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February 16, 2023 05:40
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policedataprepare.ipynb
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/TanabutT/a31e6b78793ff50d9d429b1469704691/policedataprepare.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Mqasnq0jtuXB" | |
| }, | |
| "source": [ | |
| "# Police stop in Rhode Island\n", | |
| "\n", | |
| "On a typical day in the United States, police officers make more than 50,000 traffic stops. Our team is gathering, analyzing, and releasing records from millions of traffic stops by law enforcement agencies across the country. Our goal is to help researchers, journalists, and policymakers investigate and improve interactions between police and the public\n", | |
| "\n", | |
| "the Stanford Open Policing Project dataset and analyze the impact of gender and race on police behavior\n", | |
| "\n", | |
| "cradit https://openpolicing.stanford.edu/\n", | |
| "\n", | |
| "population:\n", | |
| "https://datacommons.org/tools/timeline#place=geoId%2F44&statsVar=Count_Person_BlackOrAfricanAmericanAlone%2C0%2C14%2C2%2CCount_Person__Count_Person_HispanicOrLatino%2C0%2C14%2C3%2CCount_Person__Count_Person_WhiteAlone%2C0%2C14%2C7%2CCount_Person__Count_Person_AsianAlone%2C0%2C14%2C1__Count_Person_SomeOtherRaceAlone%2C0%2C14%2C5%2CCount_Person__Count_Person_NativeHawaiianOrOtherPacificIslanderAlone%2C0%2C14%2C4\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "KcYExvJCtuXE" | |
| }, | |
| "source": [ | |
| "" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "rO6hyEXQtuXE" | |
| }, | |
| "source": [ | |
| "## Problem \n", | |
| "### Do police bias with black and Hispanic drivers and searched more often than whites?\n", | |
| "### Are traffic stops prone to racial bias?\n", | |
| "<br>\n", | |
| "เนื่องจากการปฏิบัติงานของเจ้าหน้าที่ตำรวจในอเมริกานั้น ต้อง interact กับ ผู้คนหลายเชื้อชาติ หลายกลุ่มชาติพันธุ์ ซึ่งทำให้เกิด bias ขึ้นระหว่างทำหน้าที่โดยไม่รู้ตัว dataset นี่เป็นการศึกษา ของ Stanford ที่ต้องการจะ improve interactions between police and the public เราจึ่งมาวิเคราะห์กันว่าจะได้อะไรกันบ้างจากชุดข้อมูลนี้\n", | |
| "\n", | |
| "\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "jckUwS2ytuXF" | |
| }, | |
| "source": [ | |
| "### <b>คำถามที่จะต้องการหา</b>\n", | |
| "\n", | |
| "\n", | |
| "<br>\n", | |
| "1.คนส่วนใหญ่โดนเรียกด้วยสาเหตุอะไร<br>\n", | |
| "2.คนที่โดนตำรวจเรียก มีใครบ้าง ผู้หญิ่งผู้ชาย คนผิวขาว ผิวดำ เชื้อชาติอะไรบ้าง กลุ่มไหนเป็นเท่าไหร่<br>\n", | |
| "3.คนที่โดนตำรวจเรียก โดนค้นตัว ค้นรถ หรือไม่ มีแนวโน้มว่า gender or race ใดไหมที่โดนมากกว่าปกติ หา bias<br>\n", | |
| "4.มีความเชื่อมโยงหรือไม่ระหว่าง race กับการ โดนจับในที่สุด<br>\n", | |
| "5.มีความเชื่อมโยงหรือไม่ระหว่าง race กับการ ค้นหาเจอของผิด กฏหมาย\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "scrolled": false, | |
| "id": "l-m_WtkftuXG", | |
| "outputId": "4e825f1d-f352-4563-b11c-2ef519e90149" | |
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| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>raw_row_number</th>\n", | |
| " <th>date</th>\n", | |
| " <th>time</th>\n", | |
| " <th>zone</th>\n", | |
| " <th>subject_race</th>\n", | |
| " <th>subject_sex</th>\n", | |
| " <th>department_id</th>\n", | |
| " <th>type</th>\n", | |
| " <th>arrest_made</th>\n", | |
