{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploration in Criminal Justice and Corrections Data <a id=\"0\"></a>\n",
    "\n",
    "I've repeatedly read the statistic that the US makes up around 4% of the global population, but makes up more than 20% of the prison population. Well defined questions are crucial tools for making sense of raw data, and this massive asymmetry is provokes some important questions, such as those listed below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Why isn't our prison population proportional to our regular population? \n",
    "  * Do other countries have fewer criminals? \n",
    "  * Do we have a drastically different approach to criminal justice than the rest of the world?\n",
    "  * Are our prison sentances just much longer?\n",
    "* What are the most common crimes? \n",
    "  * How many people are in for those crimes?\n",
    "* How many people enter the prison system each month?\n",
    "  * How many exit?\n",
    "* Recidivism\n",
    "\n",
    "    * How many prisoners have been to prison at least once before?\n",
    "    * What is the recidivism rate for 1st time prisoners who served in public prisons?\n",
    "    * And for private prisons?\n",
    "    * Which prisons have the lowest recidivism rates?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "\n",
    "1. [Exploration in Criminal Justice and Corrections Data](#0)<br>\n",
    "2. [Datasets](#1)<br>\n",
    "3. [Total Imprisonment Rates](#2)<br>\n",
    "    3a. [Observations](#3)<br>\n",
    "4. [Imprisonment by Race](#4)<br>\n",
    "    4a. [Observations](#5)<br>\n",
    "5. [Imprisonment by Gender](#6)<br>\n",
    "    5a. [Observations](#7)<br>\n",
    "6. [To Be Continued](#8)<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datasets <a id=\"1\"></a>\n",
    "With some questions in hand, I can start trying to gather data that could shed light on the situation. I may not be able to get data with the resolution needed to answer some of these questions (eg I may not be able to find data broken down by month).\n",
    "\n",
    "The government makes some data publicly available. While government data is rarely current, often involves a bit of cleanup, and always involves hunting through old government sites, it's the only source for much of this extremely valuable information. \n",
    "From the Bureau of Justice Statistics, I found a government project called the [Prisoner Series](https://www.bjs.gov/index.cfm?ty=pbse&sid=40) and I downloaded the [most recent data](http://www.bjs.gov/index.cfm?ty=pbdetail&iid=6187)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "from IPython.core.display import display, HTML\n",
    "import os\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Notebook Styling \n",
    "# pd.options.display.max_columns = None\n",
    "# display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
    "pd.set_option('display.float_format',lambda x: '%.5f' % x)\n",
    "plt.rcParams['figure.figsize'] = 10,10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Total Imprisonment Rates (Table p16f01) <a id=\"2\"></a>\n",
    "The values in the by-race dataset are [per 100k population]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "CSV_PATH = os.path.join('data', 'prison', 'p16f01.csv')\n",
    "total_df = pd.read_csv(CSV_PATH, encoding='latin1', header=12, index_col='Year', parse_dates=['Year'])\n",
    "total_df.dropna(inplace=True)\n",
    "total_df.index = total_df.index.values.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>All ages</th>\n",
       "      <th>Age 18 or older</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>131.00000</td>\n",
       "      <td>183.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>133.00000</td>\n",
       "      <td>185.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>138.00000</td>\n",
       "      <td>191.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>153.00000</td>\n",
       "      <td>211.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>170.00000</td>\n",
       "      <td>232.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      All ages  Age 18 or older\n",
       "1978 131.00000        183.00000\n",
       "1979 133.00000        185.00000\n",
       "1980 138.00000        191.00000\n",
       "1981 153.00000        211.00000\n",
       "1982 170.00000        232.00000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21576ece5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"whitegrid\"):\n",
    "    fig, ax = plt.subplots(figsize=(10,7))\n",
    "    ax.plot(total_df['All ages'])\n",
    "    ax.plot(total_df['Age 18 or older'])\n",
    "    ax.set_title('Total Imprisonment Rates (table: p16f01)')\n",
