notebook_jupyter_desktop_with_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "b1b1c30b-5503-4896-a6ae-3fde8c58a857",
    "_execution_state": "idle",
    "_uuid": "6253fc3613cae37331065f8a7b510ab3ee469e8a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "capstone.pdf\n",
      "fastest_routes_test.csv\n",
      "fastest_routes_train_part_1.csv\n",
      "fastest_routes_train_part_2.csv\n",
      "sample_submission.csv\n",
      "test.csv\n",
      "test_data.csv\n",
      "train.csv\n",
      "train_data.csv\n",
      "weather_data_nyc_centralpark_2016.csv\n",
      "xgb_model.sav\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ankit/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "/home/ankit/anaconda3/lib/python3.6/site-packages/sklearn/grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "import xgboost\n",
    "%matplotlib inline\n",
    "\n",
    "from subprocess import check_output\n",
    "print(check_output([\"ls\", \"/home/ankit/Downloads/nytaxi_kaggle_challenge/all\"]).decode(\"utf8\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "2731cf63-63f6-493e-8aec-ad0b8510b11e",
    "_execution_state": "idle",
    "_uuid": "7c3dd1c5411eb81518d34c98ed6843c12b6238bc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75.11792755126953\n"
     ]
    },
    {
     "data": {
      "text/plain": [
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       "1458641    113\n",
       "1458642      5\n",
       "1458643     96\n",
       "Name: yday, Length: 1458644, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/home/ankit/Downloads/nytaxi_kaggle_challenge/all/train.csv')\n",
    "df.passenger_count = df.passenger_count.astype(np.uint8)\n",
    "df.vendor_id = df.vendor_id.astype(np.uint8)\n",
    "df.trip_duration = df.trip_duration.astype(np.uint32)\n",
    "for c in [c for c in df.columns if c.endswith('tude')]:\n",
    "    df.loc[:,c] = df[c].astype(np.float32)\n",
    "print(df.memory_usage().sum()/2**20)\n",
    "df.pickup_datetime=pd.to_datetime(df.pickup_datetime)\n",
    "df.dropoff_datetime=pd.to_datetime(df.dropoff_datetime)\n",
    "df['pu_hour'] = df.pickup_datetime.dt.hour\n",
    "df['yday'] = df.pickup_datetime.dt.dayofyear\n",
    "df['wday'] = df.pickup_datetime.dt.dayofweek\n",
    "df['month'] = df.pickup_datetime.dt.month\n",
    "df['yday']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f54630d7080>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pI8nJyVFLIKtXr46ZM2di5syZuHv3Ln766SfI5XJYWlrCx8cHhoaGGDduHMaNG4cnT54I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\nCCGEEEIIj1HBTgghhBBCCI/9P6BtXI73PVXfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5470ec8a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('white')\n",
    "sns.set_context(\"paper\",font_scale=2)\n",
    "corr = df.corr()\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "f, ax = plt.subplots(figsize=(11,9))\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=0.3, center=0,\n",
    "           square=True, linewidths=0.5, cbar_kws={\"shrink\":0.5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5463e24e80>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+YxwOh5kxY4Z7vCb2vmzZMnPHHXeYyZMnm8zMTLNx40Yzf/58s27dOmNMzex5586dplWr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5470ec8128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=1, nrows=1)\n",
    "sns.distplot(df['trip_duration']/3600,ax=ax,bins=100,kde=False,hist_kws={'log':True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5461697908>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OVFRUlJYvX66nnnpKq1ev9tsNqadjk6RHHnlEqamp1dpuvfXWBq7w2ixatEi33HKLxo0b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\nAIwICQCA0f8FBylKzDuwkpMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5468014dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=1, nrows=1)\n",
    "ax.set_xlim(0,30)\n",
    "sns.distplot(df['trip_duration']/3600,ax=ax,bins=1000,kde=False,hist_kws={'log':True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "0096fc06-fdc4-4e69-800b-ef8612d3a87a",
    "_execution_state": "idle",
    "_uuid": "24be42e88b116df8b0ccdc9d5275b33aafd52934",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def haversine(lon1, lat1, lon2, lat2):\n",
    "\n",
    "    lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\n",
    "\n",
    "    dlon = lon2 - lon1\n",
    "    dlat = lat2 - lat1\n",
    "\n",
    "    a = np.sin(dlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.0)**2\n",
    "\n",
    "    c = 2 * np.arcsin(np.sqrt(a))\n",
    "    km = 6367 * c\n",
    "    miles = km *  0.621371\n",
    "    return miles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "e328c381-57ff-4eb1-a422-24c7c746de60",
    "_execution_state": "idle",
    "_uuid": "37c0d27a3f54f11cb4505b31dab8211d9f50b5d5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           0.930648\n",
       "1           1.121125\n",
       "2           3.965003\n",
       "3           0.922343\n",
       "4           0.738123\n",
       "5           0.682497\n",
       "6           0.823550\n",
       "7           3.548984\n",
       "8           0.813771\n",
       "9           3.180107\n",
       "10          2.363445\n",
       "11          2.342851\n",
       "12          1.154512\n",
       "13          0.616070\n",
       "14          3.963637\n",
       "15          0.407793\n",
       "16          2.128822\n",
       "17          1.576486\n",
       "18          2.859673\n",
       "19          0.809115\n",
       "20          1.556046\n",
       "21          1.070706\n",
       "22          1.283586\n",
       "23          3.027261\n",
       "24         12.793934\n",
       "25          2.831383\n",
       "26          3.760678\n",
       "27          2.321638\n",
       "28          1.567889\n",
       "29          6.172352\n",
       "             ...    \n",
       "1458614     0.833911\n",
       "1458615     1.717840\n",
       "1458616     0.608786\n",
       "1458617     0.407056\n",
       "1458618     0.727804\n",
       "1458619     1.402978\n",
       "1458620     2.570888\n",
       "1458621     2.734856\n",
       "1458622     0.453036\n",
       "1458623     1.036291\n",
       "1458624     0.483906\n",
       "1458625     2.272269\n",
       "1458626     0.798126\n",
       "1458627     1.352633\n",
       "1458628     0.843864\n",
       "1458629     1.589372\n",
       "1458630     0.968900\n",
       "1458631     1.164204\n",
       "1458632     2.468720\n",
       "1458633     1.883035\n",
       "1458634     3.265959\n",
       "1458635     0.382993\n",
       "1458636     0.577830\n",
       "1458637     2.196656\n",
       "1458638     1.439036\n",
       "1458639     0.760751\n",
       "1458640     3.756833\n",
       "1458641     4.858918\n",
       "1458642     0.678270\n",
       "1458643     0.704183\n",
       "Name: distance, Length: 1458644, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['distance'] = haversine(df.pickup_longitude, df.pickup_latitude,\n",
    "                                           df.dropoff_longitude, df.dropoff_latitude)\n",
    "df['distance']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "bc07643c-8d28-4bc6-adf6-c42a16951ad0",
    "_execution_state": "idle",
    "_uuid": "c58addc802a84283bdb6995f87c916f091469cff"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>maximum temperature</th>\n",
       "      <th>minimum temperature</th>\n",
       "      <th>average temperature</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>snow fall</th>\n",
       "      <th>snow depth</th>\n",
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       "      <td>1-1-2016</td>\n",
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       "      <td>2-1-2016</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4-1-2016</td>\n",
       "      <td>36</td>\n",
       "      <td>14</td>\n",
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       "      <td>5-1-2016</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6-1-2016</td>\n",
       "      <td>41</td>\n",
       "      <td>25</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7-1-2016</td>\n",
       "      <td>46</td>\n",
       "      <td>31</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8-1-2016</td>\n",
       "      <td>46</td>\n",
       "      <td>31</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9-1-2016</td>\n",
       "      <td>47</td>\n",
       "      <td>40</td>\n",
       "      <td>43.5</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10-1-2016</td>\n",
       "      <td>59</td>\n",
       "      <td>40</td>\n",
       "      <td>49.5</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11-1-2016</td>\n",
       "      <td>40</td>\n",
       "      <td>26</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12-1-2016</td>\n",
       "      <td>44</td>\n",
       "      <td>25</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>T</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13-1-2016</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14-1-2016</td>\n",
       "      <td>38</td>\n",
       "      <td>22</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>T</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15-1-2016</td>\n",
       "      <td>51</td>\n",
       "      <td>34</td>\n",
       "      <td>42.5</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16-1-2016</td>\n",
       "      <td>52</td>\n",
       "      <td>42</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17-1-2016</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18-1-2016</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
       "      <td>24.5</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19-1-2016</td>\n",
       "      <td>28</td>\n",
       "      <td>16</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20-1-2016</td>\n",
       "      <td>37</td>\n",
       "      <td>27</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21-1-2016</td>\n",
       "      <td>36</td>\n",
       "      <td>26</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22-1-2016</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>25.5</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23-1-2016</td>\n",
       "      <td>27</td>\n",
       "      <td>24</td>\n",
       "      <td>25.5</td>\n",
       "      <td>2.31</td>\n",
       "      <td>27.3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24-1-2016</td>\n",
       "      <td>35</td>\n",
       "      <td>20</td>\n",
       "      <td>27.5</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25-1-2016</td>\n",
       "      <td>39</td>\n",
       "      <td>28</td>\n",
       "      <td>33.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26-1-2016</td>\n",
       "      <td>48</td>\n",
       "      <td>38</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27-1-2016</td>\n",
       "      <td>47</td>\n",
       "      <td>34</td>\n",
       "      <td>40.5</td>\n",
       "      <td>T</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28-1-2016</td>\n",
       "      <td>42</td>\n",
       "      <td>32</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29-1-2016</td>\n",
       "      <td>41</td>\n",
       "      <td>30</td>\n",
       "      <td>35.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30-1-2016</td>\n",
       "      <td>39</td>\n",
       "      <td>28</td>\n",
       "      <td>33.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>2-12-2016</td>\n",
       "      <td>51</td>\n",
       "      <td>40</td>\n",
       "      <td>45.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>3-12-2016</td>\n",
       "      <td>47</td>\n",
       "      <td>41</td>\n",
       "      <td>44.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>4-12-2016</td>\n",
       "      <td>47</td>\n",
       "      <td>39</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>5-12-2016</td>\n",
       "      <td>49</td>\n",
       "      <td>38</td>\n",
       "      <td>43.5</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>6-12-2016</td>\n",
       "      <td>46</td>\n",
       "      <td>37</td>\n",
       "      <td>41.5</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>7-12-2016</td>\n",
       "      <td>46</td>\n",
       "      <td>40</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>8-12-2016</td>\n",
       "      <td>45</td>\n",
       "      <td>35</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>9-12-2016</td>\n",
       "      <td>39</td>\n",
       "      <td>29</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>10-12-2016</td>\n",
       "      <td>35</td>\n",
       "      <td>28</td>\n",
       "      <td>31.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>11-12-2016</td>\n",
       "      <td>35</td>\n",
       "      <td>28</td>\n",
