analytics_engineer_case.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5f3c45aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing necessary libraries\n",
    "import os\n",
    "import pandas as pd\n",
    "from pandas.io import sql\n",
    "import datetime\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "sns.set(style=\"darkgrid\")\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b7bf55cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 235 ms, sys: 22.7 ms, total: 258 ms\n",
      "Wall time: 262 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "nyc_data_green = pd.read_csv('green_tripdata_2022-01.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "63cdc428",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.12 s, sys: 691 ms, total: 5.81 s\n",
      "Wall time: 6.06 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "nyc_data_yellow = pd.read_csv('yellow_tripdata_2022-01.csv',low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b8c1413",
   "metadata": {},
   "source": [
    "## Dataset information |  size in MB ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d5474b71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 62495 entries, 0 to 62494\n",
      "Data columns (total 20 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   VendorID               62495 non-null  int64  \n",
      " 1   lpep_pickup_datetime   62495 non-null  object \n",
      " 2   lpep_dropoff_datetime  62495 non-null  object \n",
      " 3   store_and_fwd_flag     56200 non-null  object \n",
      " 4   RatecodeID             56200 non-null  float64\n",
      " 5   PULocationID           62495 non-null  int64  \n",
      " 6   DOLocationID           62495 non-null  int64  \n",
      " 7   passenger_count        56200 non-null  float64\n",
      " 8   trip_distance          62495 non-null  float64\n",
      " 9   fare_amount            62495 non-null  float64\n",
      " 10  extra                  62495 non-null  float64\n",
      " 11  mta_tax                62495 non-null  float64\n",
      " 12  tip_amount             62495 non-null  float64\n",
      " 13  tolls_amount           62495 non-null  float64\n",
      " 14  ehail_fee              0 non-null      float64\n",
      " 15  improvement_surcharge  62495 non-null  float64\n",
      " 16  total_amount           62495 non-null  float64\n",
      " 17  payment_type           56200 non-null  float64\n",
      " 18  trip_type              56200 non-null  float64\n",
      " 19  congestion_surcharge   56200 non-null  float64\n",
      "dtypes: float64(14), int64(3), object(3)\n",
      "memory usage: 9.5+ MB\n",
      "CPU times: user 25.2 ms, sys: 284 µs, total: 25.5 ms\n",
      "Wall time: 24.4 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "nyc_data_green.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bcc866ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2463931 entries, 0 to 2463930\n",
      "Data columns (total 18 columns):\n",
      " #   Column                 Dtype  \n",
      "---  ------                 -----  \n",
      " 0   VendorID               int64  \n",
      " 1   tpep_pickup_datetime   object \n",
      " 2   tpep_dropoff_datetime  object \n",
      " 3   passenger_count        float64\n",
      " 4   trip_distance          float64\n",
      " 5   RatecodeID             float64\n",
      " 6   store_and_fwd_flag     object \n",
      " 7   PULocationID           int64  \n",
      " 8   DOLocationID           int64  \n",
      " 9   payment_type           int64  \n",
      " 10  fare_amount            float64\n",
      " 11  extra                  float64\n",
      " 12  mta_tax                float64\n",
      " 13  tip_amount             float64\n",
      " 14  tolls_amount           float64\n",
      " 15  improvement_surcharge  float64\n",
      " 16  total_amount           float64\n",
      " 17  congestion_surcharge   float64\n",
      "dtypes: float64(11), int64(4), object(3)\n",
      "memory usage: 338.4+ MB\n"
     ]
    }
   ],
   "source": [
    "nyc_data_yellow.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "78ea44c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(62495, 20)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting the dimension of the dataset\n",
    "nyc_data_green.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8e8b6235",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2463931, 18)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting the dimension of the dataset\n",
    "nyc_data_yellow.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fb97064b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VendorID                   int64\n",
       "lpep_pickup_datetime      object\n",
       "lpep_dropoff_datetime     object\n",
       "store_and_fwd_flag        object\n",
       "RatecodeID               float64\n",
       "PULocationID               int64\n",
       "DOLocationID               int64\n",
       "passenger_count          float64\n",
       "trip_distance            float64\n",
       "fare_amount              float64\n",
       "extra                    float64\n",
       "mta_tax                  float64\n",
       "tip_amount               float64\n",
       "tolls_amount             float64\n",
       "ehail_fee                float64\n",
       "improvement_surcharge    float64\n",
       "total_amount             float64\n",
       "payment_type             float64\n",
       "trip_type                float64\n",
       "congestion_surcharge     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the data types\n",
    "nyc_data_green.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "783e3a17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VendorID                   int64\n",
       "tpep_pickup_datetime      object\n",
       "tpep_dropoff_datetime     object\n",
       "passenger_count          float64\n",
       "trip_distance            float64\n",
       "RatecodeID               float64\n",
       "store_and_fwd_flag        object\n",
       "PULocationID               int64\n",
       "DOLocationID               int64\n",
       "payment_type               int64\n",
       "fare_amount              float64\n",
       "extra                    float64\n",
       "mta_tax                  float64\n",
       "tip_amount               float64\n",
       "tolls_amount             float64\n",
       "improvement_surcharge    float64\n",
       "total_amount             float64\n",
       "congestion_surcharge     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the data types\n",
    "nyc_data_yellow.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "167e47ee",
   "metadata": {},
   "source": [
    "## Reduce dataset memory size ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c98467a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def reduce_mem_usage(df):\n",
    "    \"\"\" iterate through all the columns of a dataframe and modify the data type\n",
    "        to reduce memory usage.        \n",
    "    \"\"\"\n",
    "    start_mem = df.memory_usage().sum() / 1024**2\n",
    "    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))\n",
    "    \n",
    "    for col in df.columns:\n",
    "        col_type = df[col].dtype\n",
    "        \n",
    "        if col_type != object and col_type.name != 'category' and 'datetime' not in col_type.name:\n",
    "            c_min = df[col].min()\n",
    "            c_max = df[col].max()\n",
    "            if str(col_type)[:3] == 'int':\n",
    "                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
    "                    df[col] = df[col].astype(np.int8)\n",
    "                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
    "                    df[col] = df[col].astype(np.int16)\n",
    "                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
    "                    df[col] = df[col].astype(np.int32)\n",
    "                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
    "                    df[col] = df[col].astype(np.int64)  \n",
    "            else:\n",
    "                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
    "                    df[col] = df[col].astype(np.float16)\n",
    "                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
    "                    df[col] = df[col].astype(np.float32)\n",
    "                else:\n",
    "                    df[col] = df[col].astype(np.float64)\n",
    "        elif 'datetime' not in col_type.name:\n",
    "            df[col] = df[col].astype('category')\n",
    "\n",
    "    end_mem = df.memory_usage().sum() / 1024**2\n",
    "    print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n",
    "    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "adf3ecd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of dataframe is 338.37 MB\n",
      "Memory usage after optimization is: 187.27 MB\n",
      "Decreased by 44.7%\n"
     ]
    },
    {
     "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>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",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-01-01 00:35:40</td>\n",
       "      <td>2022-01-01 00:53:29</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>142</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.650391</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>21.950001</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-01-01 00:33:43</td>\n",
       "      <td>2022-01-01 00:42:07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>236</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>13.300000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:53:21</td>\n",
       "      <td>2022-01-01 01:02:19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>166</td>\n",
       "      <td>166</td>\n",
       "      <td>1</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.759766</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>10.560000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:25:21</td>\n",
       "      <td>2022-01-01 00:35:23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>114</td>\n",
       "      <td>68</td>\n",
       "      <td>2</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:36:48</td>\n",
       "      <td>2022-01-01 01:14:20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>68</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>30.299999</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
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       "      <th>2463926</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:36:53</td>\n",
       "      <td>2022-01-31 23:42:51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.390625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>13.690000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2463927</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:44:22</td>\n",
       "      <td>2022-01-31 23:55:01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>16.799999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.351562</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>24.450001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463928</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:39:00</td>\n",
       "      <td>2022-01-31 23:50:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113</td>\n",
       "      <td>246</td>\n",
       "      <td>0</td>\n",
       "      <td>11.220000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>16.520000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2463929</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:36:42</td>\n",
