Oppenheim project 2 Racial representation in NYC population vs. frequency of arrest by race..ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1a99ff9c",
   "metadata": {},
   "source": [
    "<h1>Sara Oppenheim, PROJECT 2!</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15e6d6ba",
   "metadata": {},
   "source": [
    "<h2>My goal is to analyze NYPD arrest data to determine if citywide arrest rates reflect the racial composition of NYC as recorded in the 2021 US Census.</h2>  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "101d63fb",
   "metadata": {},
   "source": [
    "<h3>Data sources</h3>  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f18843ee",
   "metadata": {},
   "source": [
    "**Arrest data** obtained from NYC Open Data (https://data.cityofnewyork.us/Public-Safety/NYPD-Arrest-Data-Year-to-Date-/uip8-fykc)   \n",
    "\n",
    "**Census data** for all of NYC obtained from the US Census Bureau (https://www.census.gov/quickfacts/fact/table/newyorkcitynewyork/RHI225221)   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19a7c084",
   "metadata": {},
   "source": [
    "<h4>Import the packages I may need</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "305b07ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sc\n",
    "import sympy as sy\n",
    "import matplotlib as matplot\n",
    "import matplotlib.pyplot as plt\n",
    "import statistics as stat\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4411e239",
   "metadata": {},
   "source": [
    "<h4>Convert the arrest and census data to dataframes</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "da1cba63",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a dataframe with the year-to-date arrest data. \n",
    "missing_values = [\"(null)\"]\n",
    "arrests=pd.read_csv(\"nyc_arrest_ytd_uip8-fykc.csv\",na_values=missing_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6f116d89",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a dataframe with the 2021 census data for NYC. \n",
    "census=pd.read_csv(\"Census_NYC_QuickFacts.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93fd32cd",
   "metadata": {},
   "source": [
    "<h4>Prepare the census data for use in my analysis</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9680f829",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fact</th>\n",
       "      <th>Fact Note</th>\n",
       "      <th>New York city, New York</th>\n",
       "      <th>Value Note for New York city, New York</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Population Estimates, July 1 2021, (V2021)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8,467,513</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Population estimates base, April 1, 2020, (V2021)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8,804,190</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Population, percent change - April 1, 2020 (es...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Population, Census, April 1, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8,804,190</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Population, Census, April 1, 2010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8,175,133</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Persons under 5 years, percent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Persons under 18 years, percent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.70</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Persons 65 years and over, percent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.90</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Female persons, percent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>White alone, percent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Black or African American alone, percent</td>\n",
       "      <td>(a)</td>\n",
       "      <td>23.80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>American Indian and Alaska Native alone, percent</td>\n",
       "      <td>(a)</td>\n",
       "      <td>0.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Asian alone, percent</td>\n",
       "      <td>(a)</td>\n",
       "      <td>14.30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Native Hawaiian and Other Pacific Islander alo...</td>\n",
       "      <td>(a)</td>\n",
       "      <td>0.10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Two or More Races, percent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.60</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Hispanic or Latino, percent</td>\n",
       "      <td>(b)</td>\n",
       "      <td>28.90</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>White alone, not Hispanic or Latino, percent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.90</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Veterans, 2016-2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>142,897</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Foreign born persons, percent, 2016-2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Housing units, July 1, 2021, (V2021)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>X</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Fact Fact Note  \\\n",
       "0          Population Estimates, July 1 2021, (V2021)       NaN   \n",
       "1   Population estimates base, April 1, 2020, (V2021)       NaN   \n",
       "2   Population, percent change - April 1, 2020 (es...       NaN   \n",
       "3                   Population, Census, April 1, 2020       NaN   \n",
       "4                   Population, Census, April 1, 2010       NaN   \n",
       "5                      Persons under 5 years, percent       NaN   \n",
       "6                     Persons under 18 years, percent       NaN   \n",
       "7                  Persons 65 years and over, percent       NaN   \n",
       "8                             Female persons, percent       NaN   \n",
       "9                                White alone, percent       NaN   \n",
