Herndon_VCPA_Analysis_202303.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The below shows analysis of the 2022 HPD Stops data. If viewing this to review the results, the shaded text is code which can be ignored. I have tried to label each set of results clearly so the plots, tables, and printed text hopefully is clear.\n",
    "\n",
    "Notes/Reminders that may require follow-up action:\n",
    "- Question: Does HPD keep data on traffic crashes? \n",
    "- Also, do we want to request the raw data that HPD submits to the state? It also contains locations of stops which is something that is not made publicly available. It would be interesting to see where most stops are made. I previously FOIA'ed this info for FCPD from the Virginia State Police. It only cost me $35. FCPD now puts their submitted spreadsheets on their website.\n",
    "- How does their data collection system work? Is date automatically logged? What about time (even though not required by VCPA)? Can they start tracking times?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import pandas as pd\n",
    "from pylis import code\n",
    "\n",
    "module_path = os.path.abspath(os.path.join('../..', \"openpolicedata\"))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)\n",
    "\n",
    "import openpolicedata as opd\n",
    "import numpy as np\n",
    "\n",
    "pd.set_option('display.max_colwidth',100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def section_num_to_title(sect_num):\n",
    "    try:\n",
    "        h = code.get_section_details(sect_num)\n",
    "    except:\n",
    "        return None\n",
    "    if len(h['ChapterList'])>0:\n",
    "        return h['ChapterList'][0]['SectionTitle']\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def to_percent(x):\n",
    "    return np.round(x/x.sum()*100,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15848"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "src = opd.Source(\"Virginia\")\n",
    "data = src.load_from_url(year=[2020,2030], agency=\"Herndon Police Department\")\n",
    "df = data.table\n",
    "\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total traffic violations by race and ethnicity since VCPA start\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "WHITE                                                 6505\n",
       "HISPANIC OR LATINO                                    4039\n",
       "BLACK OR AFRICAN AMERICAN                             2483\n",
       "ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER    1105\n",
       "UNKNOWN                                                342\n",
       "AMERICAN INDIAN OR ALASKA NATIVE                       122\n",
       "Name: race_eth, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def add_cols(df):\n",
    "    df[\"year\"] = df[\"incident_date\"].dt.year\n",
    "    df[\"month\"] = df[\"incident_date\"].dt.to_period(\"M\")\n",
    "    df[\"quarter\"] = df[\"incident_date\"].dt.to_period(\"Q\")\n",
    "\n",
    "    df[\"race_eth\"] = df[\"race\"].copy()\n",
    "    df.loc[df[\"ethnicity\"] == \"HISPANIC OR LATINO\", \"race_eth\"] = \"HISPANIC OR LATINO\"\n",
    "    df.loc[df[\"ethnicity\"] == \"UNKNOWN\", \"race_eth\"] = \"UNKNOWN\"\n",
    "\n",
    "    df[\"age_range\"] = pd.cut(df[\"age\"].astype(int),[-1,15,25,35,45,55,65, 200],labels=[\"0-15\",\"16-25\",\"26-35\",\"36-45\",\"46-55\",\"56-65\",\"65+\"])\n",
    "\n",
    "add_cols(df)\n",
    "map = {x : section_num_to_title(x) for x in df[\"specific_violation\"].unique()}\n",
    "df[\"section_title\"] = df[\"specific_violation\"].map(map)\n",
    "df[\"section_title\"].head()\n",
    "\n",
    "tv = df[df[\"reason_for_stop\"]==\"TRAFFIC VIOLATION\"]\n",
    "tv.reset_index(inplace=True)\n",
    "\n",
    "print(\"Total traffic violations by race and ethnicity since VCPA start\")\n",
    "tv[\"race_eth\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What is spike in Early 2022?\n",
      "Stops for traffic violations are actually lower than previous years after that\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tv_by_month = df.groupby([\"month\"]).size()\n",
    "ax = tv_by_month.plot(grid=True, xlabel=\"\",ylabel=\"Count\", title=\"# of Stops for Traffic Violations\",marker=\"o\")\n",
    "\n",
    "print(\"What is spike in Early 2022?\")\n",
    "print(\"Stops for traffic violations are actually lower than previous years after that\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The highest increase in stops for traffic violations by count (not %) involves White individuals\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tv_by_month = tv.groupby([\"month\",\"race_eth\"]).size().unstack(\"race_eth\")\n",
    "ax = tv_by_month.plot(grid=True, xlabel=\"\",ylabel=\"Count\", title=\"# of Stops for Traffic Violations by Month\",marker=\"o\")\n",
    "ax.legend(bbox_to_anchor=(1.1, 1.05))\n",
    "\n",
    "print(\"The highest increase in stops for traffic violations by count (not %) involves White individuals\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Increase in traffic violatins affected Whites the greatest\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2021 Average Month</th>\n",
       "      <th>Mar 2022</th>\n",
       "      <th>% Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>WHITE</th>\n",
       "      <td>226.0</td>\n",
       "      <td>400</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HISPANIC OR LATINO</th>\n",
       "      <td>143.8</td>\n",
       "      <td>198</td>\n",
       "      <td>37.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <td>82.2</td>\n",
       "      <td>120</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER</th>\n",
       "      <td>43.9</td>\n",
       "      <td>64</td>\n",
       "      <td>45.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNKNOWN</th>\n",
       "      <td>19.2</td>\n",
       "      <td>1</td>\n",
       "      <td>-94.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMERICAN INDIAN OR ALASKA NATIVE</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    2021 Average Month  \\\n",
       "WHITE                                                            226.0   \n",
       "HISPANIC OR LATINO                                               143.8   \n",
       "BLACK OR AFRICAN AMERICAN                                         82.2   \n",
       "ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER                43.9   \n",
       "UNKNOWN                                                           19.2   \n",
       "AMERICAN INDIAN OR ALASKA NATIVE                                   7.0   \n",
       "\n",
       "                                                    Mar 2022  % Difference  \n",
       "WHITE                                                    400          77.0  \n",
       "HISPANIC OR LATINO                                       198          37.7  \n",
       "BLACK OR AFRICAN AMERICAN                                120          46.0  \n",
       "ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER        64          45.8  \n",
       "UNKNOWN                                                    1         -94.8  \n",
       "AMERICAN INDIAN OR ALASKA NATIVE                           0        -100.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_month = \"Mar 2022\"\n",
    "\n",
    "tv_2021 = tv[tv[\"incident_date\"].dt.year == 2021]\n",
    "df_high = tv[tv[\"month\"]==high_month]\n",
    "\n",
    "tv_2021 = tv_2021[\"race_eth\"].value_counts() / 12\n",
    "tv_2021.name = \"2021 Average Month\"\n",
    "df_high = df_high[\"race_eth\"].value_counts()\n",
    "df_high.name = high_month\n",
    "df_high = pd.concat([tv_2021, df_high],axis=1).round(1).fillna(0).convert_dtypes()\n",
    "df_high[\"% Difference\"] = round((df_high[high_month] - df_high[\"2021 Average Month\"]) / df_high[\"2021 Average Month\"] * 100,1)\n",
    "\n",
    "print(\"Increase in traffic violatins affected Whites the greatest\")\n",
    "df_high\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall stops for traffic violations were down slightly except for Black individuals which stayed the same\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2021 Average Month</th>\n",
       "      <th>2022 Average Month</th>\n",
       "      <th>% Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>WHITE</th>\n",
       "      <td>226.0</td>\n",
       "      <td>217.6</td>\n",
       "      <td>-3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HISPANIC OR LATINO</th>\n",
       "      <td>143.8</td>\n",
       "      <td>132.6</td>\n",
       "      <td>-7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <td>82.2</td>\n",
       "      <td>82.8</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER</th>\n",
       "      <td>43.9</td>\n",
       "      <td>34.5</td>\n",
       "      <td>-21.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNKNOWN</th>\n",
       "      <td>19.2</td>\n",
       "      <td>1.1</td>\n",
       "      <td>-94.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMERICAN INDIAN OR ALASKA NATIVE</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>-88.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    2021 Average Month  \\\n",
       "WHITE                                                            226.0   \n",
       "HISPANIC OR LATINO                                               143.8   \n",
       "BLACK OR AFRICAN AMERICAN                                         82.2   \n",
       "ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER                43.9   \n",
       "UNKNOWN                                                           19.2   \n",
       "AMERICAN INDIAN OR ALASKA NATIVE                                   7.0   \n",
       "\n",
       "                                                    2022 Average Month  \\\n",
       "WHITE                                                            217.6   \n",
       "HISPANIC OR LATINO                                               132.6   \n",
       "BLACK OR AFRICAN AMERICAN                                         82.8   \n",
       "ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER                34.5   \n",
       "UNKNOWN                                                            1.1   \n",
       "AMERICAN INDIAN OR ALASKA NATIVE                                   0.8   \n",
       "\n",
       "                                                    % Difference  \n",
       "WHITE                                                       -3.7  \n",
       "HISPANIC OR LATINO                                          -7.8  \n",
       "BLACK OR AFRICAN AMERICAN                                    0.7  \n",
       "ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER         -21.4  \n",
       "UNKNOWN                                                    -94.3  \n",
       "AMERICAN INDIAN OR ALASKA NATIVE                           -88.6  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tv_2021 = tv[tv[\"incident_date\"].dt.year == 2021]\n",
    "tv_2022 = tv[tv[\"incident_date\"].dt.year == 2022]\n",
    "\n",
    "tv_2021 = tv_2021[\"race_eth\"].value_counts() / 12\n",
    "tv_2021.name = \"2021 Average Month\"\n",
    "tv_2022 = tv_2022[\"race_eth\"].value_counts() / 12\n",
    "tv_2022.name = \"2022 Average Month\"\n",
    "df_high = pd.concat([tv_2021, tv_2022],axis=1).round(1).fillna(0).convert_dtypes()\n",
    "df_high[\"% Difference\"] = round((df_high[\"2022 Average Month\"] - df_high[\"2021 Average Month\"]) / df_high[\"2021 Average Month\"] * 100,1)\n",
    "\n",
    "print(\"Overall stops for traffic violations were down slightly except for Black individuals which stayed the same\")\n",
    "df_high\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29f38bab910>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "residency = None#\"OTHER VIRGINIA JURISDICTION RESIDENT\" #\"RESIDENT OF CITY/COUNTY OF STOP\"#\n",
    "group = tv.copy()\n",
    "if residency is not None:\n",
    "    group = group[group[\"residency\"]==residency]\n",
    "group = group.groupby([\"quarter\", \"race_eth\"]).size().unstack(\"race_eth\").fillna(0)\n",
    "group = group.div(group.sum(axis=1), axis=0)*100\n",
    "ax = group.plot.bar(stacked=True, figsize=[10,5], rot=0, title=\"Traffic Violations by Quarter\")\n",
    "ax.legend(bbox_to_anchor=(1.05, 1.02))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black individuals are stopped at about twice their population (consistent with last year). Is there anything that is different about Black stops? (age, violation type, etc.)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "residency = \"RESIDENT OF CITY/COUNTY OF STOP\"\n",
    "group = tv.copy()\n",
    "if residency is not None:\n",
    "    group = group[group[\"residency\"]==residency]\n",
    "group = group.groupby([\"quarter\", \"race_eth\"]).size().unstack(\"race_eth\").fillna(0)\n",
    "group = group.div(group.sum(axis=1), axis=0)*100\n",
    "\n",
    "# Population percentages from https://www.census.gov/quickfacts/fact/table/herndontownvirginia/PST045222 for Population Estimates, July 1, 2022, (V2022)\n",
    "\n",
    "pop_perc = [0.8, 16.1, 7.3, 36.7,np.nan,35.4]\n",
    "d = {x:y for x,y in zip(group.columns, pop_perc)}\n",
    "pops = pd.Series(d, name=\"Population\")\n",
    "group = pd.concat([group,pd.DataFrame(pops).transpose()],axis=0)\n",
    "\n",
    "ax = group.plot.bar(stacked=True, figsize=[10,5], rot=0, title=\"Traffic Violations of Herndon Resisdents by Quarter vs.  Herndon Population (on right)\")\n",
    "ax.legend(bbox_to_anchor=(1.05, 1.05))\n",
    "\n",
    "print(\"Black individuals are stopped at about twice their population (consistent with last year). Is there anything that is different about Black stops? (age, violation type, etc.)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stops of residents per 1000 people for 2022\n",
      "Black Herndon residents were stopped 157.6 times per 1000 people in 2022 based on 280 stops out of 1777.0 people\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "AMERICAN INDIAN OR ALASKA NATIVE                       35.9\n",
       "ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER     37.8\n",
       "BLACK OR AFRICAN AMERICAN                             157.6\n",
       "HISPANIC OR LATINO                                     74.6\n",
       "UNKNOWN                                                 NaN\n",
       "WHITE                                                  93.8\n",
       "dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year = 2022\n",
    "tv_res = tv[tv[\"residency\"]==\"RESIDENT OF CITY/COUNTY OF STOP\"]\n",
    "tv_res = tv_res[tv_res[\"year\"]==year]\n",
    "total_pop = 24339\n",
    "\n",
    "pop_counts = round(pops/100 * total_pop)\n",
    "\n",
    "num_stops = tv_res[\"race_eth\"].value_counts()\n",
    "stops_per_1000_people = round(num_stops / pop_counts * 1000,1)\n",
    "\n",
    "print(f\"Stops of residents per 1000 people for {year}\")\n",
    "people = pop_counts[\"BLACK OR AFRICAN AMERICAN\"]\n",
    "num = num_stops[\"BLACK OR AFRICAN AMERICAN\"]\n",
    "rate = stops_per_1000_people[\"BLACK OR AFRICAN AMERICAN\"]\n",
    "print(f\"Black Herndon residents were stopped {rate} times per 1000 people in {year} based on {num} stops out of {people} people\")\n",
    "stops_per_1000_people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What is increase in early 2022 in equipment violations?\n",
      "Terry stops remain low\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tv_by_month = df.groupby([\"quarter\",\"reason_for_stop\"]).size().unstack(\"reason_for_stop\")\n",
    "ax = tv_by_month.drop(columns=\"TRAFFIC VIOLATION\").plot(grid=True, xlabel=\"\",ylabel=\"Count\", figsize=[10,5],\n",
    "                                                        title=\"# of Stops for Reasons Other Than Traffic Violations\",marker=\"o\")\n",
    "# ax.legend(bbox_to_anchor=(1.1, 1.05))\n",
    "print(\"What is increase in early 2022 in equipment violations?\")\n",
    "print(\"Terry stops remain low\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looks like increase in Equipment Violations affects Whites most\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tv_by_month = df[df[\"reason_for_stop\"]==\"EQUIPMENT VIOLATION\"].groupby([\"month\",\"race_eth\"]).size().unstack(\"race_eth\")\n",
    "ax = tv_by_month.plot(grid=True, xlabel=\"\",ylabel=\"Count\", title=\"# of Stops for Equipment Violations\",marker=\"o\")\n",
    "ax.legend(bbox_to_anchor=(1.1, 1.05))\n",