    "    ax.set_xlabel('Year')\n",
    "    ax.set_ylabel('People imprisoned (per relevant 100k US population)')\n",
    "    ax.legend()\n",
    "    ax.set_ylim([0, 1.1*max([total_df['All ages'].max(), \n",
    "                             total_df['Age 18 or older'].max()])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observations <a id=\"3\"></a>\n",
    "The imprisonment rate normalized to the entire population of US residents is lower than the imprisonment rate normalized to the population of US residents that are 18 or older. This indicates that the imprisonment rate for people under age 18 is much lower than for people 18 or older. That fits with my intuition.\n",
    "\n",
    "We also see that the imprisonment rate climbs steadily from 1980 up through 1999, dips, and peaks around 2007-2008, at which point it starts trending down. \n",
    "In 1978, 183 people were in prison per every 100k US residents 18 or older. In 2007, 670 people were in prison per every 100k US residents 18 or older. That's a 266% increase in the imprisonment rate over that 29 year span. That's huge.\n",
    "\n",
    "### New Questions:\n",
    "* What was responsible for the increase in the rate of imprisonment? What was responsible for the decrease?\n",
    "    * Was it proportional to the actual crime rates? \n",
    "    * Was it a product of different enforcement policies?\n",
    "    \n",
    "To answer these new questions, we will probably have to look at other sets of data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imprisonment by Race (Table p16f02) <a id=\"4\"></a>\n",
    "Imprisonment rate of sentenced prisoners under the jurisdiction of state or federal correctional authorities, per 100,000 U.S. residents age 18 or older, by race and Hispanic origin, December 31, 2006–2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>White/*</th>\n",
       "      <th>Black/*</th>\n",
       "      <th>Hispanic</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>324.00000</td>\n",
       "      <td>2261.00000</td>\n",
       "      <td>1073.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>317.00000</td>\n",
       "      <td>2233.00000</td>\n",
       "      <td>1094.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>316.00000</td>\n",
       "      <td>2196.00000</td>\n",
       "      <td>1057.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>308.00000</td>\n",
       "      <td>2134.00000</td>\n",
       "      <td>1060.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>307.00000</td>\n",
       "      <td>2059.00000</td>\n",
       "      <td>1014.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>299.00000</td>\n",
       "      <td>1973.00000</td>\n",
       "      <td>990.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>293.00000</td>\n",
       "      <td>1873.00000</td>\n",
       "      <td>949.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>291.00000</td>\n",
       "      <td>1817.00000</td>\n",
       "      <td>922.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>289.00000</td>\n",
       "      <td>1754.00000</td>\n",
       "      <td>893.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>281.00000</td>\n",
       "      <td>1670.00000</td>\n",
       "      <td>862.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>274.00000</td>\n",
       "      <td>1608.00000</td>\n",
       "      <td>856.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       White/*    Black/*   Hispanic\n",
       "Year                                \n",
       "2006 324.00000 2261.00000 1073.00000\n",
       "2007 317.00000 2233.00000 1094.00000\n",
       "2008 316.00000 2196.00000 1057.00000\n",
       "2009 308.00000 2134.00000 1060.00000\n",
       "2010 307.00000 2059.00000 1014.00000\n",
       "2011 299.00000 1973.00000  990.00000\n",
       "2012 293.00000 1873.00000  949.00000\n",
       "2013 291.00000 1817.00000  922.00000\n",
       "2014 289.00000 1754.00000  893.00000\n",
       "2015 281.00000 1670.00000  862.00000\n",
       "2016 274.00000 1608.00000  856.00000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CSV_PATH = os.path.join('data', 'prison', 'p16f02.csv')\n",
    "race_df = pd.read_csv(CSV_PATH, encoding='latin1', header=12)\n",
    "race_df.dropna(inplace=True)\n",
    "race_df.rename(columns={'Unnamed: 0': 'Year'}, inplace=True)\n",
    "race_df.set_index('Year', inplace=True)\n",
    "race_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x215770555c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"whitegrid\"):\n",
    "    fig, ax = plt.subplots(figsize=(10,7))\n",
    "    ax.plot(race_df['White/*'])\n",
