pronojitsaha/Udacity_Project.ipynb

## Udacity_Project.ipynb
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "1. Introduction and Background"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "When the New York subway opened in 1904, it launched an unprecedented era of growth and prosperity for the newly unified New York City. One hundred years later, the city's reliance on its underground rapid transit system is greater than ever. NYC Transit keeps New York moving 24 hours a day, seven days a week, as its subways speed through underground tunnels and elevated structures in the boroughs of Manhattan, Brooklyn, Queens, and the Bronx. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this analysis I will be looking into NYC subway ridership patterns and how rain plays its part in the same. Specifically I am going to look at data from the month of May 2011. The primary question that I am looking to answer is \"how does rain affect ridership in the New York City Subway system?\". My initial hypothesis is that there is a significant difference between ridership values on rainy and non-rainy days. We will carry out some statistical tests and visualizations to confirm the same. Along the way we will also develop a model to predict the subway ridership given various attributes about weather.  "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "N.B. This analysis is a final project of the online course at Udacity titled Intro to Data Science: https://www.udacity.com/course/ud359. As such throughout this notebook we will be writing python functions which will produce the desired result only when submitted to the Udacity course page."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "2. Data Wrangling"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First off, we will explore the data a bit to get a sense of what it looks like and then process the data into a desired form so that later its easy to do analysis on it. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy\n",
      "import pandas\n",
      "import pandasql\n",
      "import scipy.stats\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We begin with importing the underground weather data of NYC and explore certain temperature values. The data can be obtained from here- https://www.dropbox.com/s/7sf0yqc9ykpq3w8/weather_underground.csv"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weather_data = pandas.read_csv('data/weather_underground.csv')\n",
      "weather_data.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Index: 8 entries, count to max\n",
        "Data columns (total 69 columns):\n",
        "maxpressurem                          8  non-null values\n",
        "maxdewptm                             8  non-null values\n",
        "maxpressurei                          8  non-null values\n",
        "maxdewpti                             8  non-null values\n",
        "since1julheatingdegreedaysnormal      8  non-null values\n",
        "heatingdegreedaysnormal               8  non-null values\n",
        "since1sepcoolingdegreedaysnormal      1  non-null values\n",
        "hail                                  8  non-null values\n",
        "since1julsnowfallm                    8  non-null values\n",
        "since1julheatingdegreedays            8  non-null values\n",
        "maxvisi                               8  non-null values\n",
        "since1sepheatingdegreedaysnormal      1  non-null values\n",
        "heatingdegreedays                     8  non-null values\n",
        "mindewptm                             8  non-null values\n",
        "since1sepheatingdegreedays            1  non-null values\n",
        "maxwspdm                              8  non-null values\n",
        "since1julsnowfalli                    8  non-null values\n",
        "since1sepcoolingdegreedays            1  non-null values\n",
        "snow                                  8  non-null values\n",
        "meanvism                              8  non-null values\n",
        "meandewptm                            8  non-null values\n",
        "snowdepthm                            8  non-null values\n",
        "meanvisi                              8  non-null values\n",
        "fog                                   8  non-null values\n",
        "snowdepthi                            8  non-null values\n",
        "minvism                               8  non-null values\n",
        "since1jancoolingdegreedays            8  non-null values\n",
        "minvisi                               8  non-null values\n",
        "coolingdegreedaysnormal               8  non-null values\n",
        "gdegreedays                           8  non-null values\n",
        "maxwspdi                              8  non-null values\n",
        "meanwindspdi                          8  non-null values\n",
        "meanpressurei                         8  non-null values\n",
        "monthtodateheatingdegreedaysnormal    8  non-null values\n",
        "meanwindspdm                          8  non-null values\n",
        "meanpressurem                         8  non-null values\n",
        "tornado                               8  non-null values\n",
        "mindewpti                             8  non-null values\n",
        "mintempi                              8  non-null values\n",
        "meandewpti                            8  non-null values\n",
        "rain                                  8  non-null values\n",
        "mintempm                              8  non-null values\n",
        "minhumidity                           8  non-null values\n",
        "precipsource                          1  non-null values\n",
        "minwspdi                              8  non-null values\n",
        "meanwdird                             8  non-null values\n",
        "meanwdire                             1  non-null values\n",
        "minwspdm                              8  non-null values\n",
        "monthtodatesnowfalli                  8  non-null values\n",
        "monthtodatecoolingdegreedaysnormal    8  non-null values\n",
        "monthtodatesnowfallm                  8  non-null values\n",
        "maxhumidity                           8  non-null values\n",
        "coolingdegreedays                     8  non-null values\n",
        "maxtempm                              8  non-null values\n",
        "minpressurei                          8  non-null values\n",
        "monthtodatecoolingdegreedays          8  non-null values\n",
        "maxtempi                              8  non-null values\n",
        "minpressurem                          8  non-null values\n",
        "humidity                              1  non-null values\n",
        "precipi                               8  non-null values\n",
        "snowfalli                             8  non-null values\n",
        "since1jancoolingdegreedaysnormal      8  non-null values\n",
        "precipm                               8  non-null values\n",
        "snowfallm                             8  non-null values\n",
        "thunder                               8  non-null values\n",
        "monthtodateheatingdegreedays          8  non-null values\n",
        "meantempi                             8  non-null values\n",
        "maxvism                               8  non-null values\n",
        "meantempm                             8  non-null values\n",
        "dtypes: float64(69)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Index: 8 entries, count to max\n",
        "Data columns (total 69 columns):\n",
        "maxpressurem                          8  non-null values\n",
        "maxdewptm                             8  non-null values\n",