    "print(\"Looks like increase in Equipment Violations affects Whites most\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shown are the top violations for equipment violations in Mar 2022\n",
      "\n",
      "Mar 2022 is an example of a month where equipment violations were highest\n",
      "Oct 2021 is an example of a month where equipment violations were lowest\n",
      "\n",
      "Increase in equipment violations is due to high numbers of:\n",
      "\t1. Vehicle registration expirations\n",
      "\t2. Safety inspection expirations\n",
      "\n",
      "I guess this was a focus in early 2022. Unsure why this would mainly affect White individuals\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>month</th>\n",
       "      <th>2021-10</th>\n",
       "      <th>2022-03</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>specific_violation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46.2-646</th>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>Expiration and renewal of registration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-1158</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>Frequency of inspection; scope of inspection</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-1030</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>When lights to be lighted; number of lights to be lighted at any time; use of warning lights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-613</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Infractions relating to registration, licensing, and certificates of title; penalties</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42-9</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "month               2021-10  2022-03  \\\n",
       "specific_violation                     \n",
       "46.2-646                  7       24   \n",
       "46.2-1158                 4        9   \n",
       "46.2-1030                 7        8   \n",
       "46.2-613                  0        4   \n",
       "42-9                      0        1   \n",
       "\n",
       "month                                                                                                Description  \n",
       "specific_violation                                                                                                \n",
       "46.2-646                                                                  Expiration and renewal of registration  \n",
       "46.2-1158                                                           Frequency of inspection; scope of inspection  \n",
       "46.2-1030           When lights to be lighted; number of lights to be lighted at any time; use of warning lights  \n",
       "46.2-613                   Infractions relating to registration, licensing, and certificates of title; penalties  \n",
       "42-9                                                                                                        None  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low_month = \"Oct 2021\"\n",
    "high_month = \"Mar 2022\"\n",
    "df_ev = df[df[\"reason_for_stop\"]==\"EQUIPMENT VIOLATION\"]\n",
    "df_ev = df_ev[(df_ev[\"month\"]==high_month) | (df_ev[\"month\"]==low_month)]\n",
    "df_ev = df_ev.groupby([\"specific_violation\",\"month\"]).size().unstack(\"month\").fillna(0).convert_dtypes()\n",
    "\n",
    "# df_ev = pd.DataFrame(df_ev)\n",
    "df_ev = df_ev.sort_values(high_month, ascending=False)\n",
    "df_ev[\"Description\"] = [map[x] for x in df_ev.index]\n",
    "\n",
    "print(f\"Shown are the top violations for equipment violations in {high_month}\\n\")\n",
    "print(f\"{high_month} is an example of a month where equipment violations were highest\")\n",
    "print(f\"{low_month} is an example of a month where equipment violations were lowest\\n\")\n",
    "print(\"Increase in equipment violations is due to high numbers of:\")\n",
    "print(\"\\t1. Vehicle registration expirations\")\n",
    "print(\"\\t2. Safety inspection expirations\\n\")\n",
    "print(\"I guess this was a focus in early 2022. Unsure why this would mainly affect White individuals\")\n",
    "df_ev.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29f38c98d60>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ev = df[df[\"reason_for_stop\"]==\"EQUIPMENT VIOLATION\"]\n",
    "df_ev = df_ev[df_ev[\"specific_violation\"].apply(lambda x: x in [\"46.2-646\", \"46.2-1158\"] if pd.notnull(x) else False)]\n",
    "\n",
    "df_ev = df_ev.groupby([\"month\",\"action_taken\"]).size().unstack(\"action_taken\")\n",
    "ax = df_ev.plot(grid=True, xlabel=\"\",ylabel=\"Count\", marker=\"o\",title=\"Result of Stops for Equipment Violations\")\n",
    "ax.legend(bbox_to_anchor=(1.1, 1.05))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking at the registration and safety inspection violations by race and action taken also shows this mainly affected Whites\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>2021-10</th>\n",
       "      <th>2022-03</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race_eth</th>\n",
       "      <th>action_taken</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER</th>\n",
       "      <th>CITATION/SUMMONS</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WARNING ISSUED</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>CITATION/SUMMONS</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WARNING ISSUED</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">HISPANIC OR LATINO</th>\n",
       "      <th>CITATION/SUMMONS</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WARNING ISSUED</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">WHITE</th>\n",
       "      <th>CITATION/SUMMONS</th>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WARNING ISSUED</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "month                                                                2021-10  \\\n",
       "race_eth                                           action_taken                \n",
       "ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER CITATION/SUMMONS        1   \n",
       "                                                   WARNING ISSUED          2   \n",
       "BLACK OR AFRICAN AMERICAN                          CITATION/SUMMONS        1   \n",
       "                                                   WARNING ISSUED          0   \n",
       "HISPANIC OR LATINO                                 CITATION/SUMMONS        2   \n",
       "                                                   WARNING ISSUED          1   \n",
       "WHITE                                              CITATION/SUMMONS        3   \n",
       "                                                   WARNING ISSUED          1   \n",
       "\n",
       "month                                                                2022-03  \n",
       "race_eth                                           action_taken               \n",
       "ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER CITATION/SUMMONS        2  \n",
       "                                                   WARNING ISSUED          1  \n",
       "BLACK OR AFRICAN AMERICAN                          CITATION/SUMMONS        2  \n",
       "                                                   WARNING ISSUED          4  \n",
       "HISPANIC OR LATINO                                 CITATION/SUMMONS        0  \n",
       "                                                   WARNING ISSUED          1  \n",
       "WHITE                                              CITATION/SUMMONS       17  \n",
       "                                                   WARNING ISSUED          6  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low_month = \"Oct 2021\"\n",
    "high_month = \"Mar 2022\"\n",
    "df_ev = df[df[\"reason_for_stop\"]==\"EQUIPMENT VIOLATION\"]\n",
    "df_ev = df_ev[df_ev[\"specific_violation\"].apply(lambda x: x in [\"46.2-646\", \"46.2-1158\"] if pd.notnull(x) else False)]\n",
    "df_ev = df_ev[(df_ev[\"month\"]==high_month) | (df_ev[\"month\"]==low_month)]\n",
    "\n",
    "df_ev[\"race_eth\"].value_counts()\n",
    "df_ev = df_ev.groupby([\"race_eth\",\"action_taken\",\"month\"]).size().unstack(\"month\").fillna(0).convert_dtypes()\n",
    "\n",
    "print(\"Looking at the registration and safety inspection violations by race and action taken also shows this mainly affected Whites\")\n",
    "df_ev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shown are the top violations for traffic violations in Mar 2022\n",
      "\n",
      "Mar 2022 is an example of a month where equipment violations were highest\n",
      "Mostly big increases across the board\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2021 Average Month</th>\n",
       "      <th>Mar 2022</th>\n",
       "      <th>% Difference</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46.2-830</th>\n",
       "      <td>108.6</td>\n",
       "      <td>184</td>\n",
       "      <td>69.4</td>\n",
       "      <td>Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcemen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-874</th>\n",
       "      <td>39.4</td>\n",
       "      <td>89</td>\n",
       "      <td>125.9</td>\n",
       "      <td>Maximum speed limit in business and residence districts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-818</th>\n",
       "      <td>28.8</td>\n",
       "      <td>76</td>\n",
       "      <td>163.9</td>\n",
       "      <td>Stopping vehicle of another; blocking access to premises; damaging or threatening commercial veh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-833</th>\n",
       "      <td>24.2</td>\n",
       "      <td>45</td>\n",
       "      <td>86.0</td>\n",
       "      <td>Traffic lights; penalty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-646</th>\n",
       "      <td>33.8</td>\n",
       "      <td>38</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Expiration and renewal of registration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-1158</th>\n",
       "      <td>43.4</td>\n",
       "      <td>35</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>Frequency of inspection; scope of inspection</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-875</th>\n",
       "      <td>19.1</td>\n",
       "      <td>30</td>\n",
       "      <td>57.1</td>\n",
       "      <td>Maximum speed limit on certain other highways in cities and towns</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-821</th>\n",
       "      <td>11.8</td>\n",
       "      <td>27</td>\n",
       "      <td>128.8</td>\n",
       "      <td>Vehicles before entering certain highways shall stop or yield right-of-way</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-715</th>\n",
       "      <td>6.3</td>\n",
       "      <td>26</td>\n",
       "      <td>312.7</td>\n",
       "      <td>Display of license plates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-300</th>\n",
       "      <td>18.6</td>\n",
       "      <td>24</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Driving without license prohibited; penalties</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           2021 Average Month  Mar 2022  % Difference  \\\n",
       "46.2-830                108.6       184          69.4   \n",
       "46.2-874                 39.4        89         125.9   \n",
       "46.2-818                 28.8        76         163.9   \n",
       "46.2-833                 24.2        45          86.0   \n",
       "46.2-646                 33.8        38          12.4   \n",
       "46.2-1158                43.4        35         -19.4   \n",
       "46.2-875                 19.1        30          57.1   \n",
       "46.2-821                 11.8        27         128.8   \n",
       "46.2-715                  6.3        26         312.7   \n",
       "46.2-300                 18.6        24          29.0   \n",
       "\n",
       "                                                                                                   Description  \n",
       "46.2-830   Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcemen...  \n",
       "46.2-874                                               Maximum speed limit in business and residence districts  \n",
       "46.2-818   Stopping vehicle of another; blocking access to premises; damaging or threatening commercial veh...  \n",
       "46.2-833                                                                               Traffic lights; penalty  \n",
       "46.2-646                                                                Expiration and renewal of registration  \n",
       "46.2-1158                                                         Frequency of inspection; scope of inspection  \n",
       "46.2-875                                     Maximum speed limit on certain other highways in cities and towns  \n",
       "46.2-821                            Vehicles before entering certain highways shall stop or yield right-of-way  \n",
       "46.2-715                                                                             Display of license plates  \n",
       "46.2-300                                                         Driving without license prohibited; penalties  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_month = \"Mar 2022\"\n",
    "tv_2021 = tv[tv[\"incident_date\"].dt.year == 2021]\n",
    "df_high = tv[tv[\"month\"]==high_month]\n",
    "\n",
    "tv_2021 = tv_2021[\"specific_violation\"].value_counts() / 12\n",
    "tv_2021.name = \"2021 Average Month\"\n",
    "df_high = df_high[\"specific_violation\"].value_counts()\n",
    "df_high.name = high_month\n",
    "df_high = pd.concat([tv_2021, df_high],axis=1).round(1).fillna(0).convert_dtypes()\n",
    "df_high[\"% Difference\"] = round((df_high[high_month] - df_high[\"2021 Average Month\"]) / df_high[\"2021 Average Month\"] * 100,1)\n",
    "df_high = df_high.sort_values(high_month, ascending=False)\n",
    "df_high[\"Description\"] = [map[x] for x in df_high.index]\n",
    "\n",
    "print(f\"Shown are the top violations for traffic violations in {high_month}\\n\")\n",
    "print(f\"{high_month} is an example of a month where equipment violations were highest\")\n",
    "print(\"Mostly big increases across the board\")\n",
    "df_high.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early 2022 increase mainly in citations\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "group = tv.groupby([\"month\", \"action_taken\"]).size().unstack(\"action_taken\").fillna(0)\n",
    "\n",
    "ax = group.plot(grid=True, xlabel=\"\",ylabel=\"Count\", marker=\"o\")\n",
    "ax.legend(bbox_to_anchor=(1.1, 1.05))\n",
    "\n",
    "print(\"Early 2022 increase mainly in citations\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Increase in traffic violations was mainly non-Herndon residents.\n",
      "It would seem that this was most likely due to whereve stops are being made\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "group = tv.groupby([\"month\", \"residency\"]).size().unstack(\"residency\").fillna(0)\n",
    "\n",
    "ax = group.plot(grid=True, xlabel=\"\",ylabel=\"Count\", marker=\"o\")\n",
    "ax.legend(bbox_to_anchor=(1.1, 1.05))\n",
    "\n",
    "print(\"Increase in traffic violations was mainly non-Herndon residents.\")\n",
    "print(\"It would seem that this was most likely due to whereve stops are being made\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of stops ending in warnings went down in 2022\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "residency = None#\"RESIDENT OF CITY/COUNTY OF STOP\"#\"OTHER VIRGINIA JURISDICTION RESIDENT\" #\n",
    "group = tv.copy()\n",
    "if residency is not None:\n",
    "    group = group[group[\"residency\"]==residency]\n",
    "group = group.groupby([\"quarter\", \"action_taken\"]).size().unstack(\"action_taken\").fillna(0)\n",
    "group = group.div(group.sum(axis=1), axis=0)*100\n",
    "ax = group.plot.bar(stacked=True, figsize=[10,5], rot=0)\n",
    "ax.set_ylabel(\"%\")\n",
    "ax.legend(bbox_to_anchor=(1.1, 1.05))\n",
    "\n",
    "print(\"Percentage of stops ending in warnings went down in 2022\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Comparison of % of traffic violations stops ending in warning\n",
      "Warning rate went down for Black, Whites, and Latinos\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "      <th>Diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race_eth</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMERICAN INDIAN OR ALASKA NATIVE</th>\n",
