1. NYCTaxiTip.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import requests\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from datetime import datetime\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "url_Sep2015 = 'https://storage.googleapis.com/tlc-trip-data/2015/green_tripdata_2015-09.csv'\n",
    "fname = \"green_tripdata_2015-09.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "r = requests.get(url_Sep2015)\n",
    "with open(\"Data\\green_tripdata_2015-09.csv\", \"wb\") as code:\n",
    "    code.write(r.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sep15df = pd.read_csv(\"Data\\green_tripdata_2015-09.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows: 1494926 Number of columns: 21\n"
     ]
    }
   ],
   "source": [
    "print \"Number of rows:\", sep15df.shape[0], \"Number of columns:\", sep15df.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'VendorID', u'lpep_pickup_datetime', u'Lpep_dropoff_datetime',\n",
       "       u'Store_and_fwd_flag', u'RateCodeID', u'Pickup_longitude',\n",
       "       u'Pickup_latitude', u'Dropoff_longitude', u'Dropoff_latitude',\n",
       "       u'Passenger_count', u'Trip_distance', u'Fare_amount', u'Extra',\n",
       "       u'MTA_tax', u'Tip_amount', u'Tolls_amount', u'Ehail_fee',\n",
       "       u'improvement_surcharge', u'Total_amount', u'Payment_type',\n",
       "       u'Trip_type '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sep15df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>RateCodeID</th>\n",
       "      <th>Pickup_longitude</th>\n",
       "      <th>Pickup_latitude</th>\n",
       "      <th>Dropoff_longitude</th>\n",
       "      <th>Dropoff_latitude</th>\n",
       "      <th>Passenger_count</th>\n",
       "      <th>Trip_distance</th>\n",
       "      <th>Fare_amount</th>\n",
       "      <th>Extra</th>\n",
       "      <th>MTA_tax</th>\n",
       "      <th>Tip_amount</th>\n",
       "      <th>Tolls_amount</th>\n",
       "      <th>Ehail_fee</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>Total_amount</th>\n",
       "      <th>Payment_type</th>\n",
       "      <th>Trip_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1494926.000000</td>\n",
       "      <td>1494926.000000</td>\n",
       "      <td>1494926.000000</td>\n",
       "      <td>1494926.000000</td>\n",
       "      <td>1494926.000000</td>\n",
       "      <td>1494926.000000</td>\n",
       "      <td>1494926.000000</td>\n",
       "      <td>1494926.000000</td>\n",
       "      <td>1494926.000000</td>\n",
       "      <td>1494926.00000</td>\n",
       "      <td>1494926.000000</td>\n",
       "      <td>1494926.000000</td>\n",
       "      <td>1494926.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1494926.000000</td>\n",
       "      <td>1494926.000000</td>\n",
       "      <td>1494926.000000</td>\n",
       "      <td>1494922.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.782045</td>\n",
       "      <td>1.097653</td>\n",
       "      <td>-73.830839</td>\n",
       "      <td>40.691144</td>\n",
       "      <td>-73.837278</td>\n",
       "      <td>40.692905</td>\n",
       "      <td>1.370598</td>\n",
       "      <td>2.968141</td>\n",
       "      <td>12.543198</td>\n",
       "      <td>0.35128</td>\n",
       "      <td>0.486641</td>\n",
       "      <td>1.235727</td>\n",
       "      <td>0.123105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.292099</td>\n",
       "      <td>15.032146</td>\n",
       "      <td>1.540559</td>\n",
       "      <td>1.022353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.412857</td>\n",
       "      <td>0.635944</td>\n",
       "      <td>2.776082</td>\n",
       "      <td>1.530882</td>\n",
       "      <td>2.677911</td>\n",
       "      <td>1.476698</td>\n",
       "      <td>1.039426</td>\n",
       "      <td>3.076621</td>\n",
       "      <td>10.082781</td>\n",
       "      <td>0.36631</td>\n",
       "      <td>0.085045</td>\n",
       "      <td>2.431476</td>\n",
       "      <td>0.891014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.050740</td>\n",
       "      <td>11.553155</td>\n",
       "      <td>0.523293</td>\n",
       "      <td>0.147829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-83.319077</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-83.427841</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-475.000000</td>\n",
       "      <td>-1.00000</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-50.000000</td>\n",
       "      <td>-15.290000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.300000</td>\n",
       "      <td>-475.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-73.959610</td>\n",
       "      <td>40.698952</td>\n",
       "      <td>-73.967819</td>\n",
       "      <td>40.698776</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>8.160000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-73.945358</td>\n",
       "      <td>40.746738</td>\n",
       "      <td>-73.945038</td>\n",
       "      <td>40.747284</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.980000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>11.760000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-73.917480</td>\n",
       "      <td>40.802551</td>\n",
       "      <td>-73.910133</td>\n",
       "      <td>40.790146</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.740000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>18.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.177261</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.799343</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>603.100000</td>\n",
       "      <td>580.500000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>95.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>581.300000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             VendorID      RateCodeID  Pickup_longitude  Pickup_latitude  \\\n",
       "count  1494926.000000  1494926.000000    1494926.000000   1494926.000000   \n",
       "mean         1.782045        1.097653        -73.830839        40.691144   \n",
       "std          0.412857        0.635944          2.776082         1.530882   \n",
       "min          1.000000        1.000000        -83.319077         0.000000   \n",
       "25%          2.000000        1.000000        -73.959610        40.698952   \n",
       "50%          2.000000        1.000000        -73.945358        40.746738   \n",
       "75%          2.000000        1.000000        -73.917480        40.802551   \n",
       "max          2.000000       99.000000          0.000000        43.177261   \n",
       "\n",
       "       Dropoff_longitude  Dropoff_latitude  Passenger_count   Trip_distance  \\\n",
       "count     1494926.000000    1494926.000000   1494926.000000  1494926.000000   \n",
       "mean          -73.837278         40.692905         1.370598        2.968141   \n",
       "std             2.677911          1.476698         1.039426        3.076621   \n",
       "min           -83.427841          0.000000         0.000000        0.000000   \n",
       "25%           -73.967819         40.698776         1.000000        1.100000   \n",
       "50%           -73.945038         40.747284         1.000000        1.980000   \n",
       "75%           -73.910133         40.790146         1.000000        3.740000   \n",
       "max             0.000000         42.799343         9.000000      603.100000   \n",
       "\n",
       "          Fare_amount          Extra         MTA_tax      Tip_amount  \\\n",
       "count  1494926.000000  1494926.00000  1494926.000000  1494926.000000   \n",
       "mean        12.543198        0.35128        0.486641        1.235727   \n",
       "std         10.082781        0.36631        0.085045        2.431476   \n",
       "min       -475.000000       -1.00000       -0.500000      -50.000000   \n",
       "25%          6.500000        0.00000        0.500000        0.000000   \n",
       "50%          9.500000        0.50000        0.500000        0.000000   \n",
