Assignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This is the NYC Taxi fare predictor "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Importing necessary Packages</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import linear_model \n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> Data Cleaning </h1>\n",
    "\n",
    "As the Data set is too large we are taking the first One Million Entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('C:\\Dhruv Course Data\\\\train.csv',nrows=40000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <h1>1) Data Cleaning</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A) We first calculate the Mean, Max, Min values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fare_amount</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>40000.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.349100</td>\n",
       "      <td>-72.509747</td>\n",
       "      <td>39.932891</td>\n",
       "      <td>-72.498153</td>\n",
       "      <td>39.920173</td>\n",
       "      <td>1.663300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.634925</td>\n",
       "      <td>10.403879</td>\n",
       "      <td>6.315765</td>\n",
       "      <td>10.439964</td>\n",
       "      <td>6.065730</td>\n",
       "      <td>1.284104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-75.414728</td>\n",
       "      <td>-74.006893</td>\n",
       "      <td>-84.654241</td>\n",
       "      <td>-74.006377</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>-73.992060</td>\n",
       "      <td>40.734847</td>\n",
       "      <td>-73.991172</td>\n",
       "      <td>40.734332</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.500000</td>\n",
       "      <td>-73.981744</td>\n",
       "      <td>40.752705</td>\n",
       "      <td>-73.980199</td>\n",
       "      <td>40.753341</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.500000</td>\n",
       "      <td>-73.967090</td>\n",
       "      <td>40.767346</td>\n",
       "      <td>-73.963766</td>\n",
       "      <td>40.768155</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>40.783472</td>\n",
       "      <td>401.083332</td>\n",
       "      <td>40.851027</td>\n",
       "      <td>43.415190</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        fare_amount  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "count  40000.000000      40000.000000     40000.000000       40000.000000   \n",
       "mean      11.349100        -72.509747        39.932891         -72.498153   \n",
       "std        9.634925         10.403879         6.315765          10.439964   \n",
       "min       -3.000000        -75.414728       -74.006893         -84.654241   \n",
       "25%        6.000000        -73.992060        40.734847         -73.991172   \n",
       "50%        8.500000        -73.981744        40.752705         -73.980199   \n",
       "75%       12.500000        -73.967090        40.767346         -73.963766   \n",
       "max      200.000000         40.783472       401.083332          40.851027   \n",
       "\n",
       "       dropoff_latitude  passenger_count  \n",
       "count      40000.000000     40000.000000  \n",
       "mean          39.920173         1.663300  \n",
       "std            6.065730         1.284104  \n",
       "min          -74.006377         0.000000  \n",
       "25%           40.734332         1.000000  \n",
       "50%           40.753341         1.000000  \n",
       "75%           40.768155         2.000000  \n",
       "max           43.415190         6.000000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>B) From the above data, we find the following anomalies- </b>\n",
    "<br>\n",
    "1) The minimum fare is- 44.9$ (Which is not possible)\n",
    "</br><br>\n",
    "2) Pickup & Dropoff Latitude and Longitude are beyond the normal range of -90 to +90 and -180 to +180 respectively.\n",
    "</br>\n",
    "<br>\n",
    "3) The maximum passenger count is 208, which is not possible for a car.\n",
    "</br>\n",
    "<br>\n",
    "3) A lot of data Values are equal to zero.\n",
    "</br>\n",
    "\n",
    "<br> <b>To remove these anomalies we first set the limits to the following values-</b> </br>\n",
    "<br> 1) Max Passengers are limited to 6.</br>\n",
    "<br> 2) Latitude and Longitude are limted to -90 to +90 and -180 to +180 respectively.</br>\n",
    "<br> 3) Taking entries with fare >0 </br>\n",
    "<br> 4) Removing Entries with all values=0 </br>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39197"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_1=df[df.passenger_count<=6] \n",
    "df_clean_1=df_clean_1[df_clean_1.pickup_longitude>=-180]\n",
    "df_clean_1=df_clean_1[df_clean_1.pickup_longitude<=180]\n",
    "df_clean_1=df_clean_1[df_clean_1.pickup_latitude>=-90]\n",
    "df_clean_1=df_clean_1[df_clean_1.pickup_latitude<=90]\n",
    "df_clean_1=df_clean_1[df_clean_1.pickup_latitude!=0]\n",
    "df_clean_1=df_clean_1[df_clean_1.pickup_longitude!=0]\n",
    "df_clean_1=df_clean_1[df_clean_1.dropoff_longitude>=-180]\n",
    "df_clean_1=df_clean_1[df_clean_1.dropoff_longitude<=180]\n",
    "df_clean_1=df_clean_1[df_clean_1.dropoff_latitude>=-90]\n",
    "df_clean_1=df_clean_1[df_clean_1.dropoff_latitude<=90]\n",
    "df_clean_1=df_clean_1[df_clean_1.fare_amount>0] \n",
    "df_clean_1=df_clean_1[df_clean_1.fare_amount<300] \n",
    "df_clean_1=df_clean_1[df_clean_1.dropoff_latitude!=0]\n",
    "df_clean_1=df_clean_1[df_clean_1.dropoff_longitude!=0]\n",
    "df_clean_1=df_clean_1[df_clean_1.pickup_longitude!=0]\n",
    "df_clean_1=df_clean_1[df_clean_1.pickup_latitude!=0]\n",
    "len(df_clean_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>C) From the http://www.nyc.gov/html/tlc/html/passenger/taxicab_rate.shtml NYC Taxi and Limousine Commision we get to know that the minimum taxi fare is 2.5$</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39196"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_1=df_clean_1[df_clean_1.fare_amount>=2.5]  \n",
    "len(df_clean_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>D) Further Points-</b>\n",
    "  <br> 1) We also notice that the min pickup_longitude and pickup_latitude are -128.175,-121.39. Which even though not a pair but would correspond to pickups in the southern hemishphere which is an anomaly for a taxi.</br>\n",
    "  <br>\n",
    "   2) Similarly for the dropoff Longitude and Latitude. </br>\n",
    "   \n",
    "   <b> To solve this- </b>\n",
    "    \n",
    "   We limit the latitude and longitude to the rough boundries of places upto 215 miles from NYC. (Assuming cabs are generally travel less than 215 miles)\n",
    "   <br>38.9<latitude<43\n",
    "   <br>-76<longitude<-70\n",
    "   \n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39169"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Approx distance between one degree of longitude at New York is 53miles and distance in between latitude is 69\n",
    "def longitude(df_clean_1):\n",
    "    df_clean_1['abs_diff_longitude_miles'] = ((df_clean_1.dropoff_longitude - df_clean_1.pickup_longitude).abs())*53\n",
    "    df_clean_1['abs_diff_latitude_miles'] = ((df_clean_1.dropoff_latitude - df_clean_1.pickup_latitude).abs())*69\n",
    "    return  df_clean_1\n",
    "df_clean_1=longitude(df_clean_1)\n",
    "df_clean_1=df_clean_1[df_clean_1.pickup_latitude>=-38.9]\n",
    "df_clean_1=df_clean_1[df_clean_1.pickup_latitude<=43]\n",
    "df_clean_1=df_clean_1[df_clean_1.dropoff_longitude>=-76]\n",
    "df_clean_1=df_clean_1[df_clean_1.dropoff_longitude<=-70]\n",
    "df_clean_1=df_clean_1[df_clean_1.pickup_latitude>=-38.9]\n",
    "df_clean_1=df_clean_1[df_clean_1.pickup_latitude<=43]\n",
    "df_clean_1=df_clean_1[df_clean_1.dropoff_longitude>=-76]\n",
    "df_clean_1=df_clean_1[df_clean_1.dropoff_longitude<=-70]\n",
    "df_clean_1=df_clean_1[df_clean_1.abs_diff_longitude_miles<=130]\n",
    "df_clean_1=df_clean_1[df_clean_1.abs_diff_latitude_miles<=130]\n",
    "len(df_clean_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>2) Finding Correlations </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_datetime</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>abs_diff_longitude_miles</th>\n",
       "      <th>abs_diff_latitude_miles</th>\n",
       "      <th>pickup_day_of_hour</th>\n",
       "      <th>pickup_day_of_week</th>\n",
       "      <th>pickup_month</th>\n",
       "      <th>pickup_date</th>\n",
       "      <th>pickup_day_of_week_name</th>\n",
       "      <th>pickup_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.000000100</td>\n",
       "      <td>4.50</td>\n",
       "      <td>2009-06-15 17:26:21</td>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
       "      <td>0.143153</td>\n",
       "      <td>0.623829</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.000000200</td>\n",
       "      <td>16.90</td>\n",
       "      <td>2010-01-05 16:52:16</td>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "      <td>1.949340</td>\n",
       "      <td>4.878369</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.000000490</td>\n",
       "      <td>5.70</td>\n",
       "      <td>2011-08-18 00:35:00</td>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "      <td>0.450712</td>\n",
       "      <td>0.738852</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.000000100</td>\n",
       "      <td>7.70</td>\n",
       "      <td>2012-04-21 04:30:42</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>0.235161</td>\n",
       "      <td>1.721481</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.30</td>\n",
       "      <td>2010-03-09 07:51:00</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "      <td>0.606320</td>\n",
       "      <td>1.087026</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011-01-06 09:50:45.000000200</td>\n",
       "      <td>12.10</td>\n",
       "      <td>2011-01-06 09:50:45</td>\n",
       "      <td>-74.000964</td>\n",
       "      <td>40.731630</td>\n",
       "      <td>-73.972892</td>\n",
       "      <td>40.758233</td>\n",
       "      <td>1</td>\n",
       "      <td>1.487816</td>\n",
       "      <td>1.835607</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012-11-20 20:35:00.000000100</td>\n",
       "      <td>7.50</td>\n",
       "      <td>2012-11-20 20:35:00</td>\n",
       "      <td>-73.980002</td>\n",
       "      <td>40.751662</td>\n",
       "      <td>-73.973802</td>\n",
       "      <td>40.764842</td>\n",
       "      <td>1</td>\n",
       "      <td>0.328600</td>\n",
       "      <td>0.909420</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2012-01-04 17:22:00.000000810</td>\n",
       "      <td>16.50</td>\n",
       "      <td>2012-01-04 17:22:00</td>\n",
       "      <td>-73.951300</td>\n",
       "      <td>40.774138</td>\n",
       "      <td>-73.990095</td>\n",
       "      <td>40.751048</td>\n",
       "      <td>1</td>\n",
       "      <td>2.056135</td>\n",
       "      <td>1.593210</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2012-12-03 13:10:00.000000125</td>\n",
       "      <td>9.00</td>\n",
       "      <td>2012-12-03 13:10:00</td>\n",
       "      <td>-74.006462</td>\n",
       "      <td>40.726713</td>\n",
       "      <td>-73.993078</td>\n",
       "      <td>40.731628</td>\n",
       "      <td>1</td>\n",
       "      <td>0.709352</td>\n",
       "      <td>0.339135</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009-09-02 01:11:00.000000830</td>\n",
       "      <td>8.90</td>\n",
       "      <td>2009-09-02 01:11:00</td>\n",
       "      <td>-73.980658</td>\n",
       "      <td>40.733873</td>\n",
       "      <td>-73.991540</td>\n",
       "      <td>40.758138</td>\n",
       "      <td>2</td>\n",
       "      <td>0.576746</td>\n",
       "      <td>1.674285</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012-04-08 07:30:50.000000200</td>\n",
       "      <td>5.30</td>\n",
       "      <td>2012-04-08 07:30:50</td>\n",
       "      <td>-73.996335</td>\n",
       "      <td>40.737142</td>\n",
       "      <td>-73.980721</td>\n",
       "      <td>40.733559</td>\n",
       "      <td>1</td>\n",
       "      <td>0.827542</td>\n",
       "      <td>0.247227</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2009-11-06 01:04:03.000000200</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2009-11-06 01:04:03</td>\n",
       "      <td>-73.991601</td>\n",
       "      <td>40.744712</td>\n",
       "      <td>-73.983081</td>\n",
       "      <td>40.744682</td>\n",
       "      <td>2</td>\n",
       "      <td>0.451560</td>\n",
       "      <td>0.002070</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013-07-02 19:54:00.000000232</td>\n",
       "      <td>7.00</td>\n",
       "      <td>2013-07-02 19:54:00</td>\n",
       "      <td>-74.005360</td>\n",
       "      <td>40.728867</td>\n",
       "      <td>-74.008913</td>\n",
       "      <td>40.710907</td>\n",
       "      <td>1</td>\n",
       "      <td>0.188309</td>\n",
       "      <td>1.239240</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2011-04-05 17:11:05.000000100</td>\n",
       "      <td>7.70</td>\n",
       "      <td>2011-04-05 17:11:05</td>\n",
       "      <td>-74.001821</td>\n",
       "      <td>40.737547</td>\n",
       "      <td>-73.998060</td>\n",
       "      <td>40.722788</td>\n",
       "      <td>2</td>\n",
       "      <td>0.199333</td>\n",
       "      <td>1.018371</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2014-02-19 07:22:00.000000740</td>\n",
       "      <td>12.50</td>\n",
       "      <td>2014-02-19 07:22:00</td>\n",
       "      <td>-73.986430</td>\n",
       "      <td>40.760465</td>\n",
       "      <td>-73.988990</td>\n",
       "      <td>40.737075</td>\n",
       "      <td>1</td>\n",
       "      <td>0.135680</td>\n",
       "      <td>1.613910</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2009-07-22 16:08:00.000000163</td>\n",
       "      <td>5.30</td>\n",
       "      <td>2009-07-22 16:08:00</td>\n",
       "      <td>-73.981060</td>\n",
       "      <td>40.737690</td>\n",
       "      <td>-73.994177</td>\n",
       "      <td>40.728412</td>\n",
       "      <td>1</td>\n",
       "      <td>0.695201</td>\n",
       "      <td>0.640182</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2010-07-07 14:52:00.000000440</td>\n",
       "      <td>5.30</td>\n",
       "      <td>2010-07-07 14:52:00</td>\n",
       "      <td>-73.969505</td>\n",
       "      <td>40.784843</td>\n",
       "      <td>-73.958732</td>\n",
       "      <td>40.783357</td>\n",
       "      <td>1</td>\n",
       "      <td>0.570969</td>\n",
       "      <td>0.102534</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014-12-06 20:36:22.000000800</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2014-12-06 20:36:22</td>\n",
       "      <td>-73.979815</td>\n",
       "      <td>40.751902</td>\n",
       "      <td>-73.979446</td>\n",
       "      <td>40.755481</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019557</td>\n",
       "      <td>0.246951</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2010-09-07 13:18:00.000000175</td>\n",
       "      <td>10.50</td>\n",
       "      <td>2010-09-07 13:18:00</td>\n",
       "      <td>-73.985382</td>\n",
       "      <td>40.747858</td>\n",
       "      <td>-73.978377</td>\n",
       "      <td>40.762070</td>\n",
       "      <td>1</td>\n",
       "      <td>0.371265</td>\n",
       "      <td>0.980628</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2013-02-12 12:15:46.000000200</td>\n",
       "      <td>11.50</td>\n",
       "      <td>2013-02-12 12:15:46</td>\n",
       "      <td>-73.957954</td>\n",
       "      <td>40.779252</td>\n",
       "      <td>-73.961250</td>\n",
       "      <td>40.758787</td>\n",
       "      <td>1</td>\n",
       "      <td>0.174688</td>\n",
       "      <td>1.412085</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2009-08-06 18:17:23.000000100</td>\n",
       "      <td>4.50</td>\n",
       "      <td>2009-08-06 18:17:23</td>\n",
       "      <td>-73.991707</td>\n",