        "maxpressurei                          8  non-null values\n",
        "maxdewpti                             8  non-null values\n",
        "since1julheatingdegreedaysnormal      8  non-null values\n",
        "heatingdegreedaysnormal               8  non-null values\n",
        "since1sepcoolingdegreedaysnormal      1  non-null values\n",
        "hail                                  8  non-null values\n",
        "since1julsnowfallm                    8  non-null values\n",
        "since1julheatingdegreedays            8  non-null values\n",
        "maxvisi                               8  non-null values\n",
        "since1sepheatingdegreedaysnormal      1  non-null values\n",
        "heatingdegreedays                     8  non-null values\n",
        "mindewptm                             8  non-null values\n",
        "since1sepheatingdegreedays            1  non-null values\n",
        "maxwspdm                              8  non-null values\n",
        "since1julsnowfalli                    8  non-null values\n",
        "since1sepcoolingdegreedays            1  non-null values\n",
        "snow                                  8  non-null values\n",
        "meanvism                              8  non-null values\n",
        "meandewptm                            8  non-null values\n",
        "snowdepthm                            8  non-null values\n",
        "meanvisi                              8  non-null values\n",
        "fog                                   8  non-null values\n",
        "snowdepthi                            8  non-null values\n",
        "minvism                               8  non-null values\n",
        "since1jancoolingdegreedays            8  non-null values\n",
        "minvisi                               8  non-null values\n",
        "coolingdegreedaysnormal               8  non-null values\n",
        "gdegreedays                           8  non-null values\n",
        "maxwspdi                              8  non-null values\n",
        "meanwindspdi                          8  non-null values\n",
        "meanpressurei                         8  non-null values\n",
        "monthtodateheatingdegreedaysnormal    8  non-null values\n",
        "meanwindspdm                          8  non-null values\n",
        "meanpressurem                         8  non-null values\n",
        "tornado                               8  non-null values\n",
        "mindewpti                             8  non-null values\n",
        "mintempi                              8  non-null values\n",
        "meandewpti                            8  non-null values\n",
        "rain                                  8  non-null values\n",
        "mintempm                              8  non-null values\n",
        "minhumidity                           8  non-null values\n",
        "precipsource                          1  non-null values\n",
        "minwspdi                              8  non-null values\n",
        "meanwdird                             8  non-null values\n",
        "meanwdire                             1  non-null values\n",
        "minwspdm                              8  non-null values\n",
        "monthtodatesnowfalli                  8  non-null values\n",
        "monthtodatecoolingdegreedaysnormal    8  non-null values\n",
        "monthtodatesnowfallm                  8  non-null values\n",
        "maxhumidity                           8  non-null values\n",
        "coolingdegreedays                     8  non-null values\n",
        "maxtempm                              8  non-null values\n",
        "minpressurei                          8  non-null values\n",
        "monthtodatecoolingdegreedays          8  non-null values\n",
        "maxtempi                              8  non-null values\n",
        "minpressurem                          8  non-null values\n",
        "humidity                              1  non-null values\n",
        "precipi                               8  non-null values\n",
        "snowfalli                             8  non-null values\n",
        "since1jancoolingdegreedaysnormal      8  non-null values\n",
        "precipm                               8  non-null values\n",
        "snowfallm                             8  non-null values\n",
        "thunder                               8  non-null values\n",
        "monthtodateheatingdegreedays          8  non-null values\n",
        "meantempi                             8  non-null values\n",
        "maxvism                               8  non-null values\n",
        "meantempm                             8  non-null values\n",
        "dtypes: float64(69)"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The number of rainy days in May. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "q = \"\"\"\n",
      "    select count(*) from weather_data where cast(rain as integer) = 1\n",
      "    \"\"\"\n",
      "rainy_days = pandasql.sqldf(q.lower(), locals())\n",
      "rainy_days"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>count(*)</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "   count(*)\n",
        "0        10"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Mean temperature on rainy days and non-rainy days. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "q = ''' select avg(cast (mintempi as integer)) from weather_data \n",
      "where cast(rain as integer) = 1\n",
      "'''\n",
      "\n",
      "p = ''' select avg(cast (mintempi as integer)) from weather_data \n",
      "where cast(rain as integer) = 0\n",
      "'''\n",
      "\n",
      "mean_temp_rainydays = pandasql.sqldf(q.lower(), locals())\n",
      "mean_temp_nonrainy = pandasql.sqldf(p.lower(), locals())\n",
      "mean_temp_rainydays, mean_temp_nonrainy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "(   avg(cast (mintempi as integer))\n",
        "0                               56,\n",
        "    avg(cast (mintempi as integer))\n",
        "0                             56.3)"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We now gather the NYC subway data from the MTA website. Here is a link to an example MTA Subway turnstile text file: http://web.mta.info/developers/data/nyct/turnstile/turnstile_110507.txt. There are multiple such files with each file containing the information of a particular turnstile. These files are scrapped from the MTA website and stored on the Udacity servers and then passed through the argument filenames in the below function, when the function is submitted to the udacity servers.\n",
      "\n",
      "As we can see from the example file above, there are numerous data points included in each row of the MTA Subway turnstile text file. We want to write a function that will update each row in the text file so there is only one entry per row. A few examples below:  \n",
      "A002,R051,02-00-00,05-28-11,00:00:00,REGULAR,003178521,001100739  \n",
      "A002,R051,02-00-00,05-28-11,04:00:00,REGULAR,003178541,001100746  \n",
      "A002,R051,02-00-00,05-28-11,08:00:00,REGULAR,003178559,001100775  \n",
      "\n",
      "Sample input file: https://www.dropbox.com/s/mpin5zv4hgrx244/turnstile_110528.txt  \n",
      "Sample updated file: https://www.dropbox.com/s/074xbgio4c39b7h/solution_turnstile_110528.txt\n",
      "\n",
      "We then combine these data into a single file."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def fix_turnstile_data(filenames):\n",
      "    for name in filenames:\n",
      "        with open(name,'rb') as f:\n",
      "            reader = csv.reader(f)\n",
      "            with open('updated_' + name,'wb') as f:\n",
