data_cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "55a434c8-0480-41f1-be9b-6f84ee8adde5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import fsspec\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dab1b4ff-8c18-41df-80d9-76280e430a4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the datasets\n",
    "yellow_taxi_data = pd.read_csv(\"D://Users//pshab//Downloads//Yellow_Taxi.csv\")\n",
    "green_taxi_data = pd.read_csv(\"D://Users//pshab//Downloads//Green_Taxi.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "232adc58-fdf9-46e1-afc3-abd8eb8b99b4",
   "metadata": {},
   "outputs": [
    {
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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>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>0</th>\n",
       "      <td>2</td>\n",
       "      <td>4/30/2023, 8:52:10 PM</td>\n",
       "      <td>4/30/2023, 9:05:26 PM</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>244</td>\n",
       "      <td>213</td>\n",
       "      <td>1</td>\n",
       "      <td>6.99</td>\n",
       "      <td>28.90</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4/30/2023, 8:29:49 PM</td>\n",
       "      <td>4/30/2023, 8:50:11 PM</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>6.60</td>\n",
       "      <td>30.30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.55</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4/30/2023, 8:25:19 PM</td>\n",
       "      <td>4/30/2023, 8:32:12 PM</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>244</td>\n",
       "      <td>244</td>\n",
       "      <td>1</td>\n",
       "      <td>1.34</td>\n",
       "      <td>9.30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>4/30/2023, 8:07:06 PM</td>\n",
       "      <td>4/30/2023, 8:27:33 PM</td>\n",
       "      <td>N</td>\n",
       "      <td>5</td>\n",
       "      <td>82</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>7.79</td>\n",
       "      <td>22.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.29</td>\n",
       "      <td>6.55</td>\n",
       "      <td>-</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.57</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>4/30/2023, 8:43:31 PM</td>\n",
       "      <td>4/30/2023, 8:46:59 PM</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>169</td>\n",
       "      <td>1</td>\n",
       "      <td>0.70</td>\n",
       "      <td>6.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</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  4/30/2023, 8:52:10 PM  4/30/2023, 9:05:26 PM                  N   \n",
       "1         2  4/30/2023, 8:29:49 PM  4/30/2023, 8:50:11 PM                  N   \n",
       "2         2  4/30/2023, 8:25:19 PM  4/30/2023, 8:32:12 PM                  N   \n",
       "3         2  4/30/2023, 8:07:06 PM  4/30/2023, 8:27:33 PM                  N   \n",
       "4         2  4/30/2023, 8:43:31 PM  4/30/2023, 8:46:59 PM                  N   \n",
       "\n",
       "   RatecodeID  PULocationID  DOLocationID  passenger_count  trip_distance  \\\n",
       "0           1           244           213                1           6.99   \n",
       "1           1            33           100                1           6.60   \n",
       "2           1           244           244                1           1.34   \n",
       "3           5            82            75                1           7.79   \n",
       "4           1            69           169                1           0.70   \n",
       "\n",
       "   fare_amount  extra  mta_tax  tip_amount  tolls_amount ehail_fee  \\\n",
       "0        28.90    1.0      0.5        0.00          0.00         -   \n",
       "1        30.30    1.0      0.5        5.00          0.00         -   \n",
       "2         9.30    1.0      0.5        2.36          0.00         -   \n",
       "3        22.73    0.0      0.0        2.29          6.55         -   \n",
       "4         6.50    1.0      0.5        0.00          0.00         -   \n",
       "\n",
       "   improvement_surcharge  total_amount  payment_type  trip_type  \\\n",
       "0                    1.0         31.40             1          1   \n",
       "1                    1.0         40.55             1          1   \n",
       "2                    1.0         14.16             1          1   \n",
       "3                    1.0         32.57             1          1   \n",
       "4                    1.0          9.00             2          1   \n",
       "\n",
       "   congestion_surcharge  \n",
       "0                  0.00  \n",
       "1                  2.75  \n",
       "2                  0.00  \n",
