2.4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1d0a4b24-1937-4aa2-bd52-8bde3651d412",
   "metadata": {},
   "source": [
    "# 2.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5546007e-cd9f-409c-8551-59e08f59cd79",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "import psycopg2 as db\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import requests\n",
    "import json\n",
    "import plotly.express as px\n",
    "import dash\n",
    "from datetime import timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "35e539f1-c28e-45ee-81da-3ea31dc56143",
   "metadata": {},
   "outputs": [],
   "source": [
    "postPass=os.environ[\"POSTGRES_PASS\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eaacacb8-3402-487b-bc95-8faed7f5a4af",
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(\"postgresql+psycopg2://{user}:{pw}@localhost/{db}\"\n",
    ".format(user=\"jupyter\", pw=postPass, db=\"expunge\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d1e81dc-b498-485b-9f2a-0df9f7f31c8f",
   "metadata": {},
   "source": [
    "## Making Conviction Rate table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "114896a5-5276-4519-9654-d8ec5b9fd1ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "con_query = \"\"\"\n",
    "SELECT race, c.fips, code_section, COUNT(*) AS con_people, f.locality\n",
    "FROM charges c\n",
    "LEFT JOIN fips f\n",
    "    ON CAST(c.fips AS INT) = f.fips\n",
    "WHERE (disposition_code='Guilty' OR disposition_code='Guilty In Absentia') AND not code_section='MISSING'\n",
    "\n",
    "GROUP BY race, c.fips, code_section, f.locality\n",
    "ORDER BY con_people desc\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "convictions = pd.read_sql(con_query, con=engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "de1300d4-3491-4d43-97cc-ddee50015ae8",
   "metadata": {},
   "outputs": [],
   "source": [
    "charged_query = \"\"\"\n",
    "SELECT race, c.fips, code_section, COUNT(*) AS charged_people, f.locality\n",
    "FROM charges c\n",
    "LEFT JOIN fips f\n",
    "    ON CAST (c.fips AS INT) = f.fips\n",
    "\n",
    "WHERE not code_section='MISSING'\n",
    "GROUP BY race, c.fips, code_section, f.locality\n",
    "ORDER BY charged_people desc\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "charged = pd.read_sql(charged_query, con=engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "57c9a9df-0a3c-489a-af21-177f28d77e05",
   "metadata": {},
   "outputs": [],
   "source": [
    "con_charge_table = pd.merge(convictions, charged, on=['race', 'fips', 'code_section', 'locality'])\n",
    "conviction_rate = con_charge_table.assign(con_rate = con_charge_table.con_people/ con_charge_table.charged_people)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a00f0440-c5ab-41bb-8ee1-324d81a10346",
   "metadata": {},
   "outputs": [],
   "source": [
    "conviction_rate.sort_values(by='con_rate', ascending = False)\n",
    "conviction_rate['nontraffic'] = ['46.' not in x for x in conviction_rate['code_section']]\n",
    "#nontraffic = ['46.' not in x for x in conviction_rate['code_section']]\n",
    "#conviction_rate.loc[nontraffic].query(\"charged_people > 500\").sort_values(by='con_rate', ascending = False)\n",
    "conviction_rate.to_sql('conviction_rate', con=engine, chunksize=1000, if_exists='replace', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c66e2d0-cb28-4948-aa16-7c6869c05e2c",
   "metadata": {},
   "source": [
    "## 2.4 table "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "08306e4c-56ef-41c1-ad12-7e1f75766982",
   "metadata": {},
   "outputs": [],
   "source": [
    "race_fips_code_query = \"\"\"\n",
    "SELECT race AS Race, code_section, CAST(fips AS INT) as FIPS, locality AS Jurisdiction, con_people AS Total_Convictions, charged_people AS Total_Charge, con_rate AS Conviction_rate\n",
    "FROM conviction_rate_with_code\n",
    "WHERE charged_people >= 30 AND NOT race='Unknown'\n",
    "GROUP BY race, fips, code_section, con_rate, con_people, charged_people, locality\n",
    "ORDER BY con_rate desc\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "rfc = pd.read_sql(race_fips_code_query, con=engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "69310eb4-2b9b-47e4-addd-19c53a5d5a2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>race_fips_cs</th>\n",
       "      <th>total_convictions</th>\n",
       "      <th>total_charge</th>\n",
       "      <th>conviction_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>White | 127 | B.46.2-357</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>White | 131 | CO2/A.46.2-862</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Asian or Pacific Islander | 043 | C.46.2-862</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hispanic | 063 | B.46.2-301</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>White | 810 | 18.2-374.1:1(D)</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hispanic | 199 | 28.2-201</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>White | 023 | C.18.2-266</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Black | 760 | 46.2-000</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Hispanic | 075 | C.46.2-862</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>White | 139 | 18.2-374.1</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>White | 117 | 46.2-861.1(A)</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Black | 069 | 46.2-862</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>White | 121 | 18.2-374.1(B..2)</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>White | 167 | 46.2-1143</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Asian or Pacific Islander | 171 | C.46.2-862</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>White | 069 | 158-6.2</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>American Indian or Alaskan Native | 023 | C.46...</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>American Indian or Alaskan Native | 081 | A.46...</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Black | 119 | 18.2-472.1</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Hispanic | 199 | 4VAC20-280-30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         race_fips_cs  total_convictions  \\\n",