      "                writer = csv.writer(f)\n",
      "                output = []\n",
      "                for row in reader:\n",
      "                    for i in range(0,(len(row[3::])/5)):\n",
      "                        output.append(row[0:3] + row[(i*5)+3:((i+1)*5)+3])\n",
      "                writer.writerows(output)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we take an list of files as input, each of which have have the columns 'C/A, UNIT, SCP, DATEn, TIMEn, DESCn, ENTRIESn, EXITSn', as processed above and then consolidate them into one file located at output file.\n",
      "\n",
      "For example, if file_1 has:  \n",
      "    \n",
      "'C/A, UNIT, SCP, DATEn, TIMEn, DESCn, ENTRIESn, EXITSn'  \n",
      "    line 1 ...  \n",
      "    line 2 ...  \n",
      "        \n",
      "and another file, file_2 has:  \n",
      "    'C/A, UNIT, SCP, DATEn, TIMEn, DESCn, ENTRIESn, EXITSn'  \n",
      "    line 3 ...  \n",
      "    line 4 ...  \n",
      "    line 5 ...  \n",
      "      \n",
      "We need to combine file_1 and file_2 into a master_file like below:  \n",
      "     'C/A, UNIT, SCP, DATEn, TIMEn, DESCn, ENTRIESn, EXITSn'  \n",
      "    line 1 ...  \n",
      "    line 2 ...  \n",
      "    line 3 ...  \n",
      "    line 4 ...  \n",
      "    line 5 ... \n",
      "    \n",
      "The following function does this very task."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def create_master_turnstile_file(filenames, output_file):\n",
      "    with open(output_file, 'w') as master_file:       \n",
      "        master_file.write('C/A,UNIT,SCP,DATEn,TIMEn,DESCn,ENTRIESn,EXITSn\\n')\n",
      "        for filename in filenames:\n",
      "            with open(filename, 'r') as f:\n",
      "               read_data = f.read()\n",
      "               f.closed\n",
      "            master_file.write(read_data)\n",
      "        master_file.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this way we can consolidate the data into a desired format. The udacity team then gave us a data frame (named 'df') containing the entire consolidated MTA Subway Turnstile data obtained through the above process. \n",
      "\n",
      "The ENTRIESn and EXITSn columns of the data frame 'df' stores the the cumulative entry and exit numbers of a particular turnstile. We now create the Hourly Entries and Hourly Exists column which turns these cumulative numbers to a count of entries and exits, respectively, since the last reading. We do this by assigning to the column the difference between ENTRIESn and EXITSn of the current row and the previous row. If there is any NaN, we fill/replace it with 1 and 0, respectively."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_hourly_entries(df):\n",
      "    df['ENTRIESn_hourly'] = (df['ENTRIESn']-df['ENTRIESn'].shift(1)).fillna(1)\n",
      "    df['EXITSn_hourly'] = (df['EXITSn']-df['EXITSn'].shift(1)).fillna(0)\n",
      "    return df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "3. Data Analysis"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Out data is now in a format that is suitable to carry out our analysis to find the effect of rain on subway ridership. The processed data can be downloaded from here - https://www.dropbox.com/s/meyki2wl9xfa7yk/turnstile_data_master_with_weather.csv"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dataframe = pandas.read_csv('data/turnstile_data_master_with_weather.csv')\n",
      "dataframe.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Unnamed: 0</th>\n",
        "      <th>Hour</th>\n",
        "      <th>ENTRIESn_hourly</th>\n",
        "      <th>EXITSn_hourly</th>\n",
        "      <th>maxpressurei</th>\n",
        "      <th>maxdewpti</th>\n",
        "      <th>mindewpti</th>\n",
        "      <th>minpressurei</th>\n",
        "      <th>meandewpti</th>\n",
        "      <th>meanpressurei</th>\n",
        "      <th>fog</th>\n",
        "      <th>rain</th>\n",
        "      <th>meanwindspdi</th>\n",
        "      <th>mintempi</th>\n",
        "      <th>meantempi</th>\n",
        "      <th>maxtempi</th>\n",
        "      <th>precipi</th>\n",
        "      <th>thunder</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 131951.000000</td>\n",
        "      <td> 131951.000000</td>\n",
        "      <td> 131951.000000</td>\n",
        "      <td> 131951.000000</td>\n",
        "      <td> 131951.000000</td>\n",
        "      <td> 131951.000000</td>\n",
        "      <td> 131951.000000</td>\n",
        "      <td> 131951.000000</td>\n",
        "      <td> 131951.000000</td>\n",
        "      <td> 131951.000000</td>\n",
        "      <td> 131951.000000</td>\n",
        "      <td> 131951.000000</td>\n",
        "      <td> 131951.000000</td>\n",
        "      <td> 131951.000000</td>\n",
        "      <td> 131951.000000</td>\n",
        "      <td> 131951.000000</td>\n",
        "      <td> 131951.000000</td>\n",
        "      <td> 131951</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>  65975.000000</td>\n",
        "      <td>     10.896158</td>\n",
        "      <td>   1095.348478</td>\n",
        "      <td>    886.890838</td>\n",
        "      <td>     30.031894</td>\n",
        "      <td>     57.241302</td>\n",
        "      <td>     48.259013</td>\n",
        "      <td>     29.892714</td>\n",
        "      <td>     52.703526</td>\n",
        "      <td>     29.965077</td>\n",
        "      <td>      0.167100</td>\n",
        "      <td>      0.334245</td>\n",
        "      <td>      5.543065</td>\n",
        "      <td>     56.169775</td>\n",
        "      <td>     64.269729</td>\n",
        "      <td>     71.769968</td>\n",
        "      <td>      0.172276</td>\n",
        "      <td>      0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>  38091.117022</td>\n",
        "      <td>      6.892084</td>\n",
        "      <td>   2337.015421</td>\n",
        "      <td>   2008.604886</td>\n",
        "      <td>      0.125689</td>\n",
        "      <td>      8.770891</td>\n",
        "      <td>     11.305312</td>\n",
        "      <td>      0.146384</td>\n",
        "      <td>      9.943590</td>\n",
        "      <td>      0.130461</td>\n",
        "      <td>      0.373066</td>\n",
        "      <td>      0.471728</td>\n",
        "      <td>      1.982441</td>\n",
        "      <td>      6.338875</td>\n",
        "      <td>      6.568289</td>\n",
        "      <td>      7.627218</td>\n",
        "      <td>      0.429005</td>\n",
        "      <td>      0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>     29.740000</td>\n",
        "      <td>     39.000000</td>\n",
        "      <td>     22.000000</td>\n",
        "      <td>     29.540000</td>\n",
        "      <td>     31.000000</td>\n",
        "      <td>     29.640000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      1.000000</td>\n",
        "      <td>     46.000000</td>\n",
        "      <td>     55.000000</td>\n",
        "      <td>     58.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>  32987.500000</td>\n",
        "      <td>      5.000000</td>\n",
        "      <td>     39.000000</td>\n",
        "      <td>     32.000000</td>\n",
        "      <td>     29.960000</td>\n",
        "      <td>     50.000000</td>\n",
        "      <td>     38.000000</td>\n",
        "      <td>     29.840000</td>\n",
        "      <td>     45.000000</td>\n",
        "      <td>     29.910000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      5.000000</td>\n",
        "      <td>     52.000000</td>\n",
        "      <td>     60.000000</td>\n",
