Lesson07_TLombardi_v3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lesson 7 Homework\n",
    "### TLombardi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook uses three ensemble methods, AdaBoost, GDM and XGBoost, to compare predictions of NYC taxi trip durations using big data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import sklearn as skl\n",
    "from sklearn import model_selection\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Implement the haversine function to calculate the distance between two lon/lat coordinates and vectorize it using numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adapted formula from comments in https://gist.github.com/rochacbruno/2883505\n",
    "def distance_vectorize(s_lat, s_lng, e_lat, e_lng, df):\n",
    "    \n",
    "    # approximate radius of earth in km\n",
    "    R = 6371.0\n",
    "    \n",
    "    s_lat = s_lat*np.pi/180.0                      \n",
    "    s_lng = np.deg2rad(s_lng)     \n",
    "    e_lat = np.deg2rad(e_lat)                       \n",
    "    e_lng = np.deg2rad(e_lng)  \n",
    "    \n",
    "    d = np.sin((e_lat - s_lat)/2)**2 + np.cos(s_lat)*np.cos(e_lat) * np.sin((e_lng - s_lng)/2)**2\n",
    "    \n",
    "    df['haversine_distance_km'] = 2 * R * np.arcsin(np.sqrt(d))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Read the train.csv from [Kaggle.com](https://www.kaggle.com/c/new-york-city-taxi-fare-prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read file paths\n",
    "train_filepath = \"C:/Users/TVL/Desktop/JHU/SP20_GeoAI/Lesson02/new-york-city-taxi-fare-prediction/train.csv\"\n",
    "test_filepath = \"C:/Users/TVL/Desktop/JHU/SP20_GeoAI/Lesson02/new-york-city-taxi-fare-prediction/test.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the files\n",
    "train = pd.read_csv(train_filepath, \n",
    "                         delimiter=',',\n",
    "                         index_col=0, \n",
    "                         nrows = 500_000, \n",
    "                         parse_dates=[\"pickup_datetime\",\n",
    "                                      \"dropoff_datetime\"])\n",
    "test = pd.read_csv(test_filepath,\n",
    "                  parse_dates=[\"pickup_datetime\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>trip_duration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\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>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>id2377394</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-12 00:43:35</td>\n",
       "      <td>2016-06-12 00:54:38</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.980415</td>\n",
       "      <td>40.738564</td>\n",
       "      <td>-73.999481</td>\n",
       "      <td>40.731152</td>\n",
       "      <td>N</td>\n",
       "      <td>663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>id3858529</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-19 11:35:24</td>\n",
       "      <td>2016-01-19 12:10:48</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.979027</td>\n",
       "      <td>40.763939</td>\n",
       "      <td>-74.005333</td>\n",
       "      <td>40.710087</td>\n",
       "      <td>N</td>\n",
       "      <td>2124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>id3504673</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-04-06 19:32:31</td>\n",
       "      <td>2016-04-06 19:39:40</td>\n",
       "      <td>1</td>\n",
       "      <td>-74.010040</td>\n",
       "      <td>40.719971</td>\n",
       "      <td>-74.012268</td>\n",
       "      <td>40.706718</td>\n",
       "      <td>N</td>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>id2181028</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-26 13:30:55</td>\n",
       "      <td>2016-03-26 13:38:10</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.973053</td>\n",
       "      <td>40.793209</td>\n",
       "      <td>-73.972923</td>\n",
       "      <td>40.782520</td>\n",
       "      <td>N</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           vendor_id     pickup_datetime    dropoff_datetime  passenger_count  \\\n",
       "id                                                                              \n",
       "id2875421          2 2016-03-14 17:24:55 2016-03-14 17:32:30                1   \n",
       "id2377394          1 2016-06-12 00:43:35 2016-06-12 00:54:38                1   \n",
       "id3858529          2 2016-01-19 11:35:24 2016-01-19 12:10:48                1   \n",
       "id3504673          2 2016-04-06 19:32:31 2016-04-06 19:39:40                1   \n",
       "id2181028          2 2016-03-26 13:30:55 2016-03-26 13:38:10                1   \n",
       "\n",
       "           pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "id                                                                \n",
       "id2875421        -73.982155        40.767937         -73.964630   \n",
       "id2377394        -73.980415        40.738564         -73.999481   \n",
       "id3858529        -73.979027        40.763939         -74.005333   \n",
       "id3504673        -74.010040        40.719971         -74.012268   \n",
       "id2181028        -73.973053        40.793209         -73.972923   \n",
       "\n",
       "           dropoff_latitude store_and_fwd_flag  trip_duration  \n",
       "id                                                             \n",
       "id2875421         40.765602                  N            455  \n",
       "id2377394         40.731152                  N            663  \n",
       "id3858529         40.710087                  N           2124  \n",
       "id3504673         40.706718                  N            429  \n",
       "id2181028         40.782520                  N            435  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list first few rows (datapoints)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vendor_id                      int64\n",
       "pickup_datetime       datetime64[ns]\n",
       "dropoff_datetime      datetime64[ns]\n",
       "passenger_count                int64\n",
       "pickup_longitude             float64\n",
       "pickup_latitude              float64\n",
       "dropoff_longitude            float64\n",
       "dropoff_latitude             float64\n",
       "store_and_fwd_flag            object\n",
       "trip_duration                  int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check datatypes\n",
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 500000 entries, id2875421 to id0013408\n",
      "Data columns (total 10 columns):\n",
      "vendor_id             500000 non-null int64\n",
      "pickup_datetime       500000 non-null datetime64[ns]\n",
      "dropoff_datetime      500000 non-null datetime64[ns]\n",
      "passenger_count       500000 non-null int64\n",
      "pickup_longitude      500000 non-null float64\n",
      "pickup_latitude       500000 non-null float64\n",
      "dropoff_longitude     500000 non-null float64\n",
      "dropoff_latitude      500000 non-null float64\n",
      "store_and_fwd_flag    500000 non-null object\n",
      "trip_duration         500000 non-null int64\n",
      "dtypes: datetime64[ns](2), float64(4), int64(3), object(1)\n",
      "memory usage: 42.0+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vendor_id             0\n",
       "pickup_datetime       0\n",
       "dropoff_datetime      0\n",
       "passenger_count       0\n",
       "pickup_longitude      0\n",
       "pickup_latitude       0\n",
       "dropoff_longitude     0\n",
       "dropoff_latitude      0\n",
       "store_and_fwd_flag    0\n",
       "trip_duration         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Locate null data\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-27 13:08:24.0000002</td>\n",
       "      <td>2015-01-27 13:08:24+00:00</td>\n",
       "      <td>-73.973320</td>\n",
       "      <td>40.763805</td>\n",
       "      <td>-73.981430</td>\n",
       "      <td>40.743835</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-27 13:08:24.0000003</td>\n",
       "      <td>2015-01-27 13:08:24+00:00</td>\n",
       "      <td>-73.986862</td>\n",
       "      <td>40.719383</td>\n",
       "      <td>-73.998886</td>\n",
       "      <td>40.739201</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2011-10-08 11:53:44.0000002</td>\n",