       "      <td>11.9</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER</th>\n",
       "      <td>40.0</td>\n",
       "      <td>41.1</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <td>45.1</td>\n",
       "      <td>36.0</td>\n",
       "      <td>-9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HISPANIC OR LATINO</th>\n",
       "      <td>40.1</td>\n",
       "      <td>33.1</td>\n",
       "      <td>-7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNKNOWN</th>\n",
       "      <td>49.8</td>\n",
       "      <td>61.5</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WHITE</th>\n",
       "      <td>33.2</td>\n",
       "      <td>27.1</td>\n",
       "      <td>-6.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    2021  2022  Diff\n",
       "race_eth                                                            \n",
       "AMERICAN INDIAN OR ALASKA NATIVE                    11.9  20.0   8.1\n",
       "ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER  40.0  41.1   1.1\n",
       "BLACK OR AFRICAN AMERICAN                           45.1  36.0  -9.1\n",
       "HISPANIC OR LATINO                                  40.1  33.1  -7.0\n",
       "UNKNOWN                                             49.8  61.5  11.7\n",
       "WHITE                                               33.2  27.1  -6.1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = None\n",
    "for y in [2021,2022]:\n",
    "    tv_year = tv[tv[\"incident_date\"].dt.year == y]\n",
    "    # tv_year = tv_year[tv_year[\"english_speaking\"]==\"YES\"]\n",
    "    tv_year = tv_year.groupby([\"action_taken\", \"race_eth\"]).size().fillna(0).unstack(\"race_eth\")\n",
    "    tv_year = tv_year.loc[\"WARNING ISSUED\"] / tv_year.sum() * 100\n",
    "    tv_year.name = y\n",
    "    if df2 is None:\n",
    "        df2 = tv_year\n",
    "    else:\n",
    "        df2 = pd.concat([df2,tv_year],axis=1)\n",
    "\n",
    "df2 = df2.round(1)\n",
    "df2[\"Diff\"] = df2[2022] - df2[2021]\n",
    "\n",
    "print(\"Comparison of % of traffic violations stops ending in warning\")\n",
    "print(\"Warning rate went down for Black, Whites, and Latinos\")\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most stops are people who don't live in TOH (see total column)\n",
      "No evidence that they are more likely to cite people from out of town (such as out of state with different licenses)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>action_taken</th>\n",
       "      <th>ARREST</th>\n",
       "      <th>CITATION/SUMMONS</th>\n",
       "      <th>NO ENFORCEMENT ACTION</th>\n",
       "      <th>WARNING ISSUED</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>residency</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OTHER VIRGINIA JURISDICTION RESIDENT</th>\n",
       "      <td>0.6</td>\n",
       "      <td>70.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>29.0</td>\n",
       "      <td>4793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUT OF STATE RESIDENT</th>\n",
       "      <td>0.3</td>\n",
       "      <td>56.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>43.1</td>\n",
       "      <td>877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESIDENT OF CITY/COUNTY OF STOP</th>\n",
       "      <td>0.8</td>\n",
       "      <td>64.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>34.5</td>\n",
       "      <td>3128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNKNOWN</th>\n",
       "      <td>1.1</td>\n",
       "      <td>24.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.7</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "action_taken                          ARREST  CITATION/SUMMONS  \\\n",
       "residency                                                        \n",
       "OTHER VIRGINIA JURISDICTION RESIDENT     0.6              70.2   \n",
       "OUT OF STATE RESIDENT                    0.3              56.2   \n",
       "RESIDENT OF CITY/COUNTY OF STOP          0.8              64.2   \n",
       "UNKNOWN                                  1.1              24.2   \n",
       "\n",
       "action_taken                          NO ENFORCEMENT ACTION  WARNING ISSUED  \\\n",
       "residency                                                                     \n",
       "OTHER VIRGINIA JURISDICTION RESIDENT                    0.3            29.0   \n",
       "OUT OF STATE RESIDENT                                   0.3            43.1   \n",
       "RESIDENT OF CITY/COUNTY OF STOP                         0.5            34.5   \n",
       "UNKNOWN                                                 0.0            74.7   \n",
       "\n",
       "action_taken                          Total  \n",
       "residency                                    \n",
       "OTHER VIRGINIA JURISDICTION RESIDENT   4793  \n",
       "OUT OF STATE RESIDENT                   877  \n",
       "RESIDENT OF CITY/COUNTY OF STOP        3128  \n",
       "UNKNOWN                                  91  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "year = 2022\n",
    "group = tv.copy()\n",
    "if year is not None:\n",
    "    group = group[group[\"year\"]==year]\n",
    "group = tv.groupby([\"residency\", \"action_taken\"]).size().unstack(\"action_taken\").fillna(0)\n",
    "total = group.sum(axis=1)\n",
    "group = group.div(group.sum(axis=1), axis=0)*100\n",
    "ax = group.plot.bar(stacked=True, figsize=[10,5])\n",
    "ax.legend(bbox_to_anchor=(1.1, 1.05))\n",
    "group[\"Total\"] = total\n",
    "\n",
    "print(\"Most stops are people who don't live in TOH (see total column)\")\n",
    "print(\"No evidence that they are more likely to cite people from out of town (such as out of state with different licenses)\")\n",
    "round(group.convert_dtypes(),1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Breakdown of traffic violations stops by race and residency\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>residency</th>\n",
       "      <th>OTHER VIRGINIA JURISDICTION RESIDENT</th>\n",
       "      <th>OUT OF STATE RESIDENT</th>\n",
       "      <th>RESIDENT OF CITY/COUNTY OF STOP</th>\n",
       "      <th>UNKNOWN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race_eth</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMERICAN INDIAN OR ALASKA NATIVE</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER</th>\n",
       "      <td>7.7</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.5</td>\n",
       "      <td>12.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <td>17.3</td>\n",
       "      <td>20.8</td>\n",
       "      <td>14.7</td>\n",
       "      <td>17.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HISPANIC OR LATINO</th>\n",
       "      <td>23.5</td>\n",
       "      <td>33.2</td>\n",
       "      <td>33.8</td>\n",
       "      <td>19.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNKNOWN</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WHITE</th>\n",
       "      <td>51.0</td>\n",
       "      <td>38.2</td>\n",
       "      <td>42.9</td>\n",
       "      <td>49.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "residency                                           OTHER VIRGINIA JURISDICTION RESIDENT  \\\n",
       "race_eth                                                                                   \n",
       "AMERICAN INDIAN OR ALASKA NATIVE                                                     0.1   \n",
       "ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER                                   7.7   \n",
       "BLACK OR AFRICAN AMERICAN                                                           17.3   \n",
       "HISPANIC OR LATINO                                                                  23.5   \n",
       "UNKNOWN                                                                              0.4   \n",
       "WHITE                                                                               51.0   \n",
       "\n",
       "residency                                           OUT OF STATE RESIDENT  \\\n",
       "race_eth                                                                    \n",
       "AMERICAN INDIAN OR ALASKA NATIVE                                      0.0   \n",
       "ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER                    7.2   \n",
       "BLACK OR AFRICAN AMERICAN                                            20.8   \n",
       "HISPANIC OR LATINO                                                   33.2   \n",
       "UNKNOWN                                                               0.7   \n",
       "WHITE                                                                38.2   \n",
       "\n",
       "residency                                           RESIDENT OF CITY/COUNTY OF STOP  \\\n",
       "race_eth                                                                              \n",
       "AMERICAN INDIAN OR ALASKA NATIVE                                                0.4   \n",
       "ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER                              7.5   \n",
       "BLACK OR AFRICAN AMERICAN                                                      14.7   \n",
       "HISPANIC OR LATINO                                                             33.8   \n",
       "UNKNOWN                                                                         0.7   \n",
       "WHITE                                                                          42.9   \n",
       "\n",
       "residency                                           UNKNOWN  \n",
       "race_eth                                                     \n",
       "AMERICAN INDIAN OR ALASKA NATIVE                        0.0  \n",
       "ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER     12.1  \n",
       "BLACK OR AFRICAN AMERICAN                              17.6  \n",
       "HISPANIC OR LATINO                                     19.8  \n",
       "UNKNOWN                                                 1.1  \n",
       "WHITE                                                  49.5  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group = tv.groupby([\"race_eth\", \"residency\"]).size().unstack(\"residency\").fillna(0).convert_dtypes()\n",
    "print(\"Breakdown of traffic violations stops by race and residency\")\n",
    "round(group / group.sum() * 100,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arrest rates during stops for traffic violations by year\n",
      "Arrest rates during stops for traffic violations remain low\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race_eth</th>\n",
       "      <th>AMERICAN INDIAN OR ALASKA NATIVE</th>\n",
       "      <th>ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC OR LATINO</th>\n",
       "      <th>UNKNOWN</th>\n",
       "      <th>WHITE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>1.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth  AMERICAN INDIAN OR ALASKA NATIVE  \\\n",
       "year                                         \n",
       "2020                                   0.0   \n",
       "2021                                   1.2   \n",
       "2022                                   0.0   \n",
       "\n",
       "race_eth  ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER  \\\n",
       "year                                                           \n",
       "2020                                                     0.0   \n",
       "2021                                                     0.4   \n",
       "2022                                                     0.2   \n",
       "\n",
       "race_eth  BLACK OR AFRICAN AMERICAN  HISPANIC OR LATINO  UNKNOWN  WHITE  \n",
       "year                                                                     \n",
       "2020                            0.3                 2.9      3.5    1.2  \n",
       "2021                            0.4                 1.9      0.0    0.7  \n",
       "2022                            0.6                 0.9      0.0    0.3  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "residency = None#\"OTHER VIRGINIA JURISDICTION RESIDENT\"# \"RESIDENT OF TOWN/CITY/COUNTY OF STOP\"\n",
    "\n",
    "tmp = tv\n",
    "\n",
    "if residency is not None:\n",
    "    tmp = tmp[tmp[\"residency\"]==residency]\n",
    "\n",
    "new_df = pd.DataFrame()\n",
    "for year in range(2020,2023):\n",
    "    action_by_race = tmp[tmp[\"year\"]==year].groupby([\"action_taken\",\"race_eth\"]).size().unstack(\"race_eth\").fillna(0).convert_dtypes()\n",
    "    action_by_race = to_percent(action_by_race)\n",
    "    action_by_race.index = pd.MultiIndex.from_product([action_by_race.index,[year]],names=[action_by_race.index.name,\"year\"])\n",
    "    new_df = pd.concat([new_df, action_by_race])\n",
    "    \n",
    "print(\"Arrest rates during stops for traffic violations by year\")\n",
    "print(\"Arrest rates during stops for traffic violations remain low\")\n",
    "new_df = new_df.sort_index()\n",
    "new_df.loc[\"ARREST\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not too many arrests during non-traffic stops in 2022 but let's look at what they are arrested for in the next cell\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race_eth</th>\n",
       "      <th>ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC OR LATINO</th>\n",
       "      <th>WHITE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reason_for_stop</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CALLS FOR SERVICE</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTHER INVESTIGATIVE DETENTION</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TERRY STOP</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRAFFIC VIOLATION</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth                       ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER  \\\n",
       "reason_for_stop                                                                     \n",
       "CALLS FOR SERVICE                                                               0   \n",
       "OTHER INVESTIGATIVE DETENTION                                                   0   \n",
       "TERRY STOP                                                                      0   \n",
       "TRAFFIC VIOLATION                                                               1   \n",
       "\n",
       "race_eth                       BLACK OR AFRICAN AMERICAN  HISPANIC OR LATINO  \\\n",
       "reason_for_stop                                                                \n",
       "CALLS FOR SERVICE                                      4                   2   \n",
       "OTHER INVESTIGATIVE DETENTION                          0                   2   \n",
       "TERRY STOP                                             1                   1   \n",
       "TRAFFIC VIOLATION                                      6                  14   \n",
       "\n",
       "race_eth                       WHITE  \n",
       "reason_for_stop                       \n",
       "CALLS FOR SERVICE                  2  \n",
       "OTHER INVESTIGATIVE DETENTION      0  \n",
       "TERRY STOP                         0  \n",
       "TRAFFIC VIOLATION                  9  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = df[df[\"action_taken\"]==\"ARREST\"]\n",
    "tmp = tmp[tmp[\"year\"]==2022]\n",
    "\n",
    "print(\"Not too many arrests during non-traffic stops in 2022 but let's look at what they are arrested for in the next cell\")\n",
    "tmp.groupby([\"reason_for_stop\", \"race_eth\"]).size().unstack(\"race_eth\").fillna(0).convert_dtypes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arrest for non-traffic violations in 2022\n",
      "No clear pattern\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reason_for_stop</th>\n",
       "      <th>race_eth</th>\n",
       "      <th>section_title</th>\n",
       "      <th>person_searched</th>\n",
       "      <th>vehicle_searched</th>\n",
       "      <th>physical_force_by_officer</th>\n",
       "      <th>english_speaking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9171</th>\n",
       "      <td>OTHER INVESTIGATIVE DETENTION</td>\n",
       "      <td>HISPANIC OR LATINO</td>\n",
       "      <td>Trespass after having been forbidden to do so; penalties</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9269</th>\n",
       "      <td>CALLS FOR SERVICE</td>\n",
       "      <td>BLACK OR AFRICAN AMERICAN</td>\n",