        "      <td>     65.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>  65975.000000</td>\n",
        "      <td>     12.000000</td>\n",
        "      <td>    279.000000</td>\n",
        "      <td>    232.000000</td>\n",
        "      <td>     30.030000</td>\n",
        "      <td>     57.000000</td>\n",
        "      <td>     51.000000</td>\n",
        "      <td>     29.910000</td>\n",
        "      <td>     54.000000</td>\n",
        "      <td>     29.960000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      5.000000</td>\n",
        "      <td>     54.000000</td>\n",
        "      <td>     63.000000</td>\n",
        "      <td>     71.000000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>  98962.500000</td>\n",
        "      <td>     17.000000</td>\n",
        "      <td>   1109.000000</td>\n",
        "      <td>    847.000000</td>\n",
        "      <td>     30.100000</td>\n",
        "      <td>     64.000000</td>\n",
        "      <td>     55.000000</td>\n",
        "      <td>     29.970000</td>\n",
        "      <td>     60.000000</td>\n",
        "      <td>     30.050000</td>\n",
        "      <td>      0.000000</td>\n",
        "      <td>      1.000000</td>\n",
        "      <td>      6.000000</td>\n",
        "      <td>     60.000000</td>\n",
        "      <td>     68.000000</td>\n",
        "      <td>     78.000000</td>\n",
        "      <td>      0.100000</td>\n",
        "      <td>      0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 131950.000000</td>\n",
        "      <td>     23.000000</td>\n",
        "      <td>  51839.000000</td>\n",
        "      <td>  45249.000000</td>\n",
        "      <td>     30.310000</td>\n",
        "      <td>     70.000000</td>\n",
        "      <td>     66.000000</td>\n",
        "      <td>     30.230000</td>\n",
        "      <td>     68.000000</td>\n",
        "      <td>     30.270000</td>\n",
        "      <td>      1.000000</td>\n",
        "      <td>      1.000000</td>\n",
        "      <td>     12.000000</td>\n",
        "      <td>     70.000000</td>\n",
        "      <td>     78.000000</td>\n",
        "      <td>     86.000000</td>\n",
        "      <td>      2.180000</td>\n",
        "      <td>      0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "          Unnamed: 0           Hour  ENTRIESn_hourly  EXITSn_hourly  \\\n",
        "count  131951.000000  131951.000000    131951.000000  131951.000000   \n",
        "mean    65975.000000      10.896158      1095.348478     886.890838   \n",
        "std     38091.117022       6.892084      2337.015421    2008.604886   \n",
        "min         0.000000       0.000000         0.000000       0.000000   \n",
        "25%     32987.500000       5.000000        39.000000      32.000000   \n",
        "50%     65975.000000      12.000000       279.000000     232.000000   \n",
        "75%     98962.500000      17.000000      1109.000000     847.000000   \n",
        "max    131950.000000      23.000000     51839.000000   45249.000000   \n",
        "\n",
        "        maxpressurei      maxdewpti      mindewpti   minpressurei  \\\n",
        "count  131951.000000  131951.000000  131951.000000  131951.000000   \n",
        "mean       30.031894      57.241302      48.259013      29.892714   \n",
        "std         0.125689       8.770891      11.305312       0.146384   \n",
        "min        29.740000      39.000000      22.000000      29.540000   \n",
        "25%        29.960000      50.000000      38.000000      29.840000   \n",
        "50%        30.030000      57.000000      51.000000      29.910000   \n",
        "75%        30.100000      64.000000      55.000000      29.970000   \n",
        "max        30.310000      70.000000      66.000000      30.230000   \n",
        "\n",
        "          meandewpti  meanpressurei            fog           rain  \\\n",
        "count  131951.000000  131951.000000  131951.000000  131951.000000   \n",
        "mean       52.703526      29.965077       0.167100       0.334245   \n",
        "std         9.943590       0.130461       0.373066       0.471728   \n",
        "min        31.000000      29.640000       0.000000       0.000000   \n",
        "25%        45.000000      29.910000       0.000000       0.000000   \n",
        "50%        54.000000      29.960000       0.000000       0.000000   \n",
        "75%        60.000000      30.050000       0.000000       1.000000   \n",
        "max        68.000000      30.270000       1.000000       1.000000   \n",
        "\n",
        "        meanwindspdi       mintempi      meantempi       maxtempi  \\\n",
        "count  131951.000000  131951.000000  131951.000000  131951.000000   \n",
        "mean        5.543065      56.169775      64.269729      71.769968   \n",
        "std         1.982441       6.338875       6.568289       7.627218   \n",
        "min         1.000000      46.000000      55.000000      58.000000   \n",
        "25%         5.000000      52.000000      60.000000      65.000000   \n",
        "50%         5.000000      54.000000      63.000000      71.000000   \n",
        "75%         6.000000      60.000000      68.000000      78.000000   \n",
        "max        12.000000      70.000000      78.000000      86.000000   \n",
        "\n",
        "             precipi  thunder  \n",
        "count  131951.000000   131951  \n",
        "mean        0.172276        0  \n",
        "std         0.429005        0  \n",
        "min         0.000000        0  \n",
        "25%         0.000000        0  \n",
        "50%         0.000000        0  \n",
        "75%         0.100000        0  \n",
        "max         2.180000        0  "
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will first examine the hourly entries in our NYC subway data and determine what distribution the data follows."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dataframe[dataframe['rain']==0]['ENTRIESn_hourly'].hist(bins=50, color= 'yellow', range=[0,12000])\n",
      "dataframe[dataframe['rain']==1]['ENTRIESn_hourly'].hist(bins=50,  range=[0,12000])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10b19a0d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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B4XAgKSkJixYtQmdnJ0QELS0t2Lp165T3OnLkCAoKCm61FhIRkZ7mqiyPPfaY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\niIjCwsJBRERh+f/VLuB8hl95oAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b199d90>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As we can see from the graph above that the data is not normally distributed so we cannot use the Welch\u2019s Test to test the difference between rainy and non rainy day subway ridership.\n",
      "\n",
      "Instead, we use a non-parametric tests like Mann Whitney test to compare the means of entries on rain and non rainy days. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with_rain_mean = numpy.mean(dataframe[dataframe['rain']==1]['ENTRIESn_hourly'])\n",
      "without_rain_mean = numpy.mean(dataframe[dataframe['rain']==0]['ENTRIESn_hourly'])\n",
      "U, p = scipy.stats.mannwhitneyu(dataframe[dataframe['rain']==1]['ENTRIESn_hourly'], dataframe[dataframe['rain']==0]['ENTRIESn_hourly'])\n",