       "10           Black or African American alone, percent       (a)   \n",
       "11   American Indian and Alaska Native alone, percent       (a)   \n",
       "12                               Asian alone, percent       (a)   \n",
       "13  Native Hawaiian and Other Pacific Islander alo...       (a)   \n",
       "14                         Two or More Races, percent       NaN   \n",
       "15                        Hispanic or Latino, percent       (b)   \n",
       "16       White alone, not Hispanic or Latino, percent       NaN   \n",
       "17                                Veterans, 2016-2020       NaN   \n",
       "18           Foreign born persons, percent, 2016-2020       NaN   \n",
       "19               Housing units, July 1, 2021, (V2021)       NaN   \n",
       "\n",
       "   New York city, New York  Value Note for New York city, New York  \n",
       "0                8,467,513                                     NaN  \n",
       "1                8,804,190                                     NaN  \n",
       "2                    -3.80                                     NaN  \n",
       "3                8,804,190                                     NaN  \n",
       "4                8,175,133                                     NaN  \n",
       "5                     6.40                                     NaN  \n",
       "6                    20.70                                     NaN  \n",
       "7                    14.90                                     NaN  \n",
       "8                    52.30                                     NaN  \n",
       "9                    41.30                                     NaN  \n",
       "10                   23.80                                     NaN  \n",
       "11                    0.40                                     NaN  \n",
       "12                   14.30                                     NaN  \n",
       "13                    0.10                                     NaN  \n",
       "14                    5.60                                     NaN  \n",
       "15                   28.90                                     NaN  \n",
       "16                   31.90                                     NaN  \n",
       "17                 142,897                                     NaN  \n",
       "18                   36.40                                     NaN  \n",
       "19                       X                                     NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Examine the census data\n",
    "census.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "66866bfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Fact', 'Fact Note', 'New York city, New York',\n",
       "       'Value Note for New York city, New York'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92e92050",
   "metadata": {},
   "source": [
    "**Only columns 1 and 3 contain useful information, I will eliminate the others.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e45ceaef",
   "metadata": {},
   "outputs": [],
   "source": [
    "census = census[['Fact', 'New York city, New York']]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfa60520",
   "metadata": {},
   "source": [
    "**The column headers are uninformative so I will rename them.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5f2f6dc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "census.rename(columns = {'Fact':'Race','New York city, New York':'Percent of NYC population'}\n",
    "              ,inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d3b9995",
   "metadata": {},
   "source": [
    "**Since I only have one data column, I want to eliminate any row with missing values.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6bee2609",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Race</th>\n",
       "      <th>Percent of NYC population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Population estimates base, April 1, 2020, (V2021)</td>\n",
       "      <td>8,804,190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Race Percent of NYC population\n",
       "0         Population Estimates, July 1 2021, (V2021)                 8,467,513\n",
       "1  Population estimates base, April 1, 2020, (V2021)                 8,804,190"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census = census.dropna()\n",
    "census.head(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f9c19832",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
       "            52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06d1cd4a",
   "metadata": {},
   "source": [
    "**I'm only interested in the racial demographics, so I filter out the unwanted rows.\n",
    "For simplicity I am only going to consider the \"single race\" census categories.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "95720d83",
   "metadata": {},
   "outputs": [],
   "source": [
    "census = census.iloc[['10','11','12','13','15','16']]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "639b968f",
   "metadata": {},
   "source": [
    "**For future operations, I want the values as numbers, not objects.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1f44d1e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Race                          object\n",
       "Percent of NYC population    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census.dtypes\n",
    "census = census.astype({\"Percent of NYC population\": float})\n",
    "census.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a0d304b",
   "metadata": {},
   "source": [
    "**I eventually want to compare census data to arrest data so I am simplifying race identifiers. I will do the same thing with the arrest data.**\n",
    "\n",
