymZ3I0O9Zl8NYT3/DOHzey8+sMXCUNd8Flq9t/FBHreKFNKMwtITDEWelt8qrKddddx8yZM1m4cCFgZfzMysry3QC1YMEChg4dypIlS5g1a1at+/LXv/6VmJgYvv32WwB27dpV5cPPV61aRfv27X3jhYWFXHvttfzjH/9g0qRJFBQUMG3aNP7+978ze/ZswMoT9Pzzz5OdnU1CQgI33HADXbt2Lbfcxx57jMsvv5z77rsPwJcRFKBnz56Vpo3w78/cuXN54oknePHFF0lPTyctLY0ePXoA8Omnn5KUlESnTp3KzfvrX/+ajIwMUlNTCQwMJCsri88//7zKz9rUbDYhtndbYnu3ZcwP4knfeZLdG7LYu/koO7/KIDjcSc9BHYgfGkOniyLrdeLV7fJweNdJa4t/63HyTxUjAp16tWHUDb2IGxBNZHRwA34643whItZNanGRjLoxnl1fZ5L6xWFWvrqDtYt202dEp+oXUgOtLgiA9eWGtQ0kIKjqZwasWrUKp9PJj3/8Y19ZcnKyb3jv3r3k5eXxzDPP8NRTT9UpCGRkZNC9e3ffeEJCQo3nffPNNxk1apTvjuWQkBCef/55xo0b5wsCZaKioujVqxcZGRkVgkBGRoZvGQD9+/ev1WcYMWIEzz33HDabjRtvvJG33nrLdxnrwoULmTFjRrn6BQUFvPTSS+zfv9+XGykmJobvf//7tWq3qdjtNronRtE9MQpXqZuDqSf4bkMWO77KIPXzw4S1DaTXYCsgRHcLr9F5peJCFwdSrcM8B1OzKSly4wiw0a1vFHED2tO9X1SDPiPDOP8FhToZMLEr/Sd04ch3p0hdc5hvV6c3yLLPiyDwmw+2sf3I6eor1kLfzhHM/V5ildNTU1MZPHhwldMXLFjAjBkzGDNmDLt27eLo0aN06NChyvqVue2225g0aRJvv/02EydOZObMmcTHx1dad/z48djtdgIDA1m/fj3btm2r0L+ePXuSl5fH6dPlv6uDBw9SVFRU6Qp+9uzZvj2Gyy67jFtvvZXOnTsDVqDzD3x/+9vfGDNmTLn5ly1bxtSpUwHr6WZ33XUXc+bMobi4mI8//rjCYag9e/bQrVs3IiIiqvxeyj4rWFlQG/pJaXXlcNq5aGA0Fw2MpqTIRdrW4+zekMXWVemkfHqIyOhg4ofGED8khnadyyfqyz1RRNrW4+zfcozDu07h8ViJCnsO7kDcgGi69mlrkuAZ1RIRYhPaEpvQloLTJdz99/ov87wIAi3RwoULWbJkCTabjeuvv57FixdX2AKvTnJyMvv27eOTTz7h008/ZejQoaxbt67SE+JnHyJR1Sq3OsvK33rrLVatWsWuXbt46aWXCAqqeNngFVdcwb59+1i2bBn//e9/GThwIKmpqcC5DweNHz+erKwsOnTowBNPPAHA0KFDycvLY9euXezYsYPhw4fTtm3bSuc/l+Y+HFQTAUEOeg/rSO9hHSnKL2VfyjF2b8hi03/T2PhxGlGxofQaEoN6rGydxw5aSfTaxIQwYGJX4pKjiYmLOC/v3jVahoa62/i8CALn2mJvLImJib4TmWfbunUru3fv5vLLLwegpKSEiy66qNZBAKzkb9dffz3XX389NpuNjz/+uEZXRSUmJpZ7TgBYGUDDwsJ8CeLKtvDXrVvH1VdfzZVXXknHjh0rLKtdu3b88Ic/5Ic//CHXXHMNX3zxxTn3gsBaUYeGhjJr1iwee+wx/vznPwMwffp0Fi5cyI4dOyocCgLo1asXBw8eJDc3t/YJAluooFCnlSRsVGfyc4rZu/kouzccZf3SfQDExEUw4rqexA1oT9uO507lbRhNzVxeUIUJEyZQXFxc7hGJGzZs4PPPP2fBggU8/vjjpKWlkZaWxpEjRzh8+HCtnvEL8OWXX/oe/VhSUsL27dvLnSM4l5tuuom1a9fy6aefAtaJ4nvvvZeHHnqoQt0RI0Zw8803V/pIyc8++4yCAiuBVm5uLnv37qVbt2416kNwcDB/+ctfeP311zlx4gRgHRKaP38+n332Gddee22FeUJCQrj99tu59957KSkpAazzEvPnz69Rmy1daGQg/cd3ZdpDg5n5+1HM+sMobpgzhEFXdDcBwGiRTBCogoiwZMkSVqxYQc+ePUlMTOTxxx+nc+fOLFy4kOuuu65c/euuu853FdHKlSvp0qWL77Vu3ToArr76al/ZjTfeyN69e7n00kvp168fAwcOZMiQIUybNq1G/QsODmbp0qU88cQTJCQk0K9fP4YOHco999xTaf05c+bwyiuvVMjtv2nTJoYMGUL//v0ZMWIEd9xxB0OHDgXOnBMoez333HMVltupUydmzJjBCy+8AEDfvn0JCQlhwoQJVT7A5oknniA6Opq+ffuSlJTE1KlTiY6O9k0fP368r81bbrmlRt9HSxTW1tx/YrR8repmMcMoY35fxoXA3CxmGIZh1IsJAoZhGK2YCQKGYRitmAkChmEYrZgJAoZhGK1YjYOAiNhF5H8i8qF3vJ2IrBCR3d73tn51HxGRPSKyS0SuaIyOG4ZhGPVXmz2B+4AdfuMPAytVNR5Y6R1HRPoC04FEYDLwdxE5L5Oi2O32ctfJP/3004B1Y9fPfvYzevbsSa9evbjmmms4ePAgYKVgTkpKKrecxx9/nD/96U8A5VIqjxs3joSEBAYMGMCoUaMou0x23LhxdOvWDf/Ld6dOnUpYWJivjXOlefa/1+Dtt99m1qxZvPLKK766AQEBvnTNDz/8sK9ufn4+UVFR5VJjl7W9aNEiXn311XL3IcybN48+ffrQp08fhg0bxtq1a33TKkudXWbJkiWICDt37gTgkksuITk5mW7duvnSXycnJ/sykh4/fhyA9PR0pkyZQnx8PD179uS+++7z3XC2evVqRIQPPvjA184111zD6tWrK+2DYRiWGgUBEekCXA38y694CvCad/g1YKpf+UJVLVbV/cAeYFiD9LaJBQcHk5KS4nuVrTB/+ctfkpuby3fffceePXuYNm0aU6ZMweOpfZ75N954gy1btjBz5kx+8Ytf+MrbtGnDl19+CcCpU6fIyMgoN19ZXp+yl/9NVRs3bmTbtm3l6t96662+up07d2bVqlWkpKT4AhtAaGgokyZN4r333vOV5eTksHbt2grPSPjwww958cUXWbt2LTt37uSf//wnP/zhD8nMzKz2My9YsIDRo0f7bq5bv349KSkp/Pa3v+UHP/iBr59lKanBypV0/fXXM3XqVHbv3s13331HXl4ejz76qK9Oly5dePLJJ6tt3zCMM2q6J/AX4CHAfy0Xo6oZAN73shSascAhv3rp3rILQkFBAa+88grPPvusL9PlrbfeSlhYmC+FQ12MHTuWPXv2+MbLcvAAvPvuu1x//fU1XtaDDz7IU089Vad+zJgxw9cuWFvtkydPJiQkpFy9P/zhDzzzzDO+RG+DBg1i5syZvjuHq5KXl8eXX37Jv//973LtVOezzz4jKCiIW2+9FbD20p599llefvllX9qLAQMGEBkZyYoVK2q8XMNo7apNICci1wBHVXWTiIyrwTIrS4tY4bZkEbkLuAsgOjq6wm57ZGSkL8VB4Kq52I5uO3sR9eLpkEjx+N+cs05hYWG59MsPPPAACQkJdOnSBREpl4Khf//+bN68mc6dO+PxeMpNKy4uxul0kpubS2lpKYWFheTm5uJ2u8nPzyc3N5e3336biy++2Fc+fPhw7r33Xk6dOsX8+fN57rnn+N3vfkdubi55eXns3bu3XN+eeeYZRo4ciapy1VVX8fzzz5OSkkJhYSGlpaXl+qOq5OXl+fL5+xs1ahS33347aWlpREVFMX/+fH70ox+Rm5tLUVERJSUl5ObmkpqaSu/evcstNzExkTfffLPCZ/O3cOFCJk6cSKdOnYiMjGTNmjW+dNX+yz+7r5s2baJfv37lpokIXbp0YcuWLRQUFOByuXjggQf4zW9+w/Dhw3G5XBQUFFToQ1lb5lCRYdQsi+go4FoRuQoIAiJEZD6QJSKdVDVDRDoBR7310wH/J5d0AY6cvVBVnQfMAyttxLhx48pN37Fjx5ksk84AsDdwwlNnAAHVZLEMDg4u96QtgC1btmC32ytkwHQ6nQQFBREeHo7NZis3PTAw0DfN6XQSHBxMeHg4drudu+66i+DgYHr06MHf/vY3X3lERARjx47l448/prS01HeeITw8nLCwMHr27Fmhb2CtGCMjI5kzZw5/+9vfuPLKK3E6neX6IyLlso2ebcqUKSxfvpxp06aRmprK1KlTfZ8vICCA8HDr4Snh4eHllhEUFORry263ExoaWqGN9957j5/97GeEh4dz00038f777/ueUeC//LP7GhgYSGBgYIXllU0vLi7G4XBwxRVX8NRTT5GSkoLD4SAkJKTSzxkUFMTAgQMr/fyG0ZpUu2ZV1UeARwC8ewIPqur/icgzwEzgae/7Uu8s7wNvisifgc5APPBNvXp55dPV12kivXr14sCBAxVSIW/evJkbbriBqKgoX2bQMidOnCAuLq7S5b3xxhsMGVJ56o/p06dz3XXX8fjjj9e6nzfffDO///3vSUysfRruGTNm8MQTT6CqTJkypdJHXvbt25dNmzYxYcIEX9nmzZvp27dvlcvNzs7ms88+IzU1FRHB7XYjIvzxj3+s9olciYmJvPPOO+XKTp8+zaFDh+jZsyfZ2dm+8kcffZQnn3wSh+O8yJRuGM2qPvcJPA1cLiK7gcu946jqNmARsB1YBsxW1YZ7KnIzCw0NZebMmTzwwAO43dbHev311wkKCmLUqFGEhYXRqVMnVq5cCVgBYNmyZYwePbrWbY0ZM4ZHHnmk0rz81XE6ndx///385S9/qfW848ePZ/fu3bzwwgtVtv3QQw8xZ84c38o3JSWFV199lbvvvrvK5b799tvccsstHDhwgLS0NA4dOkRcXFy5q4qqMnHiRAoKCnxXQbndbn7+858za9asCucrJk2axMmTJ9myZUtNP7JhtFq12lRS1dXAau9wNjCxinpPAuf9ZRqFhYXlHq84efJknn76aX7/+9/zi1/8goSEBAoLC4mOjmbdunW+rdnXX3+d2bNn8/Of/xyAuXPn0rNnz1q3LyI8+OCDlU47+9GPt912G/fee2+5OrfffrvvqV+1YbPZmDZtGosXL2bs2LGV1rn22ms5fPgwI0eO9B0amj9/frmHyl999dW+vYgRI0Zw7NixcpekAkybNo0333yzwmMrz1aW2vvuu+/md7/7HR6Ph6uuuqrKE+CPPvooU6ZMqc3HNoxWyaSSrqfMzEwmT57M3XffzV133dXc3TFq6Hz5fRnGuTREKmlz0LSeOnbsWOVzeA3DMFo6kzvIMAyjFTNBwDAMoxUzQcAwDKMVM0HAMAyjFTNBwDAMoxUzQaAalaUvXr58uS/dcVhYGAkJCSQnJ3PLLbewevXqChk3K0sfXTb/DTfcAFjppmNjY0lOTqZv374sWLCgyT+rYRitjwkC51BV+uJPP/3Ul+54yJAhvPHGG6SkpPjuZq1OWf2UlBRfcAC4//77SUlJYenSpfzoRz+itLS0sT6aYRgGYILAOdUkfXFjiI+PJyQkpEIOIsMwjIZ2Xtws9odv/sDOEzsbdJl92vVhzrA556yzbds2Bg8eXK4sIiKCbt26sWfPnnKpnP35p0cGOHjwYLlDRDfddBPBwcEAXH755TzzzDPl5t+8eTPx8fF06NABwzCMxnReBIHmoqqVZresqrzMmDFj+PDDD33js2bNKje9qsyhzz77LC+99BL79u1j2bJlde+4YRhGDZ0XQaC6LfbGUl364oZ2//338+CDD/Luu+9yyy23sHfvXoKCghq8HcMwjDLmnMA51CZ9cUO6/vrrGTJkCK+99lr1lQ3DMOrBBIFzKEtfvHjxYuLj4+nduzdBQUF1fn5vmZtuusl3iehll11WaZ3HHnuMP//5z3V6eL1hGEZNmVTSRqtkfl/GhaAhUkmbPQHDMIxWzAQBwzCMVqzaICAiQSLyjYhsEZFtIvIbb3k7EVkhIru972395nlERPaIyC4RuaIxP4BhGIZRdzXZEygGJqjqACAZmCwiw4GHgZWqGg+s9I4jIn2B6UAiMBn4u4jYG6HvhmEYRj1VGwTUkucddXpfCkwByq5hfA2Y6h2eAixU1WJV3Q/sAYY1ZKcNwzCMhlGjcwIiYheRFOAosEJV1wMxqpoB4H0vy3EQCxzymz3dW2YYhmG0MDW6Y1hV3UCyiLQBlohI0jmqV5ZPocJ1qCJyF3AXQHR0NKtXry43PTIyktzc3Jp0r1FkZ2dz7bXXApCVlYXdbqd9+/YAfPvtt/Tr189Xd9q0aTzwwANcddVVZGZmEhQUhNPp5G9/+5svv1BSUhJhYWGICG3atOHFF19k5cqVfP7557z66quAdTfy6NGjef/99+nRo4dv+bt37+a+++4jJyeH4uJiRo4cybXXXsvcuXMB2LdvH506dSI4OJjExETmzZvHunXreOSRR3zf4T333ONLhPfUU0/x2muv0b59e1wuF3PnzuWqq65q1O+zpSkqKqrwmzOMVklVa/UC5gIPAruATt6yTsAu7/AjwCN+9ZcDI861zN69e+vZtm/fXqGsucydO1efeeYZ33hoaGil9S699FLdsGGDqqq+/PLLetlll/mmde/eXY8dO6aqqo899pjecccd6vF4dOTIkbpixQpVVb3vvvv0iSeeqLDcSZMm6Xvvvecb37p1a5XtqqpmZGRo165dddOmTaqqeuzYMR00aJB++OGHFT7P9u3bNSoqSt1udw2/jQtDS/p9GUZdARu1luvws181uToo2rsHgIgEA5cBO4H3gZneajOBpd7h94HpIhIoInFAPPBNfYPV+WbEiBEcPnz4nNNEhH/84x/87Gc/Y+PGjaxcuZJf/OIXFepnZGTQpUsX37j/XkhlXnjhBWbNmsWgQYMAaN++PX/84x95+umnK9S9+OKLcTgcHD9+vDYfzzCMC0RNDgd1Al7zXuFjAxap6ocisg5YJCK3AweBGwFUdZuILAK2Ay5gtlqHk+os86mnKN7RsKmkAy/uQ8df/rJO8xYWFpZLFf3II4/wgx/8oFydZcuWMXXq1Ern95/Wv39/rrjiCiZOnMh7771HQEBAhfr3338/EyZMYOTIkUyaNIlbb72VNm3aVNm/bdu2MXPmzHJlQ4YMYdu2bRXqrl+/HpvNRnR0dJXLMwzjwlVtEFDVrcDASsqzgYlVzPMk8GS9e9dCBQcHk5KSUum0m266ifz8fNxuN5s3by43bfz48WRlZdGhQweeeOIJX/ns2bP573//y/jx4ytd5q233soVV1zBsmXLWLp0KS+++CJbtmwhMDCw0vpaRapr/7Jnn32W+fPnEx4ezltvvXXO1NiGYVy4zotU0nXdYm8Ob7zxBgMGDODhhx9m9uzZvPvuu75pq1atIjQ0lFmzZvkSxAHYbDZstnMfmevcuTO33XYbt912G0lJSaSmplZ44E2ZxMRENm7c6DuxDbBp0yb69u3rGy9LW20YRutm0kY0AqfTyRNPPMHXX3/Njh07yk0LDg7mL3/5C6+//jonTpyo0fKWLVvme95wZmYm2dnZxMZWfdXt7NmzefXVV317K9nZ2cyZM4eHHnqobh/IMIwLlgkCdVB2TqDs9fDDD1eoExwczM9//nP+9Kc/VZjWqVMnZsyYwQsvvFCj9j755BOSkpIYMGAAV1xxBc888wwdO3assn6nTp2YP38+d955J3369GHkyJHcdtttfO9736v5hzQMo1UwqaSNVsn8vowLgUklbRiGYdSLCQKGYRitmAkChmEYrZgJAoZhGK2YCQKGYRitmAkChmEYrZgJAlXIzs723QfQsWNHYmNjfeMiQnJyMklJSXzve9/j1KlT5eYdMGAAM2bMKFc2a9Ys4uLifMt47rnnAOjRowf9+vXzlX/11VekpaWRlHQmW/c333zD2LFjSUhIoE+fPtxxxx0UFBT4pufn5xMVFUVOTk65NqdOncqiRYt49dVXueeee3zl8+bNo0+fPvTp04dhw4axdu1a37Rx48axcePGSr+TJUuWICLs3GnlcbrkkktITk6mW7duREdH+z5DWloaPXr08CWlS09PZ8qUKcTHx9OzZ0/uu+8+SkpKAFi9ejUiwgcffOBr55prrjFpng2jqdQ3DWlDvM7nVNK33HJLufTP27dv16SkJO3cubPm5eX5ymfOnKmLFy+usGz/FNNl9u/fr4mJiaqqmpmZqd26ddOvvvpKVVU9Ho8uXrxYMzMzy80zffp0ffXVV33jp06d0qioKM3Pz9dXXnlFZ8+eraqqH3zwgQ4aNMjX5qZNm7Rr166akZGhqhXTUvu78cYbdfTo0Tp37txy5f7LP/tzeTweHTp0qL788suqqupyufS2227TBx98UFVVV61apV26dNFLLrnEN+/VV1+tq1atqrQPDaUl/b4Mo65oilTSxrmdnTL6zTff5Oabb2bSpEm8//779V7+Cy+8wMyZMxkxYgRgJYG74YYbiImJKVdvxowZLFy40De+ZMkSJk+eTEhISLl6f/jDH3jmmWd8D8gZNGgQM2fOrPbu5by8PL788kv+/e9/l2unOp999hlBQUG+B9rY7XaeffZZXn75Zd/ezIABA4iMjGTFihU1Xq5hGA3jvEggt2bRdxw/lFd9xVpo3zWMMd/vXa9luN1uVq5cye233+4re+utt1ixYgW7du3i+eefL3dY6Be/+IUve+h//vMf33MBxo8fj91uJzAwkPXr15drIzU1tUJa6MpMnjyZO+64g+zsbKKioli4cCE//elPK9Tbtm1bhcRzQ4YM4bXXXqtQ1997773H5MmT6d27N+3atWPz5s2+5xWcS2XtRURE0K1bN/bs2eMr+9WvfsWvfvUrLr/88mqXaRhGwzF7AnVQljsoKiqKEydO+FZcGzZsIDo6mu7duzNx4kQ2b97MyZMnffM988wzpKSkkJKSUu7BMKtWrSIlJaVCAKiNgIAArr32Wt5++22OHz9OSkoKkyZNqtG8WkXqaX8LFixg+vTpAEyfPp0FCxbUa9lnl48ZMwaANWvW1Gi5hmE0jPNiT6C+W+wNrex5Ajk5OVxzzTW88MIL3HvvvSxYsICdO3f6ng98+vRp3nnnHe644446t5WYmMimTZuYMmVKtXVnzJjBE088gaoyZcoUnE5nhTp9+/Zl06ZNTJgwwVe2efPmcmmmz5adnc1nn31GamoqIoLb7UZE+OMf/1ht8EhMTOSdd94pV3b69GkOHTpEz549yc7O9pU/+uijPPnkkzgc58XP0jAuCGZPoB4iIyN57rnn+NOf/kRxcTGLFy9m69atpKWlkZaWxtKlS2u8xVyVe+65h9dee63cXsL8+fPJzMysUHf8+PHs3r2bF154ocLVSWUeeugh5syZ41v5pqSk8Oqrr3L33XdX2Ye3336bW265hQMHDpCWlsahQ4eIi4srd1VRVSZOnEhBQQGvv/46YB1C+/nPf86sWbMqnK+YNGkSJ0+eZMuWLdUu1zCMhmGCQD0NHDiQAQMGsGjRImJjY8vl+R87dizbt28nIyOjzsuPiYlh4cKFPPjggyQkJHDxxRezZs0aIiIiKtS12WxMmzaN7Oxsxo4dW+nyrr32Wm677TZGjhxJnz59uPPOO5k/fz6dOnXy1bn66qvp0qULXbp04cYbb2TBggVcd9115ZYzbdo03nzzzWr7LyIsWbKExYsXEx8fT+/evQkKCuKpp56qtP6jjz5Kenp6tcs1DKNhVJtKWkS6Aq8DHQEPME9V/yoi7YC3gB5AGvB9VT3pnecR4HbADdyrqsvP1YZJJW00NfP7Mi4ETZVK2gX8XFUvBoYDs0WkL/AwsFJV44GV3nG806YDicBk4O/eh9QbhmEYLUy1QUBVM1R1s3c4F9gBxAJTgLLrCl8DpnqHpwALVbVYVfcDe4BhDdxvwzAMowHU6pyAiPQABgLrgRhVzQArUAAdvNVigUN+s6V7y2qtukNVhlEX5ndlGGfU+Fo8EQkD3gF+pqqnz3FpYGUTKvzXichdwF0A0dHRFXLFhIWFkZ6eTmRkZLWXIRpGTakqOTk55Ofnm/xEhkENg4CIOLECwBuq+q63OEtEOqlqhoh0Ao56y9OBrn6zdwGOnL1MVZ0HzAPrxPC4cePKTS8tLSU9Pb1cSgbDaAhBQUEMGDCg0vsoDKO1qTYIiLUZ/m9gh6r+2W/S+8BM4Gnv+1K/8jdF5M9AZyAe+Ka2HXM6ncTFxdV2NsMwDKMWarInMAq4GfhWRFK8Zb/EWvkvEpHbgYPAjQCquk1EFgHbsa4smq2q7obuuGEYhlF/1d4n0BQqu0/AMAzDOLemuk/AMAzDuECZIGAYhtGKmSBgGIbRipkgYBiG0YqZIGAYhtGKmSBgGIbRipkgYBiG0YqZIGAYhtGKmSBgGIbRipkgYBiG0YqZIGAYhtGKmSBgGIbRipkgYBiG0YqZIGAYhtGKmSBgGIbRipkgYBiG0YqZIGAYhtGKmSBgGIbRilUbBETkZRE5KiKpfmXtRGSFiOz2vrf1m/aIiOwRkV0ickVjddwwDMOov5rsCbwKTD6r7GFgparGAyu944hIX2A6kOid5+8iYm+w3hqGYRgNqtogoKpfACfOKp4CvOYdfg2Y6le+UFWLVXU/sAcY1jBdNQzDMBpaXc8JxKhqBoD3vYO3PBY45Fcv3VtmGIZhtECOBl6eVFKmlVYUuQu4CyA6OprVq1c3cFcMwzCM6tQ1CGSJSCdVzRCRTsBRb3k60NWvXhfgSGULUNV5wDyAhIQEHTduXB27YhiGYdRVXQ8HvQ/M9A7PBJb6lU8XkUARiQPigW/q10XDMAyjsVS7JyAiC4BxQHsRSQfmAk8Di0TkduAgcCOAqm4TkUXAdsAFzFZVdyP13TAMw6gnUa30kH2TSkhI0F27djV3NwzDMM4rIrJJVYfUZxkt4o5hpysPjqRAcV5zd8UwDKNVaeirg+okqDAT5l1qjYR3gqhe1qt9/JnhNt3B3iK6axiGccFoEWvV/NBu8P2/QPYeOL7Het/+HhSePFPJ5oC2cd7A0BOi4s8EitBokMquTjUMwzDOpUUEAY8tAPpOqTih4AQc320FhezdZ4LEnpXgLj5TLzDSGxh6nRUkekJAaNN9EMMwjPNMiwgCVQppB90usV7+PG7IOVR+zyF7NxxcB98uKl83IrbinkNUT+vwks2kNTIMo3Vr2UGgKjY7tO1hvXpdVn5aSQGc2Fd+zyF7D6S+DUU5Z+rZA6zDS6HREBgOgWHe93AICK++LCDMBBHDMM5752cQOJeAEOiYZL38qUJB9lmHl/Za5x1Op1tXJhXnWi//Q03n4gytGBgCI2pQFg5hHSCyizmXYRhGs7rwgkBVRCC0vfXqPuLcdV0lUOIXFIpzveOnyweLyspOHfArOw0eV9XthEZD7GC/1yAIblt1fcMwjAbWeoJAbTgCwNHOOidRH6rgKq48WOQcgsOb4fAm+G7ZmXna9YQuQ7xBYYi1R+MIrF8/DMMwqmCCQGMSAWeQ9QptX3W9ohw48j8rIKRvgn2rYetb1jSbEzr28wsMg61AYWsR9/kZhnGeM2kjWiJVOH0EDm88ExiO/A9K863pQZHQeZAVEMqCQ1iHcy/TMIyGUZxb/qKTE/usw742B9id1gUjNmfF8XNNq+O4tO9V77QRZk+gJRKByFjrVXb/hMcNx3adCQyHN8HaZ6EsP19kN+ucQllg6DTA3CNhGHXlLoWTByreo5S9B/Iy/SoKRHa1Dtl6Sq3/U3epd9gFbpf17ikF9TTbxzkXsydwPivJh4yt3qDgDQ6nDlrTxA4d+pYPDNF9zGWthlFGFfKO+q3kvVcMZu+Gk2nlL+oIifKmsPHeZ1SW0qZtnHW4tyY8Hm9A8AYF/wBxdsDwjZcNewNMuXEXkjyj3nsCJghcaPKOndlTKHsVnbKmOUOtPYTILtaPOjTKeg+JgpD2fsPtTLAwLhzFeXBib/mVfPYea7j49Jl69sAzmQfOzl1W34tEGklDZBE1QeBCp2odszy8CdI3QkYK5GZaKTlKcquYSSC4TfnAEFpJsPAPIgFh5p6H+nCVWOd8SgqsPbz6DrtKrISL9kDrxkhHgDVc9t6YZc21AVGcW34lX7bSz/V/uKH38I3/1nzZK7LreXfBhQkCRv24iq0b6AqyIf+4d/gEFByvuqyq+x7sgVXvXZSVBUZYx07tAd6VRmD5d99wYMv5Z/R4wFUIpUVnvXtfrqJzv5cW1mzl7SmtRafEOt/jDLFujgwIqzjsCLT+Vq5icJd434ut4OAuOXdZrfrSQgW1qbiSbx8P7S4CZ3Bz967BNEQQMCeGWzNHIER0tl41oWrtPucfLx8YfAHDr+zUQcjPhuKc6pdbGZujYmDwbXU6zwog1ZTZnNbKzrdyrskK3TvNXVK3/gM4gqwVjjPUWmmXraTDOnqHQ73Tzh6uZKXuP+wMbty9Lo/nTFCobQBxFzffCVBH8Jk8YSHtzJ5pDbWIPYHQuFAd8vQQAuwBBNgCCLAH4LQ5cdqdvvEAW4A17p3mq+et47RZ03zT/efxez97emRQJBEBEc39FVy4XCVQeMIKDMW5Z7ZMfSuNkgYoK/WukCop863ExVp5lq2Yq3qvSZ2azNuS9maMC1aL3hMQkcnAXwE78C9VfbqquqH2UCZ0m0CJu4RSdymlnlJKPCWUuK1XbkmuVeYdL/H41fOO19Wd/e7k3kH31nl+o6ISdwl5pXnkl+ST78onrySPAlcBRZ4iPDYPKorHDh6nE4/a8WggHvXgwYOq4lEPbnX7hhXFrW5r2FtWVr+szL++W90oZ+qpx43Y7NjEhk1sCIJdrHERa1hErOnYztQTv3p4p4sLmxRg8xRhK7Vhc/lPq+RFFW3Yzkyzie3MdMrPX25eKi5fEOw2e6V9KCvzb1sQpIVtIXvUg8vjsl7qwu1x4/K4cKubUk+pNexx+6aVekpxq7tcucvjQlWx2+zYxY7D5jjzEgd2m9037LB5x73lTpvTN09ZeUN+R6qKS10V1lll6zOXx1VuvMRTQqmnlFJ3+bq+Mr/xhtAoQUBE7MALwOVAOrBBRN5X1e2V1W9rb8vcEXPr3J6q+r5I/y+psmBxdqDp1aZXndu9kJR6SsuttPNL88u98krzKCgtsFbu1ZS5zpUvqYFVuaI9ayWsqr6g4QsO/sHDO601qE1wqiyYlQUn//r+Acut7spX5JWsuF0eF0rzH404m3/g8AUV/wDiLS8LIG51n3OFXZfPaPMoThc43OB0e99dEOSxE6QOgrVhVt+NtScwDNijqvsARGQhMAWoNAjUl4jgtFuHj3A2RgsXhuOFx5n71dyKK/iSvBrvTQU7ggl1hhLmDCPUGUqoM5TYsFjfsH+5bzwglPDfvojty83gsFsvux1xOCq8i8MBDgdityNOZyXvDsTpwGZ3Ik6HNe6wyigbdti9y3BwvNDNyu+OY1MPNo/bernd1rjbhc1jlds9bsTjtsrUW8fjxuZxnZnP9/LOqx5s7jPz2r3LKlu+3e1BBTw2Gx6b3Xq32/CI993m9247q8w7rDYpV672svEz71aZeOuId5qwbUgvioKc5QKfp2zvSt14/PeW8JuG4vG4UfXgQVFV73QPbu/0snFF0bPKwA4agKoNVRuoDfXYUbXh8Zapx3oXjw08NtweweOxpns81jyoHcV6R22ADS0bVnu58YRjWSScyAQUUEQU8IB4rDLfu2IXsIkHmw3EpthEsdkU8ZZb79Y81ru1PGuZiogHcAMukCJQD04VnC5wugWn24nT7cThFhxuxelSHG5wuDzY3eBwe7C7PN5xNzbvu93lxuZy+d6lykP1bsD6f329HuuDMo0VBGKBQ37j6cAlVdQ1mohNbBwrOEaoM5SYkJhKV9qhzlDCAsIIdYQSGhBKqMM77gwlxBGCvY6X/+VceZTinoloqQt1u1FXKbjd5cddbtTlQt0ucLnQsvHifLTUhcdtjVvTvC+3G0pLvcs4M+5vQiX9cdnseGx23Da7tVK22XHbrWG3/zTbmfFSmx23OPE47XgCy093V7Y87zkBm9sbYPyCkPgCjwd7WeAotcps6i3zlOLwG7d7PNjV++5xY9cz486yO8f9vFw4nqzQqEr/HtaKTrD53v2GbWeGRQS7za+O37AIOPyGy6bbbTacNsFuE5x2m/XuFBw2G3a7eKfZcNrL13HYBYfNquewCQ572bvfsK9e+Trhi14ldOXqOv02G5IHodRmp9TmoNTuwFU27H0V2Ry4bE7vuJ1Su4PSIMeZefxerrLptjPTXX7DpXYH7Pp5vfvcKCeGReRG4ApVvcM7fjMwTFV/6lfnLuAugOjo6MGLFi2qdFmGUWuq1svtRtxu62oXmw21W3sgiFwQV45YW+jgUfB4vFvsLrcVCN0eCA5CbDbvChqEM+8t7bxAvZWUIKVNe2mrR8Gl4PKA2u2ow2FdDNCE3+01kya02BPD6UBXv/EugP8dG6jqPGAeWPcJjBs3rpG6YhiGYVSlsa5h2wDEi0iciAQA04H3G6ktwzAMo44aZU9AVV0icg+wHOsS0ZdVdVtjtGUYhmHUXaPdJ6CqHwMfN9byDcMwjPoztzQahmG0YiYIGIZhtGImCBiGYbRiJggYhmG0Yi0ii6iI5ALN9UCBSKCO+Y7Py3abs23zmS/8dpuz7db4meNVNbJeS1DVZn8BG5ux7XmtqV3zmU27F2rb5jPX7WUOB8EHrazd5mzbfOYLv93mbNt85jpoKYeDNmo9818YhmEYtddS9gTmNXcHDMMwWqMWsSdgGIZhNI+WsifQaETELSIpfq8e56i7WkQa5LCUiKiI/Mdv3CEix0Tkw4ZYfg3av87bhz5N1F6zfl5vm3lN1VZd2m/I35d3eU36N/Zr91ER2SYiW73/U032rBAR6SIiS0Vkt4jsFZG/epNUVlX/ZyISUs82VUT+n9/4gyLyeH2WWcN2y9Zd20Rki4g8ICINvs6+4IMAUKiqyX6vtCZqNx9IEpFg7/jlwOHaLEBE6pPbaQawFiuDa23arNtTYxrg8xq1Vqe/cX2IyAjgGmCQqvYHLqP8A6Qas20B3gXeU9V4oDcQBjx5jtl+BtQrCADFwPUi0r6ey6mtsnVXItb/01VA3Z/DW4UmDQLNvaVWRkQGi8jnIrJJRJaLSCe/yf8nIl+JSKqIDKtnU/8FrvYOzwAW+PVhmLed/3nfE7zls0RksYh8AHxSl0ZFJAwYBdyOdwUhIuNE5AsRWSIi20Xkn2VbFSKSJyK/FZH1wIi6fljq9nnXiEiyX70vRaR/XTvg/Zwf+o0/LyKzvMNpIvIbEdksIt82xhb0udpv4Haq+htX9dmvEpGdIrJWRJ6rxx5aJ+C4qhYDqOpxVT1S1f+Ud+/nLw30PzUBKFLVV7xtu4H7gdtEJFRE/uT9u24VkZ+KyL1AZ2CViKyqR7surPOW9589QUS6i8hKb5srRaSbiER6f2tl/18hInJIROr88FtVPYr1EK57xGIXkWdEZIO37R/59ekh7/ewRUSerm7ZrWFPIFjOHApa4v1D/A24QVUHAy9TfksiVFVHAnd7p9XHQmC6iAQB/YH1ftN2AmNVdSDwGPCU37QRwExVrezJiDUxFVimqt8BJ0RkkLd8GPBzoB/QE7jeWx4KpKrqJaq6to5tQt0+77+AWQAi0hsIVNWt9ehDdY6r6iDgH8CDjdhOY5tK5X/jCrx/jxeBK1V1NBBdj3Y/AbqKyHci8ncRubQJ/6cSgU3+Bap6GjgI3AHEAQO9eyhvqOpzWA+zGq+q4+vRLsALwE0icvaNWc8Dr5e1CTynqjnAFuBSb53vActVtV6PPlPrme02oANW8M9R1aHAUOBOsZ7fciXWb+MSVR0A/LG65TZ5EBCRMG/ELNsam+It7yEiO0TkJbGOgX0iZw4t1If/4aDrgAQgCVghIinAr7CefFZmAYCqfgFEiEibujbsXZn1wNoqPjutdiSwWERSgWexfuBlVqjqibq2621voXd4oXcc4BtV3efdgloAjPaWu4F36tEeUOfPuxi4xrsiuQ14tb79qMa73vdN3r6er6r6G1emD7BPVfd7xxeco+45qWoeMBhrq/QY8BbwI5rmf0qwniRfWflY4J+q6vK2VZ//nwq8weZ14N6zJo0A3vQO/4cz/1NvAT/wDk/3jjeEsmdXTgJu8X7f64EoIB7r8Nwrqlrg7Xe130OjPU/gHIqA61T1tFjH2L4WkbKnjsUDM1T1ThFZBEwD5jdw+wJsU9WqDnuc/SOr7+VT7wN/AsZh/aHK/A5YparXiXWyerXftPy6NiYiUVi7zUkiolgP9VGslXJVn63IGxgaQq0+r6oWiMgKYArwfaC+J05dlN+4CTprerH33U3j/P6ra7/ezvE3fr+Kthv0obfe38pqYLWIfAvMpmn+p7ZhrRN8RCQC61G2++qx3Jr6C7AZeOUcdcr68D7wexFphxU0P6tv4yJyEdbv9ijW3/Snqrr8rDqTqeX30ByHgwR4SkS2Ap8CsUCMd9p+VU3xDjfWltouIFqsE1yIiFNE/LfCf+AtH421u1XffCAvA79V1W/PKo/kzInTWfVsw98NWLun3VW1h6p2BfZjbaEM8+4y2rA+Z30O/VSlLp/3X8BzwIYG2II7APQVkUDvrvvEei6vJbZf1d+YKtreCVwkZ66M+wF1JCIJIhLvV5QM7KBp/qdWAiEicot3eXbg/2HtPX4C/Fi8F1N4V74AuUB4Hdsrx/vbXIR1KKbMV5w5MX8T3v8p7x7TN8BfgQ/ru5ElItHAP4Hn1bqufznwk7LzDCLSW0RCsb6H28R7RZTf91Cl5tgTuAnrmORgVS0VkTTObLEU+9VzAw1xOKgcVS0RkRuA57z/KA6sCF/2+MuTIvIVEIF1eKK+7aVj/RDO9kfgNRF5gAbYSvAzAzj7ZNA7wE+Add5p/YAvgCUN2C5Qt8+rqptE5DTn3sI6J+8/f7GqHvLuRW4FdgP/q+syW3D7Vf2Nf4i1kirXtqoWisjdwDIROY61cqqrMOBv3kM6LmAP1qGheTTy/5SqqohcB/xdRH6NtRH7MfBLrPVFb2CriJQCL2Edr58H/FdEMhrgvABYQecev/F7gZdF5BdYh8du9Zv2FtbhznF1bCvYe7jHifVd/wf4s3fav7A2kjeLiHjbnqqqy8S60GKjiJRw5vupUpPeLCbW1UGPAr1U9aciMh5rhRDnrfKhqiZ56z4IhKnq403WwQuYiIwDHlTVa5q5KxWISGeswwt9VNVTx2UMAF5S1fpe0VUnzd1+dUQkTFXzvCuMF4DdqvpsE7S7Gut3t7Gx2zLqpskOB5VtKWGdQR8iIhux9gp2NlUfjJbHu2u/Hni0HgHgx1gnH3/VkH07X9qvoTu9W5XbsA7Nvdi83TFaiibbE2jpW0qGYRitUZPsCZwnW0qGYRitjkkgZxiG0Yo1yp6AiHQVkVXem7+2ich93vJ2IrJCrORPK0Skrbf8crFuN//W+z7Bb1lPinXLdYtIOWEYhnEhaZQ9AbHyhnRS1c0iEo51zf9UrOvDT6jq0yLyMNBWVeeIyEAgy5uDJAnrFutY77KGY117vVtVwxq8s4ZhGK1YkxwOEpGlWNfsPg+MU9UMb6BYraoJZ9UV4DjQuSxJlbc8zwQBwzCMhtXoJ4a9dykOxLoMMEZVMwC87x0qmWUa8D//AGAYhmE0jka9Y1isdLfvAD/z5gqqrn4i8Aes5EiGYRhGI2u0PQFvTot3sFK6lmVuzJIzecY7YSVCKqvfBSuNwS2qurex+mUYhmGc0VhXBwnwb2CHqv7Zb9L7wEzv8Exgqbd+G+Aj4BFV/bIx+mQYhmFU1FhXB40G1gDfAmWpAH6JdV5gEdAN60EQN6rqCRH5FfAIVtKrMpNU9aiI/BErMVZnrAdE/MvkEzIMw2gY5mYxwzCMVqw1PF7SMAzDqIIJAoZhGK2YCQKGYRitmAkChmEYrZgJAoZhGK2YCQKGUQci0sb73N6y8XEi8mFz9skw6sIEAcOomzbA3dVVMoyWzgQB44InIj1EZKeI/EtEUkXkDRG5TES+9D7bYpj3WRfvichWEflaRPp7531cRF4WkdUisk9E7vUu9mmgp4ikiMgz3rIwEXnb29YbUl2yLMNoARo1gZxhtCC9gBuBu4ANWHehjwauxbqb/RBW9tqp3ocavQ4ke+ftA4wHwoFdIvIP4GEgSVWTwTochJUtNxHrzvYvgVHA2kb/ZIZRD2ZPwGgt9qvqt6rqAbYBK9W6Xf5boAdWQPgPgKp+BkSJSKR33o9UtVhVj2MlPYypoo1vVDXd20aKd7mG0aKZIGC0Fv7Pp/D4jXuw9ogrO3RTllPFf143Ve9B17SeYbQYJggYhuUL4CbwHdo5rqqnz1E/F+vwkGGc18yWimFYHgdeEZGtQAFnUp5XSlWzvSeWU4H/YqVCN4zzjskiahiG0YqZw0GGYRitmAkChmEYrZgJAoZhGK2YCQKGYRitmAkChmEYrZgJAoZhGK2YCQKGYRitmAkChmEYrdj/B+FriGpO52ggAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = df#[df[\"reason_for_stop\"] == \"EQUIPMENT VIOLATION\"]\n",
    "a = a.groupby([\"month\",\"reason_for_stop\"]).size().unstack(\"reason_for_stop\")\n",
    "a.plot.line(grid=True)\n",
    "a=a.transpose()\n",
    "a[\"Total\"] = a.transpose().sum()\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'TRAFFIC VIOLATION')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "reason = \"TRAFFIC VIOLATION\"\n",
    "\n",
    "a = df[df[\"reason_for_stop\"] == reason]\n",
    "a = a.groupby([\"month\",\"race_eth\"]).size().unstack(\"race_eth\")\n",
    "\n",
    "# (a.loc[\"2021-03\"] - a.mean())/a.mean()*100\n",
    "ax = a.plot.line(grid=True, marker=\"o\")\n",
    "\n",
    "ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.2),\n",
    "          ncol=3, fancybox=True, shadow=True)\n",
    "ax.set_ylabel(reason)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "race_eth\n",
       "BLACK OR AFRICAN AMERICAN     4\n",
       "HISPANIC                     15\n",
       "UNKNOWN                       1\n",
       "WHITE                         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reason = \"TERRY STOP\"  # CALLS FOR SERVICE \n",
    "\n",
    "a = df[df[\"reason_for_stop\"] == reason]\n",
    "b = a.groupby([\"race_eth\"]).size()\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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>Race/Ethnicity</th>\n",
       "      <th colspan=\"2\" halign=\"left\">ASIAN/PACIFIC ISLANDER</th>\n",
       "      <th colspan=\"2\" halign=\"left\">BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th colspan=\"2\" halign=\"left\">HISPANIC</th>\n",
       "      <th colspan=\"2\" halign=\"left\">WHITE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metric</th>\n",
       "      <th>Count</th>\n",
       "      <th>Percent</th>\n",
       "      <th>Count</th>\n",
       "      <th>Percent</th>\n",
       "      <th>Count</th>\n",
       "      <th>Percent</th>\n",
       "      <th>Count</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>action_taken</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ARREST</th>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>35</td>\n",
       "      <td>1.9</td>\n",
       "      <td>22</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CITATION/SUMMONS</th>\n",
       "      <td>311</td>\n",
       "      <td>54.9</td>\n",
       "      <td>539</td>\n",
       "      <td>49.8</td>\n",
       "      <td>982</td>\n",
       "      <td>52.4</td>\n",
       "      <td>1827</td>\n",
       "      <td>61.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO ENFORCEMENT ACTION</th>\n",
       "      <td>18</td>\n",
       "      <td>3.2</td>\n",
       "      <td>52</td>\n",
       "      <td>4.8</td>\n",
       "      <td>97</td>\n",
       "      <td>5.2</td>\n",
       "      <td>130</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WARNING</th>\n",
       "      <td>235</td>\n",
       "      <td>41.5</td>\n",
       "      <td>487</td>\n",
       "      <td>45.0</td>\n",
       "      <td>760</td>\n",
       "      <td>40.6</td>\n",
       "      <td>974</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Race/Ethnicity        ASIAN/PACIFIC ISLANDER          \\\n",
       "Metric                                 Count Percent   \n",
       "action_taken                                           \n",
       "ARREST                                     2     0.4   \n",
       "CITATION/SUMMONS                         311    54.9   \n",
       "NO ENFORCEMENT ACTION                     18     3.2   \n",
       "WARNING                                  235    41.5   \n",
       "\n",
       "Race/Ethnicity        BLACK OR AFRICAN AMERICAN         HISPANIC          \\\n",
       "Metric                                    Count Percent    Count Percent   \n",
       "action_taken                                                               \n",
       "ARREST                                        4     0.4       35     1.9   \n",
       "CITATION/SUMMONS                            539    49.8      982    52.4   \n",
       "NO ENFORCEMENT ACTION                        52     4.8       97     5.2   \n",
       "WARNING                                     487    45.0      760    40.6   \n",
       "\n",
       "Race/Ethnicity        WHITE          \n",
       "Metric                Count Percent  \n",
       "action_taken                         \n",
       "ARREST                   22     0.7  \n",
       "CITATION/SUMMONS       1827    61.9  \n",
       "NO ENFORCEMENT ACTION   130     4.4  \n",
       "WARNING                 974    33.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df[df[\"reason_for_stop\"] == \"TERRY STOP\"]#\"TRAFFIC VIOLATION\"]\n",
    "new_df = df.copy()\n",
    "action_by_race = new_df.groupby([\"action_taken\",\"race_eth\"]).size().unstack(\"race_eth\")\n",
    "action_by_race = action_by_race[[\"BLACK OR AFRICAN AMERICAN\", \"HISPANIC\", \"WHITE\", \"ASIAN/PACIFIC ISLANDER\"]]\n",
    "perc_by_race = np.round(action_by_race / action_by_race.sum() *100,1)\n",
    "perc_by_race.columns = pd.MultiIndex.from_product([[\"Percent\"], perc_by_race.columns], names=[\"Metric\", \"Race/Ethnicity\"])\n",
    "action_by_race.columns = pd.MultiIndex.from_product([[\"Count\"], action_by_race.columns], names=[\"Metric\", \"Race/Ethnicity\"])\n",
    "A = pd.concat([action_by_race, perc_by_race], axis=1)\n",
    "A = A.swaplevel(axis=1)\n",
    "A.sort_index(axis=1, level=0, inplace=True)\n",
    "A\n",
    "\n",
    "# action_by_race.fillna(0).convert_dtypes()\n",
    "# perc_by_race.transpose()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NO     6744\n",
       "YES      58\n",
       "Name: person_searched, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Totals for people searched\n",
    "df[\"person_searched\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race_eth</th>\n",
       "      <th>AMERICAN INDIAN</th>\n",
       "      <th>ASIAN/PACIFIC ISLANDER</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC</th>\n",
       "      <th>UNKNOWN</th>\n",
       "      <th>WHITE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>person_searched</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>86.0</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1077.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>2932.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YES</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth         AMERICAN INDIAN  ASIAN/PACIFIC ISLANDER  \\\n",
       "person_searched                                            \n",
       "NO                          86.0                   562.0   \n",
       "YES                          NaN                     4.0   \n",
       "\n",
       "race_eth         BLACK OR AFRICAN AMERICAN  HISPANIC  UNKNOWN   WHITE  \n",
       "person_searched                                                        \n",
       "NO                                  1077.0    1847.0    240.0  2932.0  \n",
       "YES                                    5.0      27.0      1.0    21.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df\n",
    "# new_df = df[df[\"reason_for_stop\"] == \"TRAFFIC VIOLATION\"]\n",
    "action_by_race = new_df.groupby([\"person_searched\",\"race_eth\"]).size().unstack(\"race_eth\")\n",
    "perc_by_race = np.round(action_by_race / action_by_race.sum() *100,1)\n",
    "\n",
    "action_by_race\n",
    "perc_by_race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 8.0)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = perc_by_race.fillna(0).transpose().plot.barh(y=\"YES\", grid=True, legend=False)\n",
    "ax.set_ylabel(\"\")\n",
    "ax.set_xlabel(\"Percentage\")\n",
    "ax.set_title(\"Percentage of Stops Where the Person is Searched\")\n",
    "ax.set_xlim((0, 14.8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "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",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\"># of Searches</th>\n",
       "      <th>Search Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>WARNING</th>\n",
       "      <th>CITATION/SUMMONS</th>\n",
       "      <th>ARREST</th>\n",
       "      <th>NO ENFORCEMENT ACTION</th>\n",
       "      <th>TOTAL</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMERICAN INDIAN</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASIAN/PACIFIC ISLANDER</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HISPANIC</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNKNOWN</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WHITE</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          # of Searches                          \\\n",
