FinalProject_group022.ipynb

{
 "cells": [
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   "source": [
    "# COGS 108 - Racial & Identity Profiling in Police Traffic Stops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7ARd3AnzLPdN"
   },
   "source": [
    "Final Video Link:\n",
    "[Youtube](https://youtu.be/gfPdYHK-BWw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c7hMkVz9Lw5i"
   },
   "source": [
    "# Permissions\n",
    "\n",
    "Specify whether you want your group project to be made publicly available. Place an X in the square brackets where appropriate.\n",
    "- [X] Permission for project to be made publicly available"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7NJs3-5DMJfa"
   },
   "source": [
    "# Overview\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L2eweBOufunI"
   },
   "source": [
    "Our project aimed to determine if there were any racial disparities in police vehicular stops, specifically in the San Diego County. Using police vehicle stop data provided by the San Diego Police Department and demographic data provided by State of California demographic reports, we analyzed racial trends from 2014 - 2018 and considered whether gender, age, and time of day would influence these trends. We conclude that there is a difference between racial groups stopped, such that Black drivers and Native American drivers are stopped at a higher proportion compared to White, Hispanic, and Asian drivers, regardless of gender or age. However, time of day appears to influence the proportion of Native American drivers stopped where more are stopped near nighttime."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nn4I5yZeaS6s"
   },
   "source": [
    "# Names\n",
    "\n",
    "- Man Jiang\n",
    "- Anya Bodhisartha\n",
    "- Jiliana Tiu\n",
    "- Jingyue Xu\n",
    "- Divya Shetty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SW-JAlCDaS6s"
   },
   "source": [
    "<a id='research_question'></a>\n",
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "apNrmi0-aS6t"
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   "source": [
    "#### **Racial and Identity Profiling in Traffic Stops:**\n",
    "\n",
    "- Are ethnic minorities more likely to be stopped by the police? \n",
    "- How are traffic stop decisions biased by individual physical characteristics and are there any significant racial disparities to be noticed? \n",
    "- Are there any trends in the frequency of people being stopped by the police each year?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ce_BLW1jMc2G"
   },
   "source": [
    "# Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T4amX7fHMhfW"
   },
   "source": [
    "Reports of police brutality often headline national news as law enforcement officers are exposed to be wrongfully hurting and killing people of color. Although studies have shown that systemic racism exists in the American law system [1], much of the public remains divided on this issue. Are these breaking news stories representative of systemic racism, or exceptional events overplayed by media? This raises the question if racial bias exists in the less \"high-profile\" jobs within law enforcement that are not often highlighted by news media. In this report, we focus on vehicle stops - properly performed when the police officer has reasonable suspicion that the driver has committed a crime [4].\n",
    "\n",
    "Racial inequality is deeply rooted in US history and culture. Although the nation promotes equality, racial bias is still rampant and is known to influence some decision-making within law enforcement. Our project aims to focus on vehicle stops because it is a familiar occurence for regular citizens. We believe the familiarity of vehicle stops on our daily lives contributes to a more holistic perspective of how systemic racism may affect regular citizens. A vehicle can be pulled over for several reasons, but it starts with the police officer's suspicion of the driver that initiates the vehicle stop. We wish to explore if vehicle stops are influenced by racial biases or by other factors.\n",
    "\n",
    "A 2020 nationwide analysis of racial disparities in police stops has found preliminary evidence of racial bias against Black drivers and Hispanic drivers [1]. It was found that in general, the bar for police stops – and subsequently, the searching of drivers – was lower for Black and Hispanic groups than that of white groups. Their results further demonstrate the persistence of racial bias despite legislation change, such as the legalization of recreational marijuana, and of demographics within minority communities. Our project aims to scale down this investigation to San Diego county and advance analyses to more specific factors contributing to police stop bias.\n",
    "\n",
    "As part of California AB 953, the Racial and Identity Profiling Act (RIPA) of 2015 aims to prevent racial and identity profiling through law enforcement [2]. This act requires state and local law enforcement agencies to collect and report stop details of individuals to the California Department of Justice to improve transparency and racial justice in the system. With the government-provided database [3] from 2014 to 2021 available to the public, we plan to explore how an individual’s physical characteristics, such as race and gender, affects the chance of a traffic stop, and how the decisions after stops could vary among them. Through our results, we hope to measure the prevalence of racial bias within San Diego’s traffic law enforcement system.\n",
    "\n",
    "Discussion of RIPA 2015 is relevant to our current analysis as we are exploring if police biases violate this Act's promise of racial justice. Even though RIPA has ensured the safety and justice among citizens of California, racial bias has been embedded in our system for a long time. To determine RIPA's success in improving the transparency and racial justice in California, we will study population and vehicle stop data to conclude if certain biases affect the police's decision making.\n",
    "\n",
    "-----\n",
    "\n",
    "References:\n",
    "1. Pierson, E., Simoiu, C., Overgoor, J. et al. A large-scale analysis of racial disparities in police stops across the United States. Nat Hum Behav 4, 736–745 (2020). https://doi.org/10.1038/s41562-020-0858-1\n",
    "2. “AB-953 Law Enforcement: Racial Profiling.” Bill Text - AB-953 Law Enforcement: Racial Profiling., https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=201520160AB953.\n",
    "3. Police Vehicle Stops. (n.d.). https://data.sandiego.gov/datasets/police-vehicle-stops/\n",
    "4. “Traffic Stop.” Cornell Law School, Cornell Law School, https://www.law.cornell.edu/wex/traffic_stop. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UXjwXhOGMx-K"
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   "source": [
    "# Hypothesis"
   ]
  },
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   "source": [
    "Although California’s policies have prohibited racial and identity profiling through law enforcement, we hypothesize that inherent racial and identity profiling still exists within the law enforcement system due to the persistence of personal biases. Based on our hypothesis, we predict that minority drivers are more likely to be stopped by police officers than white drivers in San Diego.\n",
    "\n",
    "Our hypothesis is also well-supported by credible sources. According to *Census.gov*, racial distribution in San Diego County consists of major races: white (75.4%), Hispanic (34.1%), Asian (12.6%), and black (5.5%). But there is a huge gap between white and other races when it comes to vehicle stops: \"Black people were stopped by San Diego police at higher rates than white people in 106 (85%) of the 125 San Diego police beats\" [4],\n",
    "although black population in San Diego County is only 5.5%. \n",
    "\n",
    "-----\n",
    "\n",
    "References:\n",
    "\n",
    "Final san diego policing - police scorecard. (n.d.). Retrieved June 5, 2022, from https://www.policescorecard.org/san-diego/police-scorecard-san-diego-report.pdf \n",
    "\n",
    "U.S. Census Bureau. (n.d.). U.S. Census Bureau quickfacts: San Diego County, California. Retrieved June 5, 2022, from https://www.census.gov/quickfacts/fact/table/sandiegocountycalifornia/PST045221 "
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "07zTmdblaS6t"
   },
   "source": [
    "# Dataset(s)"
   ]
  },
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   },
   "source": [
    "\n",
    "**Dataset 1 - Police Vehicle Stops**\n",
    "- Link: https://data.sandiego.gov/datasets/police-vehicle-stops/\n",
    "- Number of observations: 465266 \n",
    "- Description: This dataset contains information about police vehicular stops by the San Diego Police Department from 2014 to June of 2018. The data includes information about each individual that was stopped such as: a unique identifier for the individual, the cause of stop, the location of the stop, driver demographics (age, sex, race), date and time of stop, and subsequent actions made (arrested/searched).\n",
    "\n",
    "\n",
    "**Dataset 2 - Population Demographics**\n",
    "- Link: https://dof.ca.gov/reports/demographic-reports/american-community-survey/\n",
    "- Number of observations: 54\n",
    "- Description: This dataset contains demographics estimates for 2014-2019. The data was compiled by editing the Excel files provided for the 1-year estimates under \"ACS Summary Files\" for the years 2014-2019. As such, the dataset will only include cities where the San Diego Police Department operates. The dataset includes the county/city, estimated population, estimated racial demographics, estimated family income and per capita income, and estimated poverty rate.\n",
    "\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "jf0TBAXiM6H1"
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   "source": [
    "### Combining the Data to Solve Our Question\n",
    "\n",
    "We plan to use the 'Police Vehicle Stops' dataset in order to determine if ethnic minorities are more likely to be stopped by the police. As such, we'd like to analyze the proportion of ethnic minorities stopped by the San Diego Police Department in order to more accurately compare the data for ethnic minorities stopped vs. white drivers stopped.\n",
    "\n",
    "To do so, we will use the race population estimates from 'Population Demographics' in order to calculate the proportions of various races present in cities that the San Diego Police Department operates in. By doing so, we can conclude whether minority drivers are pulled over a disproportionate amount or not."
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e4ee3bf7"
   },
   "source": [
    "## Datasets: Importing the Data Files"
   ]
  },
  {
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   "metadata": {
    "id": "6b8f6294",
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   "source": [
    "# Stops\n",
    "race_code = pd.read_csv('https://raw.githubusercontent.com/xrsAlveus/COGS108_Repo/main/Project/CheckPoint1/Data/vehicle_stops_race_codes.csv')\n",
    "df_dict = pd.read_csv('https://raw.githubusercontent.com/xrsAlveus/COGS108_Repo/main/Project/CheckPoint1/Data/vehicle_stops_dictionary.csv')\n",
    "stop_2014 = pd.read_csv('https://raw.githubusercontent.com/xrsAlveus/COGS108_Repo/main/Project/CheckPoint1/Data/vehicle_stops_2014_datasd_v1.csv')\n",
    "stop_2015 = pd.read_csv('https://raw.githubusercontent.com/xrsAlveus/COGS108_Repo/main/Project/CheckPoint1/Data/vehicle_stops_2015_datasd_v1.csv')\n",
    "stop_2016 = pd.read_csv('https://raw.githubusercontent.com/xrsAlveus/COGS108_Repo/main/Project/CheckPoint1/Data/vehicle_stops_2016_datasd_v1.csv')\n",
    "stop_2017 = pd.read_csv('https://raw.githubusercontent.com/xrsAlveus/COGS108_Repo/main/Project/CheckPoint1/Data/vehicle_stops_2017_datasd_v1.csv')\n",
    "stopAll = pd.concat([stop_2014, stop_2015, stop_2016, stop_2017])\n",
    "\n",
    "# Demographics\n",
    "demoAll = pd.read_csv('https://raw.githubusercontent.com/xrsAlveus/COGS108_Repo/main/Project/CheckPoint1/Data/SanDiegoDemographics_COGS108.csv')"
   ]
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       "   stop_id           stop_cause service_area subject_race subject_sex  \\\n",
       "0  1044975     Moving Violation          110            W           M   \n",
       "1  1044976     Moving Violation          320            W           M   \n",
       "2  1044977     Moving Violation          320            L           M   \n",
       "3  1044978     Moving Violation          610            W           M   \n",
       "4  1044980  Equipment Violation          930            H           M   \n",
       "\n",
       "  subject_age            date_time   date_stop time_stop sd_resident arrested  \\\n",
       "0          24  2014-01-01 01:25:00  2014-01-01      1:25           Y        N   \n",
       "1          42  2014-01-01 05:47:00  2014-01-01      5:47           Y        N   \n",
       "2          29  2014-01-01 07:46:00  2014-01-01      7:46           Y        N   \n",
       "3          23  2014-01-01 08:10:00  2014-01-01      8:10           Y        N   \n",
       "4          35  2014-01-01 08:35:00  2014-01-01      8:35           N        N   \n",
       "\n",
       "  searched obtained_consent contraband_found property_seized  \n",
       "0        N                N                N               N  \n",
       "1        N              NaN              NaN             NaN  \n",
       "2        N              NaN              NaN             NaN  \n",
       "3        N              NaN              NaN             NaN  \n",
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       "      <td>San Diego County</td>\n",
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       "      <td>*****</td>\n",
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       "      <td>51</td>\n",
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       "      <td>4,973</td>\n",
       "      <td>25,336</td>\n",
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       "      <td>47</td>\n",
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      "text/plain": [
       "          Geography    Year   Total_Est Total_EstErr HispanicLatino_Est  \\\n",
       "0  San Diego County  2,014   3,263,431         *****         1,083,028    \n",
       "1     Carlsbad city  2,014     112,310           52             18,178    \n",
       "2  Chula Vista city  2,014     260,977           51            149,617    \n",
       "3     El Cajon city  2,014     103,075           66             32,509    \n",
       "4    Escondido city  2,014     150,252           47             78,459    \n",
       "\n",
       "  HispanicLatino_EstErr  HispanicLatino_Pct HispanicLatino_PctErr  \\\n",
       "0                 *****                  33                 *****   \n",
       "1                4,300                   16                    4    \n",
       "2                8,871                   57                    3    \n",
       "3                4,557                   32                    4    \n",
       "4                6,216                   52                    4    \n",
       "\n",
       "  WhiteAlone_Est WhiteAlone_EstErr  ... MedianFamilyIncome_Est  \\\n",
       "0     1,520,451               825   ...                76,876    \n",
       "1              N                 N  ...               121,560    \n",
       "2        53,128             5,215   ...                76,338    \n",
       "3        52,234             4,693   ...                46,826    \n",
       "4        53,574             5,201   ...                56,999    \n",
       "\n",
       "  MedianFamilyIncome_EstErr MeanFamilyIncome_Est MeanFamilyIncome_EstErr  \\\n",
       "0                    1,259               99,383                   1,668    \n",
       "1                   10,351              145,920                  10,622    \n",
       "2                    6,017               89,498                   4,973    \n",
       "3                    7,576               64,838                   6,614    \n",
       "4                    4,691               74,639                   4,408    \n",
       "\n",
       "  PercapitaIncome_Est PercapitaIncome_EstErr PovertyRate_Est  \\\n",
       "0             31,770                    500               15   \n",
       "1             49,589                  3,568                5   \n",
       "2             25,336                  1,079               13   \n",
       "3             20,827                  1,835               23   \n",
       "4             22,246                  1,391               22   \n",
       "\n",
       "  PovertyRate_EstErr Unnamed: 40 Unnamed: 41  \n",
       "0                  1         NaN         NaN  \n",
       "1                  1         NaN         NaN  \n",
       "2                  2         NaN         NaN  \n",
       "3                  4         NaN         NaN  \n",
       "4                  3         NaN         NaN  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demoAll.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4ef_w6KMAe4h"
   },
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h2lqAs8ffB06"
   },
   "source": [
    "In the data cleaning process, we ...\n",
    "\n",
    "\n",
    "*   Extracted and renamed columns\n",
    "*   Converted values to valid types\n",
    "*   Corrected data granularity\n",
    "*   Extracted unrealistic and null values\n",
    "\n",
    "for Dataset 1 - Police Vehicle Stops and Dataset 2 - Population Demographics. \n",
    "\n",
    "These data cleaning steps simplify our data to be easily analyzed and compared. By the end of each data cleaning process, we show a graph of our cleaned datasets.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GWy2rGFvOryU"
   },
   "source": [
    "## Dataset 1 - Police Vehicle Stops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "87VFlF9XKLPG"