       "0                            White | 127 | B.46.2-357                 31   \n",
       "1                        White | 131 | CO2/A.46.2-862                 43   \n",
       "2        Asian or Pacific Islander | 043 | C.46.2-862                 35   \n",
       "3                         Hispanic | 063 | B.46.2-301                 32   \n",
       "4                       White | 810 | 18.2-374.1:1(D)                 40   \n",
       "5                           Hispanic | 199 | 28.2-201                 75   \n",
       "6                            White | 023 | C.18.2-266                109   \n",
       "7                              Black | 760 | 46.2-000                 30   \n",
       "8                         Hispanic | 075 | C.46.2-862                 64   \n",
       "9                            White | 139 | 18.2-374.1                 59   \n",
       "10                        White | 117 | 46.2-861.1(A)                 33   \n",
       "11                             Black | 069 | 46.2-862                 40   \n",
       "12                     White | 121 | 18.2-374.1(B..2)                 88   \n",
       "13                            White | 167 | 46.2-1143                 32   \n",
       "14       Asian or Pacific Islander | 171 | C.46.2-862                 43   \n",
       "15                              White | 069 | 158-6.2                 71   \n",
       "16  American Indian or Alaskan Native | 023 | C.46...                 61   \n",
       "17  American Indian or Alaskan Native | 081 | A.46...                 99   \n",
       "18                           Black | 119 | 18.2-472.1                 42   \n",
       "19                     Hispanic | 199 | 4VAC20-280-30                 30   \n",
       "\n",
       "    total_charge  conviction_rate  \n",
       "0             31              1.0  \n",
       "1             43              1.0  \n",
       "2             35              1.0  \n",
       "3             32              1.0  \n",
       "4             40              1.0  \n",
       "5             75              1.0  \n",
       "6            109              1.0  \n",
       "7             30              1.0  \n",
       "8             64              1.0  \n",
       "9             59              1.0  \n",
       "10            33              1.0  \n",
       "11            40              1.0  \n",
       "12            88              1.0  \n",
       "13            32              1.0  \n",
       "14            43              1.0  \n",
       "15            71              1.0  \n",
       "16            61              1.0  \n",
       "17            99              1.0  \n",
       "18            42              1.0  \n",
       "19            30              1.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6de9db37-6815-46d6-968f-de89d5dbeac0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# With Traffic Table\n",
    "race_fips_code_plot_query = \"\"\"\n",
    "SELECT race || ' | ' || fips || ' | ' ||  code_section as race_fips_cs, con_people AS Total_Convictions, charged_people AS Total_Charge, con_rate AS Conviction_rate\n",
    "FROM conviction_rate_with_code\n",
    "WHERE charged_people >= 30 AND NOT race='Unknown'\n",
    "GROUP BY race, fips, code_section, con_rate, con_people, charged_people, locality\n",
    "ORDER BY con_rate desc\n",
    "LIMIT 20\n",
    "\"\"\"\n",
    "\n",
    "rfc_plot = pd.read_sql(race_fips_code_plot_query, con=engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b022e62f-81ab-44f1-9d25-93ba576f1489",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='conviction_rate', ylabel='race_fips_cs'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.barplot(y='race_fips_cs', x='conviction_rate', data=rfc_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "80d5b22a-e649-400c-8178-e6a16da9730d",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc.to_csv('cr_by_race_fips_cs.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ee3498f5-bbaa-4fdd-bfe7-b079023e47a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nontraffic Table\n",
    "race_fips_code_query_nt = \"\"\"\n",
    "SELECT race || fips ||  code_section, con_people AS Total_Convictions, charged_people AS Total_Charge, con_rate AS Conviction_rate\n",
    "FROM conviction_rate_with_code\n",
    "WHERE not nontraffic AND charged_people >= 30\n",
    "GROUP BY race, fips, code_section, con_rate, con_people, charged_people, locality\n",
    "ORDER BY con_rate desc\n",
    "LIMIT 20\n",
    "\"\"\"\n",
    "\n",
    "rtf_nt = pd.read_sql(race_fips_code_query_nt, con=engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d2fdc4e-a1ec-428c-af49-2e2ca49c2359",
   "metadata": {},
   "outputs": [],
   "source": [
    "rtf_nt"
   ]
  }
 ],
 "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}