       "      <td>Concealing or taking possession of merchandise; altering price tags; transferring goods from one...</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9685</th>\n",
       "      <td>TERRY STOP</td>\n",
       "      <td>BLACK OR AFRICAN AMERICAN</td>\n",
       "      <td>Petit larceny defined; how punished</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9686</th>\n",
       "      <td>CALLS FOR SERVICE</td>\n",
       "      <td>BLACK OR AFRICAN AMERICAN</td>\n",
       "      <td>Injuring, etc., any property, monument, etc</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9797</th>\n",
       "      <td>CALLS FOR SERVICE</td>\n",
       "      <td>HISPANIC OR LATINO</td>\n",
       "      <td>Driving motor vehicle, engine, etc., while intoxicated, etc</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10395</th>\n",
       "      <td>CALLS FOR SERVICE</td>\n",
       "      <td>BLACK OR AFRICAN AMERICAN</td>\n",
       "      <td>Repealed</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10439</th>\n",
       "      <td>CALLS FOR SERVICE</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>Driving motor vehicle, engine, etc., while intoxicated, etc</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10815</th>\n",
       "      <td>TERRY STOP</td>\n",
       "      <td>HISPANIC OR LATINO</td>\n",
       "      <td>Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcemen...</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11099</th>\n",
       "      <td>CALLS FOR SERVICE</td>\n",
       "      <td>HISPANIC OR LATINO</td>\n",
       "      <td>Assault and battery; penalty</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11282</th>\n",
       "      <td>CALLS FOR SERVICE</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>Driving motor vehicle, engine, etc., while intoxicated, etc</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13034</th>\n",
       "      <td>OTHER INVESTIGATIVE DETENTION</td>\n",
       "      <td>HISPANIC OR LATINO</td>\n",
       "      <td>Cases in which courts and judges may punish summarily for contempt</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13839</th>\n",
       "      <td>CALLS FOR SERVICE</td>\n",
       "      <td>BLACK OR AFRICAN AMERICAN</td>\n",
       "      <td>Penalties for failure to appear</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     reason_for_stop                   race_eth  \\\n",
       "9171   OTHER INVESTIGATIVE DETENTION         HISPANIC OR LATINO   \n",
       "9269               CALLS FOR SERVICE  BLACK OR AFRICAN AMERICAN   \n",
       "9685                      TERRY STOP  BLACK OR AFRICAN AMERICAN   \n",
       "9686               CALLS FOR SERVICE  BLACK OR AFRICAN AMERICAN   \n",
       "9797               CALLS FOR SERVICE         HISPANIC OR LATINO   \n",
       "10395              CALLS FOR SERVICE  BLACK OR AFRICAN AMERICAN   \n",
       "10439              CALLS FOR SERVICE                      WHITE   \n",
       "10815                     TERRY STOP         HISPANIC OR LATINO   \n",
       "11099              CALLS FOR SERVICE         HISPANIC OR LATINO   \n",
       "11282              CALLS FOR SERVICE                      WHITE   \n",
       "13034  OTHER INVESTIGATIVE DETENTION         HISPANIC OR LATINO   \n",
       "13839              CALLS FOR SERVICE  BLACK OR AFRICAN AMERICAN   \n",
       "\n",
       "                                                                                             section_title  \\\n",
       "9171                                              Trespass after having been forbidden to do so; penalties   \n",
       "9269   Concealing or taking possession of merchandise; altering price tags; transferring goods from one...   \n",
       "9685                                                                   Petit larceny defined; how punished   \n",
       "9686                                                           Injuring, etc., any property, monument, etc   \n",
       "9797                                           Driving motor vehicle, engine, etc., while intoxicated, etc   \n",
       "10395                                                                                             Repealed   \n",
       "10439                                          Driving motor vehicle, engine, etc., while intoxicated, etc   \n",
       "10815  Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcemen...   \n",
       "11099                                                                         Assault and battery; penalty   \n",
       "11282                                          Driving motor vehicle, engine, etc., while intoxicated, etc   \n",
       "13034                                   Cases in which courts and judges may punish summarily for contempt   \n",
       "13839                                                                      Penalties for failure to appear   \n",
       "\n",
       "      person_searched vehicle_searched physical_force_by_officer  \\\n",
       "9171              YES               NO                        NO   \n",
       "9269              YES               NO                        NO   \n",
       "9685              YES               NO                        NO   \n",
       "9686              YES              YES                        NO   \n",
       "9797              YES               NO                        NO   \n",
       "10395             YES              YES                        NO   \n",
       "10439             YES              YES                        NO   \n",
       "10815             YES               NO                        NO   \n",
       "11099             YES               NO                        NO   \n",
       "11282             YES               NO                        NO   \n",
       "13034             YES               NO                        NO   \n",
       "13839             YES               NO                        NO   \n",
       "\n",
       "      english_speaking  \n",
       "9171                NO  \n",
       "9269               YES  \n",
       "9685               YES  \n",
       "9686               YES  \n",
       "9797                NO  \n",
       "10395              YES  \n",
       "10439              YES  \n",
       "10815              YES  \n",
       "11099               NO  \n",
       "11282               NO  \n",
       "13034              YES  \n",
       "13839              YES  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = df[df[\"action_taken\"]==\"ARREST\"]\n",
    "tmp = tmp[tmp[\"year\"]==2022]\n",
    "tmp = tmp[tmp[\"reason_for_stop\"]!=\"TRAFFIC VIOLATION\"]\n",
    "\n",
    "print(\"Arrest for non-traffic violations in 2022\")\n",
    "print(\"No clear pattern\")\n",
    "tmp[[\"reason_for_stop\",\"race_eth\",\"section_title\",\"person_searched\",\"vehicle_searched\", 'physical_force_by_officer','english_speaking']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most common arrest offenses since 2020\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <th>race_eth</th>\n",
       "      <th>AMERICAN INDIAN OR ALASKA NATIVE</th>\n",
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       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
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       "    <tr>\n",
       "      <th>specific_violation</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18.2-266</th>\n",
       "      <th>Driving motor vehicle, engine, etc., while intoxicated, etc</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-300</th>\n",
       "      <th>Driving without license prohibited; penalties</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-804</th>\n",
       "      <th>Special regulations applicable on highways laned for traffic; penalty</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-301</th>\n",
       "      <th>Driving while license, permit, or privilege to drive suspended or revoked</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.2-388</th>\n",
       "      <th>Intoxication in public; penalty; transportation of public inebriates to detoxification center</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-830</th>\n",
       "      <th>Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcement of section</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-921.1</th>\n",
       "      <th>Repealed</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-835</th>\n",
       "      <th>Right turn on steady red light after stopping</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-817</th>\n",
       "      <th>Disregarding signal by law-enforcement officer to stop; eluding police; penalties</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.2-128</th>\n",
       "      <th>Penalties for failure to appear</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth                                                                                                                 AMERICAN INDIAN OR ALASKA NATIVE  \\\n",
       "specific_violation section_title                                                                                                                            \n",
       "18.2-266           Driving motor vehicle, engine, etc., while intoxicated, etc                                                                          0   \n",
       "46.2-300           Driving without license prohibited; penalties                                                                                        0   \n",
       "46.2-804           Special regulations applicable on highways laned for traffic; penalty                                                                0   \n",
       "46.2-301           Driving while license, permit, or privilege to drive suspended or revoked                                                            0   \n",
       "18.2-388           Intoxication in public; penalty; transportation of public inebriates to detoxification center                                        0   \n",
       "46.2-830           Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcement...                                 0   \n",
       "46.2-921.1         Repealed                                                                                                                             0   \n",
       "46.2-835           Right turn on steady red light after stopping                                                                                        1   \n",
       "46.2-817           Disregarding signal by law-enforcement officer to stop; eluding police; penalties                                                    0   \n",
       "19.2-128           Penalties for failure to appear                                                                                                      0   \n",
       "\n",
       "race_eth                                                                                                                 ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER  \\\n",
       "specific_violation section_title                                                                                                                                              \n",
       "18.2-266           Driving motor vehicle, engine, etc., while intoxicated, etc                                                                                            2   \n",
       "46.2-300           Driving without license prohibited; penalties                                                                                                          0   \n",
       "46.2-804           Special regulations applicable on highways laned for traffic; penalty                                                                                  1   \n",
       "46.2-301           Driving while license, permit, or privilege to drive suspended or revoked                                                                              0   \n",
       "18.2-388           Intoxication in public; penalty; transportation of public inebriates to detoxification center                                                          0   \n",
       "46.2-830           Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcement...                                                   0   \n",
       "46.2-921.1         Repealed                                                                                                                                               0   \n",
       "46.2-835           Right turn on steady red light after stopping                                                                                                          0   \n",
       "46.2-817           Disregarding signal by law-enforcement officer to stop; eluding police; penalties                                                                      0   \n",
       "19.2-128           Penalties for failure to appear                                                                                                                        0   \n",
       "\n",
       "race_eth                                                                                                                 BLACK OR AFRICAN AMERICAN  \\\n",
       "specific_violation section_title                                                                                                                     \n",
       "18.2-266           Driving motor vehicle, engine, etc., while intoxicated, etc                                                                   7   \n",
       "46.2-300           Driving without license prohibited; penalties                                                                                 0   \n",
       "46.2-804           Special regulations applicable on highways laned for traffic; penalty                                                         1   \n",
       "46.2-301           Driving while license, permit, or privilege to drive suspended or revoked                                                     1   \n",
       "18.2-388           Intoxication in public; penalty; transportation of public inebriates to detoxification center                                 0   \n",
       "46.2-830           Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcement...                          0   \n",
       "46.2-921.1         Repealed                                                                                                                      0   \n",
       "46.2-835           Right turn on steady red light after stopping                                                                                 0   \n",
       "46.2-817           Disregarding signal by law-enforcement officer to stop; eluding police; penalties                                             1   \n",
       "19.2-128           Penalties for failure to appear                                                                                               1   \n",
       "\n",
       "race_eth                                                                                                                 HISPANIC OR LATINO  \\\n",
       "specific_violation section_title                                                                                                              \n",
       "18.2-266           Driving motor vehicle, engine, etc., while intoxicated, etc                                                           40   \n",
       "46.2-300           Driving without license prohibited; penalties                                                                          6   \n",
       "46.2-804           Special regulations applicable on highways laned for traffic; penalty                                                  1   \n",
       "46.2-301           Driving while license, permit, or privilege to drive suspended or revoked                                              3   \n",
       "18.2-388           Intoxication in public; penalty; transportation of public inebriates to detoxification center                          2   \n",
       "46.2-830           Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcement...                   3   \n",
       "46.2-921.1         Repealed                                                                                                               1   \n",
       "46.2-835           Right turn on steady red light after stopping                                                                          1   \n",
       "46.2-817           Disregarding signal by law-enforcement officer to stop; eluding police; penalties                                      0   \n",