   },
   "source": [
    "Extract columns we intend to use for analysis, and rename them for the purpose of convenience. \n",
    "\n",
    "Updated Police Vehicle Stops Columns:\n",
    "\n",
    "*   stop_id – unique identifier of a vehicle stop\n",
    "*   stop_cause – cause of stop, written by officer\n",
    "*   service_area – service area of vehicle stop\n",
    "*   race – race of driver\n",
    "*   sex – sex of driver\n",
    "*   age – age of driver\n",
    "*   date_stop – year, month, and day of vehicle stop\n",
    "*   date_time – time of day of vehicle stop\n",
    "*   arrested – Y/N if driver was arrested after vehicle stop\n",
    "*   searched – Y/N if driver was searched after vehicle stop\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "706d4293",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "stopAll = stopAll.loc[:, ['stop_id', 'stop_cause', 'service_area', 'subject_race', 'subject_sex', 'subject_age', 'date_stop', 'date_time', 'arrested', 'searched']]\n",
    "stopAll = stopAll.rename(columns = {'subject_race': 'race', 'subject_sex': 'sex', 'subject_age': 'age'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "acee47b0"
   },
   "source": [
    "### Messy or Unfaithful Data\n",
    "Does the data contain unrealistic or \"incorrect\" values?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9jIdGVS7PJxE"
   },
   "source": [
    "####  Check Data Types\n",
    "- Convert `date_stop` and `date_time` column to DateTime objects for better data manipulation\n",
    "- Convert `age` column to int "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    },
    "id": "2251207a",
    "outputId": "13c18fa7-00ab-4a27-8d37-9315e4eee714",
    "scrolled": true
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 465999 entries, 0 to 103361\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count   Dtype \n",
      "---  ------        --------------   ----- \n",
      " 0   stop_id       465999 non-null  int64 \n",
      " 1   stop_cause    465728 non-null  object\n",
      " 2   service_area  465999 non-null  object\n",
      " 3   race          465266 non-null  object\n",
      " 4   sex           465129 non-null  object\n",
      " 5   age           453538 non-null  object\n",
      " 6   date_stop     465999 non-null  object\n",
      " 7   date_time     464838 non-null  object\n",
      " 8   arrested      418727 non-null  object\n",
      " 9   searched      415885 non-null  object\n",
      "dtypes: int64(1), object(9)\n",
      "memory usage: 39.1+ MB\n"
     ]
    }
   ],
   "source": [
    "stopAll.info() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "w0Hxd7VxTeoV"
   },
   "outputs": [],
   "source": [
    "stopAll['date_stop'] = pd.to_datetime(stopAll['date_stop'])\n",
    "stopAll['date_time'] = pd.to_datetime(stopAll['date_time'])\n",
    "stopAll['age'] = pd.to_numeric(stopAll['age'], errors='coerce')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZWg1Vee9Ry1A"
   },
   "source": [
    "For the `date_time` column, we'll need to ensure all the data can be manipulated for further analysis, so we'll drop all NaN values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "id": "QZlV4tSPSHFg",
    "outputId": "e09c3341-c6a5-4ebf-d7a2-aed60f52faa8"
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   "outputs": [
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       "      <td>2018-06-25</td>\n",
       "      <td>2018-06-25 10:47:00</td>\n",
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       "      <td>N</td>\n",
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       "      <td>N</td>\n",
       "      <td>N</td>\n",
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       "      <td>1620178</td>\n",
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       "      <td>110</td>\n",
       "      <td>W</td>\n",
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       "      <td>21.0</td>\n",
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       "      <td>N</td>\n",
       "      <td>N</td>\n",
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       "      <th>464836</th>\n",
       "      <td>1620179</td>\n",
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       "      <td>110</td>\n",
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       "      <td>43.0</td>\n",
       "      <td>2018-06-25</td>\n",
       "      <td>2018-06-25 12:45:00</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
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       "      <th>464837</th>\n",
       "      <td>1620180</td>\n",
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       "      <td>N</td>\n",
       "      <td>N</td>\n",
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       "  "
      ],
      "text/plain": [
       "        stop_id           stop_cause service_area race sex   age  date_stop  \\\n",
       "0       1044975     Moving Violation          110    W   M  24.0 2014-01-01   \n",
       "1       1044976     Moving Violation          320    W   M  42.0 2014-01-01   \n",
       "2       1044977     Moving Violation          320    L   M  29.0 2014-01-01   \n",
       "3       1044978     Moving Violation          610    W   M  23.0 2014-01-01   \n",
       "4       1044980  Equipment Violation          930    H   M  35.0 2014-01-01   \n",
       "...         ...                  ...          ...  ...  ..   ...        ...   \n",
       "464833  1620175     Moving Violation          110    W   M  39.0 2018-06-25   \n",
       "464834  1620177     Moving Violation          110    W   F  32.0 2018-06-25   \n",
       "464835  1620178     Moving Violation          110    W   F  21.0 2018-06-25   \n",
       "464836  1620179     Moving Violation          110    W   M  43.0 2018-06-25   \n",
       "464837  1620180     Moving Violation          110    W   F  17.0 2018-06-25   \n",
       "\n",
       "                 date_time arrested searched  \n",
       "0      2014-01-01 01:25:00        N        N  \n",
       "1      2014-01-01 05:47:00        N        N  \n",
       "2      2014-01-01 07:46:00        N        N  \n",
       "3      2014-01-01 08:10:00        N        N  \n",
       "4      2014-01-01 08:35:00        N        N  \n",
       "...                    ...      ...      ...  \n",
       "464833 2018-06-25 10:47:00        N        N  \n",
       "464834 2018-06-25 11:34:00        N        N  \n",
       "464835 2018-06-25 11:56:00        N        N  \n",
       "464836 2018-06-25 12:45:00        N        N  \n",
       "464837 2018-06-25 13:00:00        N        N  \n",
       "\n",
       "[464838 rows x 10 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopAll = stopAll.loc[~stopAll['date_time'].isna(), :]\n",
    "stopAll.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bcde7a8a"
   },
   "source": [
    "#### Unrealistic observations? Missing Values? \n",
    "- Investigate columns of `race`, `sex`, and `age` \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AMkTJX5IvhkC",
    "outputId": "0c8d6a01-f8cc-4ba3-e955-608084b992f8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['W', 'L', 'H', 'V', 'B', 'A', 'O', 'C', 'F', 'Z', 'I', nan, 'P',\n",
       "       'J', 'X', 'K', 'U', 'G', 'S', 'D'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopAll.race.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3f65325f"
   },
   "source": [
    "The values in the `race` column are valid, with the exception of `X` and NaN values. For the purpose of our study, we dropped the observations with `X` or NaN values as shown below. With the size of our dataset, dropping these values shouldn't greatly affect the results of our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "1af860ea",
    "outputId": "98d3beb5-8691-4440-9f59-e54121ab9354",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "                                                     [key], {});\n",
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       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
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       "  "
      ],
      "text/plain": [
       "        stop_id           stop_cause service_area race sex   age  date_stop  \\\n",
       "0       1044975     Moving Violation          110    W   M  24.0 2014-01-01   \n",
       "1       1044976     Moving Violation          320    W   M  42.0 2014-01-01   \n",
       "2       1044977     Moving Violation          320    L   M  29.0 2014-01-01   \n",
       "3       1044978     Moving Violation          610    W   M  23.0 2014-01-01   \n",
       "4       1044980  Equipment Violation          930    H   M  35.0 2014-01-01   \n",
       "...         ...                  ...          ...  ...  ..   ...        ...   \n",
       "463476  1620175     Moving Violation          110    W   M  39.0 2018-06-25   \n",
       "463477  1620177     Moving Violation          110    W   F  32.0 2018-06-25   \n",
       "463478  1620178     Moving Violation          110    W   F  21.0 2018-06-25   \n",
       "463479  1620179     Moving Violation          110    W   M  43.0 2018-06-25   \n",
       "463480  1620180     Moving Violation          110    W   F  17.0 2018-06-25   \n",
       "\n",
       "                 date_time arrested searched  \n",
       "0      2014-01-01 01:25:00        N        N  \n",
       "1      2014-01-01 05:47:00        N        N  \n",
       "2      2014-01-01 07:46:00        N        N  \n",
       "3      2014-01-01 08:10:00        N        N  \n",
       "4      2014-01-01 08:35:00        N        N  \n",
       "...                    ...      ...      ...  \n",
       "463476 2018-06-25 10:47:00        N        N  \n",
       "463477 2018-06-25 11:34:00        N        N  \n",
       "463478 2018-06-25 11:56:00        N        N  \n",
       "463479 2018-06-25 12:45:00        N        N  \n",
       "463480 2018-06-25 13:00:00        N        N  \n",
       "\n",
       "[463481 rows x 10 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopAll = stopAll.replace({'race': {'X': np.NaN}})\n",
    "stopAll = stopAll.loc[~stopAll['race'].isna(), :]\n",
    "stopAll.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "84d653f9"
   },
   "source": [
    "The values in the `sex` column contain a value we don't know:\n",
    "\n",
    "***What is `X` in `array(['M', 'F', nan, 'X'], dtype=object)`?***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6b5629e1",
    "outputId": "167dbaff-286b-4d7b-898b-64fa17d572f8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M', 'F', nan, 'X'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopAll.sex.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b37607a0"
   },
   "source": [
    "From the dictionary of our datasets, `X` is not expected:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3c0789aa",
    "outputId": "0259dc8e-2fa7-4294-a279-f7d6a0a5f6fe"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Description        sex code\n",
       "Possible Values        M, F\n",
       "Name: suject_sex, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict.set_index('Field').loc['suject_sex']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e1d5de95"
   },
   "source": [
    "For now, we will convert `X` to NaN for data consistency. We will drop the NaN observations since we want to explore how gender is related to police's decisions of vehicle stops."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "fc08c714",
    "outputId": "361844bb-027c-49b5-85b5-9c4043ad2518"
   },
   "outputs": [
    {
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      "text/plain": [
       "        stop_id           stop_cause service_area race sex   age  date_stop  \\\n",
       "0       1044975     Moving Violation          110    W   M  24.0 2014-01-01   \n",
       "1       1044976     Moving Violation          320    W   M  42.0 2014-01-01   \n",
       "2       1044977     Moving Violation          320    L   M  29.0 2014-01-01   \n",
       "3       1044978     Moving Violation          610    W   M  23.0 2014-01-01   \n",
       "4       1044980  Equipment Violation          930    H   M  35.0 2014-01-01   \n",
       "...         ...                  ...          ...  ...  ..   ...        ...   \n",
       "463090  1620175     Moving Violation          110    W   M  39.0 2018-06-25   \n",
       "463091  1620177     Moving Violation          110    W   F  32.0 2018-06-25   \n",
       "463092  1620178     Moving Violation          110    W   F  21.0 2018-06-25   \n",
       "463093  1620179     Moving Violation          110    W   M  43.0 2018-06-25   \n",
       "463094  1620180     Moving Violation          110    W   F  17.0 2018-06-25   \n",
       "\n",
       "                 date_time arrested searched  \n",
       "0      2014-01-01 01:25:00        N        N  \n",
       "1      2014-01-01 05:47:00        N        N  \n",
       "2      2014-01-01 07:46:00        N        N  \n",
       "3      2014-01-01 08:10:00        N        N  \n",
       "4      2014-01-01 08:35:00        N        N  \n",
       "...                    ...      ...      ...  \n",
       "463090 2018-06-25 10:47:00        N        N  \n",
       "463091 2018-06-25 11:34:00        N        N  \n",
       "463092 2018-06-25 11:56:00        N        N  \n",
       "463093 2018-06-25 12:45:00        N        N  \n",
       "463094 2018-06-25 13:00:00        N        N  \n",
       "\n",
       "[463095 rows x 10 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopAll = stopAll.replace({'sex': {'X': np.NaN}})\n",
    "stopAll = stopAll.loc[~stopAll['sex'].isna(), :]\n",
    "stopAll.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0711e857"
   },
   "source": [
    "The values in the `age` column have unusual values: too small or too large that are out of the range of our common sense: 0, 2, 5, 185, 230, for example. There are also invalid parsing that aren't numeric: '5_', '4_', 'N', which are set as NaN in the previous step when checking data types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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     "base_uri": "https://localhost:8080/"
    },
    "id": "2d3eef3d",
    "outputId": "fbd8d703-e990-47d5-8586-0f919c1a684a"
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       "       7.000e+01, 6.600e+01, 7.200e+01, 6.100e+01, 5.900e+01, 6.900e+01,\n",
       "       7.100e+01, 5.100e+01, 8.800e+01, 6.700e+01, 8.400e+01, 7.900e+01,\n",
       "       7.700e+01, 5.000e+00, 8.100e+01, 8.300e+01, 9.700e+01, 8.900e+01,\n",
       "       7.800e+01, 7.600e+01, 9.000e+01, 8.700e+01, 8.500e+01, 8.000e+01,\n",
       "       2.300e+02, 8.600e+01, 9.300e+01, 1.500e+01, 2.000e+00, 2.110e+02,\n",
       "       9.400e+01, 8.000e+00, 2.340e+02, 1.850e+02, 1.300e+01, 1.200e+01,\n",
       "       9.200e+01, 9.000e+00, 1.530e+02, 7.000e+00, 9.500e+01, 1.400e+01,\n",
       "       9.100e+01, 6.000e+00, 4.000e+00, 1.000e+01, 2.210e+02, 1.000e+00,\n",
       "       2.550e+02, 2.240e+02, 3.000e+00, 9.600e+01, 2.040e+02, 2.230e+02,\n",
       "       2.280e+02, 2.220e+02, 9.900e+01, 2.130e+02, 1.020e+03, 3.450e+02,\n",
       "       1.250e+02, 3.700e+00, 2.430e+02, 3.990e+02, 1.100e+01, 4.560e+02,\n",
       "       2.005e+03, 2.330e+02, 7.010e+02, 3.870e+02, 1.800e+02, 1.730e+02,\n",
       "       9.800e+01, 1.000e+02, 1.190e+02, 1.630e+02, 1.140e+02, 2.120e+02,\n",
       "       2.200e+02, 1.450e+02, 1.200e+02, 1.110e+02, 2.350e+02, 2.260e+02,\n",
       "       2.030e+02, 1.430e+02])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopAll.age.unique() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "70d694bf"
   },
   "source": [
    "However, ages can still feasibly have a wide range all over the place. Are these due to manual errors? Since we are currently not dealing with ages for our vehicle stop analysis, we'll leave it like this, but we'll keep in mind how the ages could influence the validity of our observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "534becb8",
    "outputId": "88b6fa0a-d1d8-4762-99db-88add7f4b7ee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    448489.000000\n",
       "mean         37.107837\n",
       "std          14.780774\n",
       "min           0.000000\n",
       "25%          25.000000\n",
       "50%          34.000000\n",
       "75%          47.000000\n",
       "max        2005.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopAll['age'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d110e4c3"
   },
   "source": [
    "### Data Granularity\n",
    "As we observed previously, the race column has many correponding letters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
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    "outputId": "945f35c3-390d-408e-f345-b397b5a79334",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['W', 'L', 'H', 'V', 'B', 'A', 'O', 'C', 'F', 'Z', 'I', 'P', 'J',\n",
       "       'K', 'U', 'G', 'S', 'D'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopAll.race.unique()"
   ]
  },
  {
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    {
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       "      <td>PACIFIC ISLANDER</td>\n",
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       "      <th>13</th>\n",
       "      <td>S</td>\n",
       "      <td>SAMOAN</td>\n",
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       "      <th>14</th>\n",
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       "      <th>15</th>\n",
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       "      <td>VIETNAMESE</td>\n",
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       "      <th>17</th>\n",
       "      <td>Z</td>\n",
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      "text/plain": [
       "   Race Code       Description\n",
       "0          A       OTHER ASIAN\n",
       "1          B             BLACK\n",
       "2          C           CHINESE\n",
       "3          D         CAMBODIAN\n",