       "      <td>40.770505</td>\n",
       "      <td>-73.985459</td>\n",
       "      <td>40.763671</td>\n",
       "      <td>1</td>\n",
       "      <td>0.331144</td>\n",
       "      <td>0.471546</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2010-12-06 12:29:00.000000199</td>\n",
       "      <td>4.90</td>\n",
       "      <td>2010-12-06 12:29:00</td>\n",
       "      <td>-74.000632</td>\n",
       "      <td>40.747473</td>\n",
       "      <td>-73.986672</td>\n",
       "      <td>40.740577</td>\n",
       "      <td>1</td>\n",
       "      <td>0.739880</td>\n",
       "      <td>0.475824</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2009-12-10 15:37:00.000000200</td>\n",
       "      <td>6.10</td>\n",
       "      <td>2009-12-10 15:37:00</td>\n",
       "      <td>-73.969622</td>\n",
       "      <td>40.756973</td>\n",
       "      <td>-73.981152</td>\n",
       "      <td>40.759712</td>\n",
       "      <td>1</td>\n",
       "      <td>0.611090</td>\n",
       "      <td>0.188991</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2011-06-21 16:15:00.000000730</td>\n",
       "      <td>7.30</td>\n",
       "      <td>2011-06-21 16:15:00</td>\n",
       "      <td>-73.991875</td>\n",
       "      <td>40.754437</td>\n",
       "      <td>-73.977230</td>\n",
       "      <td>40.774323</td>\n",
       "      <td>3</td>\n",
       "      <td>0.776185</td>\n",
       "      <td>1.372134</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2011-06-28 19:47:00.000000168</td>\n",
       "      <td>4.50</td>\n",
       "      <td>2011-06-28 19:47:00</td>\n",
       "      <td>-73.988893</td>\n",
       "      <td>40.760160</td>\n",
       "      <td>-73.986445</td>\n",
       "      <td>40.757857</td>\n",
       "      <td>3</td>\n",
       "      <td>0.129744</td>\n",
       "      <td>0.158907</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2012-05-04 06:11:20.000000200</td>\n",
       "      <td>9.30</td>\n",
       "      <td>2012-05-04 06:11:20</td>\n",
       "      <td>-73.989258</td>\n",
       "      <td>40.690835</td>\n",
       "      <td>-74.004133</td>\n",
       "      <td>40.725690</td>\n",
       "      <td>1</td>\n",
       "      <td>0.788375</td>\n",
       "      <td>2.404995</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2013-08-11 00:52:00.000000260</td>\n",
       "      <td>4.50</td>\n",
       "      <td>2013-08-11 00:52:00</td>\n",
       "      <td>-73.981020</td>\n",
       "      <td>40.737760</td>\n",
       "      <td>-73.980668</td>\n",
       "      <td>40.730497</td>\n",
       "      <td>2</td>\n",
       "      <td>0.018656</td>\n",
       "      <td>0.501147</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2014-02-19 16:03:00.000000160</td>\n",
       "      <td>5.50</td>\n",
       "      <td>2014-02-19 16:03:00</td>\n",
       "      <td>-73.976075</td>\n",
       "      <td>40.752422</td>\n",
       "      <td>-73.981082</td>\n",
       "      <td>40.759285</td>\n",
       "      <td>1</td>\n",
       "      <td>0.265371</td>\n",
       "      <td>0.473547</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2015-06-21 21:46:34.000000100</td>\n",
       "      <td>22.54</td>\n",
       "      <td>2015-06-21 21:46:34</td>\n",
       "      <td>-74.010483</td>\n",
       "      <td>40.717667</td>\n",
       "      <td>-73.985771</td>\n",
       "      <td>40.660366</td>\n",
       "      <td>1</td>\n",
       "      <td>1.309715</td>\n",
       "      <td>3.953739</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2009-01-09 16:10:00.000000170</td>\n",
       "      <td>31.90</td>\n",
       "      <td>2009-01-09 16:10:00</td>\n",
       "      <td>-73.873027</td>\n",
       "      <td>40.773883</td>\n",
       "      <td>-73.984545</td>\n",
       "      <td>40.769545</td>\n",
       "      <td>3</td>\n",
       "      <td>5.910454</td>\n",
       "      <td>0.299322</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39969</th>\n",
       "      <td>2012-01-19 02:07:00.000000280</td>\n",
       "      <td>5.30</td>\n",
       "      <td>2012-01-19 02:07:00</td>\n",
       "      <td>-73.956615</td>\n",
       "      <td>40.775282</td>\n",
       "      <td>-73.946360</td>\n",
       "      <td>40.789245</td>\n",
       "      <td>1</td>\n",
       "      <td>0.543515</td>\n",
       "      <td>0.963447</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39970</th>\n",
       "      <td>2009-11-15 08:58:00.000000700</td>\n",
       "      <td>4.50</td>\n",
       "      <td>2009-11-15 08:58:00</td>\n",
       "      <td>-73.944667</td>\n",
       "      <td>40.779615</td>\n",
       "      <td>-73.954473</td>\n",
       "      <td>40.772742</td>\n",
       "      <td>2</td>\n",
       "      <td>0.519718</td>\n",
       "      <td>0.474237</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39971</th>\n",
       "      <td>2010-08-16 08:30:53.000000100</td>\n",
       "      <td>6.90</td>\n",
       "      <td>2010-08-16 08:30:53</td>\n",
       "      <td>-73.953730</td>\n",
       "      <td>40.766796</td>\n",
       "      <td>-73.971987</td>\n",
       "      <td>40.760502</td>\n",
       "      <td>1</td>\n",
       "      <td>0.967621</td>\n",
       "      <td>0.434286</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39972</th>\n",
       "      <td>2010-09-06 17:57:00.000000160</td>\n",
       "      <td>30.50</td>\n",
       "      <td>2010-09-06 17:57:00</td>\n",
       "      <td>-73.963335</td>\n",
       "      <td>40.808983</td>\n",
       "      <td>-73.933162</td>\n",
       "      <td>40.856657</td>\n",
       "      <td>1</td>\n",
       "      <td>1.599169</td>\n",
       "      <td>3.289506</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39973</th>\n",
       "      <td>2011-06-29 08:07:08.000000200</td>\n",
       "      <td>7.70</td>\n",
       "      <td>2011-06-29 08:07:08</td>\n",
       "      <td>-73.989497</td>\n",
       "      <td>40.753408</td>\n",
       "      <td>-73.995474</td>\n",
       "      <td>40.725047</td>\n",
       "      <td>1</td>\n",
       "      <td>0.316781</td>\n",
       "      <td>1.956909</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39974</th>\n",
       "      <td>2012-04-15 18:04:00.000000930</td>\n",
       "      <td>6.50</td>\n",
       "      <td>2012-04-15 18:04:00</td>\n",
       "      <td>-73.988885</td>\n",
       "      <td>40.721957</td>\n",
       "      <td>-73.998676</td>\n",
       "      <td>40.733844</td>\n",
       "      <td>1</td>\n",
       "      <td>0.518923</td>\n",
       "      <td>0.820203</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39975</th>\n",
       "      <td>2012-06-13 22:19:00.000000126</td>\n",
       "      <td>9.70</td>\n",
       "      <td>2012-06-13 22:19:00</td>\n",
       "      <td>-74.001110</td>\n",
       "      <td>40.731577</td>\n",
       "      <td>-73.979437</td>\n",
       "      <td>40.748977</td>\n",
       "      <td>6</td>\n",
       "      <td>1.148669</td>\n",
       "      <td>1.200600</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39976</th>\n",
       "      <td>2014-04-19 12:42:00.000000680</td>\n",
       "      <td>4.50</td>\n",
       "      <td>2014-04-19 12:42:00</td>\n",
       "      <td>-73.981820</td>\n",
       "      <td>40.779220</td>\n",
       "      <td>-73.988810</td>\n",
       "      <td>40.774280</td>\n",
       "      <td>1</td>\n",
       "      <td>0.370470</td>\n",
       "      <td>0.340860</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39977</th>\n",
       "      <td>2010-10-16 17:42:00.000000161</td>\n",
       "      <td>5.70</td>\n",
       "      <td>2010-10-16 17:42:00</td>\n",
       "      <td>-73.992778</td>\n",
       "      <td>40.768293</td>\n",
       "      <td>-73.978023</td>\n",
       "      <td>40.760010</td>\n",
       "      <td>1</td>\n",
       "      <td>0.782015</td>\n",
       "      <td>0.571527</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39978</th>\n",
       "      <td>2010-05-25 18:16:00.000000271</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2010-05-25 18:16:00</td>\n",
       "      <td>-73.981438</td>\n",
       "      <td>40.746367</td>\n",
       "      <td>-73.978373</td>\n",
       "      <td>40.751347</td>\n",
       "      <td>1</td>\n",
       "      <td>0.162445</td>\n",
       "      <td>0.343620</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39979</th>\n",
       "      <td>2012-09-02 10:00:21.000000200</td>\n",
       "      <td>5.70</td>\n",
       "      <td>2012-09-02 10:00:21</td>\n",
       "      <td>-73.983994</td>\n",
       "      <td>40.765558</td>\n",
       "      <td>-73.980229</td>\n",
       "      <td>40.780697</td>\n",
       "      <td>1</td>\n",
       "      <td>0.199545</td>\n",
       "      <td>1.044591</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39980</th>\n",
       "      <td>2009-03-15 10:06:47.000000300</td>\n",
       "      <td>8.50</td>\n",
       "      <td>2009-03-15 10:06:47</td>\n",
       "      <td>-73.973822</td>\n",
       "      <td>40.791973</td>\n",
       "      <td>-73.982658</td>\n",
       "      <td>40.759242</td>\n",
       "      <td>1</td>\n",
       "      <td>0.468308</td>\n",
       "      <td>2.258439</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39981</th>\n",
       "      <td>2011-07-12 21:49:00.000000212</td>\n",
       "      <td>5.30</td>\n",
       "      <td>2011-07-12 21:49:00</td>\n",
       "      <td>-73.975942</td>\n",
       "      <td>40.740600</td>\n",
       "      <td>-73.987587</td>\n",
       "      <td>40.744400</td>\n",
       "      <td>1</td>\n",
       "      <td>0.617185</td>\n",
       "      <td>0.262200</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39982</th>\n",
       "      <td>2012-08-12 20:57:00.000000139</td>\n",
       "      <td>4.50</td>\n",
       "      <td>2012-08-12 20:57:00</td>\n",
       "      <td>-73.994722</td>\n",
       "      <td>40.750442</td>\n",
       "      <td>-74.001602</td>\n",
       "      <td>40.746620</td>\n",
       "      <td>1</td>\n",
       "      <td>0.364640</td>\n",
       "      <td>0.263718</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39983</th>\n",
       "      <td>2009-03-04 07:16:58.000000200</td>\n",
       "      <td>45.00</td>\n",
       "      <td>2009-03-04 07:16:58</td>\n",
       "      <td>-73.999516</td>\n",
       "      <td>40.719044</td>\n",
       "      <td>-73.776921</td>\n",
       "      <td>40.644669</td>\n",
       "      <td>1</td>\n",
       "      <td>11.797535</td>\n",
       "      <td>5.131875</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39984</th>\n",
       "      <td>2011-10-12 13:43:08.000000200</td>\n",
       "      <td>10.50</td>\n",
       "      <td>2011-10-12 13:43:08</td>\n",
       "      <td>-74.003546</td>\n",
       "      <td>40.748271</td>\n",
       "      <td>-74.005095</td>\n",
       "      <td>40.718857</td>\n",
       "      <td>1</td>\n",
       "      <td>0.082097</td>\n",
       "      <td>2.029566</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39985</th>\n",
       "      <td>2012-10-08 05:57:26.000000100</td>\n",
       "      <td>28.30</td>\n",
       "      <td>2012-10-08 05:57:26</td>\n",
       "      <td>-73.972885</td>\n",
       "      <td>40.743660</td>\n",
       "      <td>-73.870458</td>\n",
       "      <td>40.773737</td>\n",
       "      <td>2</td>\n",
       "      <td>5.428631</td>\n",
       "      <td>2.075313</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39986</th>\n",
       "      <td>2013-12-21 12:15:00.000000740</td>\n",
       "      <td>6.00</td>\n",
       "      <td>2013-12-21 12:15:00</td>\n",
       "      <td>-73.962362</td>\n",
       "      <td>40.779055</td>\n",
       "      <td>-73.976242</td>\n",
       "      <td>40.781015</td>\n",
       "      <td>1</td>\n",
       "      <td>0.735640</td>\n",
       "      <td>0.135240</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39988</th>\n",
       "      <td>2012-09-10 14:57:00.000000193</td>\n",
       "      <td>10.50</td>\n",
       "      <td>2012-09-10 14:57:00</td>\n",
       "      <td>-73.957140</td>\n",
       "      <td>40.766022</td>\n",
       "      <td>-73.932918</td>\n",
       "      <td>40.795317</td>\n",
       "      <td>1</td>\n",
       "      <td>1.283766</td>\n",
       "      <td>2.021355</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39989</th>\n",
       "      <td>2014-04-04 23:09:39.000000100</td>\n",
       "      <td>5.50</td>\n",
       "      <td>2014-04-04 23:09:39</td>\n",
       "      <td>-73.964014</td>\n",
       "      <td>40.767990</td>\n",
       "      <td>-73.953486</td>\n",
       "      <td>40.764279</td>\n",
       "      <td>1</td>\n",
       "      <td>0.557984</td>\n",
       "      <td>0.256059</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39990</th>\n",
       "      <td>2015-05-27 13:52:46.000000500</td>\n",
       "      <td>7.50</td>\n",
       "      <td>2015-05-27 13:52:46</td>\n",
       "      <td>-74.000778</td>\n",
       "      <td>40.752102</td>\n",
       "      <td>-73.991478</td>\n",
       "      <td>40.759659</td>\n",
       "      <td>3</td>\n",
       "      <td>0.492912</td>\n",
       "      <td>0.521427</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39991</th>\n",
       "      <td>2009-09-15 19:18:00.000000205</td>\n",
       "      <td>12.50</td>\n",
       "      <td>2009-09-15 19:18:00</td>\n",
       "      <td>-73.988683</td>\n",
       "      <td>40.764920</td>\n",
       "      <td>-74.003685</td>\n",
       "      <td>40.740188</td>\n",
       "      <td>2</td>\n",
       "      <td>0.795106</td>\n",
       "      <td>1.706508</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39992</th>\n",
       "      <td>2012-08-21 16:07:56.000000100</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2012-08-21 16:07:56</td>\n",
       "      <td>-73.969160</td>\n",
       "      <td>40.754195</td>\n",
       "      <td>-73.976719</td>\n",
       "      <td>40.751370</td>\n",
       "      <td>2</td>\n",
       "      <td>0.400627</td>\n",
       "      <td>0.194925</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39993</th>\n",
       "      <td>2014-10-29 20:54:34.000000100</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2014-10-29 20:54:34</td>\n",
       "      <td>-73.969644</td>\n",
       "      <td>40.753418</td>\n",
       "      <td>-73.973578</td>\n",
       "      <td>40.750548</td>\n",
       "      <td>1</td>\n",
       "      <td>0.208502</td>\n",
       "      <td>0.198030</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>29</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39994</th>\n",
       "      <td>2011-05-01 16:32:00.000000168</td>\n",
       "      <td>7.70</td>\n",
       "      <td>2011-05-01 16:32:00</td>\n",
       "      <td>-73.982138</td>\n",
       "      <td>40.740228</td>\n",
       "      <td>-73.991060</td>\n",
       "      <td>40.755120</td>\n",
       "      <td>1</td>\n",
       "      <td>0.472866</td>\n",
       "      <td>1.027548</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39995</th>\n",
       "      <td>2011-12-15 11:49:54.000000600</td>\n",
       "      <td>14.10</td>\n",
       "      <td>2011-12-15 11:49:54</td>\n",
       "      <td>-73.954187</td>\n",
       "      <td>40.770259</td>\n",
       "      <td>-73.976458</td>\n",
       "      <td>40.759378</td>\n",
       "      <td>1</td>\n",
       "      <td>1.180363</td>\n",
       "      <td>0.750789</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39996</th>\n",
       "      <td>2013-06-12 21:04:00.000000130</td>\n",
       "      <td>8.50</td>\n",
       "      <td>2013-06-12 21:04:00</td>\n",
       "      <td>-74.006557</td>\n",
       "      <td>40.731787</td>\n",
       "      <td>-73.992805</td>\n",
       "      <td>40.753065</td>\n",
       "      <td>5</td>\n",
       "      <td>0.728856</td>\n",
       "      <td>1.468182</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39997</th>\n",
       "      <td>2015-04-02 07:27:02.000000300</td>\n",
       "      <td>7.50</td>\n",
       "      <td>2015-04-02 07:27:02</td>\n",