    "**This is *not okay*, but for the purposes of me being able to complete the analyses of interest I am going to merge the census data for categories \"Asian alone\" and \"Native Hawaiian and Other Pacific Islander alone\" because the arrest data only have this category: \"ASIAN / PACIFIC ISLANDER\" and I have no way of disentangling the two races. So I acknowledge that my conclusions regarding arrests vs. population for Asians and Pacific Islanders may be inaccurate.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "abada8dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Race</th>\n",
       "      <th>Percent of NYC population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Black</td>\n",
       "      <td>23.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Native American</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Asian OR Pacific Islander</td>\n",
       "      <td>14.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Asian OR Pacific Islander</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>28.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Race  Percent of NYC population\n",
       "10                      Black                       23.8\n",
       "11            Native American                        0.4\n",
       "12  Asian OR Pacific Islander                       14.3\n",
       "13  Asian OR Pacific Islander                        0.1\n",
       "15                   Hispanic                       28.9"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census['Race'].replace(['White alone, not Hispanic or Latino, percent','White alone, percent','Black or African American alone, percent','American Indian and Alaska Native alone, percent','Asian alone, percent','Native Hawaiian and Other Pacific Islander alone, percent','Hispanic or Latino, percent'],['White','White','Black','Native American','Asian OR Pacific Islander','Asian OR Pacific Islander','Hispanic'],inplace=True)\n",
    "census.columns\n",
    "census.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e755c937",
   "metadata": {},
   "source": [
    "**OK, now I'm happy with the census data. On to the arrest data!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a4d29c2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>arrest_key</th>\n",
       "      <th>arrest_date</th>\n",
       "      <th>pd_cd</th>\n",
       "      <th>pd_desc</th>\n",
       "      <th>ky_cd</th>\n",
       "      <th>ofns_desc</th>\n",
       "      <th>law_code</th>\n",
       "      <th>law_cat_cd</th>\n",
       "      <th>arrest_boro</th>\n",
       "      <th>arrest_precinct</th>\n",
       "      <th>jurisdiction_code</th>\n",
       "      <th>age_group</th>\n",
       "      <th>perp_sex</th>\n",
       "      <th>perp_race</th>\n",
       "      <th>x_coord_cd</th>\n",
       "      <th>y_coord_cd</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>geocoded_column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>238552639</td>\n",
       "      <td>2022-01-03T00:00:00.000</td>\n",
       "      <td>205.0</td>\n",
       "      <td>TRESPASS 2, CRIMINAL</td>\n",
       "      <td>352.0</td>\n",
       "      <td>CRIMINAL TRESPASS</td>\n",
       "      <td>PL 1401501</td>\n",
       "      <td>M</td>\n",
       "      <td>K</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>18-24</td>\n",
       "      <td>M</td>\n",
       "      <td>BLACK</td>\n",
       "      <td>1015028</td>\n",
       "      <td>185895</td>\n",
       "      <td>40.676863</td>\n",
       "      <td>-73.889037</td>\n",
       "      <td>POINT (-73.889037 40.676863)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>238682242</td>\n",
       "      <td>2022-01-05T00:00:00.000</td>\n",
       "      <td>397.0</td>\n",
       "      <td>ROBBERY,OPEN AREA UNCLASSIFIED</td>\n",
       "      <td>105.0</td>\n",
       "      <td>ROBBERY</td>\n",
       "      <td>PL 160050H</td>\n",
       "      <td>F</td>\n",
       "      <td>K</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>25-44</td>\n",
       "      <td>M</td>\n",
       "      <td>BLACK</td>\n",
       "      <td>1017119</td>\n",
       "      <td>183909</td>\n",
       "      <td>40.671404</td>\n",
       "      <td>-73.881509</td>\n",
       "      <td>POINT (-73.881509 40.671404)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>238723993</td>\n",
       "      <td>2022-01-06T00:00:00.000</td>\n",
       "      <td>153.0</td>\n",
       "      <td>RAPE 3</td>\n",
       "      <td>104.0</td>\n",
       "      <td>RAPE</td>\n",
       "      <td>PL 1302503</td>\n",
       "      <td>F</td>\n",
       "      <td>K</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>25-44</td>\n",
       "      <td>M</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>1003509</td>\n",
       "      <td>185018</td>\n",
       "      <td>40.674496</td>\n",
       "      <td>-73.930571</td>\n",
       "      <td>POINT (-73.9305713255961 40.6744956865259)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>238821533</td>\n",
       "      <td>2022-01-08T00:00:00.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CPL5700600</td>\n",
       "      <td>9</td>\n",
       "      <td>Q</td>\n",
       "      <td>113</td>\n",
       "      <td>3</td>\n",
       "      <td>45-64</td>\n",
       "      <td>M</td>\n",
       "      <td>WHITE HISPANIC</td>\n",
       "      <td>1046367</td>\n",
       "      <td>186986</td>\n",
       "      <td>40.679701</td>\n",
       "      <td>-73.776047</td>\n",
       "      <td>POINT (-73.77604735 40.67970059)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>239029586</td>\n",
       "      <td>2022-01-12T00:00:00.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CPL5700600</td>\n",
       "      <td>9</td>\n",
       "      <td>Q</td>\n",
       "      <td>113</td>\n",
       "      <td>3</td>\n",
       "      <td>25-44</td>\n",
       "      <td>M</td>\n",
       "      <td>WHITE HISPANIC</td>\n",
       "      <td>1046367</td>\n",
       "      <td>186986</td>\n",
       "      <td>40.679701</td>\n",
       "      <td>-73.776047</td>\n",
       "      <td>POINT (-73.77604735 40.67970059)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   arrest_key              arrest_date  pd_cd                         pd_desc  \\\n",