       "75%         15.500000        0.50000        0.500000        2.000000   \n",
       "max        580.500000       12.00000        0.500000      300.000000   \n",
       "\n",
       "         Tolls_amount  Ehail_fee  improvement_surcharge    Total_amount  \\\n",
       "count  1494926.000000          0         1494926.000000  1494926.000000   \n",
       "mean         0.123105        NaN               0.292099       15.032146   \n",
       "std          0.891014        NaN               0.050740       11.553155   \n",
       "min        -15.290000        NaN              -0.300000     -475.000000   \n",
       "25%          0.000000        NaN               0.300000        8.160000   \n",
       "50%          0.000000        NaN               0.300000       11.760000   \n",
       "75%          0.000000        NaN               0.300000       18.300000   \n",
       "max         95.750000        NaN               0.300000      581.300000   \n",
       "\n",
       "         Payment_type      Trip_type   \n",
       "count  1494926.000000  1494922.000000  \n",
       "mean         1.540559        1.022353  \n",
       "std          0.523293        0.147829  \n",
       "min          1.000000        1.000000  \n",
       "25%          1.000000        1.000000  \n",
       "50%          2.000000        1.000000  \n",
       "75%          2.000000        1.000000  \n",
       "max          5.000000        2.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sep15df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>lpep_pickup_datetime</th>\n",
       "      <th>Lpep_dropoff_datetime</th>\n",
       "      <th>Store_and_fwd_flag</th>\n",
       "      <th>RateCodeID</th>\n",
       "      <th>Pickup_longitude</th>\n",
       "      <th>Pickup_latitude</th>\n",
       "      <th>Dropoff_longitude</th>\n",
       "      <th>Dropoff_latitude</th>\n",
       "      <th>Passenger_count</th>\n",
       "      <th>...</th>\n",
       "      <th>Fare_amount</th>\n",
       "      <th>Extra</th>\n",
       "      <th>MTA_tax</th>\n",
       "      <th>Tip_amount</th>\n",
       "      <th>Tolls_amount</th>\n",
       "      <th>Ehail_fee</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>Total_amount</th>\n",
       "      <th>Payment_type</th>\n",
       "      <th>Trip_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-09-01 00:02:34</td>\n",
       "      <td>2015-09-01 00:02:38</td>\n",
       "      <td>N</td>\n",
       "      <td>5</td>\n",
       "      <td>-73.979485</td>\n",
       "      <td>40.684956</td>\n",
       "      <td>-73.979431</td>\n",
       "      <td>40.685020</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.75</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-09-01 00:04:20</td>\n",
       "      <td>2015-09-01 00:04:24</td>\n",
       "      <td>N</td>\n",
       "      <td>5</td>\n",
       "      <td>-74.010796</td>\n",
       "      <td>40.912216</td>\n",
       "      <td>-74.010780</td>\n",
       "      <td>40.912212</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-09-01 00:01:50</td>\n",
       "      <td>2015-09-01 00:04:24</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.921410</td>\n",
       "      <td>40.766708</td>\n",
       "      <td>-73.914413</td>\n",
       "      <td>40.764687</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>5.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-09-01 00:02:36</td>\n",
       "      <td>2015-09-01 00:06:42</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.921387</td>\n",
       "      <td>40.766678</td>\n",
       "      <td>-73.931427</td>\n",
       "      <td>40.771584</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>6.30</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-09-01 00:00:14</td>\n",
       "      <td>2015-09-01 00:04:20</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.955482</td>\n",
       "      <td>40.714046</td>\n",
       "      <td>-73.944412</td>\n",
       "      <td>40.714729</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>6.30</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID lpep_pickup_datetime Lpep_dropoff_datetime Store_and_fwd_flag  \\\n",
       "0         2  2015-09-01 00:02:34   2015-09-01 00:02:38                  N   \n",
       "1         2  2015-09-01 00:04:20   2015-09-01 00:04:24                  N   \n",
       "2         2  2015-09-01 00:01:50   2015-09-01 00:04:24                  N   \n",
       "3         2  2015-09-01 00:02:36   2015-09-01 00:06:42                  N   \n",
       "4         2  2015-09-01 00:00:14   2015-09-01 00:04:20                  N   \n",
       "\n",
       "   RateCodeID  Pickup_longitude  Pickup_latitude  Dropoff_longitude  \\\n",
       "0           5        -73.979485        40.684956         -73.979431   \n",
       "1           5        -74.010796        40.912216         -74.010780   \n",
       "2           1        -73.921410        40.766708         -73.914413   \n",
       "3           1        -73.921387        40.766678         -73.931427   \n",
       "4           1        -73.955482        40.714046         -73.944412   \n",
       "\n",
       "   Dropoff_latitude  Passenger_count     ...      Fare_amount  Extra  MTA_tax  \\\n",
       "0         40.685020                1     ...              7.8    0.0      0.0   \n",
       "1         40.912212                1     ...             45.0    0.0      0.0   \n",
       "2         40.764687                1     ...              4.0    0.5      0.5   \n",
       "3         40.771584                1     ...              5.0    0.5      0.5   \n",
       "4         40.714729                1     ...              5.0    0.5      0.5   \n",
       "\n",
       "   Tip_amount  Tolls_amount  Ehail_fee  improvement_surcharge  Total_amount  \\\n",
       "0        1.95             0        NaN                    0.0          9.75   \n",
       "1        0.00             0        NaN                    0.0         45.00   \n",
       "2        0.50             0        NaN                    0.3          5.80   \n",
       "3        0.00             0        NaN                    0.3          6.30   \n",
       "4        0.00             0        NaN                    0.3          6.30   \n",
       "\n",
       "   Payment_type  Trip_type   \n",
       "0             1           2  \n",
       "1             1           2  \n",
       "2             1           1  \n",
       "3             2           1  \n",
       "4             2           1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sep15df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 603.1\n"
     ]
    }
   ],
   "source": [
    "#Look at min and max of trip distance\n",
    "print min(sep15df['Trip_distance']), max(sep15df['Trip_distance'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove zero trip distance rows from data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sep15dfnz = sep15df.iloc[np.nonzero(sep15df['Trip_distance'])[0],]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min trip distance: 0.01 Max trip distance: 603.1 Number of rows: 1474334\n"
     ]
    }
   ],
   "source": [
    "trip_dist = sep15dfnz['Trip_distance'].values\n",
