By_Divisions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/clean_stop_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>citizen_race</th>\n",
       "      <th>citizen_gender</th>\n",
       "      <th>citizen_age</th>\n",
       "      <th>officer_race</th>\n",
       "      <th>officer_gender</th>\n",
       "      <th>division</th>\n",
       "      <th>beat</th>\n",
       "      <th>violation</th>\n",
       "      <th>frisked</th>\n",
       "      <th>searched</th>\n",
       "      <th>citation_issued</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>white</td>\n",
       "      <td>male</td>\n",
       "      <td>53.0</td>\n",
       "      <td>Asian</td>\n",
       "      <td>female</td>\n",
       "      <td>6TH DIVISION</td>\n",
       "      <td>BEAT 1</td>\n",
       "      <td>FAIL OF NON-OWNER/OPER TO MAINTAIN REQ INS/SEC...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>22:39:00</td>\n",
       "      <td>38.210850</td>\n",
       "      <td>-85.740058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>white</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Asian</td>\n",
       "      <td>female</td>\n",
       "      <td>6TH DIVISION</td>\n",
       "      <td>BEAT 5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>16:42:00</td>\n",
       "      <td>38.203829</td>\n",
       "      <td>-85.665319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Asian</td>\n",
       "      <td>female</td>\n",
       "      <td>6TH DIVISION</td>\n",
       "      <td>BEAT 5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>20:00:00</td>\n",
       "      <td>38.205311</td>\n",
       "      <td>-85.650115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Asian</td>\n",
       "      <td>female</td>\n",
       "      <td>6TH DIVISION</td>\n",
       "      <td>BEAT 5</td>\n",
       "      <td>OPERATING ON SUSPENDED/REVOKED OPERATORS LICEN...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>20:35:00</td>\n",
       "      <td>38.202592</td>\n",
       "      <td>-85.654212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Asian</td>\n",
       "      <td>female</td>\n",
       "      <td>6TH DIVISION</td>\n",
       "      <td>BEAT 2</td>\n",
       "      <td>NO OR EXPIRED REGISTRATION PLATES|FAILURE OF O...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-11</td>\n",
       "      <td>17:20:00</td>\n",
       "      <td>38.156730</td>\n",
       "      <td>-85.696997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  month   day  hour citizen_race citizen_gender  citizen_age  \\\n",
       "0  2015.0    1.0   1.0  22.0        white           male         53.0   \n",
       "1  2015.0    1.0   2.0  16.0        white           male          NaN   \n",
       "2  2015.0    1.0   2.0  20.0        Black         female          NaN   \n",
       "3  2015.0    1.0   2.0  20.0        Black           male         24.0   \n",
       "4  2015.0    1.0  11.0  17.0        Black           male         24.0   \n",
       "\n",
       "  officer_race officer_gender      division    beat  \\\n",
       "0        Asian         female  6TH DIVISION  BEAT 1   \n",
       "1        Asian         female  6TH DIVISION  BEAT 5   \n",
       "2        Asian         female  6TH DIVISION  BEAT 5   \n",
       "3        Asian         female  6TH DIVISION  BEAT 5   \n",
       "4        Asian         female  6TH DIVISION  BEAT 2   \n",
       "\n",
       "                                           violation  frisked  searched  \\\n",
       "0  FAIL OF NON-OWNER/OPER TO MAINTAIN REQ INS/SEC...        0         0   \n",
       "1                                                NaN        0         0   \n",
       "2                                                NaN        0         0   \n",
       "3  OPERATING ON SUSPENDED/REVOKED OPERATORS LICEN...        0         1   \n",
       "4  NO OR EXPIRED REGISTRATION PLATES|FAILURE OF O...        0         0   \n",
       "\n",
       "   citation_issued        date      time        lat        lng  \n",
       "0                1  2015-01-01  22:39:00  38.210850 -85.740058  \n",
       "1                1  2015-01-02  16:42:00  38.203829 -85.665319  \n",
       "2                0  2015-01-02  20:00:00  38.205311 -85.650115  \n",
       "3                1  2015-01-02  20:35:00  38.202592 -85.654212  \n",
       "4                1  2015-01-11  17:20:00  38.156730 -85.696997  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_division = df.groupby(['division', 'officer_race', 'citizen_race']).aggregate(np.sum)[['searched', 'frisked', 'citation_issued']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_division['stops'] = df.groupby(['division', 'officer_race', 'citizen_race'])['year'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_division.drop(['NON LMPD', '8', 'STM', '7', 'SHV', '6', '4', '5', '1', '3', 'JTN', 'ANC'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>searched</th>\n",
       "      <th>frisked</th>\n",
       "      <th>citation_issued</th>\n",
       "      <th>stops</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>division</th>\n",
       "      <th>officer_race</th>\n",
       "      <th>citizen_race</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1ST DIVISION</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">Asian</th>\n",
       "      <th>Black</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Black</th>\n",
       "      <th>Asian</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>704</td>\n",
       "      <td>978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">8TH DIVISION</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">white</th>\n",
       "      <th>Black</th>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>1487</td>\n",
       "      <td>2228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>345</td>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>379</td>\n",
       "      <td>1</td>\n",
       "      <td>5977</td>\n",
       "      <td>8388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>168 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           searched  frisked  citation_issued  \\\n",
       "division     officer_race citizen_race                                          \n",
       "1ST DIVISION Asian        Black                  10        0               14   \n",
       "                          Latino                  1        0                0   \n",
       "                          white                   9        0               12   \n",
       "             Black        Asian                   0        0                1   \n",
       "                          Black                  94        0              704   \n",
       "...                                             ...      ...              ...   \n",
       "8TH DIVISION white        Black                 122        0             1487   \n",
       "                          Latino                 36        0              345   \n",
       "                          Middle Eastern          2        0                7   \n",
       "                          Native American         0        0                7   \n",
       "                          white                 379        1             5977   \n",
       "\n",
       "                                           stops  \n",
       "division     officer_race citizen_race            \n",
       "1ST DIVISION Asian        Black               39  \n",
       "                          Latino               2  \n",
       "                          white               42  \n",
       "             Black        Asian                3  \n",
       "                          Black              978  \n",
       "...                                          ...  \n",
       "8TH DIVISION white        Black             2228  \n",
       "                          Latino             575  \n",
       "                          Middle Eastern      40  \n",
       "                          Native American      9  \n",
       "                          white             8388  \n",
       "\n",
       "[168 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_division['% searched'] = by_division['searched'] / by_division['stops']\n",
    "by_division['% cited'] = by_division['citation_issued'] / by_division['stops']\n",
    "by_division['% of all stops'] = by_division['stops'] / df.year.count()\n",
    "by_division['% of division stops'] = by_division['stops'] / by_division.groupby('division').aggregate(np.sum)['stops']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th>searched</th>\n",
       "      <th>frisked</th>\n",
       "      <th>citation_issued</th>\n",
       "      <th>stops</th>\n",
       "      <th>% searched</th>\n",
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       "      <th>division</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"22\" valign=\"top\">1ST DIVISION</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">Asian</th>\n",
       "      <th>Black</th>\n",
       "      <td>10</td>\n",
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       "      <td>0.003848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>9</td>\n",
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       "      <td>12</td>\n",
       "      <td>42</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.000379</td>\n",
       "      <td>0.004144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">Black</th>\n",
       "      <th>Asian</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>704</td>\n",
       "      <td>978</td>\n",
       "      <td>0.096115</td>\n",
       "      <td>0.719836</td>\n",
       "      <td>0.008814</td>\n",
       "      <td>0.096507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>0.002072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>404</td>\n",
       "      <td>560</td>\n",
       "      <td>0.119643</td>\n",
       "      <td>0.721429</td>\n",
       "      <td>0.005047</td>\n",
       "      <td>0.055260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Latino</th>\n",
       "      <th>Asian</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>0.137931</td>\n",
       "      <td>0.275862</td>\n",
       "      <td>0.000261</td>\n",
       "      <td>0.002862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>0.002072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Other</th>\n",
       "      <th>Black</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.000395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">white</th>\n",
       "      <th>Asian</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.000225</td>\n",
       "      <td>0.002467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>627</td>\n",
       "      <td>4</td>\n",
       "      <td>2889</td>\n",
       "      <td>4567</td>\n",
       "      <td>0.137289</td>\n",
       "      <td>0.632582</td>\n",
       "      <td>0.041159</td>\n",
       "      <td>0.450661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>146</td>\n",
       "      <td>0.054795</td>\n",
       "      <td>0.650685</td>\n",
       "      <td>0.001316</td>\n",
       "      <td>0.014407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.000198</td>\n",
       "      <td>0.002171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>406</td>\n",
       "      <td>1</td>\n",
       "      <td>2459</td>\n",
       "      <td>3659</td>\n",
       "      <td>0.110959</td>\n",
       "      <td>0.672042</td>\n",
       "      <td>0.032976</td>\n",
       "      <td>0.361062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"20\" valign=\"top\">2ND DIVISION</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">Asian</th>\n",
       "      <th>Black</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>99</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.414141</td>\n",
       "      <td>0.000892</td>\n",
       "      <td>0.014216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>0.002728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Black</th>\n",
       "      <th>Black</th>\n",
       "      <td>116</td>\n",
       "      <td>1</td>\n",
       "      <td>532</td>\n",
       "      <td>944</td>\n",
       "      <td>0.122881</td>\n",
       "      <td>0.563559</td>\n",
       "      <td>0.008508</td>\n",
       "      <td>0.135554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.001723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>225</td>\n",
       "      <td>342</td>\n",
       "      <td>0.128655</td>\n",
       "      <td>0.657895</td>\n",
       "      <td>0.003082</td>\n",
       "      <td>0.049110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Latino</th>\n",
       "      <th>Black</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>68</td>\n",
       "      <td>0.147059</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>0.000613</td>\n",
       "      <td>0.009765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.000574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>41</td>\n",
       "      <td>0.219512</td>\n",
       "      <td>0.195122</td>\n",
       "      <td>0.000370</td>\n",
       "      <td>0.005887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Other</th>\n",
       "      <th>Black</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">white</th>\n",
       "      <th>Asian</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.001149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>782</td>\n",
       "      <td>1</td>\n",
       "      <td>1800</td>\n",
       "      <td>4003</td>\n",
       "      <td>0.195353</td>\n",
       "      <td>0.449663</td>\n",
       "      <td>0.036076</td>\n",
       "      <td>0.574813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>71</td>\n",
       "      <td>0.183099</td>\n",
       "      <td>0.478873</td>\n",
       "      <td>0.000640</td>\n",
       "      <td>0.010195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>0.003303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>335</td>\n",
       "      <td>3</td>\n",
       "      <td>741</td>\n",
       "      <td>1315</td>\n",
       "      <td>0.254753</td>\n",
       "      <td>0.563498</td>\n",
       "      <td>0.011851</td>\n",
       "      <td>0.188828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"20\" valign=\"top\">3RD DIVISION</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">Asian</th>\n",
       "      <th>Asian</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>63</td>\n",
       "      <td>0.158730</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000568</td>\n",
       "      <td>0.004304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.000820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>151</td>\n",
       "      <td>0.178808</td>\n",
       "      <td>0.139073</td>\n",
       "      <td>0.001361</td>\n",
       "      <td>0.010316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Black</th>\n",
       "      <th>Asian</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>186</td>\n",