       "0   238552639  2022-01-03T00:00:00.000  205.0            TRESPASS 2, CRIMINAL   \n",
       "1   238682242  2022-01-05T00:00:00.000  397.0  ROBBERY,OPEN AREA UNCLASSIFIED   \n",
       "2   238723993  2022-01-06T00:00:00.000  153.0                          RAPE 3   \n",
       "3   238821533  2022-01-08T00:00:00.000    NaN                             NaN   \n",
       "4   239029586  2022-01-12T00:00:00.000    NaN                             NaN   \n",
       "\n",
       "   ky_cd          ofns_desc    law_code law_cat_cd arrest_boro  \\\n",
       "0  352.0  CRIMINAL TRESPASS  PL 1401501          M           K   \n",
       "1  105.0            ROBBERY  PL 160050H          F           K   \n",
       "2  104.0               RAPE  PL 1302503          F           K   \n",
       "3    NaN                NaN  CPL5700600          9           Q   \n",
       "4    NaN                NaN  CPL5700600          9           Q   \n",
       "\n",
       "   arrest_precinct  jurisdiction_code age_group perp_sex       perp_race  \\\n",
       "0               75                  0     18-24        M           BLACK   \n",
       "1               75                  0     25-44        M           BLACK   \n",
       "2               77                  0     25-44        M           WHITE   \n",
       "3              113                  3     45-64        M  WHITE HISPANIC   \n",
       "4              113                  3     25-44        M  WHITE HISPANIC   \n",
       "\n",
       "   x_coord_cd  y_coord_cd   latitude  longitude  \\\n",
       "0     1015028      185895  40.676863 -73.889037   \n",
       "1     1017119      183909  40.671404 -73.881509   \n",
       "2     1003509      185018  40.674496 -73.930571   \n",
       "3     1046367      186986  40.679701 -73.776047   \n",
       "4     1046367      186986  40.679701 -73.776047   \n",
       "\n",
       "                              geocoded_column  \n",
       "0                POINT (-73.889037 40.676863)  \n",
       "1                POINT (-73.881509 40.671404)  \n",
       "2  POINT (-73.9305713255961 40.6744956865259)  \n",
       "3            POINT (-73.77604735 40.67970059)  \n",
       "4            POINT (-73.77604735 40.67970059)  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Examine the arrest data\n",
    "arrests.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1bb044f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "arrest_key             int64\n",
       "arrest_date           object\n",
       "pd_cd                float64\n",
       "pd_desc               object\n",
       "ky_cd                float64\n",
       "ofns_desc             object\n",
       "law_code              object\n",
       "law_cat_cd            object\n",
       "arrest_boro           object\n",
       "arrest_precinct        int64\n",
       "jurisdiction_code      int64\n",
       "age_group             object\n",
       "perp_sex              object\n",
       "perp_race             object\n",
       "x_coord_cd             int64\n",
       "y_coord_cd             int64\n",
       "latitude             float64\n",
       "longitude            float64\n",
       "geocoded_column       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrests.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1bfb2af1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['arrest_key', 'arrest_date', 'pd_cd', 'pd_desc', 'ky_cd', 'ofns_desc',\n",
       "       'law_code', 'law_cat_cd', 'arrest_boro', 'arrest_precinct',\n",
       "       'jurisdiction_code', 'age_group', 'perp_sex', 'perp_race', 'x_coord_cd',\n",
       "       'y_coord_cd', 'latitude', 'longitude', 'geocoded_column'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrests.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f8dffd3",
   "metadata": {},
   "source": [
    "**How much missing data is there?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f4c4ed01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "arrest_key            0\n",
       "arrest_date           0\n",
       "pd_cd                15\n",
       "pd_desc              21\n",
       "ky_cd                21\n",
       "ofns_desc            21\n",
       "law_code              0\n",
       "law_cat_cd           10\n",
       "arrest_boro           0\n",
       "arrest_precinct       0\n",
       "jurisdiction_code     0\n",
       "age_group             0\n",
       "perp_sex              0\n",
       "perp_race             0\n",
       "x_coord_cd            0\n",
       "y_coord_cd            0\n",
       "latitude              0\n",
       "longitude             0\n",
       "geocoded_column       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrests.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d813150",
   "metadata": {},
   "source": [
    "<h4>What is in each column? Am I comfortable just dropping the columns that have missing data?</h4> \n",
    "Here I have marked in bold the columns that interest me:\n",
    "\n",
    "**ARREST_KEY**\tRandomly generated persistent ID for each arrest \n",
    "\n",
    "ARREST_DATE\tExact date of arrest for the reported event\n",
    "\n",
    "PD_CD\tThree digit internal classification code (more granular than Key Code)\n",
    "\n",
    "PD_DESC\tDescription of internal classification corresponding with PD code (more granular than Offense Description)\n",
    "\n",
    "KY_CD\tThree digit internal classification code (more general category than PD code)\n",
    "\n",
    "**OFNS_DESC**\tDescription of internal classification corresponding with KY code (more general category than PD description)\n",
    "\n",
    "LAW_CODE\tLaw code charges corresponding to the NYS Penal Law, VTL and other various local laws\n",
    "\n",
    "LAW_CAT_CD\tLevel of offense: felony, misdemeanor, violation \n",
    "\n",
    "**ARREST_BORO**\tBorough of arrest. B(Bronx), S(Staten Island), K(Brooklyn), M(Manhattan), Q(Queens)\n",
