eda_2019_jan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dateparse = lambda x: datetime.strptime(x, '%Y-%m-%d %H:%M:%S')\n",
    "df = pd.read_csv('data\\yellow_tripdata_2019-01.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01 00:46:40</td>\n",
       "      <td>2019-01-01 00:53:20</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>151</td>\n",
       "      <td>239</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>9.95</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01 00:59:47</td>\n",
       "      <td>2019-01-01 01:18:59</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>239</td>\n",
       "      <td>246</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-12-21 13:48:30</td>\n",
       "      <td>2018-12-21 13:52:40</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>236</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>5.80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-11-28 15:52:25</td>\n",
       "      <td>2018-11-28 15:55:45</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>7.55</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-11-28 15:56:57</td>\n",
       "      <td>2018-11-28 15:58:33</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>55.55</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "0         1  2019-01-01 00:46:40   2019-01-01 00:53:20                1   \n",
       "1         1  2019-01-01 00:59:47   2019-01-01 01:18:59                1   \n",
       "2         2  2018-12-21 13:48:30   2018-12-21 13:52:40                3   \n",
       "3         2  2018-11-28 15:52:25   2018-11-28 15:55:45                5   \n",
       "4         2  2018-11-28 15:56:57   2018-11-28 15:58:33                5   \n",
       "\n",
       "   trip_distance  RatecodeID store_and_fwd_flag  PULocationID  DOLocationID  \\\n",
       "0            1.5           1                  N           151           239   \n",
       "1            2.6           1                  N           239           246   \n",
       "2            0.0           1                  N           236           236   \n",
       "3            0.0           1                  N           193           193   \n",
       "4            0.0           2                  N           193           193   \n",
       "\n",
       "   payment_type  fare_amount  extra  mta_tax  tip_amount  tolls_amount  \\\n",
       "0             1          7.0    0.5      0.5        1.65           0.0   \n",
       "1             1         14.0    0.5      0.5        1.00           0.0   \n",
       "2             1          4.5    0.5      0.5        0.00           0.0   \n",
       "3             2          3.5    0.5      0.5        0.00           0.0   \n",
       "4             2         52.0    0.0      0.5        0.00           0.0   \n",
       "\n",
       "   improvement_surcharge  total_amount  congestion_surcharge  \n",
       "0                    0.3          9.95                   NaN  \n",
       "1                    0.3         16.30                   NaN  \n",
       "2                    0.3          5.80                   NaN  \n",
       "3                    0.3          7.55                   NaN  \n",
       "4                    0.3         55.55                   NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>7.667792e+06</td>\n",
       "      <td>7.667792e+06</td>\n",
       "      <td>7.667792e+06</td>\n",
       "      <td>7.667792e+06</td>\n",
       "      <td>7.667792e+06</td>\n",
       "      <td>7.667792e+06</td>\n",
       "      <td>7.667792e+06</td>\n",
       "      <td>7.667792e+06</td>\n",
       "      <td>7.667792e+06</td>\n",
       "      <td>7.667792e+06</td>\n",
       "      <td>7.667792e+06</td>\n",
       "      <td>7.667792e+06</td>\n",
       "      <td>7.667792e+06</td>\n",
       "      <td>7.667792e+06</td>\n",
       "      <td>2.811814e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>1.636775e+00</td>\n",
       "      <td>1.567078e+00</td>\n",