       "4          F          FILIPINO\n",
       "5          G         GUAMANIAN\n",
       "6          H          HISPANIC\n",
       "7          I            INDIAN\n",
       "8          J          JAPANESE\n",
       "9          K            KOREAN\n",
       "10         L           LAOTIAN\n",
       "11         O             OTHER\n",
       "12         P  PACIFIC ISLANDER\n",
       "13         S            SAMOAN\n",
       "14         U          HAWAIIAN\n",
       "15         V        VIETNAMESE\n",
       "16         W             WHITE\n",
       "17         Z      ASIAN INDIAN"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Race Code Dict\n",
    "race_code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "63b9ae2f"
   },
   "source": [
    "The level of granularity of the races in our data right now seems inconsistent. \n",
    "For instance, 'WHITE' and 'BLACK' are much more broad than 'FILIPINO', 'JAPANESE', and 'GUAMANIAN'.\n",
    "Therefore, we decided to adjust the granularity by manually defining each race code as followed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "73b4e88a"
   },
   "outputs": [],
   "source": [
    "race_dict = {'A':'Asian',\n",
    "             'B':'Black',\n",
    "             'C':'Asian',\n",
    "             'D':'Asian',\n",
    "             'F':'Asian',\n",
    "             'G':'Asian',\n",
    "             'H':'Hispanic',\n",
    "             'I':'Native American',\n",
    "             'J':'Asian',\n",
    "             'K':'Asian',\n",
    "             'L':'Asian',\n",
    "             'O':'Other',\n",
    "             'P':'Asian',\n",
    "             'S':'Asian',\n",
    "             'U':'Hawaiian',\n",
    "             'V':'Asian',\n",
    "             'W':'White',\n",
    "             'Z':'Asian'\n",
    "            }"
   ]
  },
  {
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    {
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       "      <td>HAWAIIAN</td>\n",
       "      <td>Hawaiian</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>V</td>\n",
       "      <td>VIETNAMESE</td>\n",
       "      <td>Asian</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>W</td>\n",
       "      <td>WHITE</td>\n",
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       "      <td>ASIAN INDIAN</td>\n",
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       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "  "
      ],
      "text/plain": [
       "   Race Code       Description    Race_Category\n",
       "0          A       OTHER ASIAN            Asian\n",
       "1          B             BLACK            Black\n",
       "2          C           CHINESE            Asian\n",
       "3          D         CAMBODIAN            Asian\n",
       "4          F          FILIPINO            Asian\n",
       "5          G         GUAMANIAN            Asian\n",
       "6          H          HISPANIC         Hispanic\n",
       "7          I            INDIAN  Native American\n",
       "8          J          JAPANESE            Asian\n",
       "9          K            KOREAN            Asian\n",
       "10         L           LAOTIAN            Asian\n",
       "11         O             OTHER            Other\n",
       "12         P  PACIFIC ISLANDER            Asian\n",
       "13         S            SAMOAN            Asian\n",
       "14         U          HAWAIIAN         Hawaiian\n",
       "15         V        VIETNAMESE            Asian\n",
       "16         W             WHITE            White\n",
       "17         Z      ASIAN INDIAN            Asian"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race_code['Race_Category'] = race_code['Race Code'].replace(race_dict)\n",
    "race_code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cc326f63"
   },
   "source": [
    "Join `stopAll` with the race code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2258eda0"
   },
   "outputs": [],
   "source": [
    "stopAll = pd.merge(stopAll, race_code, left_on = 'race', right_on = 'Race Code', how = 'left')\n",
    "stopAll = stopAll.rename(columns = {'Race Code':'race_code'})"
   ]
  },
  {
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      ],
      "text/plain": [
       "        stop_id           stop_cause service_area race sex   age  date_stop  \\\n",
       "0       1044975     Moving Violation          110    W   M  24.0 2014-01-01   \n",
       "1       1044976     Moving Violation          320    W   M  42.0 2014-01-01   \n",
       "2       1044977     Moving Violation          320    L   M  29.0 2014-01-01   \n",
       "3       1044978     Moving Violation          610    W   M  23.0 2014-01-01   \n",
       "4       1044980  Equipment Violation          930    H   M  35.0 2014-01-01   \n",
       "...         ...                  ...          ...  ...  ..   ...        ...   \n",
       "463090  1620175     Moving Violation          110    W   M  39.0 2018-06-25   \n",
       "463091  1620177     Moving Violation          110    W   F  32.0 2018-06-25   \n",
       "463092  1620178     Moving Violation          110    W   F  21.0 2018-06-25   \n",
       "463093  1620179     Moving Violation          110    W   M  43.0 2018-06-25   \n",
       "463094  1620180     Moving Violation          110    W   F  17.0 2018-06-25   \n",
       "\n",
       "                 date_time arrested searched race_code Description  \\\n",
       "0      2014-01-01 01:25:00        N        N         W       WHITE   \n",
       "1      2014-01-01 05:47:00        N        N         W       WHITE   \n",
       "2      2014-01-01 07:46:00        N        N         L     LAOTIAN   \n",
       "3      2014-01-01 08:10:00        N        N         W       WHITE   \n",
       "4      2014-01-01 08:35:00        N        N         H    HISPANIC   \n",
       "...                    ...      ...      ...       ...         ...   \n",
       "463090 2018-06-25 10:47:00        N        N         W       WHITE   \n",
       "463091 2018-06-25 11:34:00        N        N         W       WHITE   \n",
       "463092 2018-06-25 11:56:00        N        N         W       WHITE   \n",
       "463093 2018-06-25 12:45:00        N        N         W       WHITE   \n",
       "463094 2018-06-25 13:00:00        N        N         W       WHITE   \n",
       "\n",
       "       Race_Category  \n",
       "0              White  \n",
       "1              White  \n",
       "2              Asian  \n",
       "3              White  \n",
       "4           Hispanic  \n",
       "...              ...  \n",
       "463090         White  \n",
       "463091         White  \n",
       "463092         White  \n",
       "463093         White  \n",
       "463094         White  \n",
       "\n",
       "[463095 rows x 13 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopAll"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6a93b376"
   },
   "source": [
    "Now, we want to see what the distribution of race demographics is like in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "66237a2a",
    "outputId": "4dd2b48d-dda0-4b65-ca42-7bc3e477e6ff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "White              0.423462\n",
       "Hispanic           0.305272\n",
       "Black              0.111381\n",
       "Asian              0.078705\n",
       "Other              0.076129\n",
       "Native American    0.004908\n",
       "Hawaiian           0.000143\n",
       "Name: Race_Category, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist = stopAll['Race_Category'].value_counts(normalize = True)\n",
    "dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    },
    "id": "7fb103de",
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    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the distribution\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "title = \"Racial Distribution in Police Stops Data\"\n",
    "sns.barplot(x = dist.values, y = dist.index, palette=\"Blues_d\").set(title=title);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LspqmR6tQm0F"
   },
   "source": [
    "How does this compare to the population demographic profiles at San Diego County?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Bmtx7C-KXZgS"
   },
   "source": [
    "## Dataset 2 - Population Demographics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3iI77OA1Ps4h"
   },
   "source": [
    "Drop columns that aren't needed for our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KjXzrvjuPsYx"
   },
   "outputs": [],
   "source": [
    "demoAll = demoAll.drop(['Unnamed: 40', 'Unnamed: 41'], axis = 'columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zd6Bq4dmj0Rf"
   },
   "source": [
    "#### Check Data Types\n",
    "- All observations (except for 'Geography') are population estimates, so they need to be converted to ints\n",
    "- 'HispanicLatino_Pct', 'PovertyRate_Est', and 'PovertyRate_EstErr' are already ints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E80-36yAB8zm",
    "outputId": "7836b37f-2959-40a5-8a31-1ad3346cfd68"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Geography                       object\n",
       "Year                            object\n",
       "Total_Est                       object\n",
       "Total_EstErr                    object\n",
       "HispanicLatino_Est              object\n",
       "HispanicLatino_EstErr           object\n",
       "HispanicLatino_Pct               int64\n",
       "HispanicLatino_PctErr           object\n",
       "WhiteAlone_Est                  object\n",
       "WhiteAlone_EstErr               object\n",
       "WhiteAlone_Pct                  object\n",
       "WhiteAlone_PctErr               object\n",
       "BlackAlone_Est                  object\n",
       "BlackAlone_EstErr               object\n",
       "BlackAlone_Pct                  object\n",
       "BlackAlone_PctErr               object\n",
       "IndianAlaskaAlone_Est           object\n",
       "IndianAlaskaAlone_EstErr        object\n",
       "IndianAlaskaAlone_Pct           object\n",
       "IndianAlaskaAlone_PctErr        object\n",
       "AsianAlone_Est                  object\n",
       "AsianAlone_EstErr               object\n",
       "AsianAlone_Pct                  object\n",
       "AsianAlone_PctErr               object\n",
       "HawaiianPacificAlone_Est        object\n",
       "HawaiianPacific_EstErr          object\n",
       "HawaiianPacific_Pct             object\n",
       "HawaiianPacific_PctErr          object\n",
       "MedianHouseholdIncome_Est       object\n",
       "MedianHouseholdIncome_EstErr    object\n",
       "MeanHouseholdIncome_Est         object\n",
       "MeanHouseholdIncome_EstErr      object\n",
       "MedianFamilyIncome_Est          object\n",
       "MedianFamilyIncome_EstErr       object\n",
       "MeanFamilyIncome_Est            object\n",
       "MeanFamilyIncome_EstErr         object\n",
       "PercapitaIncome_Est             object\n",
       "PercapitaIncome_EstErr          object\n",
       "PovertyRate_Est                  int64\n",
       "PovertyRate_EstErr               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demoAll.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3jT5C0ZyCEVw"
   },
   "source": [
    "In order to convert the values to ints, we need to strip any leading or trailing spaces and remove the commas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 386
    },
    "id": "y0F-JIZvFFnU",
    "outputId": "1a88072b-c668-4cee-82ff-d9e618b5d589"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "      <th></th>\n",
       "      <th>Geography</th>\n",
       "      <th>Year</th>\n",
       "      <th>Total_Est</th>\n",
       "      <th>Total_EstErr</th>\n",
       "      <th>HispanicLatino_Est</th>\n",
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       "      <th>0</th>\n",
       "      <td>San Diego County</td>\n",
       "      <td>2014</td>\n",
       "      <td>3263431</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1083028</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Carlsbad city</td>\n",
       "      <td>2014</td>\n",
       "      <td>112310</td>\n",
       "      <td>52.0</td>\n",
       "      <td>18178</td>\n",
       "      <td>4300.0</td>\n",
       "      <td>16</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>126692</td>\n",
       "      <td>8984</td>\n",
       "      <td>121560</td>\n",
       "      <td>10351</td>\n",
       "      <td>145920</td>\n",
       "      <td>10622</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chula Vista city</td>\n",
       "      <td>2014</td>\n",
       "      <td>260977</td>\n",
       "      <td>51.0</td>\n",
       "      <td>149617</td>\n",
       "      <td>8871.0</td>\n",
       "      <td>57</td>\n",
       "      <td>3.0</td>\n",
       "      <td>53128.0</td>\n",
       "      <td>5215.0</td>\n",
       "      <td>...</td>\n",
       "      <td>80644</td>\n",
       "      <td>4067</td>\n",
       "      <td>76338</td>\n",
       "      <td>6017</td>\n",
       "      <td>89498</td>\n",
       "      <td>4973</td>\n",
       "      <td>25336</td>\n",
       "      <td>1079</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>El Cajon city</td>\n",
       "      <td>2014</td>\n",
       "      <td>103075</td>\n",
       "      <td>66.0</td>\n",
       "      <td>32509</td>\n",
       "      <td>4557.0</td>\n",
       "      <td>32</td>\n",
       "      <td>4.0</td>\n",
       "      <td>52234.0</td>\n",
       "      <td>4693.0</td>\n",
       "      <td>...</td>\n",
       "      <td>63069</td>\n",
       "      <td>5391</td>\n",
       "      <td>46826</td>\n",
       "      <td>7576</td>\n",
       "      <td>64838</td>\n",
       "      <td>6614</td>\n",
       "      <td>20827</td>\n",
       "      <td>1835</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Escondido city</td>\n",
       "      <td>2014</td>\n",
       "      <td>150252</td>\n",
       "      <td>47.0</td>\n",
       "      <td>78459</td>\n",
       "      <td>6216.0</td>\n",
       "      <td>52</td>\n",
       "      <td>4.0</td>\n",
       "      <td>53574.0</td>\n",
       "      <td>5201.0</td>\n",
       "      <td>...</td>\n",
       "      <td>69212</td>\n",
       "      <td>4444</td>\n",
       "      <td>56999</td>\n",
       "      <td>4691</td>\n",
       "      <td>74639</td>\n",
       "      <td>4408</td>\n",
       "      <td>22246</td>\n",
       "      <td>1391</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
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      ],
      "text/plain": [
       "          Geography  Year  Total_Est  Total_EstErr  HispanicLatino_Est  \\\n",
       "0  San Diego County  2014    3263431           NaN             1083028   \n",
       "1     Carlsbad city  2014     112310          52.0               18178   \n",
       "2  Chula Vista city  2014     260977          51.0              149617   \n",
       "3     El Cajon city  2014     103075          66.0               32509   \n",
       "4    Escondido city  2014     150252          47.0               78459   \n",
       "\n",
       "   HispanicLatino_EstErr  HispanicLatino_Pct  HispanicLatino_PctErr  \\\n",
       "0                    NaN                  33                    NaN   \n",
       "1                 4300.0                  16                    4.0   \n",
       "2                 8871.0                  57                    3.0   \n",
       "3                 4557.0                  32                    4.0   \n",
       "4                 6216.0                  52                    4.0   \n",
       "\n",
       "   WhiteAlone_Est  WhiteAlone_EstErr  ...  MeanHouseholdIncome_Est  \\\n",
       "0       1520451.0              825.0  ...                    89137   \n",
       "1             NaN                NaN  ...                   126692   \n",
       "2         53128.0             5215.0  ...                    80644   \n",
       "3         52234.0             4693.0  ...                    63069   \n",
       "4         53574.0             5201.0  ...                    69212   \n",
       "\n",
       "   MeanHouseholdIncome_EstErr  MedianFamilyIncome_Est  \\\n",
       "0                        1431                   76876   \n",
       "1                        8984                  121560   \n",
       "2                        4067                   76338   \n",
       "3                        5391                   46826   \n",
       "4                        4444                   56999   \n",
       "\n",
       "   MedianFamilyIncome_EstErr  MeanFamilyIncome_Est  MeanFamilyIncome_EstErr  \\\n",
       "0                       1259                 99383                     1668   \n",
       "1                      10351                145920                    10622   \n",
       "2                       6017                 89498                     4973   \n",
       "3                       7576                 64838                     6614   \n",
       "4                       4691                 74639                     4408   \n",
       "\n",
       "   PercapitaIncome_Est  PercapitaIncome_EstErr  PovertyRate_Est  \\\n",
       "0                31770                     500               15   \n",
       "1                49589                    3568                5   \n",
       "2                25336                    1079               13   \n",
       "3                20827                    1835               23   \n",
       "4                22246                    1391               22   \n",
       "\n",
       "   PovertyRate_EstErr  \n",
       "0                   1  \n",
       "1                   1  \n",
       "2                   2  \n",
       "3                   4  \n",
       "4                   3  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = demoAll.columns.drop(['Geography', 'HispanicLatino_Pct', 'PovertyRate_Est', 'PovertyRate_EstErr'])\n",