    "    ax.plot(race_df['Black/*'])\n",
    "    ax.plot(race_df['Hispanic'])\n",
    "    ax.set_title('Imprisonment Rates by Race (table: p16f02)')\n",
    "    ax.set_xlabel('Year')\n",
    "    ax.set_ylabel('People imprisoned (per 100k US population)')\n",
    "    ax.legend()\n",
    "    ax.set_ylim([0, 1.1*max([race_df['White/*'].max(), \n",
    "                         race_df['Black/*'].max(), \n",
    "                         race_df['Hispanic'].max()])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " White/* imprisonment per 100k US pop: max: 324.0, min: 274.0\n",
      " Black/* imprisonment per 100k US pop: max: 2261.0, min: 1608.0\n",
      "Hispanic imprisonment per 100k US pop: max: 1094.0, min: 856.0\n",
      "*: non-Hispanic\n"
     ]
    }
   ],
   "source": [
    "print('{:>8s} imprisonment per 100k US pop: max: {}, min: {}'\n",
    "      .format('White/*', race_df['White/*'].max(), race_df['White/*'].min()))\n",
    "print('{:>8s} imprisonment per 100k US pop: max: {}, min: {}'\n",
    "      .format('Black/*', race_df['Black/*'].max(), race_df['Black/*'].min()))\n",
    "print('{:>8s} imprisonment per 100k US pop: max: {}, min: {}'\n",
    "      .format('Hispanic', race_df['Hispanic'].max(), race_df['Hispanic'].min()))\n",
    "print('*: non-Hispanic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observations <a id=\"5\"></a>\n",
    "This is very striking. We see that there is a very significant difference in the rates of white (non-Hispanic), black (non-Hispanic), and Hispanic imprisonment. We also see that rates for all three have dropped over this time period.\n",
    "\n",
    "### New Questions:\n",
    "* What is responsible for this difference in imprisonment rates for different demographic groups?\n",
    "\n",
    "Based on prior research, I suspect that this is the result of many systemic factors, but let's continue exploring the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Breakdown by Gender (Table p16t01) <a id=\"6\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Total</th>\n",
       "      <th>Federal/a</th>\n",
       "      <th>State</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1568674.00000</td>\n",
       "      <td>193046.00000</td>\n",
       "      <td>1375628.00000</td>\n",
       "      <td>1456366.00000</td>\n",
       "      <td>112308.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1596835.00000</td>\n",
       "      <td>199618.00000</td>\n",
       "      <td>1397217.00000</td>\n",
       "      <td>1482524.00000</td>\n",
       "      <td>114311.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1608282.00000</td>\n",
       "      <td>201280.00000</td>\n",
       "      <td>1407002.00000</td>\n",
       "      <td>1493670.00000</td>\n",
       "      <td>114612.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1615487.00000</td>\n",
       "      <td>208118.00000</td>\n",
       "      <td>1407369.00000</td>\n",
       "      <td>1502002.00000</td>\n",
       "      <td>113485.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1613803.00000</td>\n",
       "      <td>209771.00000</td>\n",
       "      <td>1404032.00000</td>\n",
       "      <td>1500936.00000</td>\n",
       "      <td>112867.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>1598968.00000</td>\n",
       "      <td>216362.00000</td>\n",
       "      <td>1382606.00000</td>\n",
       "      <td>1487561.00000</td>\n",
       "      <td>111407.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>1570397.00000</td>\n",
       "      <td>217815.00000</td>\n",
       "      <td>1352582.00000</td>\n",
       "      <td>1461625.00000</td>\n",
       "      <td>108772.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>1576950.00000</td>\n",
       "      <td>215866.00000</td>\n",
       "      <td>1361084.00000</td>\n",
       "      <td>1465592.00000</td>\n",
       "      <td>111358.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>1562319.00000</td>\n",
       "      <td>210567.00000</td>\n",
       "      <td>1351752.00000</td>\n",
       "      <td>1449291.00000</td>\n",
       "      <td>113028.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1526603.00000</td>\n",
       "      <td>196455.00000</td>\n",
       "      <td>1330148.00000</td>\n",
       "      <td>1415112.00000</td>\n",
       "      <td>111491.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1505397.00000</td>\n",
       "      <td>189192.00000</td>\n",
       "      <td>1316205.00000</td>\n",
       "      <td>1393975.00000</td>\n",
       "      <td>111422.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total    Federal/a         State          Male       Female\n",