| " <th>citation_issued</th>\n", | |
| " <th>...</th>\n", | |
| " <th>reason_for_stop</th>\n", | |
| " <th>vehicle_make</th>\n", | |
| " <th>vehicle_model</th>\n", | |
| " <th>raw_BasisForStop</th>\n", | |
| " <th>raw_OperatorRace</th>\n", | |
| " <th>raw_OperatorSex</th>\n", | |
| " <th>raw_ResultOfStop</th>\n", | |
| " <th>raw_SearchResultOne</th>\n", | |
| " <th>raw_SearchResultTwo</th>\n", | |
| " <th>raw_SearchResultThree</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>1</td>\n", | |
| " <td>2005-11-22</td>\n", | |
| " <td>11:15:00</td>\n", | |
| " <td>X3</td>\n", | |
| " <td>white</td>\n", | |
| " <td>male</td>\n", | |
| " <td>200</td>\n", | |
| " <td>vehicular</td>\n", | |
| " <td>False</td>\n", | |
| " <td>True</td>\n", | |
| " <td>...</td>\n", | |
| " <td>Speeding</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>SP</td>\n", | |
| " <td>W</td>\n", | |
| " <td>M</td>\n", | |
| " <td>M</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>2</td>\n", | |
| " <td>2005-10-01</td>\n", | |
| " <td>12:20:00</td>\n", | |
| " <td>X3</td>\n", | |
| " <td>white</td>\n", | |
| " <td>male</td>\n", | |
| " <td>200</td>\n", | |
| " <td>vehicular</td>\n", | |
| " <td>False</td>\n", | |
| " <td>True</td>\n", | |
| " <td>...</td>\n", | |
| " <td>Speeding</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>SP</td>\n", | |
| " <td>W</td>\n", | |
| " <td>M</td>\n", | |
| " <td>M</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>3</td>\n", | |
| " <td>2005-10-01</td>\n", | |
| " <td>12:30:00</td>\n", | |
| " <td>X3</td>\n", | |
| " <td>white</td>\n", | |
| " <td>female</td>\n", | |
| " <td>200</td>\n", | |
| " <td>vehicular</td>\n", | |
| " <td>False</td>\n", | |
| " <td>True</td>\n", | |
| " <td>...</td>\n", | |
| " <td>Speeding</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>SP</td>\n", | |
| " <td>W</td>\n", | |
| " <td>F</td>\n", | |
| " <td>M</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>4</td>\n", | |
| " <td>2005-10-01</td>\n", | |
| " <td>12:50:00</td>\n", | |
| " <td>X3</td>\n", | |
| " <td>white</td>\n", | |
| " <td>male</td>\n", | |
| " <td>200</td>\n", | |
| " <td>vehicular</td>\n", | |
| " <td>False</td>\n", | |
| " <td>True</td>\n", | |
| " <td>...</td>\n", | |
| " <td>Speeding</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>SP</td>\n", | |
| " <td>W</td>\n", | |
| " <td>M</td>\n", | |
| " <td>M</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>5</td>\n", | |
| " <td>2005-10-01</td>\n", | |
| " <td>13:10:00</td>\n", | |
| " <td>X3</td>\n", | |
| " <td>white</td>\n", | |
| " <td>female</td>\n", | |
| " <td>200</td>\n", | |
| " <td>vehicular</td>\n", | |
| " <td>False</td>\n", | |
| " <td>True</td>\n", | |
| " <td>...</td>\n", | |
| " <td>Speeding</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>SP</td>\n", | |
| " <td>W</td>\n", | |
| " <td>F</td>\n", | |
| " <td>M</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>5 rows × 31 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " raw_row_number date time zone subject_race subject_sex \\\n", | |
| "0 1 2005-11-22 11:15:00 X3 white male \n", | |
| "1 2 2005-10-01 12:20:00 X3 white male \n", | |
| "2 3 2005-10-01 12:30:00 X3 white female \n", | |
| "3 4 2005-10-01 12:50:00 X3 white male \n", | |
| "4 5 2005-10-01 13:10:00 X3 white female \n", | |