       "      <td>2011-10-08 11:53:44+00:00</td>\n",
       "      <td>-73.982524</td>\n",
       "      <td>40.751260</td>\n",
       "      <td>-73.979654</td>\n",
       "      <td>40.746139</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2012-12-01 21:12:12.0000002</td>\n",
       "      <td>2012-12-01 21:12:12+00:00</td>\n",
       "      <td>-73.981160</td>\n",
       "      <td>40.767807</td>\n",
       "      <td>-73.990448</td>\n",
       "      <td>40.751635</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2012-12-01 21:12:12.0000003</td>\n",
       "      <td>2012-12-01 21:12:12+00:00</td>\n",
       "      <td>-73.966046</td>\n",
       "      <td>40.789775</td>\n",
       "      <td>-73.988565</td>\n",
       "      <td>40.744427</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           key           pickup_datetime  pickup_longitude  \\\n",
       "0  2015-01-27 13:08:24.0000002 2015-01-27 13:08:24+00:00        -73.973320   \n",
       "1  2015-01-27 13:08:24.0000003 2015-01-27 13:08:24+00:00        -73.986862   \n",
       "2  2011-10-08 11:53:44.0000002 2011-10-08 11:53:44+00:00        -73.982524   \n",
       "3  2012-12-01 21:12:12.0000002 2012-12-01 21:12:12+00:00        -73.981160   \n",
       "4  2012-12-01 21:12:12.0000003 2012-12-01 21:12:12+00:00        -73.966046   \n",
       "\n",
       "   pickup_latitude  dropoff_longitude  dropoff_latitude  passenger_count  \n",
       "0        40.763805         -73.981430         40.743835                1  \n",
       "1        40.719383         -73.998886         40.739201                1  \n",
       "2        40.751260         -73.979654         40.746139                1  \n",
       "3        40.767807         -73.990448         40.751635                1  \n",
       "4        40.789775         -73.988565         40.744427                1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9914 entries, 0 to 9913\n",
      "Data columns (total 7 columns):\n",
      "key                  9914 non-null object\n",
      "pickup_datetime      9914 non-null datetime64[ns, UTC]\n",
      "pickup_longitude     9914 non-null float64\n",
      "pickup_latitude      9914 non-null float64\n",
      "dropoff_longitude    9914 non-null float64\n",
      "dropoff_latitude     9914 non-null float64\n",
      "passenger_count      9914 non-null int64\n",
      "dtypes: datetime64[ns, UTC](1), float64(4), int64(1), object(1)\n",
      "memory usage: 542.3+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Clean-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = np.mean(train['trip_duration'])\n",
    "s = np.std(train['trip_duration'])\n",
    "train = train[train['trip_duration'] <= m + 2*s]\n",
    "train = train[train['trip_duration'] >= m - 2*s]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train[train['pickup_longitude'] <= -73.75]\n",
    "train = train[train['pickup_longitude'] >= -74.03]\n",
    "train = train[train['pickup_latitude'] <= 40.85]\n",
    "train = train[train['pickup_latitude'] >= 40.63]\n",
    "train = train[train['dropoff_longitude'] <= -73.75]\n",
    "train = train[train['dropoff_longitude'] >= -74.03]\n",
    "train = train[train['dropoff_latitude'] <= 40.85]\n",
    "train = train[train['dropoff_latitude'] >= 40.63]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 492545 entries, id2875421 to id0013408\n",
      "Data columns (total 10 columns):\n",
      "vendor_id             492545 non-null int64\n",
      "pickup_datetime       492545 non-null datetime64[ns]\n",
      "dropoff_datetime      492545 non-null datetime64[ns]\n",
      "passenger_count       492545 non-null int64\n",
      "pickup_longitude      492545 non-null float64\n",
      "pickup_latitude       492545 non-null float64\n",
      "dropoff_longitude     492545 non-null float64\n",
      "dropoff_latitude      492545 non-null float64\n",
      "store_and_fwd_flag    492545 non-null object\n",
      "trip_duration         492545 non-null int64\n",
      "dtypes: datetime64[ns](2), float64(4), int64(3), object(1)\n",
      "memory usage: 41.3+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Make sure to read the pickup dates as timestamps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vendor_id</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>trip_duration</th>\n",
       "      <th>DayofWeek</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2016-02-12 08:03:14</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-02-12 09:32:05</td>\n",
       "      <td>3</td>\n",
       "      <td>-73.790138</td>\n",
       "      <td>40.647095</td>\n",
       "      <td>-73.980881</td>\n",
       "      <td>40.762527</td>\n",
       "      <td>N</td>\n",
       "      <td>5331</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-03-08 13:57:02</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-08 14:12:16</td>\n",
       "      <td>2</td>\n",
       "      <td>-74.008438</td>\n",
       "      <td>40.712521</td>\n",
       "      <td>-73.990364</td>\n",
       "      <td>40.746391</td>\n",
       "      <td>N</td>\n",
       "      <td>914</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-02-20 00:06:31</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-02-20 00:13:19</td>\n",
       "      <td>2</td>\n",
       "      <td>-73.977669</td>\n",
       "      <td>40.787151</td>\n",
       "      <td>-73.984894</td>\n",
       "      <td>40.768280</td>\n",
       "      <td>N</td>\n",
       "      <td>408</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-27 17:56:40</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-27 18:02:32</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.994362</td>\n",
       "      <td>40.745697</td>\n",
       "      <td>-74.005791</td>\n",
       "      <td>40.733006</td>\n",
       "      <td>N</td>\n",
       "      <td>352</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-29 20:46:42</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-29 21:02:53</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.993736</td>\n",
       "      <td>40.762077</td>\n",
       "      <td>-73.955986</td>\n",
       "      <td>40.778721</td>\n",
       "      <td>N</td>\n",
       "      <td>971</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     vendor_id    dropoff_datetime  passenger_count  \\\n",
       "pickup_datetime                                                       \n",
       "2016-02-12 08:03:14          1 2016-02-12 09:32:05                3   \n",
       "2016-03-08 13:57:02          1 2016-03-08 14:12:16                2   \n",
       "2016-02-20 00:06:31          2 2016-02-20 00:13:19                2   \n",
       "2016-01-27 17:56:40          1 2016-01-27 18:02:32                1   \n",
       "2016-01-29 20:46:42          1 2016-01-29 21:02:53                1   \n",
       "\n",
       "                     pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "pickup_datetime                                                             \n",
       "2016-02-12 08:03:14        -73.790138        40.647095         -73.980881   \n",
       "2016-03-08 13:57:02        -74.008438        40.712521         -73.990364   \n",
       "2016-02-20 00:06:31        -73.977669        40.787151         -73.984894   \n",
       "2016-01-27 17:56:40        -73.994362        40.745697         -74.005791   \n",
       "2016-01-29 20:46:42        -73.993736        40.762077         -73.955986   \n",
       "\n",
       "                     dropoff_latitude store_and_fwd_flag  trip_duration  \\\n",
       "pickup_datetime                                                           \n",
       "2016-02-12 08:03:14         40.762527                  N           5331   \n",
       "2016-03-08 13:57:02         40.746391                  N            914   \n",
       "2016-02-20 00:06:31         40.768280                  N            408   \n",
       "2016-01-27 17:56:40         40.733006                  N            352   \n",
       "2016-01-29 20:46:42         40.778721                  N            971   \n",
       "\n",
       "                     DayofWeek  Hour  Month  \n",
       "pickup_datetime                              \n",
       "2016-02-12 08:03:14          4     8      2  \n",
       "2016-03-08 13:57:02          1    13      3  \n",
       "2016-02-20 00:06:31          5     0      2  \n",
