convictions.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import csv\n",
      "from functools import reduce\n",
      "import json\n",
      "import sys\n",
      "\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "from convictions_data.models import Disposition\n",
      "\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## What are we counting here?\n",
      "\n",
      "tl;dr: **Records != Convictions != People**\n",
      "\n",
      "Each record represents a disposition, which is refers to a court's final determination of a case or issue.  Our data set appears to only have dispositions that are related to charges where there was a conviction. So, we don't have dispositions related to charges where someone was found not guilty. \n",
      "\n",
      "There can be multiple records for a single case."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "num_records = Disposition.objects.count()\n",
      "num_cases = Disposition.objects.values('case_number').distinct().count()\n",
      "\n",
      "print(\"There are {} records in our dataset.\".format(num_records))\n",
      "print(\"These are from {} cases.\".format(num_cases))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "There are 321590 records in our dataset.\n",
        "These are from 161150 cases.\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Excluded rows\n",
      "\n",
      "It seems our data contains all dispositions relating to conviction charges from 2005-2009.  However, it is very possible that some of the cases reflected in our data have dispositions from before 2005 that don't appear in our dataset since we [decided to count convictions based on charges on the first disposition date](https://github.com/sc3/cook-convictions/issues/41), we're going to limit our analysis to dispositions whose initial date is in 2005."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from datetime import datetime\n",
      "in_analysis = Disposition.objects.in_analysis()\n",
      "cases_in_analysis = in_analysis.values('case_number').distinct()\n",
      "print(\"There are {} dispositions in cases with an initital date >= 2005\".format(in_analysis.count()))\n",
      "print(\"There are {} cases with an initial date >= 2005\".format(cases_in_analysis.count()))\n",
      "print(in_analysis.order_by('initial_date')[0].initial_date)\n",
      "print(Disposition.objects.filter(chrgdispdate__gte=datetime(year=2005,month=1,day=1)).count())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "There are 270595 dispositions in cases with an initital date >= 2005\n",
        "There are 136385 cases with an initial date >= 2005"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "2005-01-02"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "321122"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Some records did not have statutes that could easily be parsed into IUCR codes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(Disposition.objects.exclude(final_statute=\"\").filter(iucr_code=\"\").count())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "114185\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Other records did not have an address that could be geocoded"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(Disposition.objects.all().count()-Disposition.objects.has_geocodable_address().count())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "5757\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We were able to geocode all the Cook county or Chicago records that had good addresses."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cook_q = Q(county=\"Cook\", state=\"IL\")\n",
      "chi_zip_q = Q(zipcode__startswith=\"606\")\n",
      "print(Disposition.objects.filter(cook_q | chi_zip_q).has_geocodable_address().ungeocoded().count())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Some rows were misaligned. These will be fixed eventually, but for the initial pass, we should exclude them."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "unique_chrgclasses = Disposition.objects.values_list('chrgclass').distinct()\n",
      "for chrgclass in unique_chrgclasses:\n",
      "    print(chrgclass[0])\n",
      "print()\n",
      "\n",
      "without_bad_chrgclass = Disposition.objects.filter(chrgclass__regex=r'^[A-Z0-9]{0,1}$')\n",
      "print(\"Without bad charge classes:\")\n",
      "for chrgclass in without_bad_chrgclass.values_list('chrgclass').distinct():\n",
      "    print(chrgclass[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "X\n",
        "3\n",
        "1\n",
        "2\n",
        "M\n",
        "U\n",
        "Z\n",
        "4\n",
        "N\n",
        "A\n",
        "\n",
        "G\n",
        "B\n",
        "DEF SENTENCED TO PROBATION\n",
        "CREDIT DEFENDANT FOR TIME SERV\n",
        "P\n",
        "DEF SENTENCED ILLINOIS DOC\n",
        "C\n",
        "DEF SENTENCED TO COOK CNTY DOC\n",
        "DEF SENT CONDITIONAL DISCHARGE\n",
        "PROB AND OTHER DISC CONDS\n",
        "SENTENCED TO COMMUNITY SERVICE\n",
        "SENTENCED CCDOC BOOT CAMP\n",
        "T\n",
        "ELIGIBLE IMPACT PROG-BOOT CAMP\n",
        "\n",
        "Without bad charge classes:\n",
        "X"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "3\n",
        "1\n",
        "2\n",
        "M\n",
        "U\n",
        "Z\n",
        "4\n",
        "N\n",
        "A\n",
        "\n",
        "G\n",
        "B\n",
        "P\n",
        "C\n",
        "T\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## From disposition to conviction"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "in_analysis = Disposition.objects.in_analysis()\n",
      "# Get the date of the first disposition for each case\n",
      "cases_in_analysis = in_analysis.values('case_number').distinct()\n",
      "first_chrgdispdates = in_analysis.values('case_number').annotate(first_chrgdispdate=Min('chrgdispdate')).values_list('case_number', 'first_chrgdispdate')\n",
      "assert cases_in_analysis.count() == first_chrgdispdates.count()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# What are the unique charge disposition values?\n",
      "\n",
      "chrgdisps = set()\n",
      "for l in in_analysis.filter(chrgdisp__regex=r'^[A-Z]').values_list('chrgdisp').distinct():\n",
      "    chrgdisps.add(l[0])\n",
      "#chrgdisps = in_analysis.filter(chrgdisp__regex=r'^[A-Z]').values_list('chrgdisp').distinct()\n",
      "\n",
      "# Some records have misaligned columns\n",
      "for l in in_analysis.filter(chrgclass__regex=r'^[A-Z]').values_list('chrgclass').distinct():\n",
      "    if len(l[0]) > 1:\n",
      "        chrgdisps.add(l[0])\n",
      "        \n",
      "print(\"The unique charge disposition values in the records we're analysing are:\")\n",
      "for chrgdisp in sorted(chrgdisps):\n",
      "    print(chrgdisp)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The unique charge disposition values in the records we're analysing are:\n",
        "COND DISCH AND OTHER DISC COND\n",
        "CREDIT DEFENDANT FOR TIME SERV\n",
        "DEF SENT CCDOC, PERIODIC IMP\n",
        "DEF SENT CONDITIONAL DISCHARGE\n",
        "DEF SENT TO HOME CONFINEMENT\n",
        "DEF SENT TO INDETERMINATE TERM\n",
        "DEF SENT TO LIFE IMPRISONMENT\n",
        "DEF SENT TO PAY RESTITUTION\n",
        "DEF SENTENCED ILLINOIS DOC\n",
        "DEF SENTENCED TO COOK CNTY DOC\n",
        "DEF SENTENCED TO PROBATION\n",
        "ELIGIBLE IMPACT PROG-BOOT CAMP\n",
        "PROB AND OTHER DISC CONDS\n",
        "SENT PER IMP + OTH DISC COND\n",
        "SENT/CRT SUPV-SOC SERV-SPC CND\n",
        "SENT/CRT SUPV/SPEC CONDS\n",
        "SENTENCED CCDOC BOOT CAMP\n",
        "SENTENCED TO COMMUNITY SERVICE\n",
        "SUPERVISION - SOCIAL SERV DEPT\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "probatio\n",
      "for case_number, first_chrgdispdate in in_analysis.first_chrgdispdates()[:100]:\n",
      "    dispositions = in_analysis.filter(case_number=case_number, chrgdispdate=first_chrgdispdate)\\\n",
      "        .values('final_statute').annotate()\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from django.db.models import F\n",
      "\n",
      "# How are charge dispositions grouped together\n",
      "in_analysis = Disposition.objects.in_analysis()\n",
      "# Get the date of the first disposition for each case\n",
      "first_chrgdispdates = in_analysis.values('case_number').annotate(first_chrgdispdate=Min('chrgdispdate')).values_list('case_number', 'first_chrgdispdate')\n",
      "with_counts = in_analysis.values('case_number')\\\n",
      "    .annotate(num_statutes=Count('final_statute', distinct=True), num_chrgdisp=Count('chrgdisp', distinct=True))\\\n",
      "    .filter(num_chrgdisp__gt=F('num_statutes'))\n",
      "#print(with_counts.query)\n",
      "\n",
      "for case_number, first_chrgdispdate in first_chrgdispdates[0:1000]:\n",
      "    dispositions = Disposition.objects.filter(case_number=case_number, chrgdispdate=first_chrgdispdate)\n",
      "    with_counts = dispositions.values('case_number').annotate(num_statutes=Count('final_statute', distinct=True), num_chrgdisp=Count('chrgdisp', distinct=True)).filter(num_chrgdisp__gt=F('num_statutes'))        \n",
      "    if with_counts.count():\n",
      "        for final_statute, chrgdisp in dispositions.values_list('final_statute', 'chrgdisp'):\n",