       "      <td>2.801084e+00</td>\n",
       "      <td>1.058371e+00</td>\n",
       "      <td>1.655009e+02</td>\n",
       "      <td>1.637529e+02</td>\n",
       "      <td>1.291776e+00</td>\n",
       "      <td>1.240941e+01</td>\n",
       "      <td>3.280394e-01</td>\n",
       "      <td>4.968458e-01</td>\n",
       "      <td>1.827367e+00</td>\n",
       "      <td>3.169187e-01</td>\n",
       "      <td>2.993382e-01</td>\n",
       "      <td>1.568222e+01</td>\n",
       "      <td>3.289691e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>5.398204e-01</td>\n",
       "      <td>1.224431e+00</td>\n",
       "      <td>3.737529e+00</td>\n",
       "      <td>6.780889e-01</td>\n",
       "      <td>6.639180e+01</td>\n",
       "      <td>7.036445e+01</td>\n",
       "      <td>4.733229e-01</td>\n",
       "      <td>2.620721e+02</td>\n",
       "      <td>5.074789e-01</td>\n",
       "      <td>5.337843e-02</td>\n",
       "      <td>2.501213e+00</td>\n",
       "      <td>2.023665e+00</td>\n",
       "      <td>1.911711e-02</td>\n",
       "      <td>2.622932e+02</td>\n",
       "      <td>9.068695e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-3.620000e+02</td>\n",
       "      <td>-6.000000e+01</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>-6.350000e+01</td>\n",
       "      <td>-7.000000e+01</td>\n",
       "      <td>-3.000000e-01</td>\n",
       "      <td>-3.628000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.300000e+02</td>\n",
       "      <td>1.130000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e-01</td>\n",
       "      <td>8.190000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.530000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.620000e+02</td>\n",
       "      <td>1.620000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>8.500000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>1.430000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e-01</td>\n",
       "      <td>1.127000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.340000e+02</td>\n",
       "      <td>2.340000e+02</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.350000e+01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>2.330000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e-01</td>\n",
       "      <td>1.656000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>8.318000e+02</td>\n",
       "      <td>9.900000e+01</td>\n",
       "      <td>2.650000e+02</td>\n",
       "      <td>2.650000e+02</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>6.232599e+05</td>\n",
       "      <td>5.353800e+02</td>\n",
       "      <td>6.080000e+01</td>\n",
       "      <td>7.872500e+02</td>\n",
       "      <td>3.288000e+03</td>\n",
       "      <td>6.000000e-01</td>\n",
       "      <td>6.232617e+05</td>\n",
       "      <td>2.500000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           VendorID  passenger_count  trip_distance    RatecodeID  \\\n",
       "count  7.667792e+06     7.667792e+06   7.667792e+06  7.667792e+06   \n",
       "mean   1.636775e+00     1.567078e+00   2.801084e+00  1.058371e+00   \n",
       "std    5.398204e-01     1.224431e+00   3.737529e+00  6.780889e-01   \n",
       "min    1.000000e+00     0.000000e+00   0.000000e+00  1.000000e+00   \n",
       "25%    1.000000e+00     1.000000e+00   9.000000e-01  1.000000e+00   \n",
       "50%    2.000000e+00     1.000000e+00   1.530000e+00  1.000000e+00   \n",
       "75%    2.000000e+00     2.000000e+00   2.800000e+00  1.000000e+00   \n",
       "max    4.000000e+00     9.000000e+00   8.318000e+02  9.900000e+01   \n",
       "\n",
       "       PULocationID  DOLocationID  payment_type   fare_amount         extra  \\\n",
       "count  7.667792e+06  7.667792e+06  7.667792e+06  7.667792e+06  7.667792e+06   \n",
       "mean   1.655009e+02  1.637529e+02  1.291776e+00  1.240941e+01  3.280394e-01   \n",
       "std    6.639180e+01  7.036445e+01  4.733229e-01  2.620721e+02  5.074789e-01   \n",