       "      <td>2022-01-31 23:48:45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.92</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>12.400000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>15.700000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463930</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:46:00</td>\n",
       "      <td>2022-02-01 00:13:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>186</td>\n",
       "      <td>181</td>\n",
       "      <td>0</td>\n",
       "      <td>25.480000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.281250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>35.060001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2463931 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "0               1  2022-01-01 00:35:40   2022-01-01 00:53:29              2.0   \n",
       "1               1  2022-01-01 00:33:43   2022-01-01 00:42:07              1.0   \n",
       "2               2  2022-01-01 00:53:21   2022-01-01 01:02:19              1.0   \n",
       "3               2  2022-01-01 00:25:21   2022-01-01 00:35:23              1.0   \n",
       "4               2  2022-01-01 00:36:48   2022-01-01 01:14:20              1.0   \n",
       "...           ...                  ...                   ...              ...   \n",
       "2463926         2  2022-01-31 23:36:53   2022-01-31 23:42:51              NaN   \n",
       "2463927         2  2022-01-31 23:44:22   2022-01-31 23:55:01              NaN   \n",
       "2463928         2  2022-01-31 23:39:00   2022-01-31 23:50:00              NaN   \n",
       "2463929         2  2022-01-31 23:36:42   2022-01-31 23:48:45              NaN   \n",
       "2463930         2  2022-01-31 23:46:00   2022-02-01 00:13:00              NaN   \n",
       "\n",
       "         trip_distance  RatecodeID store_and_fwd_flag  PULocationID  \\\n",
       "0                 3.80         1.0                  N           142   \n",
       "1                 2.10         1.0                  N           236   \n",
       "2                 0.97         1.0                  N           166   \n",
       "3                 1.09         1.0                  N           114   \n",
       "4                 4.30         1.0                  N            68   \n",
       "...                ...         ...                ...           ...   \n",
       "2463926           1.32         NaN                NaN            90   \n",
       "2463927           4.19         NaN                NaN           107   \n",
       "2463928           2.10         NaN                NaN           113   \n",
       "2463929           2.92         NaN                NaN           148   \n",
       "2463930           8.94         NaN                NaN           186   \n",
       "\n",
       "         DOLocationID  payment_type  fare_amount  extra  mta_tax  tip_amount  \\\n",
       "0                 236             1    14.500000    3.0      0.5    3.650391   \n",
       "1                  42             1     8.000000    0.5      0.5    4.000000   \n",
       "2                 166             1     7.500000    0.5      0.5    1.759766   \n",
       "3                  68             2     8.000000    0.5      0.5    0.000000   \n",
       "4                 163             1    23.500000    0.5      0.5    3.000000   \n",
       "...               ...           ...          ...    ...      ...         ...   \n",
       "2463926           170             0     8.000000    0.0      0.5    2.390625   \n",
       "2463927            75             0    16.799999    0.0      0.5    4.351562   \n",
       "2463928           246             0    11.220000    0.0      0.5    2.000000   \n",
       "2463929           164             0    12.400000    0.0      0.5    0.000000   \n",
       "2463930           181             0    25.480000    0.0      0.5    6.281250   \n",
       "\n",
       "         tolls_amount  improvement_surcharge  total_amount  \\\n",
       "0                 0.0               0.300049     21.950001   \n",
       "1                 0.0               0.300049     13.300000   \n",
       "2                 0.0               0.300049     10.560000   \n",
       "3                 0.0               0.300049     11.800000   \n",
       "4                 0.0               0.300049     30.299999   \n",
       "...               ...                    ...           ...   \n",
       "2463926           0.0               0.300049     13.690000   \n",
       "2463927           0.0               0.300049     24.450001   \n",
       "2463928           0.0               0.300049     16.520000   \n",
       "2463929           0.0               0.300049     15.700000   \n",
       "2463930           0.0               0.300049     35.060001   \n",
       "\n",
       "         congestion_surcharge  \n",
       "0                         2.5  \n",
       "1                         0.0  \n",
       "2                         0.0  \n",
       "3                         2.5  \n",
       "4                         2.5  \n",
       "...                       ...  \n",
       "2463926                   NaN  \n",
       "2463927                   NaN  \n",
       "2463928                   NaN  \n",
       "2463929                   NaN  \n",
       "2463930                   NaN  \n",
       "\n",
       "[2463931 rows x 18 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduce_mem_usage(nyc_data_yellow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5aa8f9f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of dataframe is 9.54 MB\n",
      "Memory usage after optimization is: 7.94 MB\n",
      "Decreased by 16.8%\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>lpep_pickup_datetime</th>\n",
       "      <th>lpep_dropoff_datetime</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
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       "      <td>2022-01-01 00:20:55</td>\n",
       "      <td>2022-01-01 00:29:38</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-01-01 00:57:02</td>\n",
       "      <td>2022-01-01 01:13:14</td>\n",
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       "      <td>140</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>14.500000</td>\n",
       "      <td>3.25</td>\n",
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       "      <td>0.300049</td>\n",
       "      <td>23.156250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.75</td>\n",
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       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:07:42</td>\n",
       "      <td>2022-01-01 00:15:57</td>\n",
       "      <td>N</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:07:50</td>\n",
       "      <td>2022-01-01 00:28:52</td>\n",
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       "      <td>10.609375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>62491</th>\n",
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       "      <td>2022-02-01 00:10:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>61</td>\n",
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       "      <td>2.97</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>62492</th>\n",
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       "      <td>16.562500</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>62493</th>\n",
       "      <td>2</td>\n",
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       "      <td>2022-01-31 23:55:00</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>116</td>\n",
       "      <td>166</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.88</td>\n",
       "      <td>9.476562</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.169922</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>11.953125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62494</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:52:00</td>\n",
       "      <td>2022-02-01 00:26:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>225</td>\n",
       "      <td>179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.60</td>\n",
       "      <td>32.187500</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.230469</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>49.718750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62495 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       VendorID lpep_pickup_datetime lpep_dropoff_datetime store_and_fwd_flag  \\\n",
       "0             2  2022-01-01 00:14:21   2022-01-01 00:15:33                  N   \n",
       "1             1  2022-01-01 00:20:55   2022-01-01 00:29:38                  N   \n",
       "2             1  2022-01-01 00:57:02   2022-01-01 01:13:14                  N   \n",
       "3             2  2022-01-01 00:07:42   2022-01-01 00:15:57                  N   \n",
       "4             2  2022-01-01 00:07:50   2022-01-01 00:28:52                  N   \n",
       "...         ...                  ...                   ...                ...   \n",
       "62490         2  2022-01-31 23:25:00   2022-01-31 23:33:00                NaN   \n",
       "62491         2  2022-01-31 23:52:00   2022-02-01 00:10:00                NaN   \n",
       "62492         2  2022-01-31 23:17:00   2022-01-31 23:36:00                NaN   \n",
       "62493         2  2022-01-31 23:45:00   2022-01-31 23:55:00                NaN   \n",
       "62494         2  2022-01-31 23:52:00   2022-02-01 00:26:00                NaN   \n",
       "\n",
       "       RatecodeID  PULocationID  DOLocationID  passenger_count  trip_distance  \\\n",
       "0             1.0            42            42              1.0           0.44   \n",
       "1             1.0           116            41              1.0           2.10   \n",
       "2             1.0            41           140              1.0           3.70   \n",
       "3             1.0           181           181              1.0           1.69   \n",
       "4             1.0            33           170              1.0           6.26   \n",
       "...           ...           ...           ...              ...            ...   \n",
       "62490         NaN            40            65              NaN           1.40   \n",
       "62491         NaN            36            61              NaN           2.97   \n",
       "62492         NaN            75           167              NaN           3.70   \n",
       "62493         NaN           116           166              NaN           1.88   \n",
       "62494         NaN           225           179              NaN           9.60   \n",
       "\n",
       "       fare_amount  extra  mta_tax  tip_amount  tolls_amount  ehail_fee  \\\n",
       "0         3.500000   0.50      0.5    0.000000           0.0        NaN   \n",
       "1         9.500000   0.50      0.5    0.000000           0.0        NaN   \n",
       "2        14.500000   3.25      0.5    4.601562           0.0        NaN   \n",
       "3         8.000000   0.50      0.5    0.000000           0.0        NaN   \n",
       "4        22.000000   0.50      0.5    5.210938           0.0        NaN   \n",
       "...            ...    ...      ...         ...           ...        ...   \n",
       "62490     8.382812   0.00      0.0    1.929688           0.0        NaN   \n",
       "62491    14.921875   0.00      0.0    0.000000           0.0        NaN   \n",
       "62492    16.265625   0.00      0.0    0.000000           0.0        NaN   \n",
       "62493     9.476562   0.00      0.0    2.169922           0.0        NaN   \n",
