Yellow_Taxi_Data_for_2020.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ueYPQ4350uJX",
        "outputId": "9febe80f-24b8-482c-ed02-887992d9cd8b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from google.colab import drive\n",
        "import pathlib\n",
        "import numpy as np\n",
        "from pandas.tseries.holiday import USFederalHolidayCalendar\n",
        "from datetime import datetime, timedelta\n",
        "import gc\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "drive.mount('/content/drive',force_remount=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = \"/content/drive/MyDrive/2020-par/yellow_tripdata_2020-09.parquet\"\n",
        "data_dir = pathlib.Path(data_dir)\n",
        "df9 = pd.read_parquet(data_dir)"
      ],
      "metadata": {
        "id": "WBV5STVqsA9-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df9.shape"
      ],
      "metadata": {
        "id": "VacltdBm4dQs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df9.drop(columns=['VendorID', 'RatecodeID', 'store_and_fwd_flag', 'payment_type', 'fare_amount', 'extra', 'mta_tax', 'tip_amount', 'tolls_amount', 'improvement_surcharge', 'total_amount', 'congestion_surcharge', 'airport_fee'], inplace=True)\n",
        "print(df9[df9['trip_distance'] < 1.0].shape)\n",
        "df9 = df9[df9['trip_distance'] >= 1.0]\n",
        "print(df9.shape)\n",
        "print(df9[df9['passenger_count'] == 0].shape)\n",
        "df9 = df9[df9['passenger_count'] != 0]\n",
        "df9.shape"
      ],
      "metadata": {
        "id": "y1ZPEAB-4e2G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = \"/content/drive/MyDrive/2020-par/yellow_tripdata_2020-10.parquet\"\n",
        "data_dir = pathlib.Path(data_dir)\n",
        "df10 = pd.read_parquet(data_dir)"
      ],
      "metadata": {
        "id": "wjbVC5UAsCpy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df10.shape"
      ],
      "metadata": {
        "id": "k82PJNBF4yGs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df10.drop(columns=['VendorID', 'RatecodeID', 'store_and_fwd_flag', 'payment_type', 'fare_amount', 'extra', 'mta_tax', 'tip_amount', 'tolls_amount', 'improvement_surcharge', 'total_amount', 'congestion_surcharge', 'airport_fee'], inplace=True)\n",
        "print(df10[df10['trip_distance'] < 1.0].shape)\n",
        "df10 = df10[df10['trip_distance'] >= 1.0]\n",
        "print(df10.shape)\n",
        "print(df10[df10['passenger_count'] == 0].shape)\n",
        "df10 = df10[df10['passenger_count'] != 0]\n",
        "df10.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "AgJFOdOM41fP",
        "outputId": "5f7926b6-9291-4e58-f2ff-36f0bdbdafbf"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-37c241093284>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf10\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'VendorID'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'RatecodeID'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'store_and_fwd_flag'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'payment_type'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fare_amount'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'extra'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'mta_tax'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'tip_amount'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'tolls_amount'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'improvement_surcharge'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'total_amount'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'congestion_surcharge'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'airport_fee'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf10\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf10\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'trip_distance'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdf10\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf10\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf10\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'trip_distance'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf10\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf10\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf10\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'passenger_count'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'df10' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = \"/content/drive/MyDrive/2020-par/yellow_tripdata_2020-11.parquet\"\n",
        "data_dir = pathlib.Path(data_dir)\n",
        "df11 = pd.read_parquet(data_dir)"
      ],
      "metadata": {
        "id": "4uXmwra0sExX"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df11.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "55E8D0S35az4",
        "outputId": "5eaaef48-43b9-4644-f37f-54d785b2cefd"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1509000, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df11.drop(columns=['VendorID', 'RatecodeID', 'store_and_fwd_flag', 'payment_type', 'fare_amount', 'extra', 'mta_tax', 'tip_amount', 'tolls_amount', 'improvement_surcharge', 'total_amount', 'congestion_surcharge', 'airport_fee'], inplace=True)\n",