       "min    1.000000e+00  1.000000e+00  1.000000e+00 -3.620000e+02 -6.000000e+01   \n",
       "25%    1.300000e+02  1.130000e+02  1.000000e+00  6.000000e+00  0.000000e+00   \n",
       "50%    1.620000e+02  1.620000e+02  1.000000e+00  8.500000e+00  0.000000e+00   \n",
       "75%    2.340000e+02  2.340000e+02  2.000000e+00  1.350000e+01  5.000000e-01   \n",
       "max    2.650000e+02  2.650000e+02  4.000000e+00  6.232599e+05  5.353800e+02   \n",
       "\n",
       "            mta_tax    tip_amount  tolls_amount  improvement_surcharge  \\\n",
       "count  7.667792e+06  7.667792e+06  7.667792e+06           7.667792e+06   \n",
       "mean   4.968458e-01  1.827367e+00  3.169187e-01           2.993382e-01   \n",
       "std    5.337843e-02  2.501213e+00  2.023665e+00           1.911711e-02   \n",
       "min   -5.000000e-01 -6.350000e+01 -7.000000e+01          -3.000000e-01   \n",
       "25%    5.000000e-01  0.000000e+00  0.000000e+00           3.000000e-01   \n",
       "50%    5.000000e-01  1.430000e+00  0.000000e+00           3.000000e-01   \n",
       "75%    5.000000e-01  2.330000e+00  0.000000e+00           3.000000e-01   \n",
       "max    6.080000e+01  7.872500e+02  3.288000e+03           6.000000e-01   \n",
       "\n",
       "       total_amount  congestion_surcharge  \n",
       "count  7.667792e+06          2.811814e+06  \n",
       "mean   1.568222e+01          3.289691e-05  \n",
       "std    2.622932e+02          9.068695e-03  \n",
       "min   -3.628000e+02          0.000000e+00  \n",
       "25%    8.190000e+00          0.000000e+00  \n",
       "50%    1.127000e+01          0.000000e+00  \n",
       "75%    1.656000e+01          0.000000e+00  \n",
       "max    6.232617e+05          2.500000e+00  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_pickle(\"data\\yellow_tripdata_2019-01.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['congestion_surcharge']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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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>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01 00:46:40</td>\n",
       "      <td>2019-01-01 00:53:20</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>151</td>\n",
       "      <td>239</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01 00:59:47</td>\n",
       "      <td>2019-01-01 01:18:59</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>239</td>\n",
       "      <td>246</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-12-21 13:48:30</td>\n",
       "      <td>2018-12-21 13:52:40</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>236</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>5.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-11-28 15:52:25</td>\n",
       "      <td>2018-11-28 15:55:45</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>7.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-11-28 15:56:57</td>\n",
       "      <td>2018-11-28 15:58:33</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>55.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "0         1  2019-01-01 00:46:40   2019-01-01 00:53:20                1   \n",
       "1         1  2019-01-01 00:59:47   2019-01-01 01:18:59                1   \n",
       "2         2  2018-12-21 13:48:30   2018-12-21 13:52:40                3   \n",
       "3         2  2018-11-28 15:52:25   2018-11-28 15:55:45                5   \n",
       "4         2  2018-11-28 15:56:57   2018-11-28 15:58:33                5   \n",
       "\n",
       "   trip_distance  RatecodeID store_and_fwd_flag  PULocationID  DOLocationID  \\\n",
       "0            1.5           1                  N           151           239   \n",
       "1            2.6           1                  N           239           246   \n",
       "2            0.0           1                  N           236           236   \n",