    "print \"Min trip distance:\", min(trip_dist), \"Max trip distance:\", max(trip_dist), \"Number of rows:\", len(trip_dist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aGhsbiYmJYcWKFb6p8EVFRZSUlGA2m8nJySE5ORmAw4cPU1hYSEdHBykpKeTk\n5ADQ0dHBhg0bqK2txWazsXz5cmJiYgAoLS2lqKgIgNmzZ5ORkTFgr0lERAbGWT/we/LkSYqKiliy\nZAl1dXU8+eSTLF26dEBLCuB3v/sd48aN8y3TVFxcTFJSEuvXrychIYHi4mIA6urq2LNnD2vWrCE3\nN5fNmzdjGAYAmzZtYtGiReTn51NfX09lZSXQs4KGzWYjPz+fWbNmsWXLFqDnyyC3b99OXl4eeXl5\nbNu2jZaWlgF9XSIicuHOWlRLlizhN7/5Df/yL//CP/3TP9HU1MRHH33U558L5XK5qKioICsry1c6\n+/fv941uMjMzKS8vB6C8vJz09HQsFguxsbGMHj2ampoaPB4PbW1txMfHAzBz5kz27dvX71jTpk3j\nwIEDAFRWVpKUlITVasVqtZKYmOgrNxERCR5nvfQ3fPhwAP74xz/yxz/+8bSPKSwsvKAAr776Knff\nfTdffvmlb1tTUxN2ux2AqKgompqaAPB4PEyePNn3uOjoaNxuNxaLBYfD4dvucDhwu90AuN1uoqOj\nAQgLC2PkyJE0Nzfj8Xh82089loiIBJezFtWFltBX+eCDD4iMjGTSpElUV1ef9jGnrtoeCNXV1X2y\nZWdnA2A2mQiPiAh4vnM1fPhwbDZboGOct1DOH8rZQfkDLdTzA2zdutX3s9PpxOl0+rWfX5MpBstf\n//pXPvjgAyoqKujo6ODLL7+koKCAqKgojh8/jt1ux+PxEBUVBfSMlFwul29/l8tFdHR0nxHUqdt7\n92lsbMThcNDV1UVrays2mw2Hw9GngFwuFwkJCf0ynulkdhsGXq93wM7FULHZbDQ3Nwc6xnkL5fyh\nnB2UP9C+Dvl7f9E/V+e9evpAuOuuu3j++ecpLCxk+fLlOJ1O7r//flJTUyktLQWgrKyMtLQ0AFJT\nU9m9ezednZ00NDRQX19PfHw8drud8PBwampqMAyDXbt29dmnrKwMgL1795KYmAhAcnIyVVVVtLS0\n4PV6qaqq8s0gFBGR4BHQEdX/1nsZ7bbbbmPt2rWUlJT4pqcDjBs3jhkzZrBixQrCwsJYsGCBb5+F\nCxdSWFhIe3s7KSkpTJ06Fej5BuKCggKWLl2KzWZj2bJlAERERDBnzhweeughAObOnavV4EVEgpDJ\n6J1qJ377bFYqltu/h3HLHYGOcs6+DpcPQjV/KGcH5Q+0UM8fFxd33vsG9NKfiIjIV1FRiYhIUFNR\niYhIUFNGcxMZAAAQ3klEQVRRiYhIUFNRiYhIUFNRiYhIUFNRiYhIUFNRiYhIUFNRiYhIUFNRiYhI\nUFNRiYhIUFNRiYhIUFNRiYhIUFNRiYhIUFNRiYhIUFNRiYhIUFNRiYhIUFNRiYhIUFNRiYhIUFNR\niYhIUFNRiYhIUFNRiYhIUFNRiYhIULME8skbGxspLCykqakJk8nEjTfeyC233ILX62Xt2rU0NjYS\nExPDihUrsFqtABQVFVFSUoLZbCYnJ4fk5GQADh8+TGFhIR0dHaSkpJCTkwNAR0cHGzZsoLa2FpvN\nxvLly4mJiQGgtLSUoqIiAGbPnk1GRkYAzoKIiJxNQEdUFouF73//+6xZs4annnqK3//+99TV1VFc\nXExSUhLr168nISGB4uJiAOrq6tizZw9r1qwhNzeXzZs3YxgGAJs2bWLRokXk5+dTX19PZWUlADt2\n7MBms5Gfn8+sWbPYsmULAF6vl+3bt5OXl0deXh7btm2jpaUlMCdCRETOKKBFZbfbmThxIgAjRoxg\n7NixuN1u9u/f7xvdZGZmUl5eDkB5eTnp6elYLBZiY2MZPXo0NTU1eDwe2traiI+PB2DmzJns27cP\noM+xpk2bxoEDBwCorKwkKSkJq9WK1WolMTHRV24iIhI8guY9qoaGBo4cOcLkyZNpamrCbrcDEBUV\nRVNTEwAej4fo6GjfPtHR0bjdbjweDw6Hw7fd4XDgdrsBcLvdvn3CwsIYOXIkzc3NZzyWiIgEl4C+\nR9Wrra2N1atXM2/ePMLDw/vcZzKZApSqR3V1NdXV1b7b2dnZAJhNJsIjIgKe71wNHz4cm80W6Bjn\nLZTzh3J2UP5AC/X8AFu3bvX97HQ6cTqdfu0X8KLq7Oxk9erVzJw5k2uvvRboGUUdP34cu92Ox+Mh\nKioK6BkpuVwu374ul4vo6Og+I6hTt/fu09jYiMPhoKuri9bWVmw2Gw6Ho08BuVwuEhIS+uU708ns\nNgy8Xu/AnIQhZLPZaG5uDnSM8xbK+UM5Oyh/oH0d8vf+on+uAnrpzzAMXnjhBcaOHcusWbN821NT\nUyktLQWgrKyMtLQ03/bdu3fT2dlJQ0MD9fX1xMfHY7fbCQ8Pp6amBsMw2LVrV599ysrKANi7dy+J\niYkAJCcnU1VVRUtLC16vl6qqKt8MQhERCR4BHVH99a9/ZdeuXUyYMIF///d/B+Cuu+7itttuY+3a\ntZSUlPimpwOMGzeOGTNmsGLFCsLCwliwYIHv0tvChQspLCykvb2dlJQUpk6dCkBWVhYFBQUsXboU\nm83GsmXLAIiIiGDOnDk89NBDAMydO9c3BV5ERIKHyeid3y1++2xWKpbbv4dxyx2BjnLOvg6XD0I1\nfyhnB+UPtFDPHxcXd977Bs2sPxERkdNRUYmISFBTUYmISFBTUYmISFAL+OeoQpmlyY3ReAwA06jL\n6IxyfMUeIiJyrlRUF8BoPEb7Mw8CMHzls6CiEhEZcLr0JyIiQU1FJSIiQU1FJSIiQU1FJSIiQU1F\nJSIiQU1FJSIiQU1FJSIiQU1FJSIiQU1FJSIiQU1FJSIiQU1FJSIiQU1FJSIiQU1FJSIiQU1FJSIi\nQU1FJSIiQU1FJSIiQe2i/+LEyspKXnnlFbq7u8nKyuK2224LdCQRETnFRT2i6u7u5sUXXyQ3N5c1\na9awe/du6urqzutY5uGXEPZ/DxL2fw9iaXIPcFIRkYvXRV1Uhw4dYvTo0cTGxmKxWEhPT2f//v3n\ndSyjyUP7Mw/S/syDGI3HBjipiMjF66IuKrfbTXR0tO+2w+HA7dZoSEQkmFzURXW+ht31QyxJqWe8\nv/cyoC4BiohcuIt6MoXD4cDlcvluu1wuHA5Hn8dUV1dTXV3tu52dnc3o7977Pw/4h1MuFf6ffx60\nrAPJZrMFOsIFCeX8oZwdlD/QQj3/1q1bfT87nU6cTqdf+13UI6pvfOMb1NfX09DQQGdnJ3v27CE1\nte9Iyel0kp2d7fvn1BMdipQ/cEI5Oyh/oH0d8p/6/1J/Swou8hFVWFgY8+fP56mnnvJNTx83blyg\nY4mIyCku6qICSElJISUlJdAxRETkDC7qS3/n41yGq8FI+QMnlLOD8gfaxZzfZBiGMYBZREREBpRG\nVCIiEtRUVCIiEtQu+skU/gqFxWs3btxIRUUFkZGRrF69GgCv18vatWtpbGwkJiaGFStWYLVaASgq\nKqKkpASz2UxOTg7JycmBjE9jYyOFhYU0NTVhMpm48cYbueWWW0LmNbS3t/P444/T0dFBd3c306ZN\nIzs7O2TyQ8/6lytXrsThcLBy5cqQyn7fffcRHh6O2WwmLCyMp59+OqTyt7S08MILL/jWG128eDFj\nxowJifxHjx5l3bp1vtvHjh3jzjvvZObMmQOT35Cv1NXVZSxZssQ4duyY0dHRYfzkJz8xPvvss0DH\n6ucvf/mLcfjwYeNHP/qRb9vrr79uFBcXG4ZhGEVFRcYbb7xhGIZhfPbZZ8ZPfvITo6Ojwzh27Jix\nZMkSo6urKyC5e3k8HqO2ttYwDMP48ssvjaVLlxqfffZZSL2GtrY2wzAMo7Oz08jNzTU++eSTkMr/\n7rvvGuvXrzeeeeYZwzBC68/P4sWLjebm5j7bQil/QUGB8f777xuG0fPnp6WlJaTy9+rq6jLuvfde\n44svvhiw/Lr054eBXLx2ME2ZMsX320qv/fv3k5GRAUBmZibl5eUAlJeXk56ejsViITY2ltGjR3Po\n0KEhz3wqu93OxIkTARgxYgRjx47F7XaH1Gu45JJLAOjs7KSzsxOTyRQy+V0uFxUVFWRlZWH8/zlW\noZK9l/G/5oaFSv7W1lY+/vhjsrKygJ7PeI4cOTJk8p/qwIEDjB49mlGjRg1Yfl3688PpFq8Nlj8U\nX6WpqQm73Q5AVFQUTU1NAHg8HiZPnux7XHR0dFAtyNvQ0MCRI0eYPHlySL2G7u5uHnzwQY4dO8a3\nv/1t4uPjQyb/q6++yt13382XX37p2xYq2QFMJhNPPvkkZrOZm266iZtuuilk8jc0NBAZGcnGjRv5\n29/+xqRJk5g3b17I5D/V7t27SU9PBwbuz4+K6iJiMpku6P6h0tbWxurVq5k3bx7h4eF97gv212A2\nm3nuuedobW3lueee49NPP+1zf7Dm/+CDD4iMjGTSpEl91rY8VbBm7/Xkk09y6aWXcuLECZ588knG\njh3b5/5gzt/V1UVtbS3z588nPj6eV155heLi4j6PCeb8vTo7O/nggw+4++67+913IflVVH7wZ/Ha\nYBUVFcXx48ex2+14PB6ioqKA4H1NnZ2drF69mpkzZ3LttdcCofcaAEaOHInT6eS///u/QyL/X//6\nVz744AMqKiro6Ojgyy+/pKCgICSy97r00ksBiIyM5Nprr+XQoUMhkz86OhqHw0F8fDwA06dPp6io\nCLvdHhL5e1VUVHDFFVcQGRkJDNzfXb1H5Qd/Fq8NVqmpqZSWlgJQVlZGWlqab/vu3bvp7OykoaGB\n+vp631+SQDEMgxdeeIGxY8cya9Ys3/ZQeQ0nTpygpaUF6JkBeODAAcaOHRsS+e+66y6ef/55CgsL\nWb58OU6nk/vvvz8ksgOcPHnSd8myra2NqqoqJkyYEDL57XY7o0aN4ujRowBUVVUxfvx4vvWtb4VE\n/l6nXvaDgfu7q5Up/FRRUdFnevrtt98e6Ej9rFu3joMHD3LixAnsdjvZ2dmkpaWdcXroO++8Q0lJ\nCWFhYcybN4+pU6cGNP/HH3/MY489xoQJE3yXAe666y7i4+ND4jV8+umnFBYW0t3dTXd3N9dddx1z\n5sw56xTpYMrf6y9/+QvvvvsuDz74YMhkb2ho4LnnngN63ie8/vrruf3220MmP8CRI0f4+c9/Tmdn\nJ5dddhmLFy+mu7s7ZPK3tbVx3333sWHDBt8l+4E6/yoqEREJarr0JyIiQU1FJSIiQU1FJSIiQU1F\nJSIiQU1FJSIiQU1FJSIiQU1FJTKIDh48yPLlywft+I2Njdxzzz39FmMV+TrR56hEztH3vvc93weS\nT548ybBhwzCbe37n+8EPfsD1118/IM9TWlrK888/71uRPTIykquvvprbb7+dMWPGnPOxduzYwX/8\nx38MSDaRoaS1/kTO0euvv+77+b777mPRokUkJCT0e1xXVxdhYWEX9Fzf/OY3eeKJJzAMg4aGBt59\n911WrlzJqlWrGD9+/AUdWyRUqKhEBkh1dTUFBQXcfPPN/Pa3vyUpKYkbbriBDRs28PzzzwM9xfaP\n//iP7Ny5E4/HQ1paGvfeey/Dhg077TF7L3iYTCYuu+wyFi5cSGNjI7/85S/50Y9+RENDA/fffz9v\nvvkmZrOZ0tJStm/fzokTJ7DZbHznO99h0qRJbNq0ia6uLu655x7CwsJ4+eWX+fDDD3nrrbc4duwY\nI0eOJCsrizvuuAPAd9zFixfz9ttvc/LkSWbNmsXs2bOBnmWKiouLKSkp4cSJE4wZM4YHHniA6Oho\nPv/8c1566SVqa2uJjIzkzjvvZMaMGUPwX0C+rlRUIgOoqamJlpYWNm7cSHd3NzU1Nf0e86c//YlH\nHnmE4cOH8+yzz7J9+3a+853v+P0c06ZN4xe/+EW/7W1tbbz88ss888wzjBkzhuPHj+P1ehk7diz3\n3ntvv0t/I0aM4P7772f8+PF8+umnPPnkk0ycONG3cCj0rKq+fv16jh49Sm5uLtOnTycuLo7f/OY3\n7Nmzh9zcXMaMGcOnn37KJZdcQltbG6tWreLOO+/k4Ycf9h13/PjxjBs37hzPpkgPTaYQGUAmk4ns\n7GwsFgvDhw8/7WO+/e1v43A4iIiIYPbs2ezevfucnuPSSy/F6/We9j6z2cynn35Ke3s7drv9rOVw\n9dVX+y4fTpgwgfT0dP7yl7/0eczcuXMZNmwYl19+OZdffjlHjhwB4P333+c73/mO772yCRMmEBER\nwYcffkhsbCyZmZmYzWYmTpzItGnT2Lt37zm9RpFTaUQlMoAiIyOxWM7+1+rUb4seNWoUHo/nnJ7D\n7XYTERHRb/uIESNYvnw57777Li+88AJXXXUV99xzD3Fxcac9Tk1NDb/4xS/47LPP6OzspKOjo98l\nut5vZwV8I6beDKNHj+53zC+++IKamhpycnJ827q6upg5c+Y5vUaRU6moRAaQP9+y2tjY2Ofn3i/8\n89e+ffuYMmXKae9LTk4mOTmZjo4O3nzzTX7+85/zxBNPnPax+fn53HzzzTz88MNYLBZeeeUVmpub\n/coQHR1NfX19vxHbqFGjuPrqq3nkkUfO6TWJnI0u/YkMsT/84Q+43W68Xi/vvPNOny+aO5Pu7m4a\nGhp46aWXOHjwoG/Sw6mampooLy+nra2NsLAwRowY4Zs2b7fbcblcdHZ2+h7f1taG1WrFYrFw6NAh\ndu/e7ffXmWdlZfH2229TX1+PYRj87W9/w+v18q1vfYu///3v7Ny5k87OTjo7Ozl06BCff/65n2dH\npD+NqESGWHp6OqtWrfLN+uudSXc6n3zyie8DvZGRkTidTp5++unTXs4zDIPf/va3FBYWYjKZmDhx\nIgsXLgQgISGB8ePH84Mf/ACz2czmzZtZsGABr7/+Oi+99BJTpkxhxowZtLa2+vUabr31Vjo7O1m1\nahXNzc2MHTuWn/zkJ0RERPDwww/z2muv8dprr2EYBhMnTuSee+45v5Mlgj7wKzKkzva5KxE5PV36\nExGRoKaiEhGRoKZLfyIiEtQ0ohIRkaCmohIRkaCmohIRkaCmohIRkaCmohIRkaCmohIRkaD2/wAn\nJX5ipdPxyAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1213b7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(trip_dist, 100)\n",
    "plt.grid(True)\n",
    "plt.xlabel('Trip Distance')\n",
    "plt.ylabel('Number of Trips')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As we can see in the histogram above, the distribution is highly skwed to the right. Our hypothesis is that majority of the trips are between 0-10 miles. So will plot the x-axis between 0-10 in linear scale and rest in log scale using symlog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1f3yguLgYp9NJREQEeXl5pKWlAXD48GGKioro6OggPT2dvLw8ADo6OtiwYQNH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23901f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import ticker\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.set_xscale('symlog', basex=10, linthreshx=10)\n",
    "ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%0.2f'))\n",
    "plt.hist(trip_dist, 100)\n",
    "plt.grid(True)\n",
    "plt.xlabel('Trip Distance')\n",
    "plt.ylabel('Number of Trips')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## With this histogram, we can see that majority of the trips are between 0-5 miles and then between 5-10 miles. We will zoom into the trip distance in linear scale only between 0-10 miles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EuLg4jEZj0Arr7Lz2s7jXLgagx8IVQf1sLV0jIl2NXysIPProo0G7D0bapqVrRKSraTNs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\nEREJuP8PdFUaDTzRaeUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b6de48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(filter(lambda x: x <= 10.0, trip_dist), 100)\n",