    "demoAll[columns] = demoAll[columns].apply(lambda x: x.str.strip())\n",
    "demoAll[columns] = demoAll[columns].apply(lambda x: x.str.replace(',', ''))\n",
    "demoAll[columns] = demoAll[columns].apply(pd.to_numeric, errors='coerce')\n",
    "\n",
    "demoAll.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V0ngst8akAYu"
   },
   "source": [
    "### Missing Values? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Jk_3t9XXCZ9J"
   },
   "source": [
    "- Determine if there are any NaN values and the implications it may have on our analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Dafzx2t6j_co",
    "outputId": "0b115fbe-5c9c-4187-e252-3f5b3bd6e4c1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Geography                       False\n",
       "Year                            False\n",
       "Total_Est                       False\n",
       "Total_EstErr                     True\n",
       "HispanicLatino_Est              False\n",
       "HispanicLatino_EstErr            True\n",
       "HispanicLatino_Pct              False\n",
       "HispanicLatino_PctErr            True\n",
       "WhiteAlone_Est                   True\n",
       "WhiteAlone_EstErr                True\n",
       "WhiteAlone_Pct                   True\n",
       "WhiteAlone_PctErr                True\n",
       "BlackAlone_Est                   True\n",
       "BlackAlone_EstErr                True\n",
       "BlackAlone_Pct                   True\n",
       "BlackAlone_PctErr                True\n",
       "IndianAlaskaAlone_Est            True\n",
       "IndianAlaskaAlone_EstErr         True\n",
       "IndianAlaskaAlone_Pct            True\n",
       "IndianAlaskaAlone_PctErr         True\n",
       "AsianAlone_Est                   True\n",
       "AsianAlone_EstErr                True\n",
       "AsianAlone_Pct                   True\n",
       "AsianAlone_PctErr                True\n",
       "HawaiianPacificAlone_Est         True\n",
       "HawaiianPacific_EstErr           True\n",
       "HawaiianPacific_Pct              True\n",
       "HawaiianPacific_PctErr           True\n",
       "MedianHouseholdIncome_Est       False\n",
       "MedianHouseholdIncome_EstErr    False\n",
       "MeanHouseholdIncome_Est         False\n",
       "MeanHouseholdIncome_EstErr      False\n",
       "MedianFamilyIncome_Est          False\n",
       "MedianFamilyIncome_EstErr       False\n",
       "MeanFamilyIncome_Est            False\n",
       "MeanFamilyIncome_EstErr         False\n",
       "PercapitaIncome_Est             False\n",
       "PercapitaIncome_EstErr          False\n",
       "PovertyRate_Est                 False\n",
       "PovertyRate_EstErr              False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demoAll.isna().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nWn-JRr_Cdvq"
   },
   "source": [
    "Even though there are NaN values in the dataset, we won't remove any rows (cities) or columns (demographic) since all the values will be useful in our analysis. As such, our analysis will be limited to the demographics that have information available for that year, so we need to consider how it may affect our interpretation of results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4KUf0l7tcLfS"
   },
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Pc0pQsrEHbYM"
   },
   "source": [
    "## Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E0FCPr0kWz7N"
   },
   "source": [
    "EDA was carried out with explanations of what our investigations resulted in, including:\n",
    "\n",
    "*   Bar plots to visualize distribution of data\n",
    "*   Line plots to determine trends in police vehicle stops over time broken down by race\n",
    "*   Bar plots to observe and compare trends with regards to gender and age to race\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JXVKl8X6jnWF"
   },
   "source": [
    "Datasets:\n",
    "- Police stop data: `stopAll`\n",
    "- Demographics data: `demoAll`\n",
    "\n",
    "Let's visualize them again to refresh our memories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "id": "Ag2MuMK2j0-d",
    "outputId": "eb0ff3f9-9a9b-438d-d555-7a5865b473e9"
   },
   "outputs": [
    {
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       "      <th>searched</th>\n",
       "      <th>race_code</th>\n",
       "      <th>Description</th>\n",
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       "      <td>2014-01-01</td>\n",
       "      <td>2014-01-01 01:25:00</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
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       "      <td>WHITE</td>\n",
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       "      <td>320</td>\n",
       "      <td>W</td>\n",
       "      <td>M</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>2014-01-01 05:47:00</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>W</td>\n",
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       "      <th>2</th>\n",
       "      <td>1044977</td>\n",
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       "      <td>320</td>\n",
       "      <td>L</td>\n",
       "      <td>M</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>2014-01-01 07:46:00</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>L</td>\n",
       "      <td>LAOTIAN</td>\n",
       "      <td>Asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1044978</td>\n",
       "      <td>Moving Violation</td>\n",
       "      <td>610</td>\n",
       "      <td>W</td>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>2014-01-01 08:10:00</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>W</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>White</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1044980</td>\n",
       "      <td>Equipment Violation</td>\n",
       "      <td>930</td>\n",
       "      <td>H</td>\n",
       "      <td>M</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>2014-01-01 08:35:00</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>H</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>Hispanic</td>\n",
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       "          const element = document.querySelector('#df-014d7528-b3ab-4a83-9949-a8c4537842ae');\n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
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       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   stop_id           stop_cause service_area race sex   age  date_stop  \\\n",
       "0  1044975     Moving Violation          110    W   M  24.0 2014-01-01   \n",
       "1  1044976     Moving Violation          320    W   M  42.0 2014-01-01   \n",
       "2  1044977     Moving Violation          320    L   M  29.0 2014-01-01   \n",
       "3  1044978     Moving Violation          610    W   M  23.0 2014-01-01   \n",
       "4  1044980  Equipment Violation          930    H   M  35.0 2014-01-01   \n",
       "\n",
       "            date_time arrested searched race_code Description Race_Category  \n",
       "0 2014-01-01 01:25:00        N        N         W       WHITE         White  \n",
       "1 2014-01-01 05:47:00        N        N         W       WHITE         White  \n",
       "2 2014-01-01 07:46:00        N        N         L     LAOTIAN         Asian  \n",
       "3 2014-01-01 08:10:00        N        N         W       WHITE         White  \n",
       "4 2014-01-01 08:35:00        N        N         H    HISPANIC      Hispanic  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopAll.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    },
    "id": "5Zq8_pltj8JU",
    "outputId": "fe8bfa52-4b0f-492e-a57a-3bbf8e082a93"
   },
   "outputs": [
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      "text/plain": [
       "          Geography  Year  Total_Est  Total_EstErr  HispanicLatino_Est  \\\n",
       "0  San Diego County  2014    3263431           NaN             1083028   \n",
       "1     Carlsbad city  2014     112310          52.0               18178   \n",
       "2  Chula Vista city  2014     260977          51.0              149617   \n",
       "3     El Cajon city  2014     103075          66.0               32509   \n",
       "4    Escondido city  2014     150252          47.0               78459   \n",
       "\n",
       "   HispanicLatino_EstErr  HispanicLatino_Pct  HispanicLatino_PctErr  \\\n",
       "0                    NaN                  33                    NaN   \n",
       "1                 4300.0                  16                    4.0   \n",
       "2                 8871.0                  57                    3.0   \n",
       "3                 4557.0                  32                    4.0   \n",
       "4                 6216.0                  52                    4.0   \n",
       "\n",
       "   WhiteAlone_Est  WhiteAlone_EstErr  ...  MeanHouseholdIncome_Est  \\\n",
       "0       1520451.0              825.0  ...                    89137   \n",
       "1             NaN                NaN  ...                   126692   \n",
       "2         53128.0             5215.0  ...                    80644   \n",
       "3         52234.0             4693.0  ...                    63069   \n",
       "4         53574.0             5201.0  ...                    69212   \n",
       "\n",
       "   MeanHouseholdIncome_EstErr  MedianFamilyIncome_Est  \\\n",
       "0                        1431                   76876   \n",
       "1                        8984                  121560   \n",
       "2                        4067                   76338   \n",
       "3                        5391                   46826   \n",
       "4                        4444                   56999   \n",
       "\n",
       "   MedianFamilyIncome_EstErr  MeanFamilyIncome_Est  MeanFamilyIncome_EstErr  \\\n",
       "0                       1259                 99383                     1668   \n",
       "1                      10351                145920                    10622   \n",
       "2                       6017                 89498                     4973   \n",
       "3                       7576                 64838                     6614   \n",
       "4                       4691                 74639                     4408   \n",
       "\n",
       "   PercapitaIncome_Est  PercapitaIncome_EstErr  PovertyRate_Est  \\\n",
       "0                31770                     500               15   \n",
       "1                49589                    3568                5   \n",
       "2                25336                    1079               13   \n",
       "3                20827                    1835               23   \n",
       "4                22246                    1391               22   \n",
       "\n",
       "   PovertyRate_EstErr  \n",
       "0                   1  \n",
       "1                   1  \n",
       "2                   2  \n",
       "3                   4  \n",
       "4                   3  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demoAll.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S--XB7l019oS"
   },
   "source": [
    "### Vehicle Stops Summary\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uDqOSsf1WzR8"
   },
   "source": [
    "**Numerical Data:** First, we'll take a look at the summary statistics for our data in order to better understand the numerical data we have. \n",
    "\n",
    "This includes `stop_id` and `age`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "1sgSnfuYky6h",
    "outputId": "421294af-5584-4cb9-89a9-5675c9cd0919"
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>stop_id</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>4.630950e+05</td>\n",
       "      <td>448489.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.300272e+06</td>\n",
       "      <td>37.107837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
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       "      <th>75%</th>\n",
       "      <td>1.431956e+06</td>\n",
       "      <td>47.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
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      "text/plain": [
       "            stop_id            age\n",
       "count  4.630950e+05  448489.000000\n",
       "mean   1.300272e+06      37.107837\n",
       "std    1.620906e+05      14.780774\n",
       "min    1.927050e+05       0.000000\n",
       "25%    1.162394e+06      25.000000\n",
       "50%    1.282029e+06      34.000000\n",
       "75%    1.431956e+06      47.000000\n",
       "max    1.620206e+06    2005.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# @markdown Describe `stopAll`\n",
    "stopAll.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9grRQ-WRXdqZ"
   },
   "source": [
    "From the above description, we learn that the average age of drivers stopped is approximately 37 years old. This information may be important to consider when analyzing the racial break down of police vehicle stops. The racial identity of drivers in certain age groups can may influence our analysis. As such, we will keep this information in mind."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YrQjbLLQbwzT"
   },
   "source": [
    "**Categorical Data:** Next, we'll describe the categorical variables in `stopAll`.\n",
    "\n",
    "This includes `sex`, `age`, `arrested`, `searched`, and `Race_Category`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "1qDWQisvvxFp",
    "outputId": "85350e69-0c1f-44b9-8a3e-7129e79df7b0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jxw6zb//+/QoNDVWfPn00atQonTp1qkr7AAAArKDaAmBgYKBWrlypFi1aXNK3cOFCxcXFKS4uTt26dZMk2Ww2TZkyRTNmzFBCQoL8/Pw0b968KusDAACwimoLgH5+fvLw8Cj39ElJSXJ1dZWfn58kadiwYdq0aVOV9QEAAFiFs70LkKTJkyfLMAz5+vpq0qRJatCggVJTU+Xp6WlO06hRI9lsNmVmZlZJn7u7e7nrTUpKKrXP19e33Mu5ESUmJtq7BAAAUAa7B8CVK1fKw8ND+fn5mj17tqKjo2v8aVlvb2+5urrau4wayeoBGACAmiIvL6/UQSu73wVcfFrYxcVF4eHh2rt3r9mekpJiTpeRkSFHR0e5u7tXSR8AAIBV2DUA5ubmKjs7W5JkGIY2bNigdu3aSbowynb+/Hnt2bNHkrRq1SoFBQVVWR8AAIBVVNsp4FmzZmnz5s06efKkRo4cKXd3d8XGxioiIkJFRUWy2Wxq06aNoqKiJEmOjo6aM2eOoqKilJeXpxYtWmju3LlV1gcAAGAVDoZhGPYu4npRfC69rGsAw6eurMaqao735/zF3iUAAID/70q5xe7XAAIAAKB6EQABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgMANIr+wwN4l2JXVtx8ArobdnwUMoHK4ONfSI8sn2rsMu1kx8lV7lwAA1w1GAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMVUWwCMiYlRQECAvLy8dPjwYUnS6dOn9eijj6pPnz4KCQnRhAkTlJGRYc7j5eWlkJAQhYWFKSwsTIcOHTL7tm7dqqCgIPXu3VtPPvmkzp07V+E+AAAAK6i2ABgYGKiVK1eqRYsWZpuDg4NGjx6thIQExcfHq1WrVpo3b16J+VatWqW4uDjFxcXJy8tLknT27Fk9//zzio2N1SeffCI3NzctW7asQn0AAABWUW0B0M/PTx4eHiXa3N3d1blzZ/N1hw4dlJKSUuaytm/fLm9vb7Vu3VqSNGzYMG3cuLFCfQAAAFbhbO8CitlsNv3zn/9UQEBAifYRI0aoqKhI3bt3V0REhFxcXJSamipPT09zGk9PT6WmpkrSNfddjaSkpFL7fH19r3p5N5LExER7l2BZVj/2JI4/ACivGhMAZ86cqbp162r48OFm22effSYPDw/l5ORoypQpWrJkiZ566ik7VnmBt7e3XF1d7V1GjUQIgT1x/AHA/+Tl5ZU6aFUj7gKOiYnR0aNH9corr8jR8X8lFZ8yrlevnoYMGaK9e/ea7RefKk5JSTGnvdY+AAAAq7B7AJw/f76SkpK0ZMkSubi4mO1ZWVk6f/68JKmwsFAJCQlq166dJKlbt2767rvv9Msvv0i6cKNI3759K9QHAABgFdV2CnjWrFnavHmzTp48qZEjR8rd3V2vvPKK3njjDbVu3VrDhg2TJLVs2VJLlizRTz/9pBkzZsjBwUGFhYXy8fHRxIkTJV0YEYyOjtaYMWNks9nUrl07TZs2rUJ9AAAAVuFgGIZh7yKuF8Xn0su6BjB86spqrKrmeH/OX+xdguU9snyivUuwmxUjX7V3CQBQo1wpt9j9FDAAAACqFwEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYTLkD4LJlyy7bvnz58korBgAAAFWv3AFwyZIll21//fXXK60YAAAAVD3nsib48ssvJUk2m01fffWVLn508PHjx+Xm5lZ11QEAAKDSlRkAp02bJunCA4Wfe+45s93BwUFNmjTR9OnTq646AAAAVLoyA+DWrVslSVOnTtWcOXOqvCAAAABUrTIDYLGLw5/NZivR5+jIzcQAAADXi3IHwP/+97+Kjo7WoUOHlJeXJ0kyDEMODg46ePBglRUIAACAylXuABgZGamePXvqb3/7m2rXrl2VNQEAAKAKlTsAJicn66mnnpKDg0NV1gMAAIAqVu6L93r37q3PP/+8KmsBAABANSj3CGBeXp4mTJggX19f3XzzzSX6uDsYAADg+lHuAHjHHXfojjvuqMpaAAAAUA3KHQAnTJhQlXUAAACgmpQ7ABY/Eu5yunTpUinFAAAAoOqVOwAWPxKu2OnTp1VQUKBmzZrp008/rfTCAAAAUDXKHQCLHwlXrKioSK+//rrc3NzKnDcmJkYJCQlKTk5WfHy82rZtK0n6+eefFRkZqczMTLm7uysmJkatW7e2Sx8AAIBVXPMz3JycnDR27Fi99dZbZU4bGBiolStXqkWLFiXao6KiFB4eroSEBIWHh2vGjBl26wMAALCKCj3E94svvijXD0P7+fnJw8OjRNupU6d04MABBQcHS5KCg4N14MABZWRkVHsfAACAlZT7FHCPHj1KhL1z584pPz9fUVFR17Ti1NRUNWvWTE5OTpIujCg2bdpUqampMgyjWvsaNWp0VbUnJSWV2ufr63vV++JGkpiYaO8SLMvqx57E8QcA5VXuADh37twSr+vUqaPbbrtN9erVq/Siajpvb2+5urrau4waiRACe+L4A4D/ycvLK3XQqtwBsFOnTpIkm82mkydP6uabb5aj47WfQfbw8FBaWpqKiork5OSkoqIipaeny8PDQ4ZhVGsfAACAlZQ7weXk5Gjq1Klq3769unfvrvbt2+uZZ55Rdnb2Na24cePGateundatWydJWrdundq1a6dGjRpVex8AAICVOBiGYZRnwsjISJ09e1aTJk1SixYtlJycrAULFqhOnTqKiYm54ryzZs3S5s2bdfLkSTVs2FDu7u5av369fvzxR0VGRurMmTNq0KCBYmJidPvtt0tStfeVR/FQalmngMOnriz3Mm8k78/5i71LsLxHlk+0dwl2s2Lkq/YuAQBqlCvllnIHwPvuu09btmxRnTp1zLazZ8+qd+/e2rlzZ+VWXEMRAK+MAGh/BEAAQLEr5ZZynwJ2dXW95CdTTp8+LRcXl8qpEgAAANWi3DeBDB48WKNGjdIjjzwiT09PpaSkaMWKFRoyZEhV1gcAAIBKVu4AOG7cODVr1kzx8fFKT09X06ZNNXr0aAIgAADAdabcp4Bnz56t2267TStWrNCGDRu0YsUKtWnTRrNnz67K+gAAAFDJyh0A161bJ29v7xJt3t7e5s+qAAAA4PpQ7gDo4OAgm81Woq2oqOiSNgAAANRs5Q6Afn5+evXVV83AZ7PZtGjRIvn5+VVZcQAAAKh85b4JZNq0aRozZozuv/9+eXp6KjU1VU2aNFFsbGxV1gcAAIBKVu4A2Lx5c61Zs0bffvutUlNT5eHhofbt21foecAAAACofuUOgJLk6OioDh06qEOHDlVVDwAAAKoYw3cAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAItxtncBx48f1+OPP26+zs7OVk5Ojnbt2qWAgAC5uLjI1dVVkjR58mR169ZNkrR//37NmDFDeXl5atGihebOnavGjRtXqA8AAMAK7D4C2LJlS8XFxZn/BQYGKjg42OxfuHCh2Vcc/mw2m6ZMmaIZM2YoISFBfn5+mjdvXoX6AAAArMLuAfBi+fn5io+P16BBg644XVJSklxdXeXn5ydJGjZsmDZt2lShPgAAAKuw+yngi23dulXNmjXTXXfdZbZNnjxZhmHI19dXkyZNUoMGDZSamipPT09zmkaNGslmsykzM/Oa+9zd3ctdZ1JSUql9vr6+5V7OjSgxMdHeJViW1Y89ieMPAMqrRgXA1atXlxj9W7lypTw8PJSfn6/Zs2crOjq6Rpyy9fb2Nq9LREmEENgTxx8A/E9eXl6pg1Y15hRwWlqadu/erZCQELPNw8NDkuTi4qLw8HDt3bvXbE9JSTGny8jIkKOjo9zd3a+5DwAAwCpqTABcs2aNevTooYYNG0qScnNzlZ2dLUkyDEMbNmxQu3btJF0YgTt//rz27NkjSVq1apWCgoIq1AcAAGAVNeYU8Jo1azRt2jTz9alTpxQREaGioiLZbDa1adNGUVFRkiRHR0fNmTNHUVFRJX7OpSJ9AAAAVlFjAmBCQkKJ161atdLatWtLnf7ee+9VfHx8pfYBAABYQY05BQwAAIDqQQAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiakQADAgIUFBQkMLCwhQWFqYdO3ZIkvbv36/Q0FD16dNHo0aN0qlTp8x5qqIPAADACmpEAJSkhQsXKi4uTnFxcerWrZtsNpumTJmiGTNmKCEhQX5+fpo3b54kVUkfAGsryi+wdwl2ZfXtB6zG2d4FlCYpKUmurq7y8/OTJA0bNkyBgYF66aWXqqQPgLU5udTShodG2rsMu+n3j+X2LgFANaoxAXDy5MkyDEO+vr6aNGmSUlNT5enpafY3atRINptNmZmZVdLn7u5e7lqTkpJK7fP19S33cm5EiYmJ9i7Bsqx+7EkVO/7Yf7