| "\n", | |
| " department_id type arrest_made citation_issued ... reason_for_stop \\\n", | |
| "0 200 vehicular False True ... Speeding \n", | |
| "1 200 vehicular False True ... Speeding \n", | |
| "2 200 vehicular False True ... Speeding \n", | |
| "3 200 vehicular False True ... Speeding \n", | |
| "4 200 vehicular False True ... Speeding \n", | |
| "\n", | |
| " vehicle_make vehicle_model raw_BasisForStop raw_OperatorRace \\\n", | |
| "0 NaN NaN SP W \n", | |
| "1 NaN NaN SP W \n", | |
| "2 NaN NaN SP W \n", | |
| "3 NaN NaN SP W \n", | |
| "4 NaN NaN SP W \n", | |
| "\n", | |
| " raw_OperatorSex raw_ResultOfStop raw_SearchResultOne raw_SearchResultTwo \\\n", | |
| "0 M M NaN NaN \n", | |
| "1 M M NaN NaN \n", | |
| "2 F M NaN NaN \n", | |
| "3 M M NaN NaN \n", | |
| "4 F M NaN NaN \n", | |
| "\n", | |
| " raw_SearchResultThree \n", | |
| "0 NaN \n", | |
| "1 NaN \n", | |
| "2 NaN \n", | |
| "3 NaN \n", | |
| "4 NaN \n", | |
| "\n", | |
| "[5 rows x 31 columns]" | |
| ] | |
| }, | |
| "execution_count": 1, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Examining the dataset\n", | |
| "import missingno as msno \n", | |
| "import pandas as pd\n", | |
| "\n", | |
| "# Read 'police.csv' into a DataFrame named ri\n", | |
| "ri_2020 = pd.read_csv('dataset/ri_statewide_2020_04_01.csv', dtype={'frisk_performed':'float'}, low_memory=False)\n", | |
| "\n", | |
| "ri_2020.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "0LoxShMftuXI", | |
| "outputId": "4fa4ba6e-798e-4481-f44d-4401b4ff1b92" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x1cd01721070>" | |
| ] | |
| }, | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 1800x720 with 2 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "msno.matrix(ri_2020)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "QuAMmHOWtuXI", | |
| "outputId": "fc6e6888-65e6-413c-c0dd-f549139be245" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "<class 'pandas.core.frame.DataFrame'>\n", | |
| "RangeIndex: 509681 entries, 0 to 509680\n", | |
| "Data columns (total 31 columns):\n", | |
| " # Column Non-Null Count Dtype \n", | |
| "--- ------ -------------- ----- \n", | |
| " 0 raw_row_number 509681 non-null int64 \n", | |
| " 1 date 509671 non-null object \n", | |
| " 2 time 509671 non-null object \n", | |
| " 3 zone 509671 non-null object \n", | |
| " 4 subject_race 480608 non-null object \n", | |
| " 5 subject_sex 480584 non-null object \n", | |
| " 6 department_id 509671 non-null object \n", | |
| " 7 type 509681 non-null object \n", | |
| " 8 arrest_made 480608 non-null object \n", | |
| " 9 citation_issued 480608 non-null object \n", | |
| " 10 warning_issued 480608 non-null object \n", | |
| " 11 outcome 473840 non-null object \n", | |
| " 12 contraband_found 17762 non-null object \n", | |
| " 13 contraband_drugs 15988 non-null object \n", | |
| " 14 contraband_weapons 11795 non-null object \n", | |
| " 15 contraband_alcohol 1217 non-null object \n", | |
| " 16 contraband_other 17762 non-null object \n", | |
| " 17 frisk_performed 509681 non-null float64\n", | |
| " 18 search_conducted 509681 non-null bool \n", | |
| " 19 search_basis 17762 non-null object \n", | |
| " 20 reason_for_search 17762 non-null object \n", | |
| " 21 reason_for_stop 480608 non-null object \n", | |