       "62494    32.187500   0.00      0.0    7.230469          10.0        NaN   \n",
       "\n",
       "       improvement_surcharge  total_amount  payment_type  trip_type  \\\n",
       "0                   0.300049      4.800781           2.0        1.0   \n",
       "1                   0.300049     10.796875           2.0        1.0   \n",
       "2                   0.300049     23.156250           1.0        1.0   \n",
       "3                   0.300049      9.296875           2.0        1.0   \n",
       "4                   0.300049     31.265625           1.0        1.0   \n",
       "...                      ...           ...           ...        ...   \n",
       "62490               0.300049     10.609375           NaN        NaN   \n",
       "62491               0.300049     15.218750           NaN        NaN   \n",
       "62492               0.300049     16.562500           NaN        NaN   \n",
       "62493               0.300049     11.953125           NaN        NaN   \n",
       "62494               0.300049     49.718750           NaN        NaN   \n",
       "\n",
       "       congestion_surcharge  \n",
       "0                      0.00  \n",
       "1                      0.00  \n",
       "2                      2.75  \n",
       "3                      0.00  \n",
       "4                      2.75  \n",
       "...                     ...  \n",
       "62490                   NaN  \n",
       "62491                   NaN  \n",
       "62492                   NaN  \n",
       "62493                   NaN  \n",
       "62494                   NaN  \n",
       "\n",
       "[62495 rows x 20 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduce_mem_usage(nyc_data_green)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7154f046",
   "metadata": {},
   "source": [
    "## Doing a few transformations. ##\n",
    "\n",
    "- pickup and drop off time should be a datetime object\n",
    "- store_and_fwd_flag should be categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ba16d8ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transforming vendor_id and store_and_fwd to categorical data type\n",
    "nyc_data_green['store_and_fwd_flag'] = nyc_data_green['store_and_fwd_flag'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9627a769",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transforming pick_up and drop off date time into a datetime object\n",
    "nyc_data_green['lpep_pickup_datetime'] = pd.to_datetime(nyc_data_green['lpep_pickup_datetime'], format= '%Y-%m-%d %H:%M:%S')\n",
    "nyc_data_green['lpep_dropoff_datetime'] = pd.to_datetime(nyc_data_green['lpep_dropoff_datetime'], format='%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6b299726",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transforming vendor_id and store_and_fwd to categorical data type\n",
    "nyc_data_yellow['store_and_fwd_flag'] = nyc_data_green['store_and_fwd_flag'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "deb0e545",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transforming pick_up and drop off date time into a datetime object\n",
    "nyc_data_yellow['pickup_datetime'] = pd.to_datetime(nyc_data_yellow['tpep_pickup_datetime'], format= '%Y-%m-%d %H:%M:%S')\n",
    "nyc_data_yellow['dropoff_datetime'] = pd.to_datetime(nyc_data_yellow['tpep_dropoff_datetime'], format='%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b322df21",
   "metadata": {},
   "source": [
    "## Visualize head and tail ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e171e2fb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 177 µs, sys: 25 µs, total: 202 µs\n",
      "Wall time: 208 µs\n"
     ]
    },
    {
     "data": {
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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",
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       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
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       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <th>pickup_datetime</th>\n",
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       "      <td>2022-01-01 00:53:29</td>\n",
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       "      <td>3.80</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>142</td>\n",
       "      <td>236</td>\n",
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       "      <td>14.5</td>\n",
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       "      <td>2022-01-01 00:35:40</td>\n",
       "      <td>2022-01-01 00:53:29</td>\n",
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       "      <td>1</td>\n",
       "      <td>2022-01-01 00:33:43</td>\n",
       "      <td>2022-01-01 00:42:07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>13.300000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2022-01-01 00:33:43</td>\n",
       "      <td>2022-01-01 00:42:07</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:53:21</td>\n",
       "      <td>2022-01-01 01:02:19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>166</td>\n",
       "      <td>166</td>\n",
       "      <td>1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.759766</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>10.560000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2022-01-01 00:53:21</td>\n",
       "      <td>2022-01-01 01:02:19</td>\n",
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       "      <td>2</td>\n",
       "      <td>2022-01-01 00:25:21</td>\n",
       "      <td>2022-01-01 00:35:23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>114</td>\n",
       "      <td>68</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2022-01-01 00:25:21</td>\n",
       "      <td>2022-01-01 00:35:23</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:36:48</td>\n",
       "      <td>2022-01-01 01:14:20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>68</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>23.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>30.299999</td>\n",
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       "      <td>2022-01-01 00:36:48</td>\n",
       "      <td>2022-01-01 01:14:20</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>2022-01-01 01:09:48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>138</td>\n",
       "      <td>161</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>6.550781</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>56.349998</td>\n",
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       "      <td>2022-01-01 00:40:15</td>\n",
       "      <td>2022-01-01 01:09:48</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:20:50</td>\n",
       "      <td>2022-01-01 00:34:58</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>233</td>\n",
       "      <td>87</td>\n",
       "      <td>1</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.199219</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2022-01-01 00:20:50</td>\n",
       "      <td>2022-01-01 00:34:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:13:04</td>\n",
       "      <td>2022-01-01 00:22:45</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>238</td>\n",
       "      <td>152</td>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>12.800000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2022-01-01 00:13:04</td>\n",
       "      <td>2022-01-01 00:22:45</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:30:02</td>\n",
       "      <td>2022-01-01 00:44:49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.71</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>166</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>18.049999</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2022-01-01 00:30:02</td>\n",
       "      <td>2022-01-01 00:44:49</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:48:52</td>\n",
       "      <td>2022-01-01 00:53:28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>236</td>\n",
       "      <td>141</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2022-01-01 00:48:52</td>\n",
       "      <td>2022-01-01 00:53:28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "0         1  2022-01-01 00:35:40   2022-01-01 00:53:29              2.0   \n",
       "1         1  2022-01-01 00:33:43   2022-01-01 00:42:07              1.0   \n",
       "2         2  2022-01-01 00:53:21   2022-01-01 01:02:19              1.0   \n",
       "3         2  2022-01-01 00:25:21   2022-01-01 00:35:23              1.0   \n",
       "4         2  2022-01-01 00:36:48   2022-01-01 01:14:20              1.0   \n",
       "5         1  2022-01-01 00:40:15   2022-01-01 01:09:48              1.0   \n",
       "6         2  2022-01-01 00:20:50   2022-01-01 00:34:58              1.0   \n",
       "7         2  2022-01-01 00:13:04   2022-01-01 00:22:45              1.0   \n",
       "8         2  2022-01-01 00:30:02   2022-01-01 00:44:49              1.0   \n",
       "9         2  2022-01-01 00:48:52   2022-01-01 00:53:28              1.0   \n",
       "\n",
       "   trip_distance  RatecodeID store_and_fwd_flag  PULocationID  DOLocationID  \\\n",
       "0           3.80         1.0                  N           142           236   \n",
       "1           2.10         1.0                  N           236            42   \n",
       "2           0.97         1.0                  N           166           166   \n",
       "3           1.09         1.0                  N           114            68   \n",
       "4           4.30         1.0                  N            68           163   \n",
       "5          10.30         1.0                  N           138           161   \n",
       "6           5.07         1.0                  N           233            87   \n",
       "7           2.02         1.0                  N           238           152   \n",
       "8           2.71         1.0                  N           166           236   \n",
       "9           0.78         1.0                  N           236           141   \n",
       "\n",
       "   payment_type  fare_amount  extra  mta_tax  tip_amount  tolls_amount  \\\n",
       "0             1         14.5    3.0      0.5    3.650391      0.000000   \n",
       "1             1          8.0    0.5      0.5    4.000000      0.000000   \n",
       "2             1          7.5    0.5      0.5    1.759766      0.000000   \n",
       "3             2          8.0    0.5      0.5    0.000000      0.000000   \n",
       "4             1         23.5    0.5      0.5    3.000000      0.000000   \n",
       "5             1         33.0    3.0      0.5   13.000000      6.550781   \n",
       "6             1         17.0    0.5      0.5    5.199219      0.000000   \n",
       "7             2          9.0    0.5      0.5    0.000000      0.000000   \n",
       "8             1         12.0    0.5      0.5    2.250000      0.000000   \n",
       "9             2          5.0    0.5      0.5    0.000000      0.000000   \n",
       "\n",
       "   improvement_surcharge  total_amount  congestion_surcharge  \\\n",
       "0               0.300049     21.950001                   2.5   \n",
       "1               0.300049     13.300000                   0.0   \n",
       "2               0.300049     10.560000                   0.0   \n",
       "3               0.300049     11.800000                   2.5   \n",
       "4               0.300049     30.299999                   2.5   \n",
       "5               0.300049     56.349998                   2.5   \n",
       "6               0.300049     26.000000                   2.5   \n",