      "with_rain_mean, without_rain_mean, U, p"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "(1105.4463767458733, 1090.278780151855, 1924409167.0, 0.024999912793489721)"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As the p-value is less than 5% hence we reject the null hypothesis that the distribution between rainy and non rainy days are not different. In other words there is a statistically siginificant difference between the ridership on rainy days and non-rainy days, hence confirming our earlier hypothesis."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Linear regression to predict the subway ridership. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We now proceed with our main task of predicting subway ridership given our data set. Here we will be using Linear regression with gradient descent as our main tool for the prediction task. We will first normalize the features (or the columns of the data set) as these are on different scales."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def normalize_features(array):\n",
      "   \"\"\"\n",
      "   Normalize the features in our data set.\n",
      "   \"\"\"\n",
      "   array_normalized = (array-array.mean())/array.std()\n",
      "   mu = array.mean()\n",
      "   sigma = array.std() \n",
      "\n",
      "   return array_normalized, mu, sigma"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We now create the features array that will hold the feature values. In our model we will consider rain, precipi, Hour, meantempi and UNIT as our feature variables or the independent variables. As we are predicting the hourly entries so that will be our output value or the dependent variable. \n",
      "\n",
      "Here the feature UNIT is an unordered factor variabel as there is no natural ordering between its various levels. To include unordered factors in a linear regression model, we define one level as the \"reference level\" and add a binary variable (generally called dummy variables) for each of the remaining levels. In this way, a factor with n levels is replaced by n-1 binary variables. The pandas get_dummies command does this for us. We also add a column of ones to the feature array which represents the intrecept term in our linear regression model. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dummy_units = pandas.get_dummies(dataframe['UNIT'], prefix='unit')\n",
      "features = dataframe[['rain', 'precipi', 'Hour', 'meantempi']].join(dummy_units)\n",
      "values = dataframe[['ENTRIESn_hourly']]\n",
      "m = len(values)\n",
      "features, mu, sigma = normalize_features(features)\n",
      "features['ones'] = numpy.ones(m)\n",
      "features_array = numpy.array(features)\n",
      "values_array = numpy.array(values).flatten()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We then write two functions, one to compute the cost over the different iterations of the gradient descent function and the second is the gradient descent function itself"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def compute_cost(features, values, theta):\n",
      "    m = len(values)\n",
      "    sum_of_square_errors = numpy.square(numpy.dot(features, theta) - values).sum()\n",
      "    cost = sum_of_square_errors / (2*m)\n",
      "    return cost"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def gradient_descent(features, values, theta, alpha, num_iterations):\n",
      "    m = len(values)\n",
      "    cost_history = []\n",
      "    for i in numpy.arange(num_iterations):\n",
      "        theta = theta - alpha/m*(numpy.dot((numpy.dot(features, theta)-values),features))\n",
      "        cost_history.append(compute_cost(features, values, theta))\n",
      "    return theta, pandas.Series(cost_history)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We then proceed to make the predictions. Here alpha and num_iterations are our tuning parameters and we adjust the same to achieve a better fit for our model. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Set values for alpha, number of iterations.\n",
      "alpha = 0.01 # please feel free to play with this value\n",
      "num_iterations = 100 # please feel free to play with this value\n",
      "\n",
      "#Initialize theta, perform gradient descent\n",
      "theta_gradient_descent = numpy.zeros(len(features.columns))\n",
      "theta_gradient_descent, cost_history = gradient_descent(features_array, values_array, theta_gradient_descent, alpha, num_iterations)\n",
      "\n",
      "predictions = numpy.dot(features_array, theta_gradient_descent)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plotting the residuals. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(dataframe['ENTRIESn_hourly'] - predictions).hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x107d63dd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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0aRM9Hg8bGxsZj8fvOJcMI+QcMrpCGbL1FcrQ0FC+p2Aq5Stsds5n1rlBX72S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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107d63c50>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Computing the R-Squared value, we get a value of 36.5% which is decent considering the randomness in ridership data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def compute_r_squared(data, predictions):\n",
      "    SST = ((data - numpy.mean(data))**2).sum()\n",
      "    SSReg = ((predictions - data)**2).sum()\n",
      "    r_squared = 1 - (SSReg/SST)\n",
      "    return r_squared\n",
      "compute_r_squared(values_array, predictions)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "0.36584471198470159"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Another way of carrying out a linear regression is by using the least squares approach. Here we use the statsmodel package to run an ordinary least squares regression on the same model features as with gradient descent. As one can see from the summary that this achieves a R squared value of 46%, much better than with gradient descent. This can be attributed to the fact that least squares rehression is a simpler implementation and in many cases simple is better!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import statsmodels.formula.api as smf\n",
      "est = smf.ols(formula = 'ENTRIESn_hourly ~ rain + precipi + Hour + meantempi + C(UNIT)', data = dataframe).fit()\n",
      "est.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table class=\"simpletable\">\n",
        "<caption>OLS Regression Results</caption>\n",
        "<tr>\n",
        "  <th>Dep. Variable:</th>     <td>ENTRIESn_hourly</td> <th>  R-squared:         </th>  <td>   0.458</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.456</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   237.4</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Date:</th>             <td>Sun, 11 May 2014</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Time:</th>                 <td>18:52:13</td>     <th>  Log-Likelihood:    </th> <td>-1.1703e+06</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>131951</td>      <th>  AIC:               </th>  <td>2.342e+06</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Df Residuals:</th>          <td>131482</td>      <th>  BIC:               </th>  <td>2.346e+06</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Df Model:</th>              <td>   468</td>      <th>                     </th>      <td> </td>     \n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