x/ASupEQFw5cqV8vDwUH5+vmbPnq3o6Gj17t3b3mWVytvbW66urvYuo0biSxT2xPFXMew/4MaSl5dX6qBVjbgG0MPDQ5Lk4uKi8PBw7d27Vx4eHkpJSTGnycjIkKOjo9zd3aukDwAAwCrsHgBzc3OVnZ0tSTIMQxs2bFC7du3k7e2t8+fPa8+ePZKkVatWKSgoSJKqpA8AAMAq7H4K+NSpU4qIiFBRUZFsNpvatGmjqKgoOTo6as6cOYqKilJeXp5atGihuXPnSlKV9AEAAFiF3QNgq1attHbt2sv23XvvvYqPj6+2PgAAACuw+ylgAAAAVC8CIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEDUGLbCAnuXYFdW334AQPVxtncBQDFH51pKnDPa3mXYje/Ut+xdAgDAIhgBBABUWGFBkb1LsBsrbzuuX4wAAgAqzLmWk/427UN7l2EXz80ebO8SgKvGCCAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYu/8Q9OnTpzV16lT9+uuvcnFx0a233qro6Gg1atRIXl5eatu2rRwdL+TUOXPmyMvLS5K0detWzZkzR0VFRbrrrrv00ksvqU6dOhXqAwAAsAK7jwA6ODho9OjRSkhIUHx8vFq1aqV58+aZ/atWrVJcXJzi4uLM8Hf27Fk9//zzio2N1SeffCI3NzctW7asQn0AAABWYfcA6O7urs6dO5uvO3TooJSUlCvOs337dnl7e6t169aSpGHDhmnjxo0V6gMAALAKu58CvpjNZtM///lPBQQEmG0jRoxQUVGRunfvroiICLm4uCg1NVWenp7mNJ6enkpNTZWka+67GklJSaX2+fr6XvXybiSJiYnXPK/V953E/qso9l/FsP+uXUX2HWAPNSoAzpw5U3Xr1tXw4cMlSZ999pk8PDyUk5OjKVOmaMmSJXrqqafsXKXk7e0tV1dXe5dRI1n9S6Ci2H8Vw/6rGPbftWPfoSbKy8srddDK7qeAi8XExOjo0aN65ZVXzJs+PDw8JEn16tXTkCFDtHfvXrP94tPEKSkp5rTX2gcAAGAVNSIAzp8/X0lJSVqyZIlcXFwkSVlZWTp//rwkqbCwUAkJCWrXrp0kqVu3bvruu+/0yy+/SLpwo0jfvn0r1AcAAGAVdj8FfOTIEb3xxhtq3bq1hg0bJklq2bKlRo8erRkzZsjBwUGFhYXy8fHRxIkTJV0YEYyOjtaYMWNks9nUrl07TZs2rehTei4AABg8SURBVEJ9AAAAVmH3APiHP/xBhw4dumxffHx8qfP16tVLvXr1qtQ+AAAAK6gRp4ABAABQfQiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYjCUD4M8//6yhQ4eqT58+Gjp0qH755Rd7lwQAsKjCggJ7l2BXVt9+e3G2dwH2EBUVpfDwcIWFhSkuLk4zZszQP/7xD3uXBQCwIOdatTT/2TH2LsNuJr30hr1LsCTLBcBTp07pwIEDWr58uSQpODhYM2fOVEZGhho1anTFeQ3DkCTl5+dfcboGdWtVTrHXmby8vIovpHb9ii/jOlUZ+69+LbdKqOT6VBn7z7E+x19F1K5rua8USZWz71zr1quESq5PlfLdgcsqzivF+eViDsblWm9gSUlJeuaZZ7R+/XqzrV+/fpo7d67uuuuuK86bnZ2tw4cPV3WJAAAAlaZt27aq/7s/cK3559o1cnNzU9u2bVWrVi05ODjYuxwAAIBSGYahgoICubldenbIcgHQw8NDaWlpKioqkpOTk4qKipSeni4PD48y53V0dLwkQQMAANRUtWvXvmy75e4Cbty4sdq1a6d169ZJktatW6d27dqVef0fAADAjcJy1wBK0o8//qjIyEidOXNGDRo0UExMjG6//XZ7lwUAAFAtLBkAAQAArMxyp4ABAACsjgAIAABgMQRAAAAAiyEAAgAAWAwBsIZZsGCBoqKizNfbtm2Tl5eXjhw5YraNGTNGH3zwgTp37nzZZaSlpWnEiBHm60WLFpX5+LobTVZWltq3b69Zs2aVOe20adO0Z8+eaqjK/gICAi55ms3AgQP19ddf69VXX9WGDRuqvaawsDCdP3++2tdbVQICAhQUFKSwsDAFBQVp+vTpKigo0KJFixQTE3PNy63o/NeD/Px8vfzyy+rVq5eCgoLUv39/bdmyRZJ08ODBS45PLy8vnT171h6lliogIEDBwcGy2Wwl2srzFKnff1ZX1XvyySeflL+/vwoKCip92Zfz6KOP6tdff62WdV3OlT73qsrvv4dr4uccAbCG8ff3165du8zXu3bt0j333GO2FRUVKTExsdTwJ0nNmjXTu+++a75evHhxtb3Ra4p169bpnnvu0fr168sMv7Nnz5afn181VVZzTZw4Uf369av29cbFxZX6Q6XXq4ULFyouLk7r16/XDz/8oE8++cTeJV0XXnjhBZ04cULr16/Xpk2bNGfOHEVHR2v37t06ePCgNm3aVOnrLCwsrPRl5ubmKi4u7qrn+/1ndVW8JzMzM7Vz507deuut2rp1a6Uu+/dsNpsMw9DSpUt1yy23VOm6aprffw/XxM85AmAN4+Pjo+PHj+vkyZOSpN27d2v8+PHmXyoHDhxQvXr15Oh44X/dggUL1L9/f/Xp08ccxTp+/LgZEF988UVJ0rBhwxQWFqYzZ84oJydH06ZN0+DBgxUSEqJZs2apqKiouje1Sq1evVrjx4+Xl5eXPv30U0nSli1bFBISorCwMAUHB5v7dMSIEdq2bZskKT4+XkOGDFH//v3Vv39/ffnll+YyAwIC9Oqrr2ro0KEKCAjQe++9V/0bVoUiIyPNbbrSvpo9e7YGDx6s3r17a/78+eb8b7/9tgYNGqT+/ftr6NChOnjwoNnn5eWl2NhYDRo0SIGBgUpISCjRVzyK8+OPP2rUqFEKCQlRSEiI1qxZUx2bXmXy8vKUl5enBg0alGg/dOiQwsPDNWDAAPXr108rVqww+7Kzs/Xss88qJCREoaGhio6OvmS5hw4dUkhISIk/Fq93ycnJ2rhxo1544QW5urpKuvD80rFjx2rhwoVauHChdu7cqbCwsBIj++++++5lj6tvvvlGI0aM0MCBAzVw4EB99tlnkv73+RgTE6MBAwbogw8+qPRtmTBhghYvXnzZPz5Le59c7rO6+D157tw5de7cWRkZGeZyYmJitHjx4itu6+XEx8erR48eCg8P1+rVq8324v3y97//Xf3791dQUJCSkpI0ffp0hYSEaMiQIfrtt9/M6d98800NHjxYAwYM0NixY82+RYsW6YknntCoUaPUr18/nTlzpsQIXFpamiIiIsz3+BtvvGHWZY/P3tLWu2PHDj322GOSpFOnTunOO+/Uxo0bJUlLly41P/tiYmI0aNAghYaG6uGHH1ZycnKJ/Vns4s+5sua53Pd6lTBQ44SHhxvr1683srOzjX79+hmFhYVG7969DcMwjLfeesuYMmWKcezYMaNt27bG1q1bDcMwjLi4OGPo0KGGYRjGsWPHjE6dOpnLa9u2rZGTk2O+fu6554w1a9YYhmEYRUVFxlNPPWX861//qq7Nq3IHDx40evbsadhsNiMuLs7461//ahiGYYSEhBh79+41DMMwCgsLjezsbMMwDGP48OHmfszIyDBsNpthGIbx448/Gt26dTOX27NnT+Pll182DOPCPu7QoUOJ/Xo96Nmzp9GnTx8jNDTU/K99+/bGV199ZTzzzDPGu+++axjGlffVyJEjjYKCAiMnJ8cIDg42992pU6fM9XzxxRfGkCFDzNdt27Y1l71nzx7j/vvvL9GXk5NjFBQUGA8++KCxYcMGsy8jI6OK9kTVuXgfd+jQwZgwYYJhGIaxcOFC8/jJzs428vLyDMMwjJycHKNv377GDz/8YBiGYURGRhrR0dFGUVGRYRj/26/F83/xxRdGcHCwceTIkeretCq1detWIzQ09JL2//73v0anTp2M1atXGxERESX6SjuusrKyjLCwMCMtLc0wDMNIS0szunXrZmRlZZmfnevXr6+S7ejZs6dx6NAhIyIiwlixYkWJNsMo+31y8WfKxe/J5557znjnnXcMwzCMgoIC47777jOOHTt2xW29nLCwMGPnzp1Gbm6u0bFjR+PEiROGYRjmftm2bZthGIaxdOlSw9fX1zhw4IBhGIYRFRVlzJ8/3zAMw1i7dq0xffp08xhduXKlMWnSJMMwLhynPXr0KLGdF2//8OHDjaVLl5p9xdNV5WfvlT73Sltvbm6u0alTJyM/P9+Ij483hg4dajz//POGYRjGqFGjjJ07d5ao3zAM49///rfx5JNPmnWW9j18pXlK+16vCpZ7FvD1oFOnTvr666/l5uYmX19fOTk56dZbb9WRI0e0a9cuPfjgg5KkunXrqmfPnpKkDh06lPv6oK1bt+rbb7/V8uXLJUnnz59Xs2bNqmZj7ODDDz9UWFiYHBwc9OCDD2rWrFlKS0uTv7+/XnrpJT344IPq3r272rZte8m8x44d09NPP620tDQ5Ozvr5MmT+u2339SkSRNJMk/HtGzZUg0aNNCJEyfUpk2bat2+ilq4cGGJbR84cOAl01xpX/Xv31/Ozs5ydnZWv3799NVXX6lnz55KSkrSG2+8oaysLDk4OOiXX34psczifdehQwelp6crLy/PHOmRpJ9//lmFhYXq27ev2dawYcPK2uxqVbyP8/LyFBERUWKET7rwnnvhhRd06NAhOTg4KD09Xd9//73atGmjbdu26aOPPjJH+S9+TOXnn3+uHTt2aNmyZTfUe1a68ND6a3G542rfvn06fvy4Hn30UXM6BwcHHT16VA0bNpSrq2uJ46wqPPnkk3rooYc0ePDgEu1lvU9KM2DAAM2ePVsPPfSQtm/frttvv10tW7bUf/7zn1K39e677y6xjAMHDujMmTPy9/c3Px/Xrl2rMWPGSLrwnfLAAw9Iku666y41b95c7dq1M1/v3LlT0oXvkKSkJA0YMEDShUuT6tWrZ66ne/ful3286tmzZ7Vv3z7zu0f63/Fd1Z+9pX3uXWm9f/jDH/TNN99o586dGj9+vObOnav8/Hx99913uvfeeyVJ27dv1/vvv6/c3NxyX05wpXmu9Xv9WhAAa6DOnTvrxRdfVP369dWxY0dJUseOHfXll18qMTFR06dPlyS5uLiY8zg6Opb74DMMQ6+99ppatWpV+cXbWX5+vtatWycXFxfzGpyCggJ99NFHeu6553To0CF99dVXmjhxokaOHKk//elPJeafNGmSIiMj1atXL9lsNt1zzz3Ky8sz+y8OLE5OTjfcqfNi5dlXF8vPz9fEiRP13nvv6a677lJaWpq6d+9eYprifefk5CTpwrVXF+/PG5Grq6seeOABffbZZyW+jOfPn68mTZro5ZdflrOzs0aNGlXiOCvNbbfdpiNHjigpKemGC4Bt27bVr7/+qszMTLm7u5vt+/fvl5eXV6nzXe64MgxDXl5eWrly5SXTHz9+XHXq1JGDg0Mlb0FJt99+u3r06FEi7JTnfVIaPz8/nT17VocOHdKaNWvMAHOlbf291atX68yZMwoMDDTrcXNzMwPg779TLn598eedYRgaN27cJeG2mJubW7m26WL2+uy90nr9/f311Vdf6ZtvvtELL7ygxo0ba/369brzzjvl6uqq5ORkvfTSS/rwww/VqlUr7d27V5MnT77i+sqa51q/168F1wDWQD4+PkpOTtbmzZvVqVMnSRfe/CtXrlSDBg2uOri5ubkpJyfHfB0QEKA333zTfANlZGTo2LFjlbcBdvTpp5/qtttu0/bt27V161Zt3bpVb7/9ttasWaOffvpJXl5eevjhhxUaGqrvvvvukvmzs7PVsmVLSRc+LK1293SxK+2rjz/+WIWFhcrNzdXGjRvl7++v/Px8FRYWysPDQ5L0/vvvX/U6b7vtNjk7O5vX2UjS6dOnK74xdmSz2bR79261bt26RHt2draaN28uZ2dnHT58uMR1Pj179tSyZcvMEbGLr/tq0aKF3n77bc2fP98ud2xXpZYtWyooKEgvvPCC+QV8+PBhxcbGasKECapXr56ys7PLtSwfHx8dPXpUX331ldn27bffXvMo47WKiIjQ+++/b177Vdb75Pef1b/Xv39/LV++XLt371afPn0klX9bi/84Xr16tfnZ+Pnnn0vSVV9nFhAQoPfff19ZWVnmsr///vsy53Nzc5OPj0+JEfHi49ten71XWq+/v78++ugjNW/eXC4uLurSpYsWLVqkLl26SJJycnJUq1YtNWnSRDabTatWrSpzfdcyT1UhANZArq6uuueeeyTJ/Cv/7rvvVlpamhkIr8aoUaP00EMPmRcWP/fcc3J0dFRYWJhCQkI0evRopaWlVeo22Mvq1asVEhJSos3Hx0c2m01RUVEKDg5WWFiYdu7cWeKUSbFnn31W48eP14ABA3Ts2LESIxFW8ve//73UfXX77bebF6o/8MAD6tmzp+rVq6cnnnhCgwcP1sCBA1W3bt2rXqezs7Nee+01rVq1yrwB4j//+U9lbla1eeKJJ8wbaGw2mx5//PES/ePGjdMHH3ygkJAQLV682Bzply4cg2fPnlVwcLBCQ0P12muvlZjXw8NDK1asUGxsrD766KNq2Z7qEhUVpaZNm6pfv34KCgrSlClTNG3aNHXq1EldunTRuXPnFBoaWubPO91000167bXXtGTJEoWGhqpv375avHhxtQfA5s2bKywsTJmZmZJU5vvk95/Vv9e/f3/FxcUpMDBQderUkVT+bd2yZYtuueUW3XrrrSXaQ0JCStwMUh79+/dXaGiohg8frpCQEA0cOFCJiYnlmnfevHnau3eveXx/+OGHkuz32Xul9d5zzz06ffq0Gfi6dOmi5ORk+fv7S7pwY0dQUJD69eunIUOGmEHySq5lnqriYFT3OwLAdWvEiBEaNWqUeY0KAOD6xAggAACAxTACCAAAYDGMAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGJ4EAuCGFBAQoJMnT8rJyUl169ZVt27d9Pzzz1/TUwoq6ueff9aCBQv09ddfq7CwUJ6enho4cKAeeugh8wkWpYmMjFSzZs301FNPVVO1AKyAEUAAN6zY2Fjt27dPa9eu1YEDB/Tmm29Wew2//vqr/vSnP8nDw0Px8fFKTEzUq6++qqSkJPMJETXVjfqoQwAEQAAW0KRJE91///06ePCgJOnNN99Ur1695OPjo379+umTTz4pMf2///1v9e3b1+z/73//K0lKS0tTRESE/P39FRAQoH/84x9lrnvhwoXy8fHRs88+q6ZNm0q68DSVv//972rQoIGkC08Oue++++Tr66u//OUvOnLkiCTpX//6l+Lj47Vs2TL5+Pho7NixZdZx/vx5PfPMM+rYsaP69u2rpUuXlnje7I8//qgRI0bIz89Pf/zjH/Xpp5+afZGRkYqKitKjjz6qDh06aPny5eratWuJILh582aFhoaWf+cDqJEIgABueCdOnNCOHTt0yy23SJJatWqllStXKjExURMmTNCUKVOUnp4uSdq4caMWLVqkmJgY7d27V6+//rrc3d1ls9k0btw4eXl5afv27XrnnXf0zjvvaMeOHVdc95dffmk+t7U03bt3V0JCgr788kv93//9n/lw+KFDhyokJER//etftW/fPsXGxpZZx+LFi5WcnKwtW7Zo+fLl+vjjj831FBQUaOzYsbrvvvu0c+dOTZ8+XZMnT9ZPP/1kTrNu3TqNHTtWe/fu1YgRI+Tu7m4+M1aS4uLi1L9//6vY+wBqIgIggBvW448/Lh8fH/Xo0UONGjXSE088IUnq27evmjVrJkdHR/Xr10+33nqrvv32W0nShx9+qNGjR6t9+/ZycHDQrbfeqhYtWui7775TRkaGJkyYIBcXF7Vq1Up/+tOftGHDhivWkJmZqSZNmlxxmsGDB6tevXpycXFRRESEvv/+e2VnZ1922rLq2Lhxo8aMGaObbrpJzZs310MPPWTO+8033yg3N1ePPfaY+XD7nj17av369eY0gYGB8vX1laOjo1xdXdW/f38zRGZmZurzzz9XcHBwGXseQE3HTSAAblhLlixR165dtWvXLj399NM6ffq0GjRooLVr12r58uVKTk6WJOXm5ur06dOSpNTUVHOk8GLJyclKT0+Xn5+f2VZUVFTi9eW4u7vrt99+K7W/qKhICxYs0KZNm5SRkSFHxwt/l58+fVr169e/6jrS09Pl4eFh9jVv3tz8d3p6upo3b26uQ5I8PT2VlpZmvr54XkkKCwtT3759lZubq40bN8rPz888lQ3g+kUABHDD69SpkwYOHKiYmBhNmzZN06dP14oVK+Tj4yMnJyeFhYWZ03p4eOjXX3+9ZBkeHh5q2bKlNm/efFXr7tKlizZv3qxBgwZdtj8+Pl6ffvqpli9frpYtWyo7O1sdO3ZU8VM6HRwcrqqOJk2a6MSJE7rjjjskXTj9Xaxp06Y6ceKEbDabGQJTU1PVunXrUutv1qyZfHx8tHnzZsXFxenPf/5zubcdQM3FKWAAlvDwww9r586dys7OloODgxo1aiRJWr16tXnThXThdOzbb7+tpKQkGYaho0ePKjk5We3bt5ebm5vefPNNnT9/XkVFRTp8+LB56rg0TzzxhPbt26eYmBhzJPDo0aOaPHmyzpw5o7Nnz8rFxUUNGzbUuXPnNH/+/BLzN27cWMePHzdfl1VH37599cYbbygrK0tpaWl67733Ssxbu3ZtvfXWWyooKNDXX3+trVu3ql+/flfchrCwMC1btkyHDx/Wgw8+WI69DaCmIwACsIRGjRopLCxMS5Ys0ahRozRs2DB17dpVhw8f1r333mtO17dvX40dO1ZPP/207r33Xj3++OPKysqSk5OTYmNj9f333yswMFD+/v6aPn26cnJyrrjeW265RatWrVJycrKCg4Pl6+uriIgIeXt7y83NTf3795enp6e6deumP/7xj+rQoUOJ+QcPHqwffvhBfn5+Gj9+fJl1PP7442revLkCAwP1yCOPqE+fPnJxcZEkubi4KDY2Vtu3b5e/v79efPFFzZkzR23atLniNvTu3VvJycnq3bu36tSpcy27H0AN42AUn2cAANxw3n//fW3YsKHESOC16NWrl6Kjo9W1a9dKqgyAPTECCAA3kPT0dCUmJspms+mnn37S8uXL1atXrwotMyEhQQ4ODvL396+kKgHYGzeBAEAFjR49WomJiZe0jxkzxvzx5upSUFCgqKgoHT9+XPXr19cf//hHhYeHX/PyRowYoR9++EFz5swpcfcwgOsbp4ABAAAshj/nAAAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAW8/8A28MUvS72fmwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.countplot(x=stopAll['sex'])\n",
    "plt.title('Distribution of sex')\n",
    "\n",
    "plt.figure()\n",
    "sns.histplot(list(stopAll['age']), binwidth=5)\n",
    "plt.xlim(0,100)\n",
    "plt.title('Distribution of age')\n",
    "\n",
    "plt.figure()\n",
    "sns.countplot(x=stopAll['arrested'])\n",
    "plt.title('Distribution of arrest')\n",
    "\n",
    "plt.figure()\n",
    "sns.countplot(x=stopAll['searched'])\n",
    "plt.title('Distribution of police search')\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(x=stopAll['Race_Category'])\n",
    "plt.title('Distribution of race');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6itUscKvatol"
   },
   "source": [
    "Above, we can see the general distribution of the categorical variables of the police vehicle stops dataset. In general, with regards to gender, there appears to be twice as many males getting stopped compared to females. For age, the distribution is slightly skewed to the right, with most drivers being in their 30's. Most drivers stopped are *not* searched nor arrested. \n",
    "\n",
    "We also have an initial look at the general distribution of race for police vehicle stops, showing that a greater number of White and Hispanic drivers are stopped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EBEVYjMc2F2c"
   },
   "source": [
    "### Population Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_DqDvtZ9b8MT"
   },
   "source": [
    "We'll also look at the summary statistics for `demoAll`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "id": "Agf-rBNDkzAP",
    "outputId": "c5e415b9-34e2-4f3b-8666-aad092cda5c7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-8097f72d-a02b-4bb0-9edc-95ee754b33af\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>Year</th>\n",
       "      <th>Total_Est</th>\n",
       "      <th>Total_EstErr</th>\n",
       "      <th>HispanicLatino_Est</th>\n",
       "      <th>HispanicLatino_EstErr</th>\n",
       "      <th>HispanicLatino_Pct</th>\n",
       "      <th>HispanicLatino_PctErr</th>\n",
       "      <th>WhiteAlone_Est</th>\n",
       "      <th>WhiteAlone_EstErr</th>\n",
       "      <th>WhiteAlone_Pct</th>\n",
       "      <th>...</th>\n",
       "      <th>MeanHouseholdIncome_Est</th>\n",
       "      <th>MeanHouseholdIncome_EstErr</th>\n",
       "      <th>MedianFamilyIncome_Est</th>\n",
       "      <th>MedianFamilyIncome_EstErr</th>\n",
       "      <th>MeanFamilyIncome_Est</th>\n",
       "      <th>MeanFamilyIncome_EstErr</th>\n",
       "      <th>PercapitaIncome_Est</th>\n",
       "      <th>PercapitaIncome_EstErr</th>\n",
       "      <th>PovertyRate_Est</th>\n",
       "      <th>PovertyRate_EstErr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>5.400000e+01</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>5.400000e+01</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>5.300000e+01</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>54.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2016.500000</td>\n",
       "      <td>6.371851e+05</td>\n",
       "      <td>55.895833</td>\n",
       "      <td>2.196172e+05</td>\n",
       "      <td>6755.562500</td>\n",
       "      <td>38.018519</td>\n",
       "      <td>3.833333</td>\n",
       "      <td>2.841391e+05</td>\n",
       "      <td>5663.622642</td>\n",
       "      <td>44.358491</td>\n",
       "      <td>...</td>\n",
       "      <td>94222.444444</td>\n",
       "      <td>5745.296296</td>\n",
       "      <td>82918.314815</td>\n",
       "      <td>7031.888889</td>\n",
       "      <td>104925.000000</td>\n",
       "      <td>7129.203704</td>\n",
       "      <td>33061.555556</td>\n",
       "      <td>2105.814815</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>2.648148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.723861</td>\n",
       "      <td>1.037081e+06</td>\n",
       "      <td>19.194680</td>\n",
       "      <td>3.425487e+05</td>\n",
       "      <td>3085.988603</td>\n",
       "      <td>13.837999</td>\n",
       "      <td>1.404148</td>\n",
       "      <td>4.757875e+05</td>\n",
       "      <td>2810.866874</td>\n",
       "      <td>14.395638</td>\n",
       "      <td>...</td>\n",
       "      <td>21750.128326</td>\n",
       "      <td>3071.383528</td>\n",
       "      <td>21128.493699</td>\n",
       "      <td>4106.774512</td>\n",
       "      <td>26349.319549</td>\n",
       "      <td>3866.042052</td>\n",
       "      <td>9685.833529</td>\n",
       "      <td>1169.201551</td>\n",
       "      <td>4.748055</td>\n",
       "      <td>1.305394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>9.291000e+04</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.163800e+04</td>\n",
       "      <td>2551.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.562300e+04</td>\n",
       "      <td>825.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>60726.000000</td>\n",
       "      <td>1291.000000</td>\n",
       "      <td>46826.000000</td>\n",
       "      <td>1225.000000</td>\n",
       "      <td>64838.000000</td>\n",
       "      <td>1499.000000</td>\n",
       "      <td>20251.000000</td>\n",
       "      <td>438.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>1.031135e+05</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>3.410500e+04</td>\n",
       "      <td>4774.500000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.504700e+04</td>\n",
       "      <td>4590.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>78822.750000</td>\n",
       "      <td>3861.500000</td>\n",
       "      <td>70143.750000</td>\n",
       "      <td>4217.750000</td>\n",
       "      <td>86965.500000</td>\n",
       "      <td>4538.500000</td>\n",
       "      <td>26919.500000</td>\n",
       "      <td>1363.250000</td>\n",
       "      <td>10.250000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2016.500000</td>\n",
       "      <td>1.516065e+05</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>6.291450e+04</td>\n",
       "      <td>5680.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.795600e+04</td>\n",
       "      <td>5126.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>92290.500000</td>\n",
       "      <td>5223.000000</td>\n",
       "      <td>80486.500000</td>\n",
       "      <td>6634.500000</td>\n",
       "      <td>104034.500000</td>\n",
       "      <td>6503.500000</td>\n",
       "      <td>31015.000000</td>\n",
       "      <td>1870.500000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>2.713522e+05</td>\n",
       "      <td>72.250000</td>\n",
       "      <td>1.609518e+05</td>\n",
       "      <td>7565.500000</td>\n",
       "      <td>51.250000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.728000e+04</td>\n",
       "      <td>5904.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>103742.500000</td>\n",
       "      <td>7469.750000</td>\n",
       "      <td>90909.500000</td>\n",
       "      <td>9181.750000</td>\n",
       "      <td>118520.750000</td>\n",
       "      <td>9392.250000</td>\n",
       "      <td>36206.000000</td>\n",
       "      <td>2776.750000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>3.343364e+06</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>1.140033e+06</td>\n",
       "      <td>15399.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.523776e+06</td>\n",
       "      <td>15180.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>158306.000000</td>\n",
       "      <td>12378.000000</td>\n",
       "      <td>151104.000000</td>\n",
       "      <td>21688.000000</td>\n",
       "      <td>182342.000000</td>\n",
       "      <td>14974.000000</td>\n",
       "      <td>63079.000000</td>\n",
       "      <td>5292.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 39 columns</p>\n",
       "</div>\n",
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      ],
      "text/plain": [
       "              Year     Total_Est  Total_EstErr  HispanicLatino_Est  \\\n",
       "count    54.000000  5.400000e+01     48.000000        5.400000e+01   \n",
       "mean   2016.500000  6.371851e+05     55.895833        2.196172e+05   \n",
       "std       1.723861  1.037081e+06     19.194680        3.425487e+05   \n",
       "min    2014.000000  9.291000e+04     31.000000        1.163800e+04   \n",
       "25%    2015.000000  1.031135e+05     42.000000        3.410500e+04   \n",
       "50%    2016.500000  1.516065e+05     47.500000        6.291450e+04   \n",
       "75%    2018.000000  2.713522e+05     72.250000        1.609518e+05   \n",
