Parking Tickets 2015

Parking Tickets 2015.ipynb.json

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 "metadata": {
  "name": "",
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Import data from https://data.cityofnewyork.us/City-Government/Parking-Violations-Issued-Fiscal-Year-2015/kiv2-tbus\n",
      "* Downloaded as csv"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import re\n",
      "import collections\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "df = pd.read_csv('/Users/rducic/Documents/MyData/Parking_Violations_Issued_-_Fiscal_Year_2015.csv',  low_memory=False, parse_dates=['Issue Date'], index_col='Issue Date')\n",
      "\n",
      "\n",
      "# Only grab where Status != 0 where office is known\n",
      "\"\"\"\n",
      "mask = df['Issuer Code'] != 0\n",
      "df = df[mask]\n",
      "\"\"\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "\"\\nmask = df['Issuer Code'] != 0\\ndf = df[mask]\\n\""
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "# Group by  Date and  officer \n",
      "Daily = df.reset_index().groupby(['Issuer Code', 'Issue Date'])['Summons Number']\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "'\\nfor name, group in Daily:\\n    print name\\n    print group \\n'"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "dailyCount = Daily.count()\n",
      "dailyCount\n",
      "\n",
      "#need to know how to filter out \n",
      "#dailyCount[dailyCount['Issue Date'] > 2015/01/01]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "Issuer Code  Issue Date\n",
        "0            2011-06-24       1\n",
        "             2013-09-17       1\n",
        "             2013-12-10       1\n",
        "             2013-12-11       1\n",
        "             2014-01-09       1\n",
        "             2014-02-28       1\n",
        "             2014-03-05       2\n",
        "             2014-03-12       1\n",
        "             2014-03-20       1\n",
        "             2014-03-24       1\n",
        "             2014-03-26     747\n",
        "             2014-03-27    2084\n",
        "             2014-03-28    2227\n",
        "             2014-03-31    1659\n",
        "             2014-04-01    1620\n",
        "...\n",
        "999601       2014-08-04    1\n",
        "             2014-08-11    1\n",
        "999607       2014-06-13    1\n",
        "             2014-06-25    1\n",
        "999643       2014-07-31    1\n",
        "999658       2014-07-22    1\n",
        "             2014-08-01    1\n",
        "999666       2014-07-13    1\n",
        "999680       2014-07-28    1\n",
        "999686       2014-06-19    1\n",
        "999693       2014-07-31    1\n",
        "999779       2014-07-24    1\n",
        "999866       2014-08-06    1\n",
        "999992       2014-07-07    1\n",
        "             2014-08-07    1\n",
        "Name: Summons Number, Length: 149868, dtype: int64"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ticketByDay = Daily.agg([('dailyTix', 'count'),])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#, 'Issuer Code'\n",
      "grouped = df.groupby([lambda x: x.month])\n",
      "\"\"\"for month, group in grouped:\n",
      "         print month\n",
      "         print group\n",
      "\"\"\"\n",
      "byMonthOfficer = grouped['Summons Number'].agg(['count'])\n",
      "\n",
      "byMonthOfficer.plot()\n",
      "\n",
      "## WHAT'S A DIFFRENCE BETWEEN COUNT AND SIZE IS IT IN TREATMENT OF NULLS?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1276a9b90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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5y/DJJ7YjCTa/NQtNFsp5J0/C6tXR5LByJXz1q9HkMGAAnH227ShV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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a23c350>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Need to remove outliers \n",
      "\"\"\"\n",
      "\n",
      "OfficerYearMonth = Daily.groupby([Daily.index.get_level_values(0), Daily.index.get_level_values(1).year, Daily.index.get_level_values(1).month])\n",
      "Daily['Lower'] = OfficerYearMonth['CustomerCount'].transform( lambda x: x.quantile(q=.25) - (1.5*x.quantile(q=.75)-x.quantile(q=.25)) )\n",
      "#Daily['Upper'] = OfficerYearMonth['CustomerCount'].transform( lambda x: x.quantile(q=.75) + (1.5*x.quantile(q=.75)-x.quantile(q=.25)) )\n",
      "Daily['Outlier'] = (Daily['CustomerCount'] < Daily['Lower']) #| (Daily['CustomerCount'] > Daily['Upper']) \n",
      "\n",
      "# Remove Outliers\n",
      "Daily = Daily[Daily['Outlier'] == False]\n",
      "\n",
      "\"\"\"\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To DO:\n",
      "======"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "* Filter data for months where data is reliable \n",
      "* Compare officers and concentrate on parking enforcers \n",
      "* Detirmine if month to month performance is consistant \n",
      "* Compare number of tickets issued per day and if most tickets are issued at the begin or end of the month\n",
      "* Determine if New York city imoses quotas on officers \n",
      "\n",
      "\n",
      "Nice to see\n",
      "-----\n",
      "* tickets issued by precint \n",
      "* ticket issued against map of the city\n",
      "* ticket issued in relation to police stations\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}