       "      <td>238</td>\n",
       "      <td>0.063025</td>\n",
       "      <td>0.781513</td>\n",
       "      <td>0.002145</td>\n",
       "      <td>0.016260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>38</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.000342</td>\n",
       "      <td>0.002596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.000273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>796</td>\n",
       "      <td>950</td>\n",
       "      <td>0.064211</td>\n",
       "      <td>0.837895</td>\n",
       "      <td>0.008562</td>\n",
       "      <td>0.064904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Latino</th>\n",
       "      <th>Black</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000099</td>\n",
       "      <td>0.000752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">white</th>\n",
       "      <th>Asian</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>46</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.239130</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.003143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>1847</td>\n",
       "      <td>2516</td>\n",
       "      <td>0.047695</td>\n",
       "      <td>0.734102</td>\n",
       "      <td>0.022675</td>\n",
       "      <td>0.171893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>476</td>\n",
       "      <td>634</td>\n",
       "      <td>0.052050</td>\n",
       "      <td>0.750789</td>\n",
       "      <td>0.005714</td>\n",
       "      <td>0.043315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "      <td>0.075000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.000360</td>\n",
       "      <td>0.002733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>355</td>\n",
       "      <td>1</td>\n",
       "      <td>7880</td>\n",
       "      <td>9920</td>\n",
       "      <td>0.035786</td>\n",
       "      <td>0.794355</td>\n",
       "      <td>0.089402</td>\n",
       "      <td>0.677735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"23\" valign=\"top\">4TH DIVISION</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">Asian</th>\n",
       "      <th>Asian</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>104</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.403846</td>\n",
       "      <td>0.000937</td>\n",
       "      <td>0.007275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>0.001609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>171</td>\n",
       "      <td>0.093567</td>\n",
       "      <td>0.573099</td>\n",
       "      <td>0.001541</td>\n",
       "      <td>0.011962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Black</th>\n",
       "      <th>Asian</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.000420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>725</td>\n",
       "      <td>1084</td>\n",
       "      <td>0.076568</td>\n",
       "      <td>0.668819</td>\n",
       "      <td>0.009769</td>\n",
       "      <td>0.075831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>79</td>\n",
       "      <td>0.037975</td>\n",
       "      <td>0.632911</td>\n",
       "      <td>0.000712</td>\n",
       "      <td>0.005526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.000839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>801</td>\n",
       "      <td>1221</td>\n",
       "      <td>0.079443</td>\n",
       "      <td>0.656020</td>\n",
       "      <td>0.011004</td>\n",
       "      <td>0.085414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Latino</th>\n",
       "      <th>Asian</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>78</td>\n",
       "      <td>0.141026</td>\n",
       "      <td>0.141026</td>\n",
       "      <td>0.000703</td>\n",
       "      <td>0.005456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000225</td>\n",
       "      <td>0.001749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.000420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>88</td>\n",
       "      <td>0.170455</td>\n",
       "      <td>0.159091</td>\n",
       "      <td>0.000793</td>\n",
       "      <td>0.006156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <th>white</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">white</th>\n",
       "      <th>Asian</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>44</td>\n",
       "      <td>0.159091</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.000397</td>\n",
       "      <td>0.003078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>413</td>\n",
       "      <td>1</td>\n",
       "      <td>3351</td>\n",
       "      <td>4867</td>\n",
       "      <td>0.084857</td>\n",
       "      <td>0.688514</td>\n",
       "      <td>0.043863</td>\n",
       "      <td>0.340469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>249</td>\n",
       "      <td>379</td>\n",
       "      <td>0.073879</td>\n",
       "      <td>0.656992</td>\n",
       "      <td>0.003416</td>\n",
       "      <td>0.026513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>64</td>\n",
       "      <td>0.156250</td>\n",
       "      <td>0.140625</td>\n",
       "      <td>0.000577</td>\n",
       "      <td>0.004477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.000280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>441</td>\n",
       "      <td>1</td>\n",
       "      <td>4413</td>\n",
       "      <td>6028</td>\n",
       "      <td>0.073159</td>\n",
       "      <td>0.732084</td>\n",
       "      <td>0.054326</td>\n",
       "      <td>0.421686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"19\" valign=\"top\">5TH DIVISION</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">Asian</th>\n",
       "      <th>Black</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>0.001240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Black</th>\n",
       "      <th>Asian</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>0.000827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>575</td>\n",
       "      <td>663</td>\n",
       "      <td>0.033183</td>\n",
       "      <td>0.867270</td>\n",
       "      <td>0.005975</td>\n",
       "      <td>0.054798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>92</td>\n",
       "      <td>0.010870</td>\n",
       "      <td>0.880435</td>\n",
       "      <td>0.000829</td>\n",
       "      <td>0.007604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.001157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>2131</td>\n",
       "      <td>2379</td>\n",
       "      <td>0.021858</td>\n",
       "      <td>0.895755</td>\n",
       "      <td>0.021440</td>\n",
       "      <td>0.196628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Latino</th>\n",
       "      <th>Black</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.001488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>67</td>\n",
       "      <td>0.208955</td>\n",
       "      <td>0.402985</td>\n",
       "      <td>0.000604</td>\n",
       "      <td>0.005538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">white</th>\n",
       "      <th>Asian</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>41</td>\n",
       "      <td>0.097561</td>\n",
       "      <td>0.195122</td>\n",
       "      <td>0.000370</td>\n",
       "      <td>0.003389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>1191</td>\n",
       "      <td>1803</td>\n",
       "      <td>0.063783</td>\n",
       "      <td>0.660566</td>\n",
       "      <td>0.016249</td>\n",
       "      <td>0.149021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>218</td>\n",
       "      <td>312</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.698718</td>\n",
       "      <td>0.002812</td>\n",
       "      <td>0.025787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>46</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.065217</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.003802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>0.000579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>4804</td>\n",
       "      <td>6618</td>\n",
       "      <td>0.045331</td>\n",
       "      <td>0.725899</td>\n",
       "      <td>0.059644</td>\n",
       "      <td>0.546987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"21\" valign=\"top\">6TH DIVISION</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">Asian</th>\n",
       "      <th>Black</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>155</td>\n",
       "      <td>0.077419</td>\n",
       "      <td>0.741935</td>\n",
       "      <td>0.001397</td>\n",
       "      <td>0.008886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.001032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>104</td>\n",
       "      <td>0.067308</td>\n",
       "      <td>0.721154</td>\n",
       "      <td>0.000937</td>\n",
       "      <td>0.005962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Black</th>\n",
       "      <th>Asian</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.001032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>715</td>\n",
       "      <td>989</td>\n",
       "      <td>0.037412</td>\n",
       "      <td>0.722952</td>\n",
       "      <td>0.008913</td>\n",
       "      <td>0.056699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>114</td>\n",
       "      <td>150</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.001352</td>\n",
       "      <td>0.008599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>0.001892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>1454</td>\n",
       "      <td>1759</td>\n",
       "      <td>0.030131</td>\n",
       "      <td>0.826606</td>\n",
       "      <td>0.015853</td>\n",
       "      <td>0.100843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Latino</th>\n",
       "      <th>Black</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>0.219512</td>\n",
       "      <td>0.048780</td>\n",
       "      <td>0.000370</td>\n",
       "      <td>0.002351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.000198</td>\n",
       "      <td>0.001261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Other</th>\n",
       "      <th>Black</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.000153</td>\n",
       "      <td>0.000975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">white</th>\n",
       "      <th>Asian</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>0.022222</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.002580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>3023</td>\n",
       "      <td>4192</td>\n",
       "      <td>0.059637</td>\n",
       "      <td>0.721135</td>\n",
       "      <td>0.037780</td>\n",
       "      <td>0.240326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>690</td>\n",
       "      <td>907</td>\n",
       "      <td>0.049614</td>\n",
       "      <td>0.760750</td>\n",
       "      <td>0.008174</td>\n",
       "      <td>0.051998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>69</td>\n",
       "      <td>0.028986</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.000622</td>\n",
       "      <td>0.003956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>309</td>\n",
       "      <td>0</td>\n",
       "      <td>7495</td>\n",
       "      <td>8896</td>\n",
       "      <td>0.034735</td>\n",
       "      <td>0.842513</td>\n",
       "      <td>0.080174</td>\n",
       "      <td>0.510004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"23\" valign=\"top\">7TH DIVISION</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">Asian</th>\n",
       "      <th>Black</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.000455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>34</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.794118</td>\n",
       "      <td>0.000306</td>\n",
       "      <td>0.002581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Black</th>\n",
       "      <th>Asian</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>570</td>\n",
       "      <td>631</td>\n",
       "      <td>0.011094</td>\n",
       "      <td>0.903328</td>\n",
       "      <td>0.005687</td>\n",
       "      <td>0.047894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>217</td>\n",
       "      <td>229</td>\n",
       "      <td>0.004367</td>\n",
       "      <td>0.947598</td>\n",
       "      <td>0.002064</td>\n",
       "      <td>0.017381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>0.000531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1504</td>\n",
       "      <td>1637</td>\n",
       "      <td>0.006109</td>\n",
       "      <td>0.918754</td>\n",
       "      <td>0.014753</td>\n",
       "      <td>0.124250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Latino</th>\n",
       "      <th>Asian</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.001214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>65</td>\n",
       "      <td>0.276923</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.000586</td>\n",
       "      <td>0.004934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Other</th>\n",
       "      <th>Black</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">white</th>\n",
       "      <th>Asian</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>1470</td>\n",
       "      <td>2034</td>\n",
       "      <td>0.067847</td>\n",
       "      <td>0.722714</td>\n",
       "      <td>0.018331</td>\n",
       "      <td>0.154383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>600</td>\n",
       "      <td>748</td>\n",
       "      <td>0.037433</td>\n",
       "      <td>0.802139</td>\n",
       "      <td>0.006741</td>\n",
       "      <td>0.056774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>0.107143</td>\n",
       "      <td>0.178571</td>\n",
       "      <td>0.000252</td>\n",
       "      <td>0.002125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>0.000759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>6244</td>\n",
       "      <td>7691</td>\n",
       "      <td>0.039007</td>\n",
       "      <td>0.811858</td>\n",
       "      <td>0.069314</td>\n",
       "      <td>0.583757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"20\" valign=\"top\">8TH DIVISION</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">Asian</th>\n",
       "      <th>Asian</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.000238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>90</td>\n",
       "      <td>0.088889</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.000811</td>\n",
       "      <td>0.005357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.000536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>217</td>\n",
       "      <td>0.055300</td>\n",
       "      <td>0.341014</td>\n",
       "      <td>0.001956</td>\n",
       "      <td>0.012916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Black</th>\n",