       "max    2019.000000  3.343364e+06    111.000000        1.140033e+06   \n",
       "\n",
       "       HispanicLatino_EstErr  HispanicLatino_Pct  HispanicLatino_PctErr  \\\n",
       "count              48.000000           54.000000              48.000000   \n",
       "mean             6755.562500           38.018519               3.833333   \n",
       "std              3085.988603           13.837999               1.404148   \n",
       "min              2551.000000           10.000000               1.000000   \n",
       "25%              4774.500000           30.000000               3.000000   \n",
       "50%              5680.000000           35.000000               4.000000   \n",
       "75%              7565.500000           51.250000               5.000000   \n",
       "max             15399.000000           61.000000               6.000000   \n",
       "\n",
       "       WhiteAlone_Est  WhiteAlone_EstErr  WhiteAlone_Pct  ...  \\\n",
       "count    5.300000e+01          53.000000       53.000000  ...   \n",
       "mean     2.841391e+05        5663.622642       44.358491  ...   \n",
       "std      4.757875e+05        2810.866874       14.395638  ...   \n",
       "min      3.562300e+04         825.000000       16.000000  ...   \n",
       "25%      4.504700e+04        4590.000000       37.000000  ...   \n",
       "50%      5.795600e+04        5126.000000       44.000000  ...   \n",
       "75%      8.728000e+04        5904.000000       47.000000  ...   \n",
       "max      1.523776e+06       15180.000000       77.000000  ...   \n",
       "\n",
       "       MeanHouseholdIncome_Est  MeanHouseholdIncome_EstErr  \\\n",
       "count                54.000000                   54.000000   \n",
       "mean              94222.444444                 5745.296296   \n",
       "std               21750.128326                 3071.383528   \n",
       "min               60726.000000                 1291.000000   \n",
       "25%               78822.750000                 3861.500000   \n",
       "50%               92290.500000                 5223.000000   \n",
       "75%              103742.500000                 7469.750000   \n",
       "max              158306.000000                12378.000000   \n",
       "\n",
       "       MedianFamilyIncome_Est  MedianFamilyIncome_EstErr  \\\n",
       "count               54.000000                  54.000000   \n",
       "mean             82918.314815                7031.888889   \n",
       "std              21128.493699                4106.774512   \n",
       "min              46826.000000                1225.000000   \n",
       "25%              70143.750000                4217.750000   \n",
       "50%              80486.500000                6634.500000   \n",
       "75%              90909.500000                9181.750000   \n",
       "max             151104.000000               21688.000000   \n",
       "\n",
       "       MeanFamilyIncome_Est  MeanFamilyIncome_EstErr  PercapitaIncome_Est  \\\n",
       "count             54.000000                54.000000            54.000000   \n",
       "mean          104925.000000              7129.203704         33061.555556   \n",
       "std            26349.319549              3866.042052          9685.833529   \n",
       "min            64838.000000              1499.000000         20251.000000   \n",
       "25%            86965.500000              4538.500000         26919.500000   \n",
       "50%           104034.500000              6503.500000         31015.000000   \n",
       "75%           118520.750000              9392.250000         36206.000000   \n",
       "max           182342.000000             14974.000000         63079.000000   \n",
       "\n",
       "       PercapitaIncome_EstErr  PovertyRate_Est  PovertyRate_EstErr  \n",
       "count               54.000000        54.000000           54.000000  \n",
       "mean              2105.814815        13.055556            2.648148  \n",
       "std               1169.201551         4.748055            1.305394  \n",
       "min                438.000000         5.000000            1.000000  \n",
       "25%               1363.250000        10.250000            2.000000  \n",
       "50%               1870.500000        12.000000            2.500000  \n",
       "75%               2776.750000        15.000000            4.000000  \n",
       "max               5292.000000        27.000000            5.000000  \n",
       "\n",
       "[8 rows x 39 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demoAll.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ECltskoXZfXN"
   },
   "source": [
    "On average, it appears a greater percentage of the San Diego population appears to be White or Hispanic. The Asian and Black population make up a much smaller percentage. This will be relevant to our analysis, as we will want to compare the racial break down of vehicle stops relative to the proportion of racial groups currently in the San Diego area.\n",
    "\n",
    "> Indented block\n",
    "\n",
    "> Indented block\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZFBMws3DHnog"
   },
   "source": [
    "### Racial Distribution Over Time (2014 - 2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_wbd6ELjHzhf"
   },
   "source": [
    "To determine if there are any trends in the racial identity of those stopped by the police over time, we will visualize the data with a line plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 316
    },
    "id": "y0_EiBgYlRhs",
    "outputId": "afd9a006-96c3-4c0a-98a3-6d7a15f61765"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title\n",
    "nRace = len(stopAll['Race_Category'].value_counts(normalize = True))\n",
    "nYear = len(pd.DataFrame(pd.DatetimeIndex(stopAll['date_stop']).year).value_counts())\n",
    "racePropByYear = np.zeros((nYear, nRace))\n",
    "years = pd.unique(pd.DataFrame(pd.DatetimeIndex(stopAll['date_stop']).year)['date_stop'])\n",
    "xLabs = list(map(str, list(years)))\n",
    "races = pd.unique(stopAll['Race_Category'])\n",
    "races = [str(x) for x in races]\n",
    "races = [x for x in races if x != 'nan']\n",
    "races = sorted(list(races))\n",
    "\n",
    "#race count per year\n",
    "for y in range(len(years)):\n",
    "  raceByYear = stopAll['Race_Category'][pd.DatetimeIndex(stopAll['date_stop']).year==years[y]]\n",
    "  raceCountByYear = pd.value_counts(raceByYear)\n",
    "  #calculate proportion for race\n",
    "  for r in range(len(races)):\n",
    "    prop = raceCountByYear[races[r]]/sum(raceCountByYear)*100\n",
    "    racePropByYear[y, r] = prop\n",
    "\n",
    "#plot\n",
    "xLabs = list(map(str, list(years)))\n",
    "plt.figure()\n",
    "plt.plot(xLabs, racePropByYear)\n",
    "plt.legend(races, bbox_to_anchor=(1, 1.05))\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Proportion in percentage')\n",
    "plt.title('Proportion of police stops by race over time\\n(not normalized by population)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IeC_gEHfzjGU"
   },
   "source": [
    "Our line plot shows most drivers stopped are White, a trend that has remained consistent from 2014 to 2018. Hispanic drivers make up the second greatest percentage of drivers stopped.\n",
    "\n",
    "In 2017, there is a noticeable change for Whites, Hispanics, Blacks, and Asians. The percentages of White drivers and Black drivers increased, while the percentages for Hispanic drivers and Asian drivers decreased. The cause for this change is unknown.\n",
    "\n",
    "An interesting pattern to note is that the changes in percentage of White drivers and percentage of Hispanic drivers appear to reflect one another. When the percentage of White drivers decreased from 2015 to 2016, the percentage of Hispanic drivers increased."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1Jl8kVzkj0DN"
   },
   "source": [
    "However, as stated previously, the racial demographics of the San Diego area may influence these results. If there's a greater population of White people in San Diego, then it's much more likely for a White driver to be stopped.\n",
    "\n",
    "We'll need to consider the population demographics in our analysis. First, we'll use a line plot to illustrate the racial demographics over the same time period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301
    },
    "id": "roRUAbAyzsSj",
    "outputId": "29c6b208-74fc-4218-f8cf-16ad3891e712"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#array to store demographics of each race per year\n",
    "raceSD = np.zeros((nYear, nRace))\n",
    "raceSD[:,0] = list(demoAll['AsianAlone_Pct'][demoAll['Geography']=='San Diego County'][0:5])\n",
    "raceSD[:,1] = list(demoAll['BlackAlone_Pct'][demoAll['Geography']=='San Diego County'][0:5])\n",
    "raceSD[:,2] = list(demoAll['HawaiianPacific_Pct'][demoAll['Geography']=='San Diego County'][0:5])\n",
    "raceSD[:,3] = list(demoAll['HispanicLatino_Pct'][demoAll['Geography']=='San Diego County'][0:5])\n",
    "raceSD[:,4] = list(demoAll['IndianAlaskaAlone_Pct'][demoAll['Geography']=='San Diego County'][0:5])\n",
    "raceSD[:,5] = np.nan\n",
    "raceSD[:,6] = list(demoAll['WhiteAlone_Pct'][demoAll['Geography']=='San Diego County'][0:5])\n",
    "\n",
    "#plot\n",
    "legend = ['Asian', 'Black', 'Hawaiian/Pacific', 'Hispanic/Latino', 'Indian/Alaska', 'Other', 'White']\n",
    "plt.figure()\n",
    "plt.plot(xLabs, raceSD)\n",
    "plt.legend(legend, bbox_to_anchor=(1, 1.05))\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Proportion in percentage')\n",
    "plt.title('Racial demographics over time');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P_HzlfOLIG0S"
   },
   "source": [
    "The racial demographics over time show that the demographics have remained relatively constant. Most of the population is White and Hispanic. A small proportion of the population is Asian, and an even smaller percentage is Black. The proportion of Indian/Alaskan, Hawaiian/Pacific Islander, and Other are much lower.\n",
    "\n",
    "However, to more accurately compare differences in vehicle stops between racial groups, we should compare the counts proportional to population demographics. As such, we will estimate the proportion using the data from Dataset 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 316
    },
    "id": "MEPmhVO-I97f",
    "outputId": "403ed7a5-fb93-4cc0-a559-1df18f077ce9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#array of race population est. for each year\n",
    "raceSD_est = np.zeros((nYear, nRace))\n",
    "\n",
    "raceSD_est[:,0] = list(demoAll['AsianAlone_Est'][demoAll['Geography']=='San Diego County'][0:5])\n",
    "raceSD_est[:,1] = list(demoAll['BlackAlone_Est'][demoAll['Geography']=='San Diego County'][0:5])\n",
    "raceSD_est[:,2] = list(demoAll['HawaiianPacificAlone_Est'][demoAll['Geography']=='San Diego County'][0:5])\n",
    "raceSD_est[:,3] = list(demoAll['HispanicLatino_Est'][demoAll['Geography']=='San Diego County'][0:5])\n",
    "raceSD_est[:,4] = list(demoAll['IndianAlaskaAlone_Est'][demoAll['Geography']=='San Diego County'][0:5])\n",
    "raceSD_est[:,5] = np.nan\n",
    "raceSD_est[:,6] = list(demoAll['WhiteAlone_Est'][demoAll['Geography']=='San Diego County'][0:5])\n",
    "\n",
    "#vehicle stops per racial category: adjusted for population\n",
    "pop_prop = np.zeros((nYear, nRace))\n",
    "\n",
    "#race count per year\n",
    "for x in range(len(years)):\n",
    "    raceByYear2 = stopAll['Race_Category'][pd.DatetimeIndex(stopAll['date_stop']).year == years[x]]\n",
    "    raceCountPerYear = pd.value_counts(raceByYear2)\n",
    "    #normalization for each race\n",
    "    for i in range(len(races)):\n",
    "        race_stops = raceCountPerYear[races[i]]\n",
    "        pop_prop[x, i] = race_stops/raceSD_est[x, i] \n",
    "\n",
    "#plot\n",
    "plt.plot(xLabs, pop_prop)\n",
    "plt.legend(races, bbox_to_anchor=(1, 1.05))\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Proportion')\n",
    "plt.title('Proportion of police stops by race over time\\n(normalized by population)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hptQrB9tSjWB"
   },
   "source": [
    "By normalizing the police stop data by population, we can see how disproportionately races are stopped by police. \n",
    "The larger the percentage, the more times a person of that race has been stopped in proportion to their demographic population. These values, along with background information, may shed light onto the results of our research problem.\n",
    "\n",
    "We can observe an interesting pattern from 2014 to 2018.\n",
    "\n",
    "In 2014, the race with the largest normalized proportion of police stops is Black. The second largest proportion is Native American, then followed by Hispanic.\n",
    "\n",
    "Over time, we see a significant decrease in proportions of non-White groups. In 2018, most races have converged to a similar value as the White group. Meanwhile, White proportions remain stable between 2014 and 2018."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JhfGwsm6J9hx"
   },
   "source": [
    "### Considering Gender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wa7V2dGwKO-I"
   },
   "source": [
    "**Reason for Consideration:** \n",
    "\n",
    "We want to know if gender is a big factor in our analysis. Are the racial distribution trends the same for both males and females?\n",
    "\n",
    "As shown in the initial look at our datasets, there appears to be a greater number of male drivers stopped than females. Therefore, we'll take a look at the racial distribution for male drivers and female drivers to determine if the patterns we've seen so far may have been driven by gender."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hYr40I7yeddf"
   },
   "source": [
    "We'll group the `Race_Category` by `sex`, then determine the proportion of each race given a gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 488
    },
    "id": "HDhYy0DaKMht",
    "outputId": "90fde350-b26c-42c0-8ce8-c2f006216aef"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-83737e75-9892-42ce-acb6-2f8d3339e8af\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>sex</th>\n",
       "      <th>Race_Category</th>\n",
       "      <th>proportion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>0.463120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>0.281933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>Black</td>\n",
       "      <td>0.094055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>Asian</td>\n",
       "      <td>0.086537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.070163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>Native American</td>\n",
       "      <td>0.004075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>F</td>\n",
       "      <td>Hawaiian</td>\n",
       "      <td>0.000116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>0.401882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>0.317972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M</td>\n",
       "      <td>Black</td>\n",
       "      <td>0.120809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>M</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.079375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>M</td>\n",
       "      <td>Asian</td>\n",
       "      <td>0.074444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>M</td>\n",
       "      <td>Native American</td>\n",
       "      <td>0.005362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>M</td>\n",
       "      <td>Hawaiian</td>\n",
       "      <td>0.000157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-83737e75-9892-42ce-acb6-2f8d3339e8af')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-83737e75-9892-42ce-acb6-2f8d3339e8af button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-83737e75-9892-42ce-acb6-2f8d3339e8af');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   sex    Race_Category  proportion\n",
       "0    F            White    0.463120\n",
       "1    F         Hispanic    0.281933\n",
       "2    F            Black    0.094055\n",
       "3    F            Asian    0.086537\n",
       "4    F            Other    0.070163\n",
       "5    F  Native American    0.004075\n",
       "6    F         Hawaiian    0.000116\n",
       "7    M            White    0.401882\n",
       "8    M         Hispanic    0.317972\n",
       "9    M            Black    0.120809\n",
       "10   M            Other    0.079375\n",
       "11   M            Asian    0.074444\n",
       "12   M  Native American    0.005362\n",
       "13   M         Hawaiian    0.000157"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_sex_df = stopAll.groupby('sex')['Race_Category'].value_counts(normalize = True)\n",
    "prop_sex_df = pd.DataFrame(prop_sex_df.reset_index(name = 'proportion'))\n",
    "prop_sex_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oS5muZbsyZvG"
   },
   "source": [
    "We'll create our own palette in order to ensure the colors remain consistent for our visualizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "s18xTbF6yYp8"
   },
   "outputs": [],
   "source": [
    "unique = stopAll['Race_Category'].unique()\n",
    "palette = dict(zip(unique, sns.color_palette(['tab:pink', 'tab:blue', 'tab:red', \n",
    "                                              'tab:orange', 'tab:brown', 'tab:purple', 'tab:green'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 370
    },
    "id": "dVvp2YVYfeuF",
    "outputId": "64c601c9-aad4-4605-b21d-2c32780f981e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 5))\n",
    "plot_sex_prop = sns.barplot(x = 'sex', y = 'proportion', hue = 'Race_Category', \n",
    "                            data = prop_sex_df, palette = palette)\n",
    "plt.title('Racial Distribution Given Gender \\n(not normalized)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zg9VHsxt5YQ7"
   },
   "source": [