       "3            0.0           1                  N           193           193   \n",
       "4            0.0           2                  N           193           193   \n",
       "\n",
       "   payment_type  fare_amount  extra  mta_tax  tip_amount  tolls_amount  \\\n",
       "0             1          7.0    0.5      0.5        1.65           0.0   \n",
       "1             1         14.0    0.5      0.5        1.00           0.0   \n",
       "2             1          4.5    0.5      0.5        0.00           0.0   \n",
       "3             2          3.5    0.5      0.5        0.00           0.0   \n",
       "4             2         52.0    0.0      0.5        0.00           0.0   \n",
       "\n",
       "   improvement_surcharge  total_amount  \n",
       "0                    0.3          9.95  \n",
       "1                    0.3         16.30  \n",
       "2                    0.3          5.80  \n",
       "3                    0.3          7.55  \n",
       "4                    0.3         55.55  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7667792"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
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       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
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       "      <th>improvement_surcharge</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
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       "      <td>2019-01-01 00:46:40</td>\n",
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       "      <td>0.5</td>\n",
       "      <td>1.65</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2019-01-01 00:59:47</td>\n",
       "      <td>2019-01-01 01:18:59</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>239</td>\n",
       "      <td>246</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
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       "      <td>0.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-12-21 13:48:30</td>\n",
       "      <td>2018-12-21 13:52:40</td>\n",
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       "      <td>N</td>\n",
       "      <td>236</td>\n",
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       "      <td>4.5</td>\n",
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       "      <td>0.3</td>\n",
       "      <td>5.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-11-28 15:52:25</td>\n",
       "      <td>2018-11-28 15:55:45</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>7.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-11-28 15:56:57</td>\n",
       "      <td>2018-11-28 15:58:33</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>55.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "0         1  2019-01-01 00:46:40   2019-01-01 00:53:20                1   \n",
       "1         1  2019-01-01 00:59:47   2019-01-01 01:18:59                1   \n",
       "2         2  2018-12-21 13:48:30   2018-12-21 13:52:40                3   \n",
       "3         2  2018-11-28 15:52:25   2018-11-28 15:55:45                5   \n",
       "4         2  2018-11-28 15:56:57   2018-11-28 15:58:33                5   \n",
       "\n",
       "   trip_distance  RatecodeID store_and_fwd_flag  PULocationID  DOLocationID  \\\n",
       "0            1.5           1                  N           151           239   \n",
       "1            2.6           1                  N           239           246   \n",
       "2            0.0           1                  N           236           236   \n",
       "3            0.0           1                  N           193           193   \n",
       "4            0.0           2                  N           193           193   \n",
       "\n",
       "   payment_type  fare_amount  extra  mta_tax  tip_amount  tolls_amount  \\\n",
       "0             1          7.0    0.5      0.5        1.65           0.0   \n",
       "1             1         14.0    0.5      0.5        1.00           0.0   \n",