       "7               0.300049     12.800000                   2.5   \n",
       "8               0.300049     18.049999                   2.5   \n",
       "9               0.300049      8.800000                   2.5   \n",
       "\n",
       "      pickup_datetime    dropoff_datetime  \n",
       "0 2022-01-01 00:35:40 2022-01-01 00:53:29  \n",
       "1 2022-01-01 00:33:43 2022-01-01 00:42:07  \n",
       "2 2022-01-01 00:53:21 2022-01-01 01:02:19  \n",
       "3 2022-01-01 00:25:21 2022-01-01 00:35:23  \n",
       "4 2022-01-01 00:36:48 2022-01-01 01:14:20  \n",
       "5 2022-01-01 00:40:15 2022-01-01 01:09:48  \n",
       "6 2022-01-01 00:20:50 2022-01-01 00:34:58  \n",
       "7 2022-01-01 00:13:04 2022-01-01 00:22:45  \n",
       "8 2022-01-01 00:30:02 2022-01-01 00:44:49  \n",
       "9 2022-01-01 00:48:52 2022-01-01 00:53:28  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "nyc_data_yellow.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "07a3ab7c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>158</td>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
       "      <td>13.090000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.529297</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>19.920000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-31 23:36:07</td>\n",
       "      <td>2022-01-31 23:48:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463922</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:09:46</td>\n",
       "      <td>2022-01-31 23:20:50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>10.970000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.289062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>16.559999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-31 23:09:46</td>\n",
       "      <td>2022-01-31 23:20:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463923</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:51:47</td>\n",
       "      <td>2022-02-01 00:10:07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>246</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>26.730000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.558594</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>36.590000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-31 23:51:47</td>\n",
       "      <td>2022-02-01 00:10:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463924</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:49:00</td>\n",
       "      <td>2022-02-01 00:08:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>26.209999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-31 23:49:00</td>\n",
       "      <td>2022-02-01 00:08:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463925</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:02:51</td>\n",
       "      <td>2022-01-31 23:13:54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>224</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.769531</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>15.780000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-31 23:02:51</td>\n",
       "      <td>2022-01-31 23:13:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463926</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:36:53</td>\n",
       "      <td>2022-01-31 23:42:51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.390625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>13.690000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-31 23:36:53</td>\n",
       "      <td>2022-01-31 23:42:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463927</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:44:22</td>\n",
       "      <td>2022-01-31 23:55:01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>16.799999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.351562</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>24.450001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-31 23:44:22</td>\n",
       "      <td>2022-01-31 23:55:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463928</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:39:00</td>\n",
       "      <td>2022-01-31 23:50:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113</td>\n",
       "      <td>246</td>\n",
       "      <td>0</td>\n",
       "      <td>11.220000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>16.520000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-31 23:39:00</td>\n",
       "      <td>2022-01-31 23:50:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463929</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:36:42</td>\n",
       "      <td>2022-01-31 23:48:45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>148</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>12.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>15.700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-31 23:36:42</td>\n",
       "      <td>2022-01-31 23:48:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463930</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:46:00</td>\n",
       "      <td>2022-02-01 00:13:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>186</td>\n",
       "      <td>181</td>\n",
       "      <td>0</td>\n",
       "      <td>25.480000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.281250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>35.060001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-31 23:46:00</td>\n",
       "      <td>2022-02-01 00:13:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "2463921         2  2022-01-31 23:36:07   2022-01-31 23:48:05              NaN   \n",
       "2463922         2  2022-01-31 23:09:46   2022-01-31 23:20:50              NaN   \n",
       "2463923         2  2022-01-31 23:51:47   2022-02-01 00:10:07              NaN   \n",
       "2463924         2  2022-01-31 23:49:00   2022-02-01 00:08:00              NaN   \n",
       "2463925         2  2022-01-31 23:02:51   2022-01-31 23:13:54              NaN   \n",
       "2463926         2  2022-01-31 23:36:53   2022-01-31 23:42:51              NaN   \n",
       "2463927         2  2022-01-31 23:44:22   2022-01-31 23:55:01              NaN   \n",
       "2463928         2  2022-01-31 23:39:00   2022-01-31 23:50:00              NaN   \n",
       "2463929         2  2022-01-31 23:36:42   2022-01-31 23:48:45              NaN   \n",
       "2463930         2  2022-01-31 23:46:00   2022-02-01 00:13:00              NaN   \n",
       "\n",
       "         trip_distance  RatecodeID store_and_fwd_flag  PULocationID  \\\n",
       "2463921           3.04         NaN                NaN           158   \n",
       "2463922           2.42         NaN                NaN           233   \n",
       "2463923           7.51         NaN                NaN           246   \n",
       "2463924           8.50         NaN                NaN            18   \n",
       "2463925           1.63         NaN                NaN           224   \n",
       "2463926           1.32         NaN                NaN            90   \n",
       "2463927           4.19         NaN                NaN           107   \n",
       "2463928           2.10         NaN                NaN           113   \n",
       "2463929           2.92         NaN                NaN           148   \n",
       "2463930           8.94         NaN                NaN           186   \n",
       "\n",
       "         DOLocationID  payment_type  fare_amount  extra  mta_tax  tip_amount  \\\n",
       "2463921           261             0    13.090000    0.0      0.5    3.529297   \n",
       "2463922            79             0    10.970000    0.0      0.5    2.289062   \n",
       "2463923           116             0    26.730000    0.0      0.5    6.558594   \n",
       "2463924            75             0    25.410000    0.0      0.5    0.000000   \n",
       "2463925            90             0     9.710000    0.0      0.5    2.769531   \n",
       "2463926           170             0     8.000000    0.0      0.5    2.390625   \n",
       "2463927            75             0    16.799999    0.0      0.5    4.351562   \n",
       "2463928           246             0    11.220000    0.0      0.5    2.000000   \n",
       "2463929           164             0    12.400000    0.0      0.5    0.000000   \n",
       "2463930           181             0    25.480000    0.0      0.5    6.281250   \n",
       "\n",
       "         tolls_amount  improvement_surcharge  total_amount  \\\n",
       "2463921           0.0               0.300049     19.920000   \n",
       "2463922           0.0               0.300049     16.559999   \n",
       "2463923           0.0               0.300049     36.590000   \n",
       "2463924           0.0               0.300049     26.209999   \n",
       "2463925           0.0               0.300049     15.780000   \n",
       "2463926           0.0               0.300049     13.690000   \n",
       "2463927           0.0               0.300049     24.450001   \n",
       "2463928           0.0               0.300049     16.520000   \n",
       "2463929           0.0               0.300049     15.700000   \n",
       "2463930           0.0               0.300049     35.060001   \n",
       "\n",
       "         congestion_surcharge     pickup_datetime    dropoff_datetime  \n",
       "2463921                   NaN 2022-01-31 23:36:07 2022-01-31 23:48:05  \n",
       "2463922                   NaN 2022-01-31 23:09:46 2022-01-31 23:20:50  \n",
       "2463923                   NaN 2022-01-31 23:51:47 2022-02-01 00:10:07  \n",
       "2463924                   NaN 2022-01-31 23:49:00 2022-02-01 00:08:00  \n",
       "2463925                   NaN 2022-01-31 23:02:51 2022-01-31 23:13:54  \n",
       "2463926                   NaN 2022-01-31 23:36:53 2022-01-31 23:42:51  \n",
       "2463927                   NaN 2022-01-31 23:44:22 2022-01-31 23:55:01  \n",
       "2463928                   NaN 2022-01-31 23:39:00 2022-01-31 23:50:00  \n",
       "2463929                   NaN 2022-01-31 23:36:42 2022-01-31 23:48:45  \n",
       "2463930                   NaN 2022-01-31 23:46:00 2022-02-01 00:13:00  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_data_yellow.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a20bd483",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>lpep_pickup_datetime</th>\n",
       "      <th>lpep_dropoff_datetime</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
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       "      <td>2022-01-01 00:14:21</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-01-01 00:20:55</td>\n",
       "      <td>2022-01-01 00:29:38</td>\n",
       "      <td>N</td>\n",
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       "      <td>41</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>2022-01-01 00:57:02</td>\n",
       "      <td>2022-01-01 01:13:14</td>\n",
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       "      <td>140</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.70</td>\n",
       "      <td>14.5</td>\n",
       "      <td>3.25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.601562</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>23.156250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.75</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:07:42</td>\n",
       "      <td>2022-01-01 00:15:57</td>\n",
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       "      <td>181</td>\n",
       "      <td>181</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.69</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:07:50</td>\n",