       "2016-01-27 17:56:40          2    17      1  \n",
       "2016-01-29 20:46:42          4    20      1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add columns with month, day of week, and hour of day values\n",
    "train = train.set_index('pickup_datetime')\n",
    "train['DayofWeek'] = train.index.dayofweek\n",
    "train['Hour'] = train.index.hour\n",
    "train['Month'] = train.index.month\n",
    "test = test.set_index('pickup_datetime')\n",
    "test['DayofWeek'] = test.index.dayofweek\n",
    "test['Hour'] = test.index.hour\n",
    "test['Month'] = test.index.month\n",
    "\n",
    "# Display a random sampling of 5 rows\n",
    "train.sample(5, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>DayofWeek</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2014-03-11 20:24:02+00:00</td>\n",
       "      <td>2014-03-11 20:24:02.0000001</td>\n",
       "      <td>-73.963902</td>\n",
       "      <td>40.765338</td>\n",
       "      <td>-73.976445</td>\n",
       "      <td>40.764152</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-12-24 03:00:00+00:00</td>\n",
       "      <td>2014-12-24 03:00:00.00000018</td>\n",
       "      <td>-73.980602</td>\n",
       "      <td>40.730085</td>\n",
       "      <td>-73.978402</td>\n",
       "      <td>40.774817</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013-09-25 22:00:00+00:00</td>\n",
       "      <td>2013-09-25 22:00:00.00000053</td>\n",
       "      <td>-73.874600</td>\n",
       "      <td>40.773985</td>\n",
       "      <td>-73.960362</td>\n",
       "      <td>40.757420</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013-02-10 19:51:53+00:00</td>\n",
       "      <td>2013-02-10 19:51:53.0000002</td>\n",
       "      <td>-73.998873</td>\n",
       "      <td>40.725026</td>\n",
       "      <td>-73.960968</td>\n",
       "      <td>40.757409</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-11-11 20:35:40+00:00</td>\n",
       "      <td>2012-11-11 20:35:40.0000001</td>\n",
       "      <td>-73.789967</td>\n",
       "      <td>40.646832</td>\n",
       "      <td>-73.973090</td>\n",
       "      <td>40.684484</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    key  pickup_longitude  \\\n",
       "pickup_datetime                                                             \n",
       "2014-03-11 20:24:02+00:00   2014-03-11 20:24:02.0000001        -73.963902   \n",
       "2014-12-24 03:00:00+00:00  2014-12-24 03:00:00.00000018        -73.980602   \n",
       "2013-09-25 22:00:00+00:00  2013-09-25 22:00:00.00000053        -73.874600   \n",
       "2013-02-10 19:51:53+00:00   2013-02-10 19:51:53.0000002        -73.998873   \n",
       "2012-11-11 20:35:40+00:00   2012-11-11 20:35:40.0000001        -73.789967   \n",
       "\n",
       "                           pickup_latitude  dropoff_longitude  \\\n",
       "pickup_datetime                                                 \n",
       "2014-03-11 20:24:02+00:00        40.765338         -73.976445   \n",
       "2014-12-24 03:00:00+00:00        40.730085         -73.978402   \n",
       "2013-09-25 22:00:00+00:00        40.773985         -73.960362   \n",
       "2013-02-10 19:51:53+00:00        40.725026         -73.960968   \n",
       "2012-11-11 20:35:40+00:00        40.646832         -73.973090   \n",
       "\n",
       "                           dropoff_latitude  passenger_count  DayofWeek  Hour  \\\n",
       "pickup_datetime                                                                 \n",
       "2014-03-11 20:24:02+00:00         40.764152                2          1    20   \n",
       "2014-12-24 03:00:00+00:00         40.774817                1          2     3   \n",
       "2013-09-25 22:00:00+00:00         40.757420                1          2    22   \n",
       "2013-02-10 19:51:53+00:00         40.757409                1          6    19   \n",
       "2012-11-11 20:35:40+00:00         40.684484                1          6    20   \n",
       "\n",
       "                           Month  \n",
       "pickup_datetime                   \n",
       "2014-03-11 20:24:02+00:00      3  \n",
       "2014-12-24 03:00:00+00:00     12  \n",
       "2013-09-25 22:00:00+00:00      9  \n",
       "2013-02-10 19:51:53+00:00      2  \n",
       "2012-11-11 20:35:40+00:00     11  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.sample(5, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.reset_index(drop=True)\n",
    "test = test.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vendor_id</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",
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       "      <th>DayofWeek</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2</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>455</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-12 00:54:38</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.980415</td>\n",
       "      <td>40.738564</td>\n",
       "      <td>-73.999481</td>\n",
       "      <td>40.731152</td>\n",
       "      <td>N</td>\n",
       "      <td>663</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-19 12:10:48</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.979027</td>\n",
       "      <td>40.763939</td>\n",
       "      <td>-74.005333</td>\n",
       "      <td>40.710087</td>\n",
       "      <td>N</td>\n",
       "      <td>2124</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-04-06 19:39:40</td>\n",
       "      <td>1</td>\n",
       "      <td>-74.010040</td>\n",
       "      <td>40.719971</td>\n",
       "      <td>-74.012268</td>\n",
       "      <td>40.706718</td>\n",
       "      <td>N</td>\n",
       "      <td>429</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-26 13:38:10</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.973053</td>\n",
       "      <td>40.793209</td>\n",
       "      <td>-73.972923</td>\n",
       "      <td>40.782520</td>\n",
       "      <td>N</td>\n",
       "      <td>435</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   vendor_id    dropoff_datetime  passenger_count  pickup_longitude  \\\n",
       "0          2 2016-03-14 17:32:30                1        -73.982155   \n",
       "1          1 2016-06-12 00:54:38                1        -73.980415   \n",
       "2          2 2016-01-19 12:10:48                1        -73.979027   \n",
       "3          2 2016-04-06 19:39:40                1        -74.010040   \n",
       "4          2 2016-03-26 13:38:10                1        -73.973053   \n",
       "\n",
       "   pickup_latitude  dropoff_longitude  dropoff_latitude store_and_fwd_flag  \\\n",
       "0        40.767937         -73.964630         40.765602                  N   \n",
       "1        40.738564         -73.999481         40.731152                  N   \n",
       "2        40.763939         -74.005333         40.710087                  N   \n",
       "3        40.719971         -74.012268         40.706718                  N   \n",
       "4        40.793209         -73.972923         40.782520                  N   \n",
       "\n",
       "   trip_duration  DayofWeek  Hour  Month  \n",
       "0            455          0    17      3  \n",
       "1            663          6     0      6  \n",
       "2           2124          1    11      1  \n",
       "3            429          2    19      4  \n",
       "4            435          5    13      3  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
       "      <td>2011-10-08 11:53:44.0000002</td>\n",
       "      <td>-73.982524</td>\n",
       "      <td>40.751260</td>\n",
       "      <td>-73.979654</td>\n",
       "      <td>40.746139</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2012-12-01 21:12:12.0000002</td>\n",
       "      <td>-73.981160</td>\n",
       "      <td>40.767807</td>\n",
       "      <td>-73.990448</td>\n",
       "      <td>40.751635</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2012-12-01 21:12:12.0000003</td>\n",
       "      <td>-73.966046</td>\n",
       "      <td>40.789775</td>\n",
       "      <td>-73.988565</td>\n",
       "      <td>40.744427</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           key  pickup_longitude  pickup_latitude  \\\n",
       "0  2015-01-27 13:08:24.0000002        -73.973320        40.763805   \n",
       "1  2015-01-27 13:08:24.0000003        -73.986862        40.719383   \n",