| " 22 vehicle_make 318117 non-null object \n", | |
| " 23 vehicle_model 230088 non-null object \n", | |
| " 24 raw_BasisForStop 480608 non-null object \n", | |
| " 25 raw_OperatorRace 480608 non-null object \n", | |
| " 26 raw_OperatorSex 480608 non-null object \n", | |
| " 27 raw_ResultOfStop 480608 non-null object \n", | |
| " 28 raw_SearchResultOne 17762 non-null object \n", | |
| " 29 raw_SearchResultTwo 819 non-null object \n", | |
| " 30 raw_SearchResultThree 168 non-null object \n", | |
| "dtypes: bool(1), float64(1), int64(1), object(28)\n", | |
| "memory usage: 117.1+ MB\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "ri_2020.info()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "y5TeyRnetuXJ", | |
| "outputId": "ccff6855-d7ae-4507-9595-009618d0ac27" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "0.0 500349\n", | |
| "1.0 9332\n", | |
| "Name: frisk_performed, dtype: int64" | |
| ] | |
| }, | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "ri_2020.frisk_performed.value_counts()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "QfTKXisRtuXK", | |
| "outputId": "a053b59a-499e-4f68-f7a3-677c9c223432" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "other 9035\n", | |
| "probable cause 7767\n", | |
| "plain view 960\n", | |
| "Name: search_basis, dtype: int64" | |
| ] | |
| }, | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "ri_2020.search_basis.value_counts()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "LP5WkfzhtuXK", | |
| "outputId": "22012fe7-b1b2-4b31-d325-31dc7935c35e" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "Index(['raw_row_number', 'date', 'time', 'zone', 'subject_race', 'subject_sex',\n", | |
| " 'department_id', 'type', 'arrest_made', 'citation_issued',\n", | |
| " 'warning_issued', 'outcome', 'contraband_found', 'contraband_drugs',\n", | |
| " 'contraband_weapons', 'contraband_alcohol', 'contraband_other',\n", | |
| " 'frisk_performed', 'search_conducted', 'search_basis',\n", | |
| " 'reason_for_search', 'reason_for_stop', 'vehicle_make', 'vehicle_model',\n", | |
| " 'raw_BasisForStop', 'raw_OperatorRace', 'raw_OperatorSex',\n", | |
| " 'raw_ResultOfStop', 'raw_SearchResultOne', 'raw_SearchResultTwo',\n", | |
| " 'raw_SearchResultThree'],\n", | |
| " dtype='object')" | |
| ] | |
| }, | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "ri_2020.columns" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "4GBI1Ww4tuXL", | |
| "outputId": "26242e98-51f5-4029-a611-1eeefd1cb28b" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "X4 135349\n", | |
| "K3 113784\n", | |
| "K2 101403\n", | |
| "X3 94058\n", | |
| "K1 48362\n", | |
| "X1 16715\n", | |
| "Name: zone, dtype: int64\n", | |
| "white 344734\n", | |
| "black 68579\n", | |
| "hispanic 53125\n", | |
| "asian/pacific islander 12826\n", | |
| "other 1344\n", | |
| "Name: subject_race, dtype: int64\n", | |
| "male 349446\n", | |
| "female 131138\n", | |
| "Name: subject_sex, dtype: int64\n", | |
| "vehicular 509681\n", | |
| "Name: type, dtype: int64\n", | |
| "False 464005\n", | |
| "True 16603\n", | |
| "Name: arrest_made, dtype: int64\n", | |
| "True 428388\n", | |
| "False 52220\n", | |
| "Name: citation_issued, dtype: int64\n", | |
| "False 451759\n", | |
| "True 28849\n", | |
| "Name: warning_issued, dtype: int64\n", | |