       "Year                                                                    \n",
       "2006 1568674.00000 193046.00000 1375628.00000 1456366.00000 112308.00000\n",
       "2007 1596835.00000 199618.00000 1397217.00000 1482524.00000 114311.00000\n",
       "2008 1608282.00000 201280.00000 1407002.00000 1493670.00000 114612.00000\n",
       "2009 1615487.00000 208118.00000 1407369.00000 1502002.00000 113485.00000\n",
       "2010 1613803.00000 209771.00000 1404032.00000 1500936.00000 112867.00000\n",
       "2011 1598968.00000 216362.00000 1382606.00000 1487561.00000 111407.00000\n",
       "2012 1570397.00000 217815.00000 1352582.00000 1461625.00000 108772.00000\n",
       "2013 1576950.00000 215866.00000 1361084.00000 1465592.00000 111358.00000\n",
       "2014 1562319.00000 210567.00000 1351752.00000 1449291.00000 113028.00000\n",
       "2015 1526603.00000 196455.00000 1330148.00000 1415112.00000 111491.00000\n",
       "2016 1505397.00000 189192.00000 1316205.00000 1393975.00000 111422.00000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CSV_PATH = os.path.join('data', 'prison', 'p16t01.csv')\n",
    "sex_df = pd.read_csv(CSV_PATH, encoding='latin1', header=11, thousands=r',')\n",
    "sex_df.dropna(inplace=True, thresh=3)\n",
    "sex_df.dropna(inplace=True, axis=1, thresh=3)\n",
    "sex_df.dropna(inplace=True)\n",
    "fix = lambda x: x.split('/')[0]\n",
    "sex_df['Year'] = sex_df['Year'].apply(fix)\n",
    "sex_df['Year'] = sex_df['Year'].astype(int)\n",
    "sex_df.set_index('Year', inplace=True)\n",
    "sex_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "@ticker.FuncFormatter\n",
    "def y_formatter(x, pos):\n",
    "    return '{:4.0f}'.format(x/1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x215770969e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"whitegrid\"):\n",
    "    fig, ax = plt.subplots(figsize=(10,7))\n",
    "    ax.plot(sex_df['Male'])\n",
    "    ax.plot(sex_df['Female'])\n",
    "    ax.set_title('Imprisonment Counts by Gender (table: p16t01)')\n",
    "    ax.set_xlabel('Year')\n",
    "    ax.yaxis.set_major_formatter(y_formatter)\n",
    "    ax.set_ylabel('People Imprisoned [in thousands of people]')\n",
    "    ax.legend()\n",
    "    ax.set_ylim([0, 1.1*max(sex_df['Male'].max(),\n",
    "                            sex_df['Female'].max())])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observations <a id=\"7\"></a>\n",
    "\n",
    "The first thing that I notice is that the number of men in prison is much higher than the number of females imprisoned. Per the chart below, over the entire span of the data set (2006 to 2016), there are at least 12 men in prison for each woman in prison. This is a massive asymmetry. It doesn't feel very controversial, but should it? According to the [2010 US Census](https://www.census.gov/prod/cen2010/briefs/c2010br-03.pdf), the US population  is 50.8% female and 49.2% male. \n",
    "\n",
    "### New Questions\n",
    "\n",
    "* Why are men so much more likely to be in prison?\n",
    "    * What are the relevant differences between men and women?\n",
    "    * What is the gender breakdown of crimes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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79uwxejZtYGBgvl2Gbm5usLGxydd+ZmYmHB0d4erqipSUFKxcubJAO//73/+QnJyMO3fuYPny5YWuzxw/h/v27cOJEyeg0+mwYMECNG/eXBqV6fV6aLVaCCGQk5MDrVYrneQVHByMe/fuYePGjTAYDNi5cydSUlLQsmVLAAX7tF69evDx8cHixYuh1Wqxe/duXLx4UbqeTwgBrVYLvV4PANBqtfn+zXU6Hf766y+89NJLJm9bWcUwM5PatWujadOmhc778MMPsXDhQvj6+mLx4sXo2rWr0XY++eQT6PV66Yyr0aNH499//y102Ye7KgMCAqTrn+bPn4/ExER06NABkZGRGDVqFNq1a1ekbbG1tcXSpUtx4cIFdO7cGW3atMGUKVMK7M4whbOzM6ZMmYIxY8bA398fW7duzTf6sdR6TdGsWTNcv34dbdq0wZdffomFCxeiUqVKJn32zz//RL9+/eDr64sRI0Zg8uTJqFWrFipVqoRly5bh22+/RUBAAFauXIlly5bBzc3N7PUHBwdj2LBhGDt2LFq2bInu3btj//79Jn22TZs2eOGFF9C+fXvpWKkclUqFQYMGYcmSJQCACRMmYOvWrWjZsiWmTp1aIBgiIyMxceJE+Pn5FTjuqVKpsHTpUuzfvx9t2rTBjBkz8Mknn6BBgwaFrrtTp074559/pN2QDg4OiIiIwBtvvAE/Pz+cPn0akZGROH/+PPz8/DB8+HB06dKlQDvdu3fHkCFD8Morr6BWrVoYMWJEgWXkfg5PnDgBX1/fp/ZV9+7dsXjxYgQEBOCvv/7Cp59+Ks2bOnUqmjVrhq1bt2LZsmVo1qyZdFy7YsWKWLp0qXQse8WKFViyZIn08zN48GD89ttv8Pf3l/YMzJ8/H+fOnYO/vz8+++wzLFy4UFo+MTERzZo1Q7du3QDk/cw/fogjOjoarVu3fiYueVCIwvb3ED3jNmzYgF9++aXAaILKrp9++gmXL1/G5MmTS7uUp5o4cSKqVq1a4Fh0WdSvXz/Mnj270GPY5Q2PmRFRudC/f//SLuGZ8+TdRMoz7mYkIqJyj7sZiYio3OPIjIiIyj2GGRERlXtl8gSQ2NjY0i6BiIjKoFatWhU6vUyGGWC84KKIi4uDj4+PGap59rBvjGPfGMe+MY59Y5y5+uZpAx3uZiQionKPYUZEROUew4yIiMo9hhkREZV7DDMiIir3GGZERFTuMcyIiKjcY5gREVG5xzAjIiIAeU+efvvtt9GzZ88CD1Qtqt9//x2XL1+W3i9YsACHDh0qaYlGWSzMJk2ahLZt26J79+4F5n399dfw9vZGWlqapVZPRERFdP78eeTk5GDz5s0FnhxeGIPBYHTek2H27rvv4qWXXjJLnYWx2O2sevfujYEDB2LChAn5piclJeHQoUOoXr26pVZNRFQmrI9NwM8n4s3a5ut+tdCnVc2nLpOQkIBhw4ahVatWOHPmDLy9vdGnTx8sXLgQaWlp+Oyzz9CsWbN8n7l9+zbGjx+PtLQ09OzZE4sWLULt2rULtB0UFITevXvj4MGDGDhwIDIzM/HTTz9Br9ejTp06+OSTTxAXF4c9e/bg2LFjWLp0Kd599118//33ePnllxESEoLDhw9j3rx5MBgMaNKkCWbMmAGVSlWifjFpZHbixAmsX78eAJCWlob4ePl/HH9/f1SoUKHA9Llz52L8+PFQKBRFLJWIiEx148YNDB48GL/++iuuXr2KLVu2YM2aNXj//fexbNmyAsu7u7tj1qxZ8PPzw+bNmwsNsofUajXWrFmDbt26ITg4GOvXr8evv/6K+vXrY926dWjZsiWCgoLw/vvvY/PmzahWrZr0Wa1Wi4kTJ+KLL77Ali1bYDAY8L///a/E2ys7Mvvvf/+Lc+fO4erVq+jTpw/0ej3Gjx+PtWvXFnll0dHR8PDwQMOGDWWXjYuLK3L7T9JoNGZp51nEvjGOfWMc+8a4wvqmkSMwvWNFM6/pvuy/QUpKCjw8PJCbm4uLFy+iSpUqqFOnDi5cuABbW1tcuXKl0DauX7+OjIyMp7av0+ng5eUlLXPu3DmsXr0amZmZ0Gg08PX1hZ+fH+7cuYPExETExcVBo9FI76Ojo+Hm5ib1V8uWLbFjxw4EBASUqFdkw2z37t3YtGkTevXqBQCoWrUqMjMzi7yi7OxsLFu2DN98841Jy5vjDsu8i7Vx7Bvj2DfGsW+MK0t94+LiAmdnZ6meSpUqoW7duvDx8YGLiwvs7OwKrfXevXv5PlcYlUqFpk2bws3NDQAwcuRILFmyBA0bNsSGDRtw7Ngx+Pj4oGLFiqhRowZ8fHwQFxcnva9Tpw4cHR2lddy5cwcuLi4m9V2J7ppvZ2cHhUIh7RbMysqSXWFhbty4gYSEBPTs2RNBQUFITk5G79698e+//xarPSIiKn2ZmZmoUqUK9Ho9tmzZIk13cnIqdOBTv359JCYm4vr16wCAzZs3w9/fv8R1yI7MunbtimnTpuHevXv4+eefsX79erz++utFXpG3tzcOHz4svQ8KCsK6deukdCciovLn3XffRb9+/VCjRg14eXlJARYaGoqpU6fihx9+wOjRo6Xl1Wo15s6di3fffVc6AeSNN94ocR0KIYSQW+jgwYM4cOAAAKB9+/Zo166dbMNjx47FsWPHkJ6eDnd3d4waNQr9+vWT5j8tzGJjY/lwTgtj3xjHvjGOfWMc+8Y4cz6cs0RPmm7Xrp1JAfa4+fPnP3X+nj17itQeERGRMUbDzNfXt9DT54UQUCgUOHnypEULIyIiy1i/fj1WrVqVb1rLli3x4YcfSu9HjhyJhISEfMuMGzcOHTp0sEqNRWU0zE6dOmXNOoiIyEr69OmDPn36PHWZxYsXW6ka8zD5DiC3b9+GVquV3vMOHkREVFbIhll0dDTmzZuH1NRUuLm54ebNm2jQoAG2bdtmjfqIyEKEENAZcpGtMyBLZ0CWLufBdwOydQZkPnj/+Pxbt9LQMjMedSs7oa67Eyo7q3g3HyoTZMNswYIF+OmnnxAeHo5NmzbhyJEjDDIiK8ox5CJL/yhUMrU5yNY/CBjtgwDSG5Cty0Gm1vBg3oPp2kfzsp4IrWydATm5siczSxQKQAFgzdk70jRntRJ13B1Rt7IT6rk7oY67I+pVdkLdyk5wd2LQkfXIhplSqUSlSpWQm5uL3NxctGnTBp999pk1aiMqFUII6A250OXkfWkffNcZDNJraVpOLnT5ln2wTGGff2y69sGyBT9fsE2dIbdI9dvb2cBRpYSDnS2c1LZwUCnhaGcLT1c7OKrzXjuo8uY9XM5RZSvNk16rbKV5Tmol1EobnDsfB+eqdXDtdiau3XrwdTsL5xLvYue5ZBgeC0cXtRJ1KjuirnveKK5uZSfUq+yIOu4MOjI/2TBzdXVFZmYm/P39MW7cOLi5uUGptNjN9okkhlwBjd4g/eLX6B+EhT5Xmv5ofq7RZR99L+xzjy37cLo+FwJXzbINShsFVEobqJU2UD38srWBSmkrTXdWK6FyfDRfWtY2b5lHoWT7IGCUD4Lo0eu8eXnBZGtjuZBQ2ihQr7IT6lV2Arzzz9MbcpGQnv0g4PKC7urtLPyZeBc7Cgm6upUfG8m5O6Hug+BzY9BRMcim0pIlS6BWqzFp0iRs2bIF9+/fx8iRI61RGz0jhBC4m61H8j0Nku9qkHJPg5R7WiTf0yDlrgYp9zXI1BqgfSJoirILrDDqB8GgtrOFvZ0N1EpbqJU2sLfL++5ir5Req5UPlrGzxb30NNTw9HgsXGzzBZH6ydCRAurRsuoH02wsGCxljZ2tzaOge4IuJxcJ6Vm4fjsLVx+G3e0snE24i+1/JuHxf2oXe6U0kqvr7pjvNYOOjJENM0dHRwBARkYGOnXqZPGCqHzR5eQ+CCeNFFap97VIvpv3/uE8jb7grjJ3JxWqutrDw1WNepXtYK+0gdrusWB5InzUdjawV9o+fZkH80sSJHl3K3ixpF1Dj1EpbVC/ijPqV3HGk79FHgZd3mgu7/vVW5k4E38H287eLBB09So7oY67E+q5O+KFqi5o6OmCepWdYGdrsWcNUzkgG2Zr167FwoULYW9vD4VCIV00HR0dbY36qJQIIXAn68Fo6uEI6uFo6rER1u1MXYHPqpU28Kxgj6qu9mhWsyI8XdWo6moPzwr28HS1lwJMrbQthS2jsubxoHuSLicX8elZuH47E1dvPfyeidPx6fmCzs5WgQZVnOHt6QJvz7yA8/Z0RfUK9hzJPSdkw+ybb77B1q1beUPgZ0BurpCOEyXf1yPjWtpju/00SL6nRcpjIyptTsHRVGXnvNFUtQr2aF6rIjxd7eFZIX9YVXCw4y8QMguV0gYNqjijQSFBp80x4J9/M3Ex+T4uJN/HxeR7OH41DZtP35SWcbFXwrvqo4DzquqChp6uqOBoZ83NICuQDbNatWrBwcHBGrU8F/LOlBPQPHZyQoETEHJypeNHWn2u7LKapy77qK2CZ8U9emK4vZ2NNGpqUauiNLJ6PKw8XOyhUnJXDpUNaqUtfKq5wqeaa77pd7P1uJTyKOAuJWdgy5mbWH00R1rG09X+sRFc3tcLHs7cW1COyYZZVFQUBgwYgObNm0OlUknTp0yZYtHCSmrPhRSM/+U6bGwTpWmFPx8g/8QnlynsI08+aKDwZQp+5mGIyT+nwDgbBQqetPDYd2e1Eu5Otk8cX7Ip8Jm7t1Ph27B+Xli52sPVQcnRFD0TKjjYwb+uG/zrPtqbJIRA8j3Ng4C7L43mDl+5Lf2RZ/vgTE1vT5d8o7lalRyfqxN5yivZMJs2bRratGkDLy8v2NiUn7/Ka7s5IqCmIypUrJRvemG/r5+c9OQyigJLFLZMIe0+sdDDs9weBUve2XPGgklt92jZh9+VNgqzhE5cnAY+XlVK3A5ReaBQKFCtggOqVXBAJ28PabrekItrtzJxMeVRwP2ZcBfbziZJyziqbPFiVRc0rOqSbzTn7qwujU0hI0y6aHrSpEnWqMWsXvBwwai2Vfh8ISIyys7WBi9WdcGLVV3Qvdmj6ZnaHFx6LOAuJt/H7rgU/HTi0a75ys6qB6M4VzT0dIFtlgaqyhmo4GCHCg52PLvSymTDLCAgAD/99BM6deqUbzdjxYoVLVoYEVFpcVIr4Vu7EnxrP9qzI4TArQzdg4C7l7e7MuU+/nfs+qNLT3Y8OvnEUWWLig52cH0Qbg+/Kjo+eu3qYIeKjqp8813tlVAyCItMNsy2bNkCAFi+fLk0jafmE9HzRqFQoIqLGlVc1Gj/YmVpuiFX4EZaFv44dQGulT1xN1svfd3Jyvt+L1uP67ezpOnZesNT1+WiVuYLwScD8MlpD79c7O0segeYskw2zPhEaCIi4x6eOKKp6QgfnxomfUabY5BC7sngy/f1YNrl1AxpWmGXzDykUOQFYQVHO7g7qaXwreKshodr3veH0yo7q2Fv9+ycvSkbZtnZ2fj222+RlJSEjz76CNeuXcPVq1d5NxAiomJSK23h4WILDxf7In9WozcUGnh3s/W48yAg72TpcDtTh/i0LJy8no60LF2hZ1G72isfBZ6LPTweC79H09Vwc1SV+TM6ZcNs0qRJaNy4sfTkaU9PT7z77rsMMyKiUmBvZwt7O1tUdTU9CPWGXKRl6vDvfe2jr4xHr1Pva/Bnwh38e1+LTF3BXaC2Ngq4O6mMjPTs8wWfk8q2VC7zkQ2zGzdu4Msvv5SeYWZvb1/gOisiIiq77GxtUPXBTRHkZGpzcCujYOil3nv0+kLSfdzK0BZ6M3AHO9sCoVfVNguWPrFcNsxUKhU0Go2UtDdu3Mh3ViMRET07nNRKOKmVqONe8OkHj8vNFbiTrX8s9DR5gfdYAF75NwNHrt5GJbUCI7tbtm7ZMBs1ahSGDRuGpKQkREVF4dSpU5g7d65lqyIiojLNxkYBNycV3Jzyrrd7mri4OIvXIxtm7dq1Q6NGjXDmzBkIITB58mTedJiIiMoUo1fm/fjjj9LrW7du4eWXX0anTp0YZEREVOYYDbP169fzLmUEAAAgAElEQVRLr99//32rFENERFQcJt0zhWcvEhFRWWb0mNm9e/ewe/du5ObmIiMjA7t27co3v0uXLk9teNKkSYiJiYG7uzu2bt0KAJg3bx727t0LOzs71K5dG3PnzoWrq+tT2yEiIpJjdGTWunVr7NmzBzExMfD398fevXvzfcnp3bs3Vq5cmW9au3btsHXrVmzZsgV169bNd79HIiKi4jI6Mivp6ff+/v5ISEjIN619+/bS6xYtWmDnzp0lWgcRERFg4jEzS1i/fj06duxYWqsnIqJniOx1ZpawdOlS2Nra4rXXXjO6jDkustNoNFa5WK88Yt8Yx74xjn1jHPvGOGv0zVPDLDc3F6dPn0bLli3NtsKNGzciJiYG33333VNvRmmOJ0THxcXxSdNGsG+MY98Yx74xjn1jnLn6JjY21ui8p+5mtLGxwbx580pcwEP79+/HV199haVLl8LBwcFs7RIR0fPNpNtZ/fbbb+jSpUuRbus/duxYHDt2DOnp6ejYsSNGjRqFFStWQKfTITw8HADQvHlzzJw5s/jVExERwYQw+/bbb5GdnQ1bW1uo1WoIIaBQKHDy5Mmnfm7+/PkFpvXr16/4lRIRERkhG2YPH8pJRERUVsmemi+EwObNm7F48WIAQFJSEs6ePWvxwoiIiEwlG2bTp0/H6dOnpVtSOTo6YsaMGRYvjIiIyFSyYXb27Fl8+OGHUKvVAIAKFSpAr9dbvDAiIiJTyYaZUqmEwWCQzmRMS0uDjU2p3TiEiIioANkTQAYNGoSRI0fi1q1b+OKLL7Bz506MGTPGGrURERGZRDbMXnvtNTRu3BhHjhwBACxZsgQNGjSweGFERESmMunejBqNRtrVqNFoLF0TERFRkcge/Prvf/+LiRMn4u7du0hPT8ekSZOwZMkSa9RGRERkEtmR2bZt27Bp0ybpbMbhw4ejV69eeOeddyxeHBERkSlkR2Y1atSAVquV3ut0OtSuXduiRRERERWF7MhMpVKhW7duaNeuHRQKBQ4ePIhWrVph1qxZAIApU6ZYvEgiIqKnkQ2z4OBgBAcHS+9bt25t0YKIiIiKSjbMevXqZY06iIiIio238iAionKPYUZEROUew4yIiMo9o8fMIiIinvrBZcuWmb0YIiKi4jAaZkOGDAEA7Nq1C7du3cJrr70GIO8i6ho1alinOiIiIhMYDbOHp+AvWLAAq1evlqYHBQXhzTfftHxlREREJpI9ZpaWlob4+HjpfXx8PNLS0ixaFBERUVHIXmc2adIkDBo0CLVq1QIAJCYmYsaMGRYvjIiIyFSyYdaxY0fs2rUL//zzDwCgfv36UKlUFi+MiIjIVLK7GbOzs7Fy5Ur8+OOPaNiwIW7evIm9e/daozYiIiKTyIbZpEmTYGdnh9OnTwMAPD098eWXX1q8MCIiIlPJhtmNGzfwn//8B0pl3h5Je3t7CCEsXhgREZGpZMNMpVJBo9FAoVAAyAs3HjMjIqKyRPYEkFGjRmHYsGFISkpCVFQUTp06hblz51qjNiIiIpPIhlm7du3QqFEjnDlzBkIITJ48GW5ubrINT5o0CTExMXB3d8fWrVsBAHfu3MF7772HxMRE1KhRA19++SUqVKhQ8q0gIqLnmkk3GtbpdHB1dYWzszOuXLmC48ePy36md+/eWLlyZb5pK1asQNu2bbFr1y60bdsWK1asKF7VREREj5EdmX366afYsWMHXnjhBdjYPMo+f3//p37O398fCQkJ+aZFR0fjhx9+AACEhYVh0KBBGD9+fHHqJiIiksiG2e+//46dO3ea5aSP27dvw8PDAwDg4eHx1NtixcXFlXh9Go3GLO08i9g3xrFvjGPfGMe+Mc4afSMbZrVq1YJer7f6GYw+Pj4lbiMuLs4s7TyL2DfGsW+MY98Yx74xzlx9Exsba3SebJg5ODggLCwMbdu2zRdoU6ZMKXIh7u7uSE1NhYeHB1JTU006kYSIiEiObJgFBQUhKCjILCsLCgrCpk2bMHz4cGzatAmdO3c2S7tERPR8kw2zXr16QafT4dq1awCAevXqwc7OTrbhsWPH4tixY0hPT0fHjh0xatQoDB8+HGPGjMG6detQrVo1LFiwoMQbQEREJBtmR48excSJE1GjRg0IIZCUlIR58+bJns04f/78Qqd///33xauUiIjICNkwmzdvHr7++mvUr18fAHD16lVERUVhw4YNFi+OiIjIFLIXTev1einIgLzdjHq93qJFERERFYXsyKxJkyb44IMP0LNnTwDAr7/+iiZNmli8MCIiIlPJhtmMGTOwevVq/PDDDxBCwN/fH//3f/9njdqIiIhMIhtmKpUK4eHhCA8Px507d5CcnMxHwBARUZkie8xs0KBByMjIwJ07dxAWFoYPPviAj4AhIqIyRTbM7t+/D2dnZ+zevRu9e/fGhg0bcOjQIWvURkREZBLZMDMYDEhNTcWOHTvw8ssvW6EkIiKiopENs3feeQdDhw5F7dq10axZM8THx6Nu3bpWKI2IiMg0sieAdO3aFV27dpXe16pVC4sWLbJoUUREREVhNMy++uor/Oc//8FHH30EhUJRYH5x7ppPRERkCUbDrEGDBgDAC6SJiKjMMxpmQUFBMBgMuHTpEiZMmGDNmoiIiIrkqSeA2Nra4q+//rJWLURERMUiewJIo0aNEBERgZCQEDg6OkrTu3TpYtHCiIiITCUbZnfv3kWlSpVw9OjRfNMZZkREVFbIhhlvXUVERGWd7EXT8fHxiIiIQJs2bdC2bVuMGDEC8fHx1qiNiIjIJLJhFhUVhZCQEBw4cAB//PEHQkJCMHbsWGvURkREZBLZMBNCICwsDEqlEkqlEj179iz0ImoiIqLSInvMLCAgACtWrEBoaCgUCgW2b9+OwMBA3LlzBwBQsWJFixdJRET0NLJhtn37dgDA2rVr801fv349FAoFoqOjLVMZERGRiWTDbM+ePdaog4iIqNhkw8xgMCAmJgaJiYkwGAzS9PDwcIsWRkREZCrZMIuIiIBarYaXlxdsbGTPFyEiIrI62TBLTk7Gli1brFELERFRscgOtTp27IgDBw5YoxYiIqJikR2ZtWjRApGRkcjNzYVSqYQQAgqFAidPnrRGfURERLJkw+zjjz/G2rVr4e3tbbaLpb/77jv88ssvUCgU8PLywty5c6FWq83SNhERPX9kdzPWrVsXXl5eZguylJQUrFq1CuvXr8fWrVthMBiwbds2s7RNRETPJ9mRWZUqVTBo0CB07NgRKpVKml6SU/MNBgM0Gg2USiU0Gg08PDyK3RYREZFsmNWsWRM1a9aEXq+HXq8v8QqrVq2KIUOGoFOnTlCr1WjXrh3at29fYLm4uLgSr0uj0ZilnWcR+8Y49o1x7Bvj2DfGWaNvZMMsMjLSrCu8e/cuoqOjER0dDRcXF7z77rvYvHkzevbsmW85Hx+fEq8rLi7OLO08i9g3xrFvjGPfGMe+Mc5cfRMbG2t0ntEwmz17NiZPnoyIiIhC5y9btqxYxRw6dAg1a9aEm5sbgLwnVp86dapAmBEREZnKaJg9DJchQ4aYdYXVq1fHmTNnkJ2dDXt7exw+fBhNmjQx6zqIiOj5YjTMHgZM69atzbrC5s2b49VXX0WvXr2gVCrh4+OD/v37m3UdRET0fJE9ZmYJo0ePxujRo0tj1URE9AzinYOJiKjcK1KY5ebmIiMjw1K1EBERFYtsmEVFRSEjIwNZWVkIDQ1FSEgIVq5caY3aiIiITCIbZpcvX4azszN+//13BAYGYu/evdi8ebM1aiMiIjKJbJjl5ORAr9fj999/R+fOnWFnZ2e2+zQSERGZg2yY9e/fH0FBQcjOzoa/vz8SExPh7OxsjdqIiIhMIntq/uDBgzF48GDpfY0aNbBq1SqLFkVERFQUsiOzW7du4YMPPsCwYcMA5B1D27hxo8ULIyIiMpVsmE2cOBHt27dHamoqgLznm3FkRkREZYlsmKWnpyM0NBQ2NnmLKpVK6TUREVFZIJtKjo6OSE9Pl85gPH36NFxcXCxeGBERkalkTwCZOHEiRowYgRs3bmDAgAFIT0/HggULrFEbERGRSWTDrHHjxvjxxx9x9epVCCFQr1492NnZWaM2IiIikxgNs127dhU6/dq1awDyHqpJRERUFhgNs7179z71gwwzIiIqK4yG2dy5c61ZBxERUbGZ9HDOmJgY/P3339BqtdK0yMhIixVFRERUFLKn5k+bNg3bt2/Hjz/+CAD47bffcPPmTYsXRkREZCrZMDt16hQ++eQTuLq6IjIyEmvXrkVycrI1aiMiIjKJbJjZ29sDABwcHJCSkgI7OzskJCRYvDAiIiJTyR4ze/nll3Hv3j0MHToUvXv3hkKhQN++fa1RGxERkUlkw2zkyJEAgFdffRWdOnWCVqvl7ayIiKhMKfJF0w/xOjMiIiorjIbZ6NGj4ePjg4YNGxY6n2FGRERlhdEwW7RoEbZv346LFy+ic+fO6N69O+rUqWPN2oiIiExiNMyCg4MRHByMrKwsREdH4+OPP8adO3fw3nvvoXXr1taskYiI6KlkT81Xq9VwcXGBs7MzsrOz890FhIiIqCwwOjI7cuQItm3bhrNnz+Kll17C4MGD0bRpU2vWRkREZBKjYfb222/D29sbrVq1gk6nw+bNm7F582Zp/pQpU4q90nv37mHKlCm4dOkSFAoF5syZA19f32K3R0REz7dSuWv+7Nmz0aFDByxcuBA6nQ4ajcZi6yIiomef0TDr1auXRVaYkZGB48eP4+OPPwYAqFQqqFQqi6yLiIieDwohhLDmCuPi4jB16lS88MILuHDhAho3bozJkyfD0dFRWiY2Njbf++LSaDTSvSUpP