    "\n",
    "ARREST_PRECINCT\tPrecinct where the arrest occurred\n",
    "\n",
    "JURISDICTION_CODE\tJurisdiction responsible for arrest. Jurisdiction codes 0(Patrol), 1(Transit) and 2(Housing) represent NYPD whilst codes 3 and more represent non NYPD jurisdictions\n",
    "\n",
    "AGE_GROUP\tPerpetrator’s age within a category\n",
    "\n",
    "PERP_SEX\tPerpetrator’s sex description\n",
    "\n",
    "**PERP_RACE**\tPerpetrator’s race description\n",
    "\n",
    "X_COORD_CD\tMidblock X-coordinate for New York State Plane Coordinate System, Long Island Zone, NAD 83, units feet (FIPS 3104)\n",
    "\n",
    "Y_COORD_CD\tMidblock Y-coordinate for New York State Plane Coordinate System, Long Island Zone, NAD 83, units feet (FIPS 3104)\n",
    "\n",
    "Latitude\tLatitude coordinate for Global Coordinate System, WGS 1984, decimal degrees (EPSG 4326) \n",
    "\n",
    "Longitude\tLongitude coordinate for Global Coordinate System, WGS 1984, decimal degrees (EPSG 4326)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "540bdb98",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "**Subset the data to only include the columns of interest. Rename for clarity and elegance.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6d1ed799",
   "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>unique_key</th>\n",
       "      <th>offense</th>\n",
       "      <th>borough</th>\n",
       "      <th>Race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>238552639</td>\n",
       "      <td>CRIMINAL TRESPASS</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>BLACK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>238682242</td>\n",
       "      <td>ROBBERY</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>BLACK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>238723993</td>\n",
       "      <td>RAPE</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>WHITE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>238821533</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queens</td>\n",
       "      <td>WHITE HISPANIC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>239029586</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queens</td>\n",
       "      <td>WHITE HISPANIC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_key            offense   borough            Race\n",
       "0   238552639  CRIMINAL TRESPASS  Brooklyn           BLACK\n",
       "1   238682242            ROBBERY  Brooklyn           BLACK\n",
       "2   238723993               RAPE  Brooklyn           WHITE\n",
       "3   238821533                NaN    Queens  WHITE HISPANIC\n",
       "4   239029586                NaN    Queens  WHITE HISPANIC"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrests = arrests[[\"arrest_key\",\"ofns_desc\",\"arrest_boro\",\"perp_race\"]]\n",
    "arrests.rename(columns = {'arrest_key':'unique_key','ofns_desc':'offense',\n",
    "                          'arrest_boro':'borough','perp_race':'Race'},inplace=True)\n",
    "arrests['borough'].replace(['K','Q','B','S','M'],['Brooklyn','Queens','Bronx','Staten Island'\n",
    "                                                  ,'Manhattan'],inplace=True)\n",
    "arrests.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5825147",
   "metadata": {},
   "source": [
    "**I will use the built-in \"arrest key\" as my index because it contains data that is meaningful if I want to dig deeper into the specific arrest event.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ca74f11e",
   "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>offense</th>\n",
       "      <th>borough</th>\n",
       "      <th>Race</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>238552639</th>\n",
       "      <td>CRIMINAL TRESPASS</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>BLACK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238682242</th>\n",
       "      <td>ROBBERY</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>BLACK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238723993</th>\n",
       "      <td>RAPE</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>WHITE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238821533</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Queens</td>\n",
       "      <td>WHITE HISPANIC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239029586</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Queens</td>\n",
       "      <td>WHITE HISPANIC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      offense   borough            Race\n",
       "unique_key                                             \n",
       "238552639   CRIMINAL TRESPASS  Brooklyn           BLACK\n",
       "238682242             ROBBERY  Brooklyn           BLACK\n",
       "238723993                RAPE  Brooklyn           WHITE\n",
       "238821533                 NaN    Queens  WHITE HISPANIC\n",
       "239029586                 NaN    Queens  WHITE HISPANIC"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrests[\"unique_key\"].is_unique\n",
    "arrests = arrests.set_index(\"unique_key\")\n",
    "arrests.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "384c5670",
   "metadata": {},
   "source": [
    "**Now how much missing data is there?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9a458a86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "offense    21\n",
       "borough     0\n",
       "Race        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrests.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1320d82",
   "metadata": {},
   "source": [
    "**Since I may want to group arrests by offense, I'm dropping any rows with missing offense data.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "490764c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "offense    0\n",
       "borough    0\n",
       "Race       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrests=arrests.dropna()\n",