       "2  2011-10-08 11:53:44.0000002        -73.982524        40.751260   \n",
       "3  2012-12-01 21:12:12.0000002        -73.981160        40.767807   \n",
       "4  2012-12-01 21:12:12.0000003        -73.966046        40.789775   \n",
       "\n",
       "   dropoff_longitude  dropoff_latitude  passenger_count  DayofWeek  Hour  \\\n",
       "0         -73.981430         40.743835                1          1    13   \n",
       "1         -73.998886         40.739201                1          1    13   \n",
       "2         -73.979654         40.746139                1          5    11   \n",
       "3         -73.990448         40.751635                1          5    21   \n",
       "4         -73.988565         40.744427                1          5    21   \n",
       "\n",
       "   Month  \n",
       "0      1  \n",
       "1      1  \n",
       "2     10  \n",
       "3     12  \n",
       "4     12  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. Plot using seaborn the distribution by:\n",
    "     - month\n",
    "     - day of week\n",
    "     - hour of day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Month')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Add title\n",
    "plt.title(\"Pickups per Month\")\n",
    "\n",
    "# Bar chart showing total counts of pickup days of the week\n",
    "sns.countplot('Month', data=train)\n",
    "\n",
    "# Add label for vertical axis\n",
    "plt.xlabel(\"Month\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Day of the Week')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Add title\n",
    "plt.title(\"Pickups per Day of the Week\")\n",
    "\n",
    "# Bar chart showing total counts of pickup days of the week\n",
    "sns.countplot('DayofWeek', data=train)\n",
    "\n",
    "# Add label for vertical axis\n",
    "plt.xlabel(\"Day of the Week\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Hour of the Day')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Add title\n",
    "plt.title(\"Pickups per Hour of the Day\")\n",
    "\n",
    "# Bar chart showing total counts of pickups per hour of the day\n",
    "sns.countplot('Hour', data=train)\n",
    "\n",
    "# Add label for vertical axis\n",
    "plt.xlabel(\"Hour of the Day\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7. Append the haversine distance to the trip record and show top 10 longest records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vendor_id</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>trip_duration</th>\n",
       "      <th>DayofWeek</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>haversine_distance_km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>86424</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-04-29 06:58:11</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.789116</td>\n",
       "      <td>40.641712</td>\n",
       "      <td>-73.990295</td>\n",
       "      <td>40.832928</td>\n",
       "      <td>N</td>\n",
       "      <td>2735</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>27.191629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179013</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-14 05:12:26</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.776634</td>\n",
       "      <td>40.645370</td>\n",
       "      <td>-73.940659</td>\n",
       "      <td>40.849697</td>\n",
       "      <td>N</td>\n",
       "      <td>2128</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>26.591816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>306668</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-02-18 18:52:13</td>\n",
       "      <td>5</td>\n",
       "      <td>-73.776833</td>\n",
       "      <td>40.645378</td>\n",
       "      <td>-73.940247</td>\n",
       "      <td>40.848572</td>\n",
       "      <td>N</td>\n",
       "      <td>3183</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>26.457531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>186152</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-01 06:52:57</td>\n",
       "      <td>6</td>\n",
       "      <td>-73.781090</td>\n",
       "      <td>40.645077</td>\n",
       "      <td>-73.937271</td>\n",
       "      <td>40.849789</td>\n",
       "      <td>N</td>\n",
       "      <td>1718</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>26.291671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>483942</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-27 20:10:58</td>\n",
       "      <td>2</td>\n",
       "      <td>-73.776718</td>\n",
       "      <td>40.645321</td>\n",
       "      <td>-73.931160</td>\n",
       "      <td>40.849964</td>\n",
       "      <td>N</td>\n",
       "      <td>2128</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>26.212000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1362</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-05 05:23:19</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.777443</td>\n",
       "      <td>40.646641</td>\n",
       "      <td>-73.935776</td>\n",
       "      <td>40.848473</td>\n",
       "      <td>N</td>\n",
       "      <td>1515</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>26.107012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>424738</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-15 21:56:25</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.780144</td>\n",
       "      <td>40.645706</td>\n",
       "      <td>-73.938980</td>\n",
       "      <td>40.846321</td>\n",
       "      <td>N</td>\n",
       "      <td>2777</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>26.012738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>298817</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-08 23:42:02</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.789536</td>\n",
       "      <td>40.643532</td>\n",
       "      <td>-73.939407</td>\n",
       "      <td>40.847809</td>\n",
       "      <td>N</td>\n",
       "      <td>2128</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>25.987645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101850</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-04-01 00:14:07</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.781929</td>\n",
       "      <td>40.644650</td>\n",
       "      <td>-73.942886</td>\n",
       "      <td>40.843578</td>\n",
       "      <td>N</td>\n",
       "      <td>1808</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>25.945272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140726</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-24 21:46:54</td>\n",
       "      <td>5</td>\n",
       "      <td>-73.788177</td>\n",
       "      <td>40.641491</td>\n",
       "      <td>-73.937958</td>\n",
       "      <td>40.845161</td>\n",
       "      <td>N</td>\n",
       "      <td>1907</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>25.925179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        vendor_id    dropoff_datetime  passenger_count  pickup_longitude  \\\n",
       "86424           1 2016-04-29 06:58:11                1        -73.789116   \n",
       "179013          2 2016-05-14 05:12:26                1        -73.776634   \n",
       "306668          2 2016-02-18 18:52:13                5        -73.776833   \n",
       "186152          2 2016-01-01 06:52:57                6        -73.781090   \n",
       "483942          1 2016-01-27 20:10:58                2        -73.776718   \n",
       "1362            2 2016-06-05 05:23:19                1        -73.777443   \n",
       "424738          2 2016-01-15 21:56:25                1        -73.780144   \n",
       "298817          1 2016-03-08 23:42:02                1        -73.789536   \n",
       "101850          2 2016-04-01 00:14:07                1        -73.781929   \n",
       "140726          2 2016-01-24 21:46:54                5        -73.788177   \n",
       "\n",
       "        pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "86424         40.641712         -73.990295         40.832928   \n",
       "179013        40.645370         -73.940659         40.849697   \n",
       "306668        40.645378         -73.940247         40.848572   \n",
       "186152        40.645077         -73.937271         40.849789   \n",
       "483942        40.645321         -73.931160         40.849964   \n",