       "      <td>31.5</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>12-12-2016</td>\n",
       "      <td>46</td>\n",
       "      <td>34</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>13-12-2016</td>\n",
       "      <td>43</td>\n",
       "      <td>35</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>14-12-2016</td>\n",
       "      <td>42</td>\n",
       "      <td>34</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>15-12-2016</td>\n",
       "      <td>34</td>\n",
       "      <td>19</td>\n",
       "      <td>26.5</td>\n",
       "      <td>0</td>\n",
       "      <td>T</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>16-12-2016</td>\n",
       "      <td>27</td>\n",
       "      <td>17</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>17-12-2016</td>\n",
       "      <td>39</td>\n",
       "      <td>24</td>\n",
       "      <td>31.5</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>18-12-2016</td>\n",
       "      <td>58</td>\n",
       "      <td>31</td>\n",
       "      <td>44.5</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>19-12-2016</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>20-12-2016</td>\n",
       "      <td>33</td>\n",
       "      <td>20</td>\n",
       "      <td>26.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>21-12-2016</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>22-12-2016</td>\n",
       "      <td>49</td>\n",
       "      <td>37</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>23-12-2016</td>\n",
       "      <td>47</td>\n",
       "      <td>38</td>\n",
       "      <td>42.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>24-12-2016</td>\n",
       "      <td>47</td>\n",
       "      <td>38</td>\n",
       "      <td>42.5</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>25-12-2016</td>\n",
       "      <td>50</td>\n",
       "      <td>36</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>26-12-2016</td>\n",
       "      <td>50</td>\n",
       "      <td>33</td>\n",
       "      <td>41.5</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>27-12-2016</td>\n",
       "      <td>60</td>\n",
       "      <td>40</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>28-12-2016</td>\n",
       "      <td>40</td>\n",
       "      <td>34</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>29-12-2016</td>\n",
       "      <td>46</td>\n",
       "      <td>33</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>30-12-2016</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "      <td>36.5</td>\n",
       "      <td>0.01</td>\n",
       "      <td>T</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>31-12-2016</td>\n",
       "      <td>44</td>\n",
       "      <td>31</td>\n",
       "      <td>37.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  maximum temperature  minimum temperature  \\\n",
       "0      1-1-2016                   42                   34   \n",
       "1      2-1-2016                   40                   32   \n",
       "2      3-1-2016                   45                   35   \n",
       "3      4-1-2016                   36                   14   \n",
       "4      5-1-2016                   29                   11   \n",
       "5      6-1-2016                   41                   25   \n",
       "6      7-1-2016                   46                   31   \n",
       "7      8-1-2016                   46                   31   \n",
       "8      9-1-2016                   47                   40   \n",
       "9     10-1-2016                   59                   40   \n",
       "10    11-1-2016                   40                   26   \n",
       "11    12-1-2016                   44                   25   \n",
       "12    13-1-2016                   30                   22   \n",
       "13    14-1-2016                   38                   22   \n",
       "14    15-1-2016                   51                   34   \n",
       "15    16-1-2016                   52                   42   \n",
       "16    17-1-2016                   42                   30   \n",
       "17    18-1-2016                   31                   18   \n",
       "18    19-1-2016                   28                   16   \n",
       "19    20-1-2016                   37                   27   \n",
       "20    21-1-2016                   36                   26   \n",
       "21    22-1-2016                   30                   21   \n",
       "22    23-1-2016                   27                   24   \n",
       "23    24-1-2016                   35                   20   \n",
       "24    25-1-2016                   39                   28   \n",
       "25    26-1-2016                   48                   38   \n",
       "26    27-1-2016                   47                   34   \n",
       "27    28-1-2016                   42                   32   \n",
       "28    29-1-2016                   41                   30   \n",
       "29    30-1-2016                   39                   28   \n",
       "..          ...                  ...                  ...   \n",
       "336   2-12-2016                   51                   40   \n",
       "337   3-12-2016                   47                   41   \n",
       "338   4-12-2016                   47                   39   \n",
       "339   5-12-2016                   49                   38   \n",
       "340   6-12-2016                   46                   37   \n",
       "341   7-12-2016                   46                   40   \n",
       "342   8-12-2016                   45                   35   \n",
       "343   9-12-2016                   39                   29   \n",
       "344  10-12-2016                   35                   28   \n",
       "345  11-12-2016                   35                   28   \n",
       "346  12-12-2016                   46                   34   \n",
       "347  13-12-2016                   43                   35   \n",
       "348  14-12-2016                   42                   34   \n",
       "349  15-12-2016                   34                   19   \n",
       "350  16-12-2016                   27                   17   \n",
       "351  17-12-2016                   39                   24   \n",
       "352  18-12-2016                   58                   31   \n",
       "353  19-12-2016                   31                   23   \n",
       "354  20-12-2016                   33                   20   \n",
       "355  21-12-2016                   40                   30   \n",
       "356  22-12-2016                   49                   37   \n",
       "357  23-12-2016                   47                   38   \n",
       "358  24-12-2016                   47                   38   \n",
       "359  25-12-2016                   50                   36   \n",
       "360  26-12-2016                   50                   33   \n",
       "361  27-12-2016                   60                   40   \n",
       "362  28-12-2016                   40                   34   \n",
       "363  29-12-2016                   46                   33   \n",
       "364  30-12-2016                   40                   33   \n",
       "365  31-12-2016                   44                   31   \n",
       "\n",
       "     average temperature precipitation snow fall snow depth  \n",
       "0                   38.0          0.00       0.0          0  \n",
       "1                   36.0          0.00       0.0          0  \n",
       "2                   40.0          0.00       0.0          0  \n",
       "3                   25.0          0.00       0.0          0  \n",
       "4                   20.0          0.00       0.0          0  \n",
       "5                   33.0          0.00       0.0          0  \n",
       "6                   38.5          0.00       0.0          0  \n",
       "7                   38.5          0.00       0.0          0  \n",
       "8                   43.5             T       0.0          0  \n",
       "9                   49.5          1.80       0.0          0  \n",
       "10                  33.0          0.00       0.0          0  \n",
       "11                  34.5          0.00         T          0  \n",
       "12                  26.0          0.00       0.0          0  \n",
       "13                  30.0          0.00         T          0  \n",
       "14                  42.5             T       0.0          0  \n",
       "15                  47.0          0.24       0.0          0  \n",
       "16                  36.0          0.05       0.4          0  \n",
       "17                  24.5             T         T          T  \n",
       "18                  22.0          0.00       0.0          T  \n",
       "19                  32.0          0.00       0.0          T  \n",
       "20                  31.0          0.00       0.0          0  \n",
       "21                  25.5          0.01       0.2          0  \n",
       "22                  25.5          2.31      27.3          6  \n",
       "23                  27.5             T         T         22  \n",
       "24                  33.5          0.00       0.0         19  \n",
       "25                  43.0          0.00       0.0         17  \n",
       "26                  40.5             T       0.0          9  \n",
       "27                  37.0          0.00       0.0          6  \n",
       "28                  35.5          0.00       0.0          6  \n",
       "29                  33.5          0.00       0.0          6  \n",
       "..                   ...           ...       ...        ...  \n",
       "336                 45.5             0         0          0  \n",
       "337                 44.0             T         0          0  \n",
       "338                 43.0             0         0          0  \n",
       "339                 43.5          0.19         0          0  \n",
       "340                 41.5          0.35         0          0  \n",
       "341                 43.0          0.09         0          0  \n",
       "342                 40.0             0         0          0  \n",
       "343                 34.0             0         0          0  \n",
       "344                 31.5             0         0          0  \n",
       "345                 31.5          0.03       0.4          0  \n",
       "346                 40.0           0.5         0          0  \n",
       "347                 39.0             0         0          0  \n",
       "348                 38.0             0         0          0  \n",
       "349                 26.5             0         T          0  \n",
       "350                 22.0             0         0          0  \n",
       "351                 31.5          0.73       2.8          2  \n",
       "352                 44.5          0.04         0          1  \n",
       "353                 27.0             0         0          0  \n",
       "354                 26.5             0         0          0  \n",
       "355                 35.0             0         0          0  \n",
       "356                 43.0             0         0          0  \n",
       "357                 42.5             0         0          0  \n",
       "358                 42.5          0.47         0          0  \n",
       "359                 43.0             0         0          0  \n",
       "360                 41.5          0.02         0          0  \n",
       "361                 50.0             0         0          0  \n",
       "362                 37.0             0         0          0  \n",
       "363                 39.5          0.39         0          0  \n",
       "364                 36.5          0.01         T          0  \n",
       "365                 37.5             0         0          0  \n",
       "\n",
       "[366 rows x 7 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdf = pd.read_csv('/home/ankit/Downloads/nytaxi_kaggle_challenge/all/weather_data_nyc_centralpark_2016.csv')\n",
    "wdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "5460a272-365c-4cf3-b762-73fc2355581b",
    "_execution_state": "idle",
    "_uuid": "c899eabb0c5c723112f1c67c7b9d472d3a733872",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wdf['date']=pd.to_datetime(wdf.date,format='%d-%m-%Y')\n",
    "wdf['yday'] = wdf.date.dt.dayofyear\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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    "_execution_state": "idle",
    "_uuid": "d7cb7e9d02379be0d4b36ddfeea80fb82a24c615"
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   "outputs": [
    {
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      "text/plain": [
       "        date  maximum temperature  minimum temperature  average temperature  \\\n",