      "            print (case_number, final_statute, chrgdisp)\n",
      "    \n",
      "        print()"
     ],
     "language": "python",
     "metadata": {},
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        "2005C22045401 720-5/16A-3(A) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22045801"
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/12-4(b)(6) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22045801 720-5/12-4(b)(6) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22045901"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-550/4(d) PROB AND OTHER DISC CONDS\n",
        "2005C22045901 720-550/4(d) SENTENCED TO COMMUNITY SERVICE\n",
        "\n",
        "2005C22046001"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 625-5/6-303(d) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22046001 625-5/6-303(d) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22046101"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/16A-3(a) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22046101 720-5/16A-3(a) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22046501"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/16A-3(A) DEF SENTENCED TO PROBATION\n",
        "2005C22046501 720-5/16A-3(A) DEF SENTENCED TO COOK CNTY DOC\n",
        "\n",
        "2005C22046701"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/16A-3(a) DEF SENT CONDITIONAL DISCHARGE\n",
        "2005C22046701 720-5/16A-3(a) SENTENCED TO COMMUNITY SERVICE\n",
        "\n",
        "2005C22047101"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-570/402(c) DEF SENTENCED TO COOK CNTY DOC\n",
        "2005C22047101 720-570/402(c) CREDIT DEFENDANT FOR TIME SERV\n",
        "2005C22047101 720-570/402(c) DEF SENTENCED TO PROBATION\n",
        "\n",
        "2005C22047501"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/16-1(a)(1) PROB AND OTHER DISC CONDS\n",
        "2005C22047501 720-5/16-1(a)(1) SENTENCED TO COMMUNITY SERVICE\n",
        "\n",
        "2005C22047801"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/19-1(a) DEF SENTENCED TO PROBATION\n",
        "2005C22047801 720-5/19-1(a) DEF SENTENCED TO COOK CNTY DOC\n",
        "\n",
        "2005C22048001"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/16-5 DEF SENTENCED ILLINOIS DOC\n",
        "2005C22048001 720-5/16-5 CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22048301"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 625-5/6-303(d) DEF SENTENCED TO COOK CNTY DOC\n",
        "2005C22048301 625-5/6-303(d) CREDIT DEFENDANT FOR TIME SERV\n",
        "2005C22048301 625-5/6-303(d) DEF SENTENCED TO PROBATION\n",
        "\n",
        "2005C22048601"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 625-5/6-303(a) DEF SENTENCED TO PROBATION\n",
        "2005C22048601 625-5/6-303(a) SENTENCED TO COMMUNITY SERVICE\n",
        "\n",
        "2005C22049201"
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      {
       "output_type": "stream",
       "stream": "stdout",
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        " 720-5/16A-3(a) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22049201 720-5/16A-3(a) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22049401"
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
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        " 720-5/16A-3(a) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22049401 720-5/16A-3(a) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22049501"
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-570/402(c) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22049501 720-570/402(c) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22049801"
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/12-4(b)(11) DEF SENTENCED TO COOK CNTY DOC\n",
        "2005C22049801 720-5/12-4(b)(11) CREDIT DEFENDANT FOR TIME SERV\n",
        "2005C22049801 720-5/12-4(b)(11) DEF SENTENCED TO PROBATION\n",
        "\n",
        "2005C22050301"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/10-3 PROB AND OTHER DISC CONDS\n",
        "2005C22050301 720-5/10-3 SENTENCED TO COMMUNITY SERVICE\n",
        "\n",
        "2005C22050401"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 625-5/4-103(a)(1) DEF SENTENCED TO COOK CNTY DOC\n",
        "2005C22050401 625-5/4-103(a)(1) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22050501"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/16A-3(a) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22050501 720-5/16A-3(a) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22050601"
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-570/402(c) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22050601 720-570/402(c) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22051101"
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/17-3(a)(1) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22051101 720-5/17-3(a)(1) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22051401"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-250/8 COND DISCH AND OTHER DISC COND\n",
        "2005C22051401 720-250/8 SENTENCED TO COMMUNITY SERVICE\n",
        "\n",
        "2005C22051701"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/16A-3(a) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22051701 720-5/16A-3(a) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22051901"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 625-5/4-103(a)(1) PROB AND OTHER DISC CONDS\n",
        "2005C22051901 625-5/4-103(a)(1) SENTENCED TO COMMUNITY SERVICE\n",
        "2005C22051901 625-5/4-103(a)(1) DEF SENT TO PAY RESTITUTION\n",
        "\n",
        "2005C22052001"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/17-3(a)(2) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22052001 720-5/17-3(a)(2) CREDIT DEFENDANT FOR TIME SERV\n",
        "2005C22052001 720-5/17-3(a)(2) DEF SENTENCED ILLINOIS DOC\n",
        "\n",
        "2005C22052101"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/19-3(a) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22052101 720-5/19-3(a) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22052301"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 625-5/6-303(d) DEF SENTENCED TO COOK CNTY DOC\n",
        "2005C22052301 625-5/6-303(d) CREDIT DEFENDANT FOR TIME SERV\n",
        "2005C22052301 625-5/6-303(d) DEF SENTENCED TO PROBATION\n",
        "\n",
        "2005C22052402"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/16A-3(a) DEF SENT CONDITIONAL DISCHARGE\n",
        "2005C22052402 720-5/16A-3(a) SENTENCED TO COMMUNITY SERVICE\n",
        "\n",
        "2005C22053001"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/19-1(a) DEF SENTENCED TO PROBATION\n",
        "2005C22053001 720-5/19-1(a) DEF SENTENCED TO COOK CNTY DOC\n",
        "\n",
        "2005C22053101"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/19-1(a) DEF SENTENCED TO PROBATION\n",
        "2005C22053101 720-5/19-1(a) DEF SENT TO PAY RESTITUTION\n",
        "\n",
        "2005C22053201"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/16A-3(A) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22053201 720-5/16A-3(A) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22053401"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/12-4(b)(8) DEF SENTENCED TO COOK CNTY DOC\n",
        "2005C22053401 720-5/12-4(b)(8) CREDIT DEFENDANT FOR TIME SERV\n",
        "2005C22053401 720-5/12-4(b)(8) DEF SENTENCED TO PROBATION\n",
        "\n",
        "2005C22053501"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/19-1(a) DEF SENTENCED TO PROBATION\n",
        "2005C22053501 720-5/19-1(a) DEF SENTENCED TO COOK CNTY DOC\n",
        "2005C22053501 720-5/19-1(a) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22054201"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-570/402(c) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22054201 720-570/402(c) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22054401"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/19-1(A) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22054401 720-5/19-1(A) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22054501"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/31-4(a) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22054501 720-5/31-4(a) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22054901"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/16A-3(A) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22054901 720-5/16A-3(A) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22055201"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-570/402(c) DEF SENTENCED TO PROBATION\n",
        "2005C22055201 720-570/402(c) SENTENCED TO COMMUNITY SERVICE\n",
        "\n",
        "2005C22056701"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/21-3(a) DEF SENTENCED TO COOK CNTY DOC\n",
        "2005C22056701 720-5/21-3(a) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22056901"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 625-5/11-401(a) PROB AND OTHER DISC CONDS\n",
        "2005C22056901 625-5/11-401(a) SENTENCED TO COMMUNITY SERVICE\n",
        "\n",