    "plt.xlabel('Trip Distance')\n",
    "plt.ylabel('Number of Trips')\n",
    "plt.grid(True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now the shape of the distribution is more evident. Our hypothesis is that we can fit a lognormal distibution the data. A log normal distribution is induced in nature when the effect of iid random variables is multiplicative instead of additive. In other words, the effects are non-linear. An indication of such phenomenon is when the variance in the data is large compared to the mean values\n",
    "\n",
    "## Estimating the distribution of the random variable helps in simulation. Simulation is used for experimenting with random variable behavior when controls are altered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 3.00959682813   Variance: 9.47302923371\n"
     ]
    }
   ],
   "source": [
    "print \"Mean:\", np.mean(trip_dist), \"  Variance:\", np.var(trip_dist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We will take average of pick-up and drop of time as time of day. As we see below, the time is in 24 hour format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2    2015-09-01 00:01:50\n",
      "3    2015-09-01 00:02:36\n",
      "4    2015-09-01 00:00:14\n",
      "5    2015-09-01 00:00:39\n",
      "6    2015-09-01 00:00:52\n",
      "Name: lpep_pickup_datetime, dtype: object 1494921    2015-09-30 23:00:01\n",
      "1494922    2015-09-30 23:00:05\n",
      "1494923    2015-09-30 23:00:30\n",
      "1494924    2015-09-30 23:00:10\n",
      "1494925    2015-09-30 23:00:11\n",
      "Name: lpep_pickup_datetime, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print sep15dfnz[\"lpep_pickup_datetime\"].head(),  sep15dfnz[\"lpep_pickup_datetime\"].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#datetime.strptime(sep15dfnz[\"lpep_pickup_datetime\"].values[10000], \"%Y-%m-%d %H:%M:%S\").hour\n",
    "#sep15dfnz.drop('trip_hr', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'VendorID', u'lpep_pickup_datetime', u'Lpep_dropoff_datetime',\n",
       "       u'Store_and_fwd_flag', u'RateCodeID', u'Pickup_longitude',\n",
       "       u'Pickup_latitude', u'Dropoff_longitude', u'Dropoff_latitude',\n",
       "       u'Passenger_count', u'Trip_distance', u'Fare_amount', u'Extra',\n",
       "       u'MTA_tax', u'Tip_amount', u'Tolls_amount', u'Ehail_fee',\n",
       "       u'improvement_surcharge', u'Total_amount', u'Payment_type',\n",
       "       u'Trip_type '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sep15dfnz.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dformat = \"%Y-%m-%d %H:%M:%S\"\n",
    "sep15dfnz.loc[sep15dfnz.index,\"trip_hr\"]= [round(np.mean([datetime.strptime(x, dformat).hour, datetime.strptime(y, dformat).hour]))\\\n",
    "                          for x,y in zip(sep15dfnz[\"lpep_pickup_datetime\"].values, sep15dfnz[\"Lpep_dropoff_datetime\"].values)] ; "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#np.mean([x for (x,y) in zip(sep15dfnz['Trip_distance'].values, sep15dfnz['trip_hr'].values) if y == 5])\n",
    "tripDistMeanByHr = sep15dfnz.groupby(by=\"trip_hr\")['Trip_distance'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trip_hr\n",
       "0     2.751526\n",
       "1     3.161032\n",
       "2     3.193260\n",
       "3     3.275183\n",
       "4     3.505923\n",
       "5     4.253349\n",
       "6     4.011083\n",
       "7     3.282965\n",
       "8     2.964701\n",
       "9     3.066708\n",
       "10    3.058976\n",
       "11    2.950696\n",
       "12    3.413571\n",
       "13    2.941764\n",
       "14    2.819376\n",
       "15    2.833838\n",
       "16    2.815830\n",
       "17    2.727801\n",
       "18    2.702913\n",
       "19    2.769204\n",
       "20    2.827541\n",
       "21    2.960170\n",
       "22    3.180878\n",
       "23    3.255221\n",
       "Name: Trip_distance, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Printing mean of Trip Distance by hour\n",
    "tripDistMeanByHr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tripDistMedianByHr = sep15dfnz.groupby(by=\"trip_hr\")['Trip_distance'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trip_hr\n",
       "0     1.98\n",
       "1     2.23\n",
       "2     2.25\n",
       "3     2.30\n",
       "4     2.41\n",
       "5     3.00\n",
       "6     2.81\n",
       "7     2.10\n",
       "8     1.90\n",
       "9     2.00\n",
       "10    2.00\n",
       "11    1.90\n",
       "12    2.27\n",
       "13    1.90\n",
       "14    1.82\n",
       "15    1.82\n",
       "16    1.81\n",
       "17    1.79\n",
       "18    1.80\n",
       "19    1.88\n",
       "20    1.94\n",
       "21    2.03\n",
       "22    2.20\n",
       "23    2.29\n",
       "Name: Trip_distance, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tripDistMedianByHr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We will calculate the most common pick-up or drop off point from the whole dataset. We will round the latitude, longitude to 2 decimal places as the GPS readings beyond 2 decimal places do not matter for this problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Pickup_longitude, Pickup_latitude, Dropoff_longitude, Dropoff_latitude\n",
    "allPoints = [(round(x,2),round(y,2)) for (x,y) in zip(sep15dfnz[\"Pickup_latitude\"].values, sep15dfnz[\"Pickup_longitude\"].values)] \\\n",
    "          + [(round(x,2),round(y,2)) for (x,y) in zip(sep15dfnz[\"Dropoff_latitude\"].values, sep15dfnz[\"Dropoff_longitude\"].values)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "maxPoint = Counter(allPoints).most_common()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[((40.72, -73.96), 76320),\n",
       " ((40.81, -73.95), 72439),\n",
       " ((40.81, -73.94), 71340),\n",
       " ((40.69, -73.99), 71135),\n",
       " ((40.79, -73.95), 71097),\n",
       " ((40.8, -73.94), 58272),\n",
       " ((40.75, -73.89), 54667),\n",
       " ((40.81, -73.96), 51268),\n",
       " ((40.68, -73.98), 49165),\n",
       " ((40.7, -73.99), 48749)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maxPoint[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As we can see, the location (40.72, -73.96) has maximum number of pick-ups and drop offs (76320). Incidentally these co-ordinates are close to the JFK airport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tripIndexes = [ix for (ix,y,z) in zip(sep15dfnz.index,\n",
    "                                    zip(sep15dfnz[\"Pickup_latitude\"].values, sep15dfnz[\"Pickup_longitude\"].values),\n",
    "                                    zip(sep15dfnz[\"Dropoff_latitude\"].values, sep15dfnz[\"Dropoff_longitude\"].values)) \\\n",
    "               if ((round(y[0],2) == maxPoint[0][0][0]) and (round(y[1],2) == maxPoint[0][0][1])) or \\\n",
    "               ((round(z[0],2) == maxPoint[0][0][0]) and (round(z[1],2) == maxPoint[0][0][1]))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## So the average fare for pick-up and drop-off that location is -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.25374772294863"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(sep15dfnz.ix[tripIndexes,\"Fare_amount\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As we can see below, there are some neagative and zero fare amounts in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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llwCsXLnSjFpERKQXCOnkAhERka5yyRlPU1MTK1aswDCMDp/X+mwiIhKuSwaPzWbjlltu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f929c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(sep15dfnz[\"Fare_amount\"][tripIndexes].values, 20);\n",
    "plt.xlabel('Fare amount')\n",