       "      <th>Asian</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.000357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>799</td>\n",
       "      <td>883</td>\n",
       "      <td>0.004530</td>\n",
       "      <td>0.904870</td>\n",
       "      <td>0.007958</td>\n",
       "      <td>0.052556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>148</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.959459</td>\n",
       "      <td>0.001334</td>\n",
       "      <td>0.008809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.000357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>3806</td>\n",
       "      <td>4004</td>\n",
       "      <td>0.002747</td>\n",
       "      <td>0.950549</td>\n",
       "      <td>0.036085</td>\n",
       "      <td>0.238319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Latino</th>\n",
       "      <th>Asian</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>0.001369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.000357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>52</td>\n",
       "      <td>0.173077</td>\n",
       "      <td>0.519231</td>\n",
       "      <td>0.000469</td>\n",
       "      <td>0.003095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">white</th>\n",
       "      <th>Asian</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>109</td>\n",
       "      <td>0.036697</td>\n",
       "      <td>0.128440</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.006488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>1487</td>\n",
       "      <td>2228</td>\n",
       "      <td>0.054758</td>\n",
       "      <td>0.667415</td>\n",
       "      <td>0.020079</td>\n",
       "      <td>0.132611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>345</td>\n",
       "      <td>575</td>\n",
       "      <td>0.062609</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.005182</td>\n",
       "      <td>0.034224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle Eastern</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.175000</td>\n",
       "      <td>0.000360</td>\n",
       "      <td>0.002381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.000536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>379</td>\n",
       "      <td>1</td>\n",
       "      <td>5977</td>\n",
       "      <td>8388</td>\n",
       "      <td>0.045184</td>\n",
       "      <td>0.712566</td>\n",
       "      <td>0.075595</td>\n",
       "      <td>0.499256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           searched  frisked  citation_issued  \\\n",
       "division     officer_race citizen_race                                          \n",
       "1ST DIVISION Asian        Black                  10        0               14   \n",
       "                          Latino                  1        0                0   \n",
       "                          white                   9        0               12   \n",
       "             Black        Asian                   0        0                1   \n",
       "                          Black                  94        0              704   \n",
       "                          Latino                  0        0               17   \n",
       "                          Middle Eastern          0        0                2   \n",
       "                          Native American         0        0                3   \n",
       "                          Unknown                 0        0                1   \n",
       "                          white                  67        0              404   \n",
       "             Latino       Asian                   0        0                0   \n",
       "                          Black                   4        0                8   \n",
       "                          Latino                  0        0                1   \n",
       "                          white                   2        0                4   \n",
       "             Other        Black                   0        0                4   \n",
       "                          white                   0        0                1   \n",
       "             white        Asian                   2        0                9   \n",
       "                          Black                 627        4             2889   \n",
       "                          Latino                  8        0               95   \n",
       "                          Middle Eastern          2        0                1   \n",
       "                          Native American         1        0                1   \n",
       "                          white                 406        1             2459   \n",
       "2ND DIVISION Asian        Black                  18        0               41   \n",
       "                          Latino                  0        0                0   \n",
       "                          white                   3        0               11   \n",
       "             Black        Black                 116        1              532   \n",
       "                          Latino                  3        0                6   \n",
       "                          Middle Eastern          0        0                0   \n",
       "                          Native American         0        0                2   \n",
       "                          white                  44        0              225   \n",
       "             Latino       Black                  10        0               28   \n",
       "                          Latino                  0        0                2   \n",
       "                          Middle Eastern          0        0                0   \n",
       "                          white                   9        0                8   \n",
       "             Other        Black                   0        0                1   \n",
       "                          Middle Eastern          0        0                0   \n",
       "             white        Asian                   1        0                3   \n",
       "                          Black                 782        1             1800   \n",
       "                          Latino                 13        0               34   \n",
       "                          Middle Eastern          6        0                4   \n",
       "                          Native American         0        0                2   \n",
       "                          white                 335        3              741   \n",
       "3RD DIVISION Asian        Asian                   0        0                0   \n",
       "                          Black                  10        0                9   \n",
       "                          Latino                  1        0                2   \n",
       "                          Middle Eastern          0        0                0   \n",
       "                          white                  27        0               21   \n",
       "             Black        Asian                   0        0                0   \n",
       "                          Black                  15        1              186   \n",
       "                          Latino                  1        0               30   \n",
       "                          Middle Eastern          0        0                0   \n",
       "                          Native American         0        0                1   \n",
       "                          white                  61        0              796   \n",
       "             Latino       Black                   3        0                4   \n",
       "                          Latino                  2        0                1   \n",
       "                          white                   5        0                5   \n",
       "             white        Asian                   4        0               11   \n",
       "                          Black                 120        0             1847   \n",
       "                          Latino                 33        0              476   \n",
       "                          Middle Eastern          3        0                6   \n",
       "                          Native American         0        0                1   \n",
       "                          white                 355        1             7880   \n",
       "4TH DIVISION Asian        Asian                   0        0                0   \n",
       "                          Black                  16        0               42   \n",
       "                          Latino                  3        0               13   \n",
       "                          Middle Eastern          0        0                0   \n",
       "                          white                  16        0               98   \n",
       "             Black        Asian                   1        0                0   \n",
       "                          Black                  83        0              725   \n",
       "                          Latino                  3        0               50   \n",
       "                          Middle Eastern          1        0                3   \n",
       "                          Native American         0        0                0   \n",
       "                          white                  97        0              801   \n",
       "             Latino       Asian                   0        0                0   \n",
       "                          Black                  11        0               11   \n",
       "                          Latino                  1        0                2   \n",
       "                          Middle Eastern          0        0                1   \n",
       "                          white                  15        0               14   \n",
       "             Other        white                   0        0                0   \n",
       "             white        Asian                   7        0                6   \n",
       "                          Black                 413        1             3351   \n",
       "                          Latino                 28        0              249   \n",
       "                          Middle Eastern         10        0                9   \n",
       "                          Native American         1        0                3   \n",
       "                          white                 441        1             4413   \n",
       "5TH DIVISION Asian        Black                   2        0                1   \n",
       "                          Latino                  0        0                0   \n",
       "                          white                   0        0                3   \n",
       "             Black        Asian                   0        0                0   \n",
       "                          Black                  22        0              575   \n",
       "                          Latino                  1        0               81   \n",
       "                          Middle Eastern          0        0                0   \n",
       "                          Native American         0        0                1   \n",
       "                          white                  52        0             2131   \n",
       "             Latino       Black                   5        0                6   \n",
       "                          Latino                  0        0                1   \n",
       "                          Middle Eastern          1        0                0   \n",
       "                          white                  14        0               27   \n",
       "             white        Asian                   4        0                8   \n",
       "                          Black                 115        0             1191   \n",
       "                          Latino                 13        0              218   \n",
       "                          Middle Eastern          2        0                3   \n",
       "                          Native American         0        0                7   \n",
       "                          white                 300        0             4804   \n",
       "6TH DIVISION Asian        Black                  12        0              115   \n",
       "                          Latino                  2        0               12   \n",
       "                          white                   7        0               75   \n",
       "             Black        Asian                   0        0                0   \n",
       "                          Black                  37        0              715   \n",
       "                          Latino                  3        0              114   \n",
       "                          Middle Eastern          3        0                3   \n",
       "                          white                  53        0             1454   \n",
       "             Latino       Black                   9        0                2   \n",
       "                          Latino                  1        0                1   \n",
       "                          white                   4        0                5   \n",
       "             Other        Black                   0        0                1   \n",
       "                          Latino                  0        0                0   \n",
       "                          white                   0        0                2   \n",
       "             white        Asian                   1        0                3   \n",
       "                          Black                 250        0             3023   \n",
       "                          Latino                 45        0              690   \n",
       "                          Middle Eastern          2        0                9   \n",
       "                          Native American         0        0                7   \n",
       "                          Unknown                 0        0                1   \n",
       "                          white                 309        0             7495   \n",
       "7TH DIVISION Asian        Black                   1        0                7   \n",
       "                          Latino                  0        0                6   \n",
       "                          white                   0        0               27   \n",
       "             Black        Asian                   0        0                0   \n",
       "                          Black                   7        0              570   \n",
       "                          Latino                  1        0              217   \n",
       "                          Middle Eastern          0        0                0   \n",
       "                          Native American         0        0                3   \n",
       "                          white                  10        0             1504   \n",