       "      <td>-74.014801</td>\n",
       "      <td>40.704823</td>\n",
       "      <td>-74.015556</td>\n",
       "      <td>40.715179</td>\n",
       "      <td>1</td>\n",
       "      <td>0.040031</td>\n",
       "      <td>0.714626</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39998</th>\n",
       "      <td>2010-01-19 07:48:17.000000300</td>\n",
       "      <td>9.70</td>\n",
       "      <td>2010-01-19 07:48:17</td>\n",
       "      <td>-73.974666</td>\n",
       "      <td>40.752173</td>\n",
       "      <td>-73.997573</td>\n",
       "      <td>40.730103</td>\n",
       "      <td>1</td>\n",
       "      <td>1.214071</td>\n",
       "      <td>1.522830</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39999</th>\n",
       "      <td>2010-02-17 09:55:00.000000206</td>\n",
       "      <td>15.30</td>\n",
       "      <td>2010-02-17 09:55:00</td>\n",
       "      <td>-73.991490</td>\n",
       "      <td>40.727165</td>\n",
       "      <td>-73.995898</td>\n",
       "      <td>40.764143</td>\n",
       "      <td>1</td>\n",
       "      <td>0.233624</td>\n",
       "      <td>2.551482</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39169 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                key  fare_amount     pickup_datetime  \\\n",
       "0     2009-06-15 17:26:21.000000100         4.50 2009-06-15 17:26:21   \n",
       "1     2010-01-05 16:52:16.000000200        16.90 2010-01-05 16:52:16   \n",
       "2     2011-08-18 00:35:00.000000490         5.70 2011-08-18 00:35:00   \n",
       "3     2012-04-21 04:30:42.000000100         7.70 2012-04-21 04:30:42   \n",
       "4     2010-03-09 07:51:00.000000135         5.30 2010-03-09 07:51:00   \n",
       "5     2011-01-06 09:50:45.000000200        12.10 2011-01-06 09:50:45   \n",
       "6     2012-11-20 20:35:00.000000100         7.50 2012-11-20 20:35:00   \n",
       "7     2012-01-04 17:22:00.000000810        16.50 2012-01-04 17:22:00   \n",
       "8     2012-12-03 13:10:00.000000125         9.00 2012-12-03 13:10:00   \n",
       "9     2009-09-02 01:11:00.000000830         8.90 2009-09-02 01:11:00   \n",
       "10    2012-04-08 07:30:50.000000200         5.30 2012-04-08 07:30:50   \n",
       "12    2009-11-06 01:04:03.000000200         4.10 2009-11-06 01:04:03   \n",
       "13    2013-07-02 19:54:00.000000232         7.00 2013-07-02 19:54:00   \n",
       "14    2011-04-05 17:11:05.000000100         7.70 2011-04-05 17:11:05   \n",
       "16    2014-02-19 07:22:00.000000740        12.50 2014-02-19 07:22:00   \n",
       "17    2009-07-22 16:08:00.000000163         5.30 2009-07-22 16:08:00   \n",
       "18    2010-07-07 14:52:00.000000440         5.30 2010-07-07 14:52:00   \n",
       "19    2014-12-06 20:36:22.000000800         4.00 2014-12-06 20:36:22   \n",
       "20    2010-09-07 13:18:00.000000175        10.50 2010-09-07 13:18:00   \n",
       "21    2013-02-12 12:15:46.000000200        11.50 2013-02-12 12:15:46   \n",
       "22    2009-08-06 18:17:23.000000100         4.50 2009-08-06 18:17:23   \n",
       "23    2010-12-06 12:29:00.000000199         4.90 2010-12-06 12:29:00   \n",
       "24    2009-12-10 15:37:00.000000200         6.10 2009-12-10 15:37:00   \n",
       "25    2011-06-21 16:15:00.000000730         7.30 2011-06-21 16:15:00   \n",
       "27    2011-06-28 19:47:00.000000168         4.50 2011-06-28 19:47:00   \n",
       "28    2012-05-04 06:11:20.000000200         9.30 2012-05-04 06:11:20   \n",
       "29    2013-08-11 00:52:00.000000260         4.50 2013-08-11 00:52:00   \n",
       "30    2014-02-19 16:03:00.000000160         5.50 2014-02-19 16:03:00   \n",
       "31    2015-06-21 21:46:34.000000100        22.54 2015-06-21 21:46:34   \n",
       "32    2009-01-09 16:10:00.000000170        31.90 2009-01-09 16:10:00   \n",
       "...                             ...          ...                 ...   \n",
       "39969 2012-01-19 02:07:00.000000280         5.30 2012-01-19 02:07:00   \n",
       "39970 2009-11-15 08:58:00.000000700         4.50 2009-11-15 08:58:00   \n",
       "39971 2010-08-16 08:30:53.000000100         6.90 2010-08-16 08:30:53   \n",
       "39972 2010-09-06 17:57:00.000000160        30.50 2010-09-06 17:57:00   \n",
       "39973 2011-06-29 08:07:08.000000200         7.70 2011-06-29 08:07:08   \n",
       "39974 2012-04-15 18:04:00.000000930         6.50 2012-04-15 18:04:00   \n",
       "39975 2012-06-13 22:19:00.000000126         9.70 2012-06-13 22:19:00   \n",
       "39976 2014-04-19 12:42:00.000000680         4.50 2014-04-19 12:42:00   \n",
       "39977 2010-10-16 17:42:00.000000161         5.70 2010-10-16 17:42:00   \n",
       "39978 2010-05-25 18:16:00.000000271         3.70 2010-05-25 18:16:00   \n",
       "39979 2012-09-02 10:00:21.000000200         5.70 2012-09-02 10:00:21   \n",
       "39980 2009-03-15 10:06:47.000000300         8.50 2009-03-15 10:06:47   \n",
       "39981 2011-07-12 21:49:00.000000212         5.30 2011-07-12 21:49:00   \n",
       "39982 2012-08-12 20:57:00.000000139         4.50 2012-08-12 20:57:00   \n",
       "39983 2009-03-04 07:16:58.000000200        45.00 2009-03-04 07:16:58   \n",
       "39984 2011-10-12 13:43:08.000000200        10.50 2011-10-12 13:43:08   \n",
       "39985 2012-10-08 05:57:26.000000100        28.30 2012-10-08 05:57:26   \n",
       "39986 2013-12-21 12:15:00.000000740         6.00 2013-12-21 12:15:00   \n",
       "39988 2012-09-10 14:57:00.000000193        10.50 2012-09-10 14:57:00   \n",
       "39989 2014-04-04 23:09:39.000000100         5.50 2014-04-04 23:09:39   \n",
       "39990 2015-05-27 13:52:46.000000500         7.50 2015-05-27 13:52:46   \n",
       "39991 2009-09-15 19:18:00.000000205        12.50 2009-09-15 19:18:00   \n",
       "39992 2012-08-21 16:07:56.000000100         4.10 2012-08-21 16:07:56   \n",
       "39993 2014-10-29 20:54:34.000000100         3.50 2014-10-29 20:54:34   \n",
       "39994 2011-05-01 16:32:00.000000168         7.70 2011-05-01 16:32:00   \n",
       "39995 2011-12-15 11:49:54.000000600        14.10 2011-12-15 11:49:54   \n",
       "39996 2013-06-12 21:04:00.000000130         8.50 2013-06-12 21:04:00   \n",
       "39997 2015-04-02 07:27:02.000000300         7.50 2015-04-02 07:27:02   \n",
       "39998 2010-01-19 07:48:17.000000300         9.70 2010-01-19 07:48:17   \n",
       "39999 2010-02-17 09:55:00.000000206        15.30 2010-02-17 09:55:00   \n",
       "\n",
       "       pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0            -73.844311        40.721319         -73.841610         40.712278   \n",
       "1            -74.016048        40.711303         -73.979268         40.782004   \n",
       "2            -73.982738        40.761270         -73.991242         40.750562   \n",
       "3            -73.987130        40.733143         -73.991567         40.758092   \n",
       "4            -73.968095        40.768008         -73.956655         40.783762   \n",
       "5            -74.000964        40.731630         -73.972892         40.758233   \n",
       "6            -73.980002        40.751662         -73.973802         40.764842   \n",
       "7            -73.951300        40.774138         -73.990095         40.751048   \n",
       "8            -74.006462        40.726713         -73.993078         40.731628   \n",
       "9            -73.980658        40.733873         -73.991540         40.758138   \n",
       "10           -73.996335        40.737142         -73.980721         40.733559   \n",
       "12           -73.991601        40.744712         -73.983081         40.744682   \n",
       "13           -74.005360        40.728867         -74.008913         40.710907   \n",
       "14           -74.001821        40.737547         -73.998060         40.722788   \n",
       "16           -73.986430        40.760465         -73.988990         40.737075   \n",
       "17           -73.981060        40.737690         -73.994177         40.728412   \n",
       "18           -73.969505        40.784843         -73.958732         40.783357   \n",
       "19           -73.979815        40.751902         -73.979446         40.755481   \n",
       "20           -73.985382        40.747858         -73.978377         40.762070   \n",
       "21           -73.957954        40.779252         -73.961250         40.758787   \n",
       "22           -73.991707        40.770505         -73.985459         40.763671   \n",
       "23           -74.000632        40.747473         -73.986672         40.740577   \n",
       "24           -73.969622        40.756973         -73.981152         40.759712   \n",
       "25           -73.991875        40.754437         -73.977230         40.774323   \n",
       "27           -73.988893        40.760160         -73.986445         40.757857   \n",
       "28           -73.989258        40.690835         -74.004133         40.725690   \n",
       "29           -73.981020        40.737760         -73.980668         40.730497   \n",
       "30           -73.976075        40.752422         -73.981082         40.759285   \n",
       "31           -74.010483        40.717667         -73.985771         40.660366   \n",
       "32           -73.873027        40.773883         -73.984545         40.769545   \n",
       "...                 ...              ...                ...               ...   \n",
       "39969        -73.956615        40.775282         -73.946360         40.789245   \n",
       "39970        -73.944667        40.779615         -73.954473         40.772742   \n",
       "39971        -73.953730        40.766796         -73.971987         40.760502   \n",
       "39972        -73.963335        40.808983         -73.933162         40.856657   \n",
       "39973        -73.989497        40.753408         -73.995474         40.725047   \n",
       "39974        -73.988885        40.721957         -73.998676         40.733844   \n",
       "39975        -74.001110        40.731577         -73.979437         40.748977   \n",
       "39976        -73.981820        40.779220         -73.988810         40.774280   \n",
       "39977        -73.992778        40.768293         -73.978023         40.760010   \n",
       "39978        -73.981438        40.746367         -73.978373         40.751347   \n",
       "39979        -73.983994        40.765558         -73.980229         40.780697   \n",
       "39980        -73.973822        40.791973         -73.982658         40.759242   \n",
       "39981        -73.975942        40.740600         -73.987587         40.744400   \n",
       "39982        -73.994722        40.750442         -74.001602         40.746620   \n",
       "39983        -73.999516        40.719044         -73.776921         40.644669   \n",
       "39984        -74.003546        40.748271         -74.005095         40.718857   \n",
       "39985        -73.972885        40.743660         -73.870458         40.773737   \n",
       "39986        -73.962362        40.779055         -73.976242         40.781015   \n",
       "39988        -73.957140        40.766022         -73.932918         40.795317   \n",
       "39989        -73.964014        40.767990         -73.953486         40.764279   \n",
       "39990        -74.000778        40.752102         -73.991478         40.759659   \n",
       "39991        -73.988683        40.764920         -74.003685         40.740188   \n",
       "39992        -73.969160        40.754195         -73.976719         40.751370   \n",
       "39993        -73.969644        40.753418         -73.973578         40.750548   \n",
       "39994        -73.982138        40.740228         -73.991060         40.755120   \n",
       "39995        -73.954187        40.770259         -73.976458         40.759378   \n",
       "39996        -74.006557        40.731787         -73.992805         40.753065   \n",
       "39997        -74.014801        40.704823         -74.015556         40.715179   \n",
       "39998        -73.974666        40.752173         -73.997573         40.730103   \n",
       "39999        -73.991490        40.727165         -73.995898         40.764143   \n",
       "\n",
       "       passenger_count  abs_diff_longitude_miles  abs_diff_latitude_miles  \\\n",
       "0                    1                  0.143153                 0.623829   \n",
       "1                    1                  1.949340                 4.878369   \n",
       "2                    2                  0.450712                 0.738852   \n",
       "3                    1                  0.235161                 1.721481   \n",
       "4                    1                  0.606320                 1.087026   \n",
       "5                    1                  1.487816                 1.835607   \n",
       "6                    1                  0.328600                 0.909420   \n",
       "7                    1                  2.056135                 1.593210   \n",
       "8                    1                  0.709352                 0.339135   \n",
       "9                    2                  0.576746                 1.674285   \n",
       "10                   1                  0.827542                 0.247227   \n",
       "12                   2                  0.451560                 0.002070   \n",
       "13                   1                  0.188309                 1.239240   \n",
       "14                   2                  0.199333                 1.018371   \n",
       "16                   1                  0.135680                 1.613910   \n",
       "17                   1                  0.695201                 0.640182   \n",
       "18                   1                  0.570969                 0.102534   \n",
       "19                   1                  0.019557                 0.246951   \n",
       "20                   1                  0.371265                 0.980628   \n",
       "21                   1                  0.174688                 1.412085   \n",
       "22                   1                  0.331144                 0.471546   \n",
       "23                   1                  0.739880                 0.475824   \n",
       "24                   1                  0.611090                 0.188991   \n",
       "25                   3                  0.776185                 1.372134   \n",
       "27                   3                  0.129744                 0.158907   \n",
       "28                   1                  0.788375                 2.404995   \n",
       "29                   2                  0.018656                 0.501147   \n",
       "30                   1                  0.265371                 0.473547   \n",
       "31                   1                  1.309715                 3.953739   \n",
       "32                   3                  5.910454                 0.299322   \n",
       "...                ...                       ...                      ...   \n",
       "39969                1                  0.543515                 0.963447   \n",
       "39970                2                  0.519718                 0.474237   \n",
       "39971                1                  0.967621                 0.434286   \n",
       "39972                1                  1.599169                 3.289506   \n",
       "39973                1                  0.316781                 1.956909   \n",
       "39974                1                  0.518923                 0.820203   \n",
       "39975                6                  1.148669                 1.200600   \n",
       "39976                1                  0.370470                 0.340860   \n",
       "39977                1                  0.782015                 0.571527   \n",
       "39978                1                  0.162445                 0.343620   \n",
       "39979                1                  0.199545                 1.044591   \n",
       "39980                1                  0.468308                 2.258439   \n",
       "39981                1                  0.617185                 0.262200   \n",
