arresting_agency-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of Arresting Agencies in Rogers and Love County"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install oscn==0.0.0.19\n",
    "import oscn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Request and save cases from Rogers and Love \n",
    "\n",
    "\n",
    "### Step 1. Define the data to request"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = ['2017','2018']\n",
    "counties = ['love','rogers']\n",
    "types =['CM','CF']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2.  Request and save the cases\n",
    "This takes approximately 90 minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mApplications\u001b[m\u001b[m      \u001b[34mMusic\u001b[m\u001b[m             \u001b[34manaconda2\u001b[m\u001b[m         \u001b[34morion\u001b[m\u001b[m\r\n",
      "\u001b[34mDesktop\u001b[m\u001b[m           \u001b[34mPictures\u001b[m\u001b[m          \u001b[34mgoogle-cloud-sdk\u001b[m\u001b[m  \u001b[35mp3envs\u001b[m\u001b[m\r\n",
      "\u001b[34mDocuments\u001b[m\u001b[m         \u001b[34mPublic\u001b[m\u001b[m            logfile           \u001b[34mprojects\u001b[m\u001b[m\r\n",
      "\u001b[34mDownloads\u001b[m\u001b[m         \u001b[34mSites\u001b[m\u001b[m             love.2017.CM.pckl \u001b[34mreverse\u001b[m\u001b[m\r\n",
      "\u001b[34mLibrary\u001b[m\u001b[m           Untitled.ipynb    \u001b[34mmltut\u001b[m\u001b[m             \u001b[34mtmp\u001b[m\u001b[m\r\n",
      "\u001b[34mMovies\u001b[m\u001b[m            \u001b[34mVirtualBox VMs\u001b[m\u001b[m    \u001b[34mnode_modules\u001b[m\u001b[m      \u001b[34mvenvs\u001b[m\u001b[m\r\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "import pickle\n",
    "\n",
    "for year in years:\n",
    "  for county in counties:\n",
    "    for type in types:      \n",
    "      cases=oscn.request.CaseList(type=type, year=year,county=county)\n",
    "      all_cases = [c for c in cases]\n",
    "      f = open(f'{county}.{year}.{type}.pckl', 'wb')\n",
    "      pickle.dump(all_cases, f)\n",
    "      f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2.  Retrieve the data from saved files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/owner/projects/cft/oscn-notebooks/data/love.2017.CM.pckl\n",
      " added 785\n",
      "/Users/owner/projects/cft/oscn-notebooks/data/love.2017.CF.pckl\n",
      " added 305\n",
      "/Users/owner/projects/cft/oscn-notebooks/data/rogers.2017.CM.pckl\n",
      " added 1114\n",
      "/Users/owner/projects/cft/oscn-notebooks/data/rogers.2017.CF.pckl\n",
      " added 1060\n",
      "/Users/owner/projects/cft/oscn-notebooks/data/love.2018.CM.pckl\n",
      " added 552\n",
      "/Users/owner/projects/cft/oscn-notebooks/data/love.2018.CF.pckl\n",
      " added 197\n",
      "/Users/owner/projects/cft/oscn-notebooks/data/rogers.2018.CM.pckl\n",
      " added 853\n",
      "/Users/owner/projects/cft/oscn-notebooks/data/rogers.2018.CF.pckl\n",
      " added 534\n",
      "counted case: 5400 length saved 5400\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "import pickle\n",
    "\n",
    "\n",
    "\n",
    "saved_cases = []\n",
    "case_count =0 \n",
    "for year in years:\n",
    "  for county in counties:\n",
    "    for type in types:\n",
    "        file_name = f'/Users/owner/projects/cft/oscn-notebooks/data/{county}.{year}.{type}.pckl'\n",
    "        print(file_name)\n",
    "        new_cases = pickle.load(open(file_name, 'rb'))\n",
    "        new_case_count = len(new_cases)\n",
    "        case_count += new_case_count\n",
    "        print(f' added {new_case_count}')\n",
    "        saved_cases += new_cases\n",
    "        \n",
    "print(f'counted case: {case_count} length saved {len(saved_cases)}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3.  Load the data for analysis\n",
    "This takes about 8 minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8min 4s, sys: 324 ms, total: 8min 5s\n",
      "Wall time: 8min 5s\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "columns = ['Year','County','Type','Agency','Officer','Case Number']\n",
    "\n",
    "\n",
    "\n",
    "def agency(case):\n",
    "  agency_desc = 'ARRESTING AGENCY'\n",
    "  for p in case.parties:\n",
    "    if agency_desc in p['type']:\n",
    "      return p['name']\n",
    "  return 'UNKNOWN'\n",
    "\n",
    "def officer(case):\n",
    "  agency_desc = 'ARRESTING OFFICER'\n",
    "  for p in case.parties:\n",
    "    if agency_desc in p['type']:\n",
    "      return p['name']\n",
    "  return 'UNKNOWN'\n",
    "  \n",
    "case_data = lambda c: [c.year, c.county, c.type, agency(c), officer(c),  c.case_number ]\n",
    "%time df = pd.DataFrame([case_data(c) for c in saved_cases], columns = columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1231ae9b0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "\n",
    "df['Year'].value_counts()\n",
    "# df['Type'].value_counts()\n",
    "# df['Agency'].value_counts()\n",
    "# df.filter(like='LIGHTHORSE',axis=0)\n",
    "# qf = df.query(\"Type == 'CF'\")\n",
    "\n",
    "agency_group = df.groupby(['County','Type','Year'])['Agency'].value_counts().head(5)\n",
    "\n",
    "\n",
    "\n",
    "agency_group.plot.pie(y='Agency', figsize=(5, 5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}