        "print(df11[df11['trip_distance'] < 1.0].shape)\n",
        "df11 = df11[df11['trip_distance'] >= 1.0]\n",
        "print(df11.shape)\n",
        "print(df11[df11['passenger_count'] == 0].shape)\n",
        "df11 = df11[df11['passenger_count'] != 0]\n",
        "df11.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PV8sNiYO5eDJ",
        "outputId": "541ba393-1d91-4817-c216-e1e3635a2312"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(362312, 6)\n",
            "(1146688, 6)\n",
            "(21164, 6)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1125524, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = \"/content/drive/MyDrive/2020-par/yellow_tripdata_2020-12.parquet\"\n",
        "data_dir = pathlib.Path(data_dir)\n",
        "df12 = pd.read_parquet(data_dir)"
      ],
      "metadata": {
        "id": "i1zpLE9lsHJK"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df12.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SbjP94QJ5r_C",
        "outputId": "dff7b6dc-c5ef-4407-f649-cb9af54d29e5"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1461898, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df12.drop(columns=['VendorID', 'RatecodeID', 'store_and_fwd_flag', 'payment_type', 'fare_amount', 'extra', 'mta_tax', 'tip_amount', 'tolls_amount', 'improvement_surcharge', 'total_amount', 'congestion_surcharge', 'airport_fee'], inplace=True)\n",
        "print(df12[df12['trip_distance'] < 1.0].shape)\n",
        "df12 = df12[df12['trip_distance'] >= 1.0]\n",
        "print(df12.shape)\n",
        "print(df12[df12['passenger_count'] == 0].shape)\n",
        "df12 = df12[df12['passenger_count'] != 0]\n",
        "df12.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DJq6SU7x5uT_",
        "outputId": "e371ded9-050e-461c-e8d4-ef7d6f148fa3"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(366190, 6)\n",
            "(1095708, 6)\n",
            "(20344, 6)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "(1075364, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "frames = [df9, df10, df11, df12]"
      ],
      "metadata": {
        "id": "UEazLVsUsMs5"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2020 = pd.concat(frames)\n",
        "df1 = df2 = df3= df4 = df5 = df6 = df7 = df8 = df9 = df10 = df11 = df12 = None\n",
        "del df1,df2,df3,df4,df5,df6,df7,df8,df9,df10,df11,df12,frames\n",
        "yelloTaxiData2020"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "wqYkkaMh6-gk",
        "outputId": "50d40e6c-f30e-412d-a066-f4c74f3447bf"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
              "0        2020-09-01 00:38:58   2020-09-01 01:08:12              1.0   \n",
              "1        2020-09-01 00:55:26   2020-09-01 01:15:34              1.0   \n",
              "2        2020-09-01 00:00:40   2020-09-01 00:10:25              5.0   \n",
              "3        2020-09-01 00:26:01   2020-09-01 00:35:50              1.0   \n",
              "4        2020-09-01 00:29:09   2020-09-01 01:00:33              1.0   \n",
              "...                      ...                   ...              ...   \n",
              "1461893  2020-12-31 23:44:35   2021-01-01 00:01:22              NaN   \n",
              "1461894  2020-12-31 23:41:36   2020-12-31 23:50:32              NaN   \n",
              "1461895  2020-12-31 23:01:17   2020-12-31 23:40:37              NaN   \n",
              "1461896  2020-12-31 23:31:29   2020-12-31 23:44:22              NaN   \n",
              "1461897  2020-12-31 23:12:48   2020-12-31 23:24:51              NaN   \n",
              "\n",
              "         trip_distance  PULocationID  DOLocationID  \n",
              "0                10.76           132            62  \n",
              "1                 6.37            48           129  \n",
              "2                 2.32           102            37  \n",
              "3                 1.82           137           246  \n",
              "4                 4.90            79           170  \n",
              "...                ...           ...           ...  \n",
              "1461893           9.22           143            20  \n",
              "1461894           4.79             4           262  \n",
              "1461895          28.00           210            78  \n",
              "1461896           7.08           148           129  \n",
              "1461897           2.35            18           235  \n",
              "\n",
              "[4463757 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9452b74d-ec11-4ab9-bef0-b27a6497c486\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>tpep_pickup_datetime</th>\n",
              "      <th>tpep_dropoff_datetime</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>trip_distance</th>\n",
              "      <th>PULocationID</th>\n",
              "      <th>DOLocationID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-09-01 00:38:58</td>\n",
              "      <td>2020-09-01 01:08:12</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.76</td>\n",