       "1362          40.646641         -73.935776         40.848473   \n",
       "424738        40.645706         -73.938980         40.846321   \n",
       "298817        40.643532         -73.939407         40.847809   \n",
       "101850        40.644650         -73.942886         40.843578   \n",
       "140726        40.641491         -73.937958         40.845161   \n",
       "\n",
       "       store_and_fwd_flag  trip_duration  DayofWeek  Hour  Month  \\\n",
       "86424                   N           2735          4     6      4   \n",
       "179013                  N           2128          5     4      5   \n",
       "306668                  N           3183          3    17      2   \n",
       "186152                  N           1718          4     6      1   \n",
       "483942                  N           2128          2    19      1   \n",
       "1362                    N           1515          6     4      6   \n",
       "424738                  N           2777          4    21      1   \n",
       "298817                  N           2128          1    23      3   \n",
       "101850                  N           1808          3    23      3   \n",
       "140726                  N           1907          6    21      1   \n",
       "\n",
       "        haversine_distance_km  \n",
       "86424               27.191629  \n",
       "179013              26.591816  \n",
       "306668              26.457531  \n",
       "186152              26.291671  \n",
       "483942              26.212000  \n",
       "1362                26.107012  \n",
       "424738              26.012738  \n",
       "298817              25.987645  \n",
       "101850              25.945272  \n",
       "140726              25.925179  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Append the haversine distance to the trip record \n",
    "distance_vectorize(train.pickup_latitude, \n",
    "                   train.pickup_longitude,\n",
    "                   train.dropoff_latitude,\n",
    "                   train.dropoff_longitude, \n",
    "                   train)\n",
    "\n",
    "# Show top 10 longest records\n",
    "train.nlargest(10,['haversine_distance_km'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Append the haversine distance to the trip record \n",
    "distance_vectorize(test.pickup_latitude, \n",
    "                   test.pickup_longitude,\n",
    "                   test.dropoff_latitude,\n",
    "                   test.dropoff_longitude, \n",
    "                   test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9914 entries, 0 to 9913\n",
      "Data columns (total 10 columns):\n",
      "key                      9914 non-null object\n",
      "pickup_longitude         9914 non-null float64\n",
      "pickup_latitude          9914 non-null float64\n",
      "dropoff_longitude        9914 non-null float64\n",
      "dropoff_latitude         9914 non-null float64\n",
      "passenger_count          9914 non-null int64\n",
      "DayofWeek                9914 non-null int64\n",
      "Hour                     9914 non-null int64\n",
      "Month                    9914 non-null int64\n",
      "haversine_distance_km    9914 non-null float64\n",
      "dtypes: float64(5), int64(4), object(1)\n",
      "memory usage: 774.7+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vendor_id                0\n",
       "dropoff_datetime         0\n",
       "passenger_count          0\n",
       "pickup_longitude         0\n",
       "pickup_latitude          0\n",
       "dropoff_longitude        0\n",
       "dropoff_latitude         0\n",
       "store_and_fwd_flag       0\n",
       "trip_duration            0\n",
       "DayofWeek                0\n",
       "Hour                     0\n",
       "Month                    0\n",
       "haversine_distance_km    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Locate null data\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6dREv3T6VlRYX2LHD3EObN0NDg20Hdw/1FKl+rk524M+19xFFCDaIyMfCFRE5B3i/sw8Fx61V1VkdHPYkUKaqhwLPAne3dZCq3q6qE1R1wogRIyKY3DEVFfCDH9gLf8cOKC2FW26x7eBC0FOk+rk62YE/195HlOajXwYeCFoOAawDPhvhcycC5wYi0g8YKCL3qerOz6rqhoTj/wzcHM3s3eeww+Coo6CsDG67bdd9LgSO48SJToVAVd8BJojI4GB9U5QTq+p1wHUAInIq8M1EEQi2j1bVVcHquVhQuUeor7d5SckH97kQOI4TJ6K0GioAPgGUAXkSNKNR1Z9054IiciMwU1WfAK4WkXOBJszddHl3ztkdQiEIRyZLxIerdBwnTkRxDT0GbANmsWvrocio6lRgarD8g4TtO2sNPU1Dg829RuA4TtyJIgR7q+ohabekh3HXkOM4jhGl1dB0ETko7Zb0MC4EjuM4RpQawbHAHBFZBDQCAqiqHplWy9JM6BrqKEbgQuA4ThyIIgSfSLsVGaCjGoEPYO84TpyI0nz0XQARGYr1B+gTeI3AcRzH6DRGICJni8jbwHLgFWAZ8Hy6DUs3HiNwHMcxogSLf4z1El6oqmOAMwmagvZmvEbgOI5jRBGCJlVdB+SIiKjqZKBXB4rBawSO4zghUYLFtSJSDLwA3CMia+k4m2ivwGsEjuM4RpQawSewnsVfx1xCK4Bz0mhT2tm+3aa8vNaXfiIuBI7jxIkoQnCdqjar6g5VvVNVfwVck27D0klibaCtEchcCBzHiRNRhODMNradnWpDepKO4gPgQuA4TrxoN0YgIl/CxiI4QERmJ+waAMxMt2HppKP4ALgQOI4TLzoKFv8NeA74KfCdhO2bVXVtWq1KM14jcBzHaaVd15CqblTVRcC3gGVBD+PRwIUiMrCnDEwHndUIfPB6x3HiRJQYwd8BFZF9gXuA8cD9abUqzXiNwHEcp5UoQtCiqjuATwK3qup/A3um16z04jECx3GcViL1LBaRi4DPAU8F2/LTZ1L68RqB4zhOK1GE4AvAh4Gfq+piESkHHkivWemlo/GKwTqaiUBTEzR3a3BOx3Gc3kOUNNSvA19NWK/BEtH1WjoarxhMBAoLYds264Hcv3/P2eY4jtPTdNSP4AFVvURE5gCavL83j1DWmWsIWoWgsdGFwHGcvk1HNYJvBfMLe8KQnqSzYDF4nMBxnPjQrhCo6vJgcS2wX7D8jqrWp92qNBO1RgAuBI7j9H06cg0VAL8HPgUswQLLe4nI34CrgialvZLOYgTg4xY7jhMfOmo1dD1QAuylqoeq6iHA3kAx8L2eMC4dqLYKQVFR+8d5jcBxnLjQkRB8EviiqtaFG1S1FktE98l0G5YutmwxMejfH3Jz2z/OhcBxnLjQYT+CtuIBqrqZNloR9RaiBIrBhcBxnPjQUauhFhEZALQxdEvvFYIogWJwIXAcJz50JATDgDfZVQg0WHchcBzH6SN01Hx0r540pKdw15DjOM6uRMk11KfwGoHjOM6uxE4IvEbgOI6zK7ETAq8ROI7j7Eqn2UcBRORQ4CQsSPyiqs5Lq1VpxGsEjuM4u9JpjUBErsfGH9gT2Au4X0Sui3oBEckVkTki8lQb+wpF5EERWSQir4hIWXTTu0fUGoGPW+w4TlyIUiP4LHCUqm4BEJEfA7OAn0a8xteABUBbA95/AdioqvuJyMXAzcCnI563W3iNwHEcZ1eixAiWsqtg5AGLo5xcRPYCzgbuaOeQ84C7g+WHgdNFpK0ObCnDYwSO4zi7EqVGsAV4U0T+jcUIzgBeEJFfAajqNR189lbgWmBAO/v3BJYF52kSkVqsI9v6aOZ3nc6GqQxxIXAcJy5EEYJ/BFPI9CgnFpFzgLWqOktETm3vsDa2faDXsohcCVwJMHbs2CiXb5coKagB5s6FWbNgxgxYtAgqK6GiYrcu7SSRyufqZA/+XHsfUcYsvrOb5z4ROFdEPgb0AwaKyH2q+tmEY5YDY4DlIpIHDALeb8OG24HbASZMmLBb6S2iuIaqq+Hmm602UFICa9fCpElwyy0uBqkklc/VyR78ufY+2o0RiMgDwXyOiMxOnjo7sapep6p7qWoZcDHwfJIIADwBXBYsXxgck7Yfzo4dNhh9bm6r66ctqqpMAPLyLGX1wIHmSqqqSpdljuM4maPHxywWkRuBmar6BHAncK+ILMJqAhen8lrJJLYY6igkXVMDQ4facnOzzUtKbLvjOE5fo8Mxi0UkF/i9qn50dy6iqlOBqcHyDxK2bwMu2p1zd4WoLYbKy2H1alsOhaC+3rY7juP0NTobmKYZ2C4ibfUB6HVE7UNQWQlbt5oItLTApk322crK9NvoOI7T00RpNVQPzBWRZ4CGcGMnzUazkqg1gooKCwxfeinU1sKQIXDTTR4odhynbxJFCJ4Npl5P1BoB2Ev/7LNh2TL47W+hrCytpjmO42SMdoVARO5S1ct3o/lo1hG1RhASHtfQ0PFxjuM4vZmOYgSH9pgVPUR3hSD8nOM4Tl+kI9dQkYgcQdu9f1HVTvsSZBtR00uEuBA4jhMHOhKCPYFbaD8NxGlpsShNVFfDbbdZX4BNm2DEiM6Dvy4EjuPEgY6EYJGq9qqXfXtUV1uKiE2bbJyBzZujpYwIaw4eI3Acpy8Ti6Eqq6rspZ6XZz2Ko6aM8BqB4zhxoCMh+HaPWZFmamrspR6mlC4oiJYywoXAcZw40K4QqOozPWlIOikvh40bTQjy8qB//2gpI1wIHMeJA7FwDVVWwoYN0NRkLqG6umgpIzxG4DhOHIgsBCISsdFl9lFRAR/7mKWebm6G0tJoYwt4jcBxnDjQqRCIyAkiMh8bgB4ROUxEfp92yyJQXQ0XXQQTJti8urr9Y5ua4Kij4Mkn4aGHouUNciFwHCcORKkR/Br4KLABQFXnAqek06gohE1C166FkSNbRxFrSwwaGmDpUosP7Ldf9Gt4iomuia3jOL2TSK4hVV2WtKk5DbZ0iaoqyM+H9eth27aOm4S+9ZaNNLb//tZiKCqFhTaaWWOj1SjiRii2ixbZvV2zpn2xdRyn9xJFCJaJyAmAikiBiHyTwE2USWpqrKS+fj2sWGHb2msSOn++zQ8+uGvXEGkNGMfRPRT2v1i3zsS0oMCH7HScvkgUIfgycBWWcmI5cHiwnlHKy2HAAMjJsaah9fXtNwl9802bH3RQ168T5zhBTY3d3x07TAT69/chOx2nL9KpEKjqelX9jKqOVNVSVf2sqm7oCeM6orLSXDaDBpnb5913224SumMHvP22LR94YNevE+c4QXm5uYPA7jP4kJ2O0xfpdGAaEfltG5trsQHoH0+9SdEIRxG79VbzWTc1wVe+8sHWQIsWmRiMHWs1iK4S5xpBZSVcfLHd2wEDove/cByndxHFNdQPcwe9E0yHAkOBL4jIrWm0rVMqKuCxx+BPf7KmofPnW+0gkTA+0B23EMQ7RnD88dbKqrDQAvJR+184jtO7iCIE+wGnqeptqnobMBEYD5wPnJFO46Ly8Y/bi+qBB2D8+F2bOe6uEMS5RjB/PgwebPdzzpzo/S8cx+ldRBGCPYHEXsXFwB6q2gw0psWqLvLKKxbAbGw0QQibOU6dCguC9k1dbTEUEucYwexg6KEjj8ysHY7jpJcog9f/HHhNRKZig9ScAvwkSDmRFYPaV1XB6NGwfTts3doa4LzkElsfPNhKt6WlXT93nF1Dr71m88MPz6wdjuOklyithu4ETgD+Hkwnqeodqtqgqt9Kt4FRqKmxDmV7723NHdessVZE69dbs8ecHPjmN7vXESqurqFNm+y+FhSYu81xnL5L1KRz24BVwPvAfiKS8RQTiZSX24t68GA47DDbJmK9gkVg+PDud4SKqxCEtYFDDulab2zHcXofUZLOfRGYBvwb+FEwvyG9ZnWNykrz4dfV2cu/udk6P+29t4nA8OHd7wgV1xiBu4UcJz5EqRF8DTgaWKqqHwaOANal1aouEvYpKC01t9CgQVZLOOAA2GcfE4fudoSKY4xA1VoJARxxRGZtcRwn/UQRgm2qug1ARApV9S1gXHrN6joVFda8ceZM+OtfLSFdXR20tOxeR6i4uYaqq+Gcc+Dxx2HePFiyJNMWOY6TbqIIwXIRGYwFiieLyOPAyvSatXsk1xB2pyNUnIQgzDa6ZInFBfLyuh9kdxyn99Bp81FVPT9YvEFEpgCDgH+l1aoUUFGRms5PxcUWcN6yxWoXOX14cM8w2+jmzfadS0tNEKqqvCOZ4/RlOhQCEckB5qnqIQCqGruyoQgUFZlrqaGhe/mKegs1Nfby37zZ1gcNsviKZxvte1Qvqabq1SpqNtZQPqScyqMrqShztY8rHZZvVbUFmCsiY3vInqwkLgHj8nIbeyBsdZWf79lG+yLVS6qZ9MwkajbW0