        "2005C22057101"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 625-5/11-501(c-1)(1) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22057101 625-5/11-501(c-1)(1) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22057201"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/17-3(A)(1) DEF SENT TO PAY RESTITUTION\n",
        "2005C22057201 720-5/17-3(A)(1) DEF SENTENCED TO COOK CNTY DOC\n",
        "2005C22057201 720-5/17-3(A)(1) CREDIT DEFENDANT FOR TIME SERV\n",
        "2005C22057201 720-5/17-3(A)(1) DEF SENTENCED TO PROBATION\n",
        "\n",
        "2005C22057301"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/16-1(a)(1) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22057301 720-5/16-1(a)(1) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22057401"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 625-5/6-301.2(b)(1) DEF SENTENCED TO PROBATION\n",
        "2005C22057401 625-5/6-301.2(b)(1) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22057501"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 625-5/6-303(d) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22057501 625-5/6-303(d) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22057701"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/16A-3(a) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22057701 720-5/16A-3(a) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22058301"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/16-1(a)(1) DEF SENTENCED TO PROBATION\n",
        "2005C22058301 720-5/16-1(a)(1) DEF SENT TO PAY RESTITUTION\n",
        "\n",
        "2005C22058401"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 625-5/11-501(d)(1)(A) PROB AND OTHER DISC CONDS\n",
        "2005C22058401 625-5/11-501(d)(1)(A) SENTENCED TO COMMUNITY SERVICE\n",
        "\n",
        "2005C22058601"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-570/402(c) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22058601 720-570/402(c) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22058701"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-570/402(c) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22058701 720-570/402(c) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22059102"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/18-1(a) DEF SENTENCED TO COOK CNTY DOC\n",
        "2005C22059102 720-5/18-1(a) CREDIT DEFENDANT FOR TIME SERV\n",
        "2005C22059102 720-5/18-1(a) DEF SENTENCED TO PROBATION\n",
        "\n",
        "2005C22059201"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/16A-3(a) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22059201 720-5/16A-3(a) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22059301"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/12-4(b)(8) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22059301 720-5/12-4(b)(8) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22059401"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/19-1(a) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22059401 720-5/19-1(a) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22059601"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/19-3(a) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22059601 720-5/19-3(a) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22060001"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-550/8(d) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22060001 720-550/8(d) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n",
        "2005C22060101"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 625-5/11-501(c-1)(1) PROB AND OTHER DISC CONDS\n",
        "2005C22060101 625-5/11-501(c-1)(1) SENTENCED TO COMMUNITY SERVICE\n",
        "\n",
        "2005C22060302"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720-5/16A-3(a) DEF SENTENCED ILLINOIS DOC\n",
        "2005C22060302 720-5/16A-3(a) CREDIT DEFENDANT FOR TIME SERV\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Interesting cases"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from django.db.models import Count\n",
      "\n",
      "# Print the case number with the must records, and the number of records\n",
      "most_records = Disposition.objects.values('case_number').annotate(num_records=Count('case_number')).order_by('-num_records')[0]\n",
      "print(most_records['case_number'])\n",
      "print(most_records['num_records'])\n",
      "\n",
      "# Case with the most records for a possession charge\n",
      "most_records = Disposition.objects.filter(iucr_code=\"2020\").values('case_number').annotate(num_records=Count('case_number')).order_by('-num_records')[0]\n",
      "print(most_records['case_number'])\n",
      "print(most_records['num_records'])\n",
      "\n",
      "\n",
      "dispositions = Disposition.objects.filter(case_number=most_records['case_number'])\n",
      "# In this case, all these records are likely for a single person\n",
      "print(dispositions.values('dob').distinct().count())\n",
      "\n",
      "# I think distinct tuples of case_number, dob, and statute should represent an individual conviction\n",
      "# I'm not quite sure how to take into account \n",
      "print(dispositions.values('case_number', 'dob', 'final_statute').distinct().count())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2002CR3214402\n",
        "36\n",
        "2005CR0590001"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "14\n",
        "1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "4"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Number of people\n",
      "\n",
      "The best bet we can get to identify individual people is by the ``fgrprntno`` field."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "num_with_fgrprntno = Disposition.objects.exclude(fgrprntno=\"0\").count()\n",
      "print(\"There are {} records with identifiable fgrprntno fields\".format(num_with_fgrprntno))\n",
      "print()\n",
      "num_distinct_fgrprntno = Disposition.objects.exclude(fgrprntno=\"0\").values('fgrprntno').distinct().count()\n",
      "print(\"There are {} distinct fgrprntno values (people?)\".format(num_distinct_fgrprntno))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "There are 289475 records with identifiable fgrprntno fields\n",
        "\n",
        "There are 102459 distinct fgrprntno values (people?)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Identifying unique people\n",
      "\n",
      "Within a case, it seems like a date of birth (DOB) is a pretty good way to uniquely identify people.  This would break if, for example there are twins involved in the same case.\n",
      "\n",
      "Some DOB values couldn't be parsed into meaningful dates.  These show up as NULL values in the database. I just want to make sure that there aren't any None value DOB fields with different ``fgrprntno`` or ``st_address`` values."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "no_dob = Disposition.objects.filter(dob=None)\n",
      "distinct_cases = no_dob.values('case_number').distinct().count()\n",
      "print(\"There are {} records with a null dob field.\".format(no_dob.count()))\n",
      "print(\"These records represent {} distinct cases\".format(distinct_cases))\n",
      "print(\"There should be the same number of case number/fgrprntno pairs and case number/st_address pairs:\")\n",
      "unique_address_case_number = no_dob.values('case_number', 'st_address').distinct().count()\n",
      "unique_fgrprntno_case_number = no_dob.values('case_number', 'fgrprntno').distinct().count()\n",
      "print(\"* case_number/fgrprntno: {}\".format(unique_fgrprntno_case_number))\n",
      "print(\"* case_number/st_address: {}\".format(unique_address_case_number))\n",
      "assert unique_fgrprntno_case_number == distinct_cases and unique_address_case_number == distinct_cases\n",
      "print(\"We're good.  We can use dob to uniquely identify people within a case\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "There are 557 records with a null dob field.\n",
        "These records represent 286 distinct cases\n",
        "There should be the same number of case number/fgrprntno pairs and case number/st_address pairs:\n",
        "* case_number/fgrprntno: 286"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "* case_number/st_address: 286\n",
        "We're good.  We can use dob to uniquely identify people within a case\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "How does pool of those convicted vary by crime type & demographic characteristic?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Age \n",
      "* ~~Race~~\n",
      "* Gender"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Age"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json\n",
      "convictions_by_age = Conviction.objects.counts_by_age_range()\n",