    "arrests.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eed0c033",
   "metadata": {},
   "source": [
    "**Also dropping rows where race is \"unknown.\"**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0046154b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BLACK                             501\n",
       "WHITE HISPANIC                    233\n",
       "WHITE                             114\n",
       "BLACK HISPANIC                     78\n",
       "ASIAN / PACIFIC ISLANDER           44\n",
       "AMERICAN INDIAN/ALASKAN NATIVE      5\n",
       "Name: Race, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrests=arrests[arrests[\"Race\"]!=\"UNKNOWN\"]  \n",
    "arrests[\"Race\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bcd3126",
   "metadata": {},
   "source": [
    "**I need to modify the arrest data so that the race categories match those in the census data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "663e4e1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Black                        501\n",
       "Hispanic                     311\n",
       "White                        114\n",
       "Asian OR Pacific Islander     44\n",
       "Native American                5\n",
       "Name: Race, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrests['Race'].replace(['BLACK','WHITE HISPANIC','WHITE','BLACK HISPANIC','ASIAN / PACIFIC ISLANDER','AMERICAN INDIAN/ALASKAN NATIVE'],['Black','Hispanic','White','Hispanic','Asian OR Pacific Islander','Native American'],inplace=True)\n",
    "arrests[\"Race\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "14ed9fe1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of                                    offense    borough      Race\n",
       "unique_key                                                     \n",
       "238552639                CRIMINAL TRESPASS   Brooklyn     Black\n",
       "238682242                          ROBBERY   Brooklyn     Black\n",
       "238723993                             RAPE   Brooklyn     White\n",
       "239104920     ASSAULT 3 & RELATED OFFENSES   Brooklyn     Black\n",
       "239623520     ASSAULT 3 & RELATED OFFENSES   Brooklyn     White\n",
       "...                                    ...        ...       ...\n",
       "239678172                   FELONY ASSAULT   Brooklyn     Black\n",
       "239220231     ASSAULT 3 & RELATED OFFENSES   Brooklyn     White\n",
       "241650053   CRIMINAL MISCHIEF & RELATED OF   Brooklyn  Hispanic\n",
       "240305373     ASSAULT 3 & RELATED OFFENSES   Brooklyn  Hispanic\n",
       "239457828                         BURGLARY  Manhattan  Hispanic\n",
       "\n",
       "[975 rows x 3 columns]>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrests.info"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfa32b1f",
   "metadata": {},
   "source": [
    "**Oops there are non-canonical missing values!**\n",
    "I used \"groupby\" to get a list of all the missing data values I need to deal with.\n",
    "For example:\n",
    "arrests.groupby([\"offense\"]).count()\n",
    "Only one non-standard value was found \"(null)\".\n",
    "I returned to the read_csv step and added the missing value info--see above.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55bd3442",
   "metadata": {},
   "source": [
    "**There are many offenses with very low counts. I think these could skew my results or just not be interesting so I am filtering to only include offenses with >10 arrests recorded.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3fd392dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "offense\n",
       "ASSAULT 3 & RELATED OFFENSES      177\n",
       "PETIT LARCENY                      88\n",
       "FELONY ASSAULT                     86\n",
       "MISCELLANEOUS PENAL LAW            65\n",
       "CRIMINAL MISCHIEF & RELATED OF     55\n",
       "ROBBERY                            53\n",
       "DANGEROUS DRUGS                    53\n",
       "GRAND LARCENY                      42\n",
       "DANGEROUS WEAPONS                  42\n",
       "BURGLARY                           39\n",
       "OFFENSES INVOLVING FRAUD           38\n",
       "VEHICLE AND TRAFFIC LAWS           36\n",
       "OFFENSES AGAINST PUBLIC ADMINI     29\n",
       "OFF. AGNST PUB ORD SENSBLTY &      19\n",
       "SEX CRIMES                         19\n",
       "INTOXICATED & IMPAIRED DRIVING     17\n",
       "FORGERY                            17\n",
       "RAPE                               16\n",
       "OTHER TRAFFIC INFRACTION           12\n",
       "POSSESSION OF STOLEN PROPERTY      11\n",
       "Name: borough, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_arrests=arrests.groupby(\"offense\").filter(lambda x: len(x) > 10)\n",
    "high_arrests.groupby([\"offense\"])[\"borough\"].count().sort_values(ascending=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf9f50c2",
   "metadata": {},
   "source": [
    "**Get the all-NYC arrest count: the total number of arrest records in my data set = 914.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ae542624",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "914"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_arrests[\"offense\"].count().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0203f0f",
   "metadata": {},
   "source": [
    "**The column headers are uninformative so I will rename them.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "82f4b288",
   "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>Race</th>\n",
       "      <th>Percent of NYC population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Black</td>\n",
       "      <td>23.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Native American</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Race  Percent of NYC population\n",
       "10            Black                       23.8\n",
       "11  Native American                        0.4"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census.rename(columns = {'Fact':'Race','New York city, New York':'Percent of NYC population'},inplace=True)\n",