       "                                WARNING CITATION/SUMMONS ARREST   \n",
       "AMERICAN INDIAN                       0                0      0   \n",
       "ASIAN/PACIFIC ISLANDER                1                2      1   \n",
       "BLACK OR AFRICAN AMERICAN             1                1      3   \n",
       "HISPANIC                              1                0     26   \n",
       "UNKNOWN                               0                0      1   \n",
       "WHITE                                 1                2     18   \n",
       "\n",
       "                                                      Search Rate  \n",
       "                          NO ENFORCEMENT ACTION TOTAL              \n",
       "AMERICAN INDIAN                               0     0         0.0  \n",
       "ASIAN/PACIFIC ISLANDER                        0     4         0.7  \n",
       "BLACK OR AFRICAN AMERICAN                     0     5         0.5  \n",
       "HISPANIC                                      0    27         1.4  \n",
       "UNKNOWN                                       0     1         0.4  \n",
       "WHITE                                         0    21         0.7  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actions = list(df[\"action_taken\"].unique())\n",
    "\n",
    "cols = []\n",
    "searches_action = df[df[\"action_taken\"] == a].groupby([\"person_searched\",\"race_eth\"]).size().unstack(\"race_eth\")\n",
    "\n",
    "for a in actions:\n",
    "    searches_action = df[df[\"action_taken\"] == a].groupby([\"person_searched\",\"race_eth\"]).size().unstack(\"race_eth\")\n",
    "    if \"YES\" in searches_action.index:\n",
    "        cols.append(searches_action.loc[\"YES\"])\n",
    "    else:\n",
    "        cols.append(pd.Series([0 for x in searches_action.columns], index=searches_action.columns))\n",
    "\n",
    "total = df.groupby([\"person_searched\",\"race_eth\"]).size().unstack(\"race_eth\").loc[\"YES\"]\n",
    "actions.append(\"TOTAL\")\n",
    "cols.append(total)\n",
    "actions.append(\"Search Rate\")\n",
    "cols.append(np.round(total / df[\"race_eth\"].value_counts()*100,1))\n",
    "\n",
    "last = pd.MultiIndex.from_tuples([(actions[-1], \"\")])\n",
    "col_names = pd.MultiIndex.from_product([[\"# of Searches\"], actions[:-1]]).append(last)\n",
    "df_searches = pd.DataFrame(cols).transpose().fillna(0).convert_dtypes()\n",
    "df_searches.columns = col_names\n",
    "df_searches\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race_eth</th>\n",
       "      <th>AMERICAN INDIAN</th>\n",
       "      <th>ASIAN/PACIFIC ISLANDER</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC</th>\n",
       "      <th>UNKNOWN</th>\n",
       "      <th>WHITE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>person_searched</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>85.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>1838.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>2928.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YES</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth         AMERICAN INDIAN  ASIAN/PACIFIC ISLANDER  \\\n",
       "person_searched                                            \n",
       "NO                          85.0                   561.0   \n",
       "YES                          NaN                     3.0   \n",
       "\n",
       "race_eth         BLACK OR AFRICAN AMERICAN  HISPANIC  UNKNOWN   WHITE  \n",
       "person_searched                                                        \n",
       "NO                                  1076.0    1838.0    240.0  2928.0  \n",
       "YES                                    2.0       1.0      NaN     3.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df[df[\"action_taken\"] != \"ARREST\"]\n",
    "# new_df = df[df[\"reason_for_stop\"] == \"EQUIPMENT VIOLATION\"]\n",
    "action_by_race = new_df.groupby([\"person_searched\",\"race_eth\"]).size().unstack(\"race_eth\")\n",
    "perc_by_race = np.round(action_by_race / action_by_race.sum() *100,1)\n",
    "\n",
    "action_by_race\n",
    "# perc_by_race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NO     6773\n",
       "YES      29\n",
       "Name: vehicle_searched, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Totals for vehicle search\n",
    "df[\"vehicle_searched\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race_eth</th>\n",
       "      <th>AMERICAN INDIAN</th>\n",
       "      <th>ASIAN/PACIFIC ISLANDER</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC</th>\n",
       "      <th>UNKNOWN</th>\n",
       "      <th>WHITE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vehicle_searched</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>98.8</td>\n",
       "      <td>99.3</td>\n",
       "      <td>99.6</td>\n",
       "      <td>99.3</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YES</th>\n",
       "      <td>1.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth          AMERICAN INDIAN  ASIAN/PACIFIC ISLANDER  \\\n",
       "vehicle_searched                                            \n",
       "NO                           98.8                    99.3   \n",
       "YES                           1.2                     0.7   \n",
       "\n",
       "race_eth          BLACK OR AFRICAN AMERICAN  HISPANIC  UNKNOWN  WHITE  \n",
       "vehicle_searched                                                       \n",
       "NO                                     99.6      99.3    100.0   99.8  \n",
       "YES                                     0.4       0.7      NaN    0.2  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df\n",
    "# new_df = df[df[\"reason_for_stop\"] == \"EQUIPMENT VIOLATION\"]\n",
    "action_by_race = new_df.groupby([\"vehicle_searched\",\"race_eth\"]).size().unstack(\"race_eth\")\n",
    "perc_by_race = np.round(action_by_race / action_by_race.sum() *100,1)\n",
    "\n",
    "action_by_race\n",
    "perc_by_race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race_eth</th>\n",
       "      <th>AMERICAN INDIAN</th>\n",
       "      <th>ASIAN/PACIFIC ISLANDER</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC</th>\n",
       "      <th>UNKNOWN</th>\n",
       "      <th>WHITE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vehicle_searched</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>84.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>1074.0</td>\n",
       "      <td>1835.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>2928.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YES</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth          AMERICAN INDIAN  ASIAN/PACIFIC ISLANDER  \\\n",
       "vehicle_searched                                            \n",
       "NO                           84.0                   560.0   \n",
       "YES                           1.0                     4.0   \n",
       "\n",
       "race_eth          BLACK OR AFRICAN AMERICAN  HISPANIC  UNKNOWN   WHITE  \n",
       "vehicle_searched                                                        \n",
       "NO                                   1074.0    1835.0    240.0  2928.0  \n",
       "YES                                     4.0       4.0      NaN     3.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df[df[\"action_taken\"] != \"ARREST\"]\n",
    "# new_df = df[df[\"reason_for_stop\"] == \"EQUIPMENT VIOLATION\"]\n",
    "action_by_race = new_df.groupby([\"vehicle_searched\",\"race_eth\"]).size().unstack(\"race_eth\")\n",
    "perc_by_race = np.round(action_by_race / action_by_race.sum() *100,1)\n",
    "\n",
    "action_by_race\n",
    "# perc_by_race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['incident_date', 'agency_name', 'jurisdiction', 'reason_for_stop',\n",
       "       'race', 'ethnicity', 'age', 'gender', 'action_taken', 'violation_type',\n",
       "       'specific_violation', 'person_searched', 'vehicle_searched',\n",
       "       'additional_arrest', 'person_type', 'english_speaking',\n",
       "       'physical_force_by_officer', 'physical_force_by_subject', 'residency',\n",
       "       'month', 'race_eth'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DRIVER        3119\n",
       "PEDESTRIAN      10\n",
       "MISSING          3\n",
       "Name: person_type, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"person_type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YES        2905\n",
       "NO          224\n",
       "MISSING       3\n",
       "Name: english_speaking, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"english_speaking\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>english_speaking</th>\n",
       "      <th>MISSING</th>\n",
       "      <th>NO</th>\n",
       "      <th>YES</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>action_taken</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ARREST</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CITATION/SUMMONS</th>\n",
       "      <td>100.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>58.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO ENFORCEMENT ACTION</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WARNING</th>\n",
       "      <td>NaN</td>\n",
       "      <td>35.3</td>\n",
       "      <td>38.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "english_speaking       MISSING    NO   YES\n",
       "action_taken                              \n",
       "ARREST                     NaN   2.7   0.8\n",
       "CITATION/SUMMONS         100.0  58.0  58.9\n",
       "NO ENFORCEMENT ACTION      NaN   4.0   1.4\n",
       "WARNING                    NaN  35.3  38.9"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "action_breakdown = df.groupby([\"action_taken\",\"english_speaking\"]).size().unstack(\"english_speaking\")\n",
    "np.round(action_breakdown / action_breakdown.sum()*100,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NO         3123\n",
       "YES           6\n",
       "MISSING       3\n",
       "Name: physical_force_by_officer, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns\n",
    "df[\"physical_force_by_officer\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race_eth</th>\n",
       "      <th>AMERICAN INDIAN</th>\n",
       "      <th>ASIAN/PACIFIC ISLANDER</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC</th>\n",
       "      <th>UNKNOWN</th>\n",
       "      <th>WHITE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>physical_force_by_officer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MISSING</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>9.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>868.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1452.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YES</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth                   AMERICAN INDIAN  ASIAN/PACIFIC ISLANDER  \\\n",
       "physical_force_by_officer                                            \n",
       "MISSING                                NaN                     1.0   \n",
       "NO                                     9.0                   262.0   \n",
       "YES                                    NaN                     NaN   \n",
       "\n",
       "race_eth                   BLACK OR AFRICAN AMERICAN  HISPANIC  UNKNOWN  \\\n",
       "physical_force_by_officer                                                 \n",
       "MISSING                                          NaN       NaN      NaN   \n",
       "NO                                             491.0     868.0     41.0   \n",
       "YES                                              NaN       2.0      NaN   \n",
       "\n",
       "race_eth                    WHITE  \n",
       "physical_force_by_officer          \n",
       "MISSING                       2.0  \n",
       "NO                         1452.0  \n",
       "YES                           4.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "action_breakdown = df.groupby([\"physical_force_by_officer\",\"race_eth\"]).size().unstack(\"race_eth\")\n",
    "np.round(action_breakdown / action_breakdown.sum()*100,1)\n",
    "action_breakdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>physical_force_by_subject</th>\n",
       "      <th>MISSING</th>\n",
       "      <th>NO</th>\n",
       "      <th>YES</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>physical_force_by_officer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MISSING</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3123.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YES</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "physical_force_by_subject  MISSING      NO  YES\n",
       "physical_force_by_officer                      \n",
       "MISSING                        3.0     NaN  NaN\n",
       "NO                             NaN  3123.0  NaN\n",
       "YES                            NaN     NaN  6.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"physical_force_by_officer\",\"physical_force_by_subject\"]).size().unstack(\"physical_force_by_subject\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race_eth</th>\n",
       "      <th>AMERICAN INDIAN</th>\n",