      "print(json.dumps(convictions_by_age))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[{\"drug_convictions\": 17780, \"violent_convictions\": 2157, \"total_convictions\": 43580, \"property_convictions\": 1327, \"homicide_convictions\": 9, \"affecting_women_convictions\": 102, \"age_min\": 18, \"invalid_ages\": false, \"age_max\": 24}, {\"drug_convictions\": 9234, \"violent_convictions\": 439, \"total_convictions\": 23265, \"property_convictions\": 1080, \"homicide_convictions\": 13, \"affecting_women_convictions\": 114, \"age_min\": 25, \"invalid_ages\": false, \"age_max\": 29}, {\"drug_convictions\": 6191, \"violent_convictions\": 341, \"total_convictions\": 16901, \"property_convictions\": 1201, \"homicide_convictions\": 5, \"affecting_women_convictions\": 109, \"age_min\": 30, \"invalid_ages\": false, \"age_max\": 34}, {\"drug_convictions\": 6368, \"violent_convictions\": 400, \"total_convictions\": 16820, \"property_convictions\": 1921, \"homicide_convictions\": 3, \"affecting_women_convictions\": 118, \"age_min\": 35, \"invalid_ages\": false, \"age_max\": 39}, {\"drug_convictions\": 6030, \"violent_convictions\": 369, \"total_convictions\": 15443, \"property_convictions\": 1921, \"homicide_convictions\": 1, \"affecting_women_convictions\": 80, \"age_min\": 40, \"invalid_ages\": false, \"age_max\": 44}, {\"drug_convictions\": 5027, \"violent_convictions\": 185, \"total_convictions\": 12081, \"property_convictions\": 1528, \"homicide_convictions\": 0, \"affecting_women_convictions\": 64, \"age_min\": 45, \"invalid_ages\": false, \"age_max\": 49}, {\"drug_convictions\": 2785, \"violent_convictions\": 90, \"total_convictions\": 6864, \"property_convictions\": 908, \"homicide_convictions\": 1, \"affecting_women_convictions\": 34, \"age_min\": 50, \"invalid_ages\": false, \"age_max\": 54}, {\"drug_convictions\": 1027, \"violent_convictions\": 31, \"total_convictions\": 2732, \"property_convictions\": 366, \"homicide_convictions\": 1, \"affecting_women_convictions\": 12, \"age_min\": 55, \"invalid_ages\": false, \"age_max\": 59}, {\"drug_convictions\": 291, \"violent_convictions\": 11, \"total_convictions\": 953, \"property_convictions\": 89, \"homicide_convictions\": 0, \"affecting_women_convictions\": 7, \"age_min\": 60, \"invalid_ages\": false, \"age_max\": 64}, {\"drug_convictions\": 139, \"violent_convictions\": 4, \"total_convictions\": 589, \"property_convictions\": 44, \"homicide_convictions\": 0, \"affecting_women_convictions\": 4, \"age_min\": 65, \"invalid_ages\": false, \"age_max\": null}, {\"drug_convictions\": 832, \"violent_convictions\": 156, \"total_convictions\": 2233, \"property_convictions\": 92, \"homicide_convictions\": 2, \"affecting_women_convictions\": 8, \"invalid_ages\": true}]\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "convictions_18_24 = convictions = Conviction.objects.all().with_ages().filter_by_age(start=18, end=24)\n",
      "record = {\n",
      "  'male': len(convictions_18_24.male()),\n",
      "  'female': len(convictions_18_24.female()),\n",
      "  'uknwn_sex': len(convictions_18_24.unkwn_sex()),\n",
      "}\n",
      "print(json.dumps(record))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "'{\"uknwn_sex\": 2, \"female\": 3809, \"male\": 39769}'"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "convictions_18_24 = convictions = Conviction.objects.all().with_ages().filter_by_age(start=18, end=24)\n",
      "\n",
      "convictions_18_24_by_community_area = []\n",
      "for ca in CommunityArea.objects.all():\n",
      "    ca_convictions = len(convictions_18_24.filter(community_area=ca))\n",
      "    record = {\n",
      "        'name': ca.name,\n",
      "        'convictions': ca_convictions,\n",
      "        # TODO: Perhaps do per capita calculation using 18-24 population\n",
      "        'convictions_per_capita': float(ca_convictions) / float(ca.total_population),\n",
      "    }\n",
      "    convictions_18_24_by_community_area.append(record)\n",
      "\n",
      "convictions_18_24_by_community_area = sorted(convictions_18_24_by_community_area,\n",
      "    key=lambda ca: ca['convictions_per_capita'])\n",
      "print(json.dumps(convictions_18_24_by_community_area))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[{\"convictions_per_capita\": 0.0002419081716580386, \"name\": \"OHARE\", \"convictions\": 5}, {\"convictions_per_capita\": 0.0007511060808725177, \"name\": \"LAKE VIEW\", \"convictions\": 73}, {\"convictions_per_capita\": 0.0009156514316753317, \"name\": \"LINCOLN PARK\", \"convictions\": 59}, {\"convictions_per_capita\": 0.0009731307779639941, \"name\": \"MOUNT GREENWOOD\", \"convictions\": 18}, {\"convictions_per_capita\": 0.000988586321560169, \"name\": \"EDISON PARK\", \"convictions\": 11}, {\"convictions_per_capita\": 0.0012145276188251781, \"name\": \"NORWOOD PARK\", \"convictions\": 52}, {\"convictions_per_capita\": 0.0015398502628365103, \"name\": \"FOREST GLEN\", \"convictions\": 29}, {\"convictions_per_capita\": 0.0016470024555309338, \"name\": \"NORTH CENTER\", \"convictions\": 55}, {\"convictions_per_capita\": 0.0021007352573400692, \"name\": \"LOOP\", \"convictions\": 42}, {\"convictions_per_capita\": 0.0021177467174925877, \"name\": \"NORTH PARK\", \"convictions\": 40}, {\"convictions_per_capita\": 0.002206693637366679, \"name\": \"ARMOUR SQUARE\", \"convictions\": 30}, {\"convictions_per_capita\": 0.0030739333981097076, \"name\": \"EDGEWATER\", \"convictions\": 174}, {\"convictions_per_capita\": 0.003142294343870181, \"name\": \"DUNNING\", \"convictions\": 134}, {\"convictions_per_capita\": 0.0032948009158429666, \"name\": \"WEST ELSDON\", \"convictions\": 59}, {\"convictions_per_capita\": 0.00332331065041937, \"name\": \"BRIDGEPORT\", \"convictions\": 105}, {\"convictions_per_capita\": 0.003332085361287907, \"name\": \"JEFFERSON PARK\", \"convictions\": 89}, {\"convictions_per_capita\": 0.003333537219401798, \"name\": \"WEST LAWN\", \"convictions\": 109}, {\"convictions_per_capita\": 0.00404505076142132, \"name\": \"EAST SIDE\", \"convictions\": 102}, {\"convictions_per_capita\": 0.004047180646455071, \"name\": \"IRVING PARK\", \"convictions\": 222}, {\"convictions_per_capita\": 0.004065548293996032, \"name\": \"WEST RIDGE\", \"convictions\": 293}, {\"convictions_per_capita\": 0.0041197076844356395, \"name\": \"NEAR NORTH SIDE\", \"convictions\": 314}, {\"convictions_per_capita\": 0.004131282992885013, \"name\": \"MONTCLARE\", \"convictions\": 54}, {\"convictions_per_capita\": 0.004179498460184778, \"name\": \"PORTAGE PARK\", \"convictions\": 266}, {\"convictions_per_capita\": 0.004199241427355059, \"name\": \"ALBANY PARK\", \"convictions\": 217}, {\"convictions_per_capita\": 0.004363183045917307, \"name\": \"CLEARING\", \"convictions\": 105}, {\"convictions_per_capita\": 0.004555275008459796, \"name\": \"LINCOLN SQUARE\", \"convictions\": 175}, {\"convictions_per_capita\": 0.004799692819659542, \"name\": \"MCKINLEY PARK\", \"convictions\": 75}, {\"convictions_per_capita\": 0.004824642790870292, \"name\": \"BEVERLY\", \"convictions\": 104}, {\"convictions_per_capita\": 0.004826327239990998, \"name\": \"AVONDALE\", \"convictions\": 193}, {\"convictions_per_capita\": 0.004907643558234794, \"name\": \"HYDE PARK\", \"convictions\": 127}, {\"convictions_per_capita\": 0.0050493653032440055, \"name\": \"GARFIELD RIDGE\", \"convictions\": 179}, {\"convictions_per_capita\": 0.005247318465931013, \"name\": \"ARCHER HEIGHTS\", \"convictions\": 68}, {\"convictions_per_capita\": 0.005392339159538862, \"name\": \"HEGEWISCH\", \"convictions\": 58}, {\"convictions_per_capita\": 0.005591054313099041, \"name\": \"UPTOWN\", \"convictions\": 315}, {\"convictions_per_capita\": 0.005826906598114824, \"name\": \"OAKLAND\", \"convictions\": 34}, {\"convictions_per_capita\": 0.00620874818212328, \"name\": \"NEAR SOUTH SIDE\", \"convictions\": 111}, {\"convictions_per_capita\": 0.0062241423497968, \"name\": \"MORGAN PARK\", \"convictions\": 170}, {\"convictions_per_capita\": 0.006252654428766929, \"name\": \"ASHBURN\", \"convictions\": 265}, {\"convictions_per_capita\": 0.006414674229974023, \"name\": \"LOGAN SQUARE\", \"convictions\": 484}, {\"convictions_per_capita\": 0.00653373887574631, \"name\": \"LOWER WEST SIDE\", \"convictions\": 232}, {\"convictions_per_capita\": 0.006634963768115942, \"name\": \"BRIGHTON PARK\", \"convictions\": 293}, {\"convictions_per_capita\": 0.0068725868725868725, \"name\": \"GAGE PARK\", \"convictions\": 267}, {\"convictions_per_capita\": 0.007507780240006781, \"name\": \"WEST TOWN\", \"convictions\": 620}, {\"convictions_per_capita\": 0.00755650662030226, \"name\": \"CALUMET HEIGHTS\", \"convictions\": 113}, {\"convictions_per_capita\": 0.007984031936127744, \"name\": \"KENWOOD\", \"convictions\": 144}, {\"convictions_per_capita\": 0.008066728174949505, \"name\": \"BELMONT CRAGIN\", \"convictions\": 647}, {\"convictions_per_capita\": 0.008297480024585127, \"name\": \"DOUGLAS\", \"convictions\": 162}, {\"convictions_per_capita\": 0.008379774641012082, \"name\": \"HERMOSA\", \"convictions\": 206}, {\"convictions_per_capita\": 0.008871759444424244, \"name\": \"ROGERS PARK\", \"convictions\": 488}, {\"convictions_per_capita\": 0.010850182436695839, \"name\": \"SOUTH LAWNDALE\", \"convictions\": 791}, {\"convictions_per_capita\": 0.011158021712907118, \"name\": \"AVALON PARK\", \"convictions\": 111}, {\"convictions_per_capita\": 0.01183015987788222, \"name\": \"NEAR WEST SIDE\", \"convictions\": 589}, {\"convictions_per_capita\": 0.012480555656043122, \"name\": \"WASHINGTON HEIGHTS\", \"convictions\": 345}, {\"convictions_per_capita\": 0.012689369416030041, \"name\": \"PULLMAN\", \"convictions\": 98}, {\"convictions_per_capita\": 0.01358695652173913, \"name\": \"RIVERDALE\", \"convictions\": 80}, {\"convictions_per_capita\": 0.014047911372526626, \"name\": \"CHATHAM\", \"convictions\": 492}, {\"convictions_per_capita\": 0.01619778346121057, \"name\": \"WEST PULLMAN\", \"convictions\": 589}, {\"convictions_per_capita\": 