       "0 2016-01-01                   42                   34                 38.0   \n",
       "1 2016-01-02                   40                   32                 36.0   \n",
       "2 2016-01-03                   45                   35                 40.0   \n",
       "3 2016-01-04                   36                   14                 25.0   \n",
       "4 2016-01-05                   29                   11                 20.0   \n",
       "\n",
       "  precipitation snow fall snow depth  yday  \n",
       "0          0.00       0.0          0     1  \n",
       "1          0.00       0.0          0     2  \n",
       "2          0.00       0.0          0     3  \n",
       "3          0.00       0.0          0     4  \n",
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     "execution_count": 13,
     "metadata": {},
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   "source": [
    "wdf.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "wdf['snowfall'] = wdf['snow fall'].replace(['T'],0.05).astype(np.float32)\n",
    "wdf['precipitation'] = wdf['precipitation'].replace(['T'],0.05).astype(np.float32)\n",
    "wdf['snowdepth'] = wdf['snow depth'].replace(['T'],0.05).astype(np.float32)\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
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    "_execution_state": "idle",
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       "      <td>2016-03-14 15:16:13</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.994484</td>\n",
       "      <td>40.745087</td>\n",
       "      <td>-73.998993</td>\n",
       "      <td>40.722710</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.563173</td>\n",
       "      <td>45.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id3863815</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 04:24:36</td>\n",
       "      <td>2016-03-14 04:37:11</td>\n",
       "      <td>3</td>\n",
       "      <td>-73.944359</td>\n",
       "      <td>40.714489</td>\n",
       "      <td>-73.910530</td>\n",
       "      <td>40.709492</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.803944</td>\n",
       "      <td>45.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id3817493</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 14:57:56</td>\n",
       "      <td>2016-03-14 15:15:26</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.952881</td>\n",
       "      <td>40.766468</td>\n",
       "      <td>-73.978630</td>\n",
       "      <td>40.761921</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.382826</td>\n",
       "      <td>45.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  vendor_id     pickup_datetime    dropoff_datetime  \\\n",
       "0  id2875421          2 2016-03-14 17:24:55 2016-03-14 17:32:30   \n",
       "1  id2129090          1 2016-03-14 14:05:39 2016-03-14 14:28:05   \n",
       "2  id0256505          1 2016-03-14 15:04:38 2016-03-14 15:16:13   \n",
       "3  id3863815          2 2016-03-14 04:24:36 2016-03-14 04:37:11   \n",
       "4  id3817493          2 2016-03-14 14:57:56 2016-03-14 15:15:26   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1        -73.982155        40.767937         -73.964630   \n",
       "1                1        -73.975090        40.758766         -73.953201   \n",
       "2                1        -73.994484        40.745087         -73.998993   \n",
       "3                3        -73.944359        40.714489         -73.910530   \n",
       "4                1        -73.952881        40.766468         -73.978630   \n",
       "\n",
       "   dropoff_latitude store_and_fwd_flag    ...     yday  wday  month  distance  \\\n",
       "0         40.765602                  N    ...       74     0      3  0.930648   \n",
       "1         40.765068                  N    ...       74     0      3  1.224526   \n",
       "2         40.722710                  N    ...       74     0      3  1.563173   \n",
       "3         40.709492                  N    ...       74     0      3  1.803944   \n",
       "4         40.761921                  N    ...       74     0      3  1.382826   \n",
       "\n",
       "   average temperature  precipitation  snow fall  snow depth snowfall  \\\n",
       "0                 45.5           0.29        0.0           0      0.0   \n",
       "1                 45.5           0.29        0.0           0      0.0   \n",
       "2                 45.5           0.29        0.0           0      0.0   \n",
       "3                 45.5           0.29        0.0           0      0.0   \n",
       "4                 45.5           0.29        0.0           0      0.0   \n",
       "\n",
       "  snowdepth  \n",
       "0       0.0  \n",
       "1       0.0  \n",
       "2       0.0  \n",
       "3       0.0  \n",
       "4       0.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df,wdf,on='yday')\n",
    "df = df.drop(['date','maximum temperature','minimum temperature'],axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f546d5e7e10>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RUcNosuVASUlJKttHjhyBWCxGjx49UFRUpPbL98GDB1FWVlbpeF26dMHcuXNhYmKitmIP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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f546d62dbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('white')\n",
    "sns.set_context(\"paper\",font_scale=2)\n",
    "corr = df.corr()\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "f, ax = plt.subplots(figsize=(11,9))\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=0.3, center=0,\n",
    "           square=True, linewidths=0.5, cbar_kws={\"shrink\":0.5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>trip_duration</th>\n",
       "      <th>pu_hour</th>\n",
       "      <th>yday</th>\n",
       "      <th>wday</th>\n",
       "      <th>month</th>\n",
       "      <th>distance</th>\n",
       "      <th>average temperature</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>snowfall</th>\n",
       "      <th>snowdepth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>vendor_id</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.287415</td>\n",
       "      <td>0.007820</td>\n",
       "      <td>0.001742</td>\n",
       "      <td>0.001528</td>\n",
       "      <td>0.004496</td>\n",
       "      <td>0.020304</td>\n",
       "      <td>0.009299</td>\n",
       "      <td>-0.006025</td>\n",
       "      <td>0.001311</td>\n",
       "      <td>-0.006221</td>\n",
       "      <td>0.008109</td>\n",
       "      <td>-0.005760</td>\n",
       "      <td>0.001385</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>0.006036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>passenger_count</th>\n",
       "      <td>0.287415</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002169</td>\n",
       "      <td>-0.005125</td>\n",
       "      <td>-0.000343</td>\n",
       "      <td>-0.002762</td>\n",
       "      <td>0.008471</td>\n",
       "      <td>0.009101</td>\n",
       "      <td>-0.001784</td>\n",
       "      <td>0.025191</td>\n",
       "      <td>-0.002143</td>\n",
       "      <td>0.010306</td>\n",
       "      <td>-0.001203</td>\n",
       "      <td>0.001415</td>\n",
       "      <td>0.000510</td>\n",
       "      <td>0.000733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_longitude</th>\n",
       "      <td>0.007820</td>\n",
       "      <td>0.002169</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.022568</td>\n",
       "      <td>0.783582</td>\n",
       "      <td>0.100190</td>\n",
       "      <td>0.026542</td>\n",
       "      <td>0.010150</td>\n",
       "      <td>0.004263</td>\n",
       "      <td>-0.016356</td>\n",
       "      <td>0.004474</td>\n",
       "      <td>0.259780</td>\n",
       "      <td>0.005547</td>\n",
       "      <td>0.001418</td>\n",
       "      <td>-0.003288</td>\n",
       "      <td>0.003602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_latitude</th>\n",
       "      <td>0.001742</td>\n",
       "      <td>-0.005125</td>\n",
       "      <td>0.022568</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.114884</td>\n",
       "      <td>0.494038</td>\n",
       "      <td>-0.029204</td>\n",
       "      <td>0.010603</td>\n",
       "      <td>-0.002516</td>\n",
       "      <td>-0.028976</td>\n",
       "      <td>-0.001439</td>\n",
       "      <td>-0.210354</td>\n",
       "      <td>-0.002011</td>\n",
       "      <td>-0.004180</td>\n",
       "      <td>-0.005058</td>\n",
       "      <td>-0.000112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <td>0.001528</td>\n",
       "      <td>-0.000343</td>\n",
       "      <td>0.783582</td>\n",
       "      <td>0.114884</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.124873</td>\n",
       "      <td>0.014678</td>\n",
       "      <td>-0.022455</td>\n",
       "      <td>0.004612</td>\n",
       "      <td>-0.001130</td>\n",
       "      <td>0.004758</td>\n",
       "      <td>0.134033</td>\n",
       "      <td>0.005502</td>\n",
       "      <td>0.001660</td>\n",
       "      <td>0.000774</td>\n",
       "      <td>0.001092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <td>0.004496</td>\n",
       "      <td>-0.002762</td>\n",
       "      <td>0.100190</td>\n",
       "      <td>0.494038</td>\n",
       "      <td>0.124873</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.020677</td>\n",
       "      <td>0.013612</td>\n",
       "      <td>-0.001554</td>\n",
       "      <td>-0.021762</td>\n",
       "      <td>-0.000607</td>\n",
       "      <td>-0.142128</td>\n",
       "      <td>-0.000997</td>\n",
       "      <td>-0.002710</td>\n",
       "      <td>-0.002434</td>\n",
       "      <td>0.000454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trip_duration</th>\n",
       "      <td>0.020304</td>\n",
       "      <td>0.008471</td>\n",
       "      <td>0.026542</td>\n",
       "      <td>-0.029204</td>\n",
       "      <td>0.014678</td>\n",
       "      <td>-0.020677</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003690</td>\n",
       "      <td>0.006625</td>\n",
       "      <td>-0.000708</td>\n",
       "      <td>0.006607</td>\n",
       "      <td>0.094777</td>\n",
       "      <td>0.004574</td>\n",
       "      <td>-0.002590</td>\n",
       "      <td>-0.000190</td>\n",
       "      <td>0.002716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pu_hour</th>\n",
       "      <td>0.009299</td>\n",
       "      <td>0.009101</td>\n",
       "      <td>0.010150</td>\n",
       "      <td>0.010603</td>\n",
       "      <td>-0.022455</td>\n",
       "      <td>0.013612</td>\n",
       "      <td>0.003690</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003340</td>\n",
       "      <td>-0.084255</td>\n",
       "      <td>-0.003438</td>\n",
       "      <td>-0.015813</td>\n",
       "      <td>-0.004948</td>\n",
       "      <td>-0.028656</td>\n",
       "      <td>-0.048589</td>\n",
       "      <td>0.014215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yday</th>\n",
       "      <td>-0.006025</td>\n",
       "      <td>-0.001784</td>\n",
       "      <td>0.004263</td>\n",
       "      <td>-0.002516</td>\n",
       "      <td>0.004612</td>\n",
       "      <td>-0.001554</td>\n",
       "      <td>0.006625</td>\n",
       "      <td>-0.003340</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.013615</td>\n",
       "      <td>0.985657</td>\n",
       "      <td>0.016443</td>\n",
       "      <td>0.837212</td>\n",
       "      <td>-0.031407</td>\n",
       "      <td>-0.077248</td>\n",
       "      <td>-0.229351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wday</th>\n",
       "      <td>0.001311</td>\n",
       "      <td>0.025191</td>\n",
       "      <td>-0.016356</td>\n",
       "      <td>-0.028976</td>\n",
       "      <td>-0.001130</td>\n",
       "      <td>-0.021762</td>\n",
       "      <td>-0.000708</td>\n",
       "      <td>-0.084255</td>\n",
       "      <td>-0.013615</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.014544</td>\n",
       "      <td>0.007599</td>\n",
       "      <td>-0.019799</td>\n",
       "      <td>-0.041673</td>\n",
       "      <td>0.028427</td>\n",
       "      <td>-0.034647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>-0.006221</td>\n",
       "      <td>-0.002143</td>\n",
       "      <td>0.004474</td>\n",
       "      <td>-0.001439</td>\n",
       "      <td>0.004758</td>\n",
       "      <td>-0.000607</td>\n",
       "      <td>0.006607</td>\n",
       "      <td>-0.003438</td>\n",
       "      <td>0.985657</td>\n",
       "      <td>-0.014544</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.015668</td>\n",
       "      <td>0.814295</td>\n",
       "      <td>-0.022469</td>\n",