       "39982                1                  0.364640                 0.263718   \n",
       "39983                1                 11.797535                 5.131875   \n",
       "39984                1                  0.082097                 2.029566   \n",
       "39985                2                  5.428631                 2.075313   \n",
       "39986                1                  0.735640                 0.135240   \n",
       "39988                1                  1.283766                 2.021355   \n",
       "39989                1                  0.557984                 0.256059   \n",
       "39990                3                  0.492912                 0.521427   \n",
       "39991                2                  0.795106                 1.706508   \n",
       "39992                2                  0.400627                 0.194925   \n",
       "39993                1                  0.208502                 0.198030   \n",
       "39994                1                  0.472866                 1.027548   \n",
       "39995                1                  1.180363                 0.750789   \n",
       "39996                5                  0.728856                 1.468182   \n",
       "39997                1                  0.040031                 0.714626   \n",
       "39998                1                  1.214071                 1.522830   \n",
       "39999                1                  0.233624                 2.551482   \n",
       "\n",
       "       pickup_day_of_hour  pickup_day_of_week  pickup_month  pickup_date  \\\n",
       "0                      17                   0             6           15   \n",
       "1                      16                   1             1            5   \n",
       "2                       0                   3             8           18   \n",
       "3                       4                   5             4           21   \n",
       "4                       7                   1             3            9   \n",
       "5                       9                   3             1            6   \n",
       "6                      20                   1            11           20   \n",
       "7                      17                   2             1            4   \n",
       "8                      13                   0            12            3   \n",
       "9                       1                   2             9            2   \n",
       "10                      7                   6             4            8   \n",
       "12                      1                   4            11            6   \n",
       "13                     19                   1             7            2   \n",
       "14                     17                   1             4            5   \n",
       "16                      7                   2             2           19   \n",
       "17                     16                   2             7           22   \n",
       "18                     14                   2             7            7   \n",
       "19                     20                   5            12            6   \n",
       "20                     13                   1             9            7   \n",
       "21                     12                   1             2           12   \n",
       "22                     18                   3             8            6   \n",
       "23                     12                   0            12            6   \n",
       "24                     15                   3            12           10   \n",
       "25                     16                   1             6           21   \n",
       "27                     19                   1             6           28   \n",
       "28                      6                   4             5            4   \n",
       "29                      0                   6             8           11   \n",
       "30                     16                   2             2           19   \n",
       "31                     21                   6             6           21   \n",
       "32                     16                   4             1            9   \n",
       "...                   ...                 ...           ...          ...   \n",
       "39969                   2                   3             1           19   \n",
       "39970                   8                   6            11           15   \n",
       "39971                   8                   0             8           16   \n",
       "39972                  17                   0             9            6   \n",
       "39973                   8                   2             6           29   \n",
       "39974                  18                   6             4           15   \n",
       "39975                  22                   2             6           13   \n",
       "39976                  12                   5             4           19   \n",
       "39977                  17                   5            10           16   \n",
       "39978                  18                   1             5           25   \n",
       "39979                  10                   6             9            2   \n",
       "39980                  10                   6             3           15   \n",
       "39981                  21                   1             7           12   \n",
       "39982                  20                   6             8           12   \n",
       "39983                   7                   2             3            4   \n",
       "39984                  13                   2            10           12   \n",
       "39985                   5                   0            10            8   \n",
       "39986                  12                   5            12           21   \n",
       "39988                  14                   0             9           10   \n",
       "39989                  23                   4             4            4   \n",
       "39990                  13                   2             5           27   \n",
       "39991                  19                   1             9           15   \n",
       "39992                  16                   1             8           21   \n",
       "39993                  20                   2            10           29   \n",
       "39994                  16                   6             5            1   \n",
       "39995                  11                   3            12           15   \n",
       "39996                  21                   2             6           12   \n",
       "39997                   7                   3             4            2   \n",
       "39998                   7                   1             1           19   \n",
       "39999                   9                   2             2           17   \n",
       "\n",
       "      pickup_day_of_week_name  pickup_year  \n",
       "0                      Monday         2009  \n",
       "1                     Tuesday         2010  \n",
       "2                    Thursday         2011  \n",
       "3                    Saturday         2012  \n",
       "4                     Tuesday         2010  \n",
       "5                    Thursday         2011  \n",
       "6                     Tuesday         2012  \n",
       "7                   Wednesday         2012  \n",
       "8                      Monday         2012  \n",
       "9                   Wednesday         2009  \n",
       "10                     Sunday         2012  \n",
       "12                     Friday         2009  \n",
       "13                    Tuesday         2013  \n",
       "14                    Tuesday         2011  \n",
       "16                  Wednesday         2014  \n",
       "17                  Wednesday         2009  \n",
       "18                  Wednesday         2010  \n",
       "19                   Saturday         2014  \n",
       "20                    Tuesday         2010  \n",
       "21                    Tuesday         2013  \n",
       "22                   Thursday         2009  \n",
       "23                     Monday         2010  \n",
       "24                   Thursday         2009  \n",
       "25                    Tuesday         2011  \n",
       "27                    Tuesday         2011  \n",
       "28                     Friday         2012  \n",
       "29                     Sunday         2013  \n",
       "30                  Wednesday         2014  \n",
       "31                     Sunday         2015  \n",
       "32                     Friday         2009  \n",
       "...                       ...          ...  \n",
       "39969                Thursday         2012  \n",
       "39970                  Sunday         2009  \n",
       "39971                  Monday         2010  \n",
       "39972                  Monday         2010  \n",
       "39973               Wednesday         2011  \n",
       "39974                  Sunday         2012  \n",
       "39975               Wednesday         2012  \n",
       "39976                Saturday         2014  \n",
       "39977                Saturday         2010  \n",
       "39978                 Tuesday         2010  \n",
       "39979                  Sunday         2012  \n",
       "39980                  Sunday         2009  \n",
       "39981                 Tuesday         2011  \n",
       "39982                  Sunday         2012  \n",
       "39983               Wednesday         2009  \n",
       "39984               Wednesday         2011  \n",
       "39985                  Monday         2012  \n",
       "39986                Saturday         2013  \n",
       "39988                  Monday         2012  \n",
       "39989                  Friday         2014  \n",
       "39990               Wednesday         2015  \n",
       "39991                 Tuesday         2009  \n",
       "39992                 Tuesday         2012  \n",
       "39993               Wednesday         2014  \n",
       "39994                  Sunday         2011  \n",
       "39995                Thursday         2011  \n",
       "39996               Wednesday         2013  \n",
       "39997                Thursday         2015  \n",
       "39998                 Tuesday         2010  \n",
       "39999               Wednesday         2010  \n",
       "\n",
       "[39169 rows x 16 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def prepare_time(df):\n",
    "    df['key'] = pd.to_datetime(df['key'])\n",
    "    df['pickup_datetime'] = pd.to_datetime(df['pickup_datetime'])\n",
    "    df['pickup_day_of_hour'] = df['pickup_datetime'].dt.hour\n",
    "    df['pickup_day_of_week'] = df['pickup_datetime'].dt.weekday\n",
    "    df['pickup_month'] = df['pickup_datetime'].dt.month\n",
    "    df['pickup_date'] = df['pickup_datetime'].dt.day\n",
    "    df['pickup_day_of_week_name'] = df['pickup_datetime'].dt.weekday_name\n",
    "    df['pickup_year'] = df['pickup_datetime'].dt.year\n",
    "    return df\n",
    "\n",
    "df_clean_1=prepare_time(df_clean_1)\n",
    "df_clean_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def EucledianDistanceTaxiFare(df):\n",
    "    df['EucledianDistance'] = np.sqrt(np.power(df['abs_diff_longitude_miles'],2)+np.power(df['abs_diff_latitude_miles'],2))\n",
    "    return df;\n",
    "df_clean_1=EucledianDistanceTaxiFare(df_clean_1)\n",
    "#corr = df_clean_1.corr(method='pearson')\n",
    "#print corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xe701470>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1052c8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#There are no rides greater than 40 miles in the cleaned data set.\n",
    "tmp=df_clean_1\n",
    "tmp['pickup_year'].hist(bins=80, figsize=(14,6),color = \"skyblue\", ec=\"skyblue\")\n",
    "plt.xlabel('Ride distance in miles')\n",
    "plt.ylabel('Number of Trips')\n",
    "plt.title('Ride distance-miles')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fare_amount</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>abs_diff_longitude_miles</th>\n",
       "      <th>abs_diff_latitude_miles</th>\n",
       "      <th>pickup_day_of_hour</th>\n",
       "      <th>pickup_day_of_week</th>\n",
       "      <th>pickup_month</th>\n",
       "      <th>pickup_date</th>\n",
       "      <th>pickup_year</th>\n",
       "      <th>EucledianDistance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>39169.000000</td>\n",
       "      <td>39169.000000</td>\n",
       "      <td>39169.000000</td>\n",
       "      <td>39169.000000</td>\n",
       "      <td>39169.000000</td>\n",
       "      <td>39169.000000</td>\n",
       "      <td>39169.000000</td>\n",
       "      <td>39169.000000</td>\n",
       "      <td>39169.000000</td>\n",
       "      <td>39169.000000</td>\n",
       "      <td>39169.000000</td>\n",
       "      <td>39169.000000</td>\n",
       "      <td>39169.000000</td>\n",
       "      <td>39169.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.341876</td>\n",
       "      <td>-73.975100</td>\n",
       "      <td>40.750909</td>\n",
       "      <td>-73.974059</td>\n",
       "      <td>40.751334</td>\n",
       "      <td>1.665092</td>\n",
       "      <td>1.221685</td>\n",
       "      <td>1.495455</td>\n",
       "      <td>13.481018</td>\n",
       "      <td>3.035819</td>\n",
       "      <td>6.269729</td>\n",
       "      <td>15.683142</td>\n",
       "      <td>2011.745666</td>\n",
       "      <td>2.108553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.590514</td>\n",
       "      <td>0.040580</td>\n",
       "      <td>0.035064</td>\n",
       "      <td>0.039073</td>\n",
       "      <td>0.036228</td>\n",
       "      <td>1.285075</td>\n",
       "      <td>1.924363</td>\n",
       "      <td>2.007788</td>\n",
       "      <td>6.513141</td>\n",
       "      <td>1.957451</td>\n",
       "      <td>3.457733</td>\n",
       "      <td>8.676648</td>\n",
       "      <td>1.863570</td>\n",
       "      <td>2.649014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>-75.414728</td>\n",
       "      <td>39.603178</td>\n",
       "      <td>-75.354559</td>\n",
       "      <td>39.604972</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>-73.992300</td>\n",
       "      <td>40.736508</td>\n",
       "      <td>-73.991362</td>\n",
       "      <td>40.735858</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.324360</td>\n",
       "      <td>0.479895</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>0.789258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.500000</td>\n",
       "      <td>-73.982050</td>\n",
       "      <td>40.753458</td>\n",
       "      <td>-73.980612</td>\n",
       "      <td>40.754060</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.679990</td>\n",
       "      <td>0.981939</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>1.349659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.500000</td>\n",
       "      <td>-73.968323</td>\n",
       "      <td>40.767755</td>\n",
       "      <td>-73.965532</td>\n",
       "      <td>40.768473</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.281006</td>\n",
       "      <td>1.883562</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>2.448321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>-72.856968</td>\n",
       "      <td>42.160275</td>\n",
       "      <td>-72.854940</td>\n",
       "      <td>42.168717</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>46.316647</td>\n",
       "      <td>80.638420</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>80.638421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        fare_amount  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "count  39169.000000      39169.000000     39169.000000       39169.000000   \n",
       "mean      11.341876        -73.975100        40.750909         -73.974059   \n",
       "std        9.590514          0.040580         0.035064           0.039073   \n",
       "min        2.500000        -75.414728        39.603178         -75.354559   \n",