0.017201559410971307, \"name\": \"CHICAGO LAWN\", \"convictions\": 931}, {\"convictions_per_capita\": 0.017696386730792945, \"name\": \"SOUTH CHICAGO\", \"convictions\": 574}, {\"convictions_per_capita\": 0.017710037601538504, \"name\": \"GREATER GRAND CROSSING\", \"convictions\": 617}, {\"convictions_per_capita\": 0.0178988805511133, \"name\": \"SOUTH DEERING\", \"convictions\": 291}, {\"convictions_per_capita\": 0.0182449387747401, \"name\": \"AUBURN GRESHAM\", \"convictions\": 967}, {\"convictions_per_capita\": 0.018313302098187004, \"name\": \"SOUTH SHORE\", \"convictions\": 899}, {\"convictions_per_capita\": 0.018356643356643356, \"name\": \"BURNSIDE\", \"convictions\": 63}, {\"convictions_per_capita\": 0.01882912115534025, \"name\": \"GRAND BOULEVARD\", \"convictions\": 412}, {\"convictions_per_capita\": 0.018847332409856022, \"name\": \"ROSELAND\", \"convictions\": 898}, {\"convictions_per_capita\": 0.020168877059336423, \"name\": \"NEW CITY\", \"convictions\": 879}, {\"convictions_per_capita\": 0.022222222222222223, \"name\": \"FULLER PARK\", \"convictions\": 68}, {\"convictions_per_capita\": 0.024505023953872895, \"name\": \"WOODLAWN\", \"convictions\": 578}, {\"convictions_per_capita\": 0.03263981675892346, \"name\": \"WASHINGTON PARK\", \"convictions\": 342}, {\"convictions_per_capita\": 0.034227941176470586, \"name\": \"HUMBOLDT PARK\", \"convictions\": 1862}, {\"convictions_per_capita\": 0.03551101961734076, \"name\": \"ENGLEWOOD\", \"convictions\": 1173}, {\"convictions_per_capita\": 0.037434122145292965, \"name\": \"WEST ENGLEWOOD\", \"convictions\": 1449}, {\"convictions_per_capita\": 0.03991524913140401, \"name\": \"AUSTIN\", \"convictions\": 3975}, {\"convictions_per_capita\": 0.04308725596220616, \"name\": \"NORTH LAWNDALE\", \"convictions\": 1514}, {\"convictions_per_capita\": 0.044158326208318666, \"name\": \"EAST GARFIELD PARK\", \"convictions\": 878}, {\"convictions_per_capita\": 0.07182406209573092, \"name\": \"WEST GARFIELD PARK\", \"convictions\": 1388}]\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Sex"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from django.db.models import Count\n",
      "\n",
      "values_qs = Disposition.objects.values('sex').annotate(num_records=Count('pk')).distinct()\n",
      "for d in values_qs:\n",
      "    print(d)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'sex': '', 'num_records': 247}\n",
        "{'sex': '720-5/19-1(a)', 'num_records': 3}\n",
        "{'sex': 'female', 'num_records': 39041}\n",
        "{'sex': 'male', 'num_records': 255896}\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "num_records = Disposition.objects.all().count()\n",
      "valid_sex = Disposition.objects.filter(sex__in=['male', 'female']).count()\n",
      "print(\"We have to exclude {} records because they have an invalid sex value is specified.\".format(num_records - valid_sex))\n",
      "print()\n",
      "\n",
      "dispositions = Disposition.objects.filter(sex__in=['male', 'female']).values('case_number', 'dob', 'statute').distinct()\n",
      "num_dispositions = convictions.count()\n",
      "num_male = dispositions.filter(sex='male').count()\n",
      "num_female = dispositions.filter(sex='female').count()\n",
      "assert num_dispositions == num_male + num_female\n",
      "print(\"Of the records we could consider, there were {} convictions of males and {} convictions of females.\".format(num_male, num_female))\n",
      "times_male = float(num_male / num_female)\n",
      "print(\"That's {} times the number of convictions of males as the number of convictions of females.\".format(times_male))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "We have to exclude 301 records because they have an invalid sex value is specified.\n",
        "\n",
        "Of the records we could consider, there were 162244 convictions of males and 23275 convictions of females."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "That's 6.970741138560688 times the number of convictions of males as the number of convictions of females.\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(Conviction.objects.all().male().count())\n",
      "print(Conviction.objects.all().female().count())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "122863\n",
        "18485"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## To what degree does the pool of those convicted vary spatially?  \n",
      "\n",
      "* Community Area - City only\n",
      "* Police District - city only\n",
      "* City vs. Suburban?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from convictions_data.models import CommunityArea\n",
      "\n",
      "chi_dispositions = Disposition.objects.exclude(community_area=None).values('case_number', 'dob', 'statute', 'community_area__name').distinct()\n",
      "chi_dispositions_by_ca = chi_dispositions.values('community_area__name').annotate(count=Count('pk')).order_by('-count')\n",
      "assert len(chi_dispositions_by_ca) == 77\n",
      "\n",
      "# Calculate the convictions for each community area\n",
      "total = 0\n",
      "dispositions_by_ca = []\n",
      "for ca in CommunityArea.objects.all():\n",
      "    num_ca_dispositions = ca.disposition_set.values('case_number', 'dob', 'statute').distinct().count()\n",
      "    dispositions_by_ca.append({\n",
      "      'name': ca.name,\n",
      "      'count': num_ca_dispositions,\n",
      "    })\n",
      "    total += num_ca_dispositions\n",
      "assert total == chi_dispositions.count()\n",
      "dispositions_by_ca = sorted(dispositions_by_ca, key=lambda x: x['count'])\n",
      "\n",
      "for ca in dispositions_by_ca:\n",
      "    print(\"{}: {}\".format(ca['name'], ca['count']))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "EDISON PARK: 53\n",
        "OHARE: 77\n",
        "FOREST GLEN: 123\n",
        "ARMOUR SQUARE: 125\n",
        "MOUNT GREENWOOD: 143\n",
        "NORTH PARK: 181\n",
        "HEGEWISCH: 211\n",
        "BURNSIDE: 214\n",
        "ARCHER HEIGHTS: 221\n",
        "MONTCLARE: 229\n",
        "OAKLAND: 280\n",
        "WEST ELSDON: 282\n",
        "NORWOOD PARK: 294\n",
        "FULLER PARK: 315\n",
        "CLEARING: 316\n",
        "MCKINLEY PARK: 323\n",
        "RIVERDALE: 329\n",
        "NORTH CENTER: 339\n",
        "JEFFERSON PARK: 381\n",
        "PULLMAN: 403\n",
        "LINCOLN PARK: 404\n",
        "EAST SIDE: 480\n",
        "AVALON PARK: 481\n",
        "BEVERLY: 482\n",
        "BRIDGEPORT: 513\n",
        "WEST LAWN: 545\n",
        "CALUMET HEIGHTS: 566\n",
        "KENWOOD: 575\n",
        "DUNNING: 578\n",
        "NEAR SOUTH SIDE: 612\n",
        "HYDE PARK: 660\n",
        "LAKE VIEW: 716\n",
        "MORGAN PARK: 748\n",
        "LINCOLN SQUARE: 762\n",
        "ALBANY PARK: 807\n",
        "HERMOSA: 867\n",
        "GARFIELD RIDGE: 885\n",
        "AVONDALE: 912\n",
        "LOOP: 933\n",
        "ASHBURN: 940\n",
        "EDGEWATER: 983\n",
        "GAGE PARK: 1052\n",
        "SOUTH DEERING: 1069\n",
        "PORTAGE PARK: 1127\n",
        "BRIGHTON PARK: 1211\n",
        "WEST RIDGE: 1241\n",
        "DOUGLAS: 1252\n",
        "WASHINGTON PARK: 1279\n",
        "IRVING PARK: 1279\n",
        "LOWER WEST SIDE: 1369\n",
        "WASHINGTON HEIGHTS: 1428\n",
        "NEAR NORTH SIDE: 1867\n",
        "GRAND BOULEVARD: 2024\n",
        "UPTOWN: 2096\n",
        "SOUTH CHICAGO: 2165\n",
        "CHATHAM: 2231\n",
        "ROGERS PARK: 2234\n",
        "WEST PULLMAN: 2260\n",
        "WOODLAWN: 2381\n",
        "LOGAN SQUARE: 2458\n",
        "GREATER GRAND CROSSING: 2461\n",
        "WEST TOWN: 2611\n",
        "BELMONT CRAGIN: 2672\n",
        "NEAR WEST SIDE: 3565\n",
        "SOUTH LAWNDALE: 3576\n",
        "SOUTH SHORE: 3690\n",
        "CHICAGO LAWN: 3737\n",
        "NEW CITY: 3817\n",
        "ROSELAND: 3974\n",
        "EAST GARFIELD PARK: 4027\n",
        "AUBURN GRESHAM: 4124\n",
        "ENGLEWOOD: 4645\n",
        "WEST GARFIELD PARK: 5505\n",
        "WEST ENGLEWOOD: 5601\n",
        "NORTH LAWNDALE: 6076\n",
        "HUMBOLDT PARK: 7279\n",
        "AUSTIN: 15383\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from convictions_data.models import Conviction\n",
      "\n",
      "convictions = Conviction.objects.filter(community_area__name=\"ROGERS PARK\")\n",
      "print(\"There are {} convictions in Rogers Park\".format(convictions.count()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "There are 1707 convictions in Rogers Park\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get Homicides\n",
      "homicide_convictions = Conviction.objects.filter(iucr_code=\"0110\")\n",
      "print(\"There are {} homicide convictions\".format(homicide_convictions.count()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "There are 31 homicide convictions\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(\"There are {} convictiosn that we are able to identify\".format(Conviction.objects.all().count()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "There are 141461 convictiosn that we are able to identify\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get Cannabis Convictions\n",
      "mj_convictions = Conviction.objects.filter(iucr_code__startswith=\"18\")\n",