       "      <td>2022-01-01 00:28:52</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>2022-01-01 00:54:09</td>\n",
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       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>8.296875</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:13:38</td>\n",
       "      <td>2022-01-01 00:33:50</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>66</td>\n",
       "      <td>67</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.47</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>23.796875</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:43:00</td>\n",
       "      <td>2022-01-01 00:49:20</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40</td>\n",
       "      <td>195</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.15</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>7.300781</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:41:04</td>\n",
       "      <td>2022-01-01 00:47:04</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>112</td>\n",
       "      <td>80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.30</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>7.300781</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-01 00:51:07</td>\n",
       "      <td>2022-01-01 01:09:31</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>256</td>\n",
       "      <td>186</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.75</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.210938</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>25.265625</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID lpep_pickup_datetime lpep_dropoff_datetime store_and_fwd_flag  \\\n",
       "0         2  2022-01-01 00:14:21   2022-01-01 00:15:33                  N   \n",
       "1         1  2022-01-01 00:20:55   2022-01-01 00:29:38                  N   \n",
       "2         1  2022-01-01 00:57:02   2022-01-01 01:13:14                  N   \n",
       "3         2  2022-01-01 00:07:42   2022-01-01 00:15:57                  N   \n",
       "4         2  2022-01-01 00:07:50   2022-01-01 00:28:52                  N   \n",
       "5         1  2022-01-01 00:47:57   2022-01-01 00:54:09                  N   \n",
       "6         2  2022-01-01 00:13:38   2022-01-01 00:33:50                  N   \n",
       "7         2  2022-01-01 00:43:00   2022-01-01 00:49:20                  N   \n",
       "8         2  2022-01-01 00:41:04   2022-01-01 00:47:04                  N   \n",
       "9         2  2022-01-01 00:51:07   2022-01-01 01:09:31                  N   \n",
       "\n",
       "   RatecodeID  PULocationID  DOLocationID  passenger_count  trip_distance  \\\n",
       "0         1.0            42            42              1.0           0.44   \n",
       "1         1.0           116            41              1.0           2.10   \n",
       "2         1.0            41           140              1.0           3.70   \n",
       "3         1.0           181           181              1.0           1.69   \n",
       "4         1.0            33           170              1.0           6.26   \n",
       "5         1.0           150           210              1.0           1.30   \n",
       "6         1.0            66            67              1.0           6.47   \n",
       "7         1.0            40           195              1.0           1.15   \n",
       "8         1.0           112            80              1.0           1.30   \n",
       "9         1.0           256           186              1.0           4.75   \n",
       "\n",
       "   fare_amount  extra  mta_tax  tip_amount  tolls_amount  ehail_fee  \\\n",
       "0          3.5   0.50      0.5    0.000000           0.0        NaN   \n",
       "1          9.5   0.50      0.5    0.000000           0.0        NaN   \n",
       "2         14.5   3.25      0.5    4.601562           0.0        NaN   \n",
       "3          8.0   0.50      0.5    0.000000           0.0        NaN   \n",
       "4         22.0   0.50      0.5    5.210938           0.0        NaN   \n",
       "5          7.0   0.50      0.5    0.000000           0.0        NaN   \n",
       "6         22.5   0.50      0.5    0.000000           0.0        NaN   \n",
       "7          6.0   0.50      0.5    0.000000           0.0        NaN   \n",
       "8          6.0   0.50      0.5    0.000000           0.0        NaN   \n",
       "9         17.0   0.50      0.5    4.210938           0.0        NaN   \n",
       "\n",
       "   improvement_surcharge  total_amount  payment_type  trip_type  \\\n",
       "0               0.300049      4.800781           2.0        1.0   \n",
       "1               0.300049     10.796875           2.0        1.0   \n",
       "2               0.300049     23.156250           1.0        1.0   \n",
       "3               0.300049      9.296875           2.0        1.0   \n",
       "4               0.300049     31.265625           1.0        1.0   \n",
       "5               0.300049      8.296875           2.0        1.0   \n",
       "6               0.300049     23.796875           2.0        1.0   \n",
       "7               0.300049      7.300781           2.0        1.0   \n",
       "8               0.300049      7.300781           2.0        1.0   \n",
       "9               0.300049     25.265625           1.0        1.0   \n",
       "\n",
       "   congestion_surcharge  \n",
       "0                  0.00  \n",
       "1                  0.00  \n",
       "2                  2.75  \n",
       "3                  0.00  \n",
       "4                  2.75  \n",
       "5                  0.00  \n",
       "6                  0.00  \n",
       "7                  0.00  \n",
       "8                  0.00  \n",
       "9                  2.75  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_data_green.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "947ac069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>lpep_pickup_datetime</th>\n",
       "      <th>lpep_dropoff_datetime</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>ehail_fee</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>trip_type</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62485</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 22:01:00</td>\n",
       "      <td>2022-01-31 22:13:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>244</td>\n",
       "      <td>151</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.69</td>\n",
       "      <td>19.015625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.121094</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>26.187500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62486</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 22:54:00</td>\n",
       "      <td>2022-01-31 23:10:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>188</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.12</td>\n",
       "      <td>13.898438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.160156</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>17.359375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62487</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:23:00</td>\n",
       "      <td>2022-01-31 23:39:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>179</td>\n",
       "      <td>112</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.80</td>\n",
       "      <td>16.484375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.730469</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>20.515625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62488</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:50:00</td>\n",
       "      <td>2022-02-01 00:11:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "      <td>239</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.04</td>\n",
       "      <td>23.453125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.828125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>32.343750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62489</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:19:00</td>\n",
       "      <td>2022-01-31 23:37:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>152</td>\n",
       "      <td>233</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.71</td>\n",
       "      <td>25.406250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.269531</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>34.718750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62490</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:25:00</td>\n",
       "      <td>2022-01-31 23:33:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40</td>\n",
       "      <td>65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.40</td>\n",
       "      <td>8.382812</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.929688</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>10.609375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62491</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:52:00</td>\n",
       "      <td>2022-02-01 00:10:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.97</td>\n",
       "      <td>14.921875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>15.218750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62492</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:17:00</td>\n",
       "      <td>2022-01-31 23:36:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
       "      <td>167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.70</td>\n",
       "      <td>16.265625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>16.562500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62493</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:45:00</td>\n",
       "      <td>2022-01-31 23:55:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116</td>\n",
       "      <td>166</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.88</td>\n",
       "      <td>9.476562</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.169922</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>11.953125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62494</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-31 23:52:00</td>\n",
       "      <td>2022-02-01 00:26:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>225</td>\n",
       "      <td>179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.60</td>\n",
       "      <td>32.187500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.230469</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>49.718750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       VendorID lpep_pickup_datetime lpep_dropoff_datetime store_and_fwd_flag  \\\n",
       "62485         2  2022-01-31 22:01:00   2022-01-31 22:13:00                NaN   \n",
       "62486         2  2022-01-31 22:54:00   2022-01-31 23:10:00                NaN   \n",
       "62487         2  2022-01-31 23:23:00   2022-01-31 23:39:00                NaN   \n",
       "62488         2  2022-01-31 23:50:00   2022-02-01 00:11:00                NaN   \n",
       "62489         2  2022-01-31 23:19:00   2022-01-31 23:37:00                NaN   \n",
       "62490         2  2022-01-31 23:25:00   2022-01-31 23:33:00                NaN   \n",
       "62491         2  2022-01-31 23:52:00   2022-02-01 00:10:00                NaN   \n",
       "62492         2  2022-01-31 23:17:00   2022-01-31 23:36:00                NaN   \n",
       "62493         2  2022-01-31 23:45:00   2022-01-31 23:55:00                NaN   \n",