       "      <td>-0.079563</td>\n",
       "      <td>-0.268631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance</th>\n",
       "      <td>0.008109</td>\n",
       "      <td>0.010306</td>\n",
       "      <td>0.259780</td>\n",
       "      <td>-0.210354</td>\n",
       "      <td>0.134033</td>\n",
       "      <td>-0.142128</td>\n",
       "      <td>0.094777</td>\n",
       "      <td>-0.015813</td>\n",
       "      <td>0.016443</td>\n",
       "      <td>0.007599</td>\n",
       "      <td>0.015668</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.018631</td>\n",
       "      <td>0.001317</td>\n",
       "      <td>-0.003175</td>\n",
       "      <td>0.000548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average temperature</th>\n",
       "      <td>-0.005760</td>\n",
       "      <td>-0.001203</td>\n",
       "      <td>0.005547</td>\n",
       "      <td>-0.002011</td>\n",
       "      <td>0.005502</td>\n",
       "      <td>-0.000997</td>\n",
       "      <td>0.004574</td>\n",
       "      <td>-0.004948</td>\n",
       "      <td>0.837212</td>\n",
       "      <td>-0.019799</td>\n",
       "      <td>0.814295</td>\n",
       "      <td>0.018631</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020519</td>\n",
       "      <td>-0.094767</td>\n",
       "      <td>-0.170218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipitation</th>\n",
       "      <td>0.001385</td>\n",
       "      <td>0.001415</td>\n",
       "      <td>0.001418</td>\n",
       "      <td>-0.004180</td>\n",
       "      <td>0.001660</td>\n",
       "      <td>-0.002710</td>\n",
       "      <td>-0.002590</td>\n",
       "      <td>-0.028656</td>\n",
       "      <td>-0.031407</td>\n",
       "      <td>-0.041673</td>\n",
       "      <td>-0.022469</td>\n",
       "      <td>0.001317</td>\n",
       "      <td>0.020519</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.329554</td>\n",
       "      <td>-0.023126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>snowfall</th>\n",
       "      <td>0.000207</td>\n",
       "      <td>0.000510</td>\n",
       "      <td>-0.003288</td>\n",
       "      <td>-0.005058</td>\n",
       "      <td>0.000774</td>\n",
       "      <td>-0.002434</td>\n",
       "      <td>-0.000190</td>\n",
       "      <td>-0.048589</td>\n",
       "      <td>-0.077248</td>\n",
       "      <td>0.028427</td>\n",
       "      <td>-0.079563</td>\n",
       "      <td>-0.003175</td>\n",
       "      <td>-0.094767</td>\n",
       "      <td>0.329554</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.083143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>snowdepth</th>\n",
       "      <td>0.006036</td>\n",
       "      <td>0.000733</td>\n",
       "      <td>0.003602</td>\n",
       "      <td>-0.000112</td>\n",
       "      <td>0.001092</td>\n",
       "      <td>0.000454</td>\n",
       "      <td>0.002716</td>\n",
       "      <td>0.014215</td>\n",
       "      <td>-0.229351</td>\n",
       "      <td>-0.034647</td>\n",
       "      <td>-0.268631</td>\n",
       "      <td>0.000548</td>\n",
       "      <td>-0.170218</td>\n",
       "      <td>-0.023126</td>\n",
       "      <td>0.083143</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     vendor_id  passenger_count  pickup_longitude  \\\n",
       "vendor_id             1.000000         0.287415          0.007820   \n",
       "passenger_count       0.287415         1.000000          0.002169   \n",
       "pickup_longitude      0.007820         0.002169          1.000000   \n",
       "pickup_latitude       0.001742        -0.005125          0.022568   \n",
       "dropoff_longitude     0.001528        -0.000343          0.783582   \n",
       "dropoff_latitude      0.004496        -0.002762          0.100190   \n",
       "trip_duration         0.020304         0.008471          0.026542   \n",
       "pu_hour               0.009299         0.009101          0.010150   \n",
       "yday                 -0.006025        -0.001784          0.004263   \n",
       "wday                  0.001311         0.025191         -0.016356   \n",
       "month                -0.006221        -0.002143          0.004474   \n",
       "distance              0.008109         0.010306          0.259780   \n",
       "average temperature  -0.005760        -0.001203          0.005547   \n",
       "precipitation         0.001385         0.001415          0.001418   \n",
       "snowfall              0.000207         0.000510         -0.003288   \n",
       "snowdepth             0.006036         0.000733          0.003602   \n",
       "\n",
       "                     pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "vendor_id                   0.001742           0.001528          0.004496   \n",
       "passenger_count            -0.005125          -0.000343         -0.002762   \n",
       "pickup_longitude            0.022568           0.783582          0.100190   \n",
       "pickup_latitude             1.000000           0.114884          0.494038   \n",
       "dropoff_longitude           0.114884           1.000000          0.124873   \n",
       "dropoff_latitude            0.494038           0.124873          1.000000   \n",
       "trip_duration              -0.029204           0.014678         -0.020677   \n",
       "pu_hour                     0.010603          -0.022455          0.013612   \n",
       "yday                       -0.002516           0.004612         -0.001554   \n",
       "wday                       -0.028976          -0.001130         -0.021762   \n",
       "month                      -0.001439           0.004758         -0.000607   \n",
       "distance                   -0.210354           0.134033         -0.142128   \n",
       "average temperature        -0.002011           0.005502         -0.000997   \n",
       "precipitation              -0.004180           0.001660         -0.002710   \n",
       "snowfall                   -0.005058           0.000774         -0.002434   \n",
       "snowdepth                  -0.000112           0.001092          0.000454   \n",
       "\n",
       "                     trip_duration   pu_hour      yday      wday     month  \\\n",
       "vendor_id                 0.020304  0.009299 -0.006025  0.001311 -0.006221   \n",
       "passenger_count           0.008471  0.009101 -0.001784  0.025191 -0.002143   \n",
       "pickup_longitude          0.026542  0.010150  0.004263 -0.016356  0.004474   \n",
       "pickup_latitude          -0.029204  0.010603 -0.002516 -0.028976 -0.001439   \n",
       "dropoff_longitude         0.014678 -0.022455  0.004612 -0.001130  0.004758   \n",
       "dropoff_latitude         -0.020677  0.013612 -0.001554 -0.021762 -0.000607   \n",
       "trip_duration             1.000000  0.003690  0.006625 -0.000708  0.006607   \n",
       "pu_hour                   0.003690  1.000000 -0.003340 -0.084255 -0.003438   \n",
       "yday                      0.006625 -0.003340  1.000000 -0.013615  0.985657   \n",
       "wday                     -0.000708 -0.084255 -0.013615  1.000000 -0.014544   \n",
       "month                     0.006607 -0.003438  0.985657 -0.014544  1.000000   \n",
       "distance                  0.094777 -0.015813  0.016443  0.007599  0.015668   \n",
       "average temperature       0.004574 -0.004948  0.837212 -0.019799  0.814295   \n",
       "precipitation            -0.002590 -0.028656 -0.031407 -0.041673 -0.022469   \n",
       "snowfall                 -0.000190 -0.048589 -0.077248  0.028427 -0.079563   \n",
       "snowdepth                 0.002716  0.014215 -0.229351 -0.034647 -0.268631   \n",
       "\n",
       "                     distance  average temperature  precipitation  snowfall  \\\n",
       "vendor_id            0.008109            -0.005760       0.001385  0.000207   \n",
       "passenger_count      0.010306            -0.001203       0.001415  0.000510   \n",
       "pickup_longitude     0.259780             0.005547       0.001418 -0.003288   \n",
       "pickup_latitude     -0.210354            -0.002011      -0.004180 -0.005058   \n",
       "dropoff_longitude    0.134033             0.005502       0.001660  0.000774   \n",
       "dropoff_latitude    -0.142128            -0.000997      -0.002710 -0.002434   \n",
       "trip_duration        0.094777             0.004574      -0.002590 -0.000190   \n",
       "pu_hour             -0.015813            -0.004948      -0.028656 -0.048589   \n",
       "yday                 0.016443             0.837212      -0.031407 -0.077248   \n",
       "wday                 0.007599            -0.019799      -0.041673  0.028427   \n",
       "month                0.015668             0.814295      -0.022469 -0.079563   \n",
       "distance             1.000000             0.018631       0.001317 -0.003175   \n",
       "average temperature  0.018631             1.000000       0.020519 -0.094767   \n",
       "precipitation        0.001317             0.020519       1.000000  0.329554   \n",
       "snowfall            -0.003175            -0.094767       0.329554  1.000000   \n",
       "snowdepth            0.000548            -0.170218      -0.023126  0.083143   \n",
       "\n",
       "                     snowdepth  \n",
       "vendor_id             0.006036  \n",
       "passenger_count       0.000733  \n",
       "pickup_longitude      0.003602  \n",
       "pickup_latitude      -0.000112  \n",
       "dropoff_longitude     0.001092  \n",
       "dropoff_latitude      0.000454  \n",
       "trip_duration         0.002716  \n",
       "pu_hour               0.014215  \n",
       "yday                 -0.229351  \n",
       "wday                 -0.034647  \n",
       "month                -0.268631  \n",
       "distance              0.000548  \n",
       "average temperature  -0.170218  \n",
       "precipitation        -0.023126  \n",
       "snowfall              0.083143  \n",
       "snowdepth             1.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>total_distance</th>\n",
       "      <th>total_travel_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id2875421</td>\n",
       "      <td>2009.1</td>\n",
       "      <td>164.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id2377394</td>\n",
       "      <td>2513.2</td>\n",
       "      <td>332.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id3504673</td>\n",
       "      <td>1779.4</td>\n",
       "      <td>235.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id2181028</td>\n",
       "      <td>1614.9</td>\n",
       "      <td>140.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id0801584</td>\n",
       "      <td>1393.5</td>\n",
       "      <td>189.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  total_distance  total_travel_time\n",
       "0  id2875421          2009.1              164.9\n",
       "1  id2377394          2513.2              332.0\n",
       "2  id3504673          1779.4              235.8\n",
       "3  id2181028          1614.9              140.1\n",
       "4  id0801584          1393.5              189.4"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastest1 = pd.read_csv('/home/ankit/Downloads/nytaxi_kaggle_challenge/all/fastest_routes_train_part_1.csv')\n",
    "fastest2 = pd.read_csv('/home/ankit/Downloads/nytaxi_kaggle_challenge/all/fastest_routes_train_part_2.csv')\n",
    "fastest = pd.concat([fastest1,fastest2],ignore_index=True)\n",
    "fastest = fastest.drop(['step_location_list','step_direction','step_maneuvers','travel_time_per_step','distance_per_step','street_for_each_step','number_of_steps','starting_street','end_street'],axis=1)\n",
    "fastest.head() #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>...</th>\n",
       "      <th>month</th>\n",
       "      <th>distance</th>\n",
       "      <th>average temperature</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>snow fall</th>\n",
       "      <th>snow depth</th>\n",
       "      <th>snowfall</th>\n",
       "      <th>snowdepth</th>\n",
       "      <th>total_distance</th>\n",
       "      <th>total_travel_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id2875421</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 17:24:55</td>\n",
       "      <td>2016-03-14 17:32:30</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.982155</td>\n",
       "      <td>40.767937</td>\n",
       "      <td>-73.964630</td>\n",
       "      <td>40.765602</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0.930648</td>\n",
       "      <td>45.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009.1</td>\n",
       "      <td>164.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id2129090</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-14 14:05:39</td>\n",
