{
 "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": [
    "try:\n",
    "    import openpolicedata as opd\n",
    "except:\n",
    "    import sys\n",
    "    sys.path.append('../openpolicedata')\n",
    "    import openpolicedata as opd\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race_eth</th>\n",
       "      <th>AMERICAN INDIAN</th>\n",
       "      <th>ASIAN/PACIFIC ISLANDER</th>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <th>HISPANIC</th>\n",
       "      <th>UNKNOWN</th>\n",
       "      <th>WHITE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>person_searched</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>88</td>\n",
       "      <td>1557</td>\n",
       "      <td>3574</td>\n",
       "      <td>4001</td>\n",
       "      <td>3291</td>\n",
       "      <td>8157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YES</th>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>338</td>\n",
       "      <td>513</td>\n",
       "      <td>12</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_eth         AMERICAN INDIAN  ASIAN/PACIFIC ISLANDER  \\\n",
       "person_searched                                            \n",
       "NO                            88                    1557   \n",
       "YES                            1                      57   \n",
       "\n",
       "race_eth         BLACK OR AFRICAN AMERICAN  HISPANIC  UNKNOWN  WHITE  \n",
       "person_searched                                                       \n",
       "NO                                    3574      4001     3291   8157  \n",
       "YES                                    338       513       12    346  "
      ]
     },
     "execution_count": 2,
     "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_from_url(year=year, table_type='STOPS', 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": 3,
   "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</th>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASIAN/PACIFIC ISLANDER</th>\n",
       "      <td>1614</td>\n",
       "      <td>57</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLACK OR AFRICAN AMERICAN</th>\n",
       "      <td>3912</td>\n",
       "      <td>338</td>\n",
       "      <td>8.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HISPANIC</th>\n",
       "      <td>4514</td>\n",
       "      <td>513</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNKNOWN</th>\n",
       "      <td>3303</td>\n",
       "      <td>12</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WHITE</th>\n",
       "      <td>8503</td>\n",
       "      <td>346</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           # of Stops  # of Searches  % of Stops With Search\n",
       "race_eth                                                                    \n",
       "AMERICAN INDIAN                    89              1                     1.1\n",
       "ASIAN/PACIFIC ISLANDER           1614             57                     3.5\n",
       "BLACK OR AFRICAN AMERICAN        3912            338                     8.6\n",
       "HISPANIC                         4514            513                    11.4\n",
       "UNKNOWN                          3303             12                     0.4\n",
       "WHITE                            8503            346                     4.1"
      ]
     },
     "execution_count": 3,
     "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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Percentage of Stops Where the Person is Searched')"
      ]
     },
     "execution_count": 4,
     "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": "3.8.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "a73158d29711b2da05ac73de25b71e5d8cae591f14917bba77a9573b5c85a0ce"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}