       "2             1          4.5    0.5      0.5        0.00           0.0   \n",
       "3             2          3.5    0.5      0.5        0.00           0.0   \n",
       "4             2         52.0    0.0      0.5        0.00           0.0   \n",
       "\n",
       "   improvement_surcharge  total_amount  \n",
       "0                    0.3          9.95  \n",
       "1                    0.3         16.30  \n",
       "2                    0.3          5.80  \n",
       "3                    0.3          7.55  \n",
       "4                    0.3         55.55  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_indices = df[(df.tpep_pickup_datetime == df.tpep_dropoff_datetime )|(df.PULocationID == df.DOLocationID)|(df.passenger_count <= 0)| (df.passenger_count >= 3) | (df.PULocationID == 0)| (df.DOLocationID == 0)|(df.PULocationID == 264)| (df.DOLocationID == 264)|(df.PULocationID == 265)| (df.DOLocationID == 265)|(df.trip_distance < 0.1) ].index\n",
    "\n",
    "df.drop(drop_indices, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6001383"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = np.arange(1, len(df) + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of          VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "1               1  2019-01-01 00:46:40   2019-01-01 00:53:20                1   \n",
       "2               1  2019-01-01 00:59:47   2019-01-01 01:18:59                1   \n",
       "3               1  2019-01-01 00:21:28   2019-01-01 00:28:37                1   \n",
       "4               1  2019-01-01 00:32:01   2019-01-01 00:45:39                1   \n",
       "5               1  2019-01-01 00:57:32   2019-01-01 01:09:32                2   \n",
       "...           ...                  ...                   ...              ...   \n",
       "6001379         2  2019-01-31 23:04:25   2019-01-31 23:17:09                2   \n",
       "6001380         2  2019-01-31 23:32:34   2019-01-31 23:41:40                1   \n",
       "6001381         2  2019-01-31 23:47:48   2019-02-01 00:01:08                1   \n",
       "6001382         2  2019-01-31 23:31:54   2019-01-31 23:50:36                2   \n",
       "6001383         2  2019-01-31 23:57:36   2019-02-01 00:18:39                1   \n",
       "\n",
       "         trip_distance  RatecodeID store_and_fwd_flag  PULocationID  \\\n",
       "1                 1.50           1                  N           151   \n",
       "2                 2.60           1                  N           239   \n",
       "3                 1.30           1                  N           163   \n",
       "4                 3.70           1                  N           229   \n",
       "5                 2.10           1                  N           141   \n",
       "...                ...         ...                ...           ...   \n",
       "6001379           4.15           1                  N           186   \n",
       "6001380           1.34           1                  N            68   \n",
       "6001381           1.45           1                  N           161   \n",
       "6001382           4.28           1                  N           186   \n",
       "6001383           4.79           1                  N           263   \n",
       "\n",
       "         DOLocationID  payment_type  fare_amount  extra  mta_tax  tip_amount  \\\n",
       "1                 239             1          7.0    0.5      0.5        1.65   \n",
       "2                 246             1         14.0    0.5      0.5        1.00   \n",
       "3                 229             1          6.5    0.5      0.5        1.25   \n",
       "4                   7             1         13.5    0.5      0.5        3.70   \n",
       "5                 234             1         10.0    0.5      0.5        1.70   \n",
       "...               ...           ...          ...    ...      ...         ...   \n",