       "      <td>2016-03-14 14:28:05</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.975090</td>\n",
       "      <td>40.758766</td>\n",
       "      <td>-73.953201</td>\n",
       "      <td>40.765068</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1.224526</td>\n",
       "      <td>45.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2782.9</td>\n",
       "      <td>244.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id0256505</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-14 15:04:38</td>\n",
       "      <td>2016-03-14 15:16:13</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.994484</td>\n",
       "      <td>40.745087</td>\n",
       "      <td>-73.998993</td>\n",
       "      <td>40.722710</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1.563173</td>\n",
       "      <td>45.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3124.4</td>\n",
       "      <td>419.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id3863815</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 04:24:36</td>\n",
       "      <td>2016-03-14 04:37:11</td>\n",
       "      <td>3</td>\n",
       "      <td>-73.944359</td>\n",
       "      <td>40.714489</td>\n",
       "      <td>-73.910530</td>\n",
       "      <td>40.709492</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1.803944</td>\n",
       "      <td>45.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3327.8</td>\n",
       "      <td>280.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id3817493</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 14:57:56</td>\n",
       "      <td>2016-03-14 15:15:26</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.952881</td>\n",
       "      <td>40.766468</td>\n",
       "      <td>-73.978630</td>\n",
       "      <td>40.761921</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1.382826</td>\n",
       "      <td>45.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3354.8</td>\n",
       "      <td>270.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  vendor_id     pickup_datetime    dropoff_datetime  \\\n",
       "0  id2875421          2 2016-03-14 17:24:55 2016-03-14 17:32:30   \n",
       "1  id2129090          1 2016-03-14 14:05:39 2016-03-14 14:28:05   \n",
       "2  id0256505          1 2016-03-14 15:04:38 2016-03-14 15:16:13   \n",
       "3  id3863815          2 2016-03-14 04:24:36 2016-03-14 04:37:11   \n",
       "4  id3817493          2 2016-03-14 14:57:56 2016-03-14 15:15:26   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1        -73.982155        40.767937         -73.964630   \n",
       "1                1        -73.975090        40.758766         -73.953201   \n",
       "2                1        -73.994484        40.745087         -73.998993   \n",
       "3                3        -73.944359        40.714489         -73.910530   \n",
       "4                1        -73.952881        40.766468         -73.978630   \n",
       "\n",
       "   dropoff_latitude store_and_fwd_flag        ...         month  distance  \\\n",
       "0         40.765602                  N        ...             3  0.930648   \n",
       "1         40.765068                  N        ...             3  1.224526   \n",
       "2         40.722710                  N        ...             3  1.563173   \n",
       "3         40.709492                  N        ...             3  1.803944   \n",
       "4         40.761921                  N        ...             3  1.382826   \n",
       "\n",
       "   average temperature  precipitation  snow fall  snow depth  snowfall  \\\n",
       "0                 45.5           0.29        0.0           0       0.0   \n",
       "1                 45.5           0.29        0.0           0       0.0   \n",
       "2                 45.5           0.29        0.0           0       0.0   \n",
       "3                 45.5           0.29        0.0           0       0.0   \n",
       "4                 45.5           0.29        0.0           0       0.0   \n",
       "\n",
       "   snowdepth total_distance total_travel_time  \n",
       "0        0.0         2009.1             164.9  \n",
       "1        0.0         2782.9             244.6  \n",
       "2        0.0         3124.4             419.9  \n",
       "3        0.0         3327.8             280.8  \n",
       "4        0.0         3354.8             270.3  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df,fastest,on='id',how='outer')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mask = ((df.trip_duration > 60) & (df.distance < 0.05))\n",
    "df = df[~mask]\n",
    "mask = (df.trip_duration < 60) \n",
    "df = df[~mask]\n",
    "mask =  df.trip_duration > 79200\n",
    "df = df[~mask]\n",
    "mask = df.distance/(df.trip_duration/3600) > 60\n",
    "df = df[~mask]\n",
    "df.trip_duration = df.trip_duration.astype(np.uint16)\n",
    "df = df[df.passenger_count > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m = df.groupby(['wday','vendor_id'])[['trip_duration']].apply(np.median)\n",
    "m.name = 'trip_duration_median'\n",
    "df = df.join(m, on=['wday','vendor_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f546d5895f8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zZszAlStXcPXq1ac/v1CVGhKUl5fjf//7H5RKJYDKe5i9e/eWPCd86tSpKCgo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\nw9AhIiK9YegQEZHe/H/qCFh1u9PGuwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f546d675f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(y='trip_duration_median', x='wday',data=df, fit_reg=False, hue='vendor_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m = df.groupby(['pu_hour','vendor_id'])[['trip_duration']].apply(np.median)\n",
    "m.name ='trip_duration_median_hour'\n",
    "df = df.join(m, on=['pu_hour','vendor_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f5468014b00>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JwTfffIN//etfeOWVVwAA/fr1w+DBg9Vq+uKLLwAAn3/+OWxsbAAAjo6OmDRp\nEn744Qf0799f1bdHjx5YsmRJrT//999/jytXriA6Oho9evQAAHTv3h1yuVwnoSN69trJkycRFxdX\n5zem2ql8j0jbPSMx/YhIU2BgIM6ePYsnT54AABITE3Hnzh0EBgbi5s2bSE9Px4svvoji4mLVV58+\nfVBQUIAbN26oziOVStVmoXXp0gUAVDswX758GYIgYOjQoao+TZo0wcCBA9XquXz5Mvz9/VWBAwB+\nfn6wt7fXuFxXMYBq4/Lly2jZsqUqcACgTZs2OntGU9RIZ9GiRTA3N8ecOXOwbNkytG/fHmZm6rkl\nkUhw4MABnRRH6qoLmIojmZr2IyLtAgICYG5ujm+//RZjxozBiRMn4OjoiG7duqnu6bz99ttav/eP\nP/5Q/dna2lrtd6RUKgUA1dqVf/75J5o1a6Zxs7/yiOLPP//Ec889p/FerVq1wv3799XaWrZsWdOP\nqdX9+/e1nqNVq1bIycmp07kBkaFTftOqXbt2AKD6V0BdnTlzBpGRkbh69SokEgk6duyI+fPnq4ak\nmZmZWLt2LU6dOoWCggJ4e3tj0aJFcHNzUztPQUEBNm3ahGPHjiErKwseHh4IDQ2Fr6+vTuokosbB\nxsYGAwcOxMmTJzF69GjExcUhKKjsH2zNmzcHAHzyySdwd3fX+F4nJ6cav0/r1q3x9OlTjVlmDx48\n0Oj38OFDje9/8OAB2rRpo9YmqeNaW23atKnyvXRBVOicPn1aJ29a0b59+7BixQpMmDAB7777LkpL\nS5GUlIT8/HwAgCAICAkJgVKpxEcffQQ7OztERkZi8uTJiImJgYODg+pcixcvxpkzZxAWFgZnZ2dE\nRUVh6tSp2L9/Pzw8PHReOxGZrpdeegmhoaE4ffo00tPTERgYCADo1KkT2rZti7S0NIwZM6ZO7+Hl\n5QWJRIJvv/1WdU+noKAAZ86cUevXvXt37Nu3Dzk5OapLbBcuXMCjR490vq+Zl5cXHj58iIsXL6ou\nsf3555/473//q3W0JVatN3GriZycHKxcuRLTpk1TXcusSKlUIjw8HPPnz8eUKVNU7RWvSSoUCvz6\n66/YvXs3+vTpAwDw8fGBXC7Hjh07VDfMrl27htjYWISHh6tu/vn6+iIoKAibN29GRESEHj+pYdT0\nuR8+H0RUd4MHD0aTJk2wdOlSdOzYEV27dgUAmJmZYeHChQgLC0NOTg769+8PqVSKu3fv4tSpU/j0\n009r/KCli4sLhgwZgo8//hhPnz5VzV6zsLBASUmJqt+bb76J6OhovP3225g2bRqys7Oxbt069OjR\no873cCobMGAAPD09MWfOHMz3kaX5AAAgAElEQVSbN081e618hFdXoiYSiJWfn4+jR49qXHMsd+jQ\nIZiZmeH111+v8hynT59GmzZtVIEDlO1aOnjwYCgUClWbQqGAVCpV/WsEACwsLBAUFISEhAQUFhbq\n4BPVv5o+98Png4jqxsrKCoMGDcKff/6pmrVWLjAwENu2bcPVq1fx/vvvY9asWYiOjoanp6fqvk1N\nhYeHY+DAgVi1ahUWLlyIbt26qc2aA8oe4Pzyyy8hlUoxZ84crFy5En379sW2bdvq/Dkrk0gk2LZt\nG1xdXbFkyRKsWLECI0aMwAsvvKCbNxD06M8//xTc3NyEn376SevxSZMmCa+99ppw6NAhQS6XCx4e\nHsKQIUOEr776StVnzJgxwltvvaXxvZGRkYKrq6uQk5MjCIIgfPDBB8KwYcM0+h0/flxwdXUVfv/9\nd6013L17V3B1dRXu3r1bm49IREQi6PXy2rPcv38f9+/fx9q1azF37lw4OzsjLi4Oy5cvR3FxMd54\n4w1kZmbC0dFR43vLh3pZWVmwtrZGZmYmZDLNTdPK+2VmNoBN07itNRGZuHoNHUEQkJubi9WrV2PY\nsGEAgL59+yItLU01WUAQBK2zMYRKK1zXtJ/R4rbWRKRjgiCo3RuqzNzcvM6z3cTS6z2dZykfhfTr\n10+t3d/fHw8ePMD9+/chk8m0jlLK28q3zJbJZFqncJf30zYKMhrVbWtNRFRLR44cgaenZ5VfR44c\nMXhN9TrScXFxwaVLlzTay0cnZmZmcHFxwY8//qjRJyUlBe3bt4e1tbXqXKdOnUJeXp7azJGUlBRI\npVJ06NBBT5+CiMg4DR48GAcPHqzyuJhninSlXkc65Us/JCQkqLUnJCTAwcEBrVu3hlwuR0ZGBs6f\nP686npOTg++++w4BAQGqNrlcjqKiIrUleoqLi3HixAn4+/tzkVIianRatGgBLy+vKr/K14gzpHod\n6QwcOBC9e/fG0qVL8fjxYzg7OyM+Ph4JCQlYtWoVgLLlKHx8fDB//nyEhYWpHg4VBAHTpk1TncvD\nwwOBgYEIDw9HcXExnJycEB0dDaVSiXXr1tXXR6yZZZnAMpna1tYSCXhPh4hMjkTQ4532p0+f4osv\nvsCIESOqHMbl5ORg/fr1iI+PR1ZWFjp16oR33nlH9XQuULbczpo1a6BQKNSWwam8BEV+fj42btyI\n2NhYZGVlwd3dHaGhodVu+6pUKiGXy6FQKOplqFmu48LjSMR4WFkAecVlu41W+WwNZ7kRUQNV69DJ\ny8vDkydPtM4Oa9++fZ0LMxRjCJ3qVoDWCB5tkwsYPETUQIi6vFZSUoLIyEhERUVpXRCuXFJSUp0L\nIy2qm+XG4CGiBkBU6Kxduxa7d++Gq6srhg8frrO1eIiIqHEQFTrHjh3DoEGDTGLxTGPCBTqJqLEQ\nNWX66dOnGtuokm7UaIHOqi6hGdOltWWyv7+IyOjcu3cPK1asQHBwMLp37w43NzcolUqDvb+o0PHy\n8sKdO3f0VUujd2t1kOqrSpUDxtgCp7rXRFTvbt++jZMnT8LOzg69evUy+PuLCp358+fjyJEj+OWX\nX/RVD9XEssy/v4wFl/IhahB8fX3x008/4fPPP8fw4cMN/v6i7uns3LkTLVu2xKRJk+Dh4QFHR0e1\n/b+Bsr0YNm3apNMiiYhINyr/zjY0UaETHx+v+vPVq1dx9epVjT6GXrGUiKiheZxbiNQHOejcygYt\nrBvXEl2iQufatWv6qoMaur+W8tHaTkQqX/+Wji8SbqKkVIC5mQRv+XfCq90bzgP1dVW/4ywyLcY8\nyYHICDzKLVQFDgCUlAr4IuEmHucW1nNlhlOrBT8LCwvx3//+F0qlEhKJBI6OjvDx8eFKzsSgIarG\nzQc5qsApV1IqIPVBDnpa29dTVYYlOnSOHz+OlStX4vHjx6p11yQSCVq0aIElS5YgMDBQ50USEZmC\nzq1sYG4mUQseczMJOreyqceqDEtU6Hz//fcIDQ1F27Zt8cEHH6BLly4AyjZKi46ORmhoKGxtbdG/\nf3+9FEtE1JC1sLbEW/6d1O7pTPXv1KgmE4gKnYiICHTq1An79++Hra2tqn3IkCEYP348goODsX37\ndoYOEVEVXu3eHv1dWtXr7LXyzS6vXLkCoGxAYW9vD3t7e/j5+en1vUWFTlJSEmbNmqUWOOVsbW0x\ncuRIbNu2TWfFERGZohbWlvV6D+f9999Xe/3xxx8DAPz8/LBnzx69vrfoezolJSVVHistLa1TMURE\npH/Xr1+vt/cWNWXa09MTBw4cQGam5gyl7OxsHDp0CM8//7zOiiMiItMiaqQTEhKCt99+G4GBgRg7\ndiy6dOkCiUSC5ORkHDx4EI8ePcLSpUv1VSsRETVwokLnhRdewMaNG7Fy5Up89tlnkEgkqmnTbdq0\nwcaNG9GvXz+9FEpERA2f6Hs6L774IoYMGYL//e9/qj0YnJyc4OnpCXNzc50XSA1Lxc3ouAkdEVVW\nqxUJzM3N0a1bN3Tr1k3X9VADVnn3044LjzN4iEgN114jndC23XZ17UTUOFU70nF3d4eZmRkuXboE\nS0tLuLu7P3PrAolEonXLAyIiompDZ8SIEZBIJKp7NeWviYiIakMilE8/a6SUSiXkcjkUCgWcnJzq\nu5wGTdulNN7TIaKKRN3TOXr0qGrGmjZpaWk4evRonYuihqlywDBwiKgyUbPXFi1ahLVr11Y5Ivjt\nt9+waNEijBgxQifFUcPDoCEyXnFxcTh+/DiuXLmChw8fol27dhg2bBimT58OGxvDbK8gKnSedSWu\nsLCQz+oQERmpL774Au3atcOcOXPg4OCAq1evYuvWrTh37hz27dsHMzP9T2h+Zujk5OQgKytL9frJ\nkydIT0/X6JeVlYXY2Fi0bdtWtxUSEZFOREREwN7+79Wt/fz80Lx5cyxYsADnzp1D37599V7DM0Nn\n165dqu0KJBIJwsPDER4errWvIAiYO3eubiskIiKdqBg45by8vAAAGRkZBqnhmaFTvqGPIAjYtm0b\nhg4dCjc3N41+1tbW6NatG3r27Kn7KomITMnTR8CDG0Cr54Bm9bevDgCcP38eAFQ7QetbjUKnPHjS\n09Mxbtw4dO/eXe+FERGZpMSDwM+fAaXFgJkF0CcE8BpdL6VkZGTg008/Rb9+/VQjHn0TNZFg1apV\n+qqDiMj05T78O3CAsv/9+TOgS4DBRzy5ubkICQmBubm5QX+312rBz5KSEqSmpiIzM1PrjDZfX98a\nnefcuXOYPHmyRrutrS1++eUXAH8/vKnNhQsXYGdnp3pdUFCATZs24dixY8jKyoKHhwdCQ0NrXA8R\nkV49TP47cMqVFpddavtHb4OVUVBQgJCQECiVSuzZswcODg4Ge2/RoRMREYGdO3ciJyenyj5JSUmi\nzrlkyRK1oZ22adfTp09HQECAWpu1tbXa68WLF+PMmTMICwuDs7MzoqKiMHXqVOzfvx8eHh6iaiIi\n0rlWz5VdUqsYPGYWZe0GUlRUhNmzZyMxMRH/93//p/UevT6JCp29e/di06ZN6NOnD/r164cNGzZg\nypQpsLS0xOHDh9G2bVtMnDhRdBFdunSBt7d3tX2cnZ2r7XPt2jXExsYiPDwco0aNAlA24goKCsLm\nzZsREREhui4iIp1qZl92D6fiPZ2+7xrs0lppaSlCQ0Nx9uxZREZGPvP3rj6ICp3o6Gj4+vpi165d\nePz4MTZs2ICBAweib9++ePPNN/Haa68hLy9PX7VWS6FQQCqVIjAwUNVmYWGBoKAgREZGorCwEJaW\nlvVSGxGRitfosns49TB77eOPP0ZcXBxmzJgBKysrXLp0SXXMwcHBIJfZRD1+evv2bQwdOrTsG/96\ncrW4uGyY2KJFC4wZMwZRUVGiiwgNDYWHhwd69+6NefPmaX34dP369ejatSt69uyJGTNm4Pr162rH\nk5OT4ejoCCsrK7V2FxcXFBUV4fbt26LrIiLSi2b2ZfdwDDx54IcffgBQdpskODhY7evAgQMGqUHU\nSMfKygoWFmXf0qxZM0gkEjx8+FB1vFWrVtUuCFqZra0t3nrrLfj6+sLGxgZXr17F9u3bcf78eRw9\nehQtW7aEpaUlgoOD4e/vD3t7e6SmpiIiIgLjxo3DwYMHVXPLMzMzIZPJNN6jefPmquNERI3Z6dOn\n67sEcaHj5OSEmzdvAgCkUik6duyI+Ph4jBgxAoIg4NSpU2jVqlWNz9e1a1d07dpV9drPzw++vr4Y\nM2YMvvzyS8yZMwdt2rTB8uXLVX169eqF/v37IygoCJ999hnWrVsHoOzhVW17/TTynRtqZlmFsF7G\ncCYi/RF1ec3f3x/x8fEoLS0FAAQHB+O7777D0KFD8eKLLyIhIQGvvfZanQry9PREx44dceXKlSr7\ntGvXDj179kRiYqKqTSaT4cmTJxp9y0c42kZBBPXA0faaiEiHRIXOO++8g127dqlCZ8qUKQgNDYWN\njQ1sbW3x/vvvIyQkpM5F1WR0Unlk4+LigrS0NI2JDCkpKZBKpejQoUOd6zI5VQUMg4eI9ERU6Fhb\nW6Nz586q+zoAMG3aNBw5cgSHDh3CjBkz6ry1QWJiIm7dulXtUjvp6em4ePGiWh+5XI6ioiLExcWp\n2oqLi3HixAn4+/tz5hoRkRGo8T2d/Px8+Pj4YNasWZg5c6ZO3nzevHlwcnKCp6cnbG1tkZSUhO3b\nt6s977N69WqUlpbC29sb9vb2uHnzJiIjI2FmZobp06erzuXh4YHAwECEh4ejuLgYTk5OiI6OhlKp\nVN33ISKi+lXj0GnatClkMhlatGihszd3dXVFbGwsvvrqK+Tn56NVq1YYNmwYZs+erVqC28XFBdHR\n0Thy5Ahyc3PRokUL9OnTBzNnzkTnzp3Vzrdq1Sps3LgRmzZtQlZWFtzd3bFjxw54enrqrGaTsixT\n+6U0TiYgIj2RCCKmdy1cuBCZmZn47LPP9FmTQZWv7aZQKKrchtvkcfYaERmIqNBJS0tDSEgIunbt\nivHjx8PZ2RlNmzbV6Ff5AU1jxtAhIjIcUc/pyOVySCQS/P7774iJidHaRyKR4OrVqzopjoiITIuo\n0BkxYoTWBzCJiIhqQlTorF69Wl91EBFRIyDqOR0iIqK6EDXS0bb6szbt27evVTFERGTaRIVOQEBA\nje7piN05lIiIGgdRoTNz5kyN0CkpKcGdO3dw+vRpuLi4YODAgTotkIiITIeo0Jk9e3aVx5RKJcaN\nGwd3d/c6F0VERKZJZxMJnJycMG7cOGzbtk1XpyQiIhOj09lr9vb2SElJ0eUpiYjIhOgsdAoLC3Hs\n2DHVQp1ERESVibqns2jRIq3tWVlZuHTpEh4+fIi5c+fqpDAiIjI9okLnyJEjWttlMhk6dOiA+fPn\nY8SIETopjIiITI+o0Ll27Zq+6iAiokaAy+AQEZHBMHSIiMhgqr28JpfLRZ9QIpHg