{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Simple Data Analysis Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The example shows how to do basic data analysis of the datasets. The example uses 2021 Fairfax County Virginia traffic stop data to analyze race/ethnicity values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import openpolicedata as opd\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race_eth</th>\n",
       "      <th>AMERICAN INDIAN OR ALASKA NATIVE</th>\n",
       "      <th>ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>UNKNOWN</th>\n",
       "      <th>WHITE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERSON SEARCHED</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>92</td>\n",
       "      <td>1619</td>\n",
       "      <td>4192</td>\n",
       "      <td>3405</td>\n",
       "      <td>12934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YES</th>\n",
       "      <td>3</td>\n",
       "      <td>121</td>\n",
       "      <td>934</td>\n",
       "      <td>40</td>\n",
       "      <td>1809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth         AMERICAN INDIAN OR ALASKA NATIVE  \\\n",
       "PERSON SEARCHED                                     \n",
       "NO                                             92   \n",
       "YES                                             3   \n",
       "\n",
       "race_eth         ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER  \\\n",
       "PERSON SEARCHED                                                       \n",
       "NO                                                            1619    \n",
       "YES                                                            121    \n",
       "\n",
       "race_eth         BLACK OR AFRICAN AMERICAN  UNKNOWN  WHITE  \n",
       "PERSON SEARCHED                                             \n",
       "NO                                    4192     3405  12934  \n",
       "YES                                    934       40   1809  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agency_comp = \"Fairfax County Police Department\"\n",
    "year = 2021\n",
    "src = opd.Source(source_name=\"Virginia\")\n",
    "t_ffx = src.load(table_type='STOPS', year=year, agency=agency_comp)\n",
    "\n",
    "# Make a copy of the table so that we can make changes without changing the original table.\n",
    "df_ffx = t_ffx.table.copy()\n",
    "\n",
    "# Race and ethnicity are saved in different columns in Virginia's data but analysis is typically done on a combined race/ethnicity column\n",
    "# containing Hispanic of all races, White Non-Hispanic, Black Non-Hispanic, Asian Non-Hispanic, etc. groups.\n",
    "# Create combined race/ethnicity category\n",
    "df_ffx[\"race_eth\"] = df_ffx[\"RACE\"]   # Default the value of the race/ethnicity to the race\n",
    "\n",
    "# For all rows where the ethnicity is HISPANIC, set \"race_eth\" column to HISPANIC\n",
    "df_ffx.loc[df_ffx[\"ETHNICITY\"] == \"HISPANIC\", \"race_eth\"] = \"HISPANIC\"\n",
    "# For all rows where the ethnicity is UNKNOWN, set \"race_eth\" column to UNKNOWN\n",
    "df_ffx.loc[df_ffx[\"ETHNICITY\"] == \"UNKNOWN\", \"race_eth\"] = \"UNKNOWN\"\n",
    "\n",
    "# Find the number of searches of persons by race and ethnicity\n",
    "# groupby groups the rows of the table based on [\"person_searched\",\"race_eth\"]\n",
    "# size() finds the number of rows in each group (i.e. the number of searches for each race/ethnicity group)\n",
    "# unstack just makes the resulting table more presentable\n",
    "searches = df_ffx.groupby([\"PERSON SEARCHED\",\"race_eth\"]).size().unstack(\"race_eth\")\n",
    "\n",
    "searches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's find the percent of stops that end in the person being searched for each race/ethnicity group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># of Stops</th>\n",
       "      <th># of Searches</th>\n",
       "      <th>% of Stops With Search</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>95</td>\n",
       "      <td>3</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER</th>\n",
       "      <td>1740</td>\n",
       "      <td>121</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <td>5126</td>\n",
       "      <td>934</td>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNKNOWN</th>\n",
       "      <td>3445</td>\n",
       "      <td>40</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WHITE</th>\n",
       "      <td>14743</td>\n",
       "      <td>1809</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    # of Stops  # of Searches  \\\n",
       "race_eth                                                                        \n",
       "AMERICAN INDIAN OR ALASKA NATIVE                            95              3   \n",
       "ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER        1740            121   \n",
       "BLACK OR AFRICAN AMERICAN                                 5126            934   \n",
       "UNKNOWN                                                   3445             40   \n",
       "WHITE                                                    14743           1809   \n",
       "\n",
       "                                                    % of Stops With Search  \n",
       "race_eth                                                                    \n",
       "AMERICAN INDIAN OR ALASKA NATIVE                                       3.2  \n",
       "ASIAN OR NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER                     7.0  \n",
       "BLACK OR AFRICAN AMERICAN                                             18.2  \n",
       "UNKNOWN                                                                1.2  \n",
       "WHITE                                                                 12.3  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The total number of searches for each group is the sum of each column\n",
    "number_of_stops = searches.sum()\n",
    "# The number of searches for each group is the number of Yes's for each group\n",
    "number_of_searches = searches.loc[\"YES\"]\n",
    "\n",
    "# Calculate the search rate (% of people search over total people stopped)\n",
    "percent_stops_with_search = np.round(number_of_searches/number_of_stops*100,1)\n",
    "\n",
    "# Create a DataFrame out of the 3 metrics calculated above\n",
    "searches_df = pd.DataFrame([number_of_stops, number_of_searches, percent_stops_with_search], \n",
    "    index=[\"# of Stops\", \"# of Searches\", \"% of Stops With Search\"])\n",
    "searches_df = searches_df.transpose()\n",
    "searches_df[\"# of Stops\"] = searches_df[\"# of Stops\"].astype(int)\n",
    "searches_df[\"# of Searches\"] = searches_df[\"# of Searches\"].astype(int)\n",
    "# searches.dropna(inplace=True)\n",
    "searches_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Percentage of Stops Where the Person is Searched')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = searches_df.plot.barh(y=\"% of Stops With Search\", grid=True, legend=False)\n",
    "ax.set_ylabel(\"\")\n",
    "ax.set_xlabel(\"Percentage\")\n",
    "ax.set_title(\"Percentage of Stops Where the Person is Searched\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('opd')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "a73158d29711b2da05ac73de25b71e5d8cae591f14917bba77a9573b5c85a0ce"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}