       "62494         2  2022-01-31 23:52:00   2022-02-01 00:26:00                NaN   \n",
       "\n",
       "       RatecodeID  PULocationID  DOLocationID  passenger_count  trip_distance  \\\n",
       "62485         NaN           244           151              NaN           4.69   \n",
       "62486         NaN            25           188              NaN           3.12   \n",
       "62487         NaN           179           112              NaN           3.80   \n",
       "62488         NaN           112           239              NaN           6.04   \n",
       "62489         NaN           152           233              NaN           6.71   \n",
       "62490         NaN            40            65              NaN           1.40   \n",
       "62491         NaN            36            61              NaN           2.97   \n",
       "62492         NaN            75           167              NaN           3.70   \n",
       "62493         NaN           116           166              NaN           1.88   \n",
       "62494         NaN           225           179              NaN           9.60   \n",
       "\n",
       "       fare_amount  extra  mta_tax  tip_amount  tolls_amount  ehail_fee  \\\n",
       "62485    19.015625    0.0      0.0    4.121094           0.0        NaN   \n",
       "62486    13.898438    0.0      0.0    3.160156           0.0        NaN   \n",
       "62487    16.484375    0.0      0.0    3.730469           0.0        NaN   \n",
       "62488    23.453125    0.0      0.0    5.828125           0.0        NaN   \n",
       "62489    25.406250    0.0      0.0    6.269531           0.0        NaN   \n",
       "62490     8.382812    0.0      0.0    1.929688           0.0        NaN   \n",
       "62491    14.921875    0.0      0.0    0.000000           0.0        NaN   \n",
       "62492    16.265625    0.0      0.0    0.000000           0.0        NaN   \n",
       "62493     9.476562    0.0      0.0    2.169922           0.0        NaN   \n",
       "62494    32.187500    0.0      0.0    7.230469          10.0        NaN   \n",
       "\n",
       "       improvement_surcharge  total_amount  payment_type  trip_type  \\\n",
       "62485               0.300049     26.187500           NaN        NaN   \n",
       "62486               0.300049     17.359375           NaN        NaN   \n",
       "62487               0.300049     20.515625           NaN        NaN   \n",
       "62488               0.300049     32.343750           NaN        NaN   \n",
       "62489               0.300049     34.718750           NaN        NaN   \n",
       "62490               0.300049     10.609375           NaN        NaN   \n",
       "62491               0.300049     15.218750           NaN        NaN   \n",
       "62492               0.300049     16.562500           NaN        NaN   \n",
       "62493               0.300049     11.953125           NaN        NaN   \n",
       "62494               0.300049     49.718750           NaN        NaN   \n",
       "\n",
       "       congestion_surcharge  \n",
       "62485                   NaN  \n",
       "62486                   NaN  \n",
       "62487                   NaN  \n",
       "62488                   NaN  \n",
       "62489                   NaN  \n",
       "62490                   NaN  \n",
       "62491                   NaN  \n",
       "62492                   NaN  \n",
       "62493                   NaN  \n",
       "62494                   NaN  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_data_green.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e13fbab",
   "metadata": {},
   "source": [
    "## visualize NAN values ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "71fb04e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 15 s, sys: 1.33 s, total: 16.3 s\n",
      "Wall time: 16 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "sns.heatmap(nyc_data_yellow.isnull(), cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9e67c4e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(nyc_data_green.isnull(), cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23c20138",
   "metadata": {},
   "source": [
    "## Verify for missing values ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "80e7309e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def chk_missing_values(df):\n",
    "    mis_val = df.isnull().sum()\n",
    "    mis_val_percent = 100 * df.isnull().sum() / len(df)\n",
    "    df_mz = pd.concat([mis_val, mis_val_percent], axis=1)\n",
    "    df_mz = df_mz.rename(\n",
    "        columns = {0 : 'Missing Values', 1 : '% of Total Values'})\n",
    "    df_mz['Total Missing Values'] = df_mz['Missing Values']\n",
    "    df_mz['% Total Missing Values'] = 100 * df_mz['Total Missing Values'] / len(df)\n",
    "    df_mz['Data Type'] = df.dtypes\n",
    "    df_mz = df_mz[df_mz.iloc[:,1] != 0].sort_values('% of Total Values', ascending=False).round(1)\n",
    "    print (\"\\nThe dataframe has \" + str(df.shape[1]) + \" columns and \" + str(df.shape[0]) + \" Rows.\\n\"\n",
    "           \"\\nThere are \" + str(df_mz.shape[0]) +\" columns that have missing values.\")\n",
    "    return df_mz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ab226baf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The dataframe has 20 columns and 2463931 Rows.\n",
      "\n",
      "There are 4 columns that have missing values.\n",
      "CPU times: user 250 ms, sys: 19.7 ms, total: 269 ms\n",
      "Wall time: 272 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "      <th>Total Missing Values</th>\n",
       "      <th>% Total Missing Values</th>\n",
       "      <th>Data Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <td>2407731</td>\n",
       "      <td>97.7</td>\n",
       "      <td>2407731</td>\n",
       "      <td>97.7</td>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>passenger_count</th>\n",
       "      <td>71503</td>\n",
       "      <td>2.9</td>\n",
       "      <td>71503</td>\n",
       "      <td>2.9</td>\n",
       "      <td>float16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RatecodeID</th>\n",
       "      <td>71503</td>\n",
       "      <td>2.9</td>\n",
       "      <td>71503</td>\n",
       "      <td>2.9</td>\n",
       "      <td>float16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <td>71503</td>\n",
       "      <td>2.9</td>\n",
       "      <td>71503</td>\n",
       "      <td>2.9</td>\n",
       "      <td>float16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Missing Values  % of Total Values  Total Missing Values  \\\n",
       "store_and_fwd_flag           2407731               97.7               2407731   \n",
       "passenger_count                71503                2.9                 71503   \n",
       "RatecodeID                     71503                2.9                 71503   \n",
       "congestion_surcharge           71503                2.9                 71503   \n",
       "\n",
       "                      % Total Missing Values Data Type  \n",
       "store_and_fwd_flag                      97.7  category  \n",
       "passenger_count                          2.9   float16  \n",
       "RatecodeID                               2.9   float16  \n",
       "congestion_surcharge                     2.9   float16  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "chk_missing_values(nyc_data_yellow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e9b99057",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The dataframe has 20 columns and 62495 Rows.\n",
      "\n",
      "There are 7 columns that have missing values.\n",
      "CPU times: user 19.3 ms, sys: 339 µs, total: 19.6 ms\n",
      "Wall time: 18.1 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        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>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "      <th>Total Missing Values</th>\n",
       "      <th>% Total Missing Values</th>\n",
       "      <th>Data Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ehail_fee</th>\n",
       "      <td>62495</td>\n",
       "      <td>100.0</td>\n",
       "      <td>62495</td>\n",
       "      <td>100.0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <td>6295</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6295</td>\n",
       "      <td>10.1</td>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RatecodeID</th>\n",
       "      <td>6295</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6295</td>\n",
       "      <td>10.1</td>\n",
       "      <td>float16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>passenger_count</th>\n",
       "      <td>6295</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6295</td>\n",
       "      <td>10.1</td>\n",
       "      <td>float16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_type</th>\n",
       "      <td>6295</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6295</td>\n",
       "      <td>10.1</td>\n",
       "      <td>float16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trip_type</th>\n",
       "      <td>6295</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6295</td>\n",
       "      <td>10.1</td>\n",
       "      <td>float16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <td>6295</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6295</td>\n",
       "      <td>10.1</td>\n",
       "      <td>float16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Missing Values  % of Total Values  Total Missing Values  \\\n",
       "ehail_fee                      62495              100.0                 62495   \n",
       "store_and_fwd_flag              6295               10.1                  6295   \n",
       "RatecodeID                      6295               10.1                  6295   \n",
       "passenger_count                 6295               10.1                  6295   \n",
       "payment_type                    6295               10.1                  6295   \n",
       "trip_type                       6295               10.1                  6295   \n",
       "congestion_surcharge            6295               10.1                  6295   \n",
       "\n",
       "                      % Total Missing Values Data Type  \n",
       "ehail_fee                              100.0   float64  \n",
       "store_and_fwd_flag                      10.1  category  \n",
       "RatecodeID                              10.1   float16  \n",
       "passenger_count                         10.1   float16  \n",
       "payment_type                            10.1   float16  \n",
       "trip_type                               10.1   float16  \n",
       "congestion_surcharge                    10.1   float16  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "chk_missing_values(nyc_data_green)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "47d29d80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop column with 100% null values\n",
    "df_green = nyc_data_green.drop(['ehail_fee'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "906a8b09",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "nyc_data_yellow = nyc_data_yellow.drop(['store_and_fwd_flag'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8189332",
   "metadata": {},
   "source": [
    "## Check and remove duplicates ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "93a38d73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 37.9 ms, sys: 8.21 ms, total: 46.1 ms\n",
      "Wall time: 48 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df_green.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "545eeddb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.65 s, sys: 268 ms, total: 2.92 s\n",