    "plt.ylabel('Number of Trips')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sep15dfnz.loc[tripIndexes, \"Fare_amount\"][sep15dfnz.loc[tripIndexes, \"Fare_amount\"]<=0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We assume that fares <=0 is a data error and we recalculate the average fare based on non-zero and absolute values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.262762850703769"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sep15dfnzfare = sep15dfnz.loc[tripIndexes, :][sep15dfnz.loc[tripIndexes, \"Fare_amount\"]<>0]\n",
    "np.mean(np.abs(sep15dfnzfare[\"Fare_amount\"].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lMTExZ81wKisrG82ATistLaW0tNR4npGRgd1uN577a0/ia8G+tLQkWzLearUR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15b08668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.abs(sep15dfnzfare[\"Fare_amount\"].values), 20);\n",
    "plt.xlabel('Fare Amount')\n",
    "plt.ylabel('Number of Trips')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zooming into fares below $60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hwoWEw2EATNO8Zf2WlhZWrFiBw+Fg3rx5zJ8/n87OTrKyshgaGsLr9QKwcuVKmpubyc/P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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7205ea20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(abs(sep15dfnzfareall.loc[abs(sep15dfnzfareall['Fare_amount'])<60, 'Fare_amount'].values), 20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As seen above, fare amount is also a skewed distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Hx5OQkEB9fT1Op5OOjg5cLhdw8ky5vXv3kpKSQk1NDbm5uQDMmTOHF1544YLziojIhQvq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\nOJWNiIgY7v8BcaDBEN5h66MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x71fd43c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(sep15dfnzfare[\"Tolls_amount\"].values, 50);\n",
    "plt.xlabel('Tolls Amount')\n",
    "plt.ylabel('Number of Trips')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tolls amount is pretty constant as expected. Most of the green taxis are paying 1 or 2 tolls on entering the City. So is the MTA Tax amount constant as seen below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YOt/5znfO2WaxWPB6vbz55pu0tLSErTAREYk8Fwydf1xv7cSJExQXF/Puu+8y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\nImKa/w8T9RYZUIFFIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16d79780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(sep15dfnzfare[\"MTA_tax\"].values, 50);\n",
    "plt.xlabel('MTA Tax')\n",
    "plt.ylabel('Number of Trips')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As can be seen below, the fare amount in yellow is sometime exceeding total amount in red. This implies there are still some quality issues with the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dIYQQXiNFRwghhNdI0RFCCOE1UnSEEEJ4jQyZbsVkHjYhRGsjRacVk3nYhBCt\njXSvCSGE8Bpp6bQCapA/zqn9AFCiLuAcF41+a56PUwkhxNXzadEpLi4mLS2NsrIyFEVh3LhxTJgw\ngcrKSlJSUiguLiYsLIyFCxfSuXNnADIyMsjKykKn05GUlMSgQYMAOHHiBGlpaVRXVxMbG0tSUpIv\nX5pHKcZybUocXacqXKEdfJxICCGujU+71wwGA7/5zW9Yvnw5zz33HP/+978pKCggMzOTmJgYUlNT\nGThwIJmZmQAUFBSwa9culi9fzhNPPMHatWtRVRWANWvWMHv2bFasWEFRUREHDhzw5UsTQghxGT4t\nOsHBwURGRgLg7+9Pjx49sFqt5ObmkpBQO8nlmDFj2Lt3LwB79+4lLi4Og8FAeHg4Xbt2JT8/H5vN\nht1ux2KxABAfH8+ePXt88pp8pW4kW8fcXIxnzvg6jhBCXFaLOadz9uxZTp06Rd++fSkrKyM4OBiA\noKAgysrKALDZbPTt21fbJiQkBKvVisFgwGw2a8vNZjNWq9W7L8DHZCSbEKI1aBGj1+x2O8nJyUyf\nPp2OHTs2+J6iKD5KJYQQwtN83tKpqakhOTmZ+Ph4br31VqC2dVNaWkpwcDA2m42goCCgtgVTUlKi\nbVtSUkJISMglLZuSkpIGLZ86eXl55OX9Z9RXYmIiJpOpyXyK4o9ep0PR6VAUpfbf1Ty+1u1+fKzl\n+PGxoijoFAW9Xo9Op0Ovr73BW/3Hfn5+GK7wupqD0Wi84vvZEkhOz2kNGUFyNodNmzZpj6Ojo4mO\njnZrO58WHVVVee211+jRowd33nmntnzo0KFs376dSZMmkZ2dzbBhw7TlqampTJw4EavVSlFRERaL\nBUVR6NixI/n5+VgsFnbs2MH48eMv2d/l3piKioomMxqNdvQuF3qXC1VVa/9dzeNr3e7Hx9p79eNj\nVVWpsfTENUVFtVTiGHMz+q15uFwunE4nANXV1Vy8wutqDiaT6YrvZ0sgOT2nNWQEyelpJpOJxMTE\na9rWp0Xn6NGj7Nixg169evHHP/4RgPvuu49JkyaRkpJCVlaWNmQaICIigpEjR7Jw4UL0ej0zZ87U\nut9mzZpFWloaDoeD2NhYBg8e7LPX1dzq7ioqw6eFEK2NT4vOzTffzDvvvHPZ7y1evPiyyydPnszk\nyZMvWd6nTx+Sk5M9mk8IIYRn+fycjvC8+hOBgkwGKoRoOaTotEH1h0+DDKEWQrQcLWLItBBCiPZB\nik4r5+plllWJAAAZT0lEQVTbC+fUftpEoEII0ZJJ91orJyPZhBCtibR0hBBCeI20dH7CYPgBna52\n1gOXK9zHaYQQom2RovMTev0ZjMatAFRV/TdtrTFoPHMG/enTgAylFkJ4nxSddkZ/+jSdf7zwVoZS\nCyG8TYpOO1D/YlHdxYs+TiOEaM+k6LQD9S8WtS9Y4OM0Qoj2rG2dsBBCCNGiSdFpQ+RCUSFESyfd\na22IXCgqhGjppKUjhBDCa6ToCCGE8BrpXmujXH17wdTax2pPP9+GEUKIH0nRaaPqzu8A1HRMuOL6\nMlOBEMIbpOi0Yz+9aLTj888DMlOBEKL5SNFpB9Qgf5xT+9U+rtfVJheNCiG8TYpOO6Azll9VV5sQ\nQjQXGb0mhBDCa6Sl08401tUmhBDeIEXnMvTl5ehsNtSLJ1CDzL6O41HudLXVH2AAQFAQlJUBMrJN\nCHF9pOhchlJejt+OHailCtXxYyDU14m8q/4AA6gdZFD3tYxsE0JcDzmnI4QQwmukpdOONTi/M8i9\ncz0Nut5+0u2GydS8gYUQrZ4UnXas/vkdZ8BI7XF11//WChDFDT8iP722p0G3W1SUl5ILIVqrNlV0\nDhw4wIYNG3C5XIwdO5ZJkyb5OlKrVL8Y2fvej9L9Qm0RKm7846LodCiffkrH6moZeCCEaFSbKTou\nl4t169axePFizGYzf/rTnxg6dCgRERG+jtaqKZ0vYuz0MYYuuxsWIC6d3aDjypUYnc4GLaALzz2n\nzekGUoSEaO/aTNE5fvw4Xbt2JTw8HIC4uDhyc3Ol6HhQ/QIEDbvh1EH+1PyyLy5VbVCMFP9SOux5\nr/aLYgNVY+/7TxG6hhaRTEwqROvWZoqO1WolJCRE+9psNnP8+HEfJmr7fnpOyM+cg+pyNShGSrfK\nBl11+i7fYsjOxtU1ElfIjRi+rn3svDEavx++Qq3qglrVRStG9QsTND4xqRQjIVqHNlN0PEVVg6jx\nH4Fr0I24KiNRzV18HanVaVCM/EZqy+u3lKq6hjV43MG1GcPu3dhD78fVvbdWjOoXJl3RKWoSEqh+\n9L/RFZ1Cf6ONzmWfg70UxVmN39f/16CA4R8M2MFur31sLwV/fzgdQMczasOC5s5jpKAJcb0UVVVV\nX4fwhGPHjvGPf/yDJ598EoCMjAwURWkwmCAvL4+8vDzt68TERK/nFEKItmDTpk3a4+joaKKjo93a\nrs1cHHrTTTdRVFTE2bNnqampYdeuXQwdOrTBOtHR0SQmJmr/6r9pLZnk9CzJ6TmtISNITk/btGlT\ng9+l7hYcaEPda3q9nhkzZvDcc89pQ6ZlEIEQQrQsbaboAMTGxhIbG+vrGEIIIRqhX7JkyRJfh/Cl\nuiHWLZ3k9CzJ6TmtISNITk+71pxtZiCBEEKIlq/NDCQQQgjR8knREUII4TVtaiDB1WiJk4MWFxeT\nlpZGWVkZiqIwbtw4JkyYQGVlJSkpKRQXFxMWFsbChQvp3Lmzr+PicrlYtGgRZrOZRYsWtcic58+f\n57XXXqOgoACAOXPm0K1btxaX8/333ycrKwuAXr16MWfOHKqqqnyec9WqVezfv5/AwECSk5MBmvw5\nZ2RkkJWVhU6nIykpiUGDBvks58aNG/niiy8wGAzccMMNzJkzh06dOrW4nHW2bNnCW2+9xbp16wgI\nCPBZzsYyfvjhh3z88cfodDpiY2OZNm3atWVU2yGn06nOmzdP/eGHH9Tq6mr1D3/4g/r999/7OpZq\ns9nUkydPqqqqqhcvXlQfeeQR9fvvv1c3btyoZmZmqqqqqhkZGepbb73lw5T/sWXLFjU1NVV94YUX\nVFVVW2TOlStXqlu3blVVVVVramrU8+fPt7icJSUl6ty5c1WHw6GqqqouX75czcrKahE5Dx06pJ44\ncUL93e9+py1rLNf333+v/uEPf1Crq6vVH374QZ03b57qdDp9lvPLL7/U9v/WW2+12Jyqqqrnzp1T\nn332WXXOnDlqRUWFT3NeLuPBgwfVv/71r2p1dbWqqqpaVlZ2zRnbZfda/clBDQaDNjmorwUHBxMZ\nGQmAv78/PXr0wGq1kpubS0JCAgBjxoxh7969PkxZq6SkhP379zN27FjUH8eitLScFy5c4MiRI4wd\nOxaovZarU6dOLS4ngNPppKqqSvvfbDa3iJz9+/e/pHXVWK69e/cSFxeHwWAgPDycrl27em3+w8vl\njImJQaer/RXXt29fSkpKWmROgPT0dK3lUMdXOS+X8eOPP+aee+7BYKjtHAsMDLzmjO2ye601TA56\n9uxZTp06Rd++fSkrKyM4OBiAoKAgyurNBeYrb775JtOmTePixYvaspaW8+zZswQGBrJq1Sq+/fZb\nevfuzfTp01tcTrPZzF133cWcOXMwGo0MGjSImJiYFpezTmO5bDYbffv21dYLCQnBarX6JONPbdu2\njdGjRwMtL+fevXsxm83ceOONDZa3pJxFRUUcOnSIt99+Gz8/P+6//35uuumma8rYLls6LZ3dbic5\nOZnp06fTsWPHBt9TFMVHqf5j3759BAYG0rt3b62V81MtIafT6eTkyZP813/9F8uWLcPf35/MzMwG\n67SEnJWVleTm5pKWlsbq1aux2+3k5OQ0WKcl5LycK+VqCbk3b96MwWDQis7l+CpnVVUVGRkZDeaB\nbOyYAt/ldDqdnD9/nueee45p06aRkpLS6LpXytguWzpms1lrakNtV5HZbPZhov+oqakhOTmZ+Ph4\nbr31VqD2r8nS0lKCg4Ox2WwEBQX5NOPRo0fZt28f+/fvp7q6mosXL7Jy5coWlzMkJASz2YzFYgFg\nxIgRZGRkEBwc3KJyHjx4kPDwcEwmEwDDhw/n2LFjLS5nncZ+zi3xuNq+fTv79+9n8eLF2rKWlPOH\nH37g3LlzPPbYY0BtL8yiRYt47rnnWlTOkJAQhg8fDoDFYkFRFMrLy68pY7ts6bgzOagvqKrKa6+9\nRo8ePbjzzju15UOHDmX79u0AZGdnM2zYMB8lrHXffffx6quvkpaWxoIFC4iOjmb+/PktLmdwcDCh\noaEUFhYC8NVXX9GzZ09uueWWFpUzLCyM/Px8HA4Hqqry1VdfERER0eJy1mns5zx06FB27txJTU0N\nZ8+epaioSCv4vnDgwAHee+89HnvsMYxGo7a8JeXs1asXa9asIS0tjbS0NMxmM8uWLSM4OLhF5Rw2\nbBhff/01AIWFhdTU1BAYGHhNGdvtjAT79+9vMGT6nnvu8XUkjhw5wtNPP02vXr20Jup9992HxWLx\n+dDZxhw6dIgtW7bw+OOPt8gh06dOnWL16tXU1NRow2ZdLleLy7lp0yY+++wzdDodvXv35uGHH8Zu\nt/s858svv8zhw4cpLy8nODiYxMREhg0b1miuzZs3k5WVhV6vZ/r06QwePNgnOX/5y1+SmZlJTU2N\nNvw4KiqKWbNmtYicFRUVBAUFkZiYyO233659f968ebzwwgtaZl/kvFzG2267jVdffZVTp05hMBh4\n4IEHtJmlrzZjuy06QgghvK9ddq8JIYTwDSk6QgghvEaKjhBCCK+RoiOEEMJrpOgIIYTwGik6Qggh\nvEaKjmgxlixZwrZt24DaK8mfeuopHycSLVVr+HxMnTqVH374wdcxWpx2OQ3O5cydO5eysjJtVlpF\nUUhNTdUmNmxOaWlpfPrpp7z66qte2d/VysvL45VXXuHVV191a/0VK1ZgMBiYM2eOtuzQoUMkJyeT\nnJzc6GtsCfN01bdkyRLy8/PR6/XassWLFzeY4LC5bNq0iXfffZfnnnvOp1f1N+bs2bPMnz+ft99+\nWztmrqT+++nn50f//v2ZNWuW1z/z999/v/ZZq6qqws/PT3sNDz744GXnaLvaY8Bdbe3Yd4cUnXoW\nLVrEwIEDr2nbumtsr/YXp91u5/PPP6dTp07k5ORw9913X9