       "25%        6.000000        -73.992300        40.736508         -73.991362   \n",
       "50%        8.500000        -73.982050        40.753458         -73.980612   \n",
       "75%       12.500000        -73.968323        40.767755         -73.965532   \n",
       "max      200.000000        -72.856968        42.160275         -72.854940   \n",
       "\n",
       "       dropoff_latitude  passenger_count  abs_diff_longitude_miles  \\\n",
       "count      39169.000000     39169.000000              39169.000000   \n",
       "mean          40.751334         1.665092                  1.221685   \n",
       "std            0.036228         1.285075                  1.924363   \n",
       "min           39.604972         0.000000                  0.000000   \n",
       "25%           40.735858         1.000000                  0.324360   \n",
       "50%           40.754060         1.000000                  0.679990   \n",
       "75%           40.768473         2.000000                  1.281006   \n",
       "max           42.168717         6.000000                 46.316647   \n",
       "\n",
       "       abs_diff_latitude_miles  pickup_day_of_hour  pickup_day_of_week  \\\n",
       "count             39169.000000        39169.000000        39169.000000   \n",
       "mean                  1.495455           13.481018            3.035819   \n",
       "std                   2.007788            6.513141            1.957451   \n",
       "min                   0.000000            0.000000            0.000000   \n",
       "25%                   0.479895            9.000000            1.000000   \n",
       "50%                   0.981939           14.000000            3.000000   \n",
       "75%                   1.883562           19.000000            5.000000   \n",
       "max                  80.638420           23.000000            6.000000   \n",
       "\n",
       "       pickup_month   pickup_date   pickup_year  EucledianDistance  \n",
       "count  39169.000000  39169.000000  39169.000000       39169.000000  \n",
       "mean       6.269729     15.683142   2011.745666           2.108553  \n",
       "std        3.457733      8.676648      1.863570           2.649014  \n",
       "min        1.000000      1.000000   2009.000000           0.000000  \n",
       "25%        3.000000      8.000000   2010.000000           0.789258  \n",
       "50%        6.000000     16.000000   2012.000000           1.349659  \n",
       "75%        9.000000     23.000000   2013.000000           2.448321  \n",
       "max       12.000000     31.000000   2015.000000          80.638421  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Can only tuple-index with a MultiIndex",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m\u001b[0m",
      "\u001b[1;31mValueError\u001b[0mTraceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-7ac33af28407>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_clean_1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"EucledianDistance\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'go-'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Data points\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbbox_to_anchor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1.05\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mborderaxespad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\kelad\\Anaconda2\\lib\\site-packages\\pandas\\core\\series.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    807\u001b[0m             \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_bool_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    808\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 809\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_with\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    810\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    811\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_with\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\kelad\\Anaconda2\\lib\\site-packages\\pandas\\core\\series.pyc\u001b[0m in \u001b[0;36m_get_with\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    820\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    821\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 822\u001b[1;33m                     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_values_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    823\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    824\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\kelad\\Anaconda2\\lib\\site-packages\\pandas\\core\\series.pyc\u001b[0m in \u001b[0;36m_get_values_tuple\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    866\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 867\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Can only tuple-index with a MultiIndex'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    868\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    869\u001b[0m         \u001b[1;31m# If key is contained, would have returned by now\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Can only tuple-index with a MultiIndex"
     ]
    }
   ],
   "source": [
    "plt.plot(df_clean_1[\"EucledianDistance\"][:,-1], 'go-', label=\"Data points\")\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdc3a198>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_1['EucledianDistance'].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x191e2cf8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(16,6))\n",
    "axs.set_title('Visualizing Eucledian Distance of Ride and Taxi Fare')\n",
    "axs.set_xlabel('Ride Distance - miles')\n",
    "axs.set_ylabel('Fare for the taxi ride in USD')\n",
    "axs.scatter(tmp.EucledianDistance, tmp.fare_amount, alpha=0.5,color = \"burlywood\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1cf684a8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(16,6))\n",
    "axs.set_title('Visualizing Time of Day and Distance Travlled')\n",
    "axs.set_xlabel('Hours of the Day')\n",
    "axs.set_ylabel('Ride Distance - miles')\n",
    "axs.scatter(tmp.pickup_day_of_hour, tmp.EucledianDistance, alpha=0.5,color = \"green\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1e4f3eb8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(16,6))\n",
    "axs.set_title('Visualizing Time of Day and Taxi Fare')\n",
    "axs.set_xlabel('Hours of the Day')\n",
    "axs.set_ylabel('Taxi Fare in USD')\n",
    "axs.scatter(tmp.pickup_day_of_hour, tmp.fare_amount, alpha=0.5,color = \"black\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # 4) Plot of our own"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp_Inde=df_clean_1[df_clean_1.pickup_month==7]\n",
    "tmp_Inde=df_clean_1[df_clean_1.pickup_date==4]\n",
    "tmp_chris=df_clean_1[df_clean_1.pickup_month==12]\n",
    "tmp_chris=df_clean_1[df_clean_1.pickup_date==25]\n",
    "tmp_NY=df_clean_1[(df_clean_1.pickup_month==1) | (df_clean_1.pickup_month==12)]\n",
    "tmp_NY=df_clean_1[(df_clean_1.pickup_date==1)|(df_clean_1.pickup_date==31)]\n",
    "tmp_NY=df_clean_1[(df_clean_1.pickup_day_of_hour<7)]\n",
    "tmp_weekend= df_clean_1[(df_clean_1.pickup_day_of_week_name=='Saturday') | (df_clean_1.pickup_day_of_week_name=='Sunday')]\n",
    "tmp_weekday=df_clean_1[(df_clean_1.pickup_day_of_week_name=='Monday') | (df_clean_1.pickup_day_of_week_name=='Tuesday')| (df_clean_1.pickup_day_of_week_name=='Wednesday')| (df_clean_1.pickup_day_of_week_name=='Thurday')| (df_clean_1.pickup_day_of_week_name=='Friday')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_holiday = [['Christmas',tmp_chris['fare_amount'].mean()],['New Year',tmp_NY['fare_amount'].mean()],['Independence Day',tmp_Inde['fare_amount'].mean()],['Year Average',df_clean_1['fare_amount'].mean()]]\n",
    "df_holidays=pd.DataFrame(data_holiday,columns=['Holiday','AverageFare'])\n",
    "data_workdays = [['Weekday',tmp_weekend['fare_amount'].mean()],['Weekend',tmp_weekday['fare_amount'].mean()]]\n",
    "df_workdays=pd.DataFrame(data_workdays,columns=['Weekdays','AverageFare'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4 artists>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(16,6))\n",
    "axs.set_title('Visualizing Average fare and Prominent Holidays')\n",
    "axs.set_xlabel('Holidays')\n",
    "axs.set_ylabel('Average Fare of Cab')\n",
    "axs.bar(['Christmas','New Year','Independence Day', 'Year Average'], height=[df_holidays.AverageFare[0],df_holidays.AverageFare[1],df_holidays.AverageFare[2],df_holidays.AverageFare[3]])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 2 artists>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(16,6))\n",
    "axs.set_title('Visualizing Average fare and Weekday/Weekend')\n",
    "axs.set_xlabel('Weekday/Weekend')\n",
    "axs.set_ylabel('Average Fare of Cab')\n",
    "axs.bar(['Weekday','Not Weekday'], height=[df_workdays.AverageFare[0],df_workdays.AverageFare[1]])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NOTE: There are a few more plots created after point 8."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5) Generating new feature set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding approximate commute time\n",
    "def commute_time(df):\n",
    "    df['commute_time'] = 0.5*((df['fare_amount']/0.4)-(df['EucledianDistance']/5))\n",
    "    return df\n",
    "\n",
    "df_clean_1=commute_time(df_clean_1)\n",
    "df_clean_1.head()                           \n",
    "                              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def average_speed(df):\n",
    "    df['average_speed'] = df['EucledianDistance']/(df['commute_time']/60)\n",
    "    return df\n",
    "\n",
    "df_clean_1=average_speed(df_clean_1)\n",
    "df_clean_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def time_day(df_clean_1):\n",
    "        df_clean_1['time_day']=np.where(((df_clean_1.pickup_day_of_hour>=20)|(df_clean_1.pickup_day_of_hour<=6)),0,1)\n",
    "        return df_clean_1\n",
    "def airport(df_clean_1):\n",
    "        df_clean_1['airport']=np.where((((df_clean_1.pickup_latitude>40.62)&(df_clean_1.pickup_latitude<40.66))&((df_clean_1.pickup_longitude>-73.82)&(df_clean_1.pickup_longitude<-73.74))|((df_clean_1.pickup_latitude>40.76)&(df_clean_1.pickup_latitude<40.78))&((df_clean_1.pickup_longitude>-73.88)&(df_clean_1.pickup_longitude<-73.85))|((df_clean_1.pickup_latitude>40.66)&(df_clean_1.pickup_latitude<40.72))&((df_clean_1.pickup_longitude>-74.20)&(df_clean_1.pickup_longitude<-74.129))),1,0)\n",
    "        return df_clean_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6) Setting up a simple Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_x=df_clean_1.drop(['key', 'pickup_datetime','fare_amount','pickup_day_of_week_name','commute_time','average_speed'], axis=1)\n",
    "df_y=df_clean_1.fare_amount\n",
    "df_x=time_day(df_x)\n",
    "df_x=airport(df_x)\n",
    "linreg=linear_model.LinearRegression()\n",
    "linreg.fit(df_x,df_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.15165378e+01, -1.14384153e+00,  2.00893179e+01, -1.10049725e+01,\n",
       "        5.09875838e-02,  1.95049296e+00,  8.04838726e-01, -3.14314670e-03,\n",
       "       -5.12634480e-04,  8.26434666e-02,  2.27925602e-03,  5.39586391e-01,\n",
       "        7.12490840e-01,  5.10498764e-01,  9.72522803e+00])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>abs_diff_longitude_miles</th>\n",
       "      <th>abs_diff_latitude_miles</th>\n",
       "      <th>pickup_day_of_hour</th>\n",
       "      <th>pickup_day_of_week</th>\n",
       "      <th>pickup_month</th>\n",
       "      <th>pickup_date</th>\n",
       "      <th>pickup_year</th>\n",
       "      <th>EucledianDistance</th>\n",
       "      <th>time_day</th>\n",
       "      <th>airport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
       "      <td>0.143153</td>\n",
       "      <td>0.623829</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.640043</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "      <td>1.949340</td>\n",
       "      <td>4.878369</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>5.253419</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "      <td>0.450712</td>\n",
       "      <td>0.738852</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.865473</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>0.235161</td>\n",
       "      <td>1.721481</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.737469</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "      <td>0.606320</td>\n",
       "      <td>1.087026</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2010</td>\n",
       "      <td>1.244689</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.844311        40.721319         -73.841610         40.712278   \n",
       "1        -74.016048        40.711303         -73.979268         40.782004   \n",
       "2        -73.982738        40.761270         -73.991242         40.750562   \n",
       "3        -73.987130        40.733143         -73.991567         40.758092   \n",
       "4        -73.968095        40.768008         -73.956655         40.783762   \n",
       "\n",
       "   passenger_count  abs_diff_longitude_miles  abs_diff_latitude_miles  \\\n",
       "0                1                  0.143153                 0.623829   \n",
       "1                1                  1.949340                 4.878369   \n",
       "2                2                  0.450712                 0.738852   \n",
       "3                1                  0.235161                 1.721481   \n",
       "4                1                  0.606320                 1.087026   \n",
       "\n",
       "   pickup_day_of_hour  pickup_day_of_week  pickup_month  pickup_date  \\\n",
       "0                  17                   0             6           15   \n",
       "1                  16                   1             1            5   \n",
       "2                   0                   3             8           18   \n",
       "3                   4                   5             4           21   \n",
       "4                   7                   1             3            9   \n",
       "\n",
       "   pickup_year  EucledianDistance  time_day  airport  \n",
       "0         2009           0.640043         1        0  \n",
       "1         2010           5.253419         1        0  \n",
       "2         2011           0.865473         0        0  \n",
       "3         2012           1.737469         0        0  \n",
       "4         2010           1.244689         1        0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test=pd.read_csv('C:\\Dhruv Course Data\\\\test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>abs_diff_longitude_miles</th>\n",
       "      <th>abs_diff_latitude_miles</th>\n",
       "      <th>pickup_day_of_hour</th>\n",
       "      <th>pickup_day_of_week</th>\n",
       "      <th>pickup_month</th>\n",
       "      <th>pickup_date</th>\n",
       "      <th>pickup_year</th>\n",
       "      <th>EucledianDistance</th>\n",
       "      <th>time_day</th>\n",
       "      <th>airport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-73.973320</td>\n",
       "      <td>40.763805</td>\n",
       "      <td>-73.981430</td>\n",
       "      <td>40.743835</td>\n",
       "      <td>1</td>\n",
       "      <td>0.429832</td>\n",
       "      <td>1.377926</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.443411</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-73.986862</td>\n",
       "      <td>40.719383</td>\n",
       "      <td>-73.998886</td>\n",
       "      <td>40.739201</td>\n",
       "      <td>1</td>\n",
       "      <td>0.637268</td>\n",
       "      <td>1.367397</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.508604</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-73.982524</td>\n",
       "      <td>40.751260</td>\n",
       "      <td>-73.979654</td>\n",
       "      <td>40.746139</td>\n",
       "      <td>1</td>\n",
       "      <td>0.152110</td>\n",