        "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Intercept</th>       <td> 3749.1876</td> <td>  135.360</td> <td>   27.698</td> <td> 0.000</td> <td> 3483.885  4014.490</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R002]</th> <td>-3063.1322</td> <td>  179.451</td> <td>  -17.069</td> <td> 0.000</td> <td>-3414.853 -2711.411</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R003]</th> <td>-3761.5656</td> <td>  182.322</td> <td>  -20.631</td> <td> 0.000</td> <td>-4118.913 -3404.218</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R004]</th> <td>-3435.6463</td> <td>  181.242</td> <td>  -18.956</td> <td> 0.000</td> <td>-3790.878 -3080.415</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R005]</th> <td>-3448.5882</td> <td>  181.508</td> <td>  -19.000</td> <td> 0.000</td> <td>-3804.341 -3092.836</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R006]</th> <td>-3377.0057</td> <td>  177.779</td> <td>  -18.996</td> <td> 0.000</td> <td>-3725.449 -3028.562</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R007]</th> <td>-3598.0489</td> <td>  182.878</td> <td>  -19.675</td> <td> 0.000</td> <td>-3956.487 -3239.610</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R008]</th> <td>-3563.8325</td> <td>  184.618</td> <td>  -19.304</td> <td> 0.000</td> <td>-3925.680 -3201.985</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R009]</th> <td>-3635.2992</td> <td>  182.322</td> <td>  -19.939</td> <td> 0.000</td> <td>-3992.648 -3277.951</td>\n",
        "</tr>\n",
        "<tr>\n",
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        "<tr>\n",
        "  <th>C(UNIT)[T.R450]</th> <td>-3459.9828</td> <td>  183.445</td> <td>  -18.861</td> <td> 0.000</td> <td>-3819.532 -3100.433</td>\n",
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        "<tr>\n",
        "  <th>C(UNIT)[T.R451]</th> <td>-3035.6019</td> <td>  178.962</td> <td>  -16.962</td> <td> 0.000</td> <td>-3386.363 -2684.840</td>\n",
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        "<tr>\n",
        "  <th>C(UNIT)[T.R452]</th> <td> 1784.8758</td> <td>  179.205</td> <td>    9.960</td> <td> 0.000</td> <td> 1433.637  2136.115</td>\n",
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        "<tr>\n",
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        "<tr>\n",
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        "<tr>\n",
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        "<tr>\n",
        "  <th>C(UNIT)[T.R464]</th> <td>-3968.1185</td> <td>  184.918</td> <td>  -21.459</td> <td> 0.000</td> <td>-4330.555 -3605.682</td>\n",
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        "<tr>\n",
        "  <th>C(UNIT)[T.R468]</th> <td>-3508.4527</td> <td>  179.700</td> <td>  -19.524</td> <td> 0.000</td> <td>-3860.661 -3156.245</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R469]</th> <td>-3505.2000</td> <td>  180.203</td> <td>  -19.451</td> <td> 0.000</td> <td>-3858.395 -3152.005</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R535]</th> <td>-3513.8357</td> <td>  177.319</td> <td>  -19.817</td> <td> 0.000</td> <td>-3861.377 -3166.294</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R536]</th> <td>-3500.2152</td> <td>  161.265</td> <td>  -21.705</td> <td> 0.000</td> <td>-3816.291 -3184.139</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R540]</th> <td>-3797.6507</td> <td>  129.006</td> <td>  -29.438</td> <td> 0.000</td> <td>-4050.500 -3544.801</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R541]</th> <td>-3904.4777</td> <td>  128.344</td> <td>  -30.422</td> <td> 0.000</td> <td>-4156.029 -3652.926</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R542]</th> <td>-3924.8080</td> <td>  134.086</td> <td>  -29.271</td> <td> 0.000</td> <td>-4187.613 -3662.003</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R543]</th> <td>-3961.0241</td> <td>  129.178</td> <td>  -30.663</td> <td> 0.000</td> <td>-4214.211 -3707.837</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R544]</th> <td>-3974.8126</td> <td>  132.062</td> <td>  -30.098</td> <td> 0.000</td> <td>-4233.652 -3715.974</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R545]</th> <td>-3925.3711</td> <td>  132.672</td> <td>  -29.587</td> <td> 0.000</td> <td>-4185.405 -3665.337</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R546]</th> <td>-3786.7747</td> <td>  132.899</td> <td>  -28.494</td> <td> 0.000</td> <td>-4047.254 -3526.295</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R547]</th> <td>-3892.2169</td> <td>  140.183</td> <td>  -27.765</td> <td> 0.000</td> <td>-4166.974 -3617.460</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R548]</th> <td>-3858.4017</td> <td>  144.009</td> <td>  -26.793</td> <td> 0.000</td> <td>-4140.656 -3576.147</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R549]</th> <td>-3998.5832</td> <td>  127.334</td> <td>  -31.402</td> <td> 0.000</td> <td>-4248.156 -3749.010</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R550]</th> <td>-3960.0904</td> <td>  128.071</td> <td>  -30.921</td> <td> 0.000</td> <td>-4211.108 -3709.073</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R551]</th> <td>-3933.4610</td> <td>  130.087</td> <td>  -30.237</td> <td> 0.000</td> <td>-4188.429 -3678.493</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(UNIT)[T.R552]</th> <td>-3868.1381</td> <td>  129.681</td> <td>  -29.828</td> <td> 0.000</td> <td>-4122.310 -3613.966</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>rain</th>            <td>  -20.1954</td> <td>   12.233</td> <td>   -1.651</td> <td> 0.099</td> <td>  -44.173     3.782</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>precipi</th>         <td>   43.6973</td> <td>   13.522</td> <td>    3.232</td> <td> 0.001</td> <td>   17.194    70.201</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Hour</th>            <td>   67.3966</td> <td>    0.691</td> <td>   97.533</td> <td> 0.000</td> <td>   66.042    68.751</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>meantempi</th>       <td>   -7.2718</td> <td>    0.736</td> <td>   -9.878</td> <td> 0.000</td> <td>   -8.715    -5.829</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
        "  <th>Omnibus:</th>       <td>121615.917</td> <th>  Durbin-Watson:     </th>   <td>   1.590</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Prob(Omnibus):</th>   <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>15257403.191</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Skew:</th>            <td> 4.028</td>   <th>  Prob(JB):          </th>   <td>    0.00</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Kurtosis:</th>        <td>55.059</td>   <th>  Cond. No.          </th>   <td>3.77e+04</td>  \n",
        "</tr>\n",
        "</table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
        "\"\"\"\n",
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:        ENTRIESn_hourly   R-squared:                       0.458\n",
        "Model:                            OLS   Adj. R-squared:                  0.456\n",
        "Method:                 Least Squares   F-statistic:                     237.4\n",
        "Date:                Sun, 11 May 2014   Prob (F-statistic):               0.00\n",
        "Time:                        18:52:13   Log-Likelihood:            -1.1703e+06\n",
        "No. Observations:              131951   AIC:                         2.342e+06\n",
        "Df Residuals:                  131482   BIC:                         2.346e+06\n",
        "Df Model:                         468                                         \n",
        "===================================================================================\n",
        "                      coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "-----------------------------------------------------------------------------------\n",
        "Intercept        3749.1876    135.360     27.698      0.000      3483.885  4014.490\n",
        "C(UNIT)[T.R002] -3063.1322    179.451    -17.069      0.000     -3414.853 -2711.411\n",
        "C(UNIT)[T.R003] -3761.5656    182.322    -20.631      0.000     -4118.913 -3404.218\n",
        "C(UNIT)[T.R004] -3435.6463    181.242    -18.956      0.000     -3790.878 -3080.415\n",
        "C(UNIT)[T.R005] -3448.5882    181.508    -19.000      0.000     -3804.341 -3092.836\n",