       "             Latino       Asian                   0        0                0   \n",
       "                          Black                   5        0                8   \n",
       "                          Latino                  4        0                8   \n",
       "                          Middle Eastern          0        0                0   \n",
       "                          white                  18        0               35   \n",
       "             Other        Black                   1        0                0   \n",
       "                          Latino                  0        0                0   \n",
       "                          white                   1        0                0   \n",
       "             white        Asian                   0        0                3   \n",
       "                          Black                 138        1             1470   \n",
       "                          Latino                 28        0              600   \n",
       "                          Middle Eastern          3        0                5   \n",
       "                          Native American         0        0                8   \n",
       "                          white                 300        0             6244   \n",
       "8TH DIVISION Asian        Asian                   0        0                0   \n",
       "                          Black                   8        0               24   \n",
       "                          Latino                  1        0                4   \n",
       "                          white                  12        0               74   \n",
       "             Black        Asian                   0        0                2   \n",
       "                          Black                   4        0              799   \n",
       "                          Latino                  1        0              142   \n",
       "                          Middle Eastern          0        0                1   \n",
       "                          Native American         0        0                2   \n",
       "                          white                  11        0             3806   \n",
       "             Latino       Asian                   0        0                0   \n",
       "                          Black                   0        0               11   \n",
       "                          Latino                  2        0                4   \n",
       "                          white                   9        0               27   \n",
       "             white        Asian                   4        0               14   \n",
       "                          Black                 122        0             1487   \n",
       "                          Latino                 36        0              345   \n",
       "                          Middle Eastern          2        0                7   \n",
       "                          Native American         0        0                7   \n",
       "                          white                 379        1             5977   \n",
       "\n",
       "                                           stops  % searched   % cited  \\\n",
       "division     officer_race citizen_race                                   \n",
       "1ST DIVISION Asian        Black               39    0.256410  0.358974   \n",
       "                          Latino               2    0.500000  0.000000   \n",
       "                          white               42    0.214286  0.285714   \n",
       "             Black        Asian                3    0.000000  0.333333   \n",
       "                          Black              978    0.096115  0.719836   \n",
       "                          Latino              21    0.000000  0.809524   \n",
       "                          Middle Eastern       5    0.000000  0.400000   \n",
       "                          Native American      3    0.000000  1.000000   \n",
       "                          Unknown              1    0.000000  1.000000   \n",
       "                          white              560    0.119643  0.721429   \n",
       "             Latino       Asian                1    0.000000  0.000000   \n",
       "                          Black               29    0.137931  0.275862   \n",
       "                          Latino               1    0.000000  1.000000   \n",
       "                          white               21    0.095238  0.190476   \n",
       "             Other        Black                4    0.000000  1.000000   \n",
       "                          white                2    0.000000  0.500000   \n",
       "             white        Asian               25    0.080000  0.360000   \n",
       "                          Black             4567    0.137289  0.632582   \n",
       "                          Latino             146    0.054795  0.650685   \n",
       "                          Middle Eastern      22    0.090909  0.045455   \n",
       "                          Native American      3    0.333333  0.333333   \n",
       "                          white             3659    0.110959  0.672042   \n",
       "2ND DIVISION Asian        Black               99    0.181818  0.414141   \n",
       "                          Latino               2    0.000000  0.000000   \n",
       "                          white               19    0.157895  0.578947   \n",
       "             Black        Black              944    0.122881  0.563559   \n",
       "                          Latino              12    0.250000  0.500000   \n",
       "                          Middle Eastern       2    0.000000  0.000000   \n",
       "                          Native American      3    0.000000  0.666667   \n",
       "                          white              342    0.128655  0.657895   \n",
       "             Latino       Black               68    0.147059  0.411765   \n",
       "                          Latino               4    0.000000  0.500000   \n",
       "                          Middle Eastern       1    0.000000  0.000000   \n",
       "                          white               41    0.219512  0.195122   \n",
       "             Other        Black                1    0.000000  1.000000   \n",
       "                          Middle Eastern       1    0.000000  0.000000   \n",
       "             white        Asian                8    0.125000  0.375000   \n",
       "                          Black             4003    0.195353  0.449663   \n",
       "                          Latino              71    0.183099  0.478873   \n",
       "                          Middle Eastern      23    0.260870  0.173913   \n",
       "                          Native American      5    0.000000  0.400000   \n",
       "                          white             1315    0.254753  0.563498   \n",
       "3RD DIVISION Asian        Asian                2    0.000000  0.000000   \n",
       "                          Black               63    0.158730  0.142857   \n",
       "                          Latino              12    0.083333  0.166667   \n",
       "                          Middle Eastern       1    0.000000  0.000000   \n",
       "                          white              151    0.178808  0.139073   \n",
       "             Black        Asian                1    0.000000  0.000000   \n",
       "                          Black              238    0.063025  0.781513   \n",
       "                          Latino              38    0.026316  0.789474   \n",
       "                          Middle Eastern       4    0.000000  0.000000   \n",
       "                          Native American      1    0.000000  1.000000   \n",
       "                          white              950    0.064211  0.837895   \n",
       "             Latino       Black                5    0.600000  0.800000   \n",
       "                          Latino               2    1.000000  0.500000   \n",
       "                          white               11    0.454545  0.454545   \n",
       "             white        Asian               46    0.086957  0.239130   \n",
       "                          Black             2516    0.047695  0.734102   \n",
       "                          Latino             634    0.052050  0.750789   \n",
       "                          Middle Eastern      40    0.075000  0.150000   \n",
       "                          Native American      2    0.000000  0.500000   \n",
       "                          white             9920    0.035786  0.794355   \n",
       "4TH DIVISION Asian        Asian                1    0.000000  0.000000   \n",
       "                          Black              104    0.153846  0.403846   \n",
       "                          Latino              23    0.130435  0.565217   \n",
       "                          Middle Eastern       1    0.000000  0.000000   \n",
       "                          white              171    0.093567  0.573099   \n",
       "             Black        Asian                6    0.166667  0.000000   \n",
       "                          Black             1084    0.076568  0.668819   \n",
       "                          Latino              79    0.037975  0.632911   \n",
       "                          Middle Eastern      12    0.083333  0.250000   \n",
       "                          Native American      1    0.000000  0.000000   \n",
       "                          white             1221    0.079443  0.656020   \n",
       "             Latino       Asian                8    0.000000  0.000000   \n",
       "                          Black               78    0.141026  0.141026   \n",
       "                          Latino              25    0.040000  0.080000   \n",
       "                          Middle Eastern       6    0.000000  0.166667   \n",
       "                          white               88    0.170455  0.159091   \n",
       "             Other        white                1    0.000000  0.000000   \n",
       "             white        Asian               44    0.159091  0.136364   \n",
       "                          Black             4867    0.084857  0.688514   \n",
       "                          Latino             379    0.073879  0.656992   \n",
       "                          Middle Eastern      64    0.156250  0.140625   \n",
       "                          Native American      4    0.250000  0.750000   \n",
       "                          white             6028    0.073159  0.732084   \n",
       "5TH DIVISION Asian        Black                5    0.400000  0.200000   \n",
       "                          Latino               2    0.000000  0.000000   \n",
       "                          white               15    0.000000  0.200000   \n",
       "             Black        Asian               10    0.000000  0.000000   \n",
       "                          Black              663    0.033183  0.867270   \n",
       "                          Latino              92    0.010870  0.880435   \n",
       "                          Middle Eastern      14    0.000000  0.000000   \n",
       "                          Native American      1    0.000000  1.000000   \n",
       "                          white             2379    0.021858  0.895755   \n",
       "             Latino       Black               18    0.277778  0.333333   \n",
       "                          Latino               3    0.000000  0.333333   \n",
       "                          Middle Eastern       3    0.333333  0.000000   \n",
       "                          white               67    0.208955  0.402985   \n",
       "             white        Asian               41    0.097561  0.195122   \n",
       "                          Black             1803    0.063783  0.660566   \n",
       "                          Latino             312    0.041667  0.698718   \n",
       "                          Middle Eastern      46    0.043478  0.065217   \n",
       "                          Native American      7    0.000000  1.000000   \n",
       "                          white             6618    0.045331  0.725899   \n",
       "6TH DIVISION Asian        Black              155    0.077419  0.741935   \n",
       "                          Latino              18    0.111111  0.666667   \n",
       "                          white              104    0.067308  0.721154   \n",
       "             Black        Asian               18    0.000000  0.000000   \n",
       "                          Black              989    0.037412  0.722952   \n",
       "                          Latino             150    0.020000  0.760000   \n",
       "                          Middle Eastern      33    0.090909  0.090909   \n",
       "                          white             1759    0.030131  0.826606   \n",
       "             Latino       Black               41    0.219512  0.048780   \n",
       "                          Latino               5    0.200000  0.200000   \n",
       "                          white               22    0.181818  0.227273   \n",
       "             Other        Black               13    0.000000  0.076923   \n",
       "                          Latino               1    0.000000  0.000000   \n",
       "                          white               17    0.000000  0.117647   \n",
       "             white        Asian               45    0.022222  0.066667   \n",
       "                          Black             4192    0.059637  0.721135   \n",
       "                          Latino             907    0.049614  0.760750   \n",
       "                          Middle Eastern      69    0.028986  0.130435   \n",
       "                          Native American      8    0.000000  0.875000   \n",
       "                          Unknown              1    0.000000  1.000000   \n",
       "                          white             8896    0.034735  0.842513   \n",
       "7TH DIVISION Asian        Black                8    0.125000  0.875000   \n",
       "                          Latino               6    0.000000  1.000000   \n",
       "                          white               34    0.000000  0.794118   \n",
       "             Black        Asian                2    0.000000  0.000000   \n",
       "                          Black              631    0.011094  0.903328   \n",
       "                          Latino             229    0.004367  0.947598   \n",
       "                          Middle Eastern       7    0.000000  0.000000   \n",
       "                          Native American      3    0.000000  1.000000   \n",
       "                          white             1637    0.006109  0.918754   \n",
       "             Latino       Asian                1    0.000000  0.000000   \n",
       "                          Black               16    0.312500  0.500000   \n",
       "                          Latino               8    0.500000  1.000000   \n",