       "      <td>0.353349</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.384699</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-73.981160</td>\n",
       "      <td>40.767807</td>\n",
       "      <td>-73.990448</td>\n",
       "      <td>40.751635</td>\n",
       "      <td>1</td>\n",
       "      <td>0.492264</td>\n",
       "      <td>1.115868</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.219625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-73.966046</td>\n",
       "      <td>40.789775</td>\n",
       "      <td>-73.988565</td>\n",
       "      <td>40.744427</td>\n",
       "      <td>1</td>\n",
       "      <td>1.193507</td>\n",
       "      <td>3.129012</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>3.348907</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.973320        40.763805         -73.981430         40.743835   \n",
       "1        -73.986862        40.719383         -73.998886         40.739201   \n",
       "2        -73.982524        40.751260         -73.979654         40.746139   \n",
       "3        -73.981160        40.767807         -73.990448         40.751635   \n",
       "4        -73.966046        40.789775         -73.988565         40.744427   \n",
       "\n",
       "   passenger_count  abs_diff_longitude_miles  abs_diff_latitude_miles  \\\n",
       "0                1                  0.429832                 1.377926   \n",
       "1                1                  0.637268                 1.367397   \n",
       "2                1                  0.152110                 0.353349   \n",
       "3                1                  0.492264                 1.115868   \n",
       "4                1                  1.193507                 3.129012   \n",
       "\n",
       "   pickup_day_of_hour  pickup_day_of_week  pickup_month  pickup_date  \\\n",
       "0                  13                   1             1           27   \n",
       "1                  13                   1             1           27   \n",
       "2                  11                   5            10            8   \n",
       "3                  21                   5            12            1   \n",
       "4                  21                   5            12            1   \n",
       "\n",
       "   pickup_year  EucledianDistance  time_day  airport  \n",
       "0         2015           1.443411         1        0  \n",
       "1         2015           1.508604         1        0  \n",
       "2         2011           0.384699         1        0  \n",
       "3         2012           1.219625         0        0  \n",
       "4         2012           3.348907         0        0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test=longitude(df_test)\n",
    "df_test=prepare_time(df_test)\n",
    "df_test=EucledianDistanceTaxiFare(df_test)\n",
    "df_test=time_day(df_test)\n",
    "df_test=airport(df_test)\n",
    "df_test=df_test.drop(['key', 'pickup_datetime','pickup_day_of_week_name'], axis=1)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=linreg.predict(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-11.516537833992492\n",
      "-1.143841531108411\n",
      "20.08931794150554\n",
      "-11.004972524150086\n",
      "0.050987583777515305\n",
      "1.9504929614260893\n",
      "0.8048387259684119\n",
      "-0.003143146697975667\n",
      "-0.0005126344804598278\n",
      "0.08264346656976797\n",
      "0.002279256022140269\n",
      "0.5395863910616406\n",
      "0.7124908400634912\n",
      "0.5104987639267443\n",
      "9.725228028540672\n"
     ]
    }
   ],
   "source": [
    "for i in linreg.coef_:\n",
    "    print i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_key=pd.read_csv('C:\\Dhruv Course Data\\\\test.csv')\n",
    "submission = pd.DataFrame(columns=['key','fare_amount'])\n",
    "submission.key=df_key.key\n",
    "submission.fare_amount=a\n",
    "submission.to_csv('submission_linreg.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>7) Using External Data Set</h1> <br>\n",
    "(a)The weather of the the city of New York on that day could help in predicting if rain and snow days effect taxi fares.<br>\n",
    "(b)The introduction of Uber and Lyft.<br>\n",
    "(c)The office hours of companies.<br>\n",
    "(d)Suspension of subway lines for repairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kelad\\Anaconda2\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2714: DtypeWarning: Columns (17) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "df_crime=pd.read_csv('C:\\Users\\kelad\\Downloads\\crimes-new-york-city\\NYPD_Complaint_Data_Historic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_crime_data(df,data):\n",
    "    df=df[df.Latitude>=-38.9]\n",
    "    df=df[df.Latitude<=43]\n",
    "    df=df[df.Longitude>=-76]\n",
    "    df=df[df.Longitude<=-70]\n",
    "    df = df[np.isfinite(df['Latitude'])]\n",
    "    df = df[np.isfinite(df['Longitude'])]\n",
    "    df['ConvertedLatitude']=(df['Latitude']*10000)\n",
    "    df['ConvertedLongitude']=(df['Longitude']*10000)\n",
    "    df['ConvertedLatitude'] = df['ConvertedLatitude'].astype(int)\n",
    "    df['ConvertedLongitude'] = df['ConvertedLongitude'].astype(int)\n",
    "    data['ConvertedLatitude']=(data['pickup_latitude']*10000)\n",
    "    data['ConvertedLongitude']=(data['pickup_longitude']*10000)\n",
    "    data['ConvertedLatitude'] = data['ConvertedLatitude'].astype(int)\n",
    "    data['ConvertedLongitude'] = data['ConvertedLongitude'].astype(int)    \n",
    "    l1=df['ConvertedLongitude'].tolist()\n",
    "    l2=df['ConvertedLatitude'].tolist()\n",
    "    t1=data['ConvertedLongitude'].isin(l1)\n",
    "    t2=data['ConvertedLatitude'].isin(l1)\n",
    "    t1=pd.Series(t1)\n",
    "    t2=pd.Series(t2)\n",
    "    data['crime']=t1\n",
    "    return data\n",
    "df_clean_1=add_crime_data(df_crime,df_clean_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8) Better Prediction Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_clean_1=df_clean_1[(df_clean_1.EucledianDistance<10)&(df_clean_1.fare_amount>100)]\n",
    "chk=time_day(df_clean_1)\n",
    "chk=airport(df_clean_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33818"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(chk[chk.airport==1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fare_amount</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>abs_diff_longitude_miles</th>\n",
       "      <th>abs_diff_latitude_miles</th>\n",
       "      <th>pickup_day_of_hour</th>\n",
       "      <th>pickup_day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>pickup_date</th>\n",
       "      <th>pickup_year</th>\n",
       "      <th>EucledianDistance</th>\n",
       "      <th>commute_time</th>\n",
       "      <th>average_speed</th>\n",
       "      <th>ConvertedLatitude</th>\n",
       "      <th>ConvertedLongitude</th>\n",
       "      <th>crime</th>\n",
       "      <th>time_day</th>\n",
       "      <th>airport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fare_amount</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.342889</td>\n",
       "      <td>-0.167925</td>\n",
       "      <td>0.263273</td>\n",
       "      <td>-0.140677</td>\n",
       "      <td>0.014199</td>\n",
       "      <td>0.743102</td>\n",
       "      <td>0.572863</td>\n",
       "      <td>-0.019220</td>\n",
       "      <td>0.002509</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001450</td>\n",
       "      <td>0.116807</td>\n",
       "      <td>0.754946</td>\n",
       "      <td>0.999900</td>\n",
       "      <td>0.011627</td>\n",
       "      <td>-0.167924</td>\n",
       "      <td>0.342886</td>\n",
       "      <td>-0.043292</td>\n",
       "      <td>-0.016095</td>\n",
       "      <td>0.487743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_longitude</th>\n",
       "      <td>0.342889</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.142903</td>\n",
       "      <td>0.458642</td>\n",
       "      <td>0.147233</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>0.370813</td>\n",
       "      <td>0.220855</td>\n",
       "      <td>0.016015</td>\n",
       "      <td>-0.021373</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000620</td>\n",
       "      <td>0.002755</td>\n",
       "      <td>0.323848</td>\n",
       "      <td>0.341449</td>\n",
       "      <td>0.015142</td>\n",
       "      <td>0.142907</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.177686</td>\n",
       "      <td>0.040478</td>\n",
       "      <td>0.636910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_latitude</th>\n",
       "      <td>-0.167925</td>\n",
       "      <td>0.142903</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.171180</td>\n",
       "      <td>0.512019</td>\n",
       "      <td>-0.006186</td>\n",
       "      <td>-0.132356</td>\n",
       "      <td>-0.177085</td>\n",
       "      <td>0.024123</td>\n",
       "      <td>-0.033058</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000938</td>\n",
       "      <td>-0.015750</td>\n",
       "      <td>-0.185071</td>\n",
       "      <td>-0.166648</td>\n",
       "      <td>-0.023064</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.142909</td>\n",
       "      <td>-0.172792</td>\n",
       "      <td>0.096235</td>\n",
       "      <td>-0.196820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <td>0.263273</td>\n",
       "      <td>0.458642</td>\n",
       "      <td>0.171180</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.228016</td>\n",
       "      <td>-0.001451</td>\n",
       "      <td>0.292718</td>\n",
       "      <td>0.158364</td>\n",
       "      <td>-0.037808</td>\n",
       "      <td>-0.001170</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001382</td>\n",
       "      <td>-0.000176</td>\n",
       "      <td>0.253695</td>\n",
       "      <td>0.262059</td>\n",
       "      <td>0.000198</td>\n",
       "      <td>0.171182</td>\n",
       "      <td>0.458644</td>\n",
       "      <td>-0.145938</td>\n",
       "      <td>-0.024412</td>\n",
       "      <td>0.124798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <td>-0.140677</td>\n",
       "      <td>0.147233</td>\n",
       "      <td>0.512019</td>\n",
       "      <td>0.228016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003659</td>\n",
       "      <td>-0.109357</td>\n",
       "      <td>-0.157052</td>\n",
       "      <td>0.016141</td>\n",
       "      <td>-0.026133</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.008397</td>\n",
       "      <td>-0.165184</td>\n",
       "      <td>-0.139388</td>\n",
       "      <td>0.000357</td>\n",
       "      <td>0.512020</td>\n",
       "      <td>0.147237</td>\n",
       "      <td>-0.097277</td>\n",
       "      <td>0.067748</td>\n",
       "      <td>-0.061423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>passenger_count</th>\n",
       "      <td>0.014199</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>-0.006186</td>\n",
       "      <td>-0.001451</td>\n",
       "      <td>-0.003659</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.007982</td>\n",
       "      <td>0.005902</td>\n",
       "      <td>0.016920</td>\n",
       "      <td>0.036511</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004113</td>\n",
       "      <td>0.005922</td>\n",
       "      <td>0.007755</td>\n",
       "      <td>0.014262</td>\n",
       "      <td>-0.001706</td>\n",
       "      <td>-0.006188</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>0.003148</td>\n",
       "      <td>-0.021932</td>\n",
       "      <td>0.008426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abs_diff_longitude_miles</th>\n",
       "      <td>0.743102</td>\n",
       "      <td>0.370813</td>\n",
       "      <td>-0.132356</td>\n",
       "      <td>0.292718</td>\n",
       "      <td>-0.109357</td>\n",
       "      <td>0.007982</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.508590</td>\n",
       "      <td>-0.026448</td>\n",
       "      <td>0.000680</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001178</td>\n",
       "      <td>0.020137</td>\n",
       "      <td>0.858010</td>\n",
       "      <td>0.736607</td>\n",
       "      <td>0.052811</td>\n",
       "      <td>-0.132353</td>\n",
       "      <td>0.370812</td>\n",
       "      <td>-0.129639</td>\n",
       "      <td>-0.028602</td>\n",
       "      <td>0.507270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abs_diff_latitude_miles</th>\n",
       "      <td>0.572863</td>\n",
       "      <td>0.220855</td>\n",
       "      <td>-0.177085</td>\n",
       "      <td>0.158364</td>\n",
       "      <td>-0.157052</td>\n",
       "      <td>0.005902</td>\n",
       "      <td>0.508590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.018758</td>\n",
       "      <td>0.019527</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.008412</td>\n",
       "      <td>0.863640</td>\n",
       "      <td>0.563485</td>\n",
       "      <td>0.062411</td>\n",
       "      <td>-0.177084</td>\n",
       "      <td>0.220856</td>\n",
       "      <td>-0.083204</td>\n",
       "      <td>-0.080494</td>\n",
       "      <td>0.240970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_day_of_hour</th>\n",
       "      <td>-0.019220</td>\n",
       "      <td>0.016015</td>\n",
       "      <td>0.024123</td>\n",
       "      <td>-0.037808</td>\n",
       "      <td>0.016141</td>\n",
       "      <td>0.016920</td>\n",
       "      <td>-0.026448</td>\n",
       "      <td>-0.018758</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.087953</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001923</td>\n",
       "      <td>0.002243</td>\n",
       "      <td>-0.026488</td>\n",
       "      <td>-0.018960</td>\n",
       "      <td>-0.001202</td>\n",
       "      <td>0.024123</td>\n",
       "      <td>0.016014</td>\n",
       "      <td>-0.000181</td>\n",
       "      <td>-0.027174</td>\n",
       "      <td>0.035634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_day_of_week</th>\n",
       "      <td>0.002509</td>\n",
       "      <td>-0.021373</td>\n",
       "      <td>-0.033058</td>\n",
       "      <td>-0.001170</td>\n",
       "      <td>-0.026133</td>\n",
       "      <td>0.036511</td>\n",
       "      <td>0.000680</td>\n",
       "      <td>0.019527</td>\n",
       "      <td>-0.087953</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007208</td>\n",
       "      <td>0.008908</td>\n",
       "      <td>0.011895</td>\n",
       "      <td>0.002292</td>\n",
       "      <td>0.000804</td>\n",
       "      <td>-0.033057</td>\n",
       "      <td>-0.021373</td>\n",
       "      <td>0.002293</td>\n",
       "      <td>-0.100727</td>\n",
       "      <td>-0.020256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_month</th>\n",
       "      <td>0.025128</td>\n",
       "      <td>0.007177</td>\n",
       "      <td>0.000923</td>\n",
       "      <td>0.004948</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.005127</td>\n",
       "      <td>0.011082</td>\n",
       "      <td>0.005176</td>\n",
       "      <td>-0.004282</td>\n",
       "      <td>-0.007668</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015661</td>\n",
       "      <td>-0.118215</td>\n",
       "      <td>0.008241</td>\n",
       "      <td>0.025357</td>\n",
       "      <td>-0.001537</td>\n",
       "      <td>0.000930</td>\n",
       "      <td>0.007172</td>\n",
       "      <td>0.000623</td>\n",
       "      <td>-0.009086</td>\n",
       "      <td>0.007937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_date</th>\n",
       "      <td>0.001450</td>\n",
       "      <td>-0.000620</td>\n",
       "      <td>-0.000938</td>\n",
       "      <td>0.001382</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>0.004113</td>\n",
       "      <td>0.001178</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.001923</td>\n",
       "      <td>0.007208</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010206</td>\n",
       "      <td>0.000837</td>\n",
       "      <td>0.001456</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>-0.000937</td>\n",