/aNcewb49g3xrFvjDNX32RlZaFVq1aFzjPpeWbmlJOTg/Pnz2Pq1Klo3rw5Zs2ahRUrVmDMmDH5lvPx8SnxuuLi4szSzrOIfWMc+8Y49o1x7BvjzNU3sbGxRucZPTX/008/BQDs2LGjxAU8ztPTE56enmjevDkAICQkBOfPnzfrOoiI6PliNMz2798PvV6PFStWmHWFVapUgaenJ/755x8AwOHDh9GgQQOzroOIiJ4vRncztm/fHm3atEF2djZatmwJIQQUCoX0/eTJk8Ve6dSpUzFu3Djo9XrUqlXLomdOEhHRs89omE2YMAETJkzAiBEjsHTpUrOu1MfHBxs2bDBrm0RE9PySPQFk6dKluHXrFv78808AQPPmzeHm5mbxwoiIiEwle2/GHTt2oF+/fti5cyd27NiBvn37YufOndaojYiIyCQmjczWrVsHd3d3AEBaWhrefvtthISEWLw4IiIiU8iOzIQQUpABQMWKFWHl66yJiIieSnZk1r59ewwdOhTdunUDAGzfvh0dO3a0eGFERESmkg2zCRMmYNeuXYiNjYUQAv3790dwcLA1aiMiIjKJSbez6tKlC7p06WLpWoiIiIpF9pgZERFRWccwIyKicu+pYWYwGDBu3Dhr1UJERFQsTw0zW1tbpKenQ6fTWaseIiKiIpM9AaRGjRp44403EBQUlO+BmeHh4RYtjIiIyFSyYebh4QEPDw8IIZCZmWmNmoiIiIpENswiIyMB5D2u+vGRGRERUVkhezbjqVOnEBoaitDQUADAhQsXMH36dEvXRUREZDLZMJszZw6+/vprVKxYEQDQsGFDnDhxwuKFERERmcqk68yqVauW/0M2vDyNiIjKDtljZtWqVcPJkyehUCig0+nwww8/oEGDBtaojYiIyCSyQ6zp06dj9erVSElJQceOHREXF4dp06ZZozYiIiKTyI7M3Nzc8Pnnn1ujFiIiomKRHZnFx8cjIiICbdq0Qdu2bTFixAjEx8dbozYiIiKTyIZZVFQUQkJCcODAAfzxxx8ICQnB2LFjrVEbERGRSWTDTAiBsLAwKJVKKJVK9OzZEwqFwhq1ERERmcToMbM7d+4AAAICArBixQqEhoZCoVBg+/btCAwMtFqBREREcoyGWe/evaFQKCCEAACsXbtWmqdQKDBy5EjLV0dERGQCo2G2Z88ea9ZBRERUbLKn5hsMBsTExCAxMREGg0GazkfAEBFRWSEbZhEREVCr1fDy8jLrbawMBgP69OmDqlWrYvny5WZrl4iInj+yYZacnIwtW7aYfcWrVq1CgwYNkJGRYfa2iYjo+SI71OrYsSMOHDhg1pUmJycjJiYGffv2NWu7RET0fJIdmbVo0QKRkZHIzc2FUqmEEAIKhQInT54s9krnzJmD8ePH88nVRERkFrJh9vHHH2Pt2rXw9vY2y8XSe/fuhZubG5o0aYKjR48aXS4uLq7E69JoNGZp51nEvjGOfWMc+8Y49o1x1ugb2TCrW7cuvLy8zHbXj5MnT2LPnj3Yv38/tFotMjIyMG7cOHz22Wf5lvPx8SnxuuLi4szSzrOIfWMc+8Y49o1x7BvjzNU3sbGxRufJhlmVKlUwaNAgdOzYESqVSppe3FPzo6KiEBUVBQA4evQovvnmmwJBRkREVBSyYVazZk3UrFkTer0eer3eGjUREREViWyYRUZGWmzlAQEBCAgIsFj7RET0fJANs0GDBhV6vGzVqlUWKYiIiKioZMNswoQJ0mutVotdu3bB1tbWokUREREVhWyYNWnSJN/7Vq1aYeDAgRYriIiIqKhkw+zhc80AIDc3F3/99Rf+/fdfixZFRERUFLJh9vhzzZRKJWrWrInZs2dbozYiIiKTyIYZn2tGRERlndEwO378+FM/6O/vb/ZiiIiIisNomH399deFTr948SKSk5N5DzIiIiozjIbZsmXL8r0/ceIEli1bhipVqmDq1KkWL4yIiMhUssfMDh8+jCVLlgDIe+p0u3btLF4UERFRURgNs5iYGCxbtgzOzs4YM2YMWrVqZc26iIiITGY0zCIiIuDp6YmGDRviq6++wldffZVv/pO7IYmIiEqL0TDjvReJiKi8MBpmrVu3tmYdRERExWZT2gUQERGVFMOMiIjKPZPDLCsry5J1EBERFZtsmJ08eRKhoaEIDQ0FAFy4cAHTp0+3dF1EREQmkw2zuXPn4uuvv0bFihUBAA0bNsSJEycsXhgREZGpTNrNWK1atfwfsuGhNiIiKjtkb2dVrVo1nDx5EgqFAjqdDj/88AMaNGhgjdqIiIhMIjvEmj59OlavXo2UlBQEBgYiLi4O06ZNs0ZtREREJpEdmbm5ueHzzz+3Ri1ERETFIhtms2bNKjDN2dkZTZo0wSuvvGKRooiIiIpCdjejVqtFXFwc6tSpgzp16uDixYu4e/cu1q1bh9mzZ1ujRiIioqeSHZldv34d33//PZTKvEXfeOMNDBkyBN9++y169Ohh8QKJiIjkyI7MUlJSkJ2dLb3Pzs5GamoqbG1toVKpLFocERGRKWRHZsOGDUPPnj0REBAAIQSOHz+OiIgIZGVloW3bttaokYiI6Klkw6xfv34IDAzE2bNnAQDvvfceqlatCgCYMGFCkVeYlJSE999/H7du3YKNjQ1ef/11vPXWW0Vuh4iI6CHZMAMAtVoNDw8PaLVa3LhxAzdu3IC/v3+xVmhra4uJEyeicePGyMjIQJ8+fdCuXTu88MILxWqPiIhINsx++eUXrFq1CsnJyWjYsCHOnDmDFi1aFPtJ1B4eHvDw8ACQd4p//fr1kZKSwjAjIqJikz0BZNWqVVi3bh2qV6+OH374ARs3boSbm5tZVp6QkIC4uDg0b97cLO0REdHzSXZkplKpoFarAQA6nQ4NGjTA1atXS7zizMxMjB49Gh988AGcnZ0LzI+LiyvxOjQajVnaeRaxb4xj3xjHvjGOfWOcNfpGNsw8PT1x7949vPLKKwgPD4erq6u0m7C49Ho9Ro8ejR49eqBLly6FLuPj41OidQB5gWiOdp5F7Bvj2DfGsW+MY98YZ66+iY2NNTpPNswWL14MABg1ahQCAgJw//59dOjQodjFCCEwefJk1K9fH+Hh4cVuh4iI6KGnHjPLzc1F9+7dpfetW7dG586dS3SxdGxsLDZv3owjR46gZ8+e6NmzJ/bt21fs9oiIiJ46MrOxsYG3tzdu3ryJ6tWrm2WFfn5+uHjxolnaIiIiAkzYzfjvv/+iW7duaNasGRwcHKTpy5Yts2hhREREppINs8jISGvUQUREVGyyYda6dWskJibi+vXreOmll5CdnQ2DwWCN2oiIiEwie9H0zz//jNGjR2PatGkA8u6iP3LkSIsXRkREZCrZMFu9ejXWrFkjXdhct25dpKWlWbwwIiIiU8mGmUqlyncqfk5OjkULIiIiKirZY2b+/v5YtmwZNBoNDh48iP/9738ICgqyRm1EREQmkR2ZjRs3Dm5ubvDy8sJPP/2EwMBAjBkzxhq1ERERmUR2ZBYdHY2wsDC8/vrr1qiHiIioyGRHZnv27MGrr76K8ePHIyYmhsfMiIiozJENs7lz52LXrl0ICQnBli1bEBwcjMmTJ1ujNiIiIpPIhhkA2NnZoWPHjujWrRsaN26M6OhoS9dFRERkMtljZvv378f27dtx9OhRtG7dGv369cOXX35pjdqIiIhMIhtmGzduRLdu3TBz5swSPfqFiIjIUmR3M37xxRd45ZVXpCCLjY3FjBkzLF4YERGRqWRHZkDeI6+3bNmCnTt3okaNGujSpYul6yIiIjKZ0TC7evUqtm3bhm3btqFixYoIDQ2FEAI//PCDNesjIiKSZTTMunbtCj8/Pyxbtgx16tQBAHz33XfWqouIiMhkRo+ZLVq0CJUrV8bgwYMxZcoUHD58GEIIa9ZGRERkEqMjs+DgYAQHByMrKwu///47vvvuO9y+fRsffvghgoOD0b59e2vWSUREZJTs2YyOjo547bXXsHz5cuzbtw8+Pj5YsWKFNWojIiIyiUlnMz5UsWJFDBgwAAMGDLBUPUREREVm0u2siIiIyjKGGRERlXsMMyIiKvcYZkREVO4xzIiIqNxjmBERUblXKmG2f/9+vPrqqwgODuY1a0REVGJWDzODwYCZM2di5cqV2LZtG7Zu3YrLly9buwwiInqGWD3Mzp49izp16qBWrVpQqVTo1q0boqOjrV0GERE9Q4p0BxBzSElJgaenp/S+atWqOHv2bIHlYmNjzbI+c7XzLGLfGMe+MY59Yxz7xjhL943Vw6ywO+8rFIp871u1amWtcoiI6Blg9d2Mnp6eSE5Olt6npKTAw8PD2mUQEdEzxOph1rRpU1y7dg3x8fHQ6XTYtm0bgoKCrF0GERE9Q6weZkqlEtOmTcOwYcMQGhqKrl274sUXXzTps0lJSRg0aBC6du2Kbt264fvvvwcA3LlzB+Hh4ejSpQvCw8Nx9+5dAHm7NGfNmoXg4GD06NEDf/31l9TWzZs3MWTIEHTt2hWhoaFISEgw/8ZakTn75pNPPkG3bt3QtWtXzJo1q9w/lLWofXPlyhX0798fTZo0wddff52vrWftshJz9Y2xdsozc/7cAHlncoeFheH//b//Z9XtsARz9s29e/cwevRohISEoGvXrjh16lTxihLlSEpKijh37pwQQoj79++LLl26iL///lvMmzdPLF++XAghxPLly8Unn3wihBAiJiZGDB06VOTm5opTp06Jvn37Sm0NHDhQHDhwQAghREZGhsjKyrLy1piXufomNjZW9O/fX+Tk5IicnBzx+uuviyNHjpTORplJUfvm1q1b4syZM2L+/Pli5cqVUjs5OTmic+fO4saNG0Kr1YoePXqIv//+2/obZEbm6htj7ZRn5uqbh7755hsxduxYMXz4cOtthIWYs2/ef/998fPPPwshhNBqteLu3bvFqqlc3QHEw8MDjRs3BgA4Ozujfv36SElJQXR0NMLCwgAAYWFh+P333wFAmq5QKNCiRQvcu3cPqampuHz5MnJyctCuXTsAgJOTExwcHEpno8zEXH2jUCig0+mg1+ul75UrVy617TKHovaNu7s7mjVrBqUy//lRz+JlJebqG2PtlGfm6hsASE5ORkxMDPr27Wu9DbAgc/VNRkYGjh8/LvWLSqWCq6trsWoqV2H2uISEBMTFxaF58+a4ffu2dBKJh4cH0tLSABS8DMDT0xMpKSm4du0aXF1dERkZibCwMMybNw8Gg6FUtsMSStI3vr6+CAgIQPv27dG+fXt06NABDRo0KJXtsART+saYwi4rKe+/sB9Xkr4x1s6zoqR9M2fOHIwfPx42NuX2V65RJemb+Ph4uLm5YdKkSQgLC8PkyZORlZVVrDrKZc9mZmZi9OjR+OCDD+Ds7Gx0OWHkMoCcnBycOHECEyZMwLp165CQkIANGzZYsmSrKWnfXL9+HVeuXMG+ffuwf/9+HDlyBMePH7dkyVZjat8YY6zPngUl7Rtzt1OWlHSb9u7dCzc3NzRp0sQC1ZWukvZNTk4Ozp8/jzfeeAObNm2Cg4NDsY9Fl7sw0+v1GD16NHr06IEuXboAyBvCpqamAgBSU1Ph5uYGoOBlAMnJyfDw8ICnpycaNWqEWrVqQalUonPnzjh//rz1N8bMzNE3u3fvRvPmzeHk5AQnJyd06NABp0+ftv7GmFlR+saYZ/WyEnP0jbF2yjtz9M3JkyexZ88eBAUFYezYsThy5AjGjRtn8dotzVz/pzw9PaVRfEhISLF/F5erMBNCYPLkyahfvz7Cw8Ol6UFBQdi0aRMAYNOmTejcuXO+6UIInD59Gi4uLvDw8EDTpk1x9+5daQh89OhRvPDCC9bfIDMyV99Ur14dx48fR05ODvR6PY4fP17udzMWtW+MeRYvKzFX3xhrpzwzV99ERUVh//792LNnD+bPn482bdrgs88+s2jtlmauvqlSpQo8PT3xzz//AAAOHz5c7N83ClHYvpMy6sSJE3jzzTfh5eUl7XseO3YsmjVrhjFjxiApKQnVqlXDggULULFiRQghMHPmTPzxxx9wcHDAnDlz0LRpUwDAwYMH8fHHHwMAGjdujBKHhMMAAAK2SURBVJkzZ0KlUpXatpWUufrGYDBgxowZOH78OBQKBTp06IBJkyaV8taVTFH75t9//0WfPn2QkZEBGxsbODo6Yvv27XB2dsa+ffswZ84cGAwG9OnTByNGjCjlrSsZc/XNhQsXCm0nMDCwNDevRMz5c/PQ0aNH8c0332D58uWltVlmYc6+iYuLw+TJk6HX61GrVi3MnTsXFSpUKHJN5SrMiIiIClOudjMSEREVhmFGRETlHsOMiIjKPYYZERGVewwzIiIq9xhmRFYmhMAbb7yBffv2SdO2b9+OoUOHlmJVROUbT80nKgWXLl3Cu+++i02bNkmPBlm5ciVq165d7DZzcnIKvckt0fOAYUZUSj755BM4OjoiKysLTk5OGDlyJDZu3IjVq1dDr9fD19cX06ZNg42NDaZOnYq//voLWq0WXbt2RWRkJACgY8eO6N+/P/744w+89dZb6Nq1aylvFVHp4J9xRKUkMjISvXr1gkqlwvr163Hp0iXs3r0ba9euhVKpxNSpU7Ft2zb06NEDUVFRqFixInJycjB48GCEhIRIt2BzdHTE2rVrS3lriEoXw4yolDg6OiI0NBSOjo5QqVQ4dOgQ/vzzT/Tp0wcAoNFopEfObNu2DevWrUNOTo70TL6HYRYaGlpq20BUVjDMiEqRjY1Nvmdc9enTB2PGjMm3zLVr17Bq1Sr88ssvcHV1xbhx46DVaqX55f3BskTmwLMZicqItm3bYseOHdLTHNLT03Hz5k1kZGTAyckJzs7OSE1NxYEDB0q5UqKyhyMzojLC29sbkZGRCA8PR25uLuzs7DB9+nQ0bdoUDRo0QPfu3VGrVi20bNmytEslKnN4NiMREZV73M1IRETlHsOMiIjKPYYZERGVewwzIiIq9xhmRERU7jHMiIio3GOYERFRuccwIyKicu//A5ZEmRzUSs9QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x215783d6ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sex_df['m_f_ratio'] = sex_df['Male'] / sex_df['Female']\n",
    "with sns.axes_style(\"whitegrid\"):\n",
    "    fig, ax = plt.subplots(figsize=(7,5))\n",
    "    ax.plot(sex_df['m_f_ratio'])\n",
    "    ax.set_title('Male to Female Imprisonment Ratio (table: p16t01)')\n",
    "    ax.set_xlabel('Year')\n",
    "    ax.set_ylabel('Average Number of Males imprisoned per Female')\n",
    "    ax.legend()\n",
    "    ax.set_ylim([0, 1.1*sex_df['m_f_ratio'].max()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21578322400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"whitegrid\"):\n",
    "    fig, ax = plt.subplots(figsize=(10,7))\n",
    "    ax.plot(sex_df['Federal/a'])\n",
    "    ax.plot(sex_df['State'])\n",
    "    ax.set_title('Imprisonment Counts in State and Federal Prisons (table: p16t01)')\n",
    "    ax.set_xlabel('Year')\n",
    "    ax.yaxis.set_major_formatter(y_formatter)\n",
    "    ax.set_ylabel('People Imprisoned [in thousands of people]')\n",
    "    ax.legend()\n",
    "    ax.set_ylim([0, 1.1*max(sex_df['Federal/a'].max(),\n",
    "                            sex_df['State'].max())])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations\n",
    "\n",
    "Far more people are in State prisons than are in Federal prisons. That isn't very controversial and at this level, I won't dig much deeper. It may be interesting to dig further into imprisonment counts broken down by state."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To Be Continued <a id=\"8\"></a>\n",
    "\n",
    "There are still 20 more tables that I haven't looked at yet, but so far, we've seen\n",
    "* The imprisonment rates increased by just over 266% between 1978 and 2007.\n",
    "* Black people are imprisoned at a far higher rate than either Hispanic people or non-Hispanic white people.\n",
    "* Hispanic people are imprisoned at a far higher rate than non-Hispanic white people.\n",
    "* Far more men are imprisoned than women.\n",
    "* Far more people are in state prisons than in federal prisons. \n",
    "\n",
    "Continued in [the next notebook](https://nbviewer.jupyter.org/github/MattTriano/personal_site_public/blob/master/Crime_and_Prisons_part2.ipynb)."
   ]
  }
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