      "print(\"There are only {} convictions related to the Cannabis Control Act??\".format(mj_convictions.count()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "There are only 38 convictions related to the Cannabis Control Act??\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Community Areas, Suburbs"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pprint import pprint\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "\n",
      "cas = CommunityArea.objects.all()\n",
      "# We can add custom annotations that aggregate conviction counts to community area.\n",
      "cas = cas.with_conviction_annotations()\n",
      "\n",
      "suburbs = CensusPlace.objects.chicago_suburbs().with_conviction_annotations()\n",
      "\n",
      "ca_convictions_per_capita = [ca.convictions_per_capita for ca in cas.order_by('convictions_per_capita')]\n",
      "suburbs_convictions_per_capita = [suburb.convictions_per_capita for suburb in suburbs.order_by('convictions_per_capita')]\n",
      "convictions_per_capita = ca_convictions_per_capita + suburbs_convictions_per_capita\n",
      "\n",
      "num_bins = 5\n",
      "# qcut, doesn't work very well because, making equal sized bins for both the suburbs and chicago, it\n",
      "# it has way more bins for low conviction rates\n",
      "#out, bins = pd.qcut(convictions_per_capita, num_bins, retbins=True)\n",
      "\n",
      "out, bins = pd.cut(convictions_per_capita, num_bins, retbins=True)\n",
      "\n",
      "print(\"These bins kind of suck.  We should use natural breaks\")\n",
      "print(bins)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "These bins kind of suck.  We should use natural breaks\n",
        "[-0.00021252  0.04250453  0.08500906  0.12751358  0.17001811  0.21252264]\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "\n",
      "%matplotlib inline\n",
      "\n",
      "# Let's make a histogram to see if we can manually identify natural breaks\n",
      "\n",
      "# First, let's convert to cleaner numbers and make a frequency map\n",
      "\n",
      "#cpt_map = {}\n",
      "#for cpc in convictions_per_capita:\n",
      "#    convictions_per_thousand = round(cpc * 1000)\n",
      "#    try:convictions\n",
      "#        cpt_map[convictions_per_thousand] += 1\n",
      "#    except KeyError:\n",
      "#        cpt_map[convictions_per_thousand] = 1\n",
      "        \n",
      "#pd.options.display.mpl_style = 'default'\n",
      "#df = pd.DataFrame(cpt_map, index=cpt_map.keys())\n",
      "#df.plot(kind='bar')\n",
      "\n",
      "convictions_per_thousand = [round(cpc * 1000) for cpc in convictions_per_capita]\n",
      "print(max(convictions_per_thousand))\n",
      "s = pd.Series(convictions_per_thousand)\n",
      "\n",
      "s.hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "213\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f3e33712fd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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a9Wo9q729WOsWclBEnAM8OuMXud8FrsjMgxGxGXg6Mz8cEdsBMvPu7rjHgDszc+9sX3d8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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f3e341d9630>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "\n",
      "%matplotlib inline\n",
      "\n",
      "cas = CommunityArea.objects.all().with_conviction_annotations()\n",
      "suburbs = CensusPlace.objects.chicago_suburbs().with_conviction_annotations()\n",
      "\n",
      "# CAF = crimes affecting women\n",
      "ca_caf_per_capita = [ca.affecting_women_per_capita for ca in cas.order_by('affecting_women_per_capita')]\n",
      "suburbs_caf_per_capita = [suburb.affecting_women_per_capita for suburb in suburbs.order_by('affecting_women_per_capita')]\n",
      "caf_per_capita = ca_caf_per_capita + suburbs_caf_per_capita\n",
      "\n",
      "caf_per_thousand = [round(cpc * 1000) for cpc in caf_per_capita]\n",
      "print(\"Max: {}\".format(max(caf_per_thousand)))\n",
      "print(\"Min: {}\".format(min(caf_per_thousand)))\n",
      "s = pd.Series(caf_per_thousand)\n",
      "\n",
      "s.hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Max: 2\n",
        "Min: 0\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f279c71ba58>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiRJkiRJkiRJkiRJkiQJ/h/Zhqx57RadhAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f279c715fd0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Without IUCR code"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "disp_miss = Disposition.objects.in_analysis().filter(iucr_code=\"\").values_list('final_statute').distinct()\n",
      "disp_miss.count()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "1560"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "disp_all = Disposition.objects.in_analysis().values_list('final_statute').distinct()\n",
      "disp_all.count()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "2339"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# SELECT final_statute, COUNT(*) AS num_dispositions\n",
      "# FROM convictions_data_disposition\n",
      "# WHERE initial_date >= '2005-01-01' AND chrgdispdate >= '2005-01-01'\n",
      "# AND iucr_code = \"\"\n",
      "# GROUP BY final_statute\n",
      "# ORDER BY num_dispositions DESC;\n",
      "wo_iucr = Disposition.objects.in_analysis().filter(iucr_code=\"\").values(\"final_statute\").annotate(num_dispositions=Count('id')).order_by('-num_dispositions')\n",
      "for d in wo_iucr[:10]:\n",
      "    print(d['final_statute'], d['num_dispositions'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Drug charges by amount"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = Conviction.objects.filter(final_statute='720-550/4(a)')\n",
      "print(\"{} convictions for possession of cannabis, less than 2.5 grams\".format(d.count()))\n",
      "\n",
      "d = Conviction.objects.filter(final_statute='720-550/5(a)')\n",
      "print(\"{} convictions for man/del of cannabis, less than 2.5 grams\".format(d.count()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "15 convictions for possession of cannabis, less than 2.5 grams\n",
        "24 convictions for man/del of cannabis, less than 2.5 grams"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = Conviction.objects.filter(final_statute='720-550/4(b)')\n",
      "d.count()\n",
      "print(\"{} convictions for possession of cannabis\".format(d.count()))\n",
      "\n",
      "d = Conviction.objects.filter(final_statute='720-550/5(b)')\n",
      "d.count()\n",
      "print(\"{} convictions for man/del of cannabis\".format(d.count()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "107 convictions for possession of cannabis\n",
        "298 convictions for man/del of cannabis"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = Conviction.objects.filter(final_statute='720-550/4(c)')\n",
      "print(\"{} convictions for poss cannabis\".format(d.count()))\n",
      "\n",
      "d = Conviction.objects.filter(final_statute='720-550/5(c)')\n",
      "print(\"{} convictions for man/del cannabis\".format(d.count()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "479 convictions for poss cannabis\n",
        "716 convictions for man/del cannabis"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = Conviction.objects.filter(final_statute='720-550/4(d)')\n",
      "d.count()\n",
      "\n",
      "print(\"{} convictions for poss cannabis\".format(d.count()))\n",
      "\n",
      "d = Conviction.objects.filter(final_statute='720-550/5(d)')\n",
      "d.count()\n",
      "\n",
      "print(\"{} convictions for man/del cannabis\".format(d.count()))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "961 convictions for poss cannabis\n",
        "770 convictions for man/del cannabis"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# possession of cannabis, 500 - 2000 grams\n",
      "d = Conviction.objects.filter(final_statute='720-550/4(e)')\n",
      "print(d.count())\n",
      "\n",
      "d = Conviction.objects.filter(final_statute='720-550/5(e)')\n",
      "print(d.count()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "145"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# possession of cannabis, less than 2000 - 5000 grams\n",
      "d = Conviction.objects.filter(final_statute='720-550/4(f)')\n",
      "d.count()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "17"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# possession of cannabis, 5000+ grams\n",
      "d = Conviction.objects.filter(final_statute='720-550/4(g)')\n",
      "d.count()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "15"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Possession vs. delivery in the Cannabis Control Act"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Possession vs. delivery in the Cannabis Control Act (e.g. 720-550/4(a-c)\n",
      "vs. 720-550/5(a-c) & 5.2(c-e)), particularly when looking at possession vs \n",
      "delivery of the same amounts of cannabis (under 30 g, or over 30g) "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def statute_range(chapter, act_prefix, section, subsection_start, subsection_end):\n",
      "    \"\"\"Helper function to create a list representing a range of statutes\"\"\"\n",
      "    return [\"{}-{}/{}({})\".format(chapter, act_prefix, section, chr(subsection))\n",
      "            for subsection in range(ord(subsection_start), ord(subsection_end)+1)]\n",
      "\n",