| "citation 428388\n", | |
| "warning 28849\n", | |
| "arrest 16603\n", | |
| "Name: outcome, dtype: int64\n", | |
| "False 11183\n", | |
| "True 6579\n", | |
| "Name: contraband_found, dtype: int64\n", | |
| "False 11223\n", | |
| "True 4765\n", | |
| "Name: contraband_drugs, dtype: int64\n", | |
| "False 11296\n", | |
| "True 499\n", | |
| "Name: contraband_weapons, dtype: int64\n", | |
| "True 1120\n", | |
| "False 97\n", | |
| "Name: contraband_alcohol, dtype: int64\n", | |
| "False 16771\n", | |
| "True 991\n", | |
| "Name: contraband_other, dtype: int64\n", | |
| "0.0 500349\n", | |
| "1.0 9332\n", | |
| "Name: frisk_performed, dtype: int64\n", | |
| "False 491919\n", | |
| "True 17762\n", | |
| "Name: search_conducted, dtype: int64\n", | |
| "other 9035\n", | |
| "probable cause 7767\n", | |
| "plain view 960\n", | |
| "Name: search_basis, dtype: int64\n", | |
| "Incident to Arrest 6998\n", | |
| "Probable Cause 2063\n", | |
| "Odor of Drugs/Alcohol 1872\n", | |
| "Reasonable Suspicion 1141\n", | |
| "Inventory/Tow 1101\n", | |
| " ... \n", | |
| "Probable Cause|Inventory/Tow|Incident to Arrest 1\n", | |
| "Plain View|Incident to Arrest|Terry Frisk 1\n", | |
| "Plain View|Incident to Arrest|Odor of Drugs/Alcohol 1\n", | |
| "Reasonable Suspicion|Incident to Arrest|Probable Cause 1\n", | |
| "Reasonable Suspicion|Odor of Drugs/Alcohol|Inventory/Tow 1\n", | |
| "Name: reason_for_search, Length: 188, dtype: int64\n", | |
| "Speeding 268744\n", | |
| "Other Traffic Violation 90234\n", | |
| "Equipment/Inspection Violation 61252\n", | |
| "Registration Violation 19830\n", | |
| "Seatbelt Violation 16327\n", | |
| "Special Detail/Directed Patrol 13642\n", | |
| "Call for Service 7609\n", | |
| "Violation of City/Town Ordinance 1036\n", | |
| "Motorist Assist/Courtesy 990\n", | |
| "APB 485\n", | |
| "Suspicious Person 342\n", | |
| "Warrant 117\n", | |
| "Name: reason_for_stop, dtype: int64\n", | |
| "W 344734\n", | |
| "B 68579\n", | |
| "H 44048\n", | |
| "I 12826\n", | |
| "L 9077\n", | |
| "O 814\n", | |
| "N 530\n", | |
| "Name: raw_OperatorRace, dtype: int64\n", | |
| "M 349446\n", | |
| "F 131138\n", | |
| "U 23\n", | |
| "N 1\n", | |
| "Name: raw_OperatorSex, dtype: int64\n", | |
| "M 428388\n", | |
| "W 28849\n", | |
| "D 14630\n", | |
| "N 3431\n", | |
| "A 3337\n", | |
| "P 1973\n", | |
| "Name: raw_ResultOfStop, dtype: int64\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# see rough detail with in column\n", | |
| "\n", | |
| "for i in ri_2020.columns:\n", | |
| " if i != 'raw_row_number' and i != 'date' and i != 'time' and i != 'department_id' and i != 'vehicle_make' and i != 'vehicle_model' and i != 'raw_BasisForStop'and i != 'raw_SearchResultOne' and i != 'raw_SearchResultTwo' and i != 'raw_SearchResultThree':\n", | |
| " print(ri_2020[i].value_counts())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "-H7Ks5CNtuXM", | |
| "outputId": "eb678b21-3ba1-41e4-bf32-d66021dcce1a" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "(509681, 16)\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>raw_row_number</th>\n", | |
| " <th>date</th>\n", | |
| " <th>stop_time</th>\n", | |
| " <th>district</th>\n", | |
| " <th>subject_race</th>\n", | |
| " <th>subject_sex</th>\n", | |
| " <th>arrest_made</th>\n", | |