       "19.2-128           Penalties for failure to appear                                                                                        1   \n",
       "\n",
       "race_eth                                                                                                                 UNKNOWN  \\\n",
       "specific_violation section_title                                                                                                   \n",
       "18.2-266           Driving motor vehicle, engine, etc., while intoxicated, etc                                                 4   \n",
       "46.2-300           Driving without license prohibited; penalties                                                               0   \n",
       "46.2-804           Special regulations applicable on highways laned for traffic; penalty                                       0   \n",
       "46.2-301           Driving while license, permit, or privilege to drive suspended or revoked                                   0   \n",
       "18.2-388           Intoxication in public; penalty; transportation of public inebriates to detoxification center               0   \n",
       "46.2-830           Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcement...        0   \n",
       "46.2-921.1         Repealed                                                                                                    0   \n",
       "46.2-835           Right turn on steady red light after stopping                                                               0   \n",
       "46.2-817           Disregarding signal by law-enforcement officer to stop; eluding police; penalties                           0   \n",
       "19.2-128           Penalties for failure to appear                                                                             0   \n",
       "\n",
       "race_eth                                                                                                                 WHITE  \\\n",
       "specific_violation section_title                                                                                                 \n",
       "18.2-266           Driving motor vehicle, engine, etc., while intoxicated, etc                                              34   \n",
       "46.2-300           Driving without license prohibited; penalties                                                             1   \n",
       "46.2-804           Special regulations applicable on highways laned for traffic; penalty                                     2   \n",
       "46.2-301           Driving while license, permit, or privilege to drive suspended or revoked                                 1   \n",
       "18.2-388           Intoxication in public; penalty; transportation of public inebriates to detoxification center             1   \n",
       "46.2-830           Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcement...      0   \n",
       "46.2-921.1         Repealed                                                                                                  1   \n",
       "46.2-835           Right turn on steady red light after stopping                                                             0   \n",
       "46.2-817           Disregarding signal by law-enforcement officer to stop; eluding police; penalties                         1   \n",
       "19.2-128           Penalties for failure to appear                                                                           0   \n",
       "\n",
       "race_eth                                                                                                                 Total  \n",
       "specific_violation section_title                                                                                                \n",
       "18.2-266           Driving motor vehicle, engine, etc., while intoxicated, etc                                              87  \n",
       "46.2-300           Driving without license prohibited; penalties                                                             7  \n",
       "46.2-804           Special regulations applicable on highways laned for traffic; penalty                                     5  \n",
       "46.2-301           Driving while license, permit, or privilege to drive suspended or revoked                                 5  \n",
       "18.2-388           Intoxication in public; penalty; transportation of public inebriates to detoxification center             3  \n",
       "46.2-830           Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcement...      3  \n",
       "46.2-921.1         Repealed                                                                                                  2  \n",
       "46.2-835           Right turn on steady red light after stopping                                                             2  \n",
       "46.2-817           Disregarding signal by law-enforcement officer to stop; eluding police; penalties                         2  \n",
       "19.2-128           Penalties for failure to appear                                                                           2  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = df[df[\"action_taken\"]==\"ARREST\"]\n",
    "a = tmp.groupby([\"specific_violation\",\"section_title\",\"race_eth\"]).size().unstack(\"race_eth\").fillna(0).convert_dtypes()\n",
    "a[\"Total\"] = a.sum(axis=1)\n",
    "\n",
    "print(\"Most common arrest offenses since 2020\")\n",
    "a.convert_dtypes().sort_values(\"Total\",ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No evidence that anyone is being arrested at a higher rate for drunk drive driving (unless some people are getting cited for other offense when driving drunk)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race_eth</th>\n",
       "      <th>ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC OR LATINO</th>\n",
       "      <th>UNKNOWN</th>\n",
       "      <th>WHITE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>action_taken</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ARREST</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CITATION/SUMMONS</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO ENFORCEMENT ACTION</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth               ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER  \\\n",
       "action_taken                                                                \n",
       "ARREST                                                                  2   \n",
       "CITATION/SUMMONS                                                        0   \n",
       "NO ENFORCEMENT ACTION                                                   0   \n",
       "\n",
       "race_eth               BLACK OR AFRICAN AMERICAN  HISPANIC OR LATINO  UNKNOWN  \\\n",
       "action_taken                                                                    \n",
       "ARREST                                         7                  40        4   \n",
       "CITATION/SUMMONS                               0                   3        0   \n",
       "NO ENFORCEMENT ACTION                          0                   1        0   \n",
       "\n",
       "race_eth               WHITE  \n",
       "action_taken                  \n",
       "ARREST                    34  \n",
       "CITATION/SUMMONS           1  \n",
       "NO ENFORCEMENT ACTION      0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = df[df[\"specific_violation\"]==\"18.2-266\"]\n",
    "print(\"No evidence that anyone is being arrested at a higher rate for drunk drive driving (unless some people are getting cited for other offense when driving drunk)\")\n",
    "tmp.groupby([\"action_taken\",\"race_eth\"]).size().unstack(\"race_eth\").fillna(0).convert_dtypes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Search rates for stops not ending in arrests (since those searches are often non-discretionary) by year\n",
      "Search rates remain very low although have increased slightly for Latinos\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>race_eth</th>\n",
       "      <th>AMERICAN INDIAN OR ALASKA NATIVE</th>\n",
       "      <th>ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC OR LATINO</th>\n",
       "      <th>UNKNOWN</th>\n",
       "      <th>WHITE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>searched</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">True</th>\n",
       "      <th>2020</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>1.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth       AMERICAN INDIAN OR ALASKA NATIVE  \\\n",
       "searched year                                     \n",
       "True     2020                               0.0   \n",
       "         2021                               1.2   \n",
       "         2022                               0.0   \n",
       "\n",
       "race_eth       ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER  \\\n",
       "searched year                                                       \n",
       "True     2020                                                 0.0   \n",
       "         2021                                                 0.7   \n",
       "         2022                                                 0.0   \n",
       "\n",
       "race_eth       BLACK OR AFRICAN AMERICAN  HISPANIC OR LATINO  UNKNOWN  WHITE  \n",
       "searched year                                                                 \n",
       "True     2020                        0.0                 0.2      1.1    0.0  \n",
       "         2021                        0.4                 0.2      0.0    0.1  \n",
       "         2022                        0.0                 0.6      0.0    0.1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "residency = None#\"OTHER VIRGINIA JURISDICTION RESIDENT\"# \"RESIDENT OF TOWN/CITY/COUNTY OF STOP\"\n",
    "\n",
    "# Looking at search rates. Remove arrests because they usually require searches (i.e. not discretionary)\n",
    "df_noarrest = df[df[\"action_taken\"]!=\"ARREST\"]\n",
    "\n",
    "if residency is not None:\n",
    "    df_noarrest = df_noarrest[df_noarrest[\"residency\"]==residency]\n",
    "\n",
    "import warnings\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    df_noarrest[\"searched\"] = (df_noarrest[\"person_searched\"]==\"YES\") | (df_noarrest[\"vehicle_searched\"]==\"YES\")\n",
    "\n",
    "new_df = pd.DataFrame()\n",
    "for year in range(2020,2023):\n",
    "    action_by_race = df_noarrest[df_noarrest[\"year\"]==year].groupby([\"searched\",\"race_eth\"]).size().unstack(\"race_eth\").fillna(0).convert_dtypes()\n",
    "    action_by_race = to_percent(action_by_race)\n",
    "    action_by_race.index = pd.MultiIndex.from_product([action_by_race.index,[year]],names=[action_by_race.index.name,\"year\"])\n",
    "    new_df = pd.concat([new_df, action_by_race])\n",
    "    \n",
    "print(\"Search rates for stops not ending in arrests (since those searches are often non-discretionary) by year\")\n",
    "print(\"Search rates remain very low although have increased slightly for Latinos\")\n",
    "new_df.loc[[x[0] for x in new_df.index]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Details of 2022 searches of Latinos for stops not ending in arrest\n",
      "It would be interesting to know the reason for these searches.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>action_taken</th>\n",
       "      <th>CITATION/SUMMONS</th>\n",
       "      <th>WARNING ISSUED</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reason_for_stop</th>\n",
       "      <th>section_title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">TRAFFIC VIOLATION</th>\n",
       "      <th>Altered or forged license plates or decals; use as evidence of knowledge</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Driving without license prohibited; penalties</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum speed limit in business and residence districts</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum speed limit on certain other highways in cities and towns</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Operating uninsured motor vehicle without payment of fee; verification of insurance; false evidence of insurance</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Owner to secure registration and certificate of title or certificate of ownership</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Special regulations applicable on highways laned for traffic; penalty</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "action_taken                                                                                                            CITATION/SUMMONS  \\\n",
       "reason_for_stop   section_title                                                                                                            \n",
       "TRAFFIC VIOLATION Altered or forged license plates or decals; use as evidence of knowledge                                             1   \n",
       "                  Driving without license prohibited; penalties                                                                        3   \n",
       "                  Maximum speed limit in business and residence districts                                                              1   \n",
       "                  Maximum speed limit on certain other highways in cities and towns                                                    1   \n",
       "                  Operating uninsured motor vehicle without payment of fee; verification of insurance; false eviden...                 1   \n",
       "                  Owner to secure registration and certificate of title or certificate of ownership                                    1   \n",
       "                  Special regulations applicable on highways laned for traffic; penalty                                                0   \n",
       "\n",
       "action_taken                                                                                                            WARNING ISSUED  \n",
       "reason_for_stop   section_title                                                                                                         \n",
       "TRAFFIC VIOLATION Altered or forged license plates or decals; use as evidence of knowledge                                           0  \n",
       "                  Driving without license prohibited; penalties                                                                      0  \n",
       "                  Maximum speed limit in business and residence districts                                                            0  \n",
       "                  Maximum speed limit on certain other highways in cities and towns                                                  0  \n",
       "                  Operating uninsured motor vehicle without payment of fee; verification of insurance; false eviden...               0  \n",
       "                  Owner to secure registration and certificate of title or certificate of ownership                                  0  \n",
       "                  Special regulations applicable on highways laned for traffic; penalty                                              1  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = df_noarrest[df_noarrest[\"searched\"]]\n",
    "tmp = tmp[tmp[\"year\"]==2022]\n",
    "tmp = tmp[tmp[\"race_eth\"]==\"HISPANIC OR LATINO\"]\n",
    "\n",
    "print(\"Details of 2022 searches of Latinos for stops not ending in arrest\")\n",
    "print(\"It would be interesting to know the reason for these searches.\")\n",
    "tmp.groupby([\"reason_for_stop\",\"section_title\",\"action_taken\"]).size().unstack(\"action_taken\").fillna(0).convert_dtypes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of use of force during stops\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>physical_force_by_subject</th>\n",
       "      <th>MISSING</th>\n",
       "      <th>NO</th>\n",
       "      <th>YES</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>3</td>\n",
       "      <td>3123</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>0</td>\n",
       "      <td>6130</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>0</td>\n",
       "      <td>991</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "physical_force_by_subject  MISSING    NO  YES\n",