              "      <td>132</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-09-01 00:55:26</td>\n",
              "      <td>2020-09-01 01:15:34</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.37</td>\n",
              "      <td>48</td>\n",
              "      <td>129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-09-01 00:00:40</td>\n",
              "      <td>2020-09-01 00:10:25</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.32</td>\n",
              "      <td>102</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-09-01 00:26:01</td>\n",
              "      <td>2020-09-01 00:35:50</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.82</td>\n",
              "      <td>137</td>\n",
              "      <td>246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-09-01 00:29:09</td>\n",
              "      <td>2020-09-01 01:00:33</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.90</td>\n",
              "      <td>79</td>\n",
              "      <td>170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1461893</th>\n",
              "      <td>2020-12-31 23:44:35</td>\n",
              "      <td>2021-01-01 00:01:22</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.22</td>\n",
              "      <td>143</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1461894</th>\n",
              "      <td>2020-12-31 23:41:36</td>\n",
              "      <td>2020-12-31 23:50:32</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.79</td>\n",
              "      <td>4</td>\n",
              "      <td>262</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1461895</th>\n",
              "      <td>2020-12-31 23:01:17</td>\n",
              "      <td>2020-12-31 23:40:37</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28.00</td>\n",
              "      <td>210</td>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1461896</th>\n",
              "      <td>2020-12-31 23:31:29</td>\n",
              "      <td>2020-12-31 23:44:22</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.08</td>\n",
              "      <td>148</td>\n",
              "      <td>129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1461897</th>\n",
              "      <td>2020-12-31 23:12:48</td>\n",
              "      <td>2020-12-31 23:24:51</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.35</td>\n",
              "      <td>18</td>\n",
              "      <td>235</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4463757 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9452b74d-ec11-4ab9-bef0-b27a6497c486')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9452b74d-ec11-4ab9-bef0-b27a6497c486 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9452b74d-ec11-4ab9-bef0-b27a6497c486');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2020.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jxs6j1Fb71d4",
        "outputId": "b06511f4-325c-4e69-b36b-039f64fea207"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4463757, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize=(20,10))\n",
        "plt.title(\"Visualize Outliers\")\n",
        "yelloTaxiData2020.boxplot();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 664
        },
        "id": "7BTZyvY6hW7D",
        "outputId": "bc0e9a9b-7d18-4fd5-d685-45847d79f2dc"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2020[yelloTaxiData2020['trip_distance'] > 200000].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RnGmI-0Dh_fu",
        "outputId": "569e90cc-5f29-4451-c875-d61917f83ee4"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2020 = yelloTaxiData2020[yelloTaxiData2020['trip_distance'] < 200000]"
      ],
      "metadata": {
        "id": "okNjkamviRzq"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gc.collect()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VBoRW4gAib_r",
        "outputId": "3d532739-3ecc-484f-fe85-360641a2d411"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4491"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the ride duration from the pickup and dropoff date time\n",
        "yelloTaxiData2020['tpep_dropoff_datetime'] = pd.to_datetime(yelloTaxiData2020.tpep_dropoff_datetime)\n",
        "yelloTaxiData2020['tpep_pickup_datetime'] = pd.to_datetime(yelloTaxiData2020.tpep_pickup_datetime)\n",
        "yelloTaxiData2020.loc[:, 'ride_duration'] = (yelloTaxiData2020['tpep_dropoff_datetime'] - yelloTaxiData2020['tpep_pickup_datetime']).astype('timedelta64[s]')"
      ],
      "metadata": {
        "id": "EjeVMfi6O2HJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e3304d8-254f-4cf8-990e-c6ab07b5b6d8"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1667: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.obj[key] = value\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize=(20,10))\n",
        "plt.title(\"Visualize Outliers\")\n",
        "yelloTaxiData2020.boxplot(column=['ride_duration']);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "vtr0ZpTFimgr",
        "outputId": "8f52bd65-1fcb-44f9-816f-bd9c20c162a0