    "#Since I only have one data column, I want to eliminate any row with missing values\n",
    "census = census.dropna()\n",
    "census.head(2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "352509e1",
   "metadata": {},
   "source": [
    "**Break down the all-NYC arrest counts by race and visualize.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "398d9207",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "high_arrests.sort_values(by='Race',inplace=True)\n",
    "sns.set_context(\"notebook\", font_scale=1.1, rc={\"lines.linewidth\": 2.5})\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.title(\"NYC arrests (2021 ytd) by race\")\n",
    "a=sns.countplot(x=\"Race\",data=high_arrests,palette=\"Accent_r\")\n",
    "plt.savefig(\"all-NYC arrest counts by race.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e9a4fc0",
   "metadata": {},
   "source": [
    "**Convert the arrest counts to percents.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ee4503ad",
   "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>percent of all NYC arrests</th>\n",
       "      <th>Race</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.157549</td>\n",
       "      <td>Asian OR Pacific Islander</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51.203501</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.275711</td>\n",
       "      <td>Hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.547046</td>\n",
       "      <td>Native American</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11.816193</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       percent of all NYC arrests                       Race\n",
       "label                                                       \n",
       "1                        4.157549  Asian OR Pacific Islander\n",
       "2                       51.203501                      Black\n",
       "3                       32.275711                   Hispanic\n",
       "4                        0.547046            Native American\n",
       "5                       11.816193                      White"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent = pd.DataFrame(((high_arrests.groupby(['Race']).count())/914)*100)\n",
    "percent = percent.rename(columns = {'offense':'percent of all NYC arrests'})\n",
    "percent = percent.astype({\"percent of all NYC arrests\": float})\n",
    "percent = pd.DataFrame(percent[\"percent of all NYC arrests\"])\n",
    "percent.describe\n",
    "percent['label']=[1,2,3,4,5]\n",
    "percent['Race']=[\"Asian OR Pacific Islander\",\"Black\",\"Hispanic\",\"Native American\",\"White\"]\n",
    "percent.set_index(\"label\", inplace = True)\n",
    "percent.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3853715c",
   "metadata": {},
   "source": [
    "**Visualize the NYC arrests by percent per race.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6daa956d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "percent.sort_values(by='Race',inplace=True)\n",
    "sns.set_context(\"notebook\", font_scale=1.1, rc={\"lines.linewidth\": 2.5})\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.title(\"Percent of NYC arrests (2021 ytd) by race\")\n",
    "b=sns.barplot(y=\"percent of all NYC arrests\",x=\"Race\",data=percent,palette=\"Accent_r\")\n",
    "plt.savefig(\"all-NYC arrest percents by race.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17cf4fed",
   "metadata": {},
   "source": [
    "**Visualize the census data: what percent of the NYC population does each race represent?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0549a375",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "census.sort_values(by='Race',inplace=True)\n",
    "sns.set_context(\"notebook\", font_scale=1.1, rc={\"lines.linewidth\": 2.5})\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.title(\"NYC population (2021 census) by race\")\n",
    "c=sns.barplot(y=\"Percent of NYC population\",x=\"Race\",data=census,palette=\"Accent_r\")\n",
    "plt.savefig(\"all-NYC census population by race.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53579d22",
   "metadata": {},
   "source": [
    "<h3>Now I can ask the question I was trying to get at: do the rates of arrest for each race reflect its abundance in NYC? In other words, is the arrest rate constant across races so that if race X makes up 50% of the population, they will experience ~50% of all arrests?</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b15e4c4",
   "metadata": {},
   "source": [
    "**Merge the census and arrest data so I can plot them together.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1c804c98",
   "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>percent of all NYC arrests</th>\n",
       "      <th>Race</th>\n",
       "      <th>Percent of NYC population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.157549</td>\n",
       "      <td>Asian OR Pacific Islander</td>\n",
       "      <td>14.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51.203501</td>\n",
       "      <td>Black</td>\n",
       "      <td>23.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.275711</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>28.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.547046</td>\n",
       "      <td>Native American</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11.816193</td>\n",
       "      <td>White</td>\n",
       "      <td>31.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   percent of all NYC arrests                       Race  \\\n",
       "0                    4.157549  Asian OR Pacific Islander   \n",
       "2                   51.203501                      Black   \n",
       "3                   32.275711                   Hispanic   \n",
       "4                    0.547046            Native American   \n",
       "5                   11.816193                      White   \n",
       "\n",