1KlTtS6IiIhM\nV7Who23hzoyMDNy5cwc2NjZwdnaGIAhQKpXIycnBP/7xD7Rt21ZvxRIRUcNWbejs2bNH7XViYiKm\nTp2Kjz76CGPHjoVUKgUAFBUVYd++fdi6dSs2bNigv2qJiKhBE3VPZ+3atQgKCsKECRNUgQMAUqkU\nkyZNwksvvYQ1a9bovEgiIjINokInMTERrq6uVR53dXXFlStX6lwUERGZJlGhY21tjQsXLlR5/Pz5\n82jWrFmdiyIiItMkKnSCgoJw8uRJfPLJJ1Aqlap2pVKJFStWID4+HkFBQTovkoiITIOoFQnmzp2L\nO3fu4KuvvkJUVBSkUikkEgkKCwshCAIGDBiAefPm6atWIiJq4ESFTtOmTREREYHvv/8ep0+fhlKp\nhCAIcHZ2RkBAAAYMGKCvOomIyASICp1yAwYMqFHAFBQU4OTJk/D390erVq1q81ZkAB0XHlf9+dZq\nXh4lIv3R6zI42dnZWLRoEW7cuKHPt6E6qBg42l4TEemS3tdeEwRB329BtVRVwDB4iEhfuOAnEREZ\nDEOHiIgMhqHTiFU1aYCTCYhIX+o1dM6dOwc3NzeNr169eqn1y8zMxIcffojevXvD29sbU6ZMwfXr\n1zXOV1BQgDVr1sDf3x/dunVDcHBwtSsokGbAMHCISJ9qNWVa15YsWQIvLy/Va3Nzc9WfBUFASEgI\nlEolPvroI9jZ2SEyMhKTJ09GTEwMHBwcVH0XL16MM2fOICwsDM7OzoiKisLUqVOxf/9+eHh4GPQz\nNSQMGiIyFKMInS5dusDb21vrMYVCgV9//RW7d+9Gnz59AAA+Pj6Qy+XYsWMHlixZAgC4du0aYmNj\nER4ejlGjRgEAfH19ERQUhM2bNyMiIsIwH4aIiKqk18trUqkUvr6+kMlktT7H6dOn0aZNG1XgAICt\nrS0GDx4MhUKhalMoFJBKpQgMDFS1WVhYICgoCAkJCSgsLKx1DUREpBu1GukIgoArV66oFv10cnLC\n888/D4lEotZPJpNpbASnTWhoKB4/fgw7Ozv4+/tj3rx5ql1Lk5OTtW6n4OLigqNHjyI3NxfW1tZI\nTk6Go6MjrKysNPoVFRXh9u3beO6552rzcYmISEdEh84333yDVatW4d69e2rtDg4OWLBgAYYPH17j\nc9na2uKtt96Cr68vbGxscPXqVWzfvh3nz5/H0aNH0bJlS2RmZsLR0VHje5s3bw4AyMrKgrW1NTIz\nM7WOqMr7ZWZmivmYRESkB6JCJy4uDnPmzEHr1q0REhKCTp06AQBSU1Nx8OBBzJkzBwBqHDxdu3ZF\n165dVa/9/Pzg6+uLMWPG4Msvv8ScOXMgCILGCArQXOmgpv2IiKj+iAqdbdu2oXPnzti3bx9sbW3V\njr311lsYO3Ystm3bJmq0U5mnpyc6duyo2oFUJpNpHaWUt9nZ2an6paenV9mvLveViIhIN0RNJLh9\n+zZGjx6tEThA2aWy0aNH4/bt23UuquLoxMXFReuCoSkpKWjfvj2sra1V/dLS0pCXl6fRTyqVokOH\nDnWui4iI6kZU6Dg4OKCgoKDK40VFRWrPzdRGYmIibt26he7duwMA5HI5MjIycP78eVWfnJwcfPfd\ndwgICFC1yeVyFBUVIS4uTtVWXFyMEydOwN/fH5aWlnWqi4iI6k7U5bWJEyfiiy++wGuvvYa2bduq\nHfvjjz8QHR2Nt99+u8bnmzdvHpycnODp6QlbW1skJSVh+/btaNu2LSZOnAgACAgIgI+PD+bPn4+w\nsDDVw6GCIGDatGmqc3l4eCAwMBDh4eEoLi6Gk5MToqOjoVQqsW7dOjEfk4iI9ERU6Jibm6N58+YY\nPnw4hg8frjaRID4+Hh07doREIkFUVJTa902YMEHr+VxdXREbG4uvvvoK+fn5aNWqFYYNG4bZs2fD\n3t4eAGBmZoaIiAisWbMGH3/8MQoKCuDt7Y0vv/wS7dq1UzvfqlWrsHHjRmzatAlZWVlwd3fHjh07\n4OnpKeZjEhGRnkgEEdO73N3dxb+BRIKkpCTR32coSqUScrkcCoUCTk5O9V0OEZFJEzXS+fLLL/VV\nBxERNQKiQsfPz09fdRARUSPA/XSIiMhgqh3pbN26FRKJBCEhITAzM8PWrVufeUKJRIKZM2fqrEAy\nEssqPFy7jEsKEVHtVDuRwN3dHRKJBL/99hssLS1rNJHA2CcOVMaJBDWwTMtqDgweIqqFakc65VsH\nlD9YWXErAWoktAVOeTuDh4hEqjZ0Kq7uXFpaColEgmbNmqlWbiYiIhKjxhMJSkpKMGTIEBw6dEif\n9RARkQmrcehIpVK0bNmSa5g1NlVdQuOlNSKqBVFTpocPH474+HjuUdPYVA4YBg4R1ZKoh0NfeeUV\nnDt3DlOmTMG4cePg7OyMpk2bavRzcXHRWYFkJBg0RKQDokJn7Nixqj9X3GqgsoY0ZZqIiAxHVOjM\nmjVLX3UQEVEjICp0AGDYsGFwdXXVeuzGjRuIj4+vc1FERGSaRE0k2LZtG65fv17l8Rs3bmDbtm11\nLoqIiEyTqNB51qy1vLw8mJub16kgIiIyXc+8vJaeno60tDTV69TUVFy4cEGjX2ZmJvbv3w9nZ2fd\nVkhERCbjmaFz+PBh1WrTEokEERERiIiI0OgnCALMzMzwySef6KVQIiJq+J4ZOkOGDIGjoyMEQcDi\nxYsxduxY+Pj4qPUpX5Pt+eefR/v27fVWLBERNWzPDB13d3fVlgbp6enVzl4jIiKqDp/ToRrpuPC4\n6s+3VgfVYyVE1JBxu2p6poqBo+01EVFNMXSoWlUFDIOHiGqDoUNERAbD0CEiIoNh6FC1qpo0wMkE\nRFQbDB16psoBw8AhotoSvco0NU4MGiLSBY50iIjIYBg6RERkMAwdIiIyGIYOEREZDEOHiIgMhqFD\nREQG0+inTJeUlAAA7t27V8+VEFF9c3BwgIVFo/+1qFeN/v/dP//8EwAwYcKEeq6EiOqbQqGAk5NT\nfZdh0iSCIAj1XUR9ys/Px5UrV9C6dWuYm5vXdzlEVI840tG/Rh86RERkOJxIQEREBsPQISIig2Ho\nEBGRwTB0auGPP/7Ae++9h549e6JHjx6YNWsW0tPT67ssk3fv3j2sWLECwcHB6N69O9zc3KBUKjX6\nFRQUYM2aNfD390e3bt0QHByMCxcu1EPFpisuLg6zZ8/G4MGD0a1bN7z44otYv349cnJy1PplZmbi\nww8/RO/eveHt7Y0pU6bg+vXr9VQ1GQNOJBApLy8P//znP2FpaYkPPvgAALB582bk5eXh66+/RrNm\nzeq5QtN17tw5zJkzB56enigtLUVCQoLWKa7z5s3DmTNnEBYWBmdnZ0RFReH777/H/v374eHhUU/V\nm5axY8eiXbt2kMvlcHBwwNWrV7F161Z07twZ+/btg5mZGQRBwIQJE6BUKhEWFgY7OztERkbixo0b\niImJgYODQ31/DKoPAomya9cuwd3dXbh165aq7c6dO4KHh4fwxRdf1GNlpq+kpET15//85z+Cq6ur\ncPfuXbU+SUlJgqurq3Dw4EFVW1FRkTBs2DBh+vTpBqvV1D18+FCj7ciRI4Krq6vw008/CYIgCN9+\n+63g6uoqnD17VtUnKytL8PX1FVasWGGwWsm48PKaSKdPn0b37t3RoUMHVZuzszN69OgBhUJRj5WZ\nPjOzZ/91VSgUkEqlCAwMVLVZWFggKCgICQkJKCws1GeJjYa9vb1Gm5eXFwAgIyMDQNnPSps2bdCn\nTx9VH1tbWwwePJg/K40YQ0ek5ORkuLq6arS7uLggOTm5HiqiipKTk+Ho6AgrKyu1dhcXFxQVFeH2\n7dv1VJnpO3/+PACgS5cuAKr/WUlPT0dubq5B6yPjwNARKTMzE3Z2dhrtMpkMWVlZ9VARVZSZmQmZ\nTKbR3rx5c9Vx0r2MjAx8+umn6Nevn2rEU9XPSvl/C/68NE4MnVqQSCT1XQJVQRAErf99BM6X0Zvc\n3FyEhITA3Nwcq1atUrXzvwVpw9ARyc7OTuu/lqv6Vx0Zlkwmw5MnTzTay/+baRsFUe0VFBQgJCQE\nSqUSO3fuVJuRJpPJqvxZAcCfl0aKoSOSi4sLbty4odGekpICFxeXeqiIKnJxcUFaWhry8vLU2lNS\nUiCVStUmgFDdFBUVYfbs2UhMTERkZCTc3NzUjlf3s9K+fXtYW1sbqlQyIgwdkQICAvDbb7/h7t27\nqjalUomLFy8iICCgHisjAJDL5SgqKkJcXJyqrbi4GCdOnIC/vz8sLS3rsTrTUVpaitDQUJw9exb/\n/ve/4e3trdFHLpcjIyNDNcEAAHJycvDdd9/xZ6UR4xreIo0dOxZRUVF499138f7770MikWDz5s1w\ncHBAcHBwfZdn8srD5MqVKwCA77//Hvb29rC3t4efnx88PDwQGBiI8PBwFBcXw8nJCdHR0VAqlVi3\nbl19lm5SPv74Y8TFxWHGjBmwsrLCpUuXVMccHBzg4OCAgIAA+Pj4YP78+WoPhwqCgGnTptVj9VSf\nuCJBLaSnp2PVqlX48ccfIQgC+vbti8WLF3PzJwOofAmnnJ+fH/bs2QOgbI+kjRs3IjY2FllZWXB3\nd0doaCh69+5tyFJNWkBAANLS0rQemzVrFmbPng0AePLkCdasWQOFQoGCggJ4e3tj0aJFcHd3N2S5\nZEQYOkREZDC8p0NERAbD0CEiIoNh6BARkcEwdIiIyGAYOkREZDAMHSIiMhiGDjVabm5ueO+99+q7\nDKJGhaFDREQGw9AhIiKDYegQGZmnT5/WdwlEesPQoXp1+PBhuLm54dSpU9i8eTMGDhwILy8vjB49\nGj///LOq37lz5+Dm5obDhw9rnGPSpEl1WrX48uXLmDhxIrp3746+ffsiPDwcRUVFGv1+/PFHTJw4\nET4+PvDx8cHrr7+OM2fOqPVRKpVwc3PDli1bNL5/4cKFGmvHTZo0Cb1794ZSqcTMmTPRq1cvvPzy\ny7X+LETGjqFDRmHLli2Ii4vD5MmTERISgvv372PatGm4ePGiXt83NTUV77zzDry8vLBw4UJ069YN\nu3fvRmRkpFq/+Ph4TJs2DX/88QemT5+Od999F48fP8b06dNx7NixOtVQWFiIN954A1ZWVpg3bx4m\nTZpUp/MRGTNubUBG4dGjRzhx4gRsbW0BACNGjMBLL72ENWvWYP/+/Xp73xs3bmDPnj3w8/MDAIwb\nNw6vvvoq9u7di5kzZwIo249n5cqVaN68OQ4cOAB7e3sAQHBwMF555RWsXLkSw4YNQ5MmTWpVw9On\nT/HGG2/ggw8+0M2HIjJiHOmQURg9erQqcACgffv2ePHFF3Hp0iU8fPhQb+/r4eGhChwAkEgk6N27\nNx48eIDc3FwAwP/+9z9kZGRg7NixqsAByrZbfv311/H48eM6j8gmTJhQp+8naigYOmQUOnfurNHW\nqVMnAGX3SfTF0dFRo00mkwEo2wum4vt36dJFo2/5FuV1qdHKygqtW7eu9fcTNSQMHWoQJBJJlcdK\nSkpqfV5zc/Mqj1XeagIP+c4AAAJPSURBVKq6GsqP1abO2l6WI2qIGDpkFFJTUzXabt68CQBwcnKC\nnZ0dACArK0ujnz5HQuXvDwDJyckax1JSUgD8PWIqHyVlZmZq9L17966+SiRqMBg6ZBQOHjyI7Oxs\n1ev09HTEx8eje/fuaNmyJZycnGBhYaE2jRoATpw4gYyMDL3W5unpiTZt2uDQoUNqYZKdnY3o6Gi0\naNECPXr0AADY2NigZcuWOHfunNo5fvvtN1y6dEmvdRI1BJy9RkbB3t4ewcHBGDVqFAoLCxEdHY2S\nkhKEhYUBKPtl/uqrr+Lw4cOYP38+evbsiRs3buDkyZPo0KEDiouL9VabhYUFlixZgg8++ABjxozB\n6NGjYWZmhkOHDuHevXv417/+pXaJbPz48diyZQumT5+OQYMGIS0tDQcOHICrqyuuX7+utzqJGgKG\nDhmF2bNn43//+x92796Nx48fw9XVFWvWrEGvXr1UfT788EMIggCFQoFvv/0WPj4+2LVrF1asWIG0\ntDS91vfiiy/i888/x2effYZ///vfAMpmvm3fvh0DBw5U6zt9+nRkZWXh2LFjOHv2LDw8PLB161Yc\nOnSIoUONnkSofLeUyIAOHz6MRYsWISIiAoMHD67vcohIz3hPh4iIDIaX18ikZGdnIz8/v9o+zZo1\ng7W1tYEqIqKKGDpkUlauXIkjR45U22fWrFmYPXu2gSoioop4T4dMSnJyMu7fv19tH2dnZzg7Oxuo\nIiKqiKFDREQGw4kERERkMAwdIiIyGIYOEREZDEOHiIgMhqFDREQG8/8BdhPDWvYnv7UAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f546d67dc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(y='trip_duration_median_hour', x='pu_hour',data=df, fit_reg=False, hue='vendor_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "jfk_lon = -73.778889\n",
    "jfk_lat = 40.639722\n",
    "lga_lon = -73.872611\n",
    "lga_lat = 40.77725"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['jfk_pickup_dist'] = df.apply(lambda row: haversine(jfk_lon, jfk_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\n",
    "df['lga_pickup_dist'] = df.apply(lambda row: haversine(lga_lon, lga_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\n",
    "df['jfk_dropoff_dist'] = df.apply(lambda row: haversine(jfk_lon, jfk_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)\n",
    "df['lga_dropoff_dist'] = df.apply(lambda row: haversine(lga_lon, lga_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f541af5eeb8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fsWvXLly+fBlpaWmN+sp68eJFTJgwAZ06dcLkyZPh6OiIPXv24Pjx4watHx8fj5ycHIwZ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\nfImISBuLPxGRCLH4ExGJEIs/EZEI/R8b0/C+VQv8wQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f541b02d390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=2, nrows=2, sharex=True)\n",
    "ax[0,0].set_xlim(0,50)\n",
    "\n",
    "sns.distplot(df['jfk_pickup_dist'],ax=ax[0,0],bins=100,kde=False,hist_kws={'log':True})\n",
    "sns.distplot(df['jfk_dropoff_dist'],ax=ax[0,1],bins=100,kde=False,hist_kws={'log':True})\n",
    "sns.distplot(df['lga_pickup_dist'],ax=ax[1,0],bins=100,kde=False,hist_kws={'log':True})\n",
    "sns.distplot(df['lga_dropoff_dist'],ax=ax[1,1],bins=100,kde=False,hist_kws={'log':True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>...</th>\n",
       "      <th>snow fall</th>\n",
       "      <th>snow depth</th>\n",
       "      <th>snowfall</th>\n",
       "      <th>snowdepth</th>\n",
       "      <th>total_distance</th>\n",
       "      <th>total_travel_time</th>\n",
       "      <th>trip_duration_median</th>\n",
       "      <th>trip_duration_median_hour</th>\n",
       "      <th>jfk</th>\n",
       "      <th>lga</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id2875421</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 17:24:55</td>\n",
       "      <td>2016-03-14 17:32:30</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.982155</td>\n",
       "      <td>40.767937</td>\n",
       "      <td>-73.964630</td>\n",
       "      <td>40.765602</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009.1</td>\n",
       "      <td>164.9</td>\n",
       "      <td>634.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id2129090</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-14 14:05:39</td>\n",
       "      <td>2016-03-14 14:28:05</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.975090</td>\n",
       "      <td>40.758766</td>\n",
       "      <td>-73.953201</td>\n",
       "      <td>40.765068</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2782.9</td>\n",
       "      <td>244.6</td>\n",
       "      <td>633.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id0256505</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-14 15:04:38</td>\n",
       "      <td>2016-03-14 15:16:13</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.994484</td>\n",
       "      <td>40.745087</td>\n",
       "      <td>-73.998993</td>\n",
       "      <td>40.722710</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3124.4</td>\n",
       "      <td>419.9</td>\n",
       "      <td>633.0</td>\n",
       "      <td>724.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id3863815</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 04:24:36</td>\n",
       "      <td>2016-03-14 04:37:11</td>\n",
       "      <td>3</td>\n",
       "      <td>-73.944359</td>\n",
       "      <td>40.714489</td>\n",
       "      <td>-73.910530</td>\n",
       "      <td>40.709492</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3327.8</td>\n",
       "      <td>280.8</td>\n",
       "      <td>634.0</td>\n",
       "      <td>633.5</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id3817493</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 14:57:56</td>\n",
       "      <td>2016-03-14 15:15:26</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.952881</td>\n",
       "      <td>40.766468</td>\n",
       "      <td>-73.978630</td>\n",
       "      <td>40.761921</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3354.8</td>\n",
       "      <td>270.3</td>\n",
       "      <td>634.0</td>\n",
       "      <td>735.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  vendor_id     pickup_datetime    dropoff_datetime  \\\n",
       "0  id2875421          2 2016-03-14 17:24:55 2016-03-14 17:32:30   \n",
       "1  id2129090          1 2016-03-14 14:05:39 2016-03-14 14:28:05   \n",
       "2  id0256505          1 2016-03-14 15:04:38 2016-03-14 15:16:13   \n",
       "3  id3863815          2 2016-03-14 04:24:36 2016-03-14 04:37:11   \n",
       "4  id3817493          2 2016-03-14 14:57:56 2016-03-14 15:15:26   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1        -73.982155        40.767937         -73.964630   \n",
       "1                1        -73.975090        40.758766         -73.953201   \n",
       "2                1        -73.994484        40.745087         -73.998993   \n",
       "3                3        -73.944359        40.714489         -73.910530   \n",
       "4                1        -73.952881        40.766468         -73.978630   \n",
       "\n",
       "   dropoff_latitude store_and_fwd_flag  ...    snow fall  snow depth  \\\n",
       "0         40.765602                  N  ...          0.0           0   \n",
       "1         40.765068                  N  ...          0.0           0   \n",
       "2         40.722710                  N  ...          0.0           0   \n",
       "3         40.709492                  N  ...          0.0           0   \n",
       "4         40.761921                  N  ...          0.0           0   \n",
       "\n",
       "   snowfall  snowdepth  total_distance  total_travel_time  \\\n",
       "0       0.0        0.0          2009.1              164.9   \n",
       "1       0.0        0.0          2782.9              244.6   \n",