      "Wall time: 2.92 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "nyc_data_yellow.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "300409c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 71.6 ms, sys: 4.23 ms, total: 75.8 ms\n",
      "Wall time: 74.6 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_green= df_green[~df_green.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "72a1fbf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.98 s, sys: 289 ms, total: 3.27 s\n",
      "Wall time: 3.27 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_yellow= nyc_data_yellow[~nyc_data_yellow.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "598dd7d1",
   "metadata": {},
   "source": [
    "## Perform EDA ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7dc440ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>VendorID</th>\n",
       "      <td>2463931.0</td>\n",
       "      <td>1.707819</td>\n",
       "      <td>0.502137</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>passenger_count</th>\n",
       "      <td>2392428.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trip_distance</th>\n",
       "      <td>2463931.0</td>\n",
       "      <td>5.372752</td>\n",
       "      <td>547.871399</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>1.740000</td>\n",
       "      <td>3.130000</td>\n",
       "      <td>306159.281250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RatecodeID</th>\n",
       "      <td>2392428.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PULocationID</th>\n",
       "      <td>2463931.0</td>\n",
       "      <td>166.076809</td>\n",
       "      <td>65.468057</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>265.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOLocationID</th>\n",
       "      <td>2463931.0</td>\n",
       "      <td>163.580716</td>\n",
       "      <td>70.790159</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>265.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_type</th>\n",
       "      <td>2463931.0</td>\n",
       "      <td>1.194449</td>\n",
       "      <td>0.500178</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fare_amount</th>\n",
       "      <td>2463931.0</td>\n",
       "      <td>12.946486</td>\n",
       "      <td>255.814896</td>\n",
       "      <td>-480.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>401092.312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extra</th>\n",
       "      <td>2463931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>33.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mta_tax</th>\n",
       "      <td>2463931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>16.593750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tip_amount</th>\n",
       "      <td>2463931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-125.250000</td>\n",
       "      <td>0.720215</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>889.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tolls_amount</th>\n",
       "      <td>2463931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-31.406250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>193.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <td>2463931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.300049</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>0.300049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_amount</th>\n",
       "      <td>2463931.0</td>\n",
       "      <td>19.169374</td>\n",
       "      <td>255.964096</td>\n",
       "      <td>-480.299988</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>14.690000</td>\n",
       "      <td>20.020000</td>\n",
       "      <td>401095.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <td>2392428.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count        mean         std         min  \\\n",
       "VendorID               2463931.0    1.707819    0.502137    1.000000   \n",
       "passenger_count        2392428.0         NaN    0.000000    0.000000   \n",
       "trip_distance          2463931.0    5.372752  547.871399    0.000000   \n",
       "RatecodeID             2392428.0         NaN    0.000000    1.000000   \n",
       "PULocationID           2463931.0  166.076809   65.468057    1.000000   \n",
       "DOLocationID           2463931.0  163.580716   70.790159    1.000000   \n",
       "payment_type           2463931.0    1.194449    0.500178    0.000000   \n",
       "fare_amount            2463931.0   12.946486  255.814896 -480.000000   \n",
       "extra                  2463931.0         NaN    0.000000   -4.500000   \n",
       "mta_tax                2463931.0         NaN    0.000000   -0.500000   \n",
       "tip_amount             2463931.0         NaN         NaN -125.250000   \n",
       "tolls_amount           2463931.0         NaN    0.000000  -31.406250   \n",
       "improvement_surcharge  2463931.0         NaN    0.000000   -0.300049   \n",
       "total_amount           2463931.0   19.169374  255.964096 -480.299988   \n",
       "congestion_surcharge   2392428.0         NaN    0.000000   -2.500000   \n",
       "\n",
       "                              25%         50%         75%            max  \n",
       "VendorID                 1.000000    2.000000    2.000000       6.000000  \n",
       "passenger_count          1.000000    1.000000    1.000000       9.000000  \n",
       "trip_distance            1.040000    1.740000    3.130000  306159.281250  \n",
       "RatecodeID               1.000000    1.000000    1.000000      99.000000  \n",
       "PULocationID           132.000000  162.000000  234.000000     265.000000  \n",
       "DOLocationID           113.000000  162.000000  236.000000     265.000000  \n",
       "payment_type             1.000000    1.000000    1.000000       5.000000  \n",
       "fare_amount              6.500000    9.000000   14.000000  401092.312500  \n",
       "extra                    0.000000    0.500000    2.500000      33.500000  \n",
       "mta_tax                  0.500000    0.500000    0.500000      16.593750  \n",
       "tip_amount               0.720215    2.000000    3.000000     889.000000  \n",
       "tolls_amount             0.000000    0.000000    0.000000     193.250000  \n",
       "improvement_surcharge    0.300049    0.300049    0.300049       0.300049  \n",
       "total_amount            11.300000   14.690000   20.020000  401095.625000  \n",
       "congestion_surcharge     2.500000    2.500000    2.500000       2.500000  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_yellow.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "36d8cb31",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>VendorID</th>\n",
       "      <td>62495.0</td>\n",
       "      <td>1.849508</td>\n",
       "      <td>0.357556</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RatecodeID</th>\n",
       "      <td>56200.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.862305</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PULocationID</th>\n",
       "      <td>62495.0</td>\n",
       "      <td>97.615041</td>\n",
       "      <td>62.987311</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>265.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOLocationID</th>\n",
       "      <td>62495.0</td>\n",
       "      <td>135.969902</td>\n",
       "      <td>77.590956</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>265.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>passenger_count</th>\n",
       "      <td>56200.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.877930</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trip_distance</th>\n",
       "      <td>62495.0</td>\n",
       "      <td>77.758636</td>\n",
       "      <td>2909.354004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>224481.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fare_amount</th>\n",
       "      <td>62495.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>-65.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>604.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extra</th>\n",
       "      <td>62495.0</td>\n",
       "      <td>0.347412</td>\n",
       "      <td>0.657715</td>\n",
       "      <td>-4.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mta_tax</th>\n",
       "      <td>62495.0</td>\n",
       "      <td>0.426270</td>\n",
       "      <td>0.181396</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tip_amount</th>\n",
       "      <td>62495.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>2.484375</td>\n",
       "      <td>-0.859863</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.609375</td>\n",
       "      <td>76.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tolls_amount</th>\n",
       "      <td>62495.0</td>\n",
       "      <td>0.208252</td>\n",
       "      <td>1.257812</td>\n",
       "      <td>-6.550781</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <td>62495.0</td>\n",
       "      <td>0.297607</td>\n",
       "      <td>0.037231</td>\n",
       "      <td>-0.300049</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>0.300049</td>\n",
       "      <td>0.300049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_amount</th>\n",
       "      <td>62495.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>-71.875000</td>\n",
       "      <td>9.359375</td>\n",
       "      <td>13.859375</td>\n",
       "      <td>21.046875</td>\n",
       "      <td>605.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_type</th>\n",
       "      <td>56200.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.516113</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trip_type</th>\n",
       "      <td>56200.0</td>\n",
       "      <td>1.041016</td>\n",
       "      <td>0.198120</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <td>56200.0</td>\n",
       "      <td>0.706543</td>\n",
       "      <td>1.201172</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count        mean          std        min        25%  \\\n",
       "VendorID               62495.0    1.849508     0.357556   1.000000   2.000000   \n",
       "RatecodeID             56200.0         inf     0.862305   1.000000   1.000000   \n",
       "PULocationID           62495.0   97.615041    62.987311   1.000000  55.000000   \n",
       "DOLocationID           62495.0  135.969902    77.590956   1.000000  74.000000   \n",
       "passenger_count        56200.0         inf     0.877930   0.000000   1.000000   \n",
       "trip_distance          62495.0   77.758636  2909.354004   0.000000   1.080000   \n",
       "fare_amount            62495.0         inf          inf -65.000000   7.000000   \n",
       "extra                  62495.0    0.347412     0.657715  -4.500000   0.000000   \n",
       "mta_tax                62495.0    0.426270     0.181396  -0.500000   0.500000   \n",
       "tip_amount             62495.0         inf     2.484375  -0.859863   0.000000   \n",
       "tolls_amount           62495.0    0.208252     1.257812  -6.550781   0.000000   \n",
       "improvement_surcharge  62495.0    0.297607     0.037231  -0.300049   0.300049   \n",
       "total_amount           62495.0         inf          inf -71.875000   9.359375   \n",
       "payment_type           56200.0         inf     0.516113   1.000000   1.000000   \n",
       "trip_type              56200.0    1.041016     0.198120   1.000000   1.000000   \n",
       "congestion_surcharge   56200.0    0.706543     1.201172   0.000000   0.000000   \n",