        "C(UNIT)[T.R006] -3377.0057    177.779    -18.996      0.000     -3725.449 -3028.562\n",
        "C(UNIT)[T.R007] -3598.0489    182.878    -19.675      0.000     -3956.487 -3239.610\n",
        "C(UNIT)[T.R008] -3563.8325    184.618    -19.304      0.000     -3925.680 -3201.985\n",
        "C(UNIT)[T.R009] -3635.2992    182.322    -19.939      0.000     -3992.648 -3277.951\n",
        "C(UNIT)[T.R010]   600.8519    179.701      3.344      0.001       248.642   953.062\n",
        "C(UNIT)[T.R011]  4085.4663    176.210     23.185      0.000      3740.098  4430.834\n",
        "C(UNIT)[T.R012]  3522.7797    173.735     20.277      0.000      3182.262  3863.298\n",
        "C(UNIT)[T.R013] -1464.4487    177.320     -8.259      0.000     -1811.993 -1116.905\n",
        "C(UNIT)[T.R014]    57.5593    177.777      0.324      0.746      -290.881   405.999\n",
        "C(UNIT)[T.R015] -1802.9418    170.125    -10.598      0.000     -2136.383 -1469.500\n",
        "C(UNIT)[T.R016] -2994.5701    178.245    -16.800      0.000     -3343.928 -2645.212\n",
        "C(UNIT)[T.R017]   279.9029    178.011      1.572      0.116       -68.995   628.801\n",
        "C(UNIT)[T.R018]  1989.7008    169.795     11.718      0.000      1656.906  2322.495\n",
        "C(UNIT)[T.R019] -1052.1791    175.566     -5.993      0.000     -1396.286  -708.072\n",
        "C(UNIT)[T.R020]  2551.1565    174.130     14.651      0.000      2209.865  2892.448\n",
        "C(UNIT)[T.R021]   517.4987    174.330      2.968      0.003       175.814   859.183\n",
        "C(UNIT)[T.R022]  4678.3197    173.932     26.897      0.000      4337.416  5019.224\n",
        "C(UNIT)[T.R023]  2596.8498    176.869     14.682      0.000      2250.189  2943.511\n",
        "C(UNIT)[T.R024]  -921.7015    178.011     -5.178      0.000     -1270.601  -572.802\n",
        "C(UNIT)[T.R025]  1084.9246    174.531      6.216      0.000       742.847  1427.002\n",
        "C(UNIT)[T.R027] -1000.7013    176.427     -5.672      0.000     -1346.496  -654.907\n",
        "C(UNIT)[T.R028] -1661.5265    174.944     -9.497      0.000     -2004.414 -1318.639\n",
        "C(UNIT)[T.R029]  2632.8235    176.427     14.923      0.000      2287.030  2978.617\n",
        "C(UNIT)[T.R030] -1126.1291    176.211     -6.391      0.000     -1471.500  -780.758\n",
        "C(UNIT)[T.R031]   576.6347    176.870      3.260      0.001       229.973   923.296\n",
        "C(UNIT)[T.R032]   187.7188    177.548      1.057      0.290      -160.273   535.710\n",
        "C(UNIT)[T.R033]  4502.5837    175.147     25.707      0.000      4159.299  4845.869\n",
        "C(UNIT)[T.R034] -2900.8717    179.206    -16.187      0.000     -3252.112 -2549.632\n",
        "C(UNIT)[T.R035]  -716.6872    176.870     -4.052      0.000     -1063.348  -370.026\n",
        "C(UNIT)[T.R036] -3177.0579    181.242    -17.529      0.000     -3532.290 -2821.826\n",
        "C(UNIT)[T.R037] -3102.8436    179.952    -17.243      0.000     -3455.546 -2750.141\n",
        "C(UNIT)[T.R038] -3733.9937    180.979    -20.632      0.000     -4088.709 -3379.279\n",
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        "C(UNIT)[T.R040] -2743.7902    178.963    -15.332      0.000     -3094.555 -2393.026\n",
        "C(UNIT)[T.R041] -1210.2471    175.147     -6.910      0.000     -1553.533  -866.961\n",
        "C(UNIT)[T.R042] -3287.6448    179.453    -18.320      0.000     -3639.369 -2935.921\n",
        "C(UNIT)[T.R043] -3348.8397    141.562    -23.656      0.000     -3626.298 -3071.381\n",
        "C(UNIT)[T.R044]    94.2737    171.677      0.549      0.583      -242.210   430.757\n",
        "C(UNIT)[T.R045] -1071.8885    178.720     -5.998      0.000     -1422.177  -721.600\n",
        "C(UNIT)[T.R046]  4123.0961    173.735     23.732      0.000      3782.578  4463.614\n",
        "C(UNIT)[T.R047]  1018.6038    178.010      5.722      0.000       669.708  1367.499\n",
        "C(UNIT)[T.R048] -2691.5560    177.777    -15.140      0.000     -3039.996 -2343.116\n",
        "C(UNIT)[T.R049]  -969.7381    178.011     -5.448      0.000     -1318.637  -620.839\n",
        "C(UNIT)[T.R050]   -76.6581    179.207     -0.428      0.669      -427.900   274.584\n",
        "C(UNIT)[T.R051]  1097.0014    178.245      6.154      0.000       747.644  1446.359\n",
        "C(UNIT)[T.R052] -2719.4907    182.322    -14.916      0.000     -3076.838 -2362.143\n",
        "C(UNIT)[T.R053]  -798.9082    176.647     -4.523      0.000     -1145.134  -452.683\n",
        "C(UNIT)[T.R054] -2554.6052    178.246    -14.332      0.000     -2903.964 -2205.247\n",
        "C(UNIT)[T.R055]  4281.9915    176.647     24.240      0.000      3935.766  4628.217\n",
        "C(UNIT)[T.R056] -2421.2004    177.320    -13.654      0.000     -2768.744 -2073.657\n",
        "C(UNIT)[T.R057]   707.5068    177.320      3.990      0.000       359.963  1055.051\n",
        "C(UNIT)[T.R058] -3337.7207    178.482    -18.701      0.000     -3687.543 -2987.899\n",
        "C(UNIT)[T.R059] -2731.9862    182.599    -14.962      0.000     -3089.877 -2374.095\n",
        "C(UNIT)[T.R060] -3163.5805    179.701    -17.605      0.000     -3515.791 -2811.370\n",
        "C(UNIT)[T.R061] -3294.0206    184.026    -17.900      0.000     -3654.708 -2933.333\n",
        "C(UNIT)[T.R062]  -145.0729    178.246     -0.814      0.416      -494.431   204.285\n",
        "C(UNIT)[T.R063] -2762.4410    184.026    -15.011      0.000     -3123.128 -2401.754\n",
        "C(UNIT)[T.R064] -3096.5618    181.777    -17.035      0.000     -3452.842 -2740.282\n",
        "C(UNIT)[T.R065] -3089.8675    181.777    -16.998      0.000     -3446.146 -2733.589\n",
        "C(UNIT)[T.R066] -3616.4980    184.320    -19.621      0.000     -3977.762 -3255.234\n",
        "C(UNIT)[T.R067] -3012.0594    184.918    -16.289      0.000     -3374.496 -2649.623\n",
        "C(UNIT)[T.R068] -3404.3650    184.919    -18.410      0.000     -3766.802 -3041.928\n",
        "C(UNIT)[T.R069] -2969.8314    182.048    -16.313      0.000     -3326.642 -2613.021\n",
        "C(UNIT)[T.R070] -2305.4814    176.648    -13.051      0.000     -2651.709 -1959.254\n",
        "C(UNIT)[T.R079] -1445.2609    178.245     -8.108      0.000     -1794.618 -1095.904\n",
        "C(UNIT)[T.R080]  -151.9976    178.011     -0.854      0.393      -500.896   196.901\n",
        "C(UNIT)[T.R081]  -533.3152    177.320     -3.008      0.003      -880.859  -185.771\n",
        "C(UNIT)[T.R082] -2517.2337    178.011    -14.141      0.000     -2866.132 -2168.335\n",
        "C(UNIT)[T.R083]  -999.6630    177.778     -5.623      0.000     -1348.106  -651.220\n",
        "C(UNIT)[T.R084]  3052.8894    166.191     18.370      0.000      2727.157  3378.622\n",
        "C(UNIT)[T.R085] -1580.5789    176.427     -8.959      0.000     -1926.373 -1234.785\n",
        "C(UNIT)[T.R086] -1575.5122    176.429     -8.930      0.000     -1921.309 -1229.715\n",
        "C(UNIT)[T.R087] -2760.8276    178.722    -15.448      0.000     -3111.119 -2410.536\n",
        "C(UNIT)[T.R088] -3408.1589    179.205    -19.018      0.000     -3759.398 -3056.920\n",
        "C(UNIT)[T.R089] -3448.9589    177.778    -19.400      0.000     -3797.401 -3100.516\n",
        "C(UNIT)[T.R090] -3482.2159    184.321    -18.892      0.000     -3843.481 -3120.951\n",
        "C(UNIT)[T.R091] -2777.2105    183.735    -15.115      0.000     -3137.328 -2417.094\n",
        "C(UNIT)[T.R092] -2012.9387    179.206    -11.233      0.000     -2364.180 -1661.698\n",
        "C(UNIT)[T.R093] -1935.3529    178.482    -10.843      0.000     -2285.174 -1585.531\n",
        "C(UNIT)[T.R094] -2209.7577    179.701    -12.297      0.000     -2561.968 -1857.547\n",