| " <th>citation_issued</th>\n", | |
| " <th>warning_issued</th>\n", | |
| " <th>contraband_found</th>\n", | |
| " <th>contraband_drugs</th>\n", | |
| " <th>contraband_weapons</th>\n", | |
| " <th>contraband_alcohol</th>\n", | |
| " <th>frisk_performed</th>\n", | |
| " <th>search_conducted</th>\n", | |
| " <th>reason_for_stop</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>1</td>\n", | |
| " <td>2005-11-22</td>\n", | |
| " <td>11:15:00</td>\n", | |
| " <td>X3</td>\n", | |
| " <td>white</td>\n", | |
| " <td>male</td>\n", | |
| " <td>False</td>\n", | |
| " <td>True</td>\n", | |
| " <td>False</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>False</td>\n", | |
| " <td>Speeding</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>2</td>\n", | |
| " <td>2005-10-01</td>\n", | |
| " <td>12:20:00</td>\n", | |
| " <td>X3</td>\n", | |
| " <td>white</td>\n", | |
| " <td>male</td>\n", | |
| " <td>False</td>\n", | |
| " <td>True</td>\n", | |
| " <td>False</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>False</td>\n", | |
| " <td>Speeding</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>3</td>\n", | |
| " <td>2005-10-01</td>\n", | |
| " <td>12:30:00</td>\n", | |
| " <td>X3</td>\n", | |
| " <td>white</td>\n", | |
| " <td>female</td>\n", | |
| " <td>False</td>\n", | |
| " <td>True</td>\n", | |
| " <td>False</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>False</td>\n", | |
| " <td>Speeding</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>4</td>\n", | |
| " <td>2005-10-01</td>\n", | |
| " <td>12:50:00</td>\n", | |
| " <td>X3</td>\n", | |
| " <td>white</td>\n", | |
| " <td>male</td>\n", | |
| " <td>False</td>\n", | |
| " <td>True</td>\n", | |
| " <td>False</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>False</td>\n", | |
| " <td>Speeding</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>5</td>\n", | |
| " <td>2005-10-01</td>\n", | |
| " <td>13:10:00</td>\n", | |
| " <td>X3</td>\n", | |
| " <td>white</td>\n", | |
| " <td>female</td>\n", | |
| " <td>False</td>\n", | |
| " <td>True</td>\n", | |
| " <td>False</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>False</td>\n", | |
| " <td>Speeding</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " raw_row_number date stop_time district subject_race subject_sex \\\n", | |
| "0 1 2005-11-22 11:15:00 X3 white male \n", | |
| "1 2 2005-10-01 12:20:00 X3 white male \n", | |
| "2 3 2005-10-01 12:30:00 X3 white female \n", | |
| "3 4 2005-10-01 12:50:00 X3 white male \n", | |
| "4 5 2005-10-01 13:10:00 X3 white female \n", | |
| "\n", | |
| " arrest_made citation_issued warning_issued contraband_found \\\n", | |
| "0 False True False NaN \n", | |
| "1 False True False NaN \n", | |
| "2 False True False NaN \n", | |
| "3 False True False NaN \n", | |
| "4 False True False NaN \n", | |
| "\n", | |
| " contraband_drugs contraband_weapons contraband_alcohol frisk_performed \\\n", | |
| "0 NaN NaN NaN 0.0 \n", | |
| "1 NaN NaN NaN 0.0 \n", | |
| "2 NaN NaN NaN 0.0 \n", | |
| "3 NaN NaN NaN 0.0 \n", | |
| "4 NaN NaN NaN 0.0 \n", | |
| "\n", | |
| " search_conducted reason_for_stop \n", | |
| "0 False Speeding \n", | |
| "1 False Speeding \n", | |
| "2 False Speeding \n", | |
| "3 False Speeding \n", | |
| "4 False Speeding " | |
| ] | |