       "3                  0.00  \n",
       "4                  0.00  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yellow_taxi_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae372e24-21c7-44e3-ae67-23c1bacdc6b9",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <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",
       "      <th>Airport_fee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4/30/2023, 8:33:13 PM</td>\n",
       "      <td>4/30/2023, 8:53:01 PM</td>\n",
       "      <td>0</td>\n",
       "      <td>7.80</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>138</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>33.8</td>\n",
       "      <td>7.75</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>51.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.75</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4/30/2023, 8:42:49 PM</td>\n",
       "      <td>4/30/2023, 9:11:18 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>8.10</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>138</td>\n",
       "      <td>262</td>\n",
       "      <td>1</td>\n",
       "      <td>35.9</td>\n",
       "      <td>10.25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>9.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>57.15</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4/30/2023, 8:56:34 PM</td>\n",
       "      <td>4/30/2023, 9:13:39 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>9.10</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>138</td>\n",
       "      <td>141</td>\n",
       "      <td>1</td>\n",
       "      <td>35.2</td>\n",
       "      <td>10.25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10.70</td>\n",
       "      <td>6.55</td>\n",
       "      <td>1</td>\n",
       "      <td>64.20</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>4/30/2023, 8:00:52 PM</td>\n",
       "      <td>4/30/2023, 8:20:12 PM</td>\n",
       "      <td>1</td>\n",
       "      <td>8.21</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>138</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>33.1</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>47.09</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4/30/2023, 8:05:50 PM</td>\n",
       "      <td>4/30/2023, 8:19:41 PM</td>\n",
       "      <td>0</td>\n",
       "      <td>7.90</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>138</td>\n",
       "      <td>263</td>\n",
       "      <td>1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>10.25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6.55</td>\n",
       "      <td>1</td>\n",
       "      <td>59.15</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID   tpep_pickup_datetime  tpep_dropoff_datetime  passenger_count  \\\n",
       "0         1  4/30/2023, 8:33:13 PM  4/30/2023, 8:53:01 PM                0   \n",
       "1         1  4/30/2023, 8:42:49 PM  4/30/2023, 9:11:18 PM                2   \n",
       "2         1  4/30/2023, 8:56:34 PM  4/30/2023, 9:13:39 PM                2   \n",
       "3         2  4/30/2023, 8:00:52 PM  4/30/2023, 8:20:12 PM                1   \n",
       "4         1  4/30/2023, 8:05:50 PM  4/30/2023, 8:19:41 PM                0   \n",
       "\n",
       "   trip_distance  RatecodeID store_and_fwd_flag  PULocationID  DOLocationID  \\\n",
       "0           7.80           1                  N           138            43   \n",
       "1           8.10           1                  N           138           262   \n",
       "2           9.10           1                  N           138           141   \n",
       "3           8.21           1                  N           138           140   \n",
       "4           7.90           1                  N           138           263   \n",
       "\n",
       "   payment_type  fare_amount  extra  mta_tax  tip_amount  tolls_amount  \\\n",
       "0             1         33.8   7.75      0.5        8.60          0.00   \n",
       "1             1         35.9  10.25      0.5        9.50          0.00   \n",
       "2             1         35.2  10.25      0.5       10.70          6.55   \n",
       "3             1         33.1   6.00      0.5        2.24          0.00   \n",
       "4             1         31.0  10.25      0.5        9.85          6.55   \n",
       "\n",
       "   improvement_surcharge  total_amount  congestion_surcharge  Airport_fee  \n",
       "0                      1         51.65                   0.0         1.75  \n",
       "1                      1         57.15                   2.5         1.75  \n",
       "2                      1         64.20                   2.5         1.75  \n",