       "\n",
       "                              50%         75%            max  \n",
       "VendorID                 2.000000    2.000000       2.000000  \n",
       "RatecodeID               1.000000    1.000000       5.000000  \n",
       "PULocationID            75.000000  130.000000     265.000000  \n",
       "DOLocationID           137.000000  215.000000     265.000000  \n",
       "passenger_count          1.000000    1.000000       8.000000  \n",
       "trip_distance            1.900000    3.500000  224481.375000  \n",
       "fare_amount             10.500000   17.000000     604.500000  \n",
       "extra                    0.000000    0.500000       4.500000  \n",
       "mta_tax                  0.500000    0.500000       0.500000  \n",
       "tip_amount               1.000000    2.609375      76.750000  \n",
       "tolls_amount             0.000000    0.000000      44.750000  \n",
       "improvement_surcharge    0.300049    0.300049       0.300049  \n",
       "total_amount            13.859375   21.046875     605.500000  \n",
       "payment_type             1.000000    2.000000       5.000000  \n",
       "trip_type                1.000000    1.000000       2.000000  \n",
       "congestion_surcharge     0.000000    2.750000       2.750000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_green.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0f128781",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f604a71cdf0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 455.425x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data distribution of the dataset.\n",
    "sns.displot(data=df_yellow, x=\"passenger_count\", kde=True, hue=\"payment_type\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "99fb988a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f604a6d0820>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 455.425x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data diatribution of the dataset.\n",
    "sns.displot(data=df_green, x=\"total_amount\", kde=True, hue=\"payment_type\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8fd7b94f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2009-01-01 00:34:01'), Timestamp('2022-01-31 23:57:37'))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the duration of time the dataset was collected\n",
    "df_green['lpep_pickup_datetime'].min(), df_green['lpep_pickup_datetime'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7f995237",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2009-01-01 17:05:20'), Timestamp('2022-02-01 21:01:54'))"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_green['lpep_dropoff_datetime'].min(), df_green['lpep_dropoff_datetime'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c975b5e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2008-12-31 22:23:09'), Timestamp('2022-05-18 20:41:57'))"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_yellow['pickup_datetime'].min(), df_yellow['pickup_datetime'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9bd187c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2008-12-31 23:06:56'), Timestamp('2022-05-18 20:47:45'))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_yellow['dropoff_datetime'].min(), df_yellow['dropoff_datetime'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b8c29885",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a hour column to determine which hour the taxi business is busy\n",
    "df_green['pickup_hour'] = df_green['lpep_pickup_datetime'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a84cc30c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_yellow['pickup_hour'] = df_yellow['pickup_datetime'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "dcb3532f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_green['pickup_time_of_the_day'] = df_green.pickup_hour.apply(lambda y: (y % 24 + 4)//4).replace({1: 'Late Night',\n",
    "                      2: 'Early Morning',\n",
    "                      3: 'Morning',\n",
    "                      4: 'Afternoon',\n",
    "                      5: 'Evening',\n",
    "                      6: 'Night'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0f0f4f8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_yellow['pickup_time_of_the_day'] = df_yellow.pickup_hour.apply(lambda y: (y % 24 + 4)//4).replace({1: 'Late Night',\n",
    "                      2: 'Early Morning',\n",
    "                      3: 'Morning',\n",
    "                      4: 'Afternoon',\n",
    "                      5: 'Evening',\n",
    "                      6: 'Night'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07a00d1e",
   "metadata": {},
   "source": [
    "### Check what times of the day are busy ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5cf2f1ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'pickup_time_of_the_day', order=['Early Morning', 'Morning', 'Afternoon', 'Evening', 'Night', 'Late Night'],\n",
    "              data=df_green);\n",
    "plt.title(\"Trip frequency per the time of the day\");\n",
    "plt.xlabel(\"Time of the Day\");\n",
    "plt.ylabel(\"Frequency\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "21af4ddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'pickup_time_of_the_day', order=['Early Morning', 'Morning', 'Afternoon', 'Evening', 'Night', 'Late Night'],\n",
    "              data=df_yellow);\n",
    "plt.title(\"Trip frequency per the time of the day\");\n",
    "plt.xlabel(\"Time of the Day\");\n",
    "plt.ylabel(\"Frequency\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c40cadf1",
   "metadata": {},
   "source": [
    "## Check which days of the week are busy ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2f25e408",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_yellow['pickup_day_of_the_week'] = df_yellow.pickup_datetime.apply(lambda x: x.day_name())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "806b0c09",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_green['pickup_day_of_the_week'] = df_green.lpep_pickup_datetime.apply(lambda x: x.day_name())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "821d4417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'pickup_day_of_the_week', order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'],\n",
    "              data=df_yellow);\n",
    "plt.title(\"Trip frequency per the day of the week\");\n",
    "plt.xlabel(\"Day of the Week\");\n",
    "plt.ylabel(\"Frequency\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "52006de3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'pickup_day_of_the_week', order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'],\n",
    "              data=df_green);\n",
    "plt.title(\"Trip frequency per the day of the week\");\n",
    "plt.xlabel(\"Day of the Week\");\n",
    "plt.ylabel(\"Frequency\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e21b8fd",
   "metadata": {},
   "source": [
    "## Checking which vendor is more popular ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9e414597",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SxUmSJr+J3OvsHmC3iJjexnokST1mzEETEUcCPwX+G/Df6+YTgK8WqEuS1CNaGdFcBVycmW8CRk+X/RNwXNurkiT1jFaC5gjg+vr1CEBmPg/s2e6iJEm9o5WgWQHMa2yIiLdTXfYsSdJrauXy5k8D346IJVQXAVwELAQ+VKQySVJPGPOIJjO/BfwhMEg1N3MIcHpm3lmoNklSDxjTiCYiplFdcfaWzPxw2ZIkSb1kTCOazNwMbAb2KFuOJKnXtDJH89fATRHxeeAJGh4JkJmPt7kuSVKP2GHQRMQBmflL4Iq66Z1UjwwYNQJMK1CbJKkHjGVE81Pg9ZnZDxARt2bmaa3sJCIGgOuAw4BNwGPAn2Tm2og4Fria6vs4K4D5mflUvV7b+yRJnTWWOZq+puUTxrGfEeCyzIzMPJLq0QKXRkQ/1ZdAL8jMucA9wKUAJfokSZ03lqBpfjxzc/DsUGauy8y7G5ruo7o8eh6wMTOX1e1LgLPq1yX6JEkdNpZTZ7tExL/h1YCZ1rRMZt411h3WI47zqZ7OORtY2bCdpyOiPyL2KdGXmevGWufAwIyxvlWSesLg4Mwi2x1L0DzFlndoHmpaHgF+o4V9/g2wgerigpbmejppaGgDw8PNgzlp8iv1YaLJb+3a9eNet7+/b5t/oO8waDJzzrj33CQivggcDpycmcMR8QuqU2ij/fsCw5m5rkRfu45DkjR2437wWavq79/MA07NzJfq5geAPSNi9FEDC4GbC/ZJkjqslS9sjltEHAFcRHWp9A8iAmB5Zp4WEecCV0fEHtSXIgPUI5629kmSOq9vZMR5iCZzgOXO0ahXDQ7O5IHLzut2GdrJzFt0TbvmaA6l+gP/1b4JVSZJ0g4YNJKkogwaSVJRBo0kqSiDRpJUlEEjSSrKoJEkFWXQSJKKMmgkSUUZNJKkogwaSVJRBo0kqSiDRpJUlEEjSSrKoJEkFWXQSJKKMmgkSUUZNJKkogwaSVJRBo0kqSiDRpJUlEEjSSrKoJEkFWXQSJKKMmgkSUUZNJKkogwaSVJRBo0kqSiDRpJUlEEjSSpql07sJCK+CJwBzAGOzMxH6va5wFJgABgCFmTmY6X6JEmd16kRzW3A7wErm9qXAFdm5lzgSuDqwn2SpA7ryIgmM5cBRMSv2yJiP+Bo4PfrphuBKyJiEOhrd19mri1zdJKk7elI0GzDwcCTmbkZIDM3R8Tqur2vQF9LQTMwMKMdxyhJk8bg4Mwi2+1m0OzUhoY2MDw80u0ypLYr9WGiyW/t2vXjXre/v2+bf6B386qzVcCBETENoP53Vt1eok+S1AVdG9Fk5lMR8RBwNnB9/e+Do3MpJfo6Zebr92CP3Xft5C41CWx86WXW/7+N3S5D6rhOXd58OXA6cADwvYgYyswjgIXA0oi4GHgGWNCwWom+jthj9105Z9ENnd6tdnJfv+x9rMeg0dTTqavOLgQufI32R4FjtrFO2/skSZ3nnQEkSUUZNJKkogwaSVJRBo0kqSiDRpJUlEEjSSrKoJEkFWXQSJKKMmgkSUUZNJKkogwaSVJRBo0kqSiDRpJUlEEjSSrKoJEkFWXQSJKKMmgkSUUZNJKkogwaSVJRBo0kqSiDRpJUlEEjSSrKoJEkFWXQSJKKMmgkSUUZNJKkogwaSVJRBo0kqSiDRpJUlEEjSSpql24XUEpEzAWWAgPAELAgMx/rblWSNPX08ohmCXBlZs4FrgSu7nI9kjQl9eSIJiL2A44Gfr9uuhG4IiIGM3PtDlafBtDf3zehGvbde/qE1ldvmuj/V+2y2+sHul2CdkIT+f+zYd1pzX19IyMj497wzioi5gHXZuYRDW3/AszPzB/vYPXjgHtL1idJPex4YFljQ0+OaCbofqpf1Bpgc5drkaTJYhrwRqrP0C30atCsAg6MiGmZuTkipgGz6vYdeYmmNJYkjcnPX6uxJy8GyMyngIeAs+ums4EHxzA/I0lqs56cowGIiDdRXd68N/AM1eXN2d2qJGnq6dmgkSTtHHry1Jkkaedh0EiSijJoJElFGTSSpKJ69Xs06rKI+CJwBjAHODIzH+luRVIlIgaA64DDgE3AY8Cf+PWHchzRqJTbgN8DVna5DqnZCHBZZkZmHkn1JcNLu1xTT3NEoyIycxlARHS7FGkLmbkOuLuh6T7g/O5UMzU4opE0ZUVEP1XI3N7tWnqZQSNpKvsbYANwRbcL6WWeOpM0JdUXrBwOnJyZw92up5cZNJKmnIj4PDAPeHdmvtTtenqd9zpTERFxOXA6cADwNDDU+CA6qVsi4gjgEeCnwIt18/LMPK17VfU2g0aSVJQXA0iSijJoJElFGTSSpKIMGklSUQaNJKkog0aaBCLixIh4ott1SOPhFzalcYqI7wI/ysyLm9pPAa4GDsrMV7pS3A5ExN3A9Zl5TUScCNwFvFB3Pwv8APhCZt7flQLVUxzRSOO3FJgfEX1N7ecCN+wsIRMR08bwttWZOQOYCRwLPArcGxHvKFqcpgRHNNL43QYsAY4H7gGIiL2B9wDH1HcGXgR8CHgD8I/AwsxcFxFzgOXAB4A/A14HfCkzF9fb2RO4CjgFWAP8beOOI+LNdf9bgSeBizLz9rrva1TfeD8EOKHexvfGckCZOQI8AVwcEfsAfwH89th/JdLWHNFI45SZLwI3AQsams8CHs3Mh4GPAqdSfdjPAp4BrmzazHFAAO+g+nB/c93+GaonQB4GvAt4/+gKEbEr8A/AncB+9X5uiC0f/nMOsJhqhLJsnIf498DRETF9nOtLgEEjTdRS4MyI2KNeXlC3ASwEPpmZT9Q3bvxs/d7GMwmXZOaLdTA9DBxVt58FLM7MdZm5Cri8YZ1jgRnApZm5KTPvAr4FnN3wnm9m5vczczgzN47z2FYDfVSjMWncPHUmTUBmLouIp4FTI+J+4O1UNxOF6tTVrRHReAv6zcD+Dcu/bHj9AlWAQDUCWtXQ1/hI7FnAqqZb268EDmxYblx3vA6keuzxs23YlqYwg0aauGupRjIB3JGZv6rbVwEfzMzvN69Qz9FszxrgYOAn9fLshr7VwMER0d8QNrOp7kY8qh13yz0N+HFmPt+GbWkKM2ikibsW+BTwW8DHGtqXAIsj4v2ZuTIiBoHfycxvjmGbNwEXRcQPgelU8zCjfkg1+lkUEX8J/C5wMvC2iR5IfQXdLOC8+uePJrpNyTkaaYIycwXV906ms+Wz579cL98ZEeuB+4BjxrjZS6hOhy2nmvS/rmF/m6iC5SSqZ/18BViQmY9O4DBmRcQGqsca3w8cCZyYmXdOYJsS4PNoJEmFOaKRJBVl0EiSijJoJElFGTSSpKIMGklSUQaNJKkog0aSVJRBI0kqyqCRJBX1/wE3t7vNSktY8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'VendorID', data=df_green)\n",
    "plt.title(\"Vendor Popularity\")\n",
    "plt.xlabel(\"Vendor ID\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8f28b181",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'VendorID', data=df_yellow)\n",
    "plt.title(\"Vendor Popularity\")\n",
    "plt.xlabel(\"Vendor ID\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f22ef93",
   "metadata": {},
   "source": [
    "## Why is vendor 2 more popular? ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7cdd8127",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'VendorID', hue=\"passenger_count\", data=df_yellow)\n",
    "plt.title(\"Passenger count per vendor id\")\n",
    "plt.xlabel(\"Vendor ID\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "36c41a53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'VendorID', hue=\"passenger_count\", data=df_green)\n",
    "plt.title(\"Passenger count per vendor id\")\n",
    "plt.xlabel(\"Vendor ID\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57ef7438",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}