        "C(UNIT)[T.R095] -1544.4869    179.952     -8.583      0.000     -1897.189 -1191.785\n",
        "C(UNIT)[T.R096] -1741.3124    177.548     -9.808      0.000     -2089.303 -1393.322\n",
        "C(UNIT)[T.R097] -1164.9346    173.540     -6.713      0.000     -1505.070  -824.799\n",
        "C(UNIT)[T.R098] -1870.2264    177.548    -10.534      0.000     -2218.218 -1522.235\n",
        "C(UNIT)[T.R099] -1752.4076    176.870     -9.908      0.000     -2099.070 -1405.746\n",
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        "C(UNIT)[T.R456] -3698.0692    178.482    -20.720      0.000     -4047.892 -3348.247\n",
        "C(UNIT)[T.R459] -3849.7950    227.538    -16.919      0.000     -4295.766 -3403.824\n",
        "C(UNIT)[T.R460] -2413.6067    179.205    -13.468      0.000     -2764.846 -2062.368\n",
        "C(UNIT)[T.R461]  -864.6822    178.720     -4.838      0.000     -1214.971  -514.394\n",
        "C(UNIT)[T.R462] -1931.7085    179.451    -10.765      0.000     -2283.430 -1579.988\n",
        "C(UNIT)[T.R463]  -848.5453    177.319     -4.785      0.000     -1196.086  -501.004\n",
        "C(UNIT)[T.R464] -3968.1185    184.918    -21.459      0.000     -4330.555 -3605.682\n",
        "C(UNIT)[T.R468] -3508.4527    179.700    -19.524      0.000     -3860.661 -3156.245\n",
        "C(UNIT)[T.R469] -3505.2000    180.203    -19.451      0.000     -3858.395 -3152.005\n",
        "C(UNIT)[T.R535] -3513.8357    177.319    -19.817      0.000     -3861.377 -3166.294\n",
        "C(UNIT)[T.R536] -3500.2152    161.265    -21.705      0.000     -3816.291 -3184.139\n",
        "C(UNIT)[T.R540] -3797.6507    129.006    -29.438      0.000     -4050.500 -3544.801\n",
        "C(UNIT)[T.R541] -3904.4777    128.344    -30.422      0.000     -4156.029 -3652.926\n",
        "C(UNIT)[T.R542] -3924.8080    134.086    -29.271      0.000     -4187.613 -3662.003\n",
        "C(UNIT)[T.R543] -3961.0241    129.178    -30.663      0.000     -4214.211 -3707.837\n",
        "C(UNIT)[T.R544] -3974.8126    132.062    -30.098      0.000     -4233.652 -3715.974\n",
        "C(UNIT)[T.R545] -3925.3711    132.672    -29.587      0.000     -4185.405 -3665.337\n",
        "C(UNIT)[T.R546] -3786.7747    132.899    -28.494      0.000     -4047.254 -3526.295\n",
        "C(UNIT)[T.R547] -3892.2169    140.183    -27.765      0.000     -4166.974 -3617.460\n",
        "C(UNIT)[T.R548] -3858.4017    144.009    -26.793      0.000     -4140.656 -3576.147\n",
        "C(UNIT)[T.R549] -3998.5832    127.334    -31.402      0.000     -4248.156 -3749.010\n",
        "C(UNIT)[T.R550] -3960.0904    128.071    -30.921      0.000     -4211.108 -3709.073\n",
        "C(UNIT)[T.R551] -3933.4610    130.087    -30.237      0.000     -4188.429 -3678.493\n",
        "C(UNIT)[T.R552] -3868.1381    129.681    -29.828      0.000     -4122.310 -3613.966\n",
        "rain              -20.1954     12.233     -1.651      0.099       -44.173     3.782\n",
        "precipi            43.6973     13.522      3.232      0.001        17.194    70.201\n",
        "Hour               67.3966      0.691     97.533      0.000        66.042    68.751\n",
        "meantempi          -7.2718      0.736     -9.878      0.000        -8.715    -5.829\n",
        "==============================================================================\n",
        "Omnibus:                   121615.917   Durbin-Watson:                   1.590\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         15257403.191\n",
        "Skew:                           4.028   Prob(JB):                         0.00\n",
        "Kurtosis:                      55.059   Cond. No.                     3.77e+04\n",
        "==============================================================================\n",
        "\n",
        "Warnings:\n",
        "[1] The condition number is large, 3.77e+04. This might indicate that there are\n",
        "strong multicollinearity or other numerical problems.\n",
        "\"\"\""
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "4. Data Visualizations"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Having built our model and predicted the subway ridership with fair bit of accuracy, we now go on to understand the ridership pattern more better through some visualizations. We will be using ggplot to make the visualizations. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from ggplot import *\n",
      "turnstile_viz = pandas.read_csv('data/turnstile_data_master_with_weather.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First we will plot the hourly ridership at the various turnstile posts. The peak hours seem to be noon, 5pm, 8pm and 9pm. Noon is quite surprising as one typically does not expect mid-office hours to be high traffic. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ggplot(turnstile_viz, aes('Hour', 'ENTRIESn_hourly', color = 'UNIT')) + geom_point() "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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D5OTkSIfRKdkjHUC0io+Px+124/V6Ix3KLTkcDmpqaiIdRqNiYmJITk7G4/Eoh62gPIaH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\nD/jpuw7iZAXUAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109079ad0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "<ggplot: (277903777)>"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plotting the ridership by weekdays, one can see that its more or less in line with expectation, with weekdays having higher traffic than weekends. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from datetime import datetime\n",
      "turnstile_viz['dayofweek'] = [pandas.to_datetime(turnstile_viz.DATEn[i]).weekday() for i in numpy.arange(len(turnstile_viz))]\n",
      "turnstile_daywise = turnstile_viz.groupby(['dayofweek'])\n",
      "turnstile_daywise['ENTRIESn_hourly'].sum().plot(kind = 'bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x149d8a790>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PngQA/POf/8TYsWNRWlqKH/3oRzhy5EiXf4djx45h4sSJeO655zx5zCh1cHiT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\nAOLwJiIKIA5vIqIA4vAmIgqg/wPmG0C0JHpXSgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107e3a990>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Interestingly while ploting the ridership on rainy vs non-rainy days, one does not find much difference between peak ridership values for rainy and non-rainy days. Infact we find there is an outlier (belonging to trunstile R324 with hourly ridership of around 52000) which has the single highest ridership value among all the data and its on a rainy day. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ggplot(turnstile_viz, aes('rain', 'ENTRIESn_hourly', color = 'UNIT')) + geom_point()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2+vXrd10xqedQREREpI2FQiHg0mPi/fv3M2zYsLDjM2fOJCEhgZycHBYsWMCQ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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x103c06f90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<ggplot: (277413829)>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "But on the whole the cumulative non-rainy day ridership is much more (almost double) than the rainy day ridership. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "turnstile_viz.groupby(['rain'])['ENTRIESn_hourly'].sum().plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x108951990>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x149daddd0>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Hence the above statistical tests and visualization confirm our initial hypothesis of there being a significant difference in ridership values between rainy and non-rainy days. "
     ]
    }
   ],
   "metadata": {}
  }
 ]
}