       "      <th>ASIAN/PACIFIC ISLANDER</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC</th>\n",
       "      <th>UNKNOWN</th>\n",
       "      <th>WHITE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth  AMERICAN INDIAN  ASIAN/PACIFIC ISLANDER  BLACK OR AFRICAN AMERICAN  \\\n",
       "age                                                                            \n",
       "0                     NaN                     NaN                        NaN   \n",
       "16                    NaN                     1.0                        2.0   \n",
       "17                    NaN                     2.0                        6.0   \n",
       "18                    NaN                     6.0                        4.0   \n",
       "19                    1.0                     6.0                        8.0   \n",
       "..                    ...                     ...                        ...   \n",
       "82                    NaN                     NaN                        1.0   \n",
       "83                    NaN                     NaN                        NaN   \n",
       "84                    NaN                     NaN                        NaN   \n",
       "86                    NaN                     1.0                        NaN   \n",
       "88                    NaN                     NaN                        NaN   \n",
       "\n",
       "race_eth  HISPANIC  UNKNOWN  WHITE  \n",
       "age                                 \n",
       "0              NaN      1.0    NaN  \n",
       "16            12.0      3.0    7.0  \n",
       "17            24.0      7.0   30.0  \n",
       "18            41.0      4.0   48.0  \n",
       "19            56.0      7.0   47.0  \n",
       "..             ...      ...    ...  \n",
       "82             NaN      NaN    2.0  \n",
       "83             1.0      NaN    NaN  \n",
       "84             1.0      NaN    NaN  \n",
       "86             NaN      NaN    NaN  \n",
       "88             NaN      NaN    2.0  \n",
       "\n",
       "[72 rows x 6 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"age\",\"race_eth\"]).size().unstack(\"race_eth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race_eth</th>\n",
       "      <th>AMERICAN INDIAN</th>\n",
       "      <th>ASIAN/PACIFIC ISLANDER</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC</th>\n",
       "      <th>UNKNOWN</th>\n",
       "      <th>WHITE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_range</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16-25</th>\n",
       "      <td>8.1</td>\n",
       "      <td>12.9</td>\n",
       "      <td>20.3</td>\n",
       "      <td>24.1</td>\n",
       "      <td>26.6</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26-35</th>\n",
       "      <td>29.1</td>\n",
       "      <td>31.4</td>\n",
       "      <td>31.3</td>\n",
       "      <td>28.7</td>\n",
       "      <td>28.2</td>\n",
       "      <td>28.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36-45</th>\n",
       "      <td>25.6</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>23.8</td>\n",
       "      <td>19.5</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46-55</th>\n",
       "      <td>18.6</td>\n",
       "      <td>17.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>14.1</td>\n",
       "      <td>16.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56-65</th>\n",
       "      <td>14.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>6.9</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65+</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth   AMERICAN INDIAN  ASIAN/PACIFIC ISLANDER  BLACK OR AFRICAN AMERICAN  \\\n",
       "age_range                                                                       \n",
       "16-25                  8.1                    12.9                       20.3   \n",
       "26-35                 29.1                    31.4                       31.3   \n",
       "36-45                 25.6                    24.0                       22.4   \n",
       "46-55                 18.6                    17.5                       15.0   \n",
       "56-65                 14.0                    11.1                        7.7   \n",
       "65+                    4.7                     3.0                        3.3   \n",
       "\n",
       "race_eth   HISPANIC  UNKNOWN  WHITE  \n",
       "age_range                            \n",
       "16-25          24.1     26.6   19.0  \n",
       "26-35          28.7     28.2   28.5  \n",
       "36-45          23.8     19.5   20.0  \n",
       "46-55          14.5     14.1   16.2  \n",
       "56-65           6.9      8.7   10.2  \n",
       "65+             2.1      2.9    6.1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stops_breakdown = df.groupby([\"age_range\",\"race_eth\"]).size().unstack(\"race_eth\")\n",
    "np.round(stops_breakdown / stops_breakdown.sum()*100,1)\n",
    "# stops_breakdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race_eth</th>\n",
       "      <th>AMERICAN INDIAN</th>\n",
       "      <th>ASIAN/PACIFIC ISLANDER</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC</th>\n",
       "      <th>UNKNOWN</th>\n",
       "      <th>WHITE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_range</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16-25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26-35</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36-45</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46-55</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56-65</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65+</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth   AMERICAN INDIAN  ASIAN/PACIFIC ISLANDER  BLACK OR AFRICAN AMERICAN  \\\n",
       "age_range                                                                       \n",
       "16-25                  0.0                     0.0                        0.5   \n",
       "26-35                  4.0                     0.0                        0.3   \n",
       "36-45                  0.0                     1.5                        0.8   \n",
       "46-55                  0.0                     0.0                        0.0   \n",
       "56-65                  0.0                     0.0                        0.0   \n",
       "65+                    0.0                     0.0                        0.0   \n",
       "\n",
       "race_eth   HISPANIC  UNKNOWN  WHITE  \n",
       "age_range                            \n",
       "16-25           2.0      0.0    0.9  \n",
       "26-35           1.7      1.5    0.6  \n",
       "36-45           2.5      0.0    0.7  \n",
       "46-55           1.5      0.0    1.0  \n",
       "56-65           1.6      0.0    1.0  \n",
       "65+             0.0      0.0    0.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df[df[\"action_taken\"]==\"ARREST\"]\n",
    "action_breakdown = new_df.groupby([\"age_range\",\"race_eth\"]).size().unstack(\"race_eth\")\n",
    "# np.round(action_breakdown / action_breakdown.sum()*100,1)\n",
    "np.round(action_breakdown / stops_breakdown *100,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46.2-646     74\n",
       "46.2-1030    52\n",
       "46.2-1158    33\n",
       "46.2-300      6\n",
       "46.2-716      5\n",
       "46.2-613      5\n",
       "46.2-1003     3\n",
       "46.2-715      3\n",
       "46.2-1011     3\n",
       "46.2-612      2\n",
       "46.2-1034     1\n",
       "46.2-600      1\n",
       "46.2-722      1\n",
       "46.2-818      1\n",
       "46.2-1014     1\n",
       "46.2-1013     1\n",
       "46.2-712      1\n",
       "Name: specific_violation, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = df[df[\"reason_for_stop\"] == \"EQUIPMENT VIOLATION\"]\n",
    "len(a)\n",
    "\n",
    "a = a[a[\"incident_date\"] > \"2021-04-30\"]\n",
    "# a = a[a[\"incident_date\"] > \"2021-10-31\"]\n",
    "\n",
    "a[\"specific_violation\"].value_counts()\n",
    "# a[\"incident_date\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC</th>\n",
       "      <th>UNKNOWN</th>\n",
       "      <th>WHITE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>action_taken</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CITATION/SUMMONS</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO ENFORCEMENT ACTION</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WARNING</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       BLACK OR AFRICAN AMERICAN  HISPANIC  UNKNOWN  WHITE\n",
       "action_taken                                                              \n",
       "CITATION/SUMMONS                               0         1        0      0\n",
       "NO ENFORCEMENT ACTION                          4        10        1      2\n",
       "WARNING                                        0         4        0      0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = df[df[\"reason_for_stop\"] == \"TERRY STOP\"]\n",
    "\n",
    "a.groupby([\"action_taken\",\"race_eth\"]).size().unstack(\"race_eth\").replace(np.nan,0).convert_dtypes(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC</th>\n",
       "      <th>UNKNOWN</th>\n",
       "      <th>WHITE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-03</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         BLACK OR AFRICAN AMERICAN  HISPANIC  UNKNOWN  WHITE\n",
       "month                                                       \n",
       "2021-03                          2         6        0      0\n",
       "2021-05                          1         1        1      0\n",
       "2021-06                          0         1        0      0\n",
       "2021-07                          0         3        0      0\n",
       "2021-08                          1         0        0      0\n",
       "2021-10                          0         1        0      0\n",
       "2021-11                          0         1        0      0\n",
       "2021-12                          0         2        0      2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.groupby([\"month\",\"race_eth\"]).size().unstack(\"race_eth\").replace(np.nan,0).convert_dtypes(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>incident_date</th>\n",
       "      <th>race</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>action_taken</th>\n",
       "      <th>specific_violation</th>\n",
       "      <th>person_searched</th>\n",
       "      <th>vehicle_searched</th>\n",
       "      <th>person_type</th>\n",
       "      <th>english_speaking</th>\n",
       "      <th>physical_force_by_officer</th>\n",
       "      <th>residency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>2021-03-05</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>47</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NO ENFORCEMENT ACTION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>2021-03-06</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>31</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NO ENFORCEMENT ACTION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>2021-03-06</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>22</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>NO ENFORCEMENT ACTION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>2021-03-11</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>23</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WARNING</td>\n",
       "      <td>4.1-305</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>2021-03-11</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>30</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WARNING</td>\n",
       "      <td>4.1-305</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>2021-03-16</td>\n",
       "      <td>BLACK OR AFRICAN AMERICAN</td>\n",
       "      <td>NON-HISPANIC</td>\n",
       "      <td>35</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>NO ENFORCEMENT ACTION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>2021-03-20</td>\n",
       "      <td>BLACK OR AFRICAN AMERICAN</td>\n",
       "      <td>NON-HISPANIC</td>\n",
       "      <td>24</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NO ENFORCEMENT ACTION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>2021-03-29</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>42</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NO ENFORCEMENT ACTION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    incident_date                       race     ethnicity age  gender  \\\n",
       "281    2021-03-05                      WHITE      HISPANIC  47    MALE   \n",
       "293    2021-03-06                      WHITE      HISPANIC  31    MALE   \n",
       "294    2021-03-06                      WHITE      HISPANIC  22  FEMALE   \n",
       "383    2021-03-11                      WHITE      HISPANIC  23    MALE   \n",
       "384    2021-03-11                      WHITE      HISPANIC  30    MALE   \n",
       "591    2021-03-16  BLACK OR AFRICAN AMERICAN  NON-HISPANIC  35  FEMALE   \n",
       "649    2021-03-20  BLACK OR AFRICAN AMERICAN  NON-HISPANIC  24    MALE   \n",
       "936    2021-03-29                      WHITE      HISPANIC  42    MALE   \n",
       "\n",
       "              action_taken specific_violation person_searched  \\\n",
       "281  NO ENFORCEMENT ACTION                NaN              NO   \n",
       "293  NO ENFORCEMENT ACTION                NaN              NO   \n",