    "Once again, we'll want to normalize our data by the population estimates for each racial group. Since the data we are currently looking at combines all the vehicular stops from 2014 to 2018, we'll use the average population estimates in order to normalize our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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    "id": "e-0sEnXJ0VlU",
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: RuntimeWarning: Mean of empty slice\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 383907.6,  156161.4,   13070.2, 1112585.4,   12206.4,       nan,\n",
       "       1516215.8])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raceSD_est_mean = np.nanmean(raceSD_est, axis = 0)\n",
    "raceSD_est_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pR93wcNe0YCl"
   },
   "outputs": [],
   "source": [
    "avg_asian_pop = raceSD_est_mean[0]\n",
    "avg_black_pop = raceSD_est_mean[1]\n",
    "avg_hpi_pop = raceSD_est_mean[2]\n",
    "avg_hisp_pop = raceSD_est_mean[3]\n",
    "avg_indian_pop = raceSD_est_mean[4]\n",
    "avg_white_pop = raceSD_est_mean[6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jqr5c-i30bOW"
   },
   "source": [
    "Once we have the average population estimates for each racial group, we'll use a function to calculate the normalized data values. Note that the `Race_Category` label of \"Other\" will be set to NaN since there is no demographic data available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Oqx_hkE40ge1"
   },
   "outputs": [],
   "source": [
    "def normalize_pop(df):\n",
    "    for index in df.index:\n",
    "        if df.loc[index, 'Race_Category'] == 'Asian':\n",
    "            df.loc[index, 'normal'] = df.loc[index, 'count'] / avg_asian_pop\n",
    "        elif df.loc[index, 'Race_Category'] == 'Black':\n",
    "            df.loc[index, 'normal'] = df.loc[index, 'count'] / avg_black_pop\n",
    "        elif df.loc[index, 'Race_Category'] == 'Hispanic':\n",
    "            df.loc[index, 'normal'] = df.loc[index, 'count'] / avg_hisp_pop\n",
    "        elif df.loc[index, 'Race_Category'] == 'Hawaiian':\n",
    "            df.loc[index, 'normal'] = df.loc[index, 'count'] / avg_hpi_pop\n",
    "        elif df.loc[index, 'Race_Category'] == 'Native American':\n",
    "            df.loc[index, 'normal'] = df.loc[index, 'count'] / avg_indian_pop\n",
    "        elif df.loc[index, 'Race_Category'] == 'White':\n",
    "            df.loc[index, 'normal'] = df.loc[index, 'count'] / avg_white_pop\n",
    "        else:\n",
    "            df.loc[index, 'normal'] = np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rgGKRqML0mXv"
   },
   "source": [
    "We'll need to use the counts for vehicular stops when normalizing the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 488
    },
    "id": "Mfdf0COC0pI4",
    "outputId": "79e29c91-a91c-4ef8-f655-f69359b7fc28"
   },
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      "text/plain": [
       "   sex    Race_Category   count    normal\n",
       "0    F            White   75577  0.049846\n",
       "1    F         Hispanic   46009  0.041353\n",
       "2    F            Black   15349  0.098289\n",
       "3    F            Asian   14122  0.036785\n",
       "4    F            Other   11450       NaN\n",
       "5    F  Native American     665  0.054480\n",
       "6    F         Hawaiian      19  0.001454\n",
       "7    M            White  120526  0.079491\n",
       "8    M         Hispanic   95361  0.085711\n",
       "9    M            Black   36231  0.232010\n",
       "10   M            Other   23805       NaN\n",
       "11   M            Asian   22326  0.058155\n",
       "12   M  Native American    1608  0.131734\n",
       "13   M         Hawaiian      47  0.003596"
      ]
     },
     "execution_count": 44,
     "metadata": {},
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    }
   ],
   "source": [
    "sex_df = stopAll.groupby('sex')['Race_Category'].value_counts()\n",
    "sex_df = pd.DataFrame(sex_df.reset_index(name = 'count'))\n",
    "normalize_pop(sex_df)\n",
    "sex_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 5))\n",
    "plot_sex = sns.barplot(x = 'sex', y = 'normal', hue = 'Race_Category', \n",
    "                       data = sex_df, palette = palette)\n",
    "plt.title('Racial Distribution Given Gender \\n(normalized)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FStWAlp8YubU"
   },
   "source": [
    "As shown, there doesn't appear to be a distinct difference in trends between genders. For both Females and Males, there appears to be a greater proportion of Black male drivers and Native American male drivers stopped, so they may have a greater contribution to the trend we observed. However, the racial distribution does not appear to be too affected by gender."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L8C82N7rvhqD"
   },
   "source": [
    "### Considering Age "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xSY_Z8gDv1J6"
   },
   "source": [
    "**Reason for Consideration:**\n",
    "\n",
    "We want to know if age plays a significant role in our results on racial distribution. Is the racial distribution disproportionate because more people of that race are younger or older? If so, are younger or older people more likely to be pulled over by the police? We will explore those questions in this section.\n",
    "\n",
    "As previously seen, most of the drivers stopped appear to be in their 30s or close to that range. To determine if the trend we've seen may be driven by age, we will look at age groups divided into young drivers (16 - 40) and middle-aged drivers (40+). The plot below shows the right-skewed age distribution of drivers who are pulled over."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301
    },
    "id": "ZHJAXEEOxf6T",
    "outputId": "cd1c850c-bcfe-4df3-fa18-cf9a1f24dca8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.histplot(list(stopAll['age']), binwidth=5)\n",
    "plt.xlim(0,100)\n",
    "plt.xlabel('Age');\n",
    "plt.title('Distribution of age');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LftLjTYVzHN5"
   },
   "source": [
    "We'll first look at young drivers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 370
    },
    "id": "_IuQEol60Mam",
    "outputId": "8336b04a-3237-47af-d18e-fae20e361cdf"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_young = stopAll[(stopAll['age'] > 16) & (stopAll['age'] < 40)]\n",
    "\n",
    "plt.figure(figsize = (10, 5))\n",
    "young_count_plot = sns.countplot(x = 'Race_Category', data = age_young, palette = palette);\n",
    "plt.title('Racial Distribution of Young Drivers \\n(not normalized)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qNPBPMwSFiP1"
   },
   "source": [
    "In the plot above, we plotted the counts of drivers between the ages 16 and 40. The majority of young drivers who are stopped over are White, followed by Hispanic. This trend looks similar to the population demographics of San Diego, but what if we normalize this data to the population data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qEGQlFpr7nBy"
   },
   "source": [
    "Similar to what we did in \"Considering Gender\", we'll normalize the data using population estimates for each racial group. We can use our `normalize_pop()` function to do so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "id": "04Ge3g1f7p02",
    "outputId": "6b96a83e-6e64-458a-d43f-ae39295a00ec"
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "     Race_Category   count    normal\n",
       "0            White  106656  0.070344\n",
       "1         Hispanic   90560  0.081396\n",
       "2            Black   32623  0.208906\n",
       "3            Other   22041       NaN\n",
       "4            Asian   20627  0.053729\n",
       "5  Native American    1462  0.119773\n",
       "6         Hawaiian      43  0.003290"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "young_df = pd.DataFrame(age_young.Race_Category.value_counts())\n",
    "young_df = young_df.reset_index()\n",
    "young_df.columns = ['Race_Category', 'count']\n",
    "normalize_pop(young_df)\n",
    "young_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5))\n",
    "young_norm_plot = sns.barplot(x = 'Race_Category', y = 'normal', \n",
    "                              data = young_df, palette = palette)\n",
    "plt.title('Racial Distribution of Young Drivers \\n(normalized)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ITCMvwi01E0J"
   },
   "source": [
    "We can see that among young drivers, the distribution of races appear to follow the same pattern as before, where there's a greater proportion of Black drivers stopped compared to other racial groups, and a slightly higher proportion of Native American drivers stopped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tAtTZ2WMZxYH"
   },
   "source": [
    "Now, we'll look at middle-aged drivers at least 40 years old."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 370
    },
    "id": "b3IB3KHz1VTv",
    "outputId": "87102156-4a52-49b2-bb44-b7cbcdacfa38"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_old = stopAll[stopAll['age'] >= 40]\n",
    "\n",
    "plt.figure(figsize = (10, 5))\n",
    "old_count_plot = sns.countplot(x = 'Race_Category', data = age_old, palette = palette);\n",
    "plt.title('Racial Distribution of Old Drivers \\n(not normalized)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8A7SsulhHdSk"
   },
   "source": [
    "We see the same trend as young drivers. White and Hispanic make the majority of vehicle stops for older drivers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vgO4UM7LHUXq"
   },
   "source": [
    "Again, we normalize the data by using `normalize_pop()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "id": "aaVRPEBe759V",
    "outputId": "9ae187e9-734f-42bc-8b82-94e5b3994165"
   },
   "outputs": [
    {
     "data": {
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       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-66dd22f4-91c7-47e6-918b-ca6874121a8b');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "     Race_Category  count    normal\n",
       "0            White  80281  0.052948\n",
       "1         Hispanic  47330  0.042541\n",
       "2            Black  17517  0.112172\n",
       "3            Asian  14846  0.038671\n",
       "4            Other  11577       NaN\n",
       "5  Native American    792  0.064884\n",
       "6         Hawaiian     19  0.001454"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_df = pd.DataFrame(age_old.Race_Category.value_counts())\n",
    "old_df = old_df.reset_index()\n",
    "old_df.columns = ['Race_Category', 'count']\n",
    "normalize_pop(old_df)\n",
    "old_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 370
    },
    "id": "x3ouejva78J3",
    "outputId": "8142a81a-3985-4d65-c095-4c5280324d46"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5))\n",
    "old_norm_plot = sns.barplot(x = 'Race_Category', y = 'normal', \n",
    "                              data = old_df, palette = palette)\n",
    "plt.title('Racial Distribution of Old Drivers \\n(normalized)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u5L9WeQJ1XQD"
   },
   "source": [
    "Again, the normalized racial distribution for older drivers shows a similar distribution to the normalized graph for young drivers. In the graph above, black drivers are most disproportionately stopped by the police despite age. This is followed by the Native American group.\n",
    "\n",
    "Based on these results, we can state that racial distribution of drivers in vehicle stops remains the same between young and old drivers; therefore, age does not appear to drive the trends we see."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TrVH3Watbfq0"
   },
   "source": [
    "### Considering Time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rh4CxsSubtm2"
   },
   "source": [
    "**Reason for Consideration:**\n",
    "\n",
    "In our background, we referenced a recent 2020 study that found that the time of day can influence who gets stopped (https://www.nature.com/articles/s41562-020-0858-1). The study finds that \"black drivers comprise a smaller proportion of drivers stopped after sunset, suggestive of discrimination in stop decisions\". Does this trend hold true for San Diego?\n",
    "\n",
    "To determine if the time of the stop influences the racial distributions we've seen for police vehicular stops, we will divide the data into different times of the day:\n",
    "\n",
    "- morning (5:00 AM - 11:59 AM)\n",
    "- afternoon (12:00 PM - 4:59 PM)\n",
    "- evening (5:00 PM - 10:59 PM)\n",
    "- late night/early morning (11:00 PM - 4:59 AM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CCHwn96Ddjsm"
   },
   "outputs": [],
   "source": [
    "morning = stopAll[(pd.DatetimeIndex(stopAll['date_time']).time >= datetime.time(5, 0))\n",
    "                 & (pd.DatetimeIndex(stopAll['date_time']).time <= datetime.time(11, 59))]\n",
    "afternoon = stopAll[(pd.DatetimeIndex(stopAll['date_time']).time >= datetime.time(12, 0))\n",
    "                 & (pd.DatetimeIndex(stopAll['date_time']).time <= datetime.time(16, 59))]\n",
    "evening = stopAll[(pd.DatetimeIndex(stopAll['date_time']).time >= datetime.time(17, 0))\n",
    "                 & (pd.DatetimeIndex(stopAll['date_time']).time <= datetime.time(22, 59))]\n",
    "night = stopAll[(pd.DatetimeIndex(stopAll['date_time']).time >= datetime.time(23, 0))\n",
    "                 | (pd.DatetimeIndex(stopAll['date_time']).time <= datetime.time(4, 59))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5tpm7NtZeAfo"
   },
   "source": [
    "We will normalize our data to the population before beginning our analysis. To do so, we use our `normalize_pop()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hf57EM_geXSA"
   },
   "outputs": [],
   "source": [
    "#morning\n",
    "m_df = pd.DataFrame(morning.Race_Category.value_counts())\n",
    "m_df = m_df.reset_index()\n",
    "m_df.columns = ['Race_Category', 'count']\n",
    "normalize_pop(m_df)\n",
    "\n",
    "#afternoon\n",
    "a_df = pd.DataFrame(afternoon.Race_Category.value_counts())\n",
    "a_df = a_df.reset_index()\n",
    "a_df.columns = ['Race_Category', 'count']\n",
    "normalize_pop(a_df)\n",
    "\n",
    "#evening\n",
    "e_df = pd.DataFrame(evening.Race_Category.value_counts())\n",
    "e_df = e_df.reset_index()\n",
    "e_df.columns = ['Race_Category', 'count']\n",
    "normalize_pop(e_df)\n",
    "\n",
    "#night\n",
    "n_df = pd.DataFrame(night.Race_Category.value_counts())\n",
    "n_df = n_df.reset_index()\n",
    "n_df.columns = ['Race_Category', 'count']\n",
    "normalize_pop(n_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 691
    },
    "id": "iMnrhDfyajaN",
    "outputId": "97475ddd-f394-4ce8-9ce9-452aae2eb3ba"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plots\n",
    "fig, ax = plt.subplots(2, 2, figsize = (15, 10))\n",
    "\n",
    "#morning\n",
    "sns.barplot(x = 'Race_Category', y = 'normal', data = m_df, \n",
    "            ax = ax[0, 0], palette = palette);\n",
    "ax[0, 0].set_title('Morning');\n",
    "ax[0, 0].tick_params(axis = 'x', labelsize = 8, labelrotation = 45)\n",
    "\n",
    "#afternoon\n",
    "sns.barplot(x = 'Race_Category', y = 'normal', data = a_df, \n",
    "            ax = ax[0, 1], palette = palette);\n",
    "ax[0, 1].set_title('Afternoon');\n",
    "ax[0, 1].tick_params(axis = 'x', labelsize = 8, labelrotation = 45)\n",
    "\n",
    "#evening\n",
    "sns.barplot(x = 'Race_Category', y = 'normal', data = e_df, \n",
    "            ax = ax[1, 0], palette = palette);\n",
    "ax[1, 0].set_title('Evening');\n",
    "ax[1, 0].tick_params(axis = 'x', labelsize = 8, labelrotation = 45)\n",
    "\n",
    "#night\n",
    "sns.barplot(x = 'Race_Category', y = 'normal', data = n_df, \n",
    "            ax = ax[1, 1], palette = palette);\n",
    "ax[1, 1].set_title('Night');\n",
    "ax[1, 1].tick_params(axis = 'x', labelsize = 8, labelrotation = 45)\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ij-Ok70SfZYI"
   },
   "source": [
    "The graphs above indicate that generally, the proportion of White drivers, Hispanic drivers, and Asian drivers (relative to their respective population estimates) consistently remain around 0.02 - 0.03 for all times of the day. The proportion of Black drivers also stays within a consistent range throughout the day (0.07 - 0.09), though the proportion of Black drivers stopped is much greater than the other racial groups, which matches the pattern we've previously observed.\n",
    "\n",
    "However, the proportion of Native American drivers appears to change throughout the day. In the morning, the proportion is about 0.05, and it drops to around 0.035 in the afternoon. But in the evening, the proportion spikes to about 0.07. In addition, comparing the proportion of Native American drivers to White, Hispanic, and Asian drivers reveals that around nighttime, a greater proportion of Native American drivers seem to be stopped. Yet during the day, their proportion is similar to White, Hispanic, and Asian drivers. This implies that the time of day may have some influence on the trend of more Native American drivers being stopped.\n",
    "\n",
    "The proportion of Hawaiian/Pacific Islander drivers stopped is nearly negligible to the rest of our analysis considering how small their numbers are compared to the rest of our data.\n",
    "\n",
    "There was no demographic data available for \"Other\" racial groups that may be stopped. As such, normalization of their stop data couldn't be found. Analysis of that data can't be conducted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZbiZTOeYdNJN"
   },
   "source": [
    "## Statistical Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eJ6T5A80IZIh"
   },
   "source": [