       "6001379            13             2         14.5    0.5      0.5        0.00   \n",
       "6001380           233             2          8.0    0.5      0.5        0.00   \n",
       "6001381           229             1         10.5    0.5      0.5        2.36   \n",
       "6001382           262             1         17.0    0.5      0.5        3.66   \n",
       "6001383             4             1         18.0    0.5      0.5        3.86   \n",
       "\n",
       "         tolls_amount  improvement_surcharge  total_amount  \n",
       "1                 0.0                    0.3          9.95  \n",
       "2                 0.0                    0.3         16.30  \n",
       "3                 0.0                    0.3          9.05  \n",
       "4                 0.0                    0.3         18.50  \n",
       "5                 0.0                    0.3         13.00  \n",
       "...               ...                    ...           ...  \n",
       "6001379           0.0                    0.3         15.80  \n",
       "6001380           0.0                    0.3          9.30  \n",
       "6001381           0.0                    0.3         14.16  \n",
       "6001382           0.0                    0.3         21.96  \n",
       "6001383           0.0                    0.3         23.16  \n",
       "\n",
       "[6001383 rows x 17 columns]>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['tpep_pickup_datetime'] = pd.to_datetime(df['tpep_pickup_datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"pickup_hour\"] = df.tpep_pickup_datetime.dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LmfKzZnydAABwn80WzLv7r7Pytd5XrLLPi5O8eIX6FSvt190fy79cCrNc/8ckZ34txwsAAPuST/4EAIABCOYAADCAOa8xB+7nXvzak2cZ99d+8spZxgWA+zMz5gAAMADBHAAABiCYAwDAAARzAAAYgGAOAAADEMwBAGAAgjkAAAxAMAcAgAEI5gAAMADBHAAABiCYAwDAAARzAAAYgGAOAAADEMwBAGAAgjkAAAxAMAcAgAEI5gAAMADBHAAABiCYAwDAAARzAAAYgGAOAAADWLc7G1XV1d190q5qAABwf3Xn//3W2cZ+5Lmn7HKbVYN5VT0oyUOSHFZVhyapadVDk3zzfT1AAABgYVcz5s9M8twsQvi1+Zdg/rkkr5jxuAAA4ICyajDv7t9P8vtV9fPd/Qd76ZgAAOCAs1vXmHf3H1TVv0ty9PI+3f3qmY4LAAAOKLv75s/XJPmWJNcl+fJU7iSCOQAArIHdCuZJNiY5trt7zoMBAIAD1e7ex/yGJN8054EAAMCBbHdnzA9L8uGqel+SL24vdveTZzkqAAA4wOxuMH/hnAcBAAAHut29K8tfzn0gAABwINvdu7J8Pou7sCTJwUkekOQfuvuhcx0YAAAcSHZ3xvwbl59X1elJjp/liAAA4AC0u3dl+Qrd/cYkJ67xsQAAwAFrdy9l+bGlp1+XxX3N3dMcAADWyO7eleVHl5bvTXJLktPW/GgAAOAAtbvXmD9j7gMBAIAD2W5dY15VR1bVG6rqzqq6o6peV1VHzn1wAABwoNjdN3++MsnlSb45yRFJ3jzVdqqqjqqqd1bVTVV1Y1U9Z6o/vKquqqqbp6+HTvWqqpdX1daqur6qHrc01uZp+5uravNS/fFV9aFpn5dXVa3WAwAARrW7wXx9d7+yu++dHq9Ksn4X+9yb5Je7+9uTnJDk2VV1bJLzklzd3RuSXD09T5JTkmyYHuckOT9ZhOwkL0jyPVncovEFS0H7/Gnb7fttmuo76wEAAEPa3WD+qar66ao6aHr8dJJPr7ZDd9/e3e+flj+f5KYsZttPS3LxtNnFSU6flk9L8upeeE+SQ6rq8CQnJ7mqu+/u7nuSXJVk07Tuod397u7uJK/eYayVegAAwJB2N5j/bJKfSPLJJLcnOSPJbr8htKqOTvLYJO9N8qjuvj1ZhPckj5w2OyLJrUu7bZtqq9W3rVDPKj0AAGBIuxvMX5Rkc3ev7+5HZhHUX7g7O1bVNyR5XZLndvfnVtt0hVrvQX23VdU5VbWlqrbcddddX8uuAACwpnY3mH/ndBlJkqS7785iBnxVVfWALEL5n3b366fyHdNlKJm+3jnVtyU5amn3I5Pctov6kSvUV+vxFbr7gu7e2N0b16/f1SXzAAAwn90N5l+3fGeT6Q2Zq94DfbpDyoVJburu311adXmS7XdW2ZzkTUv1p093ZzkhyWeny1CuTPKkqjp0OoYnJblyWvf5qjph6vX0HcZaqQcAAAxpdz/586VJ/mdVXZbF5SI/keTFu9jnCUmeluRDVXXdVPtPSX4ryaVVdXaSTyQ5c1p3RZJTk2xN8oVM17B3991V9aIk10zb/cY0Y58kz0ryqiQPTvLW6ZFVegAAwJB295M/X11VW5KcmMW13T/W3R/exT5/nZWvA0+Sk1bYvpM8eydjXZTkohXqW5I8ZoX6p1fqAQAAo9rdGfNMQXzVMA4AAOyZ3b3GHAAAmJFgDgAAAxDMAQBgAII5AAAMQDAHAIABCOYAADAAwRwAAAYgmAMAwAAEcwAAGIBgDgAAAxDMAQBgAII5AAAMQDAHAIABCOYAADAAwRwAAAYgmAMAwAAEcwAAGIBgDgAAAxDMAQBgAII5AAAMQDAHAIABCOYAADAAwRwAAAYgmAMAwAAEcwAAGIBgDgAAAxDMAQBgAII5AAAMQDAHAIABCOYAADAAwRwAAAYgmAMAwAAEcwAAGIBgDgAAA1i3rw9gRHed/yezjLv+WT89y7gAANz/mTEHAIABCOYAADAAwRwAAAYgmAMAwAAEcwAAGIBgDgAAAxDMAQBgALMF86q6qKrurKoblmovrKq/q6rrpsepS+ueX1Vbq+qjVXXyUn3TVNtaVect1Y+pqvdW1c1V9dqqOniqP3B6vnVaf/RcrxEAANbKnDPmr0qyaYX6y7r7uOlxRZJU1bFJzkryHdM+f1hVB1XVQUlekeSUJMcmeeq0bZK8ZBprQ5J7kpw91c9Ock93f2uSl03bAQDA0GYL5t39V0nu3s3NT0tySXd/sbs/nmRrkuOnx9bu/lh3fynJJUlOq6pKcmKSy6b9L05y+tJYF0/LlyU5adoeAACGtS+uMT+3qq6