       "3                      1         47.09                   2.5         1.75  \n",
       "4                      1         59.15                   2.5         1.75  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "green_taxi_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "92eadb2c-93c2-42c8-b367-9cb677a5be17",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pshab\\AppData\\Local\\Temp\\ipykernel_10148\\4104758355.py:2: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  yellow_taxi_data['lpep_pickup_datetime'] = pd.to_datetime(yellow_taxi_data['lpep_pickup_datetime'])\n",
      "C:\\Users\\pshab\\AppData\\Local\\Temp\\ipykernel_10148\\4104758355.py:3: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  yellow_taxi_data['lpep_dropoff_datetime'] = pd.to_datetime(yellow_taxi_data['lpep_dropoff_datetime'])\n",
      "C:\\Users\\pshab\\AppData\\Local\\Temp\\ipykernel_10148\\4104758355.py:5: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  green_taxi_data['tpep_pickup_datetime'] = pd.to_datetime(green_taxi_data['tpep_pickup_datetime'])\n",
      "C:\\Users\\pshab\\AppData\\Local\\Temp\\ipykernel_10148\\4104758355.py:6: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  green_taxi_data['tpep_dropoff_datetime'] = pd.to_datetime(green_taxi_data['tpep_dropoff_datetime'])\n"
     ]
    }
   ],
   "source": [
    "# Convert date-time columns to datetime objects\n",
    "yellow_taxi_data['lpep_pickup_datetime'] = pd.to_datetime(yellow_taxi_data['lpep_pickup_datetime'])\n",
    "yellow_taxi_data['lpep_dropoff_datetime'] = pd.to_datetime(yellow_taxi_data['lpep_dropoff_datetime'])\n",
    "\n",
    "green_taxi_data['tpep_pickup_datetime'] = pd.to_datetime(green_taxi_data['tpep_pickup_datetime'])\n",
    "green_taxi_data['tpep_dropoff_datetime'] = pd.to_datetime(green_taxi_data['tpep_dropoff_datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ca03db38-1282-421d-91db-ac61bf2b6009",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handling missing values\n",
    "yellow_taxi_data.dropna(inplace=True)\n",
    "green_taxi_data.dropna(inplace=True)\n",
    "\n",
    "# Remove rows with zero passenger counts\n",
    "yellow_taxi_data = yellow_taxi_data[yellow_taxi_data['passenger_count'] > 0]\n",
    "green_taxi_data = green_taxi_data[green_taxi_data['passenger_count'] > 0]\n",
    "\n",
    "# Filter out trips with zero trip distance\n",
    "yellow_taxi_data = yellow_taxi_data[yellow_taxi_data['trip_distance'] > 0]\n",
    "green_taxi_data = green_taxi_data[green_taxi_data['trip_distance'] > 0]\n",
    "\n",
    "# Filter out trips with zero fare amount\n",
    "yellow_taxi_data = yellow_taxi_data[yellow_taxi_data['fare_amount'] > 0]\n",
    "green_taxi_data = green_taxi_data[green_taxi_data['fare_amount'] > 0]\n",
    "\n",
    "# Remove outliers in trip distance and fare amount (using z-score)\n",
    "def remove_outliers(df, columns):\n",
    "    z_scores = (df[columns] - df[columns].mean()) / df[columns].std()\n",
    "    return df[(z_scores.abs() < 3).all(axis=1)]\n",
    "\n",
    "columns_to_check = ['trip_distance', 'fare_amount']\n",
    "yellow_taxi_data = remove_outliers(yellow_taxi_data, columns_to_check)\n",
    "green_taxi_data = remove_outliers(green_taxi_data, columns_to_check)\n",
    "\n",
    "# Convert categorical variables to appropriate data types\n",
    "yellow_taxi_data['VendorID'] = yellow_taxi_data['VendorID'].astype('category')\n",
    "yellow_taxi_data['store_and_fwd_flag'] = yellow_taxi_data['store_and_fwd_flag'].astype('category')\n",
    "yellow_taxi_data['RatecodeID'] = yellow_taxi_data['RatecodeID'].astype('category')\n",
    "yellow_taxi_data['payment_type'] = yellow_taxi_data['payment_type'].astype('category')\n",
    "yellow_taxi_data['trip_type'] = yellow_taxi_data['trip_type'].astype('category')\n",
    "\n",
    "green_taxi_data['VendorID'] = green_taxi_data['VendorID'].astype('category')\n",
    "green_taxi_data['store_and_fwd_flag'] = green_taxi_data['store_and_fwd_flag'].astype('category')\n",
    "green_taxi_data['RatecodeID'] = green_taxi_data['RatecodeID'].astype('category')\n",
    "green_taxi_data['payment_type'] = green_taxi_data['payment_type'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "13cd7a70-39a8-4b61-ad1a-58377193617b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    VendorID lpep_pickup_datetime lpep_dropoff_datetime store_and_fwd_flag  \\\n",
      "0          2  2023-04-30 20:52:10   2023-04-30 21:05:26                  N   \n",
      "1          2  2023-04-30 20:29:49   2023-04-30 20:50:11                  N   \n",
      "2          2  2023-04-30 20:25:19   2023-04-30 20:32:12                  N   \n",