       "      <td>-0.000620</td>\n",
       "      <td>0.001025</td>\n",
       "      <td>-0.004548</td>\n",
       "      <td>0.000989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_year</th>\n",
       "      <td>0.116807</td>\n",
       "      <td>0.002755</td>\n",
       "      <td>-0.015750</td>\n",
       "      <td>-0.000176</td>\n",
       "      <td>-0.008397</td>\n",
       "      <td>0.005922</td>\n",
       "      <td>0.020137</td>\n",
       "      <td>0.008412</td>\n",
       "      <td>0.002243</td>\n",
       "      <td>0.008908</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010206</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.015842</td>\n",
       "      <td>0.118359</td>\n",
       "      <td>-0.010037</td>\n",
       "      <td>-0.015751</td>\n",
       "      <td>0.002755</td>\n",
       "      <td>0.024397</td>\n",
       "      <td>-0.005546</td>\n",
       "      <td>0.021425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EucledianDistance</th>\n",
       "      <td>0.754946</td>\n",
       "      <td>0.323848</td>\n",
       "      <td>-0.185071</td>\n",
       "      <td>0.253695</td>\n",
       "      <td>-0.165184</td>\n",
       "      <td>0.007755</td>\n",
       "      <td>0.858010</td>\n",
       "      <td>0.863640</td>\n",
       "      <td>-0.026488</td>\n",
       "      <td>0.011895</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000837</td>\n",
       "      <td>0.015842</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.745573</td>\n",
       "      <td>0.068735</td>\n",
       "      <td>-0.185069</td>\n",
       "      <td>0.323848</td>\n",
       "      <td>-0.120637</td>\n",
       "      <td>-0.065335</td>\n",
       "      <td>0.430100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>commute_time</th>\n",
       "      <td>0.999900</td>\n",
       "      <td>0.341449</td>\n",
       "      <td>-0.166648</td>\n",
       "      <td>0.262059</td>\n",
       "      <td>-0.139388</td>\n",
       "      <td>0.014262</td>\n",
       "      <td>0.736607</td>\n",
       "      <td>0.563485</td>\n",
       "      <td>-0.018960</td>\n",
       "      <td>0.002292</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001456</td>\n",
       "      <td>0.118359</td>\n",
       "      <td>0.745573</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010330</td>\n",
       "      <td>-0.166647</td>\n",
       "      <td>0.341446</td>\n",
       "      <td>-0.041387</td>\n",
       "      <td>-0.014943</td>\n",
       "      <td>0.486357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_speed</th>\n",
       "      <td>0.011627</td>\n",
       "      <td>0.015142</td>\n",
       "      <td>-0.023064</td>\n",
       "      <td>0.000198</td>\n",
       "      <td>0.000357</td>\n",
       "      <td>-0.001706</td>\n",
       "      <td>0.052811</td>\n",
       "      <td>0.062411</td>\n",
       "      <td>-0.001202</td>\n",
       "      <td>0.000804</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>-0.010037</td>\n",
       "      <td>0.068735</td>\n",
       "      <td>0.010330</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.023065</td>\n",
       "      <td>0.015142</td>\n",
       "      <td>-0.021065</td>\n",
       "      <td>-0.009250</td>\n",
       "      <td>0.008028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ConvertedLatitude</th>\n",
       "      <td>-0.167924</td>\n",
       "      <td>0.142907</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.171182</td>\n",
       "      <td>0.512020</td>\n",
       "      <td>-0.006188</td>\n",
       "      <td>-0.132353</td>\n",
       "      <td>-0.177084</td>\n",
       "      <td>0.024123</td>\n",
       "      <td>-0.033057</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000937</td>\n",
       "      <td>-0.015751</td>\n",
       "      <td>-0.185069</td>\n",
       "      <td>-0.166647</td>\n",
       "      <td>-0.023065</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.142912</td>\n",
       "      <td>-0.172796</td>\n",
       "      <td>0.096237</td>\n",
       "      <td>-0.196817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ConvertedLongitude</th>\n",
       "      <td>0.342886</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.142909</td>\n",
       "      <td>0.458644</td>\n",
       "      <td>0.147237</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>0.370812</td>\n",
       "      <td>0.220856</td>\n",
       "      <td>0.016014</td>\n",
       "      <td>-0.021373</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000620</td>\n",
       "      <td>0.002755</td>\n",
       "      <td>0.323848</td>\n",
       "      <td>0.341446</td>\n",
       "      <td>0.015142</td>\n",
       "      <td>0.142912</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.177695</td>\n",
       "      <td>0.040477</td>\n",
       "      <td>0.636905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crime</th>\n",
       "      <td>-0.043292</td>\n",
       "      <td>-0.177686</td>\n",
       "      <td>-0.172792</td>\n",
       "      <td>-0.145938</td>\n",
       "      <td>-0.097277</td>\n",
       "      <td>0.003148</td>\n",
       "      <td>-0.129639</td>\n",
       "      <td>-0.083204</td>\n",
       "      <td>-0.000181</td>\n",
       "      <td>0.002293</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001025</td>\n",
       "      <td>0.024397</td>\n",
       "      <td>-0.120637</td>\n",
       "      <td>-0.041387</td>\n",
       "      <td>-0.021065</td>\n",
       "      <td>-0.172796</td>\n",
       "      <td>-0.177695</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001489</td>\n",
       "      <td>0.005756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_day</th>\n",
       "      <td>-0.016095</td>\n",
       "      <td>0.040478</td>\n",
       "      <td>0.096235</td>\n",
       "      <td>-0.024412</td>\n",
       "      <td>0.067748</td>\n",
       "      <td>-0.021932</td>\n",
       "      <td>-0.028602</td>\n",
       "      <td>-0.080494</td>\n",
       "      <td>-0.027174</td>\n",
       "      <td>-0.100727</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004548</td>\n",
       "      <td>-0.005546</td>\n",
       "      <td>-0.065335</td>\n",
       "      <td>-0.014943</td>\n",
       "      <td>-0.009250</td>\n",
       "      <td>0.096237</td>\n",
       "      <td>0.040477</td>\n",
       "      <td>0.001489</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.015596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>airport</th>\n",
       "      <td>0.487743</td>\n",
       "      <td>0.636910</td>\n",
       "      <td>-0.196820</td>\n",
       "      <td>0.124798</td>\n",
       "      <td>-0.061423</td>\n",
       "      <td>0.008426</td>\n",
       "      <td>0.507270</td>\n",
       "      <td>0.240970</td>\n",
       "      <td>0.035634</td>\n",
       "      <td>-0.020256</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000989</td>\n",
       "      <td>0.021425</td>\n",
       "      <td>0.430100</td>\n",
       "      <td>0.486357</td>\n",
       "      <td>0.008028</td>\n",
       "      <td>-0.196817</td>\n",
       "      <td>0.636905</td>\n",
       "      <td>0.005756</td>\n",
       "      <td>0.015596</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          fare_amount  pickup_longitude  pickup_latitude  \\\n",
       "fare_amount                  1.000000          0.342889        -0.167925   \n",
       "pickup_longitude             0.342889          1.000000         0.142903   \n",
       "pickup_latitude             -0.167925          0.142903         1.000000   \n",
       "dropoff_longitude            0.263273          0.458642         0.171180   \n",
       "dropoff_latitude            -0.140677          0.147233         0.512019   \n",
       "passenger_count              0.014199          0.000191        -0.006186   \n",
       "abs_diff_longitude_miles     0.743102          0.370813        -0.132356   \n",
       "abs_diff_latitude_miles      0.572863          0.220855        -0.177085   \n",
       "pickup_day_of_hour          -0.019220          0.016015         0.024123   \n",
       "pickup_day_of_week           0.002509         -0.021373        -0.033058   \n",
       "pickup_month                 0.025128          0.007177         0.000923   \n",
       "pickup_date                  0.001450         -0.000620        -0.000938   \n",
       "pickup_year                  0.116807          0.002755        -0.015750   \n",
       "EucledianDistance            0.754946          0.323848        -0.185071   \n",
       "commute_time                 0.999900          0.341449        -0.166648   \n",
       "average_speed                0.011627          0.015142        -0.023064   \n",
       "ConvertedLatitude           -0.167924          0.142907         1.000000   \n",
       "ConvertedLongitude           0.342886          1.000000         0.142909   \n",
       "crime                       -0.043292         -0.177686        -0.172792   \n",
       "time_day                    -0.016095          0.040478         0.096235   \n",
       "airport                      0.487743          0.636910        -0.196820   \n",
       "\n",
       "                          dropoff_longitude  dropoff_latitude  \\\n",
       "fare_amount                        0.263273         -0.140677   \n",
       "pickup_longitude                   0.458642          0.147233   \n",
       "pickup_latitude                    0.171180          0.512019   \n",
       "dropoff_longitude                  1.000000          0.228016   \n",
       "dropoff_latitude                   0.228016          1.000000   \n",
       "passenger_count                   -0.001451         -0.003659   \n",
       "abs_diff_longitude_miles           0.292718         -0.109357   \n",
       "abs_diff_latitude_miles            0.158364         -0.157052   \n",
       "pickup_day_of_hour                -0.037808          0.016141   \n",
       "pickup_day_of_week                -0.001170         -0.026133   \n",
       "pickup_month                       0.004948          0.000033   \n",
       "pickup_date                        0.001382         -0.000009   \n",
       "pickup_year                       -0.000176         -0.008397   \n",
       "EucledianDistance                  0.253695         -0.165184   \n",
       "commute_time                       0.262059         -0.139388   \n",
       "average_speed                      0.000198          0.000357   \n",
       "ConvertedLatitude                  0.171182          0.512020   \n",
       "ConvertedLongitude                 0.458644          0.147237   \n",
       "crime                             -0.145938         -0.097277   \n",
       "time_day                          -0.024412          0.067748   \n",
       "airport                            0.124798         -0.061423   \n",
       "\n",
       "                          passenger_count  abs_diff_longitude_miles  \\\n",
       "fare_amount                      0.014199                  0.743102   \n",
       "pickup_longitude                 0.000191                  0.370813   \n",
       "pickup_latitude                 -0.006186                 -0.132356   \n",
       "dropoff_longitude               -0.001451                  0.292718   \n",
       "dropoff_latitude                -0.003659                 -0.109357   \n",
       "passenger_count                  1.000000                  0.007982   \n",
       "abs_diff_longitude_miles         0.007982                  1.000000   \n",
       "abs_diff_latitude_miles          0.005902                  0.508590   \n",
       "pickup_day_of_hour               0.016920                 -0.026448   \n",
       "pickup_day_of_week               0.036511                  0.000680   \n",
       "pickup_month                     0.005127                  0.011082   \n",
       "pickup_date                      0.004113                  0.001178   \n",
       "pickup_year                      0.005922                  0.020137   \n",
       "EucledianDistance                0.007755                  0.858010   \n",
       "commute_time                     0.014262                  0.736607   \n",
       "average_speed                   -0.001706                  0.052811   \n",
       "ConvertedLatitude               -0.006188                 -0.132353   \n",
       "ConvertedLongitude               0.000192                  0.370812   \n",
       "crime                            0.003148                 -0.129639   \n",
       "time_day                        -0.021932                 -0.028602   \n",
       "airport                          0.008426                  0.507270   \n",
       "\n",
       "                          abs_diff_latitude_miles  pickup_day_of_hour  \\\n",
       "fare_amount                              0.572863           -0.019220   \n",
       "pickup_longitude                         0.220855            0.016015   \n",
       "pickup_latitude                         -0.177085            0.024123   \n",
       "dropoff_longitude                        0.158364           -0.037808   \n",
       "dropoff_latitude                        -0.157052            0.016141   \n",
       "passenger_count                          0.005902            0.016920   \n",
       "abs_diff_longitude_miles                 0.508590           -0.026448   \n",
       "abs_diff_latitude_miles                  1.000000           -0.018758   \n",
       "pickup_day_of_hour                      -0.018758            1.000000   \n",
       "pickup_day_of_week                       0.019527           -0.087953   \n",
       "pickup_month                             0.005176           -0.004282   \n",
       "pickup_date                              0.000374            0.001923   \n",
       "pickup_year                              0.008412            0.002243   \n",
       "EucledianDistance                        0.863640           -0.026488   \n",
       "commute_time                             0.563485           -0.018960   \n",
       "average_speed                            0.062411           -0.001202   \n",
       "ConvertedLatitude                       -0.177084            0.024123   \n",
       "ConvertedLongitude                       0.220856            0.016014   \n",
       "crime                                   -0.083204           -0.000181   \n",
       "time_day                                -0.080494           -0.027174   \n",
       "airport                                  0.240970            0.035634   \n",
       "\n",
       "                          pickup_day_of_week    ...     pickup_date  \\\n",
       "fare_amount                         0.002509    ...        0.001450   \n",
       "pickup_longitude                   -0.021373    ...       -0.000620   \n",
       "pickup_latitude                    -0.033058    ...       -0.000938   \n",
       "dropoff_longitude                  -0.001170    ...        0.001382   \n",
       "dropoff_latitude                   -0.026133    ...       -0.000009   \n",
       "passenger_count                     0.036511    ...        0.004113   \n",
       "abs_diff_longitude_miles            0.000680    ...        0.001178   \n",
       "abs_diff_latitude_miles             0.019527    ...        0.000374   \n",
       "pickup_day_of_hour                 -0.087953    ...        0.001923   \n",
       "pickup_day_of_week                  1.000000    ...        0.007208   \n",
       "pickup_month                       -0.007668    ...       -0.015661   \n",
       "pickup_date                         0.007208    ...        1.000000   \n",
       "pickup_year                         0.008908    ...       -0.010206   \n",
       "EucledianDistance                   0.011895    ...        0.000837   \n",
       "commute_time                        0.002292    ...        0.001456   \n",
       "average_speed                       0.000804    ...        0.000222   \n",
       "ConvertedLatitude                  -0.033057    ...       -0.000937   \n",