fLnU5dKodkeTWpW22TbWd1R+R5DPdfe8O9a8Ya1r/2Wl7AAAY1t4O5ucn+ZYkxyW5PclLp/pKM9q9B/XVxvoqVXVOVW2pqi133XXXascNAACz2qvBvLvv6O4vd/c/J/mjLC5VSRYz3kctbXpkkttWqX8qySFVtW6H+leMNa1/WHZySU13X9DdG7t74/r16+/rywMAgD22V4N5VR2+9PQpSbbfseXyJGdNd1Q5JsmGJO9Lck2SDdMdWA7O4g2il3d3J3lnkjOm/TcnedPSWJun5TOSvGPaHgAAhrVu15vsmar68yQ/mOSwqtqW5AVJfrCqjsvi0pJbkjwzSbr7xqq6NMmHk9yb5Nnd/eVpnHOTXJnkoCQXdfeNU4vnJbmkqn4zyQeSXDjVL0zymqramsVM+VlzvUYAAFgrswXz7n7qCuULV6ht3/7FSV68Qv2KJFesUP9Y/uVSmOX6PyY582s6WAAA2Md88icAAAxAMAcAgAHMdikLsPb+22tOnmXcZz7tylnGBQB2nxlzAAAYgGAOAAADEMwBAGAAgjkAAAxAMAcAgAEI5gAAMADBHAAABiCYAwDAAARzAAAYgGAOAAADEMwBAGAAgjkAAAxAMAcAgAEI5gAAMADBHAAABiCYAwDAAARzAAAYgGAOAAADEMwBAGAAgjkAAAxAMAcAgAEI5gAAMADBHAAABiCYAwDAAARzAAAYgGAOAAADEMwBAGAAgjkAAAxg3b4+AJI7zv/tWcZ91LN+dZZxAQBYe2bMAQBgAII5AAAMQDAHAIABCOYAADAAwRwAAAYgmAMAwAAEcwAAGIBgDgAAAxDMAQBgAII5AAAMQDAHAIABzBbMq+qiqrqzqm5Yqj28qq6qqpunr4dO9aqql1fV1qq6vqoet7TP5mn7m6tq81L98VX1oWmfl1dVrdYDAABGNueM+auSbNqhdl6Sq7t7Q5Krp+dJckqSDdPjnCTnJ4uQneQFSb4nyfFJXrAUtM+ftt2+36Zd9AAAgGHNFsy7+6+S3L1D+bQkF0/LFyc5fan+6l54T5JDqurwJCcnuaq77+7ue5JclWTTtO6h3f3u7u4kr95hrJV6AADAsPb2NeaP6u7bk2T6+sipfkSSW5e22zbVVqtvW6G+Wg8AABjWKG/+rBVqvQf1r61p1TlVtaWqttx1111f6+4AALBm9nYwv2O6DCXT1zun+rYkRy1td2SS23ZRP3KF+mo9vkp3X9DdG7t74/r16/f4RQEAwH21t4P55Um231llc5I3LdWfPt2d5YQkn50uQ7kyyZOq6tDpTZ9PSnLltO7zVXXCdDeWp+8w1ko9AABgWOvmGriq/jzJDyY5rKq2ZXF3ld9KcmlVnZ3kE0nOnDa/IsmpSbYm+UKSZyRJd99dVS9Kcs203W909/Y3lD4rizu/PDjJW6dHVukBAADDmi2Yd/dTd7LqpBW27STP3sk4FyW5aIX6liSPWaH+6ZV6AADAyEZ58ycAABzQBHMAABiAYA4AAAMQzAEAYACCOQAADEAwBwCAAQjmAAAwAMEcAAAGIJgDAMAABHMAABiAYA4AAANYt68PgL3vEy8/Y5ZxH/0Ll80yLgDAgcCMOQAADEAwBwCAAQjmAAAwAMEcAAAGIJgDAMAABHMAABiAYA4AAAMQzAEAYACCOQAADEAwBwCAAQjmAAAwAMEcAAAGIJgDAMAABHMAABiAYA4AAAMQzAEAYACCOQAADEAwBwCAAQjmAAAwAMEcAAAGIJgDAMAABHMAABiAYA4AAAMQzAEAYADr9vUBsH+75r/96Gxjf/cz3zzb2AAAe5tgDvfBa1+5abaxf/IZb5ttbABgPC5lAQCAAQjmAAAwAMEcAAAGIJgDAMAA9kkwr6pbqupDVXVdVW2Zag+vqquq6ubp66FTvarq5VW1taqur6rHLY2zedr+5qravFR//DT+1mnf2vuvEgAAdt++nDH/9919XHdvnJ6fl+Tq7t6Q5OrpeZKckmTD9DgnyfnJIsgneUGS70lyfJIXbA/z0zbnLO03360zAABgDYx0KctpSS6eli9OcvpS/dW98J4kh1TV4UlOTnJVd9/d3fckuSrJpmndQ7v73d3dSV69NBYAAAxpXwXzTvL2qrq2qs6Zao/q7tuTZPr6yKl+RJJbl/bdNtVWq29boQ4AAMPaVx8w9ITuvq2qHpnkqqr6yCrbrnR9eO9B/asHXvxQcE6SPPrRj179iAEAYEb7ZMa8u2+bvt6Z5A1ZXCN+x3QZSqavd06bb0ty1NLuRya5bRf1I1eor3QcF3T3xu7euH79+vv6sgAAYI/t9WBeVV9fVd+4fTnJk5LckOTyJNvvrLI5yZum5cuTPH26O8sJST47XepyZZInVdWh05s+n5Tkymnd56vqhOluLE9fGgsAAIa0Ly5leVSSN0x3MFyX5M+6+21VdU2SS6vq7CSfSHLmtP0VSU5NsjXJF5I8I0m6++6qelGSa6btfqO7756Wn5XkVUkenOSt0wMAAIa114N5d38syXetUP90kpNWqHeSZ+9krIuSXLRCfUuSx9zng+V+58oLT51t7JPPvmK2sQEARrpdIgAAHLAEcwAAGIBgDgAAAxDMAQBgAII5AAAMQDAHAIABCOYAADAAwRwAAAYgmAMAwAAEcwAAGIBgDgAAAxDMAQBgAII5AAAMQDAHAIABCOYAADAAwRwAAAYgmAMAwAAEcwAAGIBgDgAAAxDMAQBgAII5AAAMQDAHAIABCOYAADAAwRwAAAYgmAMAwAAEcwAAGIBgDgAAAxDMAQBgAII5AAAMQDAHAIABCOYAADAAwRwAAAYgmAMAwAAEcwAAGIBgDgAAAxDMAQBgAII5AAAMQDAHAIABCOYAADAAwRwAAAYgmAMAwAAEcwAAGIBgDgAAA9hvg3lVbaqqj1bV1qo6b18fDwAArGa/DOZVdVCSVyQ5JcmxSZ5aVcfu26MCAICd2y+DeZLjk2zt7o9195eSXJLktH18TAAAsFP7azA/IsmtS8+3TTUAABhSdfe+PoY1V1VnJjm5u39uev60JMd398/vsN05Sc6Znn5bko/uQbvDknzqPhyufgdOv/35temnn377rt/+/Nr0029/7fevunv9jsV19/14hrQtyVFLz49MctuOG3X3BUkuuC+NqmpLd2+8L2Pod2D0259fm3766bfv+u3Pr00//Q60fvvrpSzXJNlQVcdU1cFJzkpy+T4+JgAA2Kn9csa8u++tqnOTXJnkoCQXdfeN+/iwAABgp/bLYJ4k3X1Fkiv2Qqv7dCmMfgdUv/35temnn377rt/+/Nr00++A6rdfvvkTAADub/bXa8wBAOB+RTDfQ1W1qao+WlVbq+q8vdDvoqq6s6pu2Au9jqqqd1bVTVV1Y1U9Z+Z+D6qq91XVB6d+vz5nv6W+B1XVB6rqLXuh1y1V9aGquq6qtuyFfodU1WVV9ZHp7/F7Z+z1bdPr2v74XFU9d65+U89fnP6t3FBVf15VD5qx13OmPjfO9bpW+v9dVQ+vqquq6ubp66Ez9ztzeo3/XFVrdoeBnfT67enf5vVV9YaqOmTmfi+ael1XVW+vqm+es9/Sul+pqq6qw+bsV1UvrKq/W/o/eOqc/ab6z0/fA2+sqv8yZ7+qeu3Sa7ulqq6bud9xVfWe7efrqjp+5n7fVVXvnr5HvLmqHrpGvVb8Xj7XuWWVfnOdW3bWb5bzyyr91vb80t0eX+MjizeU/k2Sf53k4CQfTHLszD2fmORxSW7YC6/v8CSPm5a/Mcn/mvP1Jakk3zAtPyDJe4QogVcAAAm3SURBVJOcsBde5y8l+bMkb9kLvW5JctjcfZb6XZzk56blg5Mcspf6HpTkk1ncn3WuHkck+XiSB0/PL03yMzP1ekySG5I8JIv35Pw/STbM0Oer/n8n+S9JzpuWz0vykpn7fXsWn+fwriQbZ+71pCTrpuWX7IXX9tCl5V9I8l/n7DfVj8riBgR/u5b/93fy+l6Y5FfW+t/lKv