| }, | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "#cut down the table only interest column\n", | |
| "\n", | |
| "ri_trim = ri_2020[['raw_row_number', 'date', 'time','zone', 'subject_race', 'subject_sex',\n", | |
| " 'arrest_made', 'citation_issued', 'warning_issued', 'contraband_found', 'contraband_drugs',\n", | |
| " 'contraband_weapons', 'contraband_alcohol', 'frisk_performed',\n", | |
| " 'search_conducted', 'reason_for_stop']]\n", | |
| "\n", | |
| "#rename column\n", | |
| "ri_trim = ri_trim.rename(columns={\"time\": \"stop_time\", \"zone\": \"district\"})\n", | |
| "print(ri_trim.shape)\n", | |
| "ri_trim.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "_bMtqnWutuXN", | |
| "outputId": "880ed606-d593-4373-d3ec-8baf4409b298" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "<class 'pandas.core.frame.DataFrame'>\n", | |
| "RangeIndex: 509681 entries, 0 to 509680\n", | |
| "Data columns (total 16 columns):\n", | |
| " # Column Non-Null Count Dtype \n", | |
| "--- ------ -------------- ----- \n", | |
| " 0 raw_row_number 509681 non-null int64 \n", | |
| " 1 date 509671 non-null object \n", | |
| " 2 stop_time 509671 non-null object \n", | |
| " 3 district 509671 non-null object \n", | |
| " 4 subject_race 480608 non-null object \n", | |
| " 5 subject_sex 480584 non-null object \n", | |
| " 6 arrest_made 480608 non-null object \n", | |
| " 7 citation_issued 480608 non-null object \n", | |
| " 8 warning_issued 480608 non-null object \n", | |
| " 9 contraband_found 17762 non-null object \n", | |
| " 10 contraband_drugs 15988 non-null object \n", | |
| " 11 contraband_weapons 11795 non-null object \n", | |
| " 12 contraband_alcohol 1217 non-null object \n", | |
| " 13 frisk_performed 509681 non-null float64\n", | |
| " 14 search_conducted 509681 non-null bool \n", | |
| " 15 reason_for_stop 480608 non-null object \n", | |
| "dtypes: bool(1), float64(1), int64(1), object(13)\n", | |
| "memory usage: 58.8+ MB\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>raw_row_number</th>\n", | |
| " <th>date</th>\n", | |
| " <th>stop_time</th>\n", | |
| " <th>district</th>\n", | |
| " <th>subject_race</th>\n", | |
| " <th>subject_sex</th>\n", | |
| " <th>arrest_made</th>\n", | |
| " <th>citation_issued</th>\n", | |
| " <th>warning_issued</th>\n", | |
| " <th>contraband_found</th>\n", | |
| " <th>contraband_drugs</th>\n", | |
| " <th>contraband_weapons</th>\n", | |
| " <th>contraband_alcohol</th>\n", | |
| " <th>frisk_performed</th>\n", | |
| " <th>search_conducted</th>\n", | |
| " <th>reason_for_stop</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>1</td>\n", | |
| " <td>2005-11-22</td>\n", | |
| " <td>11:15:00</td>\n", | |
| " <td>X3</td>\n", | |
| " <td>white</td>\n", | |
| " <td>male</td>\n", | |
| " <td>False</td>\n", | |
| " <td>True</td>\n", | |
| " <td>False</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>False</td>\n", | |
| " <td>Speeding</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>2</td>\n", | |
| " <td>2005-10-01</td>\n", | |
| " <td>12:20:00</td>\n", | |
| " <td>X3</td>\n", | |
| " <td>white</td>\n", | |
| " <td>male</td>\n", | |
| " <td>False</td>\n", | |
| " <td>True</td>\n", | |
| " <td>False</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>False</td>\n", | |
| " <td>Speeding</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>3</td>\n", | |