       "year                                         \n",
       "2021                             3  3123    6\n",
       "2022                             0  6130    1\n",
       "2023                             0   991    1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Number of use of force during stops\")\n",
    "df.groupby([\"year\",\"physical_force_by_officer\"]).size().unstack(\"physical_force_by_officer\").fillna(0).convert_dtypes()\n",
    "df.groupby([\"year\",\"physical_force_by_subject\"]).size().unstack(\"physical_force_by_subject\").fillna(0).convert_dtypes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of traffic violations stops of non-English speakers by year\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "english_speaking  year\n",
       "MISSING           2021     0.0\n",
       "NO                2021     3.2\n",
       "                  2022     6.6\n",
       "                  2023     7.3\n",
       "YES               2021    43.1\n",
       "                  2022    93.4\n",
       "                  2023    92.7\n",
       "dtype: Float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "es = tv.groupby([\"english_speaking\",\"year\"]).size().fillna(0).convert_dtypes()\n",
    "tot = tv.groupby(\"year\").size().fillna(0).convert_dtypes()\n",
    "\n",
    "print(\"Percentage of traffic violations stops of non-English speakers by year\")\n",
    "(es / tot*100).round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Comparison of % of traffic violations stops ending in warning for non-English speakers\n",
      "Warning rate went up for non-English speakers while it went down overall\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "      <th>Diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>english_speaking</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MISSING</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>35.0</td>\n",
       "      <td>39.2</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YES</th>\n",
       "      <td>39.0</td>\n",
       "      <td>30.9</td>\n",
       "      <td>-8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL</th>\n",
       "      <td>38.7</td>\n",
       "      <td>31.5</td>\n",
       "      <td>-7.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  2021  2022  Diff\n",
       "english_speaking                  \n",
       "MISSING            NaN   NaN   NaN\n",
       "NO                35.0  39.2   4.2\n",
       "YES               39.0  30.9  -8.1\n",
       "TOTAL             38.7  31.5  -7.2"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = None\n",
    "for y in [2021,2022]:\n",
    "    tv_year = tv[tv[\"incident_date\"].dt.year == y]\n",
    "    tv_year = tv_year.groupby([\"action_taken\", \"english_speaking\"]).size().fillna(0).unstack(\"english_speaking\")\n",
    "    total = tv_year.sum(axis=1) / tv_year.sum(axis=1).sum() * 100\n",
    "    tv_year = tv_year.loc[\"WARNING ISSUED\"] / tv_year.sum() * 100\n",
    "    tv_year[\"TOTAL\"] = total[\"WARNING ISSUED\"]\n",
    "    tv_year.name = y\n",
    "    if df2 is None:\n",
    "        df2 = tv_year\n",
    "    else:\n",
    "        df2 = pd.concat([df2,tv_year],axis=1)\n",
    "\n",
    "df2 = df2.round(1)\n",
    "df2[\"Diff\"] = df2[2022] - df2[2021]\n",
    "\n",
    "print(\"Comparison of % of traffic violations stops ending in warning for non-English speakers\")\n",
    "print(\"Warning rate went up for non-English speakers while it went down overall\")\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Typically no action taken when stop is for Terry Stop\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race_eth</th>\n",
       "      <th>ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC OR LATINO</th>\n",
       "      <th>WHITE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>action_taken</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ARREST</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CITATION/SUMMONS</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO ENFORCEMENT ACTION</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WARNING ISSUED</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth               ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER  \\\n",
       "action_taken                                                                \n",
       "ARREST                                                                  0   \n",
       "CITATION/SUMMONS                                                        0   \n",
       "NO ENFORCEMENT ACTION                                                   1   \n",
       "WARNING ISSUED                                                          1   \n",
       "\n",
       "race_eth               BLACK OR AFRICAN AMERICAN  HISPANIC OR LATINO  WHITE  \n",
       "action_taken                                                                 \n",
       "ARREST                                         1                   1      0  \n",
       "CITATION/SUMMONS                               0                   2      1  \n",
       "NO ENFORCEMENT ACTION                          4                  17      9  \n",
       "WARNING ISSUED                                 1                   6      2  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur = df[df[\"reason_for_stop\"]==\"TERRY STOP\"]\n",
    "cur = cur[cur[\"year\"]==2022]\n",
    "cur = cur.groupby([\"action_taken\",\"race_eth\"]).size().unstack(\"race_eth\").fillna(0).convert_dtypes()\n",
    "\n",
    "print(\"Typically no action taken when stop is for Terry Stop\")\n",
    "cur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Terry stops are stops based on reasonable suspicion\n",
      "Most of the violations in the resulting action taken are not for things that police could have known in advance\n",
      "However, based on the actions taken for Terry stops, most cases only resulted in warnings\n",
      "NOTE: there is no section title for a violations if no action is taken since the section title is for the action taken not the reason for the stop\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race_eth</th>\n",
       "      <th>ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC OR LATINO</th>\n",
       "      <th>WHITE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>section_title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Child restraint devices required when transporting certain children; safety belts for passengers less than 18 years old required</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Display of license plates</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Driving while license, permit, or privilege to drive suspended or revoked</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Driving without license prohibited; penalties</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>None</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Petit larceny defined; how punished</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Special restrictions on particular licensees</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trespass after having been forbidden to do so; penalties</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcement of section</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth                                                                                              ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER  \\\n",
       "section_title                                                                                                                                              \n",
       "Child restraint devices required when transporting certain children; safety belts for passengers ...                                                   1   \n",
       "Display of license plates                                                                                                                              0   \n",
       "Driving while license, permit, or privilege to drive suspended or revoked                                                                              0   \n",
       "Driving without license prohibited; penalties                                                                                                          0   \n",
       "None                                                                                                                                                   1   \n",
       "Petit larceny defined; how punished                                                                                                                    0   \n",
       "Special restrictions on particular licensees                                                                                                           0   \n",
       "Trespass after having been forbidden to do so; penalties                                                                                               0   \n",
       "Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcement...                                                   0   \n",
       "\n",
       "race_eth                                                                                              BLACK OR AFRICAN AMERICAN  \\\n",
       "section_title                                                                                                                     \n",
       "Child restraint devices required when transporting certain children; safety belts for passengers ...                          1   \n",
       "Display of license plates                                                                                                     0   \n",
       "Driving while license, permit, or privilege to drive suspended or revoked                                                     0   \n",
       "Driving without license prohibited; penalties                                                                                 0   \n",
       "None                                                                                                                          4   \n",
       "Petit larceny defined; how punished                                                                                           1   \n",
       "Special restrictions on particular licensees                                                                                  0   \n",
       "Trespass after having been forbidden to do so; penalties                                                                      0   \n",
       "Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcement...                          0   \n",
       "\n",
       "race_eth                                                                                              HISPANIC OR LATINO  \\\n",
       "section_title                                                                                                              \n",
       "Child restraint devices required when transporting certain children; safety belts for passengers ...                   2   \n",
       "Display of license plates                                                                                              1   \n",
       "Driving while license, permit, or privilege to drive suspended or revoked                                              1   \n",
       "Driving without license prohibited; penalties                                                                          1   \n",
       "None                                                                                                                  17   \n",
       "Petit larceny defined; how punished                                                                                    0   \n",
       "Special restrictions on particular licensees                                                                           2   \n",
       "Trespass after having been forbidden to do so; penalties                                                               1   \n",
       "Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcement...                   1   \n",
       "\n",
       "race_eth                                                                                              WHITE  \n",
       "section_title                                                                                                \n",
       "Child restraint devices required when transporting certain children; safety belts for passengers ...      0  \n",
       "Display of license plates                                                                                 0  \n",
       "Driving while license, permit, or privilege to drive suspended or revoked                                 0  \n",
       "Driving without license prohibited; penalties                                                             0  \n",
       "None                                                                                                      9  \n",
       "Petit larceny defined; how punished                                                                       0  \n",
       "Special restrictions on particular licensees                                                              2  \n",
       "Trespass after having been forbidden to do so; penalties                                                  0  \n",
       "Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcement...      1  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur = df[df[\"reason_for_stop\"]==\"TERRY STOP\"]\n",
    "cur = cur[cur[\"year\"]==2022]\n",
    "cur[\"section_title\"] = cur[\"section_title\"].apply(lambda x: \"None\" if x is None else x)\n",
    "cur = cur.groupby([\"section_title\",\"race_eth\"]).size().unstack(\"race_eth\").fillna(0).convert_dtypes()\n",
    "\n",
    "print(\"Terry stops are stops based on reasonable suspicion\")\n",
    "print(\"Most of the violations in the resulting action taken are not for things that police could have known in advance\")\n",
    "print(\"However, based on the actions taken for Terry stops, most cases only resulted in warnings\")\n",
    "print(\"NOTE: there is no section title for a violations if no action is taken since the section title is for the action taken not the reason for the stop\")\n",
    "\n",
    "cur"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The rest of this will focus on if there is anything in the data that indicates why Black individuals are stopped more frequently than other groups\n",
    "\n",
    "Let's first focus on age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'# of traffic violations stops in each age group in 2022'}, xlabel='age_range', ylabel='Count'>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Focusing on residents since that is the source of the finding about more frequent Black stops\n",
    "year = 2022\n",
    "tv_res = tv[tv[\"residency\"]==\"RESIDENT OF CITY/COUNTY OF STOP\"]\n",
    "\n",
    "by_age = tv_res[tv_res[\"year\"]==year].groupby([\"age_range\",\"race_eth\"]).size().unstack(\"race_eth\").fillna(0).convert_dtypes()\n",
    "by_age = by_age.drop(columns=[\"AMERICAN INDIAN OR ALASKA NATIVE\",\"UNKNOWN\"])\n",
    "by_age = by_age.rename(columns={\"ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER\":\"AAPI\"})\n",
    "\n",
    "by_age.plot.bar(figsize=[10,5], rot=0, title=f\"# of traffic violations stops in each age group in {year}\", ylabel=\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A higher percentage of young people are pulled over for traffic violations for Blacks and Latinos\n",
      "This could result from a younger population, higher commission of traffic violations, and/or bias.\n",
      "If you assume that the young population 16-25 largely matches the demographics of Herndon High School,\n",
      "this can be evaluated a bit: https://schoolprofiles.fcps.edu/schlprfl/f?p=108:13:::NO::P0_CURRENT_SCHOOL_ID,P0_EDSL:270,0.\n",
      "HHS has a larger Latino population (49.4%) than Herndon as a whole (36.7%).\n",
      "However, the HHS Black population (6.7%) is about the same as Herndon (7.3%)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Focusing on residents since that is the source of the finding about more frequent Black stops\n",
    "year = 2022\n",
    "by_age = tv_res[tv_res[\"year\"]==year].groupby([\"age_range\",\"race_eth\"]).size().unstack(\"race_eth\").fillna(0).convert_dtypes()\n",
    "by_age = round(by_age / by_age.sum() * 100, 1)\n",
    "by_age = by_age.drop(columns=[\"AMERICAN INDIAN OR ALASKA NATIVE\",\"UNKNOWN\"])\n",