C+vH2vr1zLpmUlUL4ldOc8JiOLoGA28KSLPicgT4ZRuw7KJuMQJKithwwbLNlpS4tlG+ypVr1ZRnF/Mxm0beef9d9jevJ3igmKqXvURh+JKlBQTP0q7FVlOXPoSVFTACSfA88+bGJSWmgh4fKBvUbOxhqL8Iuq315OXk8fQ/kNBbLsTT6IEi6tFZG9gf1V9VkSKgNzOPici/bCOaIXBdR5W1R8mHXM58AsgGGySKlW9o2tfIf3EpUYQ5mo66ihrgjtwYKYtctJB+ZByZiyfAcCo4lHk5ORQ11hH+RD3AcaVKD2L/x/wMPCnYNOeWFPSzmjE0lcfho1ncKaIHNfGcQ+q6uHBlHUiAPGJESxcaGJQVuYi0Jc5f9z51DbW0tLSwvDi4dQ11tGwvYHKo90HGFeiuIauAo4BXgFQ1XdEpNM8nqqqQPjqzA8mbf8T2UtcXEPz5tn80EMza4eTXpbWLmX8iPE0NTexYcsGazV0qrcaijNRhKBRVbeLCAAikkfEF7qI5AKzsMFtfqeqr7Rx2AVBEru3gW+o6rI2znMlcCXA2LE934ApLq6hnhaCTD/XOLKibgUvL3+ZUSWjuPPcOy0+kGL8ufY+orQaqhaR7wL9ReQjwEPAk1FOrqrNqno4sBdwjIgcknTIk0CZqh6KjW1wdzvnuV1VJ6jqhBEjRkS5dEqJgxA0NsLbb1u/iUOSn1KayPRzjSOPLHgERZlYPjEtIgD+XHsjUYTgO1iSudeBLwFPA9/rykVUdRMwFTgzafsGVQ1HOfszcFRXzttTxEEIFiywlkL77tsaE3H6Fuu3rGfKkikIwifHfzLT5jhZRBTX0HnAPar6566cWERGADtUdZOI9MfGOr456ZjRqroqWD0XWNCVa/QU4YuxL8cI5s61uccH+i5/f+vvNLU0cfLYkxk9YHSmzXGyiCg1gnOBt0XkXhE5O4gRRGE0MEVE5gGvApNV9SkRuVFEzg2OuVpE3hSRucDVwOVd/QI9QRxqBK+/bnMXgr5JXWMd/1pkKcIuPOjCDFvjZBtR+hFcISL5wFnAfwG/F5HJqvrFTj43Dxu7IHn7DxKWrwOu67LVPUxfFoLqavjNb6wTWVGR9SzONJ4HJ/U8ufBJGpsbOWr0UewzZJ9Mm+NkGZFyaarqDuCfwP9hrYDOS6dR2UZfFYLktNM5OfDd72Y27XSYB+e92vcYUTSCtQ2eB2d32bpjK0+98xQAFx10UYatcbKRKB3KzhSRu4BFwIXAHZjbJzYkxgi0V/aEaJsw7XRLi7UWGjGi+2M7p8ymV6tobmnmvdr3eK/uPQYWDPQ8OLvJv9/9N/Xb6xk/fDwHlx6caXOcLCRKjeByrCfxAap6mao+rapN6TUru8jLg8JCe2Fu25Zpa1JHTc2u7qCBA7s/tnPKbNpYw+D+gwFr5bJ2y1pKCko8D0432dG8g8feegzw2oDTPp0KgaperKp/D5t5isiJIvK79JuWXfRF91B5OaxYYWkliopMCDKddrp8SDkt2kLZ4DIAlm5aytqGtZ4Hp5tMWTKF97e+T9mgMibsMSHT5jjtUV0NF10EEybYvLv+2W6eJ1ILIBE5HAsUfwqoAR7tnpW9l5ISKznX15sLJZNUV5v7pqbGXtrdzRB61VX2W2lpgZEjsyPtdOXRlUx6ZhLFBcWUFpWysn4lNRtruKHihqIzAIcAACAASURBVMwZ1Utp0RYemf8IYC2FwuwATpYRBuuKimzkqyVL4KtfhWuvtQyQLS02NTebbzpcT57mzIE//AH69bPpvffsvBHG6W1XCETkAOBi4BJgA/AgIKr64RTegl5DtvQlCH8zxcX28l67NvKz/gD9+8N++8Hq1bBjB4wZk/m00xVlFdxyxi1UvVpFfWM9w/sPZ2jRUF5e/jJn7X8WOdKHxwpNMS8te4mV9SsZVTyKk8aelGlznLZQhZ/8BGprW/+IYL07r7vOhCAqs2ZZioC84LVeVtYa9OuuEABvAf8BPq6qiwBE5BvRrepbZItrqKrKXuDr11u8orS0dXtXX+CPPALDh9vv7dxzOz++p6goq9jZXHT9lvV8/V9fZ+6audw37z4uPezSDFvXO1BVHnrzIQA+Of6T5OZ0mjne6SlULZ/Lf/5j0yuvWLM9EQtGhi/yLVss30tOju3LyWl/ErEawciRrev9+5sQRAj6dSQEF2A1giki8i+s6Whs65bZIgQ1NTaE5Pr1sHGjvci7E+CdP9/SSpSUwBlnpMfWVDC8aDjfPvHbfG/K93ho/kOMGzaOY/c6NtNmZT1zVs9h8abFDOk3hNP3OT3T5jiqsHgxTJsGL7xgVfmQIUNMCPbcs9X1UFdnpbyf/jT6NWbMsPMm5pCvq4sU9Gu3nq2qj6nqp4EDsTxB3wBGisgfRCSLXx3pIVuEoLwcVgVJOZqbbaD5rgR4w1jSxIlWk9x3X3MnZjMfGvkhLjvsMgB+Nf1XrNy8MsMWZT9hbeC8cedRkFuQYWtizNKlcO+98KUvwde/Do8+ai/roUPhvPPgl7+Ev/zFYgPNzebr726wrrLSPldX1+XzROlZ3AD8FfiriAwFLsIS0T3TNSt7N9kSI/j852HyZMjNtWnVKhg0KNpvJowv5OaaC7KlBZ56Cj7ykewfjvL8A89n4fqFvLT8JX7yn5/wyzN+Sb+8LFewDPHW+rd4Y90bFOcXc9b+Z2XanL5PcuuNT3/a/mDTpsGyhKz6gwbBSSfBySfDQQeZ+wZg3DgL8u1uC5CKim6fJ2reIABU9X1spLI/dXZsX6MnawQdtQoSgfHjLa60caM1/fzFL6L9ZsIOZJs22XlGj7bfZnfiCz2NiPC1477G0n8vZWntUqpmVDHp+EneEqYNwtrA2fufTVF+UYatyWJS0fxu6lS45hpz7ajCSy9ZSW38ePPbDhhgA4GffLL5+3PbidVUVKTmT9jN83RJCOLMu++aK2XOHHj22fS1rumsVVB1tf2+broJHngA1qyBUaOinbumxs65IhghesiQzHcg6wpF+UVcf/L1XPPMNVQvrebA4QdyzgHnZNqsrGLppqXMWDmDgtwCzh2XRS0Aso3EJptDh1rJ/aqr4BvfsJd4Q4MFaxsaWqfE9XB5ypRdW+qABfG2boUbboDDDtt1X5aS/RZmAdXVcPvt9ryHDdu9JpudEZbat22z2scee7RuP/hgeOMNK3wcf7y9wB9/HF5+2X67nVFebraHvaP79898B7KuMmbQGK4+5mp+/tLPuWP2Hew7ZF/Gj4jw5bOUVCfYe3j+wwCcsc8ZDOo3KFVm9j1uvdUCbCtWmI8UzJ3z/e93rcnmli0WZMvPt9L/0KE2X7eua+fJMC4EEQhfznl5Fs8Jg/LpcKnU1JjYLFzYmtdo1CjbPm2arR9zjBVkjj/ehGD6dLjiilaXY3tUVlq8autW+91u25b5DmTd4eS9T2bhhoU8vvBxfvbiz/jNmb9hcL/BGbOnqy9zVWVr01aeefcZvv/89ynMK6Qov4g19WuY9Mwkbjnjlm6JwZr6NUx7bxq5ksv548/fna/Ud9m2zf401dX2hxaxeW6ulYwaG+G44+wPX1Rk88QpedvnP29N+LrRUiebcCGIQE2N+dKXLWvt75Eul0p5+a4iELpx9tmntbd4KD7jx5tdq1ZZJ8K99+743BUV5s789rdN0EpLM9+BrLtcfvjlvLPhHeavn8/NL9zMTafdlJG28mG21H65/cjPyWfBugVc8fgVXDj+QvYatBcN2xuo315P/fZ6GnY02LS9AUWZtXIWjc2N5OXY37Agt4ABBQO49ZVbuyUEjy54lBZt4bSy0ygtLk31V+3d7NgB//oXPPigdd4KS/GJQ/KFTTavvz76ea++2twDYC+F+vpeWbrybpoRKC+3oKyIzVXT51KprLRUFk1N9lvdscP6nixeDHffDa+91tpyKSfHagdg7qEojBplNdZvfQseeqh3igBAXk4e3z7p2wzpN4Q31r3B3XPbHO46rbRoC/8z7X/YsHUDizYuYvGmxazbso7axlrue/0+Ji+ezEvLX2Le2nks3rSYNQ1rqN9ej6L0y+vH9ubtDMgfwICCAfTLtfX1W9YzdclU/vDqH1hRtyKyLZu2beLZmmeBLB54JlX5dLpCSws89xx8+cvm362thQMOsCDbiBG732QzbKlTWmoBu9LS9PiM04zXCCJQWWmir2rThg32gk6H6J94ov1OFy600r6IuRvDMQMKC61EH/7Wjj/eGilMnw4XX9z5+Zcvt/mYMam3vacZ2n8o3znpO3z3ue/y2FuPceDwAzlhzAlpv+6qzat4dvGzPFfzHDNXzqQgt4AcyWFQ4SAK8wrJJZfNOzZTeXQlJQUlFBcUU5xfTElBCSUFJRTlF5Gbk8tFD13E2oa1DCwcCAq1jbUs3bSUvJY8nl70NP9c9E+O3uNozjvwPD5U+qEOW0g9/tbjbG/ezrF7HsuYQVn4cMPgrKq9gNMZaAO7zssvw333tTbhHDsWLr3USk8iVhtIRdKuVLX4ySAuBBEIRf/KK030Bwyw2mM6nv38+VbD/MQn4Pe/h/PPt99z6Crac0+bh/GJww6zwszf/mYNGA44oOPfc/if2Guv1NueCQ4acRBXHH4Fd8y5g19P/zVjB41lr4Gp/3Lbmrbx0rKXmPzuZN5Y98bO7UP7D6Uwr5Cxg8bu7LhV11jH/sP356P7fbTDc4YJ9gBKCkoQEUYUj+BbJ3yLNQ1rmLpkKjNWzmDGyhnsM3gfzjvwPE7Z+5SdrqSQhu0NPL3oaSCLU01XVVkV9513zN8ZBt1uugmOPTa1vRrnzrXq8zvv2HppKXz2s/anyElwgvSBF3iqcCGISEWFdQ6cMsXcgun6/bz6qs2PPtrmy5ZZLOCtt6xGMGCAiUIYn3j5ZVi0yGJcBQWdF7T6Uo0g5Nxx57Jww0L+895/uOofV5Gbk8t7te91uRVOctD3qglXMXrAaCYvnsy0pdPY2rQVgMLcQk4aexIT95nI+ob1fHPyN9nWtI28nDyLBWxvoPLUzquLiQn2dgaaT22197LDLuPpd57mH+/8g8WbFvPr6b/mrtfu4pwDzuHM/c5kzqo5VL1axeyVs9natJXTyk5j3PBx3b+R6aSmxgKqAwaYX7W+3n7Ia9bAJZdYCebQQ20aN85+zF3l7bfhnntMCAAGD7Zq8kc/2iuacGYSvztdYORImyemCUk1yUIQNvk89NDWXFKbN7fGJ6qqrF/B6tVWMwj7FLTVomn7djsu7EzWVxARrj72al5870Wql1YzpP8QDhp+0M5hLqO0wgmDvsUFxQztP5T5a+fzqYc/xb5D92V40XAADhx2IB/Z9yOcNPakXTpqdfQy74zEBHvJDOo3iEs+dAkXHHQB05ZO4/G3HmdJ7RLunXcvVTOqWLJpCaNLRrOtaRuNzY3MXj2b6iXV2Tm+c/hDHj/efPKbN5sIqJqffv58m/7v/0wEDjywVRj233/XF3lyR7CLLrLS//Tptr+4GC64AD7+8ezPn5IluBB0gTDT55o16Tn/ihU2lZS09gsI4xNg25NjWjU15nJdvbo1iNxei6aVK+1/t8ce1mCiL9Evrx/N2kx+Tj5bdmxh7pq55EgO25u38/nHP89Z+59FruSSl5NHbk7uB5b/9ubfaNjRwObGzdQ21qIozdrMyrqVXHnklUzcZ2K7vveOXuapoCC3gIn7TOT08tOZu2Yuj7/1OH+a9ScamxtZUrsEgEGFgxhRNIKqV6uyUwiSf8giVju45RYLHr/xBrz+upXmlyyBefNsAnuZH3SQicK2bfCrX9k5Bg+G2bNbe/LusYel0b3ggtZUAE4kXAi6QFgjSJcQzJxp86OOau2J3ln6kLCglZtrAewdO6yfQFstmvqiWyiR1fWrOWDYAdRsqmFHi7XzVVU2bN3A0tqlHX52Wd0yCnILEBEEYUi/IQzrP4xtTdu44ogresL8ThERDh91OIePOpxHFjxCS0sLG7ZtoEVb2GPAHpQUZvGQnp39kI891iaw0s7rr5sQvP66+Udnz7Zp1iz7kQ8ebLUKVfvxNzbCn/9sHbqcLuNC0AXSLQQzZtg8bBIa0lFMKyxohYnk1q2zwlZbLZpCIegrgeJkyoeUs7ZhLUeMOoImbUJVqWusY1jRMG476zaaW5ppammiWW3eoi223tLMdduvY8OWDRQXFDOwcCD5ufnUNdaxz8B9Mv212mTc8HGsbVjLmEFj2N68naKCIuoa67J7SM+owdmBA6353Ikn2vr771uNYd48c/+ImFiImF909GhLoOUi0G1cCLrAsGH223v/fSuUpMq9Ul1tPd6nTLGOi13JcBoWtK65xvIhFRZaCvO2/m9hi6G+WiNIboVTv72eppYmrj3h2p1jILfHDyt+yKRnJpGfm09uTi51jXWRg76ZIPm7Zru9u8XQoXDKKTZVV5v/NBx4pX//XtmTN9vwDmVdIC/P/PGq1qs8FYTNq2tqLEaWm2vpTrrS16aiwka7O/lkc4+2V+jq6zWCsBVOaXEpa+rXUFpcGjldw+58NhP0NntTRmWltXrIy7NSTzYMtN0H8BpBFyktNZ/8mjWpaXkT5jFav94KOSNHRh5mdBfCAlF7aS9U+74QwO4FbtMd9E01vc3elLAbOfed9nEh6CIjR5q7MlVxgjA19Lvv2vrgwVbQ6Woeo733NiFZvrxtt9W6dVaQGjKkNbWK4/RKvCNYynHXUBdJdcC4vLw1ZUV+fvdTQxcWWuu55uZdB0UK6evxAcdxuo8LQRdJdV+CykoLPjc1td1PoCt05B6Kg1vIcZzu4ULQRVLdu7iiAs44w0r0TU27l7ywIyHoazmGHMdJHR4j6CLp6EuwbZt1IvvNb2zcge4SpUbgriHHcZLxGkEXGTbMmniGA8fvLuvXm6gUFUFZ2e6dK1EIwmylIV4jcBynPVwIukhOjvUlgNS4h+bPt/n48btmyO0Ow4ZZ+pbNmy0AHfL009ZZ7YUX4Ctf6ZnxQBzH6T24EHSDVAaM33zT5gcfvPvnEvmge6i62kYja2y0pqlhmmoXA8dxQlwIukEqA8ahEBxyyO6fCz4oBFVV5srKyzP308CBrR3WHMdxII1CICL9RGSGiMwVkTdF5EdtHFMoIg+KyCIReUVEytJlTyrpasC4vaFaN2+GpUut/8B++6XGtmQhePddy8cFrR3J2ktT7ThOPElnjaAROE1VDwMOB84UkeOSjvkCsFFV9wN+DdycRntSRleEIMwltGqVpVVfs6bVNRPGB8aNS10Cu2QhULV+CUVFrbGN7nRYcxyn75I2IVCjPljND6aktiycB9wdLD8MnC4djdCdJXRFCMJcQmvX2iBKdXWtrplUxgdCxoyxoPPKlSY2/fpZb+MwruE5uhzHSSatMQIRyRWR14C1wGRVfSXpkD2BZQCq2gTUAsPaOM+VIjJTRGauW7cunSZHInypRokR1NTYyHy1tba+apV1HKupSY8Q5Odb0HjmTDjvPHM9XXghjB1rwrU7HdZSTbY9Vyc1+HPtfaRVCFS1WVUPB/YCjhGR5JBoW6X/5FoDqnq7qk5Q1QkjQv9GBhk61IKvtbXWGawjyspaE8qFo+e9/ba5aRYtstL7gQemzrbqahOBcDD7/HyYM8dqADNnwkMPZYcIQPY9Vyc1+HPtffRIqyFV3QRMBc5M2rUcGAMgInnAIOD9nrBpdxCJXis45hjYssU+c8AB5hbassVG3KuuhgULWkcmSwVVVdYyKC/PRGD8eBMgbyXkOE57pLPV0AgRGRws9wcmAm8lHfYEcFmwfCHwvGpyn9jsJEqcoKHBRtcbP97G3l63Dvbc017Qzc1WYofUtuuvqbHew0OHwv7727W8lZDjOB2RzhrBaGCKiMwDXsViBE+JyI0icm5wzJ3AMBFZBFwDfCeN9qSUujor1V9yya5NQqG1uejBB8Pzz9tYAc89Z66ZUaPMFVRY2FqzSGW7/vJycwvtt5/1MgZvJeQ4TsekLemcqs4Djmhj+w8SlrcBF6XLhnRRXQ1PPmkv3BEjWnvr3nKL7Z80yUri27bZOANvvAHTpplvPhyIJhxqdfBga+KZqhJ7OJg9WE2gvt5bCTmO0zHes7gbVFVZaTsvz16yib11w+aidXX2gh850nIAhSX+8nJ7OZeU2EAyIqktsYcj+ZWWZl8rIcdxshNPQ90NwlL9unXWO7i21oQhLNUPGGBJ30TMX5+f37qvJ0rsPpKf4zhdwWsE3aC83Nw+4eD1y5a1lurLy1vTQJeWWiwgscTvJXbHcbINrxF0g7BU37+/lfbr6mD1arjxRuvANXmybR81qu2evF5idxwnm/AaQTcIS/WjRlkOn8JCa6Vz4onWL2D8eKsBvP++l/gdx8l+vEbQTcJSfXOzlfZfew2OOMLcRAMHwu23w5nJ3eccx3GyEK8R7Ca5ufChD1lNYOVK6yRWUgLf+54P/uI4Tu/AhSAFTJliL/+8PHMTlZf74C+O4/QeXAhSQE2NxQj697dexDk5ntbBcZzegwtBCigvt+aiH/oQDBli2zytg+M4vQUXghRQWWlNROvqbOwBH/zFcZzehAtBCvBOYo7j9Ga8+WiK8E5ijuP0VrxG4DiOE3NcCBzHcWKOC4HjOE7McSFwHMeJOS4EjuM4MUd6yVjxOxGRdcDSpM3DgfUZMKczstUuSI1te6vqiFQY04ueaxxsSvdzTRXZ9iyyyZ62bGn3ufY6IWgLEZmpqhMybUcy2WoXZLdtIdloo9uUPWTb984me7pqi7uGHMdxYo4LgeM4TszpK0Jwe6YNaIdstQuy27aQbLTRbcoesu17Z5M9XbKlT8QIHMdxnO7TV2oEjuM4Tjfp1UIgImeKyEIRWSQi38mwLf8rImtF5I2EbUNFZLKIvBPMh2TItjEiMkVEFojImyLytWyxr7NnKCKFIvJgsP8VESnrAZvavF9Jx5wqIrUi8low/aAH7FoiIq8H15vZxn4Rkd8G92qeiByZbpt6mijPJhOISK6IzBGRp7LAlsEi8rCIvBXcp+M7/ZCq9soJyAXeBfYBCoC5wEEZtOcU4EjgjYRtPwe+Eyx/B7g5Q7aNBo4MlgcAbwMHZdq+KM8Q+Crwx2D5YuDBTN2vpGNOBZ7q4fu1BBjewf6PAf8EBDgOeCUTv7dMP5sM2XUNcH9P/ybaseVu4IvBcgEwuLPP9OYawTHAIlVdrKrbgf8DzsuUMao6DXg/afN52EMhmH+iR40KUNVVqjo7WN4MLAD2zAL7ojzDRBsfBk4XEUmnUR3cr2znPOAeNaYDg0VkdKaNSiXZ+GxEZC/gbOCOTNoR2DIQK5TeCaCq21V1U2ef681CsCewLGF9Odn3Zx2pqqvAfsBAaYbtIXCtHAG8Qubti/IMdx6jqk1ALTCsR6zjA/crmeNFZK6I/FNEDu4BcxR4RkRmiciVbezvDf+JlNHJs+lJbgWuBVoybAdY7Xod8JfAVXWHiBR39qHeLARtlQq9CVQHiEgJ8AjwdVWty7Q9RHuGGXvOndyv2ViX/cOA24C/94BJJ6rqkcBZwFUickrS/tj8J7Lltywi5wBrVXVWpmxIIg9zUf9BVY8AGjC3b4f0ZiFYDoxJWN8LWJkhW9pjTVg1D+ZrM2WIiORjf5y/quqjWWJflGe48xgRyQMG8UEXXMpp537tRFXrVLU+WH4ayBeR4em0SVVXBvO1wGOYay2R3vCf2G06ezY9zInAuSKyBHNtniYi92XQnuXAclUNa0kPY8LQIb1ZCF4F9heRchEpwAKJT2TYpmSeAC4Lli8DHs+EEYFP/U5ggar+KmFXpu2L8gwTbbwQeF6DKFi66OB+JR4zKoxViMgx2H9pQxptKhaRAeEycAbwRtJhTwCXBq2HjgNqQ9dfXyHKs+lJVPU6Vd1LVcuw3+/zqvrZDNqzGlgmIuOCTacD86N8sNdOWCuJt7GWJ9dn2JYHgFXADkyVv4D5sp8D3gnmQzNk20mYi2Ae8FowfSwb7GvrGQI3AucGy/2Ah4BFwAxgnwzery8DXw6OqQTexFo6TQdOSLNN+wTXmhtcN7xXiTYJ8LvgXr4OTMjE7y0TzybTdgW2nUp2tBo6HJgZ3KO/A0M6+4z3LHYcx4k5vdk15DiO46QAFwLHcZyY40LgOI4Tc1wIHMdxYo4LgeM4TsyJvRCISH3S+uUiUtXDNlwUZAmckrS9TES2Bl3FF4jIDBG5rL3zOIaINAcZOt8MUkBcIyJp/a2LyC+C6/0iYZuIyPowq6uIjBYRFZGTEo5ZJyJdTpmRid9pJgju170J63nBPetWls8gM+dXE9ZP3Z2MoSJyYPBbmyMi+yZsv19EvpKwfmyQETavu9dKJ7EXgnQhIrldOPwLwFdV9cNt7HtXVY9Q1fFYh5VviMgVKTGy77JVVQ9X1YOBj2B9AH6Y5mt+CcuK+a1wg1rb7FeAMA3wCcCcYE7Q6We9qqatI1ofoAE4RET6B+sfAVbsxvkGYxltU8UngMeD/+i7Cdu/AXxLREYEhZAq7D/etDsXS5eQuBB0gIjsLSLPBUr+nIiMDbbfJSIXJhxXH8xPFcuVfj/WoSf5fJeI5ZN/Q0RuDrb9AOsk88fE0mRbqOpiLN3t1cFnjxGRl4LSyEthb0IR+Y+IHJ5w3RdF5NDdvB29ErV0DFcClUEJvSy4P7ODKXwp3ysiOzOfishfReTcxHMFn/9F8PxeF5FPB9ufAIqBV8JtCbxI8OIP5r9iV2F4KTjHCBF5REReDaYTg+3FYmNdvBo85w9k2BWRs0XkZUlziosM8k8suyfAJVjnTWDnmBp/D/6j08PfuYjcENy3qSKyWESuDj7yM2DfoBQf/t9KpDV//19FPpjdVkQOD84/T0QeE5EhIvIx4OvAFyWpNq+qa4BfYqnevwzMU9UXgnOdFTyv2WJjbRQH238UPOc3ROSPoR0i8oKI/FhEpmEdGVNPpnvBZXoCmmntofga8B5QFex7ErgsWP488Pdg+S7gwoRz1Cf0LGwAytu4zh7BuUdgiaGeBz4R7JtKG71AgTISxjcItg3GSrwAA4G8YHki8EiwfBlwa7B8ADAz0/e5h59pfRvbNgIjgSKgX7Bt//DeABUJz3cQUBPe24RzXABMxsZRGBk8z9HtXTPhN/F8sPwfoCThmn8GPh8s3w+cFCyPxVIoAPwE+GzCs38bE53LsVLm+cF5O+092hsnoB44FMuZ0y/4j55K0IMXS/j3w2D5NOC1YPkGTGQLgeFY+o/85P9UcK5aLC9TDvBy+ByS7JgHVATLNyb8v24AvtmO7TlYjbAGGBZsKwWqgaJg/Xrgu8Hy0GAumNidFay/ANyWzvuclf6qHmarqiaWni8HJgSrxwOfDJbvxdS9M2aoak0b248GpqrquuA6f8Xyhnc1a2ViaWUQcLeI7I91u88Ptj8EfF9EvoUJ2F1dvEZfJLxv+UBVUGNqxoQSVa0Wkd+JSCn2zB/RD1bjTwIeUNVmLGFfNfZcO8pxNQM4Iij15atqfVBC3Q+rEdwSHDcROCihMDpQLLfQGVhSs28G2/thQgHwYey3eoZmRzbZtKCq88RSTl8CPJ20+yRMoFHV50VkmIgMCvb9Q1UbgUYRWYuJd1vMUNXlACLyGiYWL4Q7g/MNVtXqYNPd2H+sM7tbRORPWCEvdP+dgA0K9VLwrAsSrnV68J/th4nXLKw2BJbQLm24EHSNMB9HE4FbLai+FSQc09DOZ1M1mMoR2GAcAP8DTFHV84M/ylQAVd0iIpOxgUo+RauwxRIR2Qd76a/FYgVrgMOwZ7gt4dB7gc9gsZjPt3Wqrl47eBaLgvPNDjZPx+IWpcDCYFsOcLyqbk2yXYALVHVh0vZjgcVYDqIDsNwyfZknMFfLqew6HkVHqbcbE7Y10/77Lupx3aGFXccpEOBfqvq5xINEpAir4R2pqitE5CZMEELae6+kBI8RdMxL2EsB7AURKvcS4Khg+TxaS+Id8QpQISLDxQLJl2BVxMgEL/tfYtVhsBpBGDi7POnwO4DfAq+qatrTNmcrIjIC+CPm7lPsnq1S1Rbgc5ibJ+QuzOeLqr7ZxummAZ8WG592BFajmxHBjBeD874crL8MfA2YHtgE8AwJ/t+EGM+/gf9O8BcfkXDepVjt5R7pmYFxMsn/AjeqanLsbRr230RETsWC7x3VjjZjQ1xGRlVrgY0icnKw6XN08b+bwEvYe2Af2BkD2h/ojwnG+qAmeEE3z98tXAg65mrgChGZhz38cKDsP2MPcwZwLBHUWi0d8HXAFCyD5GxVjZL2ed8gSLgA+BvmK/xLsO/nwE9F5EV2faGhNlBGHfAX4kf/IBj4JvAs9pL9UbDv98BlIjIdK0nvfHZqAb4FtH/PHsN8xXOxGM+1aml/O+NFrOQeCsFszCf9UsIxVwMTgmDkfCzACFbrywfmicgbwfpOgprCZ4CHJKH5Yl9DVZer6m/a2HUDwX3DAsEdNq8OXDQvBgHZDhtnJHEZ8IvgOodjcYIuE/zGvgA8KCJzsd/AAYFdd2OpxR+jh0dd8+yjfRQR2QNzFR0YlH6dTgiq569j1fPaTNvjOD2F1wj6ICJyKVaiuN5FIBoiMhF4C6txuQg4scJrBI7jODHHawSO4zgxx4XAcRwn5rgQOI7jxBwXAsdxnJjjUGN/ogAAABNJREFUQuA4jhNzXAgcx3Fizv8HMYjK9zsph98AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize average trip distance and average trip duration for time series variables\n",
    "fig, ax = plt.subplots(ncols=3, sharey=True)\n",
    "ax[0].plot(train.groupby('Hour').mean()['haversine_distance_km'], 'bo-', lw=2, alpha=0.7)\n",
    "ax[1].plot(train.groupby('DayofWeek').mean()['haversine_distance_km'], 'go-', lw=2, alpha=0.7)\n",
    "ax[2].plot(train.groupby('Month').mean()['haversine_distance_km'], 'ro-', lw=2, alpha=0.7)\n",
    "ax[0].set_xlabel('Hour of Day')\n",
    "ax[1].set_xlabel('Day of Week')\n",
    "ax[2].set_xlabel('Month of Year')\n",
    "ax[0].set_ylabel('Average Trip Distance')\n",
    "fig.suptitle('Average Trip Distance by Date')\n",
    "plt.show()\n",
    "fig, ax = plt.subplots(ncols=3, sharey=True)\n",
    "ax[0].plot(train.groupby('Hour').mean()['trip_duration'], 'bo-', lw=2, alpha=0.7)\n",
    "ax[1].plot(train.groupby('DayofWeek').mean()['trip_duration'], 'go-', lw=2, alpha=0.7)\n",
    "ax[2].plot(train.groupby('Month').mean()['trip_duration'], 'ro-', lw=2, alpha=0.7)\n",
    "ax[0].set_xlabel('Hour of Day')\n",
    "ax[1].set_xlabel('Day of Week')\n",
    "ax[2].set_xlabel('Month of Year')\n",
    "ax[0].set_ylabel('Average Trip Duration')\n",
    "fig.suptitle('Average Trip Duration by Date')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['log_trip_duration'] = np.log(train['trip_duration'].values + 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8. Create Dummy Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "for feature in train.columns: # Loop through all columns in the dataframe\n",
    "    if train[feature].dtype == 'object':\n",
    "        train[feature] = pd.Categorical(train[feature]).codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 492545 entries, 0 to 492544\n",
      "Data columns (total 14 columns):\n",
      "vendor_id                492545 non-null int64\n",
      "dropoff_datetime         492545 non-null datetime64[ns]\n",
      "passenger_count          492545 non-null int64\n",
      "pickup_longitude         492545 non-null float64\n",
      "pickup_latitude          492545 non-null float64\n",
      "dropoff_longitude        492545 non-null float64\n",
      "dropoff_latitude         492545 non-null float64\n",
      "store_and_fwd_flag       492545 non-null int8\n",
      "trip_duration            492545 non-null int64\n",
      "DayofWeek                492545 non-null int64\n",
      "Hour                     492545 non-null int64\n",
      "Month                    492545 non-null int64\n",
      "haversine_distance_km    492545 non-null float64\n",
      "log_trip_duration        492545 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(6), int64(6), int8(1)\n",
      "memory usage: 49.3 MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "passenger_count_train = pd.get_dummies(train['passenger_count'], prefix='pc', prefix_sep='_')\n",
    "month_train = pd.get_dummies(train['Month'], prefix='m', prefix_sep='_')\n",
    "hour_train = pd.get_dummies(train['Hour'], prefix='h', prefix_sep='_')\n",
    "dow_train = pd.get_dummies(train['DayofWeek'], prefix='dow', prefix_sep='_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "passenger_count_test = pd.get_dummies(test['passenger_count'], prefix='pc', prefix_sep='_')\n",
    "month_test = pd.get_dummies(test['Month'], prefix='m', prefix_sep='_')\n",
    "hour_test = pd.get_dummies(test['Hour'], prefix='h', prefix_sep='_')\n",
    "dow_test = pd.get_dummies(test['DayofWeek'], prefix='dow', prefix_sep='_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(['vendor_id','passenger_count','store_and_fwd_flag','Month','Hour','DayofWeek','trip_duration',\n",
    "                   'pickup_longitude','pickup_latitude','dropoff_longitude','dropoff_latitude','dropoff_datetime'],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.drop(['key','passenger_count','Month','Hour','DayofWeek',\n",
    "                   'pickup_longitude','pickup_latitude','dropoff_longitude','dropoff_latitude'],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train_Master = pd.concat([train,\n",
    "                          passenger_count_train,\n",