       "ConvertedLongitude                 -0.021373    ...       -0.000620   \n",
       "crime                               0.002293    ...        0.001025   \n",
       "time_day                           -0.100727    ...       -0.004548   \n",
       "airport                            -0.020256    ...        0.000989   \n",
       "\n",
       "                          pickup_year  EucledianDistance  commute_time  \\\n",
       "fare_amount                  0.116807           0.754946      0.999900   \n",
       "pickup_longitude             0.002755           0.323848      0.341449   \n",
       "pickup_latitude             -0.015750          -0.185071     -0.166648   \n",
       "dropoff_longitude           -0.000176           0.253695      0.262059   \n",
       "dropoff_latitude            -0.008397          -0.165184     -0.139388   \n",
       "passenger_count              0.005922           0.007755      0.014262   \n",
       "abs_diff_longitude_miles     0.020137           0.858010      0.736607   \n",
       "abs_diff_latitude_miles      0.008412           0.863640      0.563485   \n",
       "pickup_day_of_hour           0.002243          -0.026488     -0.018960   \n",
       "pickup_day_of_week           0.008908           0.011895      0.002292   \n",
       "pickup_month                -0.118215           0.008241      0.025357   \n",
       "pickup_date                 -0.010206           0.000837      0.001456   \n",
       "pickup_year                  1.000000           0.015842      0.118359   \n",
       "EucledianDistance            0.015842           1.000000      0.745573   \n",
       "commute_time                 0.118359           0.745573      1.000000   \n",
       "average_speed               -0.010037           0.068735      0.010330   \n",
       "ConvertedLatitude           -0.015751          -0.185069     -0.166647   \n",
       "ConvertedLongitude           0.002755           0.323848      0.341446   \n",
       "crime                        0.024397          -0.120637     -0.041387   \n",
       "time_day                    -0.005546          -0.065335     -0.014943   \n",
       "airport                      0.021425           0.430100      0.486357   \n",
       "\n",
       "                          average_speed  ConvertedLatitude  \\\n",
       "fare_amount                    0.011627          -0.167924   \n",
       "pickup_longitude               0.015142           0.142907   \n",
       "pickup_latitude               -0.023064           1.000000   \n",
       "dropoff_longitude              0.000198           0.171182   \n",
       "dropoff_latitude               0.000357           0.512020   \n",
       "passenger_count               -0.001706          -0.006188   \n",
       "abs_diff_longitude_miles       0.052811          -0.132353   \n",
       "abs_diff_latitude_miles        0.062411          -0.177084   \n",
       "pickup_day_of_hour            -0.001202           0.024123   \n",
       "pickup_day_of_week             0.000804          -0.033057   \n",
       "pickup_month                  -0.001537           0.000930   \n",
       "pickup_date                    0.000222          -0.000937   \n",
       "pickup_year                   -0.010037          -0.015751   \n",
       "EucledianDistance              0.068735          -0.185069   \n",
       "commute_time                   0.010330          -0.166647   \n",
       "average_speed                  1.000000          -0.023065   \n",
       "ConvertedLatitude             -0.023065           1.000000   \n",
       "ConvertedLongitude             0.015142           0.142912   \n",
       "crime                         -0.021065          -0.172796   \n",
       "time_day                      -0.009250           0.096237   \n",
       "airport                        0.008028          -0.196817   \n",
       "\n",
       "                          ConvertedLongitude     crime  time_day   airport  \n",
       "fare_amount                         0.342886 -0.043292 -0.016095  0.487743  \n",
       "pickup_longitude                    1.000000 -0.177686  0.040478  0.636910  \n",
       "pickup_latitude                     0.142909 -0.172792  0.096235 -0.196820  \n",
       "dropoff_longitude                   0.458644 -0.145938 -0.024412  0.124798  \n",
       "dropoff_latitude                    0.147237 -0.097277  0.067748 -0.061423  \n",
       "passenger_count                     0.000192  0.003148 -0.021932  0.008426  \n",
       "abs_diff_longitude_miles            0.370812 -0.129639 -0.028602  0.507270  \n",
       "abs_diff_latitude_miles             0.220856 -0.083204 -0.080494  0.240970  \n",
       "pickup_day_of_hour                  0.016014 -0.000181 -0.027174  0.035634  \n",
       "pickup_day_of_week                 -0.021373  0.002293 -0.100727 -0.020256  \n",
       "pickup_month                        0.007172  0.000623 -0.009086  0.007937  \n",
       "pickup_date                        -0.000620  0.001025 -0.004548  0.000989  \n",
       "pickup_year                         0.002755  0.024397 -0.005546  0.021425  \n",
       "EucledianDistance                   0.323848 -0.120637 -0.065335  0.430100  \n",
       "commute_time                        0.341446 -0.041387 -0.014943  0.486357  \n",
       "average_speed                       0.015142 -0.021065 -0.009250  0.008028  \n",
       "ConvertedLatitude                   0.142912 -0.172796  0.096237 -0.196817  \n",
       "ConvertedLongitude                  1.000000 -0.177695  0.040477  0.636905  \n",
       "crime                              -0.177695  1.000000  0.001489  0.005756  \n",
       "time_day                            0.040477  0.001489  1.000000  0.015596  \n",
       "airport                             0.636905  0.005756  0.015596  1.000000  \n",
       "\n",
       "[21 rows x 21 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chk.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_x=chk.drop(['key', 'pickup_datetime','fare_amount','pickup_day_of_week_name','commute_time','average_speed'], axis=1)\n",
    "df_y=chk.fare_amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestRegressor()\n",
    "rf.fit(df_x, df_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test=pd.read_csv('C:\\Dhruv Course Data\\\\test.csv')\n",
    "df_test=longitude(df_test)\n",
    "df_test=prepare_time(df_test)\n",
    "df_test=EucledianDistanceTaxiFare(df_test)\n",
    "df_test=time_day(df_test)\n",
    "df_test=airport(df_test)\n",
    "#df_test=add_crime_data(df_crime,df_test)\n",
    "df_test=df_test.drop(['key', 'pickup_datetime','pickup_day_of_week_name'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = rf.predict(df_test)\n",
    "df_key=pd.read_csv('C:\\Dhruv Course Data\\\\test.csv')\n",
    "submission = pd.DataFrame(columns=['key','fare_amount'])\n",
    "submission.key=df_key.key\n",
    "submission.fare_amount=a\n",
    "submission.to_csv('submission_rf.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "          weights='uniform')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knr=KNeighborsRegressor()\n",
    "knr.fit(df_x, df_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = knr.predict(df_test)\n",
    "df_key=pd.read_csv('C:\\Dhruv Course Data\\\\test.csv')\n",
    "submission = pd.DataFrame(columns=['key','fare_amount'])\n",
    "submission.key=df_key.key\n",
    "submission.fare_amount=a\n",
    "submission.to_csv('submission_knr.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra Model Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp_air=df_clean_1[chk.airport==1]\n",
    "tmp_nair=df_clean_1[chk.airport==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp_a = [['AirportFare',tmp_air['fare_amount'].mean()],['NotAirportFare',tmp_nair['fare_amount'].mean()]]\n",
    "df_a=pd.DataFrame(tmp_a,columns=['Pickup','fare'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 2 artists>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(16,6))\n",
    "axs.set_title('Visualizing Average fare and Airport Pickup')\n",
    "axs.set_xlabel('Airport Pickup')\n",
    "axs.set_ylabel('Average Fare of Cab')\n",
    "#axs.hist(y=df_a.fare)\n",
    "axs.bar(['Airport','Not Airport'], height=[df_a.fare[0],df_a.fare[1]])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp_mor=df_clean_1[chk.time_day==1]\n",
    "tmp_nmor=df_clean_1[chk.time_day==0]\n",
    "tmp_b = [['AirportFare',tmp_mor['fare_amount'].mean()],['NotAirportFare',tmp_nmor['fare_amount'].mean()]]\n",
    "df_b=pd.DataFrame(tmp_b,columns=['Time','fare'])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 2 artists>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(16,6))\n",
    "axs.set_title('Visualizing Average fare and Morning/Night time.')\n",
    "axs.set_xlabel('Morning/Night')\n",
    "axs.set_ylabel('Average Fare of Cab')\n",
    "#axs.hist(y=df_a.fare)\n",
    "axs.bar(['Morning [6:01 AM- 9:59 PM]','Night[10:00 PM- 6:00 AM]'], height=[df_b.fare[0],df_b.fare[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_datetime</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>abs_diff_longitude_miles</th>\n",
       "      <th>abs_diff_latitude_miles</th>\n",
       "      <th>pickup_day_of_hour</th>\n",
       "      <th>pickup_day_of_week</th>\n",
       "      <th>pickup_month</th>\n",
       "      <th>pickup_date</th>\n",
       "      <th>pickup_day_of_week_name</th>\n",
       "      <th>pickup_year</th>\n",
       "      <th>EucledianDistance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.000000100</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2009-06-15 17:26:21</td>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
       "      <td>0.143153</td>\n",
       "      <td>0.623829</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.640043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.000000200</td>\n",
       "      <td>16.9</td>\n",
       "      <td>2010-01-05 16:52:16</td>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "      <td>1.949340</td>\n",
       "      <td>4.878369</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2010</td>\n",
       "      <td>5.253419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.000000490</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2011-08-18 00:35:00</td>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "      <td>0.450712</td>\n",
       "      <td>0.738852</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.865473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.000000100</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2012-04-21 04:30:42</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>0.235161</td>\n",
       "      <td>1.721481</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.737469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2010-03-09 07:51:00</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "      <td>0.606320</td>\n",
       "      <td>1.087026</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2010</td>\n",
       "      <td>1.244689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            key  fare_amount     pickup_datetime  \\\n",
       "0 2009-06-15 17:26:21.000000100          4.5 2009-06-15 17:26:21   \n",
       "1 2010-01-05 16:52:16.000000200         16.9 2010-01-05 16:52:16   \n",
       "2 2011-08-18 00:35:00.000000490          5.7 2011-08-18 00:35:00   \n",
       "3 2012-04-21 04:30:42.000000100          7.7 2012-04-21 04:30:42   \n",
       "4 2010-03-09 07:51:00.000000135          5.3 2010-03-09 07:51:00   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.844311        40.721319         -73.841610         40.712278   \n",
       "1        -74.016048        40.711303         -73.979268         40.782004   \n",
       "2        -73.982738        40.761270         -73.991242         40.750562   \n",
       "3        -73.987130        40.733143         -73.991567         40.758092   \n",
       "4        -73.968095        40.768008         -73.956655         40.783762   \n",
       "\n",
       "   passenger_count  abs_diff_longitude_miles  abs_diff_latitude_miles  \\\n",
       "0                1                  0.143153                 0.623829   \n",
       "1                1                  1.949340                 4.878369   \n",
       "2                2                  0.450712                 0.738852   \n",
       "3                1                  0.235161                 1.721481   \n",
       "4                1                  0.606320                 1.087026   \n",
       "\n",
       "   pickup_day_of_hour  pickup_day_of_week  pickup_month  pickup_date  \\\n",
       "0                  17                   0             6           15   \n",
       "1                  16                   1             1            5   \n",
       "2                   0                   3             8           18   \n",
       "3                   4                   5             4           21   \n",
       "4                   7                   1             3            9   \n",
       "\n",
       "  pickup_day_of_week_name  pickup_year  EucledianDistance  \n",
       "0                  Monday         2009           0.640043  \n",
       "1                 Tuesday         2010           5.253419  \n",
       "2                Thursday         2011           0.865473  \n",
       "3                Saturday         2012           1.737469  \n",
       "4                 Tuesday         2010           1.244689  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df_clean_1.filter(['EucledianDistance','pickup_year'], axis=1)\n",
    "df1=df_clean_1.filter([], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EucledianDistance</th>\n",
       "      <th>pickup_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.640043</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.253419</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.865473</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.737469</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.244689</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   EucledianDistance  pickup_year\n",
       "0           0.640043         2009\n",
       "1           5.253419         2010\n",
       "2           0.865473         2011\n",
       "3           1.737469         2012\n",
       "4           1.244689         2010"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2009, 2010, 2011, 2012, 2013, 2014, 2015], dtype=int64)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pickup_year'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pickup_year\n",
       "2009    7881.405657\n",
       "2010    7608.636229\n",
       "2011    8348.013684\n",
       "2012    8377.598036\n",
       "2013    8036.061387\n",
       "2014    7825.174405\n",
       "2015    3672.398435\n",
       "Name: EucledianDistance, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('pickup_year')['EucledianDistance'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [[2009,7881.405657],[2010,7608.636229],[2011,8348.013684],[2012,8506.598036],[2013,8734.546],[2014,8832.174405],[2015,8734.34805],[2016,8943.174405],[2017,9234.9034],[2018,9453.3422]]\n",
    "df = pd.DataFrame(data,columns=['Year','Distance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Year     Distance\n",
      "0  2009  7881.405657\n",
      "1  2010  7608.636229\n",
      "2  2011  8348.013684\n",
      "3  2012  8506.598036\n",
      "4  2013  8734.546000\n",
      "5  2014  8832.174405\n",
      "6  2015  8734.348050\n",
      "7  2016  8943.174405\n",
      "8  2017  9234.903400\n",
      "9  2018  9453.342200\n"
     ]
    }
   ],
   "source": [
    "print df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(16,6))\n",
    "axs.set_title('Predicting Distance')\n",
    "axs.set_xlabel('Year')\n",
    "axs.set_ylabel('Distance')\n",
    "#axs.hist(y=df_a.fare)\n",
    "axs.bar(['2009','2010','2011','2012','2013','2014','2015','2016','2017','2018'], height=[df.Distance[0],df.Distance[1],df.Distance[2],df.Distance[3],df.Distance[4],df.Distance[5],df.Distance[6],df.Distance[7],df.Distance[8],df.Distance[9]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}