P+W5IZM2bwu9/9jq+++oqYmBgcDger\nV6/mgQceaJEHVmMURWHmzJnarRGuhcvlcvuXch1VVcnJySEgIIDs7OwWWXSuRf3388KFC6SkpJCe\nns4jjzzi1RwbN27UHs+dO5fZs2df83F/Pdrise8OKTpNOH/+PCtXruT48eM4nU769evHgw8+qE1o\nt2TJEm6++Wby8vI4efIkycnJ1NTUsH79ek6ePElgYCBTp05l5MiRje7j888/p3Pnztx1111s3bq1\nwQdv06ZNFBQU4OfnR25uLmFhYfz+979n9+7dfPDBBxgMBmbPnk1MTAxQO1ngmjVrOHr0KAEBAfzi\nF79g3LhxQO1fVCEhIfy///f/gEv/gpk7dy4///nPycnJ4dy5cwwePJi5c+fidDp5/vnnqamp4YEH\nHnCrBRgQEMCMGTN4/fXXSU5O5t1336Vr164kJCRw7Ngx0tPTOX36NKGhoSQlJTFgwIAr/iyOHj3K\nhg0bOHPmDN26dSMpKYmoqCi+/vprNmzYwEsvvQTAM888w4ULF1i6dCkATz31FHfffbdH59Zbvnw5\nR44cweFwcOONN/Lb3/6WiIgIoPZ9NhqNFBcXc+jQIR5//HG6d+/O+vXrOXLkCP7+/tx5552MHz++\n0ec/fPgwpaWlPPzww7zxxhv85je/0e5jsn37drZu3YrFYmH79u0EBAQwf/58CgsLeeedd6ipqWHa\ntGna/W4uXLjA+vXrOXDgAB06dGDcuHHcc889KIrCpk2b+OGHH5g/fz5waetlyZIl9O/fn6+//prv\nvvuOqKgoHnnkEUwmE08//TQA06dPB66+BdipUyeGDh3Kxx9/rC07ffp0o8dORUUFq1at4tChQ/To\n0UP7zHtSdXU1f/vb3/jss88AGDlyJNOmTaOmpuayx0BxcTFvvPEGhYWFGI1Ghg8fzgMPPKD9rK6k\nLR777pBzOvX8dEYgVVUZO3Ysq1at4tVXX8VoNLJu3boG6+zYsYOHHnqI9PR0TCYTzz77LLfddhtr\n165lwYIFrF27VrtV8uVkZ2czevRo4uLiKCws5MSJEw2+v2/fPhISEnjjjTeIjIzkueeeA2D16tVM\nmTKF119/XVs3NTWV0NBQXn/9dX73u9/x9ttva5P0KYpyxVbY7t27efLJJ3nllVf49ttv2b59O/7+\n/jz55JOYzWbS09N588033frQjRgxgt69e/Pyyy+zdetWHnroIaxWK8uWLWPKlCm88cYb3H///SQn\nJ1NRUdHkc1VWVvLCCy8wYcIE1q9fz8SJE1m6dCmVlZVERUVx5swZKisrqamp4bvvvqO0tBS73Y7D\n4eDEiRPcfPPNV8x7NYYMGcKKFStYu3YtvXv3ZsWKFQ2+v3PnTu699142btxIVFQUy5Yto3fv3qxe\nvZqnnnqKDz74gC+//LLR58/Ozmbo0KHaL9x9+/Y1+P7x48e58cYbWb9+PXFxcaSkpHDixAlWrlzJ\n/PnzWb9+PVVVVQCsX7+eixcv8sorr7BkyRKys7O122K70yrfuXMnc+fOZc2aNdTU1LBlyxYA/vrX\nvwKwYcMG0tPTr7rLsaKigj179tC1a1eg9q/+po6ddevW0aFDB9asWcPs2bPZvn27x7tjN2/ezPHj\nx3nxxRd58cUX+eabb3j33XcbPQbqbs+8bt06nn32WQ4ePNigiF5JWz32r0SKTj0vvvgiSUlJJCUl\n8dJLLxEQEMCtt96K0WjE39+fyZMnc+jQIW19RVFISEggIiICnU7HgQMHCA8PZ8yYMeh0OiIjIxk+\nfDi7d+++7P6Ki4vJy8tj9OjRBAUFMXDgwEvuozJgwADtDogjRoygvLycSZMmodPpGDVqFOfOnePC\nhQsUFxdz9OhRfv3rX2MwGIiMjGTs2LENnu9K0+yNHz+e4OBgAgICuOWWWzh16pRb2zVm1qxZ5OXl\nMWXKFMxmMzk5OcTGxmoTAsbExNCnTx+++OKLJp/niy++oHv37tx2223odDri4uLo0aMHubm5GI1G\nLBYLhw4d4sSJE0RGRtKvXz+OHDnCsWPH6NatmzZ54tVSVZU33nhD+0wsWrQIqL1bpr+/PwaDgV/+\n8pd8++23DW5mN2zYMKKiogD49ttvqaio4N5770Wv1xMeHs7YsWPZuXPnZfdZVVXF7t27GT16NHq9\nnuHDh5Odnd1gnbrPmKIojBo1CqvVypQpUzAYDMTExGAwGCgqKsLlcrFr1y7uu+8+/P39CQsL4667\n7mLHjh3a62uKoiiMGTOGrl27YjQaGTly5HV9Jurez+nTpzNr1iwqKiqYMWMGUPszbuzYcblcfP75\n5yQmJmI0GunZsycJCQnX/LlszKeffsqUKVMIDAwkMDCQKVOmaMfP5fbVp08fLBYLOp2OsLAw7rjj\njga/H5rS1o/9pkj3Wj1//OMfG/TtVlVV8eabb/Lll19SWVkJ1P5Fpqqq9pdDaGiotv65c+fIz88n\nKSlJW+Z0OomPj7/s/nJycoiIiNDuGDh69Gg2btzIAw88oJ0HqLstLIDRaMRkMmn7rpuu3W63Y7PZ\nCAgIwN/fX1s/NDT0kr+emlL/rxij0YjNZnN728sJCgrCZDLRs2dPoPZA++yzzxr85e50Oq/Yn/7T\nO71C7Wury9e/f3/y8vIICQmhf//+BAQEcOjQIfz8/Brtutu8ebN2Q7f4+Hht9uH6FEUhKSmpwTkd\nl8vF22+/ze7duykvL9d+ThUVFXTs2BFFURrcT+TcuXPYbLYGnwmXy0X//v0vm2vPnj3o9XqtMI8e\nPZpnn32WiooK7X479e+rU/cZ+OnnxG63U15ejtPpJCwsrMH7djV3n/zpZ8Jut7u97U/Vfz+/++47\nli1bRklJCSEhIU0eO+Xl5bhcrgbHWv3HP/X666/z6aefAjB58mQmTZrkVj6bzXbJPpo6BgoLC0lP\nT+fEiRNUVVXhcrno06ePW/tq68d+U6ToNGHLli2cOXOG559/nqCgIE6dOsXjjz/eoOjUFxoayoAB\nA/jzn//s1vNnZ2dTUlLCgw8+CNQeZJWVlXzxxRdXfQ6iS5cuVFZWYrfbtQ9fcXGx9guwQ4cOOBwO\nbf3S0lK3n9tT3RihoaHEx8fz0EMPXdV2ZrOZPXv2NFhWXFxMbGwsUPsXYXp6OqGhodxzzz107tyZ\n1157DT8/P37+859f9jknT57M5MmTr/o1fPrpp+Tm5vLUU08RFhbG+fPnmTFjRqN/EYaGhhIeHk5q\naqpbz5+dnY3dbtdG/qmqitPpZMeOHUyYMOGqsgYGBqLX6zl79qx2zqn+Z8Lf31/rhgPvfiZ69erF\n5MmTWbduHcuWLWvy2KkbjFFcXEz37t2119GYBx98UDumrkaXLl04d+5cg/eqS5cuwOVf79q1a+nT\npw8LFizA39+ff/3rX3z++edu7au9Hfv1SfdaE+x2O0ajkU6dOlFZWck//vGPJte/5ZZbOHPmDDk5\nOdTU1FBTU8Px48c5ffr0JeseO3aMs2fPsnTpUq0POTk5mbi4uEu6U9wRGhpKv379+J//+R+qq6v5\n9ttvycrK0lpZkZGR7N+/n8rKSkpLS/nggw/cfu6goCAqKiq4cOGCtiwvL4+pU6deVcbbbruNffv2\n8eWXX+JyuXA4HOTl5V3xL+/Y2FjOnDnDp59+itPpZNeuXZw+fZpbbrkFgH79+lFYWMg333yDxWIh\nIiKCc+fOcfz48UZbFNfKbrfj5+dHQEAAdrudt99+u8H3f1p8LBYL/v7+/POf/8ThcOByufjuu+/4\n5ptvLnluq9XK119/zaJFi7TPxIsvvsgvfvGLS7pe3KHT6Rg5ciR///vfsdvtnDt3jn/961/cdttt\nQO1n4vDhwxQXF3PhwoVLbuXdlMDAQBRFaXAdytmzZ5k6dWqTBaG+hIQESktLyc3NbfLY0el0DB8+\nnE2bNuFwOCgoKCA7O9vjvxDj4uLYvHkz5eXllJeX87//+7/a8XO5Y6Dul3yHDh04ffq02+dzWvux\nf72kpdOEO++8kxUrVjBz5kzMZjMTJ04kNze30fXrTrylp6eTnp6OqqpERkbywAMPXLJu3R0X67qe\n6kyYMIGnn36aysrKy54AbOpAe/TRR3n99dd56KGH6Ny5M1OnTtW6ruLj4zl48CBz587V+s7ff//9\nRp+r/r579OhBXFwc8+fPx+VysXz5ckpKSujXr1+j219OSEgIf/zjH3nrrbdITU1Fp9NhsVj47W9/\n22gGAJPJxOOPP86GDRtYu3Yt3bp1Y9GiRdq5mg4dOtCnTx+MRqN2TU2/fv0oKCho0EXhCfHx8Rw4\ncICHH36YgIAApk6dyieffNIgc/2fkU6nY9GiRaSnpzNv3jyqq6vp0aOHNpKovpycHHr37n3JyKyf\n//znvP/++9pJ9av5ZTtjxgzWr1/PvHnz8PPz44477tBuGhYTE8OoUaN47LHHCAwM5O67775k0MJP\n1e27Q4cOTJ48mcWLF+N0OnnyySeprq4mLCzM7VsqGwwGJkyYwLvvvsvQoUObPHZmzJjBqlWrtJGC\nt99+u9vnT9x17733cvHiRe3W0SNHjuTee+8FLj0GUlJSuP/++3n99dd577336N27N3FxceTl5V1x\nP6352E9JSbnuwQRyEzdxTV577TVGjRrVLENXReu0efNmAgMDueOOO3wdRbRgUnSEEEJ4jZzTEUII\n4TVSdIQQQniNFB0hhBBeI0VHCCGE10jREUII4TVSdIQQQniNFB0hhBBeI0VHCCGE1/x/7SdBVJgk\nUUEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123e60f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.abs(sep15dfnzfare[\"Total_amount\"].values), 100, color='red', alpha=0.8);\n",
    "plt.hist(np.abs(sep15dfnzfare[\"Fare_amount\"].values), 100, color='yellow', alpha=0.5);\n",
    "plt.xlabel('Fare Amount, Yellow - Fare Amount, Red - Total Amount')\n",
    "plt.ylabel('Number of Trips')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the derived variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Further filtering the dataframe for Non-zero fares\n",
    "sep15dfnzfareall = sep15dfnz.loc[sep15dfnz.loc[:, \"Fare_amount\"]<>0, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upper Manhattan is around the co-ordinates (40.82, -73.94)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sep15dfnzfareall.loc[:,'tip_ratio'] = 100*sep15dfnzfareall['Tip_amount']/sep15dfnzfareall['Fare_amount']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "index_UM = [ix for (ix, x,y) in zip(sep15dfnzfareall.index, sep15dfnzfareall['Pickup_latitude'].values, \\\n",
    "                                    sep15dfnzfareall['Pickup_longitude'].values)\\\n",
    "            if (round(x,2) == 40.82) and (round(y,2) == -73.94)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of tip ratio in upper Manhattan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IEJP3oP1effVVDhw4QFRUFMuWLQNo176XlZWRm5tLQ0MDqampZGdnm7ZP7XW1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x715bf2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tipsUM = sep15dfnzfareall.loc[index_UM, 'tip_ratio'].values\n",
    "plt.hist(tipsUM, 10);\n",
    "plt.xlabel('Tips Percentage')\n",
    "plt.ylabel('Number of Trips')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let us zoom into the tip ratio in lower percentage region for Upper Manhattan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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U1dXV2O12tmzZwpQpU664PhERuXJBBU1CQkLICsjOzmbJkiW0trZy/fXXM2fO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\nEQkpBY2IiISUgkZERELq/wMxzJNE7VR8YAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x715bf7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(filter(lambda x: x <= 50.0, tipsUM), 10)\n",
    "plt.xlabel('Tips Percentage')\n",
    "plt.ylabel('Number of Trips')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Distribution of tip ratio in outer boroughs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AFRUVTJ06FYvFQlxcHCNGjKCurg6Px0N7e7v/+WTTp09nz549AL3OlZGRwd69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\n6P4TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21179ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tipsOB = sep15dfnzfareall.loc[~sep15dfnzfareall.index.isin(index_UM), 'tip_ratio'].values\n",
    "plt.hist(tipsOB, 10);\n",
    "plt.xlabel('Tips Percentage')\n",
    "plt.ylabel('Number of Trips')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let us zoom into the tip ratio in lower percentage region for Outer Borough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QREQuW92GkJZvEBGRSy3kzwmdOXOG6upqmpubu2y/5pprvvCiRESkfwgphEpL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\nQZaSCCk8SKIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x714b4748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(filter(lambda x: x <= 50.0, tipsOB), 10)\n",