      "3          2  2023-04-30 20:07:06   2023-04-30 20:27:33                  N   \n",
      "4          2  2023-04-30 20:43:31   2023-04-30 20:46:59                  N   \n",
      "..       ...                  ...                   ...                ...   \n",
      "994        2  2023-05-01 09:44:37   2023-05-01 09:53:33                  N   \n",
      "995        2  2023-05-01 09:22:00   2023-05-01 09:33:12                  N   \n",
      "997        2  2023-05-01 09:01:12   2023-05-01 09:14:21                  N   \n",
      "998        2  2023-05-01 09:43:54   2023-05-01 09:47:41                  N   \n",
      "999        2  2023-05-01 09:19:10   2023-05-01 09:20:02                  N   \n",
      "\n",
      "    RatecodeID  PULocationID  DOLocationID  passenger_count  trip_distance  \\\n",
      "0            1           244           213                1           6.99   \n",
      "1            1            33           100                1           6.60   \n",
      "2            1           244           244                1           1.34   \n",
      "3            5            82            75                1           7.79   \n",
      "4            1            69           169                1           0.70   \n",
      "..         ...           ...           ...              ...            ...   \n",
      "994          1           236            74                6           1.21   \n",
      "995          1           226            82                1           1.46   \n",
      "997          1            74            42                1           1.93   \n",
      "998          1            33            52                1           0.51   \n",
      "999          1            92            92                1           0.17   \n",
      "\n",
      "     fare_amount  extra  mta_tax  tip_amount  tolls_amount ehail_fee  \\\n",
      "0          28.90    1.0      0.5        0.00          0.00         -   \n",
      "1          30.30    1.0      0.5        5.00          0.00         -   \n",
      "2           9.30    1.0      0.5        2.36          0.00         -   \n",
      "3          22.73    0.0      0.0        2.29          6.55         -   \n",
      "4           6.50    1.0      0.5        0.00          0.00         -   \n",
      "..           ...    ...      ...         ...           ...       ...   \n",
      "994        10.00    0.0      0.5        2.88          0.00         -   \n",
      "995        11.40    0.0      0.5        0.00          0.00         -   \n",
      "997        13.50    0.0      0.5        0.00          0.00         -   \n",
      "998         5.80    0.0      0.5        0.00          0.00         -   \n",
      "999         3.70    0.0      0.5        0.00          0.00         -   \n",
      "\n",
      "     improvement_surcharge  total_amount payment_type trip_type  \\\n",
      "0                      1.0         31.40            1         1   \n",
      "1                      1.0         40.55            1         1   \n",
      "2                      1.0         14.16            1         1   \n",
      "3                      1.0         32.57            1         1   \n",
      "4                      1.0          9.00            2         1   \n",
      "..                     ...           ...          ...       ...   \n",
      "994                    1.0         14.38            1         1   \n",
      "995                    1.0         12.90            2         1   \n",
      "997                    1.0         15.00            2         1   \n",
      "998                    1.0          7.30            1         1   \n",
      "999                    1.0          5.20            2         1   \n",
      "\n",
      "     congestion_surcharge  \n",
      "0                    0.00  \n",
      "1                    2.75  \n",
      "2                    0.00  \n",
      "3                    0.00  \n",
      "4                    0.00  \n",
      "..                    ...  \n",
      "994                  0.00  \n",
      "995                  0.00  \n",
      "997                  0.00  \n",
      "998                  0.00  \n",
      "999                  0.00  \n",
      "\n",
      "[904 rows x 20 columns]\n"
     ]
    }
   ],
   "source": [
    "cleaned_yellow_taxi_data=yellow_taxi_data\n",
    "print(cleaned_yellow_taxi_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b46fac7-7f50-48a8-b076-ca179c3c8fdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_green_taxi_data=green_taxi_data\n",
    "print(cleaned_green_taxi_data)"
   ]
  }
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