       "2       0.0        0.0          3124.4              419.9   \n",
       "3       0.0        0.0          3327.8              280.8   \n",
       "4       0.0        0.0          3354.8              270.3   \n",
       "\n",
       "   trip_duration_median  trip_duration_median_hour    jfk    lga  \n",
       "0                 634.0                      709.0  False  False  \n",
       "1                 633.0                      727.0  False  False  \n",
       "2                 633.0                      724.0  False  False  \n",
       "3                 634.0                      633.5  False  False  \n",
       "4                 634.0                      735.0  False  False  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['jfk'] = ((df['jfk_pickup_dist'] < 2) | (df['jfk_dropoff_dist'] < 2))\n",
    "df['lga'] = ((df['lga_pickup_dist'] < 2) | (df['lga_dropoff_dist'] < 2))\n",
    "df = df.drop(['jfk_pickup_dist','lga_pickup_dist','jfk_dropoff_dist','lga_dropoff_dist'],axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>...</th>\n",
       "      <th>snow depth</th>\n",
       "      <th>snowfall</th>\n",
       "      <th>snowdepth</th>\n",
       "      <th>total_distance</th>\n",
       "      <th>total_travel_time</th>\n",
       "      <th>trip_duration_median</th>\n",
       "      <th>trip_duration_median_hour</th>\n",
       "      <th>jfk</th>\n",
       "      <th>lga</th>\n",
       "      <th>workday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id2875421</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 17:24:55</td>\n",
       "      <td>2016-03-14 17:32:30</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.982155</td>\n",
       "      <td>40.767937</td>\n",
       "      <td>-73.964630</td>\n",
       "      <td>40.765602</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009.1</td>\n",
       "      <td>164.9</td>\n",
       "      <td>634.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id2129090</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-14 14:05:39</td>\n",
       "      <td>2016-03-14 14:28:05</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.975090</td>\n",
       "      <td>40.758766</td>\n",
       "      <td>-73.953201</td>\n",
       "      <td>40.765068</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2782.9</td>\n",
       "      <td>244.6</td>\n",
       "      <td>633.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id0256505</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-14 15:04:38</td>\n",
       "      <td>2016-03-14 15:16:13</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.994484</td>\n",
       "      <td>40.745087</td>\n",
       "      <td>-73.998993</td>\n",
       "      <td>40.722710</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3124.4</td>\n",
       "      <td>419.9</td>\n",
       "      <td>633.0</td>\n",
       "      <td>724.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id3863815</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 04:24:36</td>\n",
       "      <td>2016-03-14 04:37:11</td>\n",
       "      <td>3</td>\n",
       "      <td>-73.944359</td>\n",
       "      <td>40.714489</td>\n",
       "      <td>-73.910530</td>\n",
       "      <td>40.709492</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3327.8</td>\n",
       "      <td>280.8</td>\n",
       "      <td>634.0</td>\n",
       "      <td>633.5</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id3817493</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 14:57:56</td>\n",
       "      <td>2016-03-14 15:15:26</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.952881</td>\n",
       "      <td>40.766468</td>\n",
       "      <td>-73.978630</td>\n",
       "      <td>40.761921</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3354.8</td>\n",
       "      <td>270.3</td>\n",
       "      <td>634.0</td>\n",
       "      <td>735.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  vendor_id     pickup_datetime    dropoff_datetime  \\\n",
       "0  id2875421          2 2016-03-14 17:24:55 2016-03-14 17:32:30   \n",
       "1  id2129090          1 2016-03-14 14:05:39 2016-03-14 14:28:05   \n",
       "2  id0256505          1 2016-03-14 15:04:38 2016-03-14 15:16:13   \n",
       "3  id3863815          2 2016-03-14 04:24:36 2016-03-14 04:37:11   \n",
       "4  id3817493          2 2016-03-14 14:57:56 2016-03-14 15:15:26   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1        -73.982155        40.767937         -73.964630   \n",
       "1                1        -73.975090        40.758766         -73.953201   \n",
       "2                1        -73.994484        40.745087         -73.998993   \n",
       "3                3        -73.944359        40.714489         -73.910530   \n",
       "4                1        -73.952881        40.766468         -73.978630   \n",
       "\n",
       "   dropoff_latitude store_and_fwd_flag   ...    snow depth  snowfall  \\\n",
       "0         40.765602                  N   ...             0       0.0   \n",
       "1         40.765068                  N   ...             0       0.0   \n",
       "2         40.722710                  N   ...             0       0.0   \n",
       "3         40.709492                  N   ...             0       0.0   \n",
       "4         40.761921                  N   ...             0       0.0   \n",
       "\n",
       "   snowdepth  total_distance  total_travel_time  trip_duration_median  \\\n",
       "0        0.0          2009.1              164.9                 634.0   \n",
       "1        0.0          2782.9              244.6                 633.0   \n",
       "2        0.0          3124.4              419.9                 633.0   \n",
       "3        0.0          3327.8              280.8                 634.0   \n",
       "4        0.0          3354.8              270.3                 634.0   \n",
       "\n",
       "   trip_duration_median_hour    jfk    lga workday  \n",
       "0                      709.0  False  False    True  \n",
       "1                      727.0  False  False    True  \n",
       "2                      724.0  False  False    True  \n",
       "3                      633.5  False  False   False  \n",
       "4                      735.0  False  False    True  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['workday'] = ((df['pu_hour'] > 8) & (df['pu_hour'] < 18))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f396143a-380f-4d90-8a25-99b5a2d91454",
    "_execution_state": "idle",
    "_uuid": "53eb8baab05d86229fbaa11c5947f6ec085f9726"
   },
   "source": [
    "## Locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_cell_guid": "6067f4ea-649d-438b-8de3-deab1a8438c1",
    "_execution_state": "idle",
    "_uuid": "f700181673094d1b8e3cbce2f285ccc2c5000be7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f541aade438>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uxMop/UWt/T3DeyqOo9OocE/GLsVqvVK4X/HZjwjvoMKs0b2d9s+/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\nMeFI1gThnS+vUov/kjHhmDY8An4+Xvihs923pXpkFdS4nJq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qSIvN85JwpLOQmK1sQ+5A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\npo4Kwt7TZfj14iTRdmwIDzk2KfREXv4kZIUYrKwaDYH9TrrMn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DVQLanQSPnLyVLMjQ+m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\nYv3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NwAghhBBCwU9I1LDD4CSWdUy2PQPMgoMaQgghkULBTwiJK5EI2kQS/Xp2SKQ2EkII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\nPyGEEEIIIQnM/wO/FUl3t/NbgQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f541ab516d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=1, nrows=1,figsize=(12,10))\n",
    "plt.ylim(40.6, 40.9)\n",
    "plt.xlim(-74.1,-73.7)\n",
    "ax.scatter(df['pickup_longitude'],df['pickup_latitude'], s=0.01, alpha=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RMSLE: Evaluation Metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rmsle(evaluator,X,real):\n",
    "    sum = 0.0\n",
    "    predicted = evaluator.predict(X)\n",
    "    print(\"Number predicted less than 0: {}\".format(np.where(predicted < 0)[0].shape))\n",
    "\n",
    "    predicted[predicted < 0] = 0\n",
    "    for x in range(len(predicted)):\n",
    "        p = np.log(predicted[x]+1)\n",
    "        r = np.log(real[x]+1)\n",
    "        sum = sum + (p-r)**2\n",
    "    return (sum/len(predicted))**0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_cell_guid": "f36d65a0-9179-4e0e-b893-fad641510ca5",
    "_execution_state": "idle",
    "_uuid": "ff6a80de90c26ecb63b02f197f72aed5f1fc4414"
   },
   "outputs": [],
   "source": [
    "tdf = pd.read_csv('/home/ankit/Downloads/nytaxi_kaggle_challenge/all/test.csv')\n",
    "tdf.pickup_datetime=pd.to_datetime(tdf.pickup_datetime)\n",
    "#tdf.dropoff_datetime=pd.to_datetime(tdf.dropoff_datetime)\n",
    "tdf['pu_hour'] = tdf.pickup_datetime.dt.hour\n",
    "tdf['yday'] = tdf.pickup_datetime.dt.dayofyear\n",
    "tdf['wday'] = tdf.pickup_datetime.dt.dayofweek\n",
    "tdf['month'] = tdf.pickup_datetime.dt.month\n",
    "tdf['distance'] = haversine(tdf.pickup_longitude, tdf.pickup_latitude,\n",
    "                                           tdf.dropoff_longitude, tdf.dropoff_latitude)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "fastest_test = pd.read_csv('/home/ankit/Downloads/nytaxi_kaggle_challenge/all/fastest_routes_test.csv')\n",
    "tdf = pd.merge(tdf,fastest_test,on='id',how='outer')\n",
    "tdf = tdf.drop(['step_location_list','step_direction','step_maneuvers','travel_time_per_step','distance_per_step','street_for_each_step','number_of_steps','starting_street','end_street'],axis=1)\n",
    "tdf = pd.merge(tdf,wdf,on='yday')\n",
    "tdf = tdf.drop(['date','maximum temperature','minimum temperature'],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tdf['jfk_pickup_dist'] = tdf.apply(lambda row: haversine(jfk_lon, jfk_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\n",
    "tdf['lga_pickup_dist'] = tdf.apply(lambda row: haversine(lga_lon, lga_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\n",
    "tdf['jfk_dropoff_dist'] = tdf.apply(lambda row: haversine(jfk_lon, jfk_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)\n",
    "tdf['lga_dropoff_dist'] = tdf.apply(lambda row: haversine(lga_lon, lga_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tdf['jfk'] = ((tdf['jfk_pickup_dist'] < 2) | (tdf['jfk_dropoff_dist'] < 2))\n",
    "tdf['lga'] = ((tdf['lga_pickup_dist'] < 2) | (tdf['lga_dropoff_dist'] < 2))\n",
    "tdf = tdf.drop(['jfk_pickup_dist','lga_pickup_dist','jfk_dropoff_dist','lga_dropoff_dist'],axis=1)\n",
    "tdf['workday'] = ((tdf['pu_hour'] > 8) & (tdf['pu_hour'] < 18))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tdf['snowfall'] = tdf['snow fall'].replace(['T'],0.05)\n",
    "tdf['precipitation'] = tdf['precipitation'].replace(['T'],0.05)\n",
    "tdf['snowdepth'] = tdf['snow depth'].replace(['T'],0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>pu_hour</th>\n",
       "      <th>...</th>\n",
       "      <th>total_travel_time</th>\n",
       "      <th>average temperature</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>snow fall</th>\n",
       "      <th>snow depth</th>\n",
       "      <th>snowfall</th>\n",
       "      <th>snowdepth</th>\n",
       "      <th>jfk</th>\n",
       "      <th>lga</th>\n",
       "      <th>workday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id3004672</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-30 23:59:58</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.988129</td>\n",
       "      <td>40.732029</td>\n",
       "      <td>-73.990173</td>\n",
       "      <td>40.756680</td>\n",
       "      <td>N</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>424.6</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id3505355</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-30 23:59:53</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.964203</td>\n",
       "      <td>40.679993</td>\n",
       "      <td>-73.959808</td>\n",
       "      <td>40.655403</td>\n",
       "      <td>N</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>200.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id1217141</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-30 23:59:47</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.997437</td>\n",
       "      <td>40.737583</td>\n",
       "      <td>-73.986160</td>\n",
       "      <td>40.729523</td>\n",
       "      <td>N</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>193.2</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id2150126</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-30 23:59:41</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.956070</td>\n",