       "                          Middle Eastern       1    0.000000  0.000000   \n",
       "                          white               65    0.276923  0.538462   \n",
       "             Other        Black                1    1.000000  0.000000   \n",
       "                          Latino               1    0.000000  0.000000   \n",
       "                          white                1    1.000000  0.000000   \n",
       "             white        Asian               13    0.000000  0.230769   \n",
       "                          Black             2034    0.067847  0.722714   \n",
       "                          Latino             748    0.037433  0.802139   \n",
       "                          Middle Eastern      28    0.107143  0.178571   \n",
       "                          Native American     10    0.000000  0.800000   \n",
       "                          white             7691    0.039007  0.811858   \n",
       "8TH DIVISION Asian        Asian                4    0.000000  0.000000   \n",
       "                          Black               90    0.088889  0.266667   \n",
       "                          Latino               9    0.111111  0.444444   \n",
       "                          white              217    0.055300  0.341014   \n",
       "             Black        Asian                6    0.000000  0.333333   \n",
       "                          Black              883    0.004530  0.904870   \n",
       "                          Latino             148    0.006757  0.959459   \n",
       "                          Middle Eastern       6    0.000000  0.166667   \n",
       "                          Native American      3    0.000000  0.666667   \n",
       "                          white             4004    0.002747  0.950549   \n",
       "             Latino       Asian                1    0.000000  0.000000   \n",
       "                          Black               23    0.000000  0.478261   \n",
       "                          Latino               6    0.333333  0.666667   \n",
       "                          white               52    0.173077  0.519231   \n",
       "             white        Asian              109    0.036697  0.128440   \n",
       "                          Black             2228    0.054758  0.667415   \n",
       "                          Latino             575    0.062609  0.600000   \n",
       "                          Middle Eastern      40    0.050000  0.175000   \n",
       "                          Native American      9    0.000000  0.777778   \n",
       "                          white             8388    0.045184  0.712566   \n",
       "\n",
       "                                           % of all stops  % of division stops  \n",
       "division     officer_race citizen_race                                          \n",
       "1ST DIVISION Asian        Black                  0.000351             0.003848  \n",
       "                          Latino                 0.000018             0.000197  \n",
       "                          white                  0.000379             0.004144  \n",
       "             Black        Asian                  0.000027             0.000296  \n",
       "                          Black                  0.008814             0.096507  \n",
       "                          Latino                 0.000189             0.002072  \n",
       "                          Middle Eastern         0.000045             0.000493  \n",
       "                          Native American        0.000027             0.000296  \n",
       "                          Unknown                0.000009             0.000099  \n",
       "                          white                  0.005047             0.055260  \n",
       "             Latino       Asian                  0.000009             0.000099  \n",
       "                          Black                  0.000261             0.002862  \n",
       "                          Latino                 0.000009             0.000099  \n",
       "                          white                  0.000189             0.002072  \n",
       "             Other        Black                  0.000036             0.000395  \n",
       "                          white                  0.000018             0.000197  \n",
       "             white        Asian                  0.000225             0.002467  \n",
       "                          Black                  0.041159             0.450661  \n",
       "                          Latino                 0.001316             0.014407  \n",
       "                          Middle Eastern         0.000198             0.002171  \n",
       "                          Native American        0.000027             0.000296  \n",
       "                          white                  0.032976             0.361062  \n",
       "2ND DIVISION Asian        Black                  0.000892             0.014216  \n",
       "                          Latino                 0.000018             0.000287  \n",
       "                          white                  0.000171             0.002728  \n",
       "             Black        Black                  0.008508             0.135554  \n",
       "                          Latino                 0.000108             0.001723  \n",
       "                          Middle Eastern         0.000018             0.000287  \n",
       "                          Native American        0.000027             0.000431  \n",
       "                          white                  0.003082             0.049110  \n",
       "             Latino       Black                  0.000613             0.009765  \n",
       "                          Latino                 0.000036             0.000574  \n",
       "                          Middle Eastern         0.000009             0.000144  \n",
       "                          white                  0.000370             0.005887  \n",
       "             Other        Black                  0.000009             0.000144  \n",
       "                          Middle Eastern         0.000009             0.000144  \n",
       "             white        Asian                  0.000072             0.001149  \n",
       "                          Black                  0.036076             0.574813  \n",
       "                          Latino                 0.000640             0.010195  \n",
       "                          Middle Eastern         0.000207             0.003303  \n",
       "                          Native American        0.000045             0.000718  \n",
       "                          white                  0.011851             0.188828  \n",
       "3RD DIVISION Asian        Asian                  0.000018             0.000137  \n",
       "                          Black                  0.000568             0.004304  \n",
       "                          Latino                 0.000108             0.000820  \n",
       "                          Middle Eastern         0.000009             0.000068  \n",
       "                          white                  0.001361             0.010316  \n",
       "             Black        Asian                  0.000009             0.000068  \n",
       "                          Black                  0.002145             0.016260  \n",
       "                          Latino                 0.000342             0.002596  \n",
       "                          Middle Eastern         0.000036             0.000273  \n",
       "                          Native American        0.000009             0.000068  \n",
       "                          white                  0.008562             0.064904  \n",
       "             Latino       Black                  0.000045             0.000342  \n",
       "                          Latino                 0.000018             0.000137  \n",
       "                          white                  0.000099             0.000752  \n",
       "             white        Asian                  0.000415             0.003143  \n",
       "                          Black                  0.022675             0.171893  \n",
       "                          Latino                 0.005714             0.043315  \n",
       "                          Middle Eastern         0.000360             0.002733  \n",
       "                          Native American        0.000018             0.000137  \n",
       "                          white                  0.089402             0.677735  \n",
       "4TH DIVISION Asian        Asian                  0.000009             0.000070  \n",
       "                          Black                  0.000937             0.007275  \n",
       "                          Latino                 0.000207             0.001609  \n",
       "                          Middle Eastern         0.000009             0.000070  \n",
       "                          white                  0.001541             0.011962  \n",
       "             Black        Asian                  0.000054             0.000420  \n",
       "                          Black                  0.009769             0.075831  \n",
       "                          Latino                 0.000712             0.005526  \n",
       "                          Middle Eastern         0.000108             0.000839  \n",
       "                          Native American        0.000009             0.000070  \n",
       "                          white                  0.011004             0.085414  \n",
       "             Latino       Asian                  0.000072             0.000560  \n",
       "                          Black                  0.000703             0.005456  \n",
       "                          Latino                 0.000225             0.001749  \n",
       "                          Middle Eastern         0.000054             0.000420  \n",
       "                          white                  0.000793             0.006156  \n",
       "             Other        white                  0.000009             0.000070  \n",
       "             white        Asian                  0.000397             0.003078  \n",
       "                          Black                  0.043863             0.340469  \n",
       "                          Latino                 0.003416             0.026513  \n",
       "                          Middle Eastern         0.000577             0.004477  \n",
       "                          Native American        0.000036             0.000280  \n",
       "                          white                  0.054326             0.421686  \n",
       "5TH DIVISION Asian        Black                  0.000045             0.000413  \n",
       "                          Latino                 0.000018             0.000165  \n",
       "                          white                  0.000135             0.001240  \n",
       "             Black        Asian                  0.000090             0.000827  \n",
       "                          Black                  0.005975             0.054798  \n",
       "                          Latino                 0.000829             0.007604  \n",
       "                          Middle Eastern         0.000126             0.001157  \n",
       "                          Native American        0.000009             0.000083  \n",
       "                          white                  0.021440             0.196628  \n",
       "             Latino       Black                  0.000162             0.001488  \n",
       "                          Latino                 0.000027             0.000248  \n",
       "                          Middle Eastern         0.000027             0.000248  \n",
       "                          white                  0.000604             0.005538  \n",
       "             white        Asian                  0.000370             0.003389  \n",
       "                          Black                  0.016249             0.149021  \n",
       "                          Latino                 0.002812             0.025787  \n",
       "                          Middle Eastern         0.000415             0.003802  \n",
       "                          Native American        0.000063             0.000579  \n",
       "                          white                  0.059644             0.546987  \n",
       "6TH DIVISION Asian        Black                  0.001397             0.008886  \n",
       "                          Latino                 0.000162             0.001032  \n",
       "                          white                  0.000937             0.005962  \n",
       "             Black        Asian                  0.000162             0.001032  \n",
       "                          Black                  0.008913             0.056699  \n",
       "                          Latino                 0.001352             0.008599  \n",
       "                          Middle Eastern         0.000297             0.001892  \n",
       "                          white                  0.015853             0.100843  \n",
       "             Latino       Black                  0.000370             0.002351  \n",
       "                          Latino                 0.000045             0.000287  \n",
       "                          white                  0.000198             0.001261  \n",
       "             Other        Black                  0.000117             0.000745  \n",
       "                          Latino                 0.000009             0.000057  \n",
       "                          white                  0.000153             0.000975  \n",
       "             white        Asian                  0.000406             0.002580  \n",
       "                          Black                  0.037780             0.240326  \n",
       "                          Latino                 0.008174             0.051998  \n",
       "                          Middle Eastern         0.000622             0.003956  \n",
       "                          Native American        0.000072             0.000459  \n",
       "                          Unknown                0.000009             0.000057  \n",
       "                          white                  0.080174             0.510004  \n",
       "7TH DIVISION Asian        Black                  0.000072             0.000607  \n",
       "                          Latino                 0.000054             0.000455  \n",
       "                          white                  0.000306             0.002581  \n",
       "             Black        Asian                  0.000018             0.000152  \n",
       "                          Black                  0.005687             0.047894  \n",
       "                          Latino                 0.002064             0.017381  \n",
       "                          Middle Eastern         0.000063             0.000531  \n",