    "by_age = by_age.rename(columns={\"ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER\":\"AAPI\"})\n",
    "\n",
    "by_age.plot.bar(figsize=[10,5], rot=0, title=f\"% of traffic violations stops in each age group in {year}\")\n",
    "ax.set_ylabel(\"%\")\n",
    "\n",
    "print(\"A higher percentage of young people are pulled over for traffic violations for Blacks and Latinos\")\n",
    "print(\"This could result from a younger population, higher commission of traffic violations, and/or bias.\")\n",
    "print(\"If you assume that the young population 16-25 largely matches the demographics of Herndon High School,\")\n",
    "print(\"this can be evaluated a bit: https://schoolprofiles.fcps.edu/schlprfl/f?p=108:13:::NO::P0_CURRENT_SCHOOL_ID,P0_EDSL:270,0.\")\n",
    "print(\"HHS has a larger Latino population (49.4%) than Herndon as a whole (36.7%).\")\n",
    "print(\"However, the HHS Black population (6.7%) is about the same as Herndon (7.3%)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The below table shows the ratio of each race for each age group to that groups total (regardless of age) population in Herndon\n",
      "A value of 1 indicates that the % of people in group X for an age group is the same as their % population\n",
      "This is called disproportionality\n",
      "\n",
      "Latino disproportionality appears slightly high for the youngest age group but drops with age.\n",
      "White disproportionality increases with age\n",
      "Black disproportionality is highest for all age groups\n",
      "\n",
      "Note that since we are dealing with percentages, if one group is stopped less often, that will automatically boost the percentage of another groups\n",
      "Asians are stopped less often for most ages and older Latinos are rarely stopped\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race_eth</th>\n",
       "      <th>ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC OR LATINO</th>\n",
       "      <th>WHITE</th>\n",
       "      <th>COUNT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_range</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16-25</th>\n",
       "      <td>0.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26-35</th>\n",
       "      <td>0.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36-45</th>\n",
       "      <td>0.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46-55</th>\n",
       "      <td>0.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56-65</th>\n",
       "      <td>0.6</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65+</th>\n",
       "      <td>1.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth   ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER  \\\n",
       "age_range                                                       \n",
       "16-25                                                     0.3   \n",
       "26-35                                                     0.5   \n",
       "36-45                                                     0.5   \n",
       "46-55                                                     0.5   \n",
       "56-65                                                     0.6   \n",
       "65+                                                       1.3   \n",
       "\n",
       "race_eth   BLACK OR AFRICAN AMERICAN  HISPANIC OR LATINO  WHITE  COUNT  \n",
       "age_range                                                               \n",
       "16-25                            1.8                 1.2    1.0    474  \n",
       "26-35                            2.6                 0.9    1.2    463  \n",
       "36-45                            2.1                 0.9    1.2    428  \n",
       "46-55                            1.6                 0.9    1.3    262  \n",
       "56-65                            1.6                 0.7    1.5    210  \n",
       "65+                              1.7                 0.4    1.5     73  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There are very few 0-15 year olds\n",
    "tmp = tv_res[tv_res[\"year\"]==year].groupby([\"race_eth\",\"age_range\"]).size().unstack(\"age_range\").fillna(0).convert_dtypes().drop(columns=\"0-15\")\n",
    "total = tmp.sum()\n",
    "tmp = tmp / total * 100\n",
    "\n",
    "disprop = round(tmp.transpose() / pops,1).drop(columns=[\"UNKNOWN\",\"AMERICAN INDIAN OR ALASKA NATIVE\"])\n",
    "disprop[\"COUNT\"] = total\n",
    "\n",
    "print(\"The below table shows the ratio of each race for each age group to that groups total (regardless of age) population in Herndon\")\n",
    "print(\"A value of 1 indicates that the % of people in group X for an age group is the same as their % population\")\n",
    "print(\"This is called disproportionality\")\n",
    "print(\"\\nLatino disproportionality appears slightly high for the youngest age group but drops with age.\")\n",
    "print(\"White disproportionality increases with age\")\n",
    "print(\"Black disproportionality is highest for all age groups\")\n",
    "print(\"\\nNote that since we are dealing with percentages, if one group is stopped less often, that will automatically boost the percentage of another groups\")\n",
    "print(\"Asians are stopped less often for most ages and older Latinos are rarely stopped\")\n",
    "disprop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "To try to account for the underrepresentation of Asians and Latinos at certain ages. Let's normalize to the White disproportionality\n",
      "This is called disparity.\n",
      "\n",
      "Black disparity is still high although unlike for disproportionality, it does decrease with age a bit more (mainly after 45).\n",
      "Let's focus on people under 45\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>race_eth</th>\n",
       "      <th>ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC OR LATINO</th>\n",
       "      <th>WHITE</th>\n",
       "      <th>COUNT</th>\n",
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       "    <tr>\n",
       "      <th>age_range</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16-25</th>\n",
       "      <td>0.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>474.0</td>\n",
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       "      <th>26-35</th>\n",
       "      <td>0.4</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>356.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46-55</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>201.5</td>\n",
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       "    <tr>\n",
       "      <th>56-65</th>\n",
       "      <td>0.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65+</th>\n",
       "      <td>0.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth   ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER  \\\n",
       "age_range                                                       \n",
       "16-25                                                     0.3   \n",
       "26-35                                                     0.4   \n",
       "36-45                                                     0.4   \n",
       "46-55                                                     0.4   \n",
       "56-65                                                     0.4   \n",
       "65+                                                       0.9   \n",
       "\n",
       "race_eth   BLACK OR AFRICAN AMERICAN  HISPANIC OR LATINO  WHITE  COUNT  \n",
       "age_range                                                               \n",
       "16-25                            1.8                 1.2    1.0  474.0  \n",
       "26-35                            2.2                 0.8    1.0  385.8  \n",
       "36-45                            1.8                 0.8    1.0  356.7  \n",
       "46-55                            1.2                 0.7    1.0  201.5  \n",
       "56-65                            1.1                 0.5    1.0  140.0  \n",
       "65+                              1.1                 0.3    1.0   48.7  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"To try to account for the underrepresentation of Asians and Latinos at certain ages. Let's normalize to the White disproportionality\")\n",
    "print(\"This is called disparity.\")\n",
    "\n",
    "print(\"\\nBlack disparity is still high although unlike for disproportionality, it does decrease with age a bit more (mainly after 45).\")\n",
    "print(\"Let's focus on people under 45\")\n",
    "\n",
    "disprop.divide(disprop[\"WHITE\"],axis=0).round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rates of different stop results for people under 45 in 2022\n",
      "\n",
      "Blacks are warned at a higher rate than whites but the same rate as other POC. \n",
      "If we assume that POC are not more likely to warn when the officer would otherwise give a citation, this suggests that POC are either more likely to be pulled over\n",
      " for warnable/minor offenses and/or less likely to be pulled over for citable offenses\n",
      "Not sure that we have the fidelity to test this theory. We have information about the violation but it is pretty generic (i.e. data might say \"speeding violation\" not \"speeding violation for being XX MPH over speed limit\")\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race_eth</th>\n",
       "      <th>AMERICAN INDIAN OR ALASKA NATIVE</th>\n",
       "      <th>ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
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       "      <th>UNKNOWN</th>\n",
       "      <th>WHITE</th>\n",
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       "    <tr>\n",
       "      <th>action_taken</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ARREST</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CITATION/SUMMONS</th>\n",
       "      <td>66.7</td>\n",
       "      <td>63.4</td>\n",
       "      <td>64.8</td>\n",
       "      <td>63.7</td>\n",
       "      <td>25.0</td>\n",
       "      <td>71.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO ENFORCEMENT ACTION</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>33.3</td>\n",
       "      <td>35.5</td>\n",
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       "      <td>34.5</td>\n",
       "      <td>75.0</td>\n",
       "      <td>28.2</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth               AMERICAN INDIAN OR ALASKA NATIVE  \\\n",
       "action_taken                                              \n",
       "ARREST                                              0.0   \n",
       "CITATION/SUMMONS                                   66.7   \n",
       "NO ENFORCEMENT ACTION                               0.0   \n",
       "WARNING ISSUED                                     33.3   \n",
       "\n",
       "race_eth               ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER  \\\n",
       "action_taken                                                                \n",
       "ARREST                                                                0.0   \n",
       "CITATION/SUMMONS                                                     63.4   \n",
       "NO ENFORCEMENT ACTION                                                 1.1   \n",
       "WARNING ISSUED                                                       35.5   \n",
       "\n",
       "race_eth               BLACK OR AFRICAN AMERICAN  HISPANIC OR LATINO  UNKNOWN  \\\n",
       "action_taken                                                                    \n",
       "ARREST                                       0.5                 1.2      0.0   \n",
       "CITATION/SUMMONS                            64.8                63.7     25.0   \n",
       "NO ENFORCEMENT ACTION                        0.0                 0.6      0.0   \n",
       "WARNING ISSUED                              34.7                34.5     75.0   \n",
       "\n",
       "race_eth               WHITE  \n",
       "action_taken                  \n",
       "ARREST                   0.0  \n",
       "CITATION/SUMMONS        71.8  \n",
       "NO ENFORCEMENT ACTION    0.0  \n",
       "WARNING ISSUED          28.2  "
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year = 2022\n",
    "tv_u45 = tv_res[tv_res[\"age\"].apply(lambda x: 16<=int(x)<=45)]\n",
    "tv_u45 = tv_u45[tv_u45[\"year\"]==year]\n",
    "\n",
    "tmp = tv_u45.groupby([\"action_taken\",\"race_eth\"]).size().unstack(\"race_eth\").fillna(0).convert_dtypes()\n",
    "\n",
    "print(f\"Rates of different stop results for people under 45 in {year}\")\n",
    "print(\"\\nBlacks are warned at a higher rate than whites but the same rate as other POC. \")\n",
    "print(\"If we assume that POC are not more likely to warn when the officer would otherwise give a citation, this suggests that POC are either more likely to be pulled over\\n for warnable/minor offenses and/or less likely to be pulled over for citable offenses\")\n",
    "print('Not sure that we have the fidelity to test this theory. We have information about the violation but it is pretty generic (i.e. data might say \"speeding violation\" not \"speeding violation for being XX MPH over speed limit\")')\n",
    "round(tmp / tmp.sum()*100, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This shows the following for 2022:\n",
      "1. Percentage of Black/White stops for traffic violation ending in each violation and result. (46.2-830 for citations at 13.2% is % of all stops of Blacks not just citations)\n",
      "2. Total counts for each violation and result of stop for each group\n",
      "3. Ratio of Black Count to White Count\n",
      "\n",
      "Note that the number of cases gets pretty small quickly so those numbers may not have a lot of meaning. Thought about using data for all years but then we would have to deal with COVID effects on behavior\n",
      "\n",
      "Most notable is that when pulled over for 46.2-830 which I assume is mostly speeding, Blacks are given warnings about half the time while Whites are only given warnings about 1/3 of the time\n"
     ]
    },
    {
     "data": {
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       "      <th>specific_violation</th>\n",
       "      <th>section_title</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">46.2-830</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcement of section</th>\n",
       "      <th>CITATION/SUMMONS</th>\n",
       "      <td>13.2</td>\n",
       "      <td>14.6</td>\n",
       "      <td>27</td>\n",
       "      <td>72</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WARNING ISSUED</th>\n",
       "      <td>12.7</td>\n",
       "      <td>7.9</td>\n",
       "      <td>26</td>\n",
       "      <td>39</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">46.2-833</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Traffic lights; penalty</th>\n",
       "      <th>CITATION/SUMMONS</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WARNING ISSUED</th>\n",
       "      <td>2.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">46.2-646</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Expiration and renewal of registration</th>\n",
       "      <th>CITATION/SUMMONS</th>\n",
       "      <td>6.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>14</td>\n",
       "      <td>36</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WARNING ISSUED</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">46.2-1158</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Frequency of inspection; scope of inspection</th>\n",
       "      <th>CITATION/SUMMONS</th>\n",
       "      <td>6.9</td>\n",
       "      <td>8.7</td>\n",
       "      <td>14</td>\n",
       "      <td>43</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WARNING ISSUED</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">46.2-874</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Maximum speed limit in business and residence districts</th>\n",
       "      <th>CITATION/SUMMONS</th>\n",
       "      <td>4.9</td>\n",
       "      <td>6.5</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WARNING ISSUED</th>\n",
       "      <td>1.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">46.2-875</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Maximum speed limit on certain other highways in cities and towns</th>\n",
       "      <th>CITATION/SUMMONS</th>\n",
       "      <td>3.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WARNING ISSUED</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">46.2-821</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Vehicles before entering certain highways shall stop or yield right-of-way</th>\n",