| " <td>2005-10-01</td>\n", | |
| " <td>12:30:00</td>\n", | |
| " <td>X3</td>\n", | |
| " <td>white</td>\n", | |
| " <td>female</td>\n", | |
| " <td>False</td>\n", | |
| " <td>True</td>\n", | |
| " <td>False</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>False</td>\n", | |
| " <td>Speeding</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>4</td>\n", | |
| " <td>2005-10-01</td>\n", | |
| " <td>12:50:00</td>\n", | |
| " <td>X3</td>\n", | |
| " <td>white</td>\n", | |
| " <td>male</td>\n", | |
| " <td>False</td>\n", | |
| " <td>True</td>\n", | |
| " <td>False</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>False</td>\n", | |
| " <td>Speeding</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>5</td>\n", | |
| " <td>2005-10-01</td>\n", | |
| " <td>13:10:00</td>\n", | |
| " <td>X3</td>\n", | |
| " <td>white</td>\n", | |
| " <td>female</td>\n", | |
| " <td>False</td>\n", | |
| " <td>True</td>\n", | |
| " <td>False</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>False</td>\n", | |
| " <td>Speeding</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " raw_row_number date stop_time district subject_race subject_sex \\\n", | |
| "0 1 2005-11-22 11:15:00 X3 white male \n", | |
| "1 2 2005-10-01 12:20:00 X3 white male \n", | |
| "2 3 2005-10-01 12:30:00 X3 white female \n", | |
| "3 4 2005-10-01 12:50:00 X3 white male \n", | |
| "4 5 2005-10-01 13:10:00 X3 white female \n", | |
| "\n", | |
| " arrest_made citation_issued warning_issued contraband_found \\\n", | |
| "0 False True False NaN \n", | |
| "1 False True False NaN \n", | |
| "2 False True False NaN \n", | |
| "3 False True False NaN \n", | |
| "4 False True False NaN \n", | |
| "\n", | |
| " contraband_drugs contraband_weapons contraband_alcohol frisk_performed \\\n", | |
| "0 NaN NaN NaN 0.0 \n", | |
| "1 NaN NaN NaN 0.0 \n", | |
| "2 NaN NaN NaN 0.0 \n", | |
| "3 NaN NaN NaN 0.0 \n", | |
| "4 NaN NaN NaN 0.0 \n", | |
| "\n", | |
| " search_conducted reason_for_stop \n", | |
| "0 False Speeding \n", | |
| "1 False Speeding \n", | |
| "2 False Speeding \n", | |
| "3 False Speeding \n", | |
| "4 False Speeding " | |
| ] | |
| }, | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "ri_trim.info()\n", | |
| "ri_trim.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "ssLcUuxktuXN", | |
| "outputId": "87c3687a-2a29-4c50-f774-c05111c85096" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "Index(['raw_row_number', 'date', 'stop_time', 'district', 'subject_race',\n", | |
| " 'subject_sex', 'arrest_made', 'citation_issued', 'warning_issued',\n", | |
| " 'contraband_found', 'contraband_drugs', 'contraband_weapons',\n", | |
| " 'contraband_alcohol', 'frisk_performed', 'search_conducted',\n", | |
| " 'reason_for_stop'],\n", | |
| " dtype='object')" | |
| ] | |
| }, | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "ri_trim.columns" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "7mi0nYn-tuXN" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "ri_trim.to_csv('PoliceRI2020.csv')" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.8.3" | |
| }, | |
| "colab": { | |
| "provenance": [], | |
| "include_colab_link": true | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 0 | |
| } |
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