       "                          Native American        0.000027             0.000228  \n",
       "                          white                  0.014753             0.124250  \n",
       "             Latino       Asian                  0.000009             0.000076  \n",
       "                          Black                  0.000144             0.001214  \n",
       "                          Latino                 0.000072             0.000607  \n",
       "                          Middle Eastern         0.000009             0.000076  \n",
       "                          white                  0.000586             0.004934  \n",
       "             Other        Black                  0.000009             0.000076  \n",
       "                          Latino                 0.000009             0.000076  \n",
       "                          white                  0.000009             0.000076  \n",
       "             white        Asian                  0.000117             0.000987  \n",
       "                          Black                  0.018331             0.154383  \n",
       "                          Latino                 0.006741             0.056774  \n",
       "                          Middle Eastern         0.000252             0.002125  \n",
       "                          Native American        0.000090             0.000759  \n",
       "                          white                  0.069314             0.583757  \n",
       "8TH DIVISION Asian        Asian                  0.000036             0.000238  \n",
       "                          Black                  0.000811             0.005357  \n",
       "                          Latino                 0.000081             0.000536  \n",
       "                          white                  0.001956             0.012916  \n",
       "             Black        Asian                  0.000054             0.000357  \n",
       "                          Black                  0.007958             0.052556  \n",
       "                          Latino                 0.001334             0.008809  \n",
       "                          Middle Eastern         0.000054             0.000357  \n",
       "                          Native American        0.000027             0.000179  \n",
       "                          white                  0.036085             0.238319  \n",
       "             Latino       Asian                  0.000009             0.000060  \n",
       "                          Black                  0.000207             0.001369  \n",
       "                          Latino                 0.000054             0.000357  \n",
       "                          white                  0.000469             0.003095  \n",
       "             white        Asian                  0.000982             0.006488  \n",
       "                          Black                  0.020079             0.132611  \n",
       "                          Latino                 0.005182             0.034224  \n",
       "                          Middle Eastern         0.000360             0.002381  \n",
       "                          Native American        0.000081             0.000536  \n",
       "                          white                  0.075595             0.499256  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "393"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['division', 'citizen_race']).sum('searched').loc['2ND DIVISION'].loc['white', 'searched']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2nd division = 928 searches of Black motorists in 5131 stops\n",
    "# 18% of stops of a Black motorist result in a search\n",
    "#928/5131\n",
    "# 73% of all traffic stops are of Black motorist\n",
    "# 393/1723 searches/stops for white motorists\n",
    "# 22.8% of stops of white motorists result in search\n",
    "# 6999 stops total stops for division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "division\n",
       "1                   2\n",
       "1ST DIVISION    10154\n",
       "2ND DIVISION     6987\n",
       "3                   2\n",
       "3RD DIVISION    14663\n",
       "4                   7\n",
       "4TH DIVISION    14315\n",
       "5                   5\n",
       "5TH DIVISION    12114\n",
       "6                   8\n",
       "6TH DIVISION    17457\n",
       "7                   9\n",
       "7TH DIVISION    13185\n",
       "8                  14\n",
       "8TH DIVISION    16824\n",
       "ANC                 1\n",
       "JTN                 1\n",
       "NON LMPD          138\n",
       "SHV                 8\n",
       "STM                10\n",
       "Name: citizen_race, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('division')['citizen_race'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_beta(alpha, beta, ax, dist, title=None, label=None):\n",
    "\n",
    "    x = np.linspace(0.0, 1.0, beta)\n",
    "    y = dist.pdf(x)    \n",
    "    \n",
    "    lines = ax.plot(x, y, label=label)\n",
    "    ax.fill_between(x, y, alpha=0.2, color=lines[0].get_c())\n",
    "    if title: \n",
    "        ax.set_title(title)\n",
    "    ax.set_ylim(0.0, np.max(y)*1.2)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def a_b_division(race, division):\n",
    "    a = df.groupby(['division', 'citizen_race']).sum('searched').loc[division].loc[race, 'searched'] # alpha\n",
    "    b = df.groupby(['division', 'citizen_race']).count().loc[division].loc[race, 'searched'] # beta\n",
    "    return stats.beta((a + 1), b - a + 1), a, b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing search rates of Black, white citizens in 2nd division, officers all races"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((a_b_division('white', '2ND DIVISION')[0].rvs(10000)) > (a_b_division('Black', '2ND DIVISION')[0].rvs(10000))).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n",
    "#def plot_beta(alpha, beta, ax, dist, title=None, label=None):\n",
    "\n",
    "title = 'Search rates of Black and white citizens in 2nd Division, officers of all races'\n",
    "\n",
    "Black_dist, b_alpha, b_beta = a_b_division('Black', '2ND DIVISION')\n",
    "White_dist, w_alpha, w_beta = a_b_division('white', '2ND DIVISION')\n",
    "plot_beta(b_alpha, b_beta, ax, Black_dist, title=title, label=\"Black citizens\")\n",
    "plot_beta(w_alpha, w_beta, ax, White_dist, title=None, label=\"white citizens\")\n",
    "\n",
    "# title, etc\n",
    "ax.set_xlabel(\"Search Rates\")\n",
    "ax.legend()\n",
    "ax.set_xlim(0.125,0.28);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n",
    "#def plot_beta(alpha, beta, ax, dist, title=None, label=None):\n",
    "\n",
    "title = 'Search rates of Black and white citizens in 1st Division, officers of all races'\n",
    "\n",
    "Black_dist, b_alpha, b_beta = a_b_division('Black', '1ST DIVISION')\n",
    "White_dist, w_alpha, w_beta = a_b_division('white', '1ST DIVISION')\n",
    "plot_beta(b_alpha, b_beta, ax, Black_dist, title=title, label=\"Black citizens\")\n",
    "plot_beta(w_alpha, w_beta, ax, White_dist, title=None, label=\"white citizens\")\n",
    "\n",
    "# title, etc\n",
    "ax.set_xlabel(\"Search Rates\")\n",
    "ax.legend()\n",
    "ax.set_xlim(0.08,0.18);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n",
    "#def plot_beta(alpha, beta, ax, dist, title=None, label=None):\n",
    "\n",
    "title = 'Search rates of Black and white citizens in 4th Division, officers of all races'\n",
    "\n",
    "Black_dist, b_alpha, b_beta = a_b_division('Black', '4TH DIVISION')\n",
    "White_dist, w_alpha, w_beta = a_b_division('white', '4TH DIVISION')\n",
    "plot_beta(b_alpha, b_beta, ax, Black_dist, title=title, label=\"Black citizens\")\n",
    "plot_beta(w_alpha, w_beta, ax, White_dist, title=None, label=\"white citizens\")\n",
    "\n",
    "# title, etc\n",
    "ax.set_xlabel(\"Search Rates\")\n",
    "ax.legend()\n",
    "ax.set_xlim(0.05,0.12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n",
    "#def plot_beta(alpha, beta, ax, dist, title=None, label=None):\n",
    "\n",
    "title = 'Search rates of Black and white citizens in 5th Division, officers of all races'\n",
    "\n",
    "Black_dist, b_alpha, b_beta = a_b_division('Black', '5TH DIVISION')\n",
    "White_dist, w_alpha, w_beta = a_b_division('white', '5TH DIVISION')\n",
    "plot_beta(b_alpha, b_beta, ax, Black_dist, title=title, label=\"Black citizens\")\n",
    "plot_beta(w_alpha, w_beta, ax, White_dist, title=None, label=\"white citizens\")\n",
    "\n",
    "# title, etc\n",
    "ax.set_xlabel(\"Search Rates\")\n",
    "ax.legend()\n",
    "ax.set_xlim(0,0.08);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((a_b_division('Black', '1ST DIVISION')[0].rvs(10000)) > (a_b_division('Black', '5TH DIVISION')[0].rvs(10000))).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((a_b_division('Black', '1ST DIVISION')[0].rvs(10000)) > (a_b_division('Black', '5TH DIVISION')[0].rvs(10000))).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Hj3QMNLNLg+9jvC5Ajhgc5+KFErK+mvHO/79sZq14idgbeAdqnHOP4P16+bl5pyXewOv8D94pvd/j9anZjvdF0L9Zur+r8ZKbzXh9Xv42Gi/CbzW4Ge9AvxevNeGxiOmb8Q5M75nXTDwL7wrRx4A/mFkz3msPko8ZeFcFNeGdmniWQQ5izrkOvNMV5+J1PP03vD4+m8cQ/uXmFTVswbu66gW8vhX9n+st4J/w+pZUA4v9eYPpv8S7CuineO/ro3idhSPXsQ+vk+m5ZvYPgzxHsz/9QrxTJFvx6nL1n2+//1wv+NvzpP6vCe803KbgtZnZKn/aNXgHs7fw3quH8FoPRpKGt19W4p1G+iu8vjTRdjpeQvM7vNaENrwDKHg/OP4DL+7teKdO7van/RhY6G+PR4dZ/2v+e/0O3tVmX3LO/Z+hZnbOBUn/LLzP22Cy8Uo11OG9b9PwThn1X9fbeMnMv/rzXohX7qFjmHiH8i28vl2NeBfr/M9oF3TO/R6vW8RTeNvhqREW+SJei8g+4P8DPu2ce8Zf10j74mj92j8O7MTrVP19vKvohnoNVXifxVPwLlQazKj2WT8Zv8Cftx7vtNUFzrm6kYL2fyyUc/BHdH/fxrua8XVgI/Cq/9iYDXWMcV4JkgvxTqm/j7dv3Y/XhWUo5wBv+p+Ff8Hr03lgkOd8Eq/v48N4LauHA1eMMt5hj5kTMYrvnFfw9p978D4jz3KwpW+4Y+AKvO/jFn99X3TOvR+NmOOROReTFm4REZFJZWYfAP7GP/0nklCUkImIiIiETKcsRUREREKmhExEREQkZErIREREREKmhExEREQkZNEcZX7cpkyZ4ubPnx92GCIiIiIjWr9+fZ1zbmo01xkXCdn8+fNZt25d2GGIiIiIjMjMhh3ibDxGPGVpZv9pZjVm9kbEY2Vm9kcz2+rflkZMu83M3jGzt83s7GgHLCIiIpJsRtOH7AG8KsKRbgWedM4dCTzp38fMFuJVDT7WX+bf/OF2RERERGQIIyZkzrnn8Ia5iHQRsNr/fzVwccTjP3fOtfvDG7wDnBCdUEVERESS03j7kE33xy7DOVdlZtP8x2fTdwyxXf5jIiIifXR2drJr1y4OHBgwbKNIXMjJyWHOnDlkZmbG/Lmi3anfBnls0LGZzOwzwGcADjnkkCiHISIi8W7Xrl0UFhYyf/58zAb7+hAJj3OO+vp6du3axaGHHhrz5xtvHbJqM5sJ4N/W+I/vAuZGzDcHqBxsBc65Hznnljvnlk+dGtUrR0VEJAEcOHCA8vJyJWMSl8yM8vLySWvBHW9C9hhwrf//tcCvIh6/wsyyzexQ4EjglYmFKCIiyUrJmMSzydw/R1P24mfAS8BRZrbLzG4AvgucaWZbgTP9+zjn3gR+AbwFPA78jXOuO1bBi4iITER6ejoVFRUsXbqUZcuW8eKLLwKwbds2Fi1aNK51rly5cly1NdetW8fNN98MwDPPPNMbC8CqVav47//+73HFEw3PPfccy5YtIyMjg4ceemjE+f/xH/9xyGnz589n8eLFLF68mIULF/KNb3yD9vZ2ACorK7nkkkuGXfdjjz3Gd7/73SGnR27HRGLODdrFa1ItX77cqTCsiEhq2bRpE8ccc0yoMRQUFNDS0gLAE088wT/+4z/y7LPPsm3bNi644ALeeOONEdYw0MqVK7n77rtZvnz5uOO64447KCgo4JZbbhn3OqJp27ZtNDU1cffdd/ORj3xkxKQpcrv2FxSDnzJlCi0tLXzmM58hMzOT1atXDzp/2AbbT81svXNu/G/wIDSWpYiICNDU1ERpaemAx7dt28Zpp53GsmXL+rSiAdx1110sXryYpUuXcuutt/ZZrqenh2uvvZZvfOMbA9a5du1aTjnlFJYuXcoJJ5xAc3MzzzzzDBdccAHbtm1j1apV3HPPPVRUVPD8889zxx13cPfdd1NZWUlFRUXvX3p6Otu3b6e2tpaPf/zjrFixghUrVvDCCy8AXmJ3/fXXs3LlSg477DDuvfdeAFpbWzn//PNZunQpixYtYs2aNcNum/nz57NkyRLS0vqmDVVVVZx++ulUVFSwaNEinn/+eW699Vba2tqoqKjgqquuGna9BQUFrFq1ikcffZSGhoY+LZMnnngib775Zu+8K1euZP369TzwwAPcdNNNAPzyl79k0aJFLF26lNNPPx2gdzsCNDQ0cPHFF7NkyRJOOukkXn/99WG3S5jiYugkERGRMASJw4EDB6iqquKpp54aMM+0adP44x//SE5ODlu3buUTn/gE69at4/e//z2PPvooL7/8Mnl5eTQ0HCzZ2dXVxVVXXcWiRYu4/fbb+6yvo6ODyy+/nDVr1rBixQqamprIzc3tnT5//nxuvPHGPi1kTz75JACzZs1iw4YNANx33308++yzzJs3jyuvvJIvfelLfOADH2DHjh2cffbZbNq0CYDNmzfz9NNP09zczFFHHcXnPvc5Hn/8cWbNmsVvf/tbABobG8e1/X76059y9tlnc/vtt9Pd3c3+/fs57bTT+MEPftAb50iKioo49NBD2bp1K9OnT+99/IorruAXv/gF3/rWt6iqqqKyspLjjz+ejRs39s5z55138sQTTzB79mz27ds3YN3f/OY3Oe6443j00Ud56qmnuOaaa3rjGmy7TEZ5i6EoIRMRkdB969dv8lZlU1TXuXBWEd+88Nhh58nNze39gn7ppZe45pprBpym7Ozs5KabbmLDhg2kp6ezZcsWAP70pz/xqU99iry8PADKysp6l/nsZz/LZZddNiAZA3j77beZOXMmK1asALyEZKxeeOEF7r//fp5//vneWN56663e6U1NTTQ3NwNw/vnnk52dTXZ2NtOmTaO6uprFixdzyy238PWvf50LLriA0047bcwxAKxYsYLrr7+ezs5OLr74YioqKsa1nsG6T1122WWceeaZfOtb3+IXv/gFl1566YB5Tj31VK677jouu+wyPvaxjw2Y/uc//5mHH34YgA996EPU19f3Jp+DbZc5c+aMK/5o0ClLERER4OSTT6auro7a2to+j99zzz1Mnz6d1157jXXr1tHR0QF4ScRQV+GdcsopPP3004OWTBhuudGoqqrihhtuYM2aNRQUFADe6dGXXnqJDRs2sGHDBnbv3k1hYSEA2dnZvcump6fT1dXFggULWL9+PYsXL+a2227jzjvvHFcsp59+Os899xyzZ8/m6quvHteFB83NzWzbto0FCxb0eXz27NmUl5fz+uuvs2bNGq644ooBy65atYpvf/vb7Ny5k4qKCurr6/tMHyzRC7b9YNslTGohExGR0I3UkjUZNm/eTHd3N+Xl5ezfv7/38cbGRubMmUNaWhqrV6+mu9srHnDWWWdx5513cuWVV/aesgxayW644Qaee+45Lr30Uh555BEyMg5+3R599NFUVlaydu1aVqxYQXNzc59TlgCFhYU0NQ1sMezs7OSyyy7je9/7Xp8E5qyzzuIHP/gBX/3qVwHYsGHDsK1VlZWVlJWV8clPfpKCggIeeOABAG677TZOOOEEPvrRj45qm23fvp3Zs2fz6U9/mtbWVl599VWuueYaMjMz6ezsHPEUYEtLC5///Oe5+OKLKS0tHXDq9IorruCuu+6isbGRxYsXD1j+3Xff5cQTT+TEE0/k17/+NTt37uwz/fTTT+fBBx/k7//+73nmmWeYMmXKuFokJ4NayEREJGUFfcgqKiq4/PLLWb16Nenp6X3m+fznP8/q1as56aST2LJlC/n5+QCcc845fOQjH2H58uVUVFRw991391nuy1/+MsuWLePqq6+mp6en9/GsrCzWrFnDF77wBZYuXcqZZ545oCXtwgsv5JFHHunt1B948cUXWbt2Ld/85jd7466srOTee+9l3bp1LFmyhIULF7Jq1aphX/fGjRs54YQTqKio4Dvf+U7vhQcbN25kxowZA+Zfu3Ytc+bM4Ze//CWf/exnOfZYL4F+5plnqKio4LjjjuPhhx/mi1/8IgCf+cxnWLJkyZCd+j/4wQ+yaNEiTjjhBA455BB++MMfDjrfJZdcws9//nMuu+yyQad/9atfZfHixSxatIjTTz+dpUuX9pl+xx139G6XW2+9NW6v5ASVvRARkZDEQ9kL6evss8/miSeeCDuMuKKyFyIiIjKplIyFRwmZiIiISMiUkImIiIiETAmZiIiISMiUkImIiIiETAmZiIiISMiUkImISMpKT0+noqKCpUuX9hk4PHKQ67FauXIl4ynltG7dOm6++WbAq+8VOYj5qlWrxlUFP1oeeOABpk6d2lv77P777we87fTTn/60z3zBwN/DWblyJUcddRRLlizh6KOP5qabbuozFuUpp5wy7PKR22owlZWVXHLJJSPGEU9UqV9ERFJW5FiWTzzxBLfddhvPPvtsKLEsX76c5cu90lbPPPMMBQUFvYnJjTfeGEpMkS6//HJ+8IMf9HksSMiuvPLKMa/vwQcfZPny5XR0dHDbbbdx0UUX9W77yGR0MJHbajCzZs3ioYceGnNMYVILmYiICN6A3KWlpQMe37ZtG6eddhrLli3r04oGcNddd7F48WKWLl3Krbfe2me5np4err322t4q+JHWrl3LKaecwtKlSznhhBNobm7mmWee4YILLmDbtm2sWrWKe+65p7dS/x133MHdd99NZWVlbytVRUUF6enpbN++ndraWj7+8Y+zYsUKVqxYwQsvvAB4leqvv/56Vq5cyWGHHca9994LQGtrK+effz5Lly5l0aJFrFmzZlzb7NZbb+X555+noqKCe+65B/Bap8455xyOPPJIvva1r424jqysLO666y527NjBa6+9BtA7Rufll1/O7373u955r7vuOh5++OHebQXw7LPP9m6P4447rndszKCF88CBA3zqU59i8eLFHHfccTz99NOA15r3sY99bEyxxpJayEREJGUFQycdOHCAqqoqnnrqqQHzTJs2jT/+8Y/k5OSwdetWPvGJT7Bu3Tp+//vf8+ijj/Lyyy/3jmUZ6Orq4qqrrmLRokXcfvvtfdbX0dHB5Zdfzpo1a1ixYgVNTU19xrKcP38+N954IwUFBdxyyy0APPnkk4DX8hO06N133308++yzzJs3jyuvvJIvfelLfOADH2DHjh2cffbZbNq0CfDG6Hz66adpbm7mqKOO4nOf+xyPP/44s2bN4re//S3AgDEkB/Pwww/z3HPPsWDBAu655x7mzp3Ld7/7Xe6++25+85vfAF6Ss2HDBv7yl7+QnZ3NUUcdxRe+8AXmzp077LrT09NZunQpmzdv7jP80RVXXMGaNWs477zz6Ojo4Mknn+Tf//3fefnll3vnufvuu7nvvvs49dRTaWlpIScnp8+677vvPsAbFmrz5s2cddZZbNmyBWBcscaKEjIREQnf72+FPRuju84Zi+Hc7w47S+Qpy5deeolrrrmGN954o888nZ2d3HTTTWzYsIH09PTeL/M//elPfOpTnyIvLw+gd2BxgM9+9rNcdtllA5IxgLfffpuZM2eyYsUKgHENdv3CCy9w//33945z+ac//Ym33nqrd3pTUxPNzc0AnH/++WRnZ5Odnc20adOorq5m8eLF3HLLLXz961/nggsu4LTTThv2+S688EI+8YlPkJ2dzapVq7j22msHTV4BzjjjDIqLiwFYuHAh27dvH1WSM9hQjueeey4333wz7e3tPP7445x++ukDBmI/9dRT+fKXv8xVV13Fxz72MebMmdNn+p///Ge+8IUvAN7A7vPmzet9D8cbayzolKWIiAhw8sknU1dXR21tbZ/H77nnHqZPn85rr73GunXr6OjoALwEwswGXdcpp5zC008/PWDQ8JGWG42qqipuuOEG1qxZ03tqr6enh5deeokNGzawYcMGdu/eTWFhIQDZ2dm9y6anp9PV1cWCBQtYv349ixcv5rbbbuPOO+8c9jnLy8t71/PpT3+a9evXDznvYM83ku7ubjZu3DhgzMicnBxWrlzJE088wZo1a7jiiisGLHvrrbdy//3