       "294  NO ENFORCEMENT ACTION                NaN              NO   \n",
       "383                WARNING            4.1-305              NO   \n",
       "384                WARNING            4.1-305              NO   \n",
       "591  NO ENFORCEMENT ACTION                NaN              NO   \n",
       "649  NO ENFORCEMENT ACTION                NaN              NO   \n",
       "936  NO ENFORCEMENT ACTION                NaN              NO   \n",
       "\n",
       "    vehicle_searched person_type english_speaking physical_force_by_officer  \\\n",
       "281               NO         NaN              NaN                       NaN   \n",
       "293               NO         NaN              NaN                       NaN   \n",
       "294               NO         NaN              NaN                       NaN   \n",
       "383               NO         NaN              NaN                       NaN   \n",
       "384               NO         NaN              NaN                       NaN   \n",
       "591               NO         NaN              NaN                       NaN   \n",
       "649               NO         NaN              NaN                       NaN   \n",
       "936               NO         NaN              NaN                       NaN   \n",
       "\n",
       "    residency  \n",
       "281       NaN  \n",
       "293       NaN  \n",
       "294       NaN  \n",
       "383       NaN  \n",
       "384       NaN  \n",
       "591       NaN  \n",
       "649       NaN  \n",
       "936       NaN  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = a[a[\"month\"]==\"2021-03\"]\n",
    "\n",
    "b[[\"incident_date\", \"race\",\"ethnicity\",\"age\",\"gender\",'action_taken','specific_violation','person_searched','vehicle_searched',\"person_type\",'english_speaking','physical_force_by_officer','residency']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2b4f00ec400>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = df[(df[\"reason_for_stop\"] == \"TRAFFIC VIOLATION\") & (df[\"month\"] == \"2021-03\")]\n",
    "\n",
    "b = a.groupby([\"incident_date\",\"race_eth\"]).size().unstack(\"race_eth\").replace(np.nan,0).convert_dtypes(int)\n",
    "ax = b.plot(grid=True,marker='o')\n",
    "ax.set_ylabel('Count')\n",
    "ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.2),\n",
    "          ncol=3, fancybox=True, shadow=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMERICAN INDIAN</th>\n",
       "      <th>ASIAN/PACIFIC ISLANDER</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC</th>\n",
       "      <th>UNKNOWN</th>\n",
       "      <th>WHITE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_range</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16-25</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>55</td>\n",
       "      <td>23</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26-35</th>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>42</td>\n",
       "      <td>57</td>\n",
       "      <td>19</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36-45</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>55</td>\n",
       "      <td>22</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46-55</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56-65</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65+</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           AMERICAN INDIAN  ASIAN/PACIFIC ISLANDER  BLACK OR AFRICAN AMERICAN  \\\n",
       "age_range                                                                       \n",
       "16-25                    0                       4                         14   \n",
       "26-35                    5                      13                         42   \n",
       "36-45                    3                      15                         16   \n",
       "46-55                    2                       7                         18   \n",
       "56-65                    0                       7                          9   \n",
       "65+                      2                       0                          2   \n",
       "\n",
       "           HISPANIC  UNKNOWN  WHITE  \n",
       "age_range                            \n",
       "16-25            55       23     50  \n",
       "26-35            57       19     83  \n",
       "36-45            55       22     47  \n",
       "46-55            36       11     39  \n",
       "56-65            16        9     31  \n",
       "65+               5        3     15  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = df[(df[\"reason_for_stop\"] == \"TRAFFIC VIOLATION\") & (df[\"month\"] == \"2021-03\")] #  & ((df[\"ethnicity\"]==\"HISPANIC\") | (df[\"ethnicity\"]==\"UNKNOWN\"))\n",
    "\n",
    "b = a.groupby([\"age_range\",\"race_eth\"]).size().unstack(\"race_eth\").replace(np.nan,0).convert_dtypes(int)\n",
    "b\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = a[a[\"race_eth\"]==\"WHITE\"][\"specific_violation\"].value_counts(normalize=False)\n",
    "c = a[a[\"race_eth\"]==\"HISPANIC\"][\"specific_violation\"].value_counts(normalize=False)\n",
    "\n",
    "df_viol = pd.DataFrame({\"WHITE\":b, \"HISPANIC\":c})\n",
    "df_viol.sort_values(\"HISPANIC\",ascending=False, inplace=True)\n",
    "\n",
    "df_viol.fillna(0, inplace=True)\n",
    "df_viol[\"WHITE\"] = df_viol[\"WHITE\"].astype(int)\n",
    "df_viol[\"HISPANIC\"] = df_viol[\"HISPANIC\"].astype(int)\n",
    "\n",
    "df_viol = df_viol[(df_viol[\"WHITE\"]>=5) | (df_viol[\"HISPANIC\"]>=5)]\n",
    "\n",
    "titles = []\n",
    "for num in df_viol.index:\n",
    "    try:\n",
    "        titles.append(section_num_to_title(num))\n",
    "    except:\n",
    "        titles.append(\"\")\n",
    "\n",
    "df_viol[\"Section Title\"] = titles\n",
    "df_viol_orig = df_viol.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "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",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">WHITE</th>\n",
       "      <th colspan=\"2\" halign=\"left\">HISPANIC</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th># in March</th>\n",
       "      <th>% for year</th>\n",
       "      <th># in March</th>\n",
       "      <th>% for year</th>\n",
       "      <th>Section Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46.2-830</th>\n",
       "      <td>63</td>\n",
       "      <td>11.8</td>\n",
       "      <td>64</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Uniform traffic control devices on highways; d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-818.2</th>\n",
       "      <td>13</td>\n",
       "      <td>48.1</td>\n",
       "      <td>17</td>\n",
       "      <td>56.7</td>\n",
       "      <td>Use of handheld personal communications device...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-300</th>\n",
       "      <td>7</td>\n",
       "      <td>13.2</td>\n",
       "      <td>17</td>\n",
       "      <td>11.5</td>\n",
       "      <td>Driving without license prohibited; penalties</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-804</th>\n",
       "      <td>15</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Special regulations applicable on highways lan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-835</th>\n",
       "      <td>16</td>\n",
       "      <td>17.8</td>\n",
       "      <td>12</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Right turn on steady red light after stopping</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-874</th>\n",
       "      <td>29</td>\n",
       "      <td>12.1</td>\n",
       "      <td>11</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Maximum speed limit in business and residence ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42-8</th>\n",
       "      <td>14</td>\n",
       "      <td>11.6</td>\n",
       "      <td>11</td>\n",
       "      <td>14.7</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-833.1</th>\n",
       "      <td>5</td>\n",
       "      <td>71.4</td>\n",
       "      <td>11</td>\n",
       "      <td>91.7</td>\n",
       "      <td>Evasion of traffic control devices</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-833</th>\n",
       "      <td>19</td>\n",
       "      <td>15.4</td>\n",
       "      <td>8</td>\n",
       "      <td>12.3</td>\n",
       "      <td>Traffic lights; penalty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-1158</th>\n",
       "      <td>16</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8</td>\n",
       "      <td>8.8</td>\n",
       "      <td>Frequency of inspection; scope of inspection</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42-9</th>\n",
       "      <td>4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>7</td>\n",
       "      <td>11.3</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-646</th>\n",
       "      <td>16</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "      <td>8.9</td>\n",
       "      <td>Expiration and renewal of registration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-1030</th>\n",
       "      <td>6</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>When lights to be lighted; number of lights to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-875</th>\n",
       "      <td>10</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4</td>\n",
       "      <td>9.1</td>\n",
       "      <td>Maximum speed limit on certain other highways ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-821</th>\n",
       "      <td>9</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>Vehicles before entering certain highways shal...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                WHITE              HISPANIC             \\\n",
       "           # in March % for year # in March % for year   \n",
       "46.2-830           63       11.8         64       20.0   \n",
       "46.2-818.2         13       48.1         17       56.7   \n",
       "46.2-300            7       13.2         17       11.5   \n",
       "46.2-804           15       13.0         14       20.0   \n",
       "46.2-835           16       17.8         12       20.0   \n",
       "46.2-874           29       12.1         11       11.0   \n",
       "42-8               14       11.6         11       14.7   \n",
       "46.2-833.1          5       71.4         11       91.7   \n",
       "46.2-833           19       15.4          8       12.3   \n",
       "46.2-1158          16        5.4          8        8.8   \n",
       "42-9                4        5.3          7       11.3   \n",
       "46.2-646           16        7.0          5        8.9   \n",
       "46.2-1030           6        9.8          5       10.0   \n",
       "46.2-875           10        9.0          4        9.1   \n",
       "46.2-821            9       15.5          2        5.3   \n",
       "\n",
       "                                                               \n",
       "                                                Section Title  \n",
       "46.2-830    Uniform traffic control devices on highways; d...  \n",
       "46.2-818.2  Use of handheld personal communications device...  \n",
       "46.2-300        Driving without license prohibited; penalties  \n",
       "46.2-804    Special regulations applicable on highways lan...  \n",
       "46.2-835        Right turn on steady red light after stopping  \n",
       "46.2-874    Maximum speed limit in business and residence ...  \n",
       "42-8                                                           \n",
       "46.2-833.1                 Evasion of traffic control devices  \n",
       "46.2-833                              Traffic lights; penalty  \n",
       "46.2-1158        Frequency of inspection; scope of inspection  \n",
       "42-9                                                           \n",
       "46.2-646               Expiration and renewal of registration  \n",
       "46.2-1030   When lights to be lighted; number of lights to...  \n",
       "46.2-875    Maximum speed limit on certain other highways ...  \n",
       "46.2-821    Vehicles before entering certain highways shal...  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_viol = df_viol_orig.copy()\n",
    "\n",
    "tv = df[df[\"reason_for_stop\"] == \"TRAFFIC VIOLATION\"]\n",
    "violations_year_w = tv[tv[\"race_eth\"]==\"WHITE\"][\"specific_violation\"].value_counts(normalize=False)\n",
    "violations_year_h = tv[tv[\"race_eth\"]==\"HISPANIC\"][\"specific_violation\"].value_counts(normalize=False)\n",
    "\n",
    "perc_total_w = np.round(df_viol[\"WHITE\"] / violations_year_w *100,1)\n",
    "perc_total_w.dropna(inplace=True)\n",
    "perc_total_h = np.round(df_viol[\"HISPANIC\"] / violations_year_h *100,1)\n",
    "perc_total_h.dropna(inplace=True)\n",
    "\n",
    "df_viol[\"WHITE %\"] = perc_total_w\n",
    "df_viol[\"HISPANIC %\"] = perc_total_h\n",
    "df_viol.columns = pd.MultiIndex.from_tuples([(\"WHITE\",\"# in March\"),(\"HISPANIC\",\"# in March\"), (\"\",\"Section Title\",),(\"WHITE\",\"% for year\"),(\"HISPANIC\",\"% for year\")])\n",
    "df_viol = df_viol.reindex(columns=df_viol.columns[[0,3,1,4,2]])\n",
    "df_viol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "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",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">TOTAL</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th># in March</th>\n",
       "      <th>% for year</th>\n",