    "plt.xlabel('Tips Percentage')\n",
    "plt.ylabel('Number of Trips')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As can be seen, tips in upper Manhattan follow similar distribution as tips on outer Borough\n",
    "## Also a large percentage of tips are zero in both regions. We can look at the distribution of tips against payment type to see whether there are any obvious pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'VendorID', u'lpep_pickup_datetime', u'Lpep_dropoff_datetime',\n",
       "       u'Store_and_fwd_flag', u'RateCodeID', u'Pickup_longitude',\n",
       "       u'Pickup_latitude', u'Dropoff_longitude', u'Dropoff_latitude',\n",
       "       u'Passenger_count', u'Trip_distance', u'Fare_amount', u'Extra',\n",
       "       u'MTA_tax', u'Tip_amount', u'Tolls_amount', u'Ehail_fee',\n",
       "       u'improvement_surcharge', u'Total_amount', u'Payment_type',\n",
       "       u'Trip_type ', u'trip_hr', u'tip_ratio'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sep15dfnzfareall.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "labels = {1:'Credit card', 2:'Cash', 3:'No charge', 4:'Dispute', 5:'Unknown', 6:'Voided trip'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sep15dfnzfareall.loc[sep15dfnzfareall.index,'Payment_label'] = \\\n",
    "[labels[ix] for ix in sep15dfnzfareall.loc[sep15dfnzfareall.index,'Payment_type']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fNm0aJSUlAJSUlDB58mQsFgvR0dHExMRQUVGB2+2mtbWV2NhYAKZMmcLOnTtPWldqaip7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\nIoYUFCIiYuj/AyL7uqW9aRm2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x797b62b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sep15dfnzfareall.groupby(['Payment_label']).size().plot(kind='bar').set_xlabel('Payment Type');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As can be seen above, most of the payment types is cash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Wamoqe/bsAaC0tJTExESsVitWq5WEhAR/uIiISP84o0NPJpOJxx9/nJCQEG688UZuvPFG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\ntxAVFcWYMWMCXVZATJgwgSe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V39PhLpEzoUNPIiJiSIeeRETEkIJCREQMKShERMSQgkJE\nRAwpKERExND/B1pGhB91fxVXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x72d87d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sep15dfnzfareall[sep15dfnzfareall['tip_ratio']== 0].groupby(['Payment_label']).size().plot(kind='bar').set_xlabel('Payment Type');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As can be seen above, tip ratio is zero predominantly for cash payments. This might be due to tips not being reported by the drivers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>lpep_pickup_datetime</th>\n",
       "      <th>Lpep_dropoff_datetime</th>\n",
       "      <th>Store_and_fwd_flag</th>\n",
       "      <th>RateCodeID</th>\n",
       "      <th>Pickup_longitude</th>\n",
       "      <th>Pickup_latitude</th>\n",
       "      <th>Dropoff_longitude</th>\n",
       "      <th>Dropoff_latitude</th>\n",
       "      <th>Passenger_count</th>\n",
       "      <th>...</th>\n",
       "      <th>Tip_amount</th>\n",
       "      <th>Tolls_amount</th>\n",
       "      <th>Ehail_fee</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>Total_amount</th>\n",
       "      <th>Payment_type</th>\n",
       "      <th>Trip_type</th>\n",
       "      <th>trip_hr</th>\n",
       "      <th>tip_ratio</th>\n",
       "      <th>Payment_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-09-01 00:01:50</td>\n",
       "      <td>2015-09-01 00:04:24</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.92141</td>\n",
       "      <td>40.766708</td>\n",
       "      <td>-73.914413</td>\n",
       "      <td>40.764687</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>Credit card</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID lpep_pickup_datetime Lpep_dropoff_datetime Store_and_fwd_flag  \\\n",
       "2         2  2015-09-01 00:01:50   2015-09-01 00:04:24                  N   \n",
       "\n",
       "   RateCodeID  Pickup_longitude  Pickup_latitude  Dropoff_longitude  \\\n",
       "2           1         -73.92141        40.766708         -73.914413   \n",
       "\n",
       "   Dropoff_latitude  Passenger_count      ...        Tip_amount  Tolls_amount  \\\n",
       "2         40.764687                1      ...               0.5             0   \n",
       "\n",
       "   Ehail_fee  improvement_surcharge  Total_amount  Payment_type  Trip_type   \\\n",
       "2        NaN                    0.3           5.8             1           1   \n",
       "\n",
       "   trip_hr  tip_ratio  Payment_label  \n",
       "2        0       12.5    Credit card  \n",
       "\n",
       "[1 rows x 24 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sep15dfnzfareall.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2L, '2015-09-01 00:01:50', '2015-09-01 00:04:24', 'N', 1L,\n",
       "        -73.92140960693358, 40.76670837402344, -73.91441345214845,\n",
       "        40.764686584472656, 1L, 0.59, 4.0, 0.5, 0.5, 0.5, 0.0, nan, 0.3,\n",
       "        5.8, 1L, 1.0, 0.0, 12.5, 'Credit card']], dtype=object)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sep15dfnzfareall.loc[0:2,].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now we will fit a random forest model to our data to predict tip ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We first start with looking at the overall distribution of tip ratio, for ratio below 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x16386160>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+97vfce+991JVVdXrCzU3N/POO++QkZEBgMPhIDIykn379pmjkvT0dCoqKgCoqKhg6tSp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\n8Zh3HQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ba50b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(sep15dfnzfareall[sep15dfnzfareall['tip_ratio']< 50]['tip_ratio'].values, 10);\n",
    "plt.xlabel('Tip Percentage')\n",
    "plt.ylabel('Number of Trips')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get our predictors X and response variable y\n",
    "### We use the average trip time variable (trip_hr) instead of the pick-up and drop-off date time variables. We also drop the 'Ehail_fee' column as it has nan values. We also need a numeric equivalent of Store_and_fwd_flag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labels1 = {'N':0, 'Y':1}\n",
    "sep15dfnzfareall.loc[sep15dfnzfareall.index,'SF_n'] = \\\n",
    "[labels1[ix] for ix in sep15dfnzfareall.loc[sep15dfnzfareall.index,'Store_and_fwd_flag']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We will create buckets in the response variable (tip_ratio) for classification algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bins = np.array([0.0, 10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 55.0])\n",
    "sep15dfnzfareall.loc[sep15dfnzfareall.index,'tip_ratio_c'] = bins.searchsorted(sep15dfnzfareall['tip_ratio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'VendorID', u'lpep_pickup_datetime', u'Lpep_dropoff_datetime',\n",
       "       u'Store_and_fwd_flag', u'RateCodeID', u'Pickup_longitude',\n",
       "       u'Pickup_latitude', u'Dropoff_longitude', u'Dropoff_latitude',\n",
       "       u'Passenger_count', u'Trip_distance', u'Fare_amount', u'Extra',\n",
       "       u'MTA_tax', u'Tip_amount', u'Tolls_amount', u'Ehail_fee',\n",
       "       u'improvement_surcharge', u'Total_amount', u'Payment_type',\n",
       "       u'Trip_type ', u'trip_hr', u'tip_ratio', u'Payment_label', u'SF_n',\n",
       "       u'tip_ratio_c'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sep15dfnzfareall.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = sep15dfnzfareall['tip_ratio_c']\n",