    "                          month_train,\n",
    "                          hour_train,\n",
    "                          dow_train],\n",
    "                          axis=1)\n",
    "Test_Master = pd.concat([test,\n",
    "                          passenger_count_train,\n",
    "                          month_train,\n",
    "                          hour_train,\n",
    "                          dow_train],\n",
    "                          axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(492545, 46)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_Master.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>haversine_distance_km</th>\n",
       "      <th>log_trip_duration</th>\n",
       "      <th>pc_0</th>\n",
       "      <th>pc_1</th>\n",
       "      <th>pc_2</th>\n",
       "      <th>pc_3</th>\n",
       "      <th>pc_4</th>\n",
       "      <th>pc_5</th>\n",
       "      <th>pc_6</th>\n",
       "      <th>m_1</th>\n",
       "      <th>...</th>\n",
       "      <th>h_21</th>\n",
       "      <th>h_22</th>\n",
       "      <th>h_23</th>\n",
       "      <th>dow_0</th>\n",
       "      <th>dow_1</th>\n",
       "      <th>dow_2</th>\n",
       "      <th>dow_3</th>\n",
       "      <th>dow_4</th>\n",
       "      <th>dow_5</th>\n",
       "      <th>dow_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6.385098</td>\n",
       "      <td>7.661527</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.485498</td>\n",
       "      <td>6.063785</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.188588</td>\n",
       "      <td>6.077642</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   haversine_distance_km  log_trip_duration  pc_0  pc_1  pc_2  pc_3  pc_4  \\\n",
       "0               1.498521           6.122493     0     1     0     0     0   \n",
       "1               1.805507           6.498282     0     1     0     0     0   \n",
       "2               6.385098           7.661527     0     1     0     0     0   \n",
       "3               1.485498           6.063785     0     1     0     0     0   \n",
       "4               1.188588           6.077642     0     1     0     0     0   \n",
       "\n",
       "   pc_5  pc_6  m_1  ...  h_21  h_22  h_23  dow_0  dow_1  dow_2  dow_3  dow_4  \\\n",
       "0     0     0    0  ...     0     0     0      1      0      0      0      0   \n",
       "1     0     0    0  ...     0     0     0      0      0      0      0      0   \n",
       "2     0     0    1  ...     0     0     0      0      1      0      0      0   \n",
       "3     0     0    0  ...     0     0     0      0      0      1      0      0   \n",
       "4     0     0    0  ...     0     0     0      0      0      0      0      0   \n",
       "\n",
       "   dow_5  dow_6  \n",
       "0      0      0  \n",
       "1      0      1  \n",
       "2      0      0  \n",
       "3      0      0  \n",
       "4      1      0  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_Master.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(492545, 45)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test_Master.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>haversine_distance_km</th>\n",
       "      <th>pc_0</th>\n",
       "      <th>pc_1</th>\n",
       "      <th>pc_2</th>\n",
       "      <th>pc_3</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   haversine_distance_km  pc_0  pc_1  pc_2  pc_3  pc_4  pc_5  pc_6  m_1  m_2  \\\n",
       "0               2.323260     0     1     0     0     0     0     0    0    0   \n",
       "1               2.425353     0     1     0     0     0     0     0    0    0   \n",
       "2               0.618628     0     1     0     0     0     0     0    1    0   \n",
       "3               1.961033     0     1     0     0     0     0     0    0    0   \n",
       "4               5.387301     0     1     0     0     0     0     0    0    0   \n",
       "\n",
       "   ...  h_21  h_22  h_23  dow_0  dow_1  dow_2  dow_3  dow_4  dow_5  dow_6  \n",
       "0  ...     0     0     0      1      0      0      0      0      0      0  \n",
       "1  ...     0     0     0      0      0      0      0      0      0      1  \n",
       "2  ...     0     0     0      0      1      0      0      0      0      0  \n",
       "3  ...     0     0     0      0      0      1      0      0      0      0  \n",
       "4  ...     0     0     0      0      0      0      0      0      1      0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test_Master.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Define variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train, Test = skl.model_selection.train_test_split(Train_Master[0:100000], test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = Train.drop(['log_trip_duration'], axis=1)\n",
    "y_train = Train[\"log_trip_duration\"]\n",
    "X_valid = Test.drop(['log_trip_duration'], axis=1)\n",
    "y_valid = Test[\"log_trip_duration\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_valid = y_valid.reset_index().drop('index',axis = 1)\n",
    "y_train = y_train.reset_index().drop('index',axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 9. AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TVL\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.metrics import explained_variance_score\n",
    "\n",
    "ada = AdaBoostRegressor(n_estimators=50, \n",
    "                        learning_rate=0.2,\n",
    "                        loss='exponential').fit(X_train, \n",
    "                                                y_train)\n",
    "pred = ada.predict(X_valid)\n",
    "adab = ada.score(X_valid,\n",
    "                 y_valid)\n",
    "predict = ada.predict(X_valid)\n",
    "exp_ada = explained_variance_score(predict,\n",
    "                                   y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.61727447661198"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.31656495066329815"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_ada"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 10. Gradient Boost "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TVL\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "est = GradientBoostingRegressor(n_estimators=400, \n",
    "                                max_depth=5, \n",
    "                                loss='ls',\n",
    "                                min_samples_split=2,\n",
    "                                learning_rate=0.1).fit(X_train, \n",
    "                                                       y_train)\n",
    "gradient = est.score(X_valid,\n",
    "                     y_valid)\n",
    "pred = est.predict(X_valid)\n",
    "exp_est = explained_variance_score(pred,\n",
    "                                   y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6757778160850216"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gradient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5399758758782292"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_est"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 11. XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "XGB = xgb.XGBRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster=None, colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints=None,\n",
       "             learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=100, n_jobs=0, num_parallel_tree=1,\n",
       "             objective='reg:squarederror', random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method=None,\n",
       "             validate_parameters=False, verbosity=None)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XGB.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = XGB.predict(X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6741388101492065"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XGB.score(X_valid, y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_xgb = explained_variance_score(y_pred,\n",
    "                                   y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5384594175199382"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot\n",
    "from xgboost import plot_tree, plot_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = pyplot.subplots(figsize=(12,12))\n",
    "ax = plot_importance(XGB, ax=ax)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}