       "      <th>Section Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46.2-830</th>\n",
       "      <td>225</td>\n",
       "      <td>17.3</td>\n",
       "      <td>Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcemen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-874</th>\n",
       "      <td>53</td>\n",
       "      <td>11.2</td>\n",
       "      <td>Maximum speed limit in business and residence districts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-818.2</th>\n",
       "      <td>41</td>\n",
       "      <td>51.9</td>\n",
       "      <td>Use of handheld personal communications devices in certain motor vehicles; exceptions; penalty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42-8</th>\n",
       "      <td>40</td>\n",
       "      <td>13.3</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-835</th>\n",
       "      <td>38</td>\n",
       "      <td>18.4</td>\n",
       "      <td>Right turn on steady red light after stopping</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-833</th>\n",
       "      <td>38</td>\n",
       "      <td>13.1</td>\n",
       "      <td>Traffic lights; penalty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-804</th>\n",
       "      <td>37</td>\n",
       "      <td>13.4</td>\n",
       "      <td>Special regulations applicable on highways laned for traffic; penalty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-1158</th>\n",
       "      <td>36</td>\n",
       "      <td>6.9</td>\n",
       "      <td>Frequency of inspection; scope of inspection</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-646</th>\n",
       "      <td>33</td>\n",
       "      <td>8.1</td>\n",
       "      <td>Expiration and renewal of registration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-300</th>\n",
       "      <td>32</td>\n",
       "      <td>14.3</td>\n",
       "      <td>Driving without license prohibited; penalties</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-875</th>\n",
       "      <td>23</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Maximum speed limit on certain other highways in cities and towns</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-833.1</th>\n",
       "      <td>19</td>\n",
       "      <td>76.0</td>\n",
       "      <td>Evasion of traffic control devices</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-1030</th>\n",
       "      <td>18</td>\n",
       "      <td>10.4</td>\n",
       "      <td>When lights to be lighted; number of lights to be lighted at any time; use of warning lights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42-9</th>\n",
       "      <td>15</td>\n",
       "      <td>7.4</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-821</th>\n",
       "      <td>13</td>\n",
       "      <td>9.2</td>\n",
       "      <td>Vehicles before entering certain highways shall stop or yield right-of-way</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-845</th>\n",
       "      <td>6</td>\n",
       "      <td>18.8</td>\n",
       "      <td>Limitation on U-turns</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-301</th>\n",
       "      <td>5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>Driving while license, permit, or privilege to drive suspended or revoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-1078.1</th>\n",
       "      <td>5</td>\n",
       "      <td>31.2</td>\n",
       "      <td>Repealed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 TOTAL             \\\n",
       "            # in March % for year   \n",
       "46.2-830           225       17.3   \n",
       "46.2-874            53       11.2   \n",
       "46.2-818.2          41       51.9   \n",
       "42-8                40       13.3   \n",
       "46.2-835            38       18.4   \n",
       "46.2-833            38       13.1   \n",
       "46.2-804            37       13.4   \n",
       "46.2-1158           36        6.9   \n",
       "46.2-646            33        8.1   \n",
       "46.2-300            32       14.3   \n",
       "46.2-875            23       10.0   \n",
       "46.2-833.1          19       76.0   \n",
       "46.2-1030           18       10.4   \n",
       "42-9                15        7.4   \n",
       "46.2-821            13        9.2   \n",
       "46.2-845             6       18.8   \n",
       "46.2-301             5        7.6   \n",
       "46.2-1078.1          5       31.2   \n",
       "\n",
       "                                                                                                                  \n",
       "                                                                                                   Section Title  \n",
       "46.2-830     Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcemen...  \n",
       "46.2-874                                                 Maximum speed limit in business and residence districts  \n",
       "46.2-818.2        Use of handheld personal communications devices in certain motor vehicles; exceptions; penalty  \n",
       "42-8                                                                                                              \n",
       "46.2-835                                                           Right turn on steady red light after stopping  \n",
       "46.2-833                                                                                 Traffic lights; penalty  \n",
       "46.2-804                                   Special regulations applicable on highways laned for traffic; penalty  \n",
       "46.2-1158                                                           Frequency of inspection; scope of inspection  \n",
       "46.2-646                                                                  Expiration and renewal of registration  \n",
       "46.2-300                                                           Driving without license prohibited; penalties  \n",
       "46.2-875                                       Maximum speed limit on certain other highways in cities and towns  \n",
       "46.2-833.1                                                                    Evasion of traffic control devices  \n",
       "46.2-1030           When lights to be lighted; number of lights to be lighted at any time; use of warning lights  \n",
       "42-9                                                                                                              \n",
       "46.2-821                              Vehicles before entering certain highways shall stop or yield right-of-way  \n",
       "46.2-845                                                                                   Limitation on U-turns  \n",
       "46.2-301                               Driving while license, permit, or privilege to drive suspended or revoked  \n",
       "46.2-1078.1                                                                                             Repealed  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = a[\"specific_violation\"].value_counts(normalize=False)\n",
    "\n",
    "df_viol = pd.DataFrame({\"TOTAL\":b})\n",
    "df_viol.sort_values(\"TOTAL\",ascending=False, inplace=True)\n",
    "\n",
    "df_viol.fillna(0, inplace=True)\n",
    "df_viol[\"TOTAL\"] = df_viol[\"TOTAL\"].astype(int)\n",
    "\n",
    "df_viol = df_viol[(df_viol[\"TOTAL\"]>=5)]\n",
    "\n",
    "titles = []\n",
    "for num in df_viol.index:\n",
    "    try:\n",
    "        titles.append(section_num_to_title(num))\n",
    "    except:\n",
    "        titles.append(\"\")\n",
    "\n",
    "df_viol[\"Section Title\"] = titles\n",
    "\n",
    "tv = df[df[\"reason_for_stop\"] == \"TRAFFIC VIOLATION\"]\n",
    "violations_year = tv[\"specific_violation\"].value_counts(normalize=False)\n",
    "\n",
    "perc_total = np.round(df_viol[\"TOTAL\"] / violations_year *100,1)\n",
    "perc_total.dropna(inplace=True)\n",
    "\n",
    "df_viol[\"TOTAL %\"] = perc_total\n",
    "df_viol.columns = pd.MultiIndex.from_tuples([(\"TOTAL\",\"# in March\"),(\"\",\"Section Title\",),(\"TOTAL\",\"% for year\")])\n",
    "df_viol = df_viol.reindex(columns=df_viol.columns[[0,2,1]])\n",
    "df_viol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC</th>\n",
       "      <th>WHITE</th>\n",
       "      <th>TOTAL</th>\n",
       "      <th>Section Title</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>specific_violation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46.2-830</th>\n",
       "      <td>15.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>100</td>\n",
       "      <td>Uniform traffic control devices on highways; d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-874</th>\n",
       "      <td>16.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>53.1</td>\n",
       "      <td>96</td>\n",
       "      <td>Maximum speed limit in business and residence ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-818</th>\n",
       "      <td>6.4</td>\n",
       "      <td>38.5</td>\n",
       "      <td>51.3</td>\n",
       "      <td>78</td>\n",
       "      <td>Stopping vehicle of another; blocking access t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-1030</th>\n",
       "      <td>22.0</td>\n",
       "      <td>37.3</td>\n",
       "      <td>23.7</td>\n",
       "      <td>59</td>\n",
       "      <td>When lights to be lighted; number of lights to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-804</th>\n",
       "      <td>19.6</td>\n",
       "      <td>26.8</td>\n",
       "      <td>42.9</td>\n",
       "      <td>56</td>\n",
       "      <td>Special regulations applicable on highways lan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-646</th>\n",
       "      <td>24.1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>57.4</td>\n",
       "      <td>54</td>\n",
       "      <td>Expiration and renewal of registration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-1158</th>\n",
       "      <td>16.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>50</td>\n",
       "      <td>Frequency of inspection; scope of inspection</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-833</th>\n",
       "      <td>22.2</td>\n",
       "      <td>22.2</td>\n",
       "      <td>44.4</td>\n",
       "      <td>45</td>\n",
       "      <td>Traffic lights; penalty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42-8</th>\n",
       "      <td>12.5</td>\n",
       "      <td>22.5</td>\n",
       "      <td>32.5</td>\n",
       "      <td>40</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-835</th>\n",
       "      <td>27.8</td>\n",
       "      <td>36.1</td>\n",
       "      <td>30.6</td>\n",
       "      <td>36</td>\n",
       "      <td>Right turn on steady red light after stopping</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-300</th>\n",
       "      <td>12.5</td>\n",
       "      <td>78.1</td>\n",
       "      <td>9.4</td>\n",
       "      <td>32</td>\n",
       "      <td>Driving without license prohibited; penalties</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-821</th>\n",
       "      <td>16.1</td>\n",
       "      <td>25.8</td>\n",
       "      <td>38.7</td>\n",
       "      <td>31</td>\n",
       "      <td>Vehicles before entering certain highways shal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-875</th>\n",
       "      <td>19.4</td>\n",
       "      <td>16.1</td>\n",
       "      <td>61.3</td>\n",
       "      <td>31</td>\n",
       "      <td>Maximum speed limit on certain other highways ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-803</th>\n",
       "      <td>23.1</td>\n",
       "      <td>26.9</td>\n",
       "      <td>34.6</td>\n",
       "      <td>26</td>\n",
       "      <td>Keep to the right in crossing intersections or...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42-9</th>\n",
       "      <td>9.5</td>\n",
       "      <td>33.3</td>\n",
       "      <td>47.6</td>\n",
       "      <td>21</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    BLACK OR AFRICAN AMERICAN  HISPANIC  WHITE  TOTAL  \\\n",
       "specific_violation                                                      \n",
       "46.2-830                                 15.0      23.0   46.0    100   \n",
       "46.2-874                                 16.7      17.7   53.1     96   \n",
       "46.2-818                                  6.4      38.5   51.3     78   \n",
       "46.2-1030                                22.0      37.3   23.7     59   \n",
       "46.2-804                                 19.6      26.8   42.9     56   \n",
       "46.2-646                                 24.1      13.0   57.4     54   \n",
       "46.2-1158                                16.0      22.0   60.0     50   \n",
       "46.2-833                                 22.2      22.2   44.4     45   \n",
       "42-8                                     12.5      22.5   32.5     40   \n",
       "46.2-835                                 27.8      36.1   30.6     36   \n",
       "46.2-300                                 12.5      78.1    9.4     32   \n",
       "46.2-821                                 16.1      25.8   38.7     31   \n",
       "46.2-875                                 19.4      16.1   61.3     31   \n",
       "46.2-803                                 23.1      26.9   34.6     26   \n",
       "42-9                                      9.5      33.3   47.6     21   \n",
       "\n",
       "                                                        Section Title  \n",
       "specific_violation                                                     \n",
       "46.2-830            Uniform traffic control devices on highways; d...  \n",
       "46.2-874            Maximum speed limit in business and residence ...  \n",
       "46.2-818            Stopping vehicle of another; blocking access t...  \n",
       "46.2-1030           When lights to be lighted; number of lights to...  \n",
       "46.2-804            Special regulations applicable on highways lan...  \n",
       "46.2-646                       Expiration and renewal of registration  \n",
       "46.2-1158                Frequency of inspection; scope of inspection  \n",
       "46.2-833                                      Traffic lights; penalty  \n",
       "42-8                                                                   \n",
       "46.2-835                Right turn on steady red light after stopping  \n",
       "46.2-300                Driving without license prohibited; penalties  \n",
       "46.2-821            Vehicles before entering certain highways shal...  \n",
       "46.2-875            Maximum speed limit on certain other highways ...  \n",
       "46.2-803            Keep to the right in crossing intersections or...  \n",
       "42-9                                                                   "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viol_counts = dfRes.groupby([\"specific_violation\",\"race_eth\"]).size().unstack(\"race_eth\").replace(np.nan,0).convert_dtypes(int)\n",
    "total = viol_counts.sum(axis=1).astype(int)\n",
    "viol_counts[\"TOTAL\"] = total\n",
    "# viol_counts.drop(columns=\"TOTAL\").max(axis=1) == viol_counts[\"BLACK OR AFRICAN AMERICAN\"]\n",
    "viol_counts.drop(columns=[\"AMERICAN INDIAN\", \"ASIAN/PACIFIC ISLANDER\",\"UNKNOWN\"],inplace=True)\n",
    "\n",
    "viol_counts = np.round(viol_counts.divide(total,axis=0)*100,1)\n",
    "viol_counts[\"TOTAL\"] = total\n",
    "viol_counts.sort_values(\"TOTAL\",ascending=False, inplace=True)\n",
    "viol_counts\n",
    "\n",
    "viol_counts = viol_counts[viol_counts[\"TOTAL\"]>20]\n",
    "\n",
    "titles = []\n",
    "for num in viol_counts.index:\n",
    "    try:\n",
    "        titles.append(section_num_to_title(num))\n",
    "    except:\n",
    "        titles.append(\"\")\n",
    "\n",
    "viol_counts[\"Section Title\"] = titles\n",
    "viol_counts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.14113496030579242\n",
      "0.1442382057231245\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC</th>\n",
       "      <th>WHITE</th>\n",
       "      <th>TOTAL</th>\n",
       "      <th>Section Title</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>specific_violation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46.2-830</th>\n",
       "      <td>17.7</td>\n",
       "      <td>23.8</td>\n",
       "      <td>42.2</td>\n",
       "      <td>593</td>\n",
       "      <td>Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcemen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-804</th>\n",
       "      <td>21.4</td>\n",
       "      <td>27.7</td>\n",
       "      <td>42.3</td>\n",
       "      <td>220</td>\n",
       "      <td>Special regulations applicable on highways laned for traffic; penalty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-1030</th>\n",
       "      <td>20.9</td>\n",
       "      <td>25.1</td>\n",
       "      <td>35.1</td>\n",
       "      <td>211</td>\n",
       "      <td>When lights to be lighted; number of lights to be lighted at any time; use of warning lights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-874</th>\n",
       "      <td>16.5</td>\n",
       "      <td>31.7</td>\n",
       "      <td>41.5</td>\n",
       "      <td>164</td>\n",
       "      <td>Maximum speed limit in business and residence districts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-646</th>\n",
       "      <td>22.3</td>\n",
       "      <td>21.7</td>\n",
       "      <td>43.9</td>\n",
       "      <td>157</td>\n",
       "      <td>Expiration and renewal of registration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-833</th>\n",
       "      <td>19.8</td>\n",
       "      <td>26.7</td>\n",
       "      <td>36.6</td>\n",
       "      <td>131</td>\n",
       "      <td>Traffic lights; penalty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42-8</th>\n",
       "      <td>12.6</td>\n",
       "      <td>35.3</td>\n",
       "      <td>32.8</td>\n",
       "      <td>119</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42-9</th>\n",
       "      <td>7.6</td>\n",
       "      <td>39.0</td>\n",
       "      <td>23.7</td>\n",
       "      <td>118</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-835</th>\n",
       "      <td>27.5</td>\n",
       "      <td>38.2</td>\n",
       "      <td>28.4</td>\n",
       "      <td>102</td>\n",
       "      <td>Right turn on steady red light after stopping</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-875</th>\n",
       "      <td>19.1</td>\n",
       "      <td>23.4</td>\n",
       "      <td>40.4</td>\n",
       "      <td>94</td>\n",
       "      <td>Maximum speed limit on certain other highways in cities and towns</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-818</th>\n",
       "      <td>16.4</td>\n",
       "      <td>35.8</td>\n",
       "      <td>38.8</td>\n",
       "      <td>67</td>\n",
       "      <td>Stopping vehicle of another; blocking access to premises; damaging or threatening commercial veh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-821</th>\n",
       "      <td>15.3</td>\n",
       "      <td>39.0</td>\n",
       "      <td>30.5</td>\n",
       "      <td>59</td>\n",
       "      <td>Vehicles before entering certain highways shall stop or yield right-of-way</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-1158</th>\n",
       "      <td>15.4</td>\n",
       "      <td>23.1</td>\n",
       "      <td>53.8</td>\n",
       "      <td>52</td>\n",
       "      <td>Frequency of inspection; scope of inspection</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-1011</th>\n",
       "      <td>14.3</td>\n",
       "      <td>42.9</td>\n",
       "      <td>38.8</td>\n",
       "      <td>49</td>\n",
       "      <td>Headlights on motor vehicles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-803</th>\n",
       "      <td>23.1</td>\n",
       "      <td>17.9</td>\n",
       "      <td>48.7</td>\n",
       "      <td>39</td>\n",
       "      <td>Keep to the right in crossing intersections or railroads</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-888</th>\n",
       "      <td>31.6</td>\n",
       "      <td>28.9</td>\n",
       "      <td>36.8</td>\n",
       "      <td>38</td>\n",
       "      <td>Stopping on highways; removing motor vehicle from roadway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-715</th>\n",
       "      <td>20.6</td>\n",
       "      <td>41.2</td>\n",
       "      <td>35.3</td>\n",
       "      <td>34</td>\n",
       "      <td>Display of license plates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-1003</th>\n",
       "      <td>18.5</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>27</td>\n",
       "      <td>Illegal use of defective and unsafe equipment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-846</th>\n",
       "      <td>25.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20</td>\n",
       "      <td>Required position and method of turning at intersections; local regulations</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-818.2</th>\n",
       "      <td>26.3</td>\n",
       "      <td>36.8</td>\n",
       "      <td>26.3</td>\n",
       "      <td>19</td>\n",
       "      <td>Use of handheld personal communications devices in certain motor vehicles; exceptions; penalty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-845</th>\n",
       "      <td>21.1</td>\n",
       "      <td>15.8</td>\n",
       "      <td>31.6</td>\n",
       "      <td>19</td>\n",
       "      <td>Limitation on U-turns</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-878</th>\n",
       "      <td>33.3</td>\n",
       "      <td>5.6</td>\n",
       "      <td>55.6</td>\n",
       "      <td>18</td>\n",
       "      <td>Authority to change speed limits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-1034</th>\n",
       "      <td>6.2</td>\n",
       "      <td>43.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>16</td>\n",
       "      <td>When dimming headlights required</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-1078.1</th>\n",
       "      <td>18.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>18.8</td>\n",
       "      <td>16</td>\n",
       "      <td>Repealed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-826</th>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15</td>\n",
       "      <td>Stop before entering public highway or sidewalk from private road, etc.; yielding right-of-way</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-820</th>\n",
       "      <td>33.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>26.7</td>\n",
       "      <td>15</td>\n",
       "      <td>Right-of-way at uncontrolled intersections, generally</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-716</th>\n",
       "      <td>33.3</td>\n",
       "      <td>33.3</td>\n",
       "      <td>26.7</td>\n",
       "      <td>15</td>\n",
       "      <td>How license plates fastened to vehicle; altering appearance of license plates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-1014</th>\n",
       "      <td>23.1</td>\n",
       "      <td>30.8</td>\n",
       "      <td>46.2</td>\n",
       "      <td>13</td>\n",
       "      <td>Brake lights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-1078</th>\n",
       "      <td>33.3</td>\n",
       "      <td>33.3</td>\n",
       "      <td>16.7</td>\n",
       "      <td>12</td>\n",
       "      <td>Unlawful to operate motor vehicle, bicycle, electric personal assistive mobility device, electri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-825</th>\n",
       "      <td>45.5</td>\n",
       "      <td>27.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11</td>\n",
       "      <td>Left turn traffic to yield right-of-way</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    BLACK OR AFRICAN AMERICAN  HISPANIC  WHITE  TOTAL  \\\n",
       "specific_violation                                                      \n",
       "46.2-830                                 17.7      23.8   42.2    593   \n",
       "46.2-804                                 21.4      27.7   42.3    220   \n",
       "46.2-1030                                20.9      25.1   35.1    211   \n",
       "46.2-874                                 16.5      31.7   41.5    164   \n",
       "46.2-646                                 22.3      21.7   43.9    157   \n",
       "46.2-833                                 19.8      26.7   36.6    131   \n",
       "42-8                                     12.6      35.3   32.8    119   \n",
       "42-9                                      7.6      39.0   23.7    118   \n",
       "46.2-835                                 27.5      38.2   28.4    102   \n",
       "46.2-875                                 19.1      23.4   40.4     94   \n",
       "46.2-818                                 16.4      35.8   38.8     67   \n",
       "46.2-821                                 15.3      39.0   30.5     59   \n",
       "46.2-1158                                15.4      23.1   53.8     52   \n",
       "46.2-1011                                14.3      42.9   38.8     49   \n",
       "46.2-803                                 23.1      17.9   48.7     39   \n",
       "46.2-888                                 31.6      28.9   36.8     38   \n",
       "46.2-715                                 20.6      41.2   35.3     34   \n",
       "46.2-1003                                18.5      37.0   37.0     27   \n",
       "46.2-846                                 25.0      35.0   35.0     20   \n",
       "46.2-818.2                               26.3      36.8   26.3     19   \n",
       "46.2-845                                 21.1      15.8   31.6     19   \n",
       "46.2-878                                 33.3       5.6   55.6     18   \n",
       "46.2-1034                                 6.2      43.8   25.0     16   \n",
       "46.2-1078.1                              18.8      25.0   18.8     16   \n",
       "46.2-826                                  0.0      60.0   20.0     15   \n",
       "46.2-820                                 33.3      20.0   26.7     15   \n",
       "46.2-716                                 33.3      33.3   26.7     15   \n",
       "46.2-1014                                23.1      30.8   46.2     13   \n",
       "46.2-1078                                33.3      33.3   16.7     12   \n",
       "46.2-825                                 45.5      27.3    9.1     11   \n",
       "\n",
       "                                                                                                          Section Title  \n",
       "specific_violation                                                                                                       \n",
       "46.2-830            Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcemen...  \n",