       "   Percent of NYC population  \n",
       "0                       14.3  \n",
       "2                       23.8  \n",
       "3                       28.9  \n",
       "4                        0.4  \n",
       "5                       31.9  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged=percent.merge(census, left_on='Race', right_on='Race')\n",
    "merged = merged.iloc[['0','2','3','4','5']]\n",
    "merged.columns\n",
    "merged.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad3b520c",
   "metadata": {},
   "source": [
    "**Visualize the combined data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9535ecd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged.sort_values(by='Race',inplace=True)\n",
    "sns.set_context(\"notebook\", font_scale=1.1, rc={\"lines.linewidth\": 2.5})\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.title(\"NYC population (2021 census) by race\")\n",
    "c=sns.barplot(y=\"Percent of NYC population\",x=\"percent of all NYC arrests\",\n",
    "              data=merged,hue=\"Race\",palette=\"Accent_r\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85887259",
   "metadata": {},
   "source": [
    "**That was not a useful visualization.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34e3d19a",
   "metadata": {},
   "source": [
    "**I am interested in the question \"can arrest rates be explained by population level?\" in other words does knowing population make it possible to predict arrest rates. This is a regression question so I will try a regression plot.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 761,
   "id": "0c00cd22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='percent of all NYC arrests', ylabel='Percent of NYC population'>"
      ]
     },
     "execution_count": 761,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(y=\"Percent of NYC population\", \n",
    "            x=\"percent of all NYC arrests\", data=merged, ci=10, fit_reg=\"True\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74b0e8a1",
   "metadata": {},
   "source": [
    "**Although that worked, I couldn't find a way to color the points by race. So now I will try \"lmplot\" which seems to have more customization options.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "28d21b59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fef8b8969e8>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540.685x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(y=\"Percent of NYC population\", x=\"percent of all NYC arrests\", \n",
    "           palette=\"bright\",data=merged, hue=\"Race\",\n",
    "          fit_reg=True, n_boot=10000, robust=True)    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2011141",
   "metadata": {},
   "source": [
    "<h2>Conclusions</h2>\n",
    "\n",
    "I compared population data for NYC (as reported by the 2021 US Census) to NYPD arrest data for 2022 (arrest data covered January 1, 2022 through June 27, 2022.\n",
    "\n",
    "Only the Hispanic race (which in the NYPD data was split into \"Black Hispanic\" and \"White Hispanic\", and in the census data was categorized as \"Hispanic or Latino\") had arrests approximately in keeping with their population level: Hispanics make up 28.9% of the NYC population and experienced 32.3% of all arrests. \n",
    "\n",
    "Asians were \"under-arrested,\" making up 14.3% of the population but experiencing 4.2% of all arrests. Whites were similarly under-represented in arrests: they make up 41.3% of the NYC population but experienced only 11.8% of all arrests.\n",
    "\n",
    "So who accounts for those missing arrests that Asians and Whites did not experience? Blacks make up 23.8% of NYC's population, but experienced 51% of all arrests.\n",
    "\n",
    "Without exploring the data in more detail, it's hard to say exactly what's happening but it doesn't look very fair. I hope to examine these data further to see if 1) these patterns are statistically meaningful, 2) are these patterns the same across offense types? Across boroughs? And 3) whether arrest locations are in any way informative (for example are most arrests in low-income neighborhoods? If yes, is the population of Black people higher there?). \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65e7a2d7",
   "metadata": {},
   "source": [
    "<h3>The post-game analysis</h3>\n",
    "**What I was trying to do**\n",
    "I wanted to have columns for \"arrested race is X% of NYC\" \"arrested race is X% of all NYC arrests\" \"arrested race is X% of this borough's arrests\" \"arrested race per offense is X% of all NYC arrests for offense\" etc.--basically a much more fine-grained exploration of the relationship between race, population, and arrest rates. \n",
    "\n",
    "**My preliminary thoughts on accomplishing this**\n",
    "#\"arrested race is X% of NYC\" is the census data\n",
    "#\"arrested race is X% of all NYC arrests\" is (sum of NYC arrests for race X)/(sum of all NYC arrests)\n",
    "#\"arrested race is X% of this borough's arrests\" is (sum of borough arrests for race X)/(sum of all borough arrests)\n",
    "\n",
    "I need to calculate:\n",
    "Total arrests in NYC, and sum for each race\n",
    "Total arrests in each borough, and sum for each race\n",
    "How these relate to citywide census data for race"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e42bda58",
   "metadata": {},
   "source": [
    "I created this exploratory visualization but that's as far as I got with the by borough, by offense breakdown."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 729,
   "id": "c19eff36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#all-NYC arrest counts by race and offense\n",
    "sns.set_context(\"notebook\", font_scale=1.3, rc={\"lines.linewidth\": 2.5})\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize=(20,20))\n",
    "d=sns.countplot(y=\"offense\",hue=\"Race\",data=high_arrests,palette=\"Accent_r\")"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}