       "      <th>CITATION/SUMMONS</th>\n",
       "      <td>1.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WARNING ISSUED</th>\n",
       "      <td>3.4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2-818</th>\n",
       "      <th>Stopping vehicle of another; blocking access to premises; damaging or threatening commercial vehicle or operator thereof; penalties</th>\n",
       "      <th>CITATION/SUMMONS</th>\n",
       "      <td>3.9</td>\n",
       "      <td>7.1</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth                                                                                                                                  BLACK %  \\\n",
       "specific_violation section_title                                                                                        action_taken                \n",
       "46.2-830           Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcement... CITATION/SUMMONS     13.2   \n",
       "                                                                                                                        WARNING ISSUED       12.7   \n",
       "46.2-833           Traffic lights; penalty                                                                              CITATION/SUMMONS      5.9   \n",
       "                                                                                                                        WARNING ISSUED        2.5   \n",
       "46.2-646           Expiration and renewal of registration                                                               CITATION/SUMMONS      6.9   \n",
       "                                                                                                                        WARNING ISSUED        1.0   \n",
       "46.2-1158          Frequency of inspection; scope of inspection                                                         CITATION/SUMMONS      6.9   \n",
       "                                                                                                                        WARNING ISSUED        0.0   \n",
       "46.2-874           Maximum speed limit in business and residence districts                                              CITATION/SUMMONS      4.9   \n",
       "                                                                                                                        WARNING ISSUED        1.5   \n",
       "46.2-875           Maximum speed limit on certain other highways in cities and towns                                    CITATION/SUMMONS      3.4   \n",
       "                                                                                                                        WARNING ISSUED        2.0   \n",
       "46.2-821           Vehicles before entering certain highways shall stop or yield right-of-way                           CITATION/SUMMONS      1.5   \n",
       "                                                                                                                        WARNING ISSUED        3.4   \n",
       "46.2-818           Stopping vehicle of another; blocking access to premises; damaging or threatening commercial vehi... CITATION/SUMMONS      3.9   \n",
       "\n",
       "race_eth                                                                                                                                  WHITE %  \\\n",
       "specific_violation section_title                                                                                        action_taken                \n",
       "46.2-830           Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcement... CITATION/SUMMONS     14.6   \n",
       "                                                                                                                        WARNING ISSUED        7.9   \n",
       "46.2-833           Traffic lights; penalty                                                                              CITATION/SUMMONS      3.2   \n",
       "                                                                                                                        WARNING ISSUED        0.6   \n",
       "46.2-646           Expiration and renewal of registration                                                               CITATION/SUMMONS      7.3   \n",
       "                                                                                                                        WARNING ISSUED        2.0   \n",
       "46.2-1158          Frequency of inspection; scope of inspection                                                         CITATION/SUMMONS      8.7   \n",
       "                                                                                                                        WARNING ISSUED        0.4   \n",
       "46.2-874           Maximum speed limit in business and residence districts                                              CITATION/SUMMONS      6.5   \n",
       "                                                                                                                        WARNING ISSUED        2.6   \n",
       "46.2-875           Maximum speed limit on certain other highways in cities and towns                                    CITATION/SUMMONS      3.0   \n",
       "                                                                                                                        WARNING ISSUED        1.0   \n",
       "46.2-821           Vehicles before entering certain highways shall stop or yield right-of-way                           CITATION/SUMMONS      2.2   \n",
       "                                                                                                                        WARNING ISSUED        1.8   \n",
       "46.2-818           Stopping vehicle of another; blocking access to premises; damaging or threatening commercial vehi... CITATION/SUMMONS      7.1   \n",
       "\n",
       "race_eth                                                                                                                                  BLACK TOTAL  \\\n",
       "specific_violation section_title                                                                                        action_taken                    \n",
       "46.2-830           Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcement... CITATION/SUMMONS           27   \n",
       "                                                                                                                        WARNING ISSUED             26   \n",
       "46.2-833           Traffic lights; penalty                                                                              CITATION/SUMMONS           11   \n",
       "                                                                                                                        WARNING ISSUED              5   \n",
       "46.2-646           Expiration and renewal of registration                                                               CITATION/SUMMONS           14   \n",
       "                                                                                                                        WARNING ISSUED              2   \n",
       "46.2-1158          Frequency of inspection; scope of inspection                                                         CITATION/SUMMONS           14   \n",
       "                                                                                                                        WARNING ISSUED              0   \n",
       "46.2-874           Maximum speed limit in business and residence districts                                              CITATION/SUMMONS           10   \n",
       "                                                                                                                        WARNING ISSUED              2   \n",
       "46.2-875           Maximum speed limit on certain other highways in cities and towns                                    CITATION/SUMMONS            7   \n",
       "                                                                                                                        WARNING ISSUED              4   \n",
       "46.2-821           Vehicles before entering certain highways shall stop or yield right-of-way                           CITATION/SUMMONS            2   \n",
       "                                                                                                                        WARNING ISSUED              7   \n",
       "46.2-818           Stopping vehicle of another; blocking access to premises; damaging or threatening commercial vehi... CITATION/SUMMONS            8   \n",
       "\n",
       "race_eth                                                                                                                                  WHITE TOTAL  \\\n",
       "specific_violation section_title                                                                                        action_taken                    \n",
       "46.2-830           Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcement... CITATION/SUMMONS           72   \n",
       "                                                                                                                        WARNING ISSUED             39   \n",
       "46.2-833           Traffic lights; penalty                                                                              CITATION/SUMMONS           16   \n",
       "                                                                                                                        WARNING ISSUED              2   \n",
       "46.2-646           Expiration and renewal of registration                                                               CITATION/SUMMONS           36   \n",
       "                                                                                                                        WARNING ISSUED             10   \n",
       "46.2-1158          Frequency of inspection; scope of inspection                                                         CITATION/SUMMONS           43   \n",
       "                                                                                                                        WARNING ISSUED              2   \n",
       "46.2-874           Maximum speed limit in business and residence districts                                              CITATION/SUMMONS           32   \n",
       "                                                                                                                        WARNING ISSUED             13   \n",
       "46.2-875           Maximum speed limit on certain other highways in cities and towns                                    CITATION/SUMMONS           15   \n",
       "                                                                                                                        WARNING ISSUED              5   \n",
       "46.2-821           Vehicles before entering certain highways shall stop or yield right-of-way                           CITATION/SUMMONS           11   \n",
       "                                                                                                                        WARNING ISSUED              9   \n",
       "46.2-818           Stopping vehicle of another; blocking access to premises; damaging or threatening commercial vehi... CITATION/SUMMONS           35   \n",
       "\n",
       "race_eth                                                                                                                                  TOTAL RATIO  \n",
       "specific_violation section_title                                                                                        action_taken                   \n",
       "46.2-830           Uniform traffic control devices on highways; drivers to obey traffic control devices; enforcement... CITATION/SUMMONS          0.4  \n",
       "                                                                                                                        WARNING ISSUED            0.7  \n",
       "46.2-833           Traffic lights; penalty                                                                              CITATION/SUMMONS          0.7  \n",
       "                                                                                                                        WARNING ISSUED            2.5  \n",
       "46.2-646           Expiration and renewal of registration                                                               CITATION/SUMMONS          0.4  \n",
       "                                                                                                                        WARNING ISSUED            0.2  \n",
       "46.2-1158          Frequency of inspection; scope of inspection                                                         CITATION/SUMMONS          0.3  \n",
       "                                                                                                                        WARNING ISSUED            0.0  \n",
       "46.2-874           Maximum speed limit in business and residence districts                                              CITATION/SUMMONS          0.3  \n",
       "                                                                                                                        WARNING ISSUED            0.2  \n",
       "46.2-875           Maximum speed limit on certain other highways in cities and towns                                    CITATION/SUMMONS          0.5  \n",
       "                                                                                                                        WARNING ISSUED            0.8  \n",
       "46.2-821           Vehicles before entering certain highways shall stop or yield right-of-way                           CITATION/SUMMONS          0.2  \n",
       "                                                                                                                        WARNING ISSUED            0.8  \n",
       "46.2-818           Stopping vehicle of another; blocking access to premises; damaging or threatening commercial vehi... CITATION/SUMMONS          0.2  "
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = tv_u45[(tv_u45[\"race_eth\"].isin([\"BLACK OR AFRICAN AMERICAN\",\"WHITE\"]))]\n",
    "\n",
    "sort_vals = tmp[tmp[\"race_eth\"].isin([\"BLACK OR AFRICAN AMERICAN\"])][[\"specific_violation\",\"section_title\"]].value_counts()\n",
    "\n",
    "tmp = tmp[[\"specific_violation\",\"section_title\",\"action_taken\",\"race_eth\"]].value_counts(normalize=False).unstack(\"race_eth\").fillna(0)\n",
    "tmp=tmp.rename(columns={\"BLACK OR AFRICAN AMERICAN\":\"BLACK %\",\"WHITE\":\"WHITE %\"})\n",
    "total = tmp.sum()\n",
    "tmp = tmp/total*100\n",
    "\n",
    "tmp[\"BLACK TOTAL\"] = (tmp[\"BLACK %\"]/100 * total[\"BLACK %\"]).fillna(0).astype(int)\n",
    "tmp[\"WHITE TOTAL\"] = (tmp[\"WHITE %\"]/100 * total[\"WHITE %\"]).fillna(0).astype(int)\n",
    "tmp[\"TOTAL RATIO\"] = tmp[\"BLACK TOTAL\"] / tmp[\"WHITE TOTAL\"]\n",
    "\n",
    "# Sort by most common violations by Blacks regardless of result\n",
    "new_index = []\n",
    "for x in sort_vals.index:\n",
    "    for result in [\"CITATION/SUMMONS\",\"WARNING ISSUED\"]:\n",
    "        y = (x[0], x[1], result)\n",
    "        if y in tmp.index:\n",
    "            new_index.append(y)\n",
    "\n",
    "print(f\"This shows the following for {year}:\")\n",
    "print(\"1. Percentage of Black/White stops for traffic violation ending in each violation and result. (46.2-830 for citations at 13.2% is % of all stops of Blacks not just citations)\")\n",
    "print(\"2. Total counts for each violation and result of stop for each group\")\n",
    "print(\"3. Ratio of Black Count to White Count\\n\")\n",
    "\n",
    "print(f\"Note that the number of cases gets pretty small quickly so those numbers may not have a lot of meaning. Thought about using data for all years but then we would have to deal with COVID effects on behavior\\n\")\n",
    "\n",
    "print(\"Most notable is that when pulled over for 46.2-830 which I assume is mostly speeding, Blacks are given warnings about 1/2 the time while Whites are only given warnings about 1/3 of the time\")\n",
    "\n",
    "tmp.loc[new_index, :].head(15).round(1)"
   ]
  }
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