    "X = sep15dfnzfareall[['VendorID', 'RateCodeID', 'Pickup_longitude', 'Pickup_latitude', 'Dropoff_longitude', 'Dropoff_latitude', 'Passenger_count', 'Trip_distance', 'Fare_amount', 'Extra','MTA_tax', 'Tip_amount', 'Tolls_amount', 'improvement_surcharge', 'Total_amount', 'Payment_type','Trip_type ', 'trip_hr',  'SF_n']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from scipy import interp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99553704177281011"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators = 100, n_jobs=2)\n",
    "scores = cross_val_score(rf, X, y, cv=5)\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As we can see, 5-fold cross-validation accuracy is quite good for this multiclass classifier. We can also examine the ROC curves for each of the classes as below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Compute ROC curve and ROC area for each class\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Learn to predict each class against the other\n",
    "yb = label_binarize(y, classes=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, yb, test_size=.5,\n",
    "                                                    random_state=0)\n",
    "n_classes = yb.shape[1]\n",
    "classifier = OneVsRestClassifier(RandomForestClassifier(n_estimators = 20, n_jobs=2))\n",
    "y_score = classifier.fit(X_train, y_train).predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(n_classes):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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o7k/cXv0abqt7XvNUVWDTe34NFa+vrVSOr/jp+yO/oXvH5kD7xf4KqiZkwnZC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\nFZFkWwghhBBCiCry//uJdj2+F6UiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x720963c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(n_classes):\n",
    "    plt.plot(fpr[i], tpr[i], label='ROC curve of class {0} (area = {1:0.2f})'.format(i, roc_auc[i]))\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Some extension of Receiver operating characteristic to multi-class')\n",
    "plt.legend(loc=\"lower right\")\n",
    "fig_size = plt.rcParams[\"figure.figsize\"]\n",
    "fig_size[0] = 12\n",
    "fig_size[1] = 9\n",
    "plt.rcParams[\"figure.figsize\"] = fig_size\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As seen above classification error (prediction of tip ratio in the range) is zero in most ranges. As areminder these are the ranges of the tip ratio that we are trying to predict - [0.0, 10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 55.0]. Tip ratio above 55% is placed in the last range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As we see below, the Random Forest classifier with only 2 classes also yields good cross-validation accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bins1 = np.array([0.0, 20.0])\n",
    "sep15dfnzfareall.loc[sep15dfnzfareall.index,'tip_ratio_c2'] = bins1.searchsorted(sep15dfnzfareall['tip_ratio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y2 = sep15dfnzfareall['tip_ratio_c2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99916493763405168"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf2 = RandomForestClassifier(n_estimators = 100, n_jobs=2)\n",
    "scores2 = cross_val_score(rf2, X, y2, cv=5)\n",
    "scores2.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Option A: Distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating average speed column in miles/hr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dformat = \"%Y-%m-%d %H:%M:%S\"\n",
    "\n",
    "sep15dfnzfareall.loc[sep15dfnzfareall.index,\"trip_time\"]= \\\n",
    "[(datetime.strptime(y, dformat) - datetime.strptime(x, dformat)).total_seconds()/3600.0 \\\n",
    " for x,y in zip(sep15dfnzfareall[\"lpep_pickup_datetime\"].values, sep15dfnzfareall[\"Lpep_dropoff_datetime\"].values)] ;\n",
    "\n",
    "#Take non zero trip time rows\n",
    "sep15AvgSpeed = sep15dfnzfareall[sep15dfnzfareall['trip_time']<>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sep15AvgSpeed.loc[:,'avg_speed'] = sep15AvgSpeed['Trip_distance']/sep15AvgSpeed['trip_time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1470407.000000\n",
       "mean          15.925150\n",
       "std          216.751966\n",
       "min            0.000417\n",
       "25%            9.422028\n",
       "50%           11.833910\n",
       "75%           15.126050\n",
       "max       202680.000000\n",
       "Name: avg_speed, dtype: float64"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sep15AvgSpeed['avg_speed'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let us create a column for week number. We will extract the week from pick-up time for simplicity. We will get the week from the ISO week (each year has 52 weeks) returned by the datetime function isocalendar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sep15AvgSpeed.loc[sep15AvgSpeed.index,\"iso_week\"] = \\\n",
    "[datetime.strptime(x, dformat).isocalendar()[1] for x in sep15AvgSpeed[\"lpep_pickup_datetime\"].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{36, 37, 38, 39, 40}"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(sep15AvgSpeed[\"iso_week\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We run the following ANOVA test with the null hypothesis that average speed on all weeks are equal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One-way ANOVA P = 0.0463751450172\n"
     ]
    }
   ],
   "source": [
    "weeklySpeeds = sep15AvgSpeed.groupby(by=\"iso_week\")['avg_speed'].apply(lambda x: x.tolist())\n",
    "\n",
    "_, p_val = stats.f_oneway(weeklySpeeds[36], \\\n",
    "                              weeklySpeeds[37], \\\n",
    "                              weeklySpeeds[38], \\\n",
    "                              weeklySpeeds[39], \\\n",
    "                              weeklySpeeds[40])\n",
    "  \n",
    "print \"One-way ANOVA P =\", p_val "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As the p value is less than 0.05, the null hypothesis that average speed on all weeks are equal is rejected. Let us plot the distribution of speed over weeks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8+HFmzpzJE088QXh4OB6Ph4SEBN81drsdt9uNxWLBZvvLCKPNZsPtdgPgdrux2+0AmM1m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\nARVPcAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20903e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(511)\n",
    "plt.title(\"Wk 36\")\n",
    "plt.hist(filter(lambda x: x <= 70.0, weeklySpeeds[36]), bins=100);\n",
    "plt.subplot(512)\n",
    "plt.title(\"Wk 37\")\n",
    "plt.hist(filter(lambda x: x <= 70.0, weeklySpeeds[37]), bins=100);\n",
    "plt.subplot(513)\n",
    "plt.title(\"Wk 38\")\n",
    "plt.hist(filter(lambda x: x <= 70.0, weeklySpeeds[38]), bins=100);\n",
    "plt.subplot(514)\n",
    "plt.title(\"Wk 39\")\n",
    "plt.hist(filter(lambda x: x <= 70.0, weeklySpeeds[39]), bins=100);\n",
    "plt.subplot(515)\n",
    "plt.title(\"Wk 40\")\n",
    "plt.hist(filter(lambda x: x <= 70.0, weeklySpeeds[40]), bins=100);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## After visual inspection of the means and trying different combinations, we can see below that weeks 36, 39 and 40 have same average speeds at 5% level of significance. Similarly, weeks 37 and 38 have same average speeds at 5% level of significance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One-way ANOVA P = 0.492193530116\n"
     ]
    }
   ],
   "source": [
    "_, p_val = stats.f_oneway(weeklySpeeds[36],     \\\n",
    "                              weeklySpeeds[39], \\\n",
    "                              weeklySpeeds[40])\n",
    "  \n",
    "print \"One-way ANOVA P =\", p_val "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One-way ANOVA P = 0.781297410955\n"
     ]
    }
   ],
   "source": [
    "_, p_val = stats.f_oneway(weeklySpeeds[37], \\\n",
    "                              weeklySpeeds[38])\n",
    "  \n",
    "print \"One-way ANOVA P =\", p_val "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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