    "Previously through explanatory data analysis and visualizations, we've went through several factors that could be related to the chance of police vehicle stop. In this section, we continue to validate our hypothesis in a statistical approach to answer specific questions we are interested in:\n",
    "- Are the racial distribution observed in police vehicle stop be driven by one particular gender?\n",
    "- Is there a significant difference between African American drivers' mean age and White drivers' mean age among those who were stopped by police?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uxEs-9TdJLny"
   },
   "source": [
    "### Racial Distribution & Gender of Police Stops\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BD9GQq44JM67"
   },
   "source": [
    "To find out if gender could be the underlying cause of the uneven racial distribution of police vehicle stops as shown above, we conducted a permutation test to see if the racial distribution of each gender come from the same data generating process, or in other words, the same distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "qQTZYpPSJP-C",
    "outputId": "4bc75575-0793-47ab-dc72-40bff7041c0f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-d3a8688f-479f-426a-b88d-489aa4bc36b1\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race_Category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>0.086537</td>\n",
       "      <td>0.074444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>0.094055</td>\n",
       "      <td>0.120809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawaiian</th>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.000157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>0.281933</td>\n",
       "      <td>0.317972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>0.004075</td>\n",
       "      <td>0.005362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>0.070163</td>\n",
       "      <td>0.079375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>0.463120</td>\n",
       "      <td>0.401882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d3a8688f-479f-426a-b88d-489aa4bc36b1')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-d3a8688f-479f-426a-b88d-489aa4bc36b1 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-d3a8688f-479f-426a-b88d-489aa4bc36b1');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "sex                     F         M\n",
       "Race_Category                      \n",
       "Asian            0.086537  0.074444\n",
       "Black            0.094055  0.120809\n",
       "Hawaiian         0.000116  0.000157\n",
       "Hispanic         0.281933  0.317972\n",
       "Native American  0.004075  0.005362\n",
       "Other            0.070163  0.079375\n",
       "White            0.463120  0.401882"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_df2 = sex_df.pivot_table(index = 'Race_Category', columns = 'sex', values = 'count')\n",
    "df_test = stopAll[['race_code','Race_Category', 'sex']]\n",
    "obs_df = df_test.pivot_table(index = 'Race_Category', columns = 'sex', values = 'race_code', aggfunc = 'count')\n",
    "obs_df / obs_df.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BwCjMR2ZJUlF"
   },
   "source": [
    "The way we quantify how different the two categorical distributions are is by having our test statistic to be TVD, Total Variation Distance, in measuring the distance between the two probability distributions. A helper function is defined below for calculating TVD and the details of the permutation test as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3zBYS1v5JS-F",
    "outputId": "46277b8f-87e9-41ba-cc11-4fe6f140241e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07333077985584097"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def tvd(df):\n",
    "    prop = df.pivot_table(index = 'Race_Category', columns = 'sex', values = 'race_code', aggfunc = 'count')\n",
    "    dist = prop/prop.sum()\n",
    "    return dist.diff(axis = 1).iloc[:, -1].abs().sum()/2\n",
    "#Observed Test Statistic\n",
    "obs = tvd(df_test)\n",
    "obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "V7YhX2G9JZjW",
    "outputId": "b0a2c9e2-a265-4079-c847-36b9f2837605"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The permuation test:\n",
    "#Since our dataset is huge, we decided to take only 100 repetition process\n",
    "n_repetitions = 100\n",
    "diff = []\n",
    "for _ in range(n_repetitions):\n",
    "    shuffled_sex = (df_test['sex'].sample(frac=1).reset_index(drop = True))\n",
    "    shuffled_df = (df_test.assign(**{'sex': shuffled_sex}))\n",
    "    diff.append(tvd(shuffled_df))\n",
    "#p-value\n",
    "p_val = np.mean(np.array(diff) >= obs)\n",
    "p_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 284
    },
    "id": "H3dL-bKTJcSc",
    "outputId": "5fd9db52-867a-4f67-adcf-86edb353c8ac"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'TVDs in Sex Distribution (Male - Female)'\n",
    "pd.Series(diff).plot(kind='hist', density=True, ec='w', bins=10, title=title)\n",
    "plt.axvline(x=obs, color='red', linewidth=3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E8Tefwk-JhpC"
   },
   "source": [
    "An extremely small p-value shows that there is a statistically significant difference in the racial distribution between Male and Female. \n",
    "\n",
    "\n",
    "This result tells us that the racial distribution between gender is statistically different. But does this change our conclusion that minorities are more likely to be pulled over by police? Let's take a look back at our previous graph from the section 'Considering Gender':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 370
    },
    "id": "I0BHvnVxzFNf",
    "outputId": "2b65b07f-54cb-44fe-a554-7e4f351208eb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 5))\n",
    "plot_sex = sns.barplot(x = 'sex', y = 'normal', hue = 'Race_Category', \n",
    "                       data = sex_df, palette = palette)\n",
    "plt.title('Racial Distribution Given Gender \\n(normalized)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b9th8HyizR8v"
   },
   "source": [
    "This graph shows the normalized racial distribution given gender. As you can see from the graph, both Female and Male groups show that Black people are most disproportionately pulled over by police. So this trend still appears no matter what gender.\n",
    "\n",
    "Thus, these results continue to prove our hypothesis that minorities are more likely to be stopped. In this case, we can specifically state that Black drivers are more likely to be pulled over despite gender."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BQKNsIR9WD_1"
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   "source": [
    "### Age Difference between Two Major Racial Groups"
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    "From previous visualization of the normalized proportion bar charts, we discovered that young black drivers have appeared to be typically the most frequent group to be stopped by the police. To test our hypothesis, we compared the African American group with the White group in terms of the drivers' age. The latter group has consisted almost half of the stop observations so it should be representative enough to serve as a baseline for us to compare if there is really a difference in ages of Black drivers from the other groups. To do this, we will perform a student's t test using scipy. \n",
    "- Null hypothesis: The mean age of Black drivers is equal to the mean age of White drivers\n",
    "- Alternative hypothesis: The mean age of Black drivers is significantly larger than the mean age of White drivers\n"
   ]
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       "      <th>age</th>\n",
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       "      <td>37.744126</td>\n",
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       "                       age\n",
       "Race_Category             \n",
       "Asian            37.744126\n",
       "Black            35.913788\n",
       "Hawaiian         35.365079\n",
       "Hispanic         35.324839\n",
       "Native American  36.425786\n",
       "Other            35.718977\n",
       "White            38.876168"
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     "execution_count": 61,
     "metadata": {},
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   "source": [
    "stopAll.groupby('Race_Category').mean()['age'].to_frame()"
   ]
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      "text/plain": [
       "Ttest_indResult(statistic=-37.59461616210613, pvalue=2.1266526172395397e-308)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#since t test cannot handle null values\n",
    "black = stopAll.loc[stopAll['Race_Category'] == 'Black', 'age'].dropna()\n",
    "white = stopAll.loc[stopAll['Race_Category'] == 'White', 'age'].dropna()\n",
    "stats.ttest_ind(black, white)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ufe5ccanbWxY"
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   "source": [
    "The average age of Black drivers being pulled over by the police is at 35.9, which is statistically significantly lower than that of White drivers with a mean age of 38.9 ($p = 2.13 \\times 10^{-308}$). Such disparity in age between the two racial groups could implicate a number of social equality problems, such as bias against younger Black drivers or elder White drivers. These conclusions can not yet be drawn as we related back the data cleaning section where we left out the unfaithful age data untreated, yet the disparity is worth noticing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "En4jCNLme1pE"
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   "source": [
    "#Ethics and Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7Td6LQzoe4mo"
   },
   "source": [
    "Due to the nature of the data collected by the San Diego Police Department, some information may indirectly be personally identifiable information. This can include the combination of \n",
    " of vehicle stop for each subject. However, all the data used still remains anonymous. In addition, the datasets are maintained by the City of San Diego and are therefore publicly accessible for others to attempt to recreate this project and our results.\n",
    "\n",
    "\n",
    "There may be some biases in our data and our subsequent analysis: racial demographics in each San Diego area, time of vehicle stop, and subject’s age and sex. As such, these factors will be taken into account while analyzing our data, and we will attempt to determine if there is an association between any of these variables and vehicle stops that may impact the results. This analysis is not influenced by personal biases toward race or the law enforcement system. \n",
    "\n",
    "The results of our analysis are to determine correlation, so we must keep in mind that correlation does not prove causation. We can only show that minority drivers are disproportionately pulled over through our data, but cannot prove that this is because of racial bias. On a similar note, we cannot generalize all vehicle stops to be influenced by racial bias. Our results are not meant to positively or negatively endorse any group or institution, but rather to show how specific factors influence vehicle stops through data.\n",
    "\n",
    "In addition, due to the data being collected for San Diego specifically, the results from our analysis may not be applicable to other regions or states due to a difference in population demographics and police activity. Our data set was also limited to the years 2014 - 2018 and doesn't consider how current events and social movements, like the rise of the Black Lives Matter protests in 2020, may have impacted these results. As such, our data may not accurately represent the current patterns of police vehicle stop activity and shouldn't be used as conclusive evidence. \n",
    "\n",
    "Overall, throughout the completion of the project, our group considered any ethical concerns or privacy issues that may have occured and addressed them accordingly."
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "dZqCh4Vpenp1"
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   "source": [
    "#Conclusion and Discussion"
   ]
  },
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    "id": "Pd3MKU2teuzb"
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   "source": [
    "Our project's aim is to explore how race and other factors influence vehicle stop decisions in San Diego. We hypothesized that racial minorities are more likely to be stopped by police. To prove this hypothesis, we used data from *data.sandiego.gov* to understand the trend of vehicles being stopped over time (2014 - 2018), and compared it to data from *dof.ca.gov* containing population demographic of San Diego county. \n",
    "\n",
    "Our analysis included visualizing any trends of being stopped over time for each racial group while considering multiple factors: gender, age, and time of stop. By looking at the population demographics, we can observe that the majority of the population in San Diego County are White. We see a similar trend in the second dataset (Vehicle Stops) where majority of drivers getting pulled over were white. However, after normalizing the vehicle stop data to the population demographics, we observe that Black and Native American drivers are more likely to be stopped then drivers of other races, a trend that remains consistent over time. However, the proportion of non-White drivers being stopped appear to be decreasing over time, slowly approaching the proportion of White drivers stopped. \n",
    "\n",
    "When considering how gender could influence these trends, we broke down our data by gender, then by racial group. After normalizing our data, the results indicate that a greater proportion of Black male drivers (proportion = 0.23) are stopped, followed by Native American male drivers (proportion = 0.13). However, the overall racial trends appear similar to what was previously observed, implying that gender most likely doesn't drive the trend.\n",
    "\n",
    "Drivers' ages vary, from 16 years old up to 90 years old, with the majority of the drivers being 20 - 40 years old. Thus, after dividing our dataset into two categories - Young Drivers (16-40) and Old Drivers (40+) - we broke down these subsets by racial group. Our graphs demonstrated that the racial distributions follow the pattern of previous findings for both, indicating the age most likely doesn't have a large influence on the racial trends we've seen.\n",
    "\n",
    "To consider how the time of being stopped may play a role, we split the dataset into four times of the day - Morning, Afternoon, Evening, and Night - to categorize the change of vehicle stops trend. Our results indicate that regardless of time, Black drivers have highest proportion of drivers stopped. The proportions for White, Hispanic, and Asian drivers remain relatively similar and consistent throughout the day. The proportion of Native American drivers stopped are not consistent throughout the day, as there is a notable increase in the evening and in the night. This indicates that the time of day may influence the trends we previously observed, considering how the proportion of Native American drivers stopped in the morning and afternoon are somewhat similar to White and Hispanic drivers.\n",
    "\n",
    "According to our statistical analysis, there is a significant difference between the ages of Black drivers stopped and White drivers stopped. This implies that age may contribute to the large disparity between the proportion of Black drivers stopped and the proportion for non-Black drivers, though further analysis would be required for definitive evidence.\n",
    "\n",
    "Overall, our results support our hypothesis that racial minorites are more likely to be stopped by the police, though this is only true for Black and Native American drivers in the San Diego area. The proportion of Hispanic and Asian drivers appear to be the same as for White drivers. However, there are some limitations to our analysis since we didn't consider police presence. If there's a greater police presence in communities with a higher Black or Native American population, this would contribute to the trends observed. But since we do not have any data about police presence in corresponding area codes, this analysis could not be conducted. Another limitation is officer information. We do not know which officer stopped which drivers, and the trends observed may be influenced by some group of specific officers' actions. For others who want to further explore this question, we recommend creating a geospatial analysis on population and police data in specific locations in San Diego.\n",
    "\n",
    "Our analysis demonstrates that there are racial disparities in police vehicular stops for the San Diego area, and that part of this may be influenced by the time of day these stops occur. We hope this analysis will prompt further research about the cause for these trends we observed and to raise awareness about possible problems regarding systemic racism in our local communities and incentivize more action to address how biases can affect day-to-day life.\n",
    "\n"
   ]
  },
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   "metadata": {
    "id": "7v1yIiEyeoR2"
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   "source": [
    "# Team Contributions"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "id": "8sokC_Quexm_"
   },
   "source": [
    "- Man Jiang: background and prior work, data cleaning, statistical analysis\n",
    "- Anya Bodhisartha: background and prior work, hypothesis, EDA, conclusion & discussion\n",
    "- Jiliana Tiu: background and prior work, analysis descriptions, ethics & privacy, conclusion & discussion\n",
    "- Jingyue Xu: background and prior work, compile datasets, data cleaning, EDA \n",
    "- Divya Shetty: data cleaning, EDA, ethics & privacy, conclusion & discussion"
   ]
  }
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