      "possession_statutes = statute_range(720, 550, 4, 'a', 'c')\n",
      "delivery_statutes = statute_range(720, 550, 5, 'a', 'c') + statute_range(720, 550, 5.2, 'c', 'e')\n",
      "all_statutes = possession_statutes + delivery_statutes\n",
      "\n",
      "cca_convictions = Conviction.objects.filter(final_statute__in=all_statutes)\n",
      "annotated = cca_convictions.values('final_statute').annotate(num_convictions=Count('id'))\n",
      "statute2convictions = {c['final_statute']: {'count': c['num_convictions'], 'statute': c['final_statute']} for c in annotated}\n",
      "for m in Conviction.objects.filter(final_statute__in=all_statutes).values('final_statute', 'final_chrgdesc').distinct():\n",
      "    try:\n",
      "        statute2convictions[m['final_statute']]['chrgdesc'].append(m['final_chrgdesc'])\n",
      "    except KeyError:\n",
      "        statute2convictions[m['final_statute']]['chrgdesc'] = [m['final_chrgdesc']]\n",
      "print(statute2convictions)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot([1,2,3,4])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Controlled Substances Act vs. Cannibis Control Act: Delivery to persons under 18 "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "How does delivery to persons under 18 under the controlled substances act (720-570/407) compared to delivery for a person under 18 of cannabis (720-550/7) ? "
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Possession of meth vs. possession of a controlled substance"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Possession of meth (720-646/60) vs. possession of a controlled substance like heroine or cocaine (720-570/402)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Difference between manufacturing and criminal drug conspiracy"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Difference between manufacturing (720-570/401, 401.1 & 401.5) and criminal drug conspiracy (720-570/405, 405.1 & 405.2)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Overall crime counts"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Conviction.objects.count()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "141461"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from functools import reduce\n",
      "\n",
      "# There are custom QuerySet and Manager methods for filtering convictions into our main categories\n",
      "# based on crime type.\n",
      "violent_index = Conviction.objects.violent_index_crimes()\n",
      "property_index = Conviction.objects.property_index_crimes()\n",
      "drugs = Conviction.objects.drug_crimes()\n",
      "affecting_women = Conviction.objects.crimes_affecting_women()\n",
      "\n",
      "# Let's output this as JSON\n",
      "series_qss = [violent_index, property_index, drugs]\n",
      "series_labels = [\"Violent Index Crimes\", \"Property Index Crimes\",\"Drug Crimes\"]\n",
      "series = [{'label': series_labels[i], 'value': series_qss[i].count()} for i in range(len(series_qss))]\n",
      "total = reduce(lambda x, y: x + y['value'], series, 0)\n",
      "series.append({\n",
      "  'label': \"Uncategorized\",\n",
      "   'value': Conviction.objects.count() - total,\n",
      "})\n",
      "\n",
      "for item in series:\n",
      "    print(\"{}: {}\".format(item['label'], item['value']))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Violent Index Crimes: 4183\n",
        "Property Index Crimes: 10477\n",
        "Drug Crimes: 48002\n",
        "Uncategorized: 78799\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Confirm that our updated drug queries don't overlap with our other categories\n",
      "violent_index = Conviction.objects.violent_index_crimes()\n",
      "property_index = Conviction.objects.property_index_crimes()\n",
      "assert violent_index.drug_crimes().count() == 0\n",
      "assert property_index.drug_crimes().count() == 0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Drug crime counts"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "These were our original categories."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mfg_delivery_non_cannibus = Conviction.objects.mfg_delivery_non_cannabis_crimes()\n",
      "dealing_in_school_area = Conviction.objects.dealing_in_school_area_crimes()\n",
      "cannibus = Conviction.objects.cannabis_crimes()\n",
      "possession = Conviction.objects.possession_crimes()\n",
      "\n",
      "series_qss = [mfg_delivery_non_cannibus, dealing_in_school_area, cannibus, possession]\n",
      "series_labels = [\"Manufacture or delivery of non-cannibus drugs\", \"Dealing in a school Area\", \"Cannibus related\", \"Non-cannibus possession\"]\n",
      "series = [{'label': series_labels[i], 'value': series_qss[i].count()} for i in range(len(series_qss))]\n",
      "print(json.dumps(series))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[{\"value\": 3825, \"label\": \"Manufacture or delivery of non-cannibus drugs\"}, {\"value\": 4015, \"label\": \"Dealing in a school Area\"}, {\"value\": 33679, \"label\": \"Cannibus related\"}, {\"value\": 38521, \"label\": \"Non-cannibus possession\"}]\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json\n",
      "from convictions_data.models import Conviction\n",
      "\n",
      "# By class\n",
      "print(json.dumps(Conviction.objects.drug_by_class()))\n",
      "\n",
      "print()\n",
      "\n",
      "# By drug type\n",
      "print(json.dumps(Conviction.objects.drug_by_drug_type()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[{\"felony_1\": {\"value\": 5744, \"label\": \"Class 1 Felony\"}, \"unkwn_class\": {\"value\": 4530, \"label\": \"Unknown Class\"}, \"felony_2\": {\"value\": 8555, \"label\": \"Class 2 Felony\"}, \"misd_b\": {\"value\": 25, \"label\": \"Class B Misdemeanor\"}, \"misd_a\": {\"value\": 691, \"label\": \"Class A Misdemeanor\"}, \"label\": \"Manufacture or Delivery\", \"felony_x\": {\"value\": 34, \"label\": \"Class X Felony\"}, \"felony_3\": {\"value\": 1223, \"label\": \"Class 3 Felony\"}, \"felony_4\": {\"value\": 1762, \"label\": \"Class 4 Felony\"}}, {\"felony_1\": {\"value\": 554, \"label\": \"Class 1 Felony\"}, \"misd_c\": {\"value\": 32, \"label\": \"Class C Misdemeanor\"}, \"misd_b\": {\"value\": 112, \"label\": \"Class B Misdemeanor\"}, \"felony_4\": {\"value\": 32127, \"label\": \"Class 4 Felony\"}, \"unkwn_class\": {\"value\": 16, \"label\": \"Unknown Class\"}, \"felony_2\": {\"value\": 40, \"label\": \"Class 2 Felony\"}, \"misd_a\": {\"value\": 74, \"label\": \"Class A Misdemeanor\"}, \"label\": \"Possession\", \"felony_x\": {\"value\": 0, \"label\": \"Class X Felony\"}, \"felony_3\": {\"value\": 189, \"label\": \"Class 3 Felony\"}, \"no_class\": {\"value\": 1, \"label\": \"No Class\"}}]\n",
        "\n",
        "[{\"ecstasy\": {\"value\": 20, \"label\": \"Ecstasy\"}, \"lsd\": {\"value\": 2, \"label\": \"LSD\"}, \"heroin\": {\"value\": 2100, \"label\": \"Heroin\"}, \"no_drug\": {\"value\": 4524, \"label\": \"Enhancement\"}, \"amphetamine\": {\"value\": 0, \"label\": \"Amphetamine\"}, \"cocaine\": {\"value\": 3504, \"label\": \"Cocaine\"}, \"pcp\": {\"value\": 8, \"label\": \"PCP\"}, \"cannabis\": {\"value\": 3694, \"label\": \"Cannabis\"}, \"label\": \"Manufacture or Delivery\", \"other_drug\": {\"value\": 8656, \"label\": \"Other or Unspecified Drug\"}, \"meth\": {\"value\": 44, \"label\": \"Methamphetamine\"}, \"sched_1_2\": {\"value\": 16, \"label\": \"Schedule 1 & 2\"}}, {\"steroids\": {\"value\": 8, \"label\": \"Steroids\"}, \"heroin\": {\"value\": 64, \"label\": \"Heroin\"}, \"no_drug\": {\"value\": 1, \"label\": \"Enhancement\"}, \"cocaine\": {\"value\": 415, \"label\": \"Cocaine\"}, \"cannabis\": {\"value\": 1961, \"label\": \"Cannabis\"}, \"barbituric\": {\"value\": 2, \"label\": \"Barbituric Acid\"}, \"label\": \"Possession\", \"other_drug\": {\"value\": 38, \"label\": \"Other or Unspecified Drug\"}, \"meth\": {\"value\": 126, \"label\": \"Methamphetamine\"}, \"unkwn_drug\": {\"value\": 30449, \"label\": \"Unknown Drug\"}, \"ecstasy\": {\"value\": 40, \"label\": \"Ecstasy\"}, \"lsd\": {\"value\": 2, \"label\": \"LSD\"}, \"ketamine\": {\"value\": 0, \"label\": \"Ketamine\"}, \"amphetamine\": {\"value\": 0, \"label\": \"Amphetamine\"}, \"morphine\": {\"value\": 2, \"label\": \"Morphine\"}, \"pcp\": {\"value\": 2, \"label\": \"PCP\"}, \"sched_1_2\": {\"value\": 6, \"label\": \"Schedule 1 & 2\"}}]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mfg_del_other_query_methods = [\n",
      "    \"mfg_del_methaqualone_gt_30_g\",\n",
      "    \"mfg_del_att_sched_iv_class_3\",\n",
      "    \"mfg_del_cntft_sub\",\n",
      "    \"mfg_del_att_cntft_sub\",\n",
      "    \"mfg_del_class_2_other_amt\",\n",
      "    \"mfg_del_att_lookalike\",\n",
      "    \"mfg_del_unauth\",\n",
      "    \"mfg_del_chemical_breakdown\",\n",
      "]\n",
      "\n",
      "for method_name in mfg_del_other_query_methods:\n",
      "    method = getattr(Conviction.objects, method_name)\n",
      "    qs = method()\n",
      "    print(method_name, qs.count())\n",
      "    \n",
      "print(Conviction.objects.mfg_del_class_2_felony().count())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "mfg_del_methaqualone_gt_30_g 2\n",
        "mfg_del_att_sched_iv_class_3"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1\n",