309bWxkknncTmzZv7TB9uzO7xxBoraiETEZHwjdCSNRk2b95Md3c35eXl7N+/v/fxxsZG5syZQ1paGqtXr6a7uxuAs846izvvvJMrr7yy95Rl0Ep2ww038Nxzz3HppZfyyCOPkJFx8Ov26KOPprKykrVr17JixQqam5sHtPoUFhbS1NQ0IMbOzk4uu+wyvve977FgwYLex8866yx+8IMf8NWvfhXwTsVVVFQM+VorKyspKyvjk5/8JAUFBTzwwAMA3HbbbZxwwgl89KMf7TN/VVUVM2fOBOCxxx7rTZwKCwt7W+LGq7Ozk9tvv525c+eyZMmSAdOvuOIK7r//ftatW9cbZ6R3332XxYsXs3jxYl566SU2b97c57WffvrpPPjgg3zoQx9iy5Yt7Nixg6OOOopXX311QnFHm1rIREQkZQV9yCoqKrj88stZvXo16enpfeb5/Oc/z+rVqznppJPYsmUL+fn5AJxzzjl85CMfYfny5VRUVHD33Xf3We7LX/4yy5Yt4+qrr6anp6f38aysLNasWcMXvvAFli5dyplnnjmgJe3CCy/kkUce6e3UH3jxxRdZu3Yt3/zmN3vjrqys5N5772XdunUsWbKEhQsXsmrVqmFf98aNGznhhBOoqKjgO9/5Tu+FBxs3bmTGjBkD5r/33ns59thjWbp0Kffee29vYrRkyRIyMjJYunRpb6f+0brqqqtYsmQJixYtorW1lV/96leDznfWWWfx3HPP8eEPf5isrKwB0//5n/+ZRYsWsXTpUnJzczn33HP7TP/85z9Pd3c3ixcv5vLLL+eBBx7o0zIWL2y4przJsnz5cjeemi0iIpK4Nm3aNOAUlYTr7LPP5oknngg7jLgy2H5qZuudc0PX3RgHtZCJiIgIgJKxECkhExEREQmZEjIRERmTddsauPyHL/EPv3mL7p7wu72IJANdZSkiIqPW0NrBdf+1lo6uHl5+v4GC7Ay+dOaCkRccwkRLQIjE0mT2s1cLmYiIjNoPn32X1o4uvn/pUpbPK+U/nn+Plvbx1W7Kycmhvr5+Ur/0REbLOUd9ff2Ayv+xohYyEREZlZ4ex0Prd7F0TjHzpuRz2fI5fO3hjTy2YTdXnjhvzOubM2cOu3btGlCIVSRe5OTkDKj8HytKyEREZFTeqmqkvrWDq048BICjZxQxpSCLx9/cM66ELDMzk0MPPTTaYYokJJ2yFBGRUfnjWzUALJ/nVaM3M5bMKWHdtr10dfcMt6iIjEAJmYiIjMpzW2o5pCyP0vyD1dKPP6SU/R3dbNi5L7zARJKAEjIRERlRe1c3G3c3snROcZ/HjzukBAOefrsmnMBEkoQSMhERGdHbe5rp6nEcPaOoz+OFOZnMLM7htZ2NIUUmkhyUkIkAdHXAhp9Bc3XYkYjEpVd37AVgwYzCAdMOm5rPW1VNkx2SSFJRQibSthd+eBo8eiOs+gDseSPsiETizms7G8nPTmd6YfaAaUdOK6ShtYOapgMhRCaSHJSQibxwL9RuhpM+D10H4PdfCzsikbjzxu5GDi3PH7Sq/oLpBQC8vkunLUXGSwmZpLb2Flh7P8w5AZZcBos+BttfgOo3w45MJG50dffwfl0rh03NH3T64VMLMdCVliIToIRMUtsb/wPtTXDcJ737x34M0rPgpfvCjUskjuxo2E9Xj+OQssETstysdKYUZLOlunmSIxNJHkrIJLVtfQJySmDGYu9+ThHMPh7e+RNofD0RwLvCEuDQKYMnZACzSnJ4r7ZlskISSTpKyCR19XTD+8/BrOMgsl/M3BOgpRrqtoQXm0gcedtv+ZpTmjvkPHPL8tjR0EZ3j37IiIyHEjJJXZV/8U5XHnJS38eD+1uemPyYROLQOzUtlOZlkpc19PDHc0vz6OjuYffetkmMTCR5KCGT1PXOn7zbuSf2fbxwJhTOOjhdJMW9W9vCrJKhW8cA5vunM9WPTGR8lJBJ6qr8CxTMgNySgdOmHwtVG9SPTFKec47t9fuZPUJCNtc/nRn0NxORsVFCJqlrz+tQfvjg06YdDQcaYd+OyY1JJM40tnWyv6ObmcU5w85XmJNJQXYG2+pbJykykeQyoYTMzL5kZm+a2Rtm9jMzyzGzMjP7o5lt9W9LoxWsSNTsb4CmSph61ODTpx/r3e5aP3kxicShnQ1en7CRTlkCTC3MYkfD/liHJJKUxp2Qmdls4GZguXNuEZAOXAHcCjzpnDsSeNK/LxJfdr/q3U5bOPj0ssMgLQN2r528mETi0Ht1XimLkVrIAKYV5rB7nzr1i4zHRE9ZZgC5ZpYB5AGVwEXAan/6auDiCT6HSPTtXufdDtVClp4FJfO8fmYiKWx7vdfiNb1o5IRsRlEOexoPqPSFyDiMOyFzzu0G7gZ2AFVAo3PuD8B051yVP08VMC0agYpEVfUbkD8VsguHnqf8CG8IJXXslxS2o2E/+dnpw5a8CMwsyaGrx1GtQcZFxmwipyxL8VrDDgVmAflm9skxLP8ZM1tnZutqa2vHG4bI+NRtheI5w89TdphXp6xV+6ekrl0N+5lakD2qeWcWe/3MttepY7/IWE3klOWHgfedc7XOuU7gf4BTgGozmwng39YMtrBz7kfOueXOueVTp06dQBgiY9TTA3vfh5JDhp8vuAKzZlPsYxKJU7v2tY3qdCUc7Gf2vq60FBmziSRkO4CTzCzPzAw4A9gEPAZc689zLfCriYUoEmWNO6GrHUoPHX6+0vnebfUbMQ9JJB719Dj2NB5getHoWsimFmRjdrDfmYiM3sidAobgnHvZzB4CXgW6gL8APwIKgF+Y2Q14Sdul0QhUJGpqNnu3IyVkeeWQmacWMklZ1c0H6OpxvaciR5KRnkZZfhY7VfpCZMzGnZABOOe+CXyz38PteK1lIvGp5k3vtmze8POZQfFcqN0c+5hE4tAOv6VrNCUvAlMLstmp8SxFxkyV+iX11L4N2UWQUzLyvCWHQP07MQ9JJB5t8xOyGUWjayHz5s2hUrXIRMZMCZmknob3vAHER6N0HrTt9Sr7i6SY3Xu9hGxq4ej6kAFMK8qhvrWDA53dsQpLJCkpIZPUs287FI0yIQuuxNz7fuziEYlTVY0HKMjOICtj9F8V0/zkTbXIRMZGCZmklq52aKmBwlmjm794rndbtzV2MYnEqarGA5TmZ45pmel+QqbTliJjo4RMUsu+nYAbuShsoMhP3JSQSQqqbjpAeX7WmJaZ4idku9WxX2RMlJBJaml4z7sdbUKWke2VvwiWE0khNc3tlOePvv8YwBS/qv8utZCJjIkSMkkt9e96t8WzR79M4QxoUB8ySS0HOrtpbOscU4d+gJzMdPKz0qlqVB8ykbFQQiappeFdSM+C3LLRL1M4G/Zti1lIIvGopqkdYNTjWEYqzc9ijxIykTFRQiapZe82KJjmFX0dreLZXumLA00xC0sk3lQ2eqccp4yxhQygLD+LqkadshQZCyVkklr27YCCGWNbJuhvtm979OMRiVNBp/zxtJBNyc+mprk92iGJJDUlZJJamqugYPrYlglqlu3dEf14ROLUbr9TfnnB2K6yBK+Q7L79nbR3qTisyGgpIZPU0d4M7U1QOMaELEjgVBxWUkhV4wFyM9PJyxr7kMdTCr0krrpRrWQio6WETFJH4y7vdrTDJgVySyEt0zvdKZIi9jS2jbkobGBaoTcYeaX6kYmMmhIySR3BKcexJmSWBvnl0Lgz+jGJxKk9TQcoyxv76Uo4OPblbtUiExk1JWSSOoJO+QXTxr5s/rSDLWwiKaCmqZ2yMVbpD0zJD6r1749mSCJJTQmZpI59273WrrzysS9bMB2aKqMfk0gc6ulx7GvrpHScCVluVjo5mWnsUR8ykVFTQiapo3GXl4ylpY992cLpsL/OG5xcJMk1tnXS3ePGfcoSoCQ3i5pmFYcVGS0lZJI6GndB3pTxLRv0O9NpS0kBdS3eD4/xtpABlORlUqtaZCKjpoRMUkdT5fj6jwEUzfJudaWlpIBg2KTx9iEDKMnLorZFCZnIaCkhk9TQ0wOtNZA/dXzLB4nc3m1RC0kkXlX7pxpLcsdX9gKgPD+T+pYOnHPRCkskqSkhk9TQ1gA9XeNvIcufBhjs1fBJkvyCFrKSCfQhK8vPpr2rh5b2rmiFJZLUlJBJagiukBxvC1l6plcgVrXIJAXUNreTZlCYM/Yq/YEp/pBLGtNSZHSUkElqCBKy8baQAeRPUad+SQl1re0U5mSSZjbudZT7g5LXNOlKS5HRUEImqSFIpMZTgyxQMA2alJBJ8qtrbqd4Av3HgN6SGZX7lJCJjIYSMkkNzX4L2YQSsunQXO1dICCSxOpaOyackAUlM/aohUxkVJSQSWpoqoTsIq8v2HgVzICeTu9qTZEk1tDaMaErLAHys9LJTDeqlZCJjIoSMkkNzXsgr2xi6+itRaaO/ZK8nHPs299BSd7EEjIz86r1N6lTv8hoKCGT1NC8Z2KnKwEKZ3i3qkUmSay5vYvObjehorCB4rxMalvUQiYyGkrIJDW0VE88IQuu0FTpC0litf4pxmgkZGV5WdQ2d0x4PSKpQAmZJL/uTmjb65WtmIisAkjPOlhCQyQJVTdPvChsoDQ/i3oNnyQyKkrIJPm11ABu/EVhA2ZeP7RmJWSSvII+X6UT7EMGUJ6fRWtHNwc6uye8LpFkp4RMkl9vlf7pE19Xbjk0VU18PSJxqqZ3HMsonLL0T3uqY7/IyJSQSfJr2u3dFkzwlCVAfrnXH00kSdU1d2BA0QTLXkDk8Enq2C8yEiVkkvwa/YRsop36wTvt2VoLzk18XSJxqLalnYLsDNLTxj9sUqAs3xs+qXJf24TXJZLslJBJ8mveDZbmDQ4+UflTobsD9jdMfF0icaiupT0qrWNw8JSlqvWLjEwJmSS/pj1eMmZR2N2D0hfq2C9Jqr5l4lX6A4U5GaSbqQ+ZyCgoIZPk17InOq1j4I1nCQdPg4okmYbWiVfpD6SZUZibQa1KX4iMSAmZJL9oVOkPBLXMVBxWkpQ3bNLEr7AMFOdkUt+i4rAiI1FCJsmvtQbyonCFJfiJnamFTJLS/o4uDnT1RKUGWaA4N5M6tZCJjEgJmSS3jv3Q3jzxKv2BtAzIKVYfMklKtX6V/mgMmxQozsukvlUtZCIjUUImya1lj3c70Sr9kfLKoFnFYSX5VPtXQ5ZG8ZRlaV4me1s7cCoVIzIsJWSS3Jr9Iq5RTcjKvX5pIkkmuBoymn3ISvOy6OpxNLV1RW2dIslICZkkt6AlK68seuvMm6Jq/ZKUanoHFo9eH7KgtU1XWooMTwmZJLdYJGT5U+FAI3Sq2KUkl6DzfXGU6pCBN8A4QK2GTxIZ1oQSMjMrMbOHzGyzmW0ys5PNrMzM/mhmW/3bKBWAEhmH5mqvIGxOSfTWWeCf/lQ/Mkkytc3t5GWlk5kevd/qpcEA481qIRMZzkQ/df8CPO6cOxpYCmwCbgWedM4dCTzp3xcJR0s1ZBdCWnr01hkUh23SlZaSXOpa2qPaOgYH+6OpWr/I8MadkJlZEXA68GMA51yHc24fcBGw2p9tNXDxxEIUmYDW2ui2jsHBCwQad0V3vSIhq2vpiHpCVpiTQZpBjU5ZigxrIi1khwG1wH+Z2V/M7H4zywemO+eqAPzbaVGIU2R8WmogtyS66wxqmjWpOKwkl4bW6I1jGUgzozAns7fGmYgMbiIJWQawDPh359xxQCtjOD1pZp8xs3Vmtq62tnYCYYgMo7U2euNYBrIKID1bLWSSdPZGedikQFFuBnUaPklkWBNJyHYBu5xzL/v3H8JL0KrNbCaAf1sz2MLOuR8555Y755ZPnRrFGlEiAeegrQFyo3iFJYCZd9Vmi2qRSfI40NnN/o5uSvOj20IGUJKbRX2rWshEhjPuhMw5twfYaWZH+Q+dAbwFPAZc6z92LfCrCUUoMl4HGqG7I3oDi0fKLVNxWEkqwfBGZTFoISvJ1QDjIiPJmODyXwAeNLMs4D3gU3hJ3i/M7AZgB3DpBJ9DZHxa/VPh0RrHMlJeGezdFv31ioSkJhg2KYrjWAZK8jLZu98bPsnMor5+kWQwoYTMObcBWD7IpDMmsl6RqAhasGLRQpY/BXa+7J0W1ReMJIE9fkJWkhv9hKw0P4vObkdzexdFOdE/JSqSDFSpX5JXLKr0B/KneqdDD+yL/rpFQlAXg2GTAr3DJ+lKS5EhKSGT5BW0kEW7Uz8crEWmfmSSJGpjmJCV5SshExmJEjJJXi3BsElF0V93vl9er0nDJ0lyqG1pJyczjeyMKI5q4Svtrdav4rAiQ1FCJsmrpdqr0m8x2M0L/AsFVItMkkR9S0fM+neV+q1uGs9SZGhKyCR5tdZCTnFs1h1cKNCs8SwlOdTGYBzLQGFOJmZKyESGo4RMkldLTfSr9AcyciArXwOMS9JoaIn+sEmB9DSjKCej98IBERlICZkkr1gMmxQpt1Sd+iVpNOzviEmH/kBRTia1LUrIRIaihEySU08PtO2NTcmLQK6GT5Lk0NndQ/OBrt7O97FQnJtJvRIykSEpIZPkdGAf9HTFNiHLK/cuHBBJcA3+sEmxqNIfKM7N7B2eSUQGUkImyanFH9M+LwbDJgXyy6G1zmuNE0lgtc1eOYqyGCZkJXlZ7G3txDkXs+cQSWRKyCQ5BX27YjGOZSB/qtcK19YQu+cQmQQ1vUVhY5eQleVn0dHdQ0t7V8yeQySRKSGT5BTLKv2B3mr9Kg4ria26yU/IYnSVJUCZf8FAXYtOW4oMRgmZJKeWGI5jGSiY7t2qWr8kuOCUZSyvstTwSSLDU0ImyamlGtIyIKsgds8RFIdVtX5JcHXNHWSlp5GbGf1hkwLBBQPVTW0xew6RRKaETJJTc40/bJLF7jmC1jdV65cEV9vSTlFuBhbDz0tJrj+epVrIRAalhEySU2sN5JbE9jnSsyC7SKcsJeHVxXDYpEBRbiaGTlmKDEUJmSSnllqvhSzWcktVHFYSXn1rB0UxTsjS04yCnAwlZCJDUEImyWl/bWyvsAzklamFTBLe3taO3lOKsVScm6mrLEWGoIRMkk9PN7Ttg7wYjmMZyCtTtX5JaN09jsa2TkpjeIVloDg3UwOMiwxBCZkkn/0N4LoPXgUZS/lTvcKw3Sp2KYlp7/4Oelxsh00KaPgkkaEpIZPk0xoMmzRJCZnrgdba2D+XSAzU+6cQy2JYpT9QmpfF3v0dGj5JZBBKyCT5NPunEINK+rGkav2S4GqavKKwk9FCVpqXSXtXD60d3TF/LpFEo4RMkk/zJFTpDxRM6/ucIgmmehKq9AdK/VY49SMTGUgJmSSfIDnKnYxO/f7g5Y27Y/9cIjEQlKEonYSrLIPhk+palJCJ9KeETJJPS41XtDUzL/bPlVsClgZNSsgkMdU0t5ORZuRnx27YpECQkO1pPBDz5xJJNErIJPm0VPuJUgyHTQqkZUBOsU5ZSsKqa+mgKCe2wyYFSvI0fJLIUJSQSfJpqZmcKv2B3FJoVrV+SUz1Le0xr9IfKPaHT1JCJjKQEjJJPq01k9N/LJBbpuGTJGFNxjiWgfQ0ozAng9pmnbIU6U8JmSSf1rrJTcjyy71WOZEE1NDaMWkJGXitZLXq1C8ygBIySS7dnXCgcXKKwgbypkDbXuhSBXJJLM459u3v7C1HMRm84ZP0WRHpTwmZJJfWOsBNbkIWFIfVmJaSYJrauujqcZNSgyxQkpelshcig1BCJsklSIryJzEhKwiq9asfmSSW4NThZAybFCjNy6ShVcMnifSnhEySS5CQBQVbJ0O+X62/qXLynlMkCoLO9WWTMGxSoDw/m64eR2Nb56Q9p0giUEImySVIiiZj2KRAvp/8qTisJJig/MRkjGMZCJK/WpW+EOlDCZkkl6CFLHcSE7KcYrB0JWSScIKErGQST1mWFyghExmMEjJJLs17vCGTMrIn7zktzS8Oq2r9klhqmg6QZlCYkzFpzxm0xu1pUi0ykUhKyCS5tNR4wyZNNlXrlwRU19JBYU4maZMxzJgvKLFRrYRMpA8lZJJcWid52KRAXpkSMkk4dS3tFOVOXusYQH5WOhnppuGTRPpRQibJpWWSh00K5JV7yaBIAqlv6aA4Z/JqkAGYGSW5mepDJtKPEjJJLvvrJrdDfyB/KrQ3Q8f+yX9ukXGqb22f1A79gSIlZCIDKCGT5NHZBh2tk1vyItBbrV+nLSUxOOdoaO2gdBKr9AdKcjNVrV+kHyVkkjyCAb4nc9ikQL6q9UtiaW7vorPbTWoNskBpXhZ1LRrPUiSSEjJJHkFClj+JVfoDBUG1ftUik8QQnDKczGGTAqX5WTS2ddLZ3TPpzy0Sr5SQSfIIWqfCSMiC52xUQiaJoTchC6GFLEgC69VKJtJLCZkkj6Awaxid+rMKID1L41lKwqhunPxxLAMaPklkoAknZGaWbmZ/MbPf+PfLzOyPZrbVvw2hBoGkpJY9gIVTGNZMxWEloVT7A4uXhnDKstxPyGqaVRxWJBCNFrIvApsi7t8KPOmcOxJ40r8vEnvN1ZBdCGmTW+iyV24ptGj4JEkMNc3tpKcZBZM4bFIgaCFTtX6RgyaUkJnZHOB84P6Ihy8CVvv/rwYunshziIxaS3U4VfoDeeVeUiiSAGqb2ynOyZjUYZMCJb3DJ+mUpUhgoi1k/wx8DYi8VGa6c64KwL+dNtiCZvYZM1tnZutqa2snGIYI0FobzunKQF65F4NIAqhtbqcod/JrkAFkZaSRl5WuU5YiEcadkJnZBUCNc279eJZ3zv3IObfcObd86tSp4w1D5KDWkIZNCuRPhc79XsV+kThX19JOcUgJGUCxqvWL9DGRFrJTgY+Y2Tbg58CHzOwnQLWZzQTwbzXAn8Sec7C/Ppwq/YHeWmTqRybxr6G1I5RhkwJKyET6GndC5py7zTk3xzk3H7gCeMo590ngMeBaf7ZrgV9NOEqRkbQ3Q1d7OFX6A/lBQqbSFxLfenq8YZPCKAobKM3LVLV+kQixqEP2XeBMM9sKnOnfF4mtMIdNChT6Cdm+7eHFIDIKe/d30OOgND+8U5aleVk0tCohEwlE5Xpn59wzwDP+//XAGdFYr8iotfhXN4ZRpT+Q5z/3vh3hxSAyCrUt4VXpD5TmZdHW2U1rexf52SGVqhGJI6rUL8kh6LcVZgtZRjZkF2k8S4l7Qd+t8hATsrICVesXiaSETJJDUJA1zE794LXQqQ+ZxLmgIGtZfnZoMQTJYNBaJ5LqlJBJcmipBkv3WqjClDdFLWQS94KCrCV54fUhC5LBPY1tocUgEk+UkElyaK6GnGKwkHfpgmkaz1LiXk3TAbLS08jNTA8thlI/Gdyjav0igBIySRYt1eFW6Q/kT4WOFhWHlbhW2+wVhbUQhk0KFOVmkp5mVO1TC5kIKCGTZNFaG+44loHCGd6t+pFJHKtt6aA4N9wrG9PMKMnL1HiWIj4lZJIcWmvD79APUOAnZI27wo1DZBj1Le0Uh1gUNlCam0m1xrMUAZSQSTLo7vKHTQqxBlkgKA67V8VhJX7Vt3b09uEKU2l+FjVNSshEQAmZJIPWWnA9UBAHg9TnTQEMGneGHYnIoDq7e2hs6wx1HMtAeX42tc2q1i8CSsgkGTT7/bWCsSTDlJ7pXe2p0hcSp+r98SPDLAobmFLgVetvPtAZdigioVNCJokvqNJfEAcJGfjFYZWQSXwKKuOHOWxSYEqBV4usWqctRZSQSRJo8jvQhzmOZaQ8VeuX+HWwSn/4CdnUwiAh05WWIkrIJPE17vYKwsZD2Qs4WBzWubAjERmgyq+MHw8JWdBCtlu1yESUkEkSaK7ykrG08KqO95E/FTr3Q3tT2JGIDFDVeAADyuKgU3+QFKo4rIgSMkkGzVXxUYMsEBSHbVQ/Mok/1U0HKMrNICM9/MN/TmY6eVnpVDWqD5lI+J9IkYlqro6zhGymd7tPpS8k/uxpOkBJbvitY4HSvCz2qFO/iBIySQIt1fFRFDYQXO25T8VhJf7UNLVTGgf9xwIleZkqDiuCEjJJdJ1tXl+t/DgoChvIKwNLh8YdYUciMkBtc3tcdOgPlOVnUdOsqyxFlJBJYmv2a5DFS8kLgLQMyC/X8EkSd9q7utnX1smUgvhJyKYUZNPQ2kF3j65KltSmhEwSW/Me7zYeqvRHyp+m4ZMk7tT49b7K87NDjuSgKQVZ9Dioa1ErmaQ2JWSS2Br9orDxUqU/UDhDnfol7tQ0e321goKs8WCqX4tsj660lBSnhEwSWzBEUTydsgQvIdtfB536kpH4UbnPT8gK4ichC4rDVjaqFpmkNiVkktiaKiE9C7IKwo6kr8JZ3m3QgicSByr3xU+V/kBvtf69SsgktSkhk8TWVAm5ZWAWdiR9Fc/xbvduCzUMkUh7mg6QkW4U5mSEHUqv4rxMMtJMCZmkPCVkktia98RXUdhAUK2/4f1w4xCJsKfxAKV5WVgc/YBJM6MsP0vjWUrKU0Imia2lGvLKw45ioPwpXvmLvUrIJH5UNx2gNC8z7DAGKC/I6h30XCRVKSGTxOUctNbEX4d+AEvz4lK1fokjNc3tlMbBoOL9TSnIplJXWUqKU0ImievAPuhqj8+EDLxaZPtUrV/ig3OO2uZ2yuPoCsvA9MJsGlo66OjqCTsUkdAoIZPE1VTp3cZbUdhA4QxdZSlxo+lAF+1dPXFVpT8wrSgHh2qRSWpTQiaJKxiaqGhWuHEMpXAmtDVAx/6wIxHpHcB7ShxV6Q/MKMoBUMd+SWlKyCRxBSUlgisa402QKOq0pcSBoNP81ML4ayGb7idku/bqx4ukLiVkkrj27fCuZMwtDTuSwRXN9m5Vi0ziwI6GICHLCTmSgYLisDsalJBJ6lJCJomrcSfkTfGuaIxHRTO9W9Uikziwa28bZlAeR1X6A1kZaRTnZqg4rKS0OP0mExmFxl2QPzXsKIaWW+YN66RaZBIHKve1UZqbSUZ6fB72y/Kz1YdMUlp8fjJFRqNpNxTEcUJm5pXk0ClLiQO797VRFoclLwJTCrKo0lWWksKUkEli6uqA1jooiNMO/YHCWWohk7iwp7GNKXF4ujIwtTCH6qYDOOfCDkUkFErIJDE17QZc/F5hGSie7V180KOClxKenh5HTXM704rir0N/YHphNu1dPTS0doQdikgolJBJYmrc6d0Wzgw3jpGUHAJdB6C5KuxIJIXVtbbT2e2YVhi/pyyD0heV+3TaUlKTEjJJTL1FYeM9IZvn3dZvDTcOSWnB1YvT47mFzI9tp0pfSIpSQiaJKUjI4vkqS/BayABqt4Qbh6S0XQmRkHmtd9vqW0OORCQcSsgkMe3bDjklkBG/p2AA7yrL9CyoezvsSCSF7fQr4E+N41OWhTmZ5GWlKyGTlKWETBJTU5zXIAtYmtfPrf7dsCORFLazoY3czHQKsjPCDmVY0wqzVa1fUpYSMklMjXFegyxSkUpfSLgq97XFZYX+/qYX5agPmaQsJWSSeJzzrlosmB52JKNTPMe7KrS7M+xIJEVV7mujvCD+E7IZxTnsaWqnq1tlYiT1jDshM7O5Zva0mW0yszfN7Iv+42Vm9kcz2+rfxunIz5KwWmuhuyP+S14ESg6Bnm6vHpnIJHPOsXtfGzOKcsMOZUSzinPp7nEqfSEpaSItZF3AV5xzxwAnAX9jZguBW4EnnXNHAk/690WiZ1+C1CALlM73butU+kIm3979nezv6GZmcfxeYRmYU+oljdvqW0KORGTyjTshc85VOede9f9vBjYBs4GLgNX+bKuBiycYo0hfwdiQ8V6lP1Ay17ut3RxuHJKStvtXLc4qjf8WspnFXozv1elKS0k9UelDZmbzgeOAl4Hpzrkq8JI2YFo0nkOkV8M73m3RrHDjGK3sYsgqUAuZhGKbn9zMSoAWsvKCLDLSrTdmkVQy4YTMzAqAh4G/dc41jWG5z5jZOjNbV1tbO9EwJJXUvwfZRZCVH3Yko2PmteY1qPSFTL73/eQmnovCBtLMmFqQzfZ6XWkpqWdCCZmZZeIlYw865/7Hf7jazGb602cCNYMt65z7kXNuuXNu+dSpCVK+QOLD3m2Jc7oyUDwbGt4LOwpJQdsb9lOSm0lOZnrYoYzKtCLVIpPUNJGrLA34MbDJOff9iEmPAdf6/18L/Gr84YkMYt/2xOnQHyg9FFqq4UBj2JFIitnZsJ9pRfFbob+/2cW57NrbRk+PCzsUkUk1kRayU4GrgQ+Z2Qb/7zzgu8CZZrYVONO/LxIdXe1eYlM0O+xIxqb8SO+2+q1w45CUs7OhjWmF8X+6MjC3LI/2rh6qmlT6QlLLuMfRcM79GbAhJp8x3vWKDGvfDnA9XrHVRDLlCO+2agPMOznUUCR1HOjspq6lnb8qTpxuIfPK8wB4p7qZ2SXxf2WoSLSoUr8klmBMyJJDwo1jrPKnQWYe7Hkj7Egkheza24aDhEps5pZ6Cdnb1c0hRyIyuZSQSWKp90tHJErJi4AZlMyDGp2ylMnzfp1XYHVOAiVkxbmZ5Gels7VaxWEltSghk8RStxUyciE3AUfkKpsPtW97Y3GKTIIgqZmdAEVhA2bGzOIc3qtTQiapRQmZJJb6d7zWMRuq+2IcKz8COls1pqVMmndqWijKyaAwJzPsUMZkVkke79ep9IWkFiVkkljq34GSBOvQH5iywLutfjPcOCRlvFvb0jscUSKZW5pLQ2sHjW2dYYciMmmUkEni6Gj1Sl4UJ1iH/kDZ4d5t1YZQw5DUsa1+f0J16A8EV1q+V6vTlpI6lJBJ4giusCydH2oY45aVBwXTdKWlTIqghWluWeIlZIeUecOiba7SlZaSOpSQSeKo2eTdlh0abhwTUTIfajeFHYWkgHf91qUguUkkM4pzyEpP480qjWwhqUMJmSSO2s2AJV6V/khlh8He7dCpKuQSW2/v8VqXDvFP/yWS9DRjdmkum9RCJilECZkkjrq3oWAqZCTOuHwDTF0Arlv1yCTmtlY3k5luTC1IzM/LvLI8tlY341QmRlKEEjJJHDWbvOKqiWzaMd7tzlfCjUOS3ru1rcwoyiE9LQFLxACHTc2n6UAX1U3tYYciMimUkEli6Gr3TvUlcv8xgIIZkF0Eu9eFHYkkuS3VzcwtS7zTlYEjphUA8Fal+pFJalBCJomhbqt3qq/8yLAjmRgzr0Bs5athRyJJbN/+Dmqa2zlsSuJ16A8cOsVLyDbuVkImqUEJmSSGPa97t+VHhBtHNEw7Gurfg3Z1WJbYeKuyCYDDpxaEHMn4FWRnUF6QxVtVTWGHIjIplJBJYtjzBqRlQMncsCOZuOmLAAeVfwk7EklSQavS4dMSNyEDr2P/JiVkkiKUkEliqHnTG8MyLSPsSCZu+rHe7bYXwo1DktablU0U52ZSmpcVdigTcuS0QnY0tGkIJUkJSsgkMezZeHDooUSXUwzFc2H7i2FHIklqU1UThyRwh/7AwllFALy2c2/IkYjEnhIyiX/Ne2B/PUw9KuxIomf6sVC5Hnq6w45EkkxHVw/v17VyaAJ36A8cNb0QA9Zt2xd2KCIxp4RM4t9u/4rEaQvDjSOaZlZ4g6WrQKxE2bu1LXT1OI6YmvgJWX52BjOLc3h1h1rIJPkpIZP4t2sdYDAlCa6wDMxa6t2+90yoYUjyWbetAYCjZhSFHEl0HD6tgI27G1WxX5KeEjKJf1V/8cavzEz8PjG9CmdCwXR49+mwI5Eks3bbXgr9lqVksHBmEY1tnexsaAs7FJGYUkIm8c05qNyQXK1jgVkVsOMl6O4KOxJJIht27uOIaQWYJeaQSf0d63fsf/n9+pAjEYktJWQS3/a+D20Nfu2uJDPnBOjcD7vXhx2JJIm9rR3saNjPUTMKww4lauaV55Ofnc7zW+vCDkUkppSQSXwLanXNOi7cOGJhznLAYMsTYUciSWL9dq/z++LZxSFHEj1pZiycWcTL79erH5kkNSVkEt+2v+D1HSudH3Yk0ZdTDFOOhK2Phx2JJIlXtjWQZrBgevK0kAEsnVNCdVM72+v3hx2KSMwoIZP4tmstTFkAaelhRxIb806B6jehqSrsSCQJrH2/gblleeRkJtfn5fh5pQA8t6U25EhEYkcJmcSv1jqofwdmLgk7ktg59HTvdvNvwo1DEl5Lexev725kSRKdrgzMLsmlNC9T/cgkqSkhk/j1zpPe7dyTwo0jlkoP9cpfvPWrsCORBPfSO3V09zhWzC8LO5SoMzMWzy7mpffq6ezuCTsckZhQQibx650/QVZBcg2Z1J+Z10q2/QWvRVBknJ7cXENWRhqLkrCFDOADR0yhpb2Ll97V50SSkxIyiU/OwfvPwozFydt/LHDk2eB64PVfhh2JJLA/b61j4cxCMtOT87C+bF4p2Rlp/Pp19beU5JScn1xJfDWboKUa5p4QdiSxV344FM+F138ediSSoHbU72fXvjaWHVIadigxk52RzpI5xTy5qYaeHpW/kOSjhEzi0xsPA3aw03syM4MF50DVBu+KS5Ex+vXrlQCcfNiUkCOJrVMPn0JDawfrtjeEHYpI1Ckhk/i0+Tcw9WjIKw87kslxzPmQlgEv/yjsSCQB/fb1KuaX5zEjScavHMrJh5eTnZHGT1/eGXYoIlGnhEziT8N7ULsZDj0t7EgmT04JzDsVNv4CDjSGHY0kkJ0N+3mrqokPHJHcrWMAeVkZnHxYOb9/o4rmA51hhyMSVUrIJP6sXw0YHPHhsCOZXBVXeWNbvvzDsCORBPLgy9sx4INHTws7lElx/pKZtHf18Miru8MORSSqlJBJfOnphtd+DrMqoCA1vmB6TV0AMyvgf/8dOlrDjkYSQHeP46H1u1g8u5hphcl9ujJw1PRC5pbmsvqlbercL0lFCZnEl61/hJY9cMyFYUcSjuXXQ1sDvPAvYUciCeDxN6qoa+ng3EUzww5l0pgZHz1uNu/WtvLEW3vCDkckapSQSXz58z9BbhnMT6H+Y5FmLoFDToIX/xWa9WUjQ3POcd/T7zK1MJuTD0+Ri198Hzp6OtOLsvn+H7bgnFrJJDkoIZP4seMV2PkKLL4E0jPDjiY8J90E3Z3w2M1hRyJx7NkttbxV1cTFFbNJT7Oww5lU6WnG5cvnsrWmhf/5i/qSSXJQQibxwTn4w99BdiEsvDjsaMJVMgeO+yRsfQJe/UnY0Ugc6u5xfPs3m5hakM25i2aEHU4oPnT0dA6bks8//Pot9rZ2hB2OyIQpIZP48MbDsGstHP8pyMoLO5rwHXcVTFsIv/sK7NkYdjQSZ/7j+fd4p7aFa0+Zl7RDJY0kPc344hlH0nygi9sf3ahTl5LwUvOTLPGluRp++xUoPRQWfiTsaOJDWgac9Q+QmQv/76Owd1vYEUmc2FzVxPf/sIVlh5Rw+pFTww4nVIdNLeDjx8/mdxv38MPn3gs7HJEJUUIm4eo8AGs+6dXfOuObXiIinrxyOO+foGM/3H+mhlUSapvbuf6BteRmpfPFMxZgllp9xwZz1YnzWDG/lLse38xD61XBXxKXEjIJT2ebl4ztegVO+wqUzQ87ovhTfhhc+M/Q3QH/8SFY+2Po6Qk7KgnBzob9XLrqRepaO7j9vGMoy88KO6S4kGbG184+mqNmFHLLL1/nB09tVX0ySUgxS8jM7Bwze9vM3jGzW2P1PJKg6rbCj8+Ed/4EJ/0NLDg77Iji15Qj4eP3Q9nh8Nsvww9Pg/ee9y6EkKTnnOO3r1dy3r3PU93Uzt+fv5BjZhaFHVZcyclM59sXLeaE+WXc/YctXPajl9hc1RR2WCJjYrHoCGlm6cAW4ExgF7AW+IRz7q3B5l++fLlbt25d1OOQOFS3FV66Dzb8BNKy4K++CoetDDuqxOB64O3HYe39XvHYssNg6ZVw1Dkw7VhIU4N3Mmnv6uYPb+7hx3/exoad+zikLJe/O/cYZpfqopehOOf4/Rt7eODFbRzo7Ob0BVO45Pi5fPiY6eRmpYcdniQRM1vvnFse1XXGKCE7GbjDOXe2f/82AOfc/x1sfiVkwxjq/Rn08TiY1/V4w/60N3l/TVXQ8K53peD2F6FxJ1gaHHEGnPBZyA93QOSx7v/j+bSM9SM24uydbdjWP5C25fdY3dveMtlFuKlH48qPhNL5uILpUDgD8qbgMnK8iwMyciAjx7ufluG9DxiMoh+SG+Mrn4zGu7Fv19i+hvG85M6uHpoOdNJ8oIu9+zvYVref9+ta2Li7kdd2NtLR3UNZfhaXLJvDeYtnply9sfFqauvkofU7eertWhrbOklPM46aXsjCWUUcOiWfuaW5TCnIpig3k+LcTHKz0slMSyMzw8hISyMz3dQ/T4aVSAnZJcA5zrm/9u9fDZzonLtpsPmXzcp0z3+6tO86hlr3IIe9wR4bah1DzTvY4TRWMaRZ6p1qanR5vNxzDC/1LOS33SdSS+nICwFuyHdBAGZRx4lpmzghbTML0nZxhO2m2PaPeT09ztuDe0jDAa731vw/mUxmkGaQbl4fKRm/Hgfdzrt1bnyJ80j0DqWegjtrop6QxeqStsH2zz6fAzP7DPAZ/257wZ21b8QolkQ2BagLO4joaAL2AE8D9010ZUm0XSZmO/DSwbvaLgNpmwxO22Vw2i6D03YZ6KhorzBWCdkuYG7E/TlAZeQMzrkfAT8CMLN10c40k4G2y+C0XQan7TKQtsngtF0Gp+0yOG2Xgcws6v2sYtULeC1wpJkdamZZwBXAYzF6LhEREZGEFpMWMudcl5ndBDwBpAP/6ZxTVUsRERGRQcSsLLpz7nfA70Y5+49iFUeC03YZnLbL4LRdBtI2GZy2y+C0XQan7TJQ1LdJTK6yFBEREZHRUyVJERERkZDFJCEbadgk89zrT3/dzJaNtKyZlZnZH81sq387ukJWcWK828TM5prZ02a2yczeNLMvRixzh5ntNrMN/t95k/maomGC+8o2M9vov/Z1EY8n9L4CE9pfjorYHzaYWZOZ/a0/LRX2l6PN7CUzazezW0azbKLvL+PdJjq2DLuvpPKxZaj9JdWPLVf5x9rXzexFM1s60rJj3l+cc1H9w+vE/y5wGJAFvAYs7DfPecDv8eqVnQS8PNKywF3Arf7/twLfi3bssfqb4DaZCSzz/y/EG5Iq2CZ3ALeE/frC2C7+tG3AlEHWm7D7SjS2S7/17AHmpdD+Mg1YAXwn8rWm+LFlqG2S6seWQbeLPy2Vjy1Dbpd+60m1Y8spQKn//7nEIG+JRQvZCcA7zrn3nHMdwM+Bi/rNcxHw387zv0CJmc0cYdmLgNX+/6uBi2MQe6yMe5s456qcc68COOeagU3A7MkMPoYmsq8MJ5H3FYjedjkDeNc5tz32IU+KEbeLc67GObcW6BzDsom8v4x7m6T6sWWYfWU4ibyvQPS2SyoeW150zu317/4vXn3VkZYd0/4Si4RsNrAz4v4uBn7Ih5pnuGWnO+eqwDuQ4GXxiWIi26SXmc0HjgNejnj4Jr8J9T8TsPl8otvFAX8ws/XmjfwQSOR9BaK0v+DV//tZv8eSfX8Zz7KJvL9MZJv0StFjy3BS+dgyGql+bLkB7wzFSMuOaX+JRUI24rBJw8wzmmUT0US2iTfRrAB4GPhb51yT//C/A4cDFUAV8E8TjnRyTXS7nOqcW4bXfPw3ZnZ6NIMLUTT2lyzgI8AvI6anwv4Si2Xj2YRfVwofW4aTyseW4VeQ4scWM/sgXkL29bEuO5JYJGQjDps0zDzDLVsdnJLxb2uiGHOsTWSbYGaZeAfMB51z/xPM4Jyrds51O+d6gP/AazpNJBPaLs654LYGeISDrz+R9xWY4HbxnQu86pyrDh5Ikf1lPMsm8v4ykW2S6seWIaX4sWUkKXtsMbMlwP3ARc65+lEsO6b9JRYJ2WiGTXoMuMY8JwGNfnPecMs+Blzr/38t8KsYxB4r494mZmbAj4FNzrnvRy7Qr8/QR4FEG6B9Itsl38wKAcwsHziLg68/kfcVmNhnKPAJ+p1SSJH9ZTzLJvL+Mu5tomPL4HRsGVFKHlvM7BDgf4CrnXNbRrns2PaX0VyBMNY/vCvAtuBdeXC7/9iNwI3+/wbc50/fCCwfbln/8XLgSWCrf1sWi9hj9TfebQJ8AK/583Vgg/93nj/t//nzvu6/8TPDfp2TuF0Ow7ua5TXgzWTaVyayXfxpeUA9UNxvnamwv8zA+8XaBOzz/y8aatlk2F/Gu010bBlyu6T6sWW4z1AqH1vuB/ZGfFbWDbfsePYXVeoXERERCZkq9YuIiIiETAmZiIiISMiUkImIiIiETAmZiIiISMiUkImIiIiETAmZiMSUmd1uZm/6w6psMLMTY/hcK83sN6OY7wEze9+P5zUzO2MUy/xddKIUERlICZmIxIyZnQxcACxzzi0BPkzfcd/Gu96Mia4D+KpzrgL4W2DVKOZXQiYiMaOETERiaSZQ55xrB3DO1Tl/WBozO97MnvUHcH4iYoiRT5vZWr/l6mEzy/Mff8DMvm9mTwPfM7MjzOxP/nyvmtnh/nMWmNlDZrbZzB70K9IP5yUiBhI2s0f9mN40f2BpM/sukOu3qD3oP/ZJM3vFf+yHZpbu/z1gZm+Y2UYz+1L0NqWIJDMlZCISS38A5prZFjP7NzP7K+gdQ/FfgUucc8cD/wl8x1/mf5xzK5xzS4FNeAP5BhYAH3bOfQV4ELjPn+8UvEGNAY7Da/VaiFd1/dQRYjwHeDTi/vV+TMuBm82s3Dl3K9DmnKtwzl1lZscAl+MNQl0BdANX4Q2uPNs5t8g5txj4r9FuKBFJbdFo9hcRGZRzrsXMjgdOAz4IrDGzW4F1wCLgj34DVjoHE6pFZvZtoAQoAJ6IWOUvnXPd/liDs51zj/jPcwDAX9crzrld/v0NwHzgz4OE9/+Z2V3ANOCkiMdvNrOP+v/PBY7EGy4m0hnA8cBa/zlz8QYO/jVwmJn9K/BbvIRURGRESshEJKacc93AM8AzZrYRb5Dd9cCbzrmTB1nkAeBi59xrZnYdsDJiWqt/O9xpyPaI/7sZ+jj3VbzBgm8GVgPHm9lKvH5uJzvn9pvZM0DOIMsasNo5d9uACWZLgbOBvwEuA64fJlYREUCnLEUkhszsKDM7MuKhCmA78DYw1e/0j5llmtmx/jyFQJV/WvOqwdbrnGsCdpnZxf7y2UFfs7FwzvUA/wKkmdnZQDGw10/GjqZvy1mnHxN4AwVfYmbT/OcvM7N5ZjYFSHPOPQz8PbBsrDGJSGpSC5mIxFIB8K9mVgJ0Ae8An3HOdZjZJcC9ZlaMdyz6Z+BNvETmZbzEbSNegjaYq4EfmtmdQCdw6XgCdM45/xTp14DzgBvN7HW8pPF/I2b9EfC6mb3q9yP7BvAHM0vzn/9vgDbgv/zHAAa0oImIDMacc2HHICIiIpLSdMpSREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERCpoRMREREJGRKyERERERC9v8DJvjDb9lRmqMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n",
    "#def plot_beta(alpha, beta, ax, dist, title=None, label=None):\n",
    "\n",
    "title = 'Search rates of Black citizens in 1st Division and 5th Division, officers of all races'\n",
    "\n",
    "Black_dist, b_alpha, b_beta = a_b_division('Black', '1ST DIVISION')\n",
    "White_dist, w_alpha, w_beta = a_b_division('Black', '5TH DIVISION')\n",
    "plot_beta(b_alpha, b_beta, ax, Black_dist, title=title, label=\"Black citizens, 1st Division\")\n",
    "plot_beta(w_alpha, w_beta, ax, White_dist, title=None, label=\"Black citizens, 5th Division\")\n",
    "\n",
    "# title, etc\n",
    "ax.set_xlabel(\"Search Rates\")\n",
    "ax.legend()\n",
    "ax.set_xlim(0,0.2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n",
    "#def plot_beta(alpha, beta, ax, dist, title=None, label=None):\n",
    "\n",
    "title = 'Search rates of Black citizens in 5th Division, white citizens in 1st Division'\n",
    "\n",
    "Black_dist, b_alpha, b_beta = a_b_division('Black', '5TH DIVISION')\n",
    "White_dist, w_alpha, w_beta = a_b_division('white', '1ST DIVISION')\n",
    "plot_beta(b_alpha, b_beta, ax, Black_dist, title=title, label=\"Black citizens, 5th Division\")\n",
    "plot_beta(w_alpha, w_beta, ax, White_dist, title=None, label=\"white citizens, 1st Division\")\n",
    "\n",
    "# title, etc\n",
    "ax.set_xlabel(\"Search Rates\")\n",
    "ax.legend()\n",
    "ax.set_xlim(0,0.2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n",
    "#def plot_beta(alpha, beta, ax, dist, title=None, label=None):\n",
    "\n",
    "title = 'Search rates of Black and white citizens in 6th Division'\n",
    "\n",
    "Black_dist, b_alpha, b_beta = a_b_division('Black', '6TH DIVISION')\n",
    "White_dist, w_alpha, w_beta = a_b_division('white', '6TH DIVISION')\n",
    "plot_beta(b_alpha, b_beta, ax, Black_dist, title=title, label=\"Black citizens, 6TH Division\")\n",
    "plot_beta(w_alpha, w_beta, ax, White_dist, title=None, label=\"white citizens, 6TH Division\")\n",
    "\n",
    "# title, etc\n",
    "ax.set_xlabel(\"Search Rates\")\n",
    "ax.legend()\n",
    "ax.set_xlim(0,0.2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>citizen_race</th>\n",
       "      <th>citizen_gender</th>\n",
       "      <th>citizen_age</th>\n",
       "      <th>officer_race</th>\n",
       "      <th>officer_gender</th>\n",
       "      <th>division</th>\n",
       "      <th>beat</th>\n",
       "      <th>violation</th>\n",
       "      <th>frisked</th>\n",
       "      <th>searched</th>\n",
       "      <th>citation_issued</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>white</td>\n",
       "      <td>male</td>\n",
       "      <td>53.0</td>\n",
       "      <td>Asian</td>\n",
       "      <td>female</td>\n",
       "      <td>6TH DIVISION</td>\n",
       "      <td>BEAT 1</td>\n",
       "      <td>FAIL OF NON-OWNER/OPER TO MAINTAIN REQ INS/SEC...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>22:39:00</td>\n",
       "      <td>38.210850</td>\n",
       "      <td>-85.740058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>white</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Asian</td>\n",
       "      <td>female</td>\n",
       "      <td>6TH DIVISION</td>\n",
       "      <td>BEAT 5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>16:42:00</td>\n",
       "      <td>38.203829</td>\n",
       "      <td>-85.665319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Asian</td>\n",
       "      <td>female</td>\n",
       "      <td>6TH DIVISION</td>\n",
       "      <td>BEAT 5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>20:00:00</td>\n",
       "      <td>38.205311</td>\n",
       "      <td>-85.650115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Asian</td>\n",
       "      <td>female</td>\n",
       "      <td>6TH DIVISION</td>\n",
       "      <td>BEAT 5</td>\n",
       "      <td>OPERATING ON SUSPENDED/REVOKED OPERATORS LICEN...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>20:35:00</td>\n",
       "      <td>38.202592</td>\n",
       "      <td>-85.654212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Asian</td>\n",
       "      <td>female</td>\n",
       "      <td>6TH DIVISION</td>\n",
       "      <td>BEAT 2</td>\n",
       "      <td>NO OR EXPIRED REGISTRATION PLATES|FAILURE OF O...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-11</td>\n",
       "      <td>17:20:00</td>\n",
       "      <td>38.156730</td>\n",
       "      <td>-85.696997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  month   day  hour citizen_race citizen_gender  citizen_age  \\\n",
       "0  2015.0    1.0   1.0  22.0        white           male         53.0   \n",
       "1  2015.0    1.0   2.0  16.0        white           male          NaN   \n",
       "2  2015.0    1.0   2.0  20.0        Black         female          NaN   \n",
       "3  2015.0    1.0   2.0  20.0        Black           male         24.0   \n",
       "4  2015.0    1.0  11.0  17.0        Black           male         24.0   \n",
       "\n",
       "  officer_race officer_gender      division    beat  \\\n",
       "0        Asian         female  6TH DIVISION  BEAT 1   \n",
       "1        Asian         female  6TH DIVISION  BEAT 5   \n",
       "2        Asian         female  6TH DIVISION  BEAT 5   \n",
       "3        Asian         female  6TH DIVISION  BEAT 5   \n",
       "4        Asian         female  6TH DIVISION  BEAT 2   \n",
       "\n",
       "                                           violation  frisked  searched  \\\n",
       "0  FAIL OF NON-OWNER/OPER TO MAINTAIN REQ INS/SEC...        0         0   \n",
       "1                                                NaN        0         0   \n",
       "2                                                NaN        0         0   \n",
       "3  OPERATING ON SUSPENDED/REVOKED OPERATORS LICEN...        0         1   \n",
       "4  NO OR EXPIRED REGISTRATION PLATES|FAILURE OF O...        0         0   \n",
       "\n",
       "   citation_issued        date      time        lat        lng  \n",
       "0                1  2015-01-01  22:39:00  38.210850 -85.740058  \n",
       "1                1  2015-01-02  16:42:00  38.203829 -85.665319  \n",
       "2                0  2015-01-02  20:00:00  38.205311 -85.650115  \n",
       "3                1  2015-01-02  20:35:00  38.202592 -85.654212  \n",
       "4                1  2015-01-11  17:20:00  38.156730 -85.696997  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "division      citizen_race   \n",
       "1             Black                  2\n",
       "1ST DIVISION  Asian                 30\n",
       "              Black               5627\n",
       "              Latino               174\n",
       "              Middle Eastern        27\n",
       "              Native American        6\n",
       "              Unknown                1\n",
       "              white               4289\n",
       "2ND DIVISION  Asian                  8\n",
       "              Black               5131\n",
       "              Latino                90\n",
       "              Middle Eastern        27\n",
       "              Native American        8\n",
       "              white               1723\n",
       "3             Black                  1\n",
       "              Latino                 1\n",
       "3RD DIVISION  Asian                 49\n",
       "              Black               2827\n",
       "              Latino               688\n",
       "              Middle Eastern        45\n",
       "              Native American        3\n",
       "              white              11051\n",
       "4             Black                  3\n",
       "              white                  4\n",
       "4TH DIVISION  Asian                 60\n",
       "              Black               6142\n",
       "              Latino               507\n",
       "              Middle Eastern        83\n",
       "              Native American        5\n",
       "              white               7518\n",
       "5             Black                  1\n",
       "              white                  4\n",
       "5TH DIVISION  Asian                 51\n",
       "              Black               2493\n",
       "              Latino               410\n",
       "              Middle Eastern        63\n",
       "              Native American        8\n",
       "              white               9089\n",
       "6             Black                  3\n",
       "              Latino                 1\n",
       "              white                  4\n",
       "6TH DIVISION  Asian                 64\n",
       "              Black               5398\n",
       "              Latino              1081\n",
       "              Middle Eastern       103\n",
       "              Native American        8\n",
       "              Unknown                1\n",
       "              white              10802\n",
       "7             Black                  2\n",
       "              Latino                 3\n",
       "              white                  4\n",
       "7TH DIVISION  Asian                 16\n",
       "              Black               2694\n",
       "              Latino               992\n",
       "              Middle Eastern        36\n",
       "              Native American       13\n",
       "              white               9434\n",
       "8             Black                  4\n",
       "              Latino                 2\n",
       "              Middle Eastern         1\n",
       "              white                  7\n",
       "8TH DIVISION  Asian                120\n",
       "              Black               3229\n",
       "              Latino               738\n",
       "              Middle Eastern        46\n",
       "              Native American       12\n",
       "              white              12679\n",
       "ANC           white                  1\n",
       "JTN           white                  1\n",
       "NON LMPD      Asian                  3\n",
       "              Black                 41\n",
       "              Latino                 2\n",
       "              Middle Eastern         3\n",
       "              white                 89\n",
       "SHV           Black                  3\n",
       "              white                  5\n",
       "STM           Black                  1\n",
       "              white                  9\n",
       "Name: citizen_race, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['division', 'citizen_race'])['citizen_race'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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