       "46.2-804                                          Special regulations applicable on highways laned for traffic; penalty  \n",
       "46.2-1030                  When lights to be lighted; number of lights to be lighted at any time; use of warning lights  \n",
       "46.2-874                                                        Maximum speed limit in business and residence districts  \n",
       "46.2-646                                                                         Expiration and renewal of registration  \n",
       "46.2-833                                                                                        Traffic lights; penalty  \n",
       "42-8                                                                                                                     \n",
       "42-9                                                                                                                     \n",
       "46.2-835                                                                  Right turn on steady red light after stopping  \n",
       "46.2-875                                              Maximum speed limit on certain other highways in cities and towns  \n",
       "46.2-818            Stopping vehicle of another; blocking access to premises; damaging or threatening commercial veh...  \n",
       "46.2-821                                     Vehicles before entering certain highways shall stop or yield right-of-way  \n",
       "46.2-1158                                                                  Frequency of inspection; scope of inspection  \n",
       "46.2-1011                                                                                  Headlights on motor vehicles  \n",
       "46.2-803                                                       Keep to the right in crossing intersections or railroads  \n",
       "46.2-888                                                      Stopping on highways; removing motor vehicle from roadway  \n",
       "46.2-715                                                                                      Display of license plates  \n",
       "46.2-1003                                                                 Illegal use of defective and unsafe equipment  \n",
       "46.2-846                                    Required position and method of turning at intersections; local regulations  \n",
       "46.2-818.2               Use of handheld personal communications devices in certain motor vehicles; exceptions; penalty  \n",
       "46.2-845                                                                                          Limitation on U-turns  \n",
       "46.2-878                                                                               Authority to change speed limits  \n",
       "46.2-1034                                                                              When dimming headlights required  \n",
       "46.2-1078.1                                                                                                    Repealed  \n",
       "46.2-826                 Stop before entering public highway or sidewalk from private road, etc.; yielding right-of-way  \n",
       "46.2-820                                                          Right-of-way at uncontrolled intersections, generally  \n",
       "46.2-716                                  How license plates fastened to vehicle; altering appearance of license plates  \n",
       "46.2-1014                                                                                                  Brake lights  \n",
       "46.2-1078           Unlawful to operate motor vehicle, bicycle, electric personal assistive mobility device, electri...  \n",
       "46.2-825                                                                        Left turn traffic to yield right-of-way  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_warn_res = dfRes[dfRes[\"action_taken\"]==\"WARNING\"]\n",
    "df_warn = df[df[\"action_taken\"]==\"WARNING\"]\n",
    "\n",
    "print(len(dfRes) / len(df))\n",
    "print(len(df_warn_res) / len(df_warn))\n",
    "\n",
    "# df_warn = df_warn_res\n",
    "\n",
    "df_warn = df_warn.groupby([\"specific_violation\",\"race_eth\"]).size().unstack(\"race_eth\").fillna(0).convert_dtypes(int)\n",
    "\n",
    "total = df_warn.sum(axis=1).astype(int)\n",
    "df_warn[\"TOTAL\"] = total\n",
    "# viol_counts.drop(columns=\"TOTAL\").max(axis=1) == viol_counts[\"BLACK OR AFRICAN AMERICAN\"]\n",
    "df_warn.drop(columns=[ \"AMERICAN INDIAN\", \"ASIAN/PACIFIC ISLANDER\",\"UNKNOWN\"],inplace=True)\n",
    "\n",
    "df_warn = np.round(df_warn.divide(total,axis=0)*100,1)\n",
    "df_warn[\"TOTAL\"] = total\n",
    "df_warn.sort_values(\"TOTAL\",ascending=False, inplace=True)\n",
    "\n",
    "df_warn.divide(df_warn[\"TOTAL\"],axis=0)\n",
    "df_warn[\"TOTAL\"] = total\n",
    "df_warn = df_warn[df_warn[\"TOTAL\"] >10]\n",
    "\n",
    "titles = []\n",
    "for num in df_warn.index:\n",
    "    try:\n",
    "        titles.append(section_num_to_title(num))\n",
    "    except:\n",
    "        titles.append(\"\")\n",
    "\n",
    "df_warn[\"Section Title\"] = titles\n",
    "\n",
    "df_warn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC</th>\n",
       "      <th>WHITE</th>\n",
       "      <th>TOTAL</th>\n",
       "      <th>Section Title</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>specific_violation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18.2-266</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>41</td>\n",
       "      <td>Driving motor vehicle, engine, etc., while intoxicated, etc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-301</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Driving while license, permit, or privilege to drive suspended or revoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-804</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Special regulations applicable on highways laned for traffic; penalty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.2-388</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Intoxication in public; penalty; transportation of public inebriates to detoxification center</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42-9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-300</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Driving without license prohibited; penalties</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-921.1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Repealed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.2-191</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Definitions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.2-128</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Penalties for failure to appear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-1030</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>When lights to be lighted; number of lights to be lighted at any time; use of warning lights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-600</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Owner to secure registration and certificate of title or certificate of ownership</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-817</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Disregarding signal by law-enforcement officer to stop; eluding police; penalties</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-830</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcemen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-833.1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Evasion of traffic control devices</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-835</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Right turn on steady red light after stopping</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-860</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Failing to give proper signals</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL</th>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "      <td>22</td>\n",
       "      <td>65</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    BLACK OR AFRICAN AMERICAN  HISPANIC  WHITE  TOTAL  \\\n",
       "specific_violation                                                      \n",
       "18.2-266                                    2        20     17     41   \n",
       "46.2-301                                    0         3      1      4   \n",
       "46.2-804                                    1         1      0      3   \n",
       "18.2-388                                    0         1      1      2   \n",
       "42-9                                        0         0      2      2   \n",
       "46.2-300                                    0         2      0      2   \n",
       "46.2-921.1                                  0         1      1      2   \n",
       "18.2-191                                    0         1      0      1   \n",
       "19.2-128                                    0         1      0      1   \n",
       "46.2-1030                                   0         1      0      1   \n",
       "46.2-600                                    0         1      0      1   \n",
       "46.2-817                                    1         0      0      1   \n",
       "46.2-830                                    0         1      0      1   \n",
       "46.2-833.1                                  0         1      0      1   \n",
       "46.2-835                                    0         0      0      1   \n",
       "46.2-860                                    0         1      0      1   \n",
       "TOTAL                                       4        35     22     65   \n",
       "\n",
       "                                                                                                          Section Title  \n",
       "specific_violation                                                                                                       \n",
       "18.2-266                                                    Driving motor vehicle, engine, etc., while intoxicated, etc  \n",
       "46.2-301                                      Driving while license, permit, or privilege to drive suspended or revoked  \n",
       "46.2-804                                          Special regulations applicable on highways laned for traffic; penalty  \n",
       "18.2-388                  Intoxication in public; penalty; transportation of public inebriates to detoxification center  \n",
       "42-9                                                                                                                     \n",
       "46.2-300                                                                  Driving without license prohibited; penalties  \n",
       "46.2-921.1                                                                                                     Repealed  \n",
       "18.2-191                                                                                                    Definitions  \n",
       "19.2-128                                                                                Penalties for failure to appear  \n",
       "46.2-1030                  When lights to be lighted; number of lights to be lighted at any time; use of warning lights  \n",
       "46.2-600                              Owner to secure registration and certificate of title or certificate of ownership  \n",
       "46.2-817                              Disregarding signal by law-enforcement officer to stop; eluding police; penalties  \n",
       "46.2-830            Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcemen...  \n",
       "46.2-833.1                                                                           Evasion of traffic control devices  \n",
       "46.2-835                                                                  Right turn on steady red light after stopping  \n",
       "46.2-860                                                                                 Failing to give proper signals  \n",
       "TOTAL                                                                                                                    "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df[df[\"action_taken\"] == \"ARREST\"]\n",
    "\n",
    "viol_counts = new_df.groupby([\"specific_violation\",\"race_eth\"]).size().unstack(\"race_eth\").replace(np.nan,0).convert_dtypes(int)\n",
    "total = viol_counts.sum(axis=1).astype(int)\n",
    "viol_counts[\"TOTAL\"] = total\n",
    "# viol_counts.drop(columns=\"TOTAL\").max(axis=1) == viol_counts[\"BLACK OR AFRICAN AMERICAN\"]\n",
    "viol_counts.drop(columns=[\"AMERICAN INDIAN\", \"ASIAN/PACIFIC ISLANDER\",\"UNKNOWN\"],inplace=True)\n",
    "\n",
    "# viol_counts = np.round(viol_counts.divide(total,axis=0)*100,1)\n",
    "viol_counts[\"TOTAL\"] = total\n",
    "viol_counts.sort_values(\"TOTAL\",ascending=False, inplace=True)\n",
    "\n",
    "titles = []\n",
    "for num in viol_counts.index:\n",
    "    try:\n",
    "        titles.append(section_num_to_title(num))\n",
    "    except:\n",
    "        titles.append(\"\")\n",
    "\n",
    "viol_counts[\"Section Title\"] = titles\n",
    "\n",
    "t = viol_counts.sum()\n",
    "t[\"Section Title\"] = \"\"\n",
    "t.name = \"TOTAL\"\n",
    "\n",
    "viol_counts.append(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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