        "mfg_del_cntft_sub"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1\n",
        "mfg_del_att_cntft_sub"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1\n",
        "mfg_del_class_2_other_amt"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 8283\n",
        "mfg_del_att_lookalike"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 364\n",
        "mfg_del_unauth"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3\n",
        "mfg_del_chemical_breakdown"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1\n",
        "8555"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import csv, sys\n",
      "import io\n",
      "from convictions_data.query.drugs import *\n",
      "from convictions_data.models import Conviction, Disposition\n",
      "\n",
      "def csv_out(disps):\n",
      "    writer = csv.DictWriter(sys.stdout, fieldnames=['final_chrgdesc', 'final_statute'])\n",
      "    writer.writeheader()\n",
      "    for d in disps:\n",
      "        writer.writerow(d)\n",
      "\n",
      "def chrgdesc_statute_pairs(qs, filter_by='final_chrgdesc'):\n",
      "    return sorted(qs.values('final_chrgdesc', 'final_statute').distinct(),\n",
      "        key=lambda d: d['final_chrgdesc'])\n",
      "\n",
      "diffqs = Disposition.objects.filter(possession_chrgdesc_query).exclude(poss_query)\n",
      "pairs = chrgdesc_statute_pairs(diffqs)\n",
      "csv_out(pairs)\n",
      "\n",
      "#dispqs = Disposition.objects.filter(possession_chrgdesc_query)\n",
      "#pairs = chrgdesc_statute_pairs(dispqs, 'final_statute')\n",
      "#csv_out(pairs)\n",
      "\n",
      "# BOOKMARK\n",
      "\n",
      "#q = mfg_del_lsd_class_2_query\n",
      "#disps = chrgdesc_statute_pairs(Disposition.objects.filter(q))\n",
      "#csv_out(disps)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "final_chrgdesc,final_statute\r\n",
        "ATTEMPT POSS CONTROL SUBSTANCE,720 5/8-4 570 401(D)\r\n",
        "BRING/POSS/CANNABIS/PENAL INST,720-5/31A-1.1(e)\r\n",
        "BRING/POSS/CON SUB/PENAL INST,720-5/31A-1.1(g)\r\n",
        "POSS ANY SUB WITH INTENT,720-570/401.5(a-5)\r\n",
        "POSS ANY SUB WITH INTENT,720-570/401(C)(2)\r\n",
        "POSS CANN 2000<5000GR W/INTENT,720-550/5(F)\r\n",
        "POSS CANNABIS PENAL INST,720-5/31A-1.1 B I\r\n",
        "POSS CANNABIS W/I DEL,720-550/5.2(D)\r\n",
        "POSS CON SUB W/INTENT TO DELIV,720-570/401(D)\r\n",
        "POSS CONT SUB,56.5-1402-B\r\n",
        "POSS CONT SUB SCH PROP,56.5.1407-B(2)\r\n",
        "POSS CONT SUB W/ INT DELIVER,720-570/401(C)(2)\r\n",
        "POSS CONT SUB W/INT TO DEL,720-570/401(C)(1)\r\n",
        "POSS CONT SUB W/INTENT TO DEL,720-570/401(D)\r\n",
        "POSS CONT SUB/WITH INTENT DEL,720-570/401(C)(1)\r\n",
        "POSS CONT SUBSTANCE W/ INTENT,720-570/401(C)(1)\r\n",
        "POSS CONTR SUB W/INT DELIVER,720-570/401(C)(1)\r\n",
        "POSS CONTROL SUB INTENT DEL,720-570/401(C)(1)\r\n",
        "POSS CONTROLLED SUB PENAL,720-5/31A-1.1(B)\r\n",
        "POSS CONTROLLED SUB PENAL INST,720-5/31A-1.1(B)\r\n",
        "POSS CONTROLLED SUB PENAL INST,720-5/31A-1.1(b)\r\n",
        "POSS CONTROLLED SUB W/INT DEL,720-570/401(D)\r\n",
        "POSS CONTROLLED SUB W/INTENT D,720-570/401(C)(1)\r\n",
        "POSS CTL SUBST W/INT TO DEL,720-570/401 (C)\r\n",
        "POSS CTL SUBST W/INT TO DEL,720-570/401 (C)(2)\r\n",
        "POSS CTL SUBST W/INTENT TO DEL,720-570/401 (C) (2)\r\n",
        "POSS MANU CHEM/<15 GRAMS METH,720-570/401(d-5)\r\n",
        "POSS OF A CON SUB W/INT TO DEL,720-570/401(A)(1)(A)\r\n",
        "POSS OF A CONTROL SUB WITH INT,720-570/401(C)(2)\r\n",
        "POSS OF A CONTROLLED SUB W/INT,720-570/401(C)(1)\r\n",
        "POSS OF CANABIS,720-550/5(C)\r\n",
        "POSS OF CANN 2.5 LESS THAN 10G,720-550/5(B)\r\n",
        "POSS OF CANNABIS,720-550/5(D)\r\n",
        "POSS OF CANNABIS W/INT TO DEL,720-550/5E\r\n",
        "POSS OF CONT SUB W/INT TO DEL,720-570/401(C)(1)\r\n",
        "POSS OF CONT SUBST W/INT DELIV,720-570/401(C)(7.5) (A)\r\n",
        "POSS W/INT TO DEL. 01-14 GR HE,720-570/401(C)(1)\r\n",
        "POSS W/INTENT 1-15 GR HEROIN/A,720-570/401(C)(1)\r\n",
        "POSS W/INTENT TO DELIVER/COCAI,720-570/401(C)(2)\r\n",
        "POSS WITH INTENT TO DELIVER,720-570/401(D)\r\n",
        "POSS.CANN.W/INTENT,720-550/5(C)\r\n",
        "POSS.CANN.W/INTENT,720-550/5-C\r\n",
        "POSS.CANN.W/INTENT TO DELIVER,720-550/5(C)\r\n",
        "POSS.CANNABIS,720-550/5(C)\r\n",
        "POSS.CANNABIS,720-550/5-B\r\n",
        "POSS.CANNABIS,720-550/5-C\r\n",
        "POSS.CANNABIS W/INTENT,720-550/5-C\r\n",
        "POSS.CONT SUB W/INTENT,720-570/401(C)(2)\r\n",
        "POSS.CONT SUB.,720-570/401-D\r\n",
        "POSS.CONT.SUB.W/INTENT,720-550/5(B)\r\n",
        "POSS.CONT.SUB.W/INTENT TO DEL.,720-570/401(C)(2)\r\n",
        "POSS.CONT.SUBS.PENAL INST.,720-5/31A-1.1(A)\r\n",
        "POSS.OF CANNABIS,720-550/5(C)\r\n",
        "POSS.OF CANNABISW/INTENT,720-550/5/(B)\r\n",
        "POSS/INT DEL 1>15 GR COCAINE,720-570/401(C)(2)\r\n",
        "POSSESION WITH INTENT TO DEL,720-570/401(C)(7.5)(I)\r\n",
        "POSSESS CANNABIS W/I DEL,720-550/5(D)\r\n",
        "POSSESS CANNABIS W/INTENTMS,720-550/5(C)\r\n",
        "POSSESS CANNIBIS PENAL IN,720-5/31A-1.1(B)\r\n",
        "POSSESS CANNIBIS PENAL INST,720-5/31A-1.1(B)\r\n",
        "POSSESS CANNIBIS PENAL INST,720-5/31A-1.1(b)\r\n",
        "POSSESS COCAINE W/I DEL,720-570/401(C)(2)\r\n",
        "POSSESS HEROIN W/I DEL,720-570/401(C)(1)\r\n",
        "POSSESSION CANNABIS,720-550/5(B)\r\n",
        "POSSESSION CONT SUB W/ INT,720-570/401(C)\r\n",
        "POSSESSION OF CANNABIS,720-550/5(B)\r\n",
        "POSSESSION OF CANNABIS.,720-550/5(C)\r\n",
        "POSSESSION OF CANNIBIS,720-550/5(C)\r\n",
        "POSSESSION WITH INTENT TO DEL,720-570/401(C)(1)\r\n",
        "POSSESSION WITH INTENT TO DELI,720-570/401(C)(2)\r\n",
        "POSSN W/INT DLV 1 - 14 GR HERO,720 570/401(C)(1)\r\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Charge description to category mappings"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dispositions = Disposition.objects.filter(final_chrgdesc=\"ATT.FORGERY\")\n",
      "for d in dispositions:\n",
      "    print(d.case_number, d.final_statute)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2007CR1475501 720-5/8-4(720-5/17-3(A)(1)\n",
        "2007CR1475501 720-5/8-4(720-5/17-3(A)(1)\n",
        "2007CR1475501 720-5/8-4(720-5/17-3(A)(1)\n",
        "2008CR0853801 720-5/8-4(720-5/17-3(A)(2)\n",
        "2008CR0853801 720-5/8-4(720-5/17-3(A)(2)\n",
        "2008CR1582701 720-5(8-4)/17-3(A)(2)\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "l = Conviction.objects.values_list('final_chrgdesc').distinct()\n",
      "print(len(l))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2613\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from convictions_data.models import Conviction\n",
      "print(Conviction.objects.crimes_affecting_women().count())\n",
      "print(Conviction.objects.crimes_affecting_women().male().count())\n",
      "print(Conviction.objects.crimes_affecting_women().female().count())\n",
      "print(Conviction.objects.crimes_affecting_women().unkwn_sex().count())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "652\n",
        "595"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "54"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "3"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(Conviction.objects.filter(final_statute__icontains=\"8-4\").count())\n",
      "print(Disposition.objects.filter(final_statute__icontains=\"8-4\").count())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2514\n",
        "6363"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Sexual and Domestic Violence"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Sexual and Domestic Violence, disparity across community areas\n",
      "from convictions_data.models import CommunityArea, CensusPlace\n",
      "\n",
      "cas = CommunityArea.objects.all().with_conviction_annotations()\n",
      "places = CensusPlace.objects.all().with_conviction_annotations()\n",
      "\n",
      "cas = cas.order_by('num_affecting_women')\n",
      "ca_last = cas.last()\n",
      "print(ca_last.name, ca_last.num_affecting_women, ca_last.affecting_women_per_capita)\n",
      "\n",
      "places = places.order_by('num_affecting_women')\n",
      "place_last = places.last()\n",
      "print(place_last.name, place_last.num_affecting_women, place_last.affecting_women_per_capita)\n",
      "\n",
      "cas = cas.order_by('affecting_women_per_capita')\n",
      "ca_last = cas.last()\n",
      "print(ca_last.name, ca_last.num_affecting_women, ca_last.affecting_women_per_capita)\n",
      "\n",
      "places = places.order_by('affecting_women_per_capita')\n",
      "place_last = places.last()\n",
      "print(place_last.name, place_last.num_affecting_women, place_last.affecting_women_per_capita)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "AUSTIN 28 0.00028116401903882074\n",
        "Cicero"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21 0.00025200403206451305\n",
        "WASHINGTON PARK"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 7 0.0006680664248902462\n",
        "Golf"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1 0.0016863406408094434\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}