3+/fR/4YHT80fO/ee5tP6lSf7zzK/v7UlOmZZPTfKumftdk+QHpuWfTfKiNeq14vfyuc4tq/Sb69yys36znF9W6bem5xcz5nvm+CRbu/tj3f2lJJckOW3Oht39V0nunrPHUq/bu/v90/Lnk9yUGT85tRf+fnr6gOkx65sfqurIJD+c5I/n7LMvTLMtT0xyYZJ095e6+zN7qf1JSf6mu/925j7rkjy4qtZlEZq/6nMK1si3J3lPd3+hu+9N8pdJnrLWTXby//u0LH7AyvT19Dn7dfdN3b0nH7K2J73ePv15Jsl7svisiTn7fW7p6ddnDc8vq5ybX5bkP65lr130m8VO+j0ryW919xenbe6cuV+SpKoqyU8k+fOZ+3WS7bPWD8sanl920u/bkvzVtHxVkh9fo147+14+y7llZ/1mPLfsrN8s55dV+q3p+UUw3zNHJLl16fm2zBhc96WqOjrJY7OYxZ6zz0HTryfvTHJVd8/aL8nvZfFN859n7rNdJ3l7VV1bi0+cndO/TnJXklfW4lKdP66qr5+553ZnZQ2/aa6ku/8uye8k+USS25N8trvfPlO7G5I8saoeUVUPyWL27Khd7LNWHtXdtyeLbwhJHrmX+u5tP5vkrXM3qaoXV9WtSX4qyX+eudeTk/xdd39wzj47OHf6dfpFa3nZ0078myTfX1Xvraq/rKrvnrnfdt+f5I7uvnnmPs9N8tvTv5ffSfL8mfvdkOTJ0/KZmeEcs8P38tnPLXsrO+xGv1nOLzv2W8vzi2C+Z2qF2n53e5uq+oYkr0vy3B1+Ilxz3f3l7j4ui59sj6+qx8zVq6p+JMmd3X3tXD1W8ITuflySU5I8u6qeOGOvdVn8qvT87n5skn/I4teVs6rFh3k9Ocl/n7nPoVnM+ByT5JuTfH1V/fQcvbr7pix+FXpVkrdlcdnavavuxG6rql/L4s/zT+fu1d2/1t1HTb3OnavP9APcr2Xm8L+D85N8S5Ljsvhh9aUz91uX5NAkJyT51SSXTrPZc3tqZv7Bf/KsJL84/Xv5xUy/fZzRz2bxfeHaLC6R+NJaDr43v5eP1G+u88tK/dby/CKY75lt+cqfaI/MfL9K3yeq6gFZ/MP70+5+/d7qO11y8a4km2Zs84QkT66qW7K4DOnEqvqTGfulu2+bvt6Z5A1ZXA41l21Jti391uGyLIL63E5J8v7uvmPmPj+U5OPdfVd3/1OS1yf5d3M16+4Lu/tx3f3ELH4FPfds3XZ3VNXhSTJ9XbPLBUZQVZuT/EiSn+rp4sy95M+yRpcK7MS3ZPFD4wenc8yRSd5fVd80V8PuvmOa3PjnJH+Uec8vyeIc8/rpMsT3ZfGbxzV7g+tKpsvWfizJa+fsM9mcxXklWUw0zPrn2d0f6e4ndffjs/jB42/WauydfC+f7dyyt7PDzvrNdX7Zjdd3n88vgvmeuSbJhqo6ZpolPCvJ5fv4mNbMNPNxYZKbuvt390K/9dvfNV1VD84ieH1krn7d/fzuPrK7j87i7+4d3T3LjGuSVNXXV9U3bl/O4o0ps91dp7s/meTWqvq2qXRSkg/P1W/J3prN+kSSE6rqIdO/1ZOyuNZvFlX1yOnro7MIBnvjNSaLc8rmaXlzkjftpb6zq6pNSZ6X5Mnd/YW90G/D0tMnZ97zy4e6+5HdffR0jtmWxRvGPjlXz+0ha/KUzHh+mbwxyYlT73+TxRvMPzVzzx9K8pHu3jZzn2Qx0fYD0/KJmfmH8aVzzNcl+T+T/Nc1Gndn38tnObfsg+ywYr+5zi+r9Fvb88uO7wb12O13556axTty/ybJr+2Ffn+exa8o/ymLE/3ZM/b6viwuzbk+yXXT49QZ+31nkg9M/W7IGr7jfjd6/2BmvitLFtd8f3B63LiX/r0cl2TL9Gf6xiSHztzvIUk+neRhe+nv7denk98NSV6T6e4QM/X6f7P4weaDSU6aqcdX/f9O8ogkV2cRCq5O8vCZ+z1lWv5ikjuSXDljr61ZvE9n+/llLe+SslK/103/Vq5P8uYs3rA1W78d1t+Stb0ry0qv7zVJPjS9vsuTHD5zv4OT/Mn0Z/r+JCfO/eeZ5FVJ/sNa9dnF6/u+JNdO/+ffm+TxM/d7ThZ54n8l+a1MH/64Br1W/F4+17lllX5znVt21m+W88sq/db0/OKTPwEAYAAuZQEAgAEI5gAAMADBHAAABiCYAwDAAARzAAAYgGAOAAADEMwB9mNV9cdVdewq619YVb8yU++jq2ruD7sB2G+s29cHAMB8uvvn9vUxrLWqWtfd9+7r4wBYa2bMAfYD0+z0R6rq4qq6vqouq6qHVNW7qmrjtM2mqnp/VX2wqq5eYYz/vareWlUP3mG/w6rqlmn5Z6rqTVX1tqr6aFW9YBeHdlBV/VFV3VhVb6+qB0/jHFdV75mO9Q1VdehUX63vf6+qNyd5+9r8qQGMRTAH2H98W5ILuvs7k3wuyf+xfUVVrU/yR0l+vLu/K8mZyztW1blJfjTJ6d39/+2iz/FJfirJcUnO3B6kd2JDkld093ck+UySH5/qr07yvOlYP5RkVwE/Sb43yebuPnE3tgW43xHMAfYft3b3/5iW/yTJ9y2tOyHJX3X3x5Oku+9eWve0JKdkEdq/uBt9ruruT08B/vU79NnRx7v7umn52iRHV9XDkhzS3X851S9O8sTd7Hv3rjcDuH8SzAH2H73K81ph/XY3JDk6yZFLtXvzL98jHvQ19NnRctD/cnb93qbV+v7DLvYFuF8TzAH2H4+uqu+dlp+a5K+X1r07yQ9U1TFJUlUPX1r3gSTPTHJ5VX3zVLslyeOn5TN26PO/VdXDp+vFT0/yP/I16O7PJrmnqr5/Kj0tyfbZ89X6AuzXBHOA/cdNSTZX1fVJHp7k/O0ruvuuJOckeX1VfTDJa5d37O6/TvIrSf6iqg5L8jtJnlVV/zPJYTv0+eskr0lyXZLXdfeWPTjWzUl+ezrW45L8xlRfrS/Afq26V/sNJAD3B1V1dJK3dPdjZu7zM0k2dve5c/YBOBCZMQcAgAGYMQfgPqmqRyT5qvuiJzmpuz+9t48H4P5KMAcAgAG4lAUAAAYgmAMAwAAEcwAAGIBgDgAAAxDMAQBgAP8/czjNgdwP6pgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.countplot(x=\"pickup_hour\", data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.countplot(x=\"pickup_hour\", data=df[df['PULocationID']==138])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.countplot(x=\"pickup_hour\", data=df[df['DOLocationID']==138])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>pickup_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [VendorID, tpep_pickup_datetime, tpep_dropoff_datetime, passenger_count, trip_distance, RatecodeID, store_and_fwd_flag, PULocationID, DOLocationID, payment_type, fare_amount, extra, mta_tax, tip_amount, tolls_amount, improvement_surcharge, total_amount, pickup_hour]\n",
       "Index: []"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['PULocationID'] == df['DOLocationID'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}