       "      <td>40.771900</td>\n",
       "      <td>-73.986427</td>\n",
       "      <td>40.730469</td>\n",
       "      <td>N</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>494.8</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id1598245</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-30 23:59:33</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.970215</td>\n",
       "      <td>40.761475</td>\n",
       "      <td>-73.961510</td>\n",
       "      <td>40.755890</td>\n",
       "      <td>N</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>103.2</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  vendor_id     pickup_datetime  passenger_count  \\\n",
       "0  id3004672          1 2016-06-30 23:59:58                1   \n",
       "1  id3505355          1 2016-06-30 23:59:53                1   \n",
       "2  id1217141          1 2016-06-30 23:59:47                1   \n",
       "3  id2150126          2 2016-06-30 23:59:41                1   \n",
       "4  id1598245          1 2016-06-30 23:59:33                1   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.988129        40.732029         -73.990173         40.756680   \n",
       "1        -73.964203        40.679993         -73.959808         40.655403   \n",
       "2        -73.997437        40.737583         -73.986160         40.729523   \n",
       "3        -73.956070        40.771900         -73.986427         40.730469   \n",
       "4        -73.970215        40.761475         -73.961510         40.755890   \n",
       "\n",
       "  store_and_fwd_flag  pu_hour   ...    total_travel_time  average temperature  \\\n",
       "0                  N       23   ...                424.6                 76.0   \n",
       "1                  N       23   ...                200.0                 76.0   \n",
       "2                  N       23   ...                193.2                 76.0   \n",
       "3                  N       23   ...                494.8                 76.0   \n",
       "4                  N       23   ...                103.2                 76.0   \n",
       "\n",
       "   precipitation  snow fall  snow depth  snowfall  snowdepth    jfk    lga  \\\n",
       "0            0.0        0.0           0       0.0          0  False  False   \n",
       "1            0.0        0.0           0       0.0          0  False  False   \n",
       "2            0.0        0.0           0       0.0          0  False  False   \n",
       "3            0.0        0.0           0       0.0          0  False  False   \n",
       "4            0.0        0.0           0       0.0          0  False  False   \n",
       "\n",
       "  workday  \n",
       "0   False  \n",
       "1   False  \n",
       "2   False  \n",
       "3   False  \n",
       "4   False  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('/home/ankit/Downloads/nytaxi_kaggle_challenge/all/train_data.csv',index=False)\n",
    "tdf.to_csv('/home/ankit/Downloads/nytaxi_kaggle_challenge/all/test_data.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ankit/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2698: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('/home/ankit/Downloads/nytaxi_kaggle_challenge/all/train_data.csv')\n",
    "tdf = pd.read_csv('/home/ankit/Downloads/nytaxi_kaggle_challenge/all/test_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benchmark Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id0771704</td>\n",
       "      <td>200.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id3274209</td>\n",
       "      <td>141.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id2756455</td>\n",
       "      <td>324.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id3684027</td>\n",
       "      <td>84.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id3101285</td>\n",
       "      <td>294.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  trip_duration\n",
       "0  id0771704          200.2\n",
       "1  id3274209          141.5\n",
       "2  id2756455          324.6\n",
       "3  id3684027           84.2\n",
       "4  id3101285          294.7"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "benchmark = fastest_test[['id','total_travel_time']]\n",
    "benchmark = benchmark.rename(index=str, columns={\"total_travel_time\": \"trip_duration\"})\n",
    "benchmark.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "benchmark['trip_duration'].isnull().values.any()\n",
    "benchmark.to_csv('benchmark.csv',index=False)\n",
    "#RMSLE=0.990"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_cell_guid": "da4346dd-73a7-4412-8222-3fc122e4020c",
    "_execution_state": "idle",
    "_uuid": "39ebf018303a0b31cea169a5bc184d9290ea249a",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features = df[['vendor_id','passenger_count','pickup_latitude','pickup_longitude','dropoff_latitude','dropoff_longitude','pu_hour','wday','month','workday','precipitation','snowfall','snowdepth','total_distance','total_travel_time','jfk','lga']]\n",
    "target = df['trip_duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_cell_guid": "58acd8d6-8b32-43ba-bf48-7e16d5c52244",
    "_execution_state": "idle",
    "_uuid": "1035773aa16ec621683056b10197c4ce36bf500a",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tfeatures = tdf[['vendor_id','passenger_count','pickup_latitude','pickup_longitude','dropoff_latitude','dropoff_longitude','pu_hour','wday','month','workday','precipitation','snowfall','snowdepth','total_distance','total_travel_time','jfk','lga']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b0bbeea7-80a5-450b-864e-6f540293b8e2",
    "_execution_state": "idle",
    "_uuid": "790a8b8ce345eb618bbef88fe8fec940abf8c75c"
   },
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_cell_guid": "3efc1ba1-55cb-4039-b8d3-991e43f6c22e",
    "_execution_state": "idle",
    "_uuid": "1785c513750bbef82f98065c4dc9b4deeee2dd5f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number predicted less than 0: (14,)\n",
      "Number predicted less than 0: (9,)\n",
      "Number predicted less than 0: (17,)\n",
      "Number predicted less than 0: (12,)\n",
      "[ 0.43999626  0.44022776  0.43897465  0.44073689]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "reg = linear_model.LinearRegression()\n",
    "cv = ShuffleSplit(n_splits=4, test_size=0.1, random_state=0)\n",
    "print(cross_val_score(reg, features, np.ravel(target), cv=cv, scoring=rmsle))\n",
    "reg.fit(features, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.43998374750000002"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean([0.43999617,  0.44022755 , 0.43897449,0.44073678])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(625134, 17)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfeatures.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(490,)\n"
     ]
    }
   ],
   "source": [
    "pred = reg.predict(tfeatures)\n",
    "print(np.where(pred < 0)[0].shape)\n",
    "pred[pred < 0]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "_cell_guid": "1baff0f1-e3cf-40b4-af2d-2a3856dfbc81",
    "_execution_state": "idle",
    "_uuid": "70381e4a5b6a713d2dc671ce72686149210d790f",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tdf['trip_duration']=pred.astype(int)\n",
    "out = tdf[['id','trip_duration']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "_cell_guid": "0721f21f-2888-4ab3-b3bd-f54968156bdb",
    "_execution_state": "idle",
    "_uuid": "74f10d64578769e0eddbe133fb3ca32f6f26e28b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out['trip_duration'].isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "_cell_guid": "31941b96-6946-47d6-8b72-28842f417e30",
    "_execution_state": "idle",
    "_uuid": "3ca4d926dc29d9767614e8e05dd313379ae5bdb5",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "out.to_csv('pred_linear.csv',index=False)\n",
    "#RMSLE=0.535"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-nearest Neighbors Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "_cell_guid": "55f8b4ca-315d-402d-a85b-f18d1f521c54",
    "_execution_state": "idle",
    "_uuid": "6db76ebe986844f2484ab081ece300d4b0f8fa8f",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number predicted less than 0: (0,)\n",
      "Number predicted less than 0: (0,)\n",
      "Number predicted less than 0: (0,)\n",
      "Number predicted less than 0: (0,)\n",
      "[ 0.42011203  0.4194096   0.41947696  0.41968832]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=1, n_neighbors=10, p=2,\n",
       "          weights='uniform')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "neigh = KNeighborsRegressor(n_neighbors=10)\n",
    "cv = ShuffleSplit(n_splits=4, test_size=0.1, random_state=0)\n",
    "print(cross_val_score(neigh, features, np.ravel(target), cv=cv,scoring=rmsle))\n",
    "neigh.fit(features,target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.41967126500000002"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean([0.42010954, 0.41940803, 0.41947931, 0.41968818])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0,)\n"
     ]
    }
   ],
   "source": [
    "pred = neigh.predict(tfeatures)\n",
    "print(np.where(pred < 0)[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "_cell_guid": "04e4bf99-8113-42f5-ba0d-a419e2842afe",
    "_execution_state": "idle",
    "_uuid": "849b36bc0db626910c1ad2b4657e6cb944ddc748",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tdf['trip_duration']=pred.astype(int)\n",
    "out = tdf[['id','trip_duration']]\n",
    "out.to_csv('pred_knn.csv',index=False)\n",
    "#RMSLE=0.505"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number predicted less than 0: (0,)\n",
      "Number predicted less than 0: (0,)\n",
      "Number predicted less than 0: (0,)\n",
      "Number predicted less than 0: (0,)\n",
      "[ 0.35106126  0.34992806  0.34971324  0.34965259]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf = RandomForestRegressor()\n",
    "cv = ShuffleSplit(n_splits=4, test_size=0.1, random_state=0)\n",
    "print(cross_val_score(rf, features, np.ravel(target), cv=cv,scoring=rmsle))\n",
    "rf = rf.fit(features,np.ravel(target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.35075301749999999"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean([0.35085909, 0.35181223, 0.34976928, 0.35057147])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0,)\n"
     ]
    }
   ],
   "source": [
    "pred = rf.predict(tfeatures)\n",
    "print(np.where(pred < 0)[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tdf['trip_duration']=pred.astype(int)\n",
    "out = tdf[['id','trip_duration']]\n",
    "out.to_csv('pred_rf.csv',index=False)\n",
    "#RMSLE=0.473"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "_cell_guid": "6e34d8d0-402d-4115-b00a-4d56414d2085",
    "_execution_state": "idle",
    "_uuid": "12c7db13fe177d3573b0b3ec0c260738ecc56f4c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number predicted less than 0: (1,)\n",
      "Number predicted less than 0: (0,)\n",
      "Number predicted less than 0: (0,)\n",
      "Number predicted less than 0: (0,)\n",
      "[ 0.34347665  0.34339981  0.34196098  0.34286028]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.08, max_delta_step=0,\n",
       "       max_depth=7, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=0.75)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = xgboost.XGBRegressor(n_estimators=100, learning_rate=0.08, gamma=0, subsample=0.75,\n",
    "                           colsample_bytree=1, max_depth=7)\n",
    "\n",
    "cv = ShuffleSplit(n_splits=4, test_size=0.1, random_state=0)\n",
    "print(cross_val_score(reg, features, np.ravel(target), cv=cv,scoring=rmsle))\n",
    "reg.fit(features,target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0,)\n"
     ]
    }
   ],
   "source": [
    "pred = reg.predict(tfeatures)\n",
    "print(np.where(pred < 0)[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "_cell_guid": "ac0340eb-fdfd-4998-a698-a8f440b1e632",
    "_execution_state": "idle",
    "_uuid": "723ceeca2a5f05925711eda23e672d38a01b1d8f",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred[pred < 0] = 0\n",
    "tdf['trip_duration']=pred.astype(int)\n",
    "out = tdf[['id','trip_duration']]\n",
    "out['trip_duration'].isnull().values.any()\n",
    "out.to_csv('pred_xgboost.csv',index=False)\n",
    "#RMSLE=0.463"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZsAAABVCAYAAACfDUmIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f541ad695f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xgboost import plot_tree\n",
    "plot_tree(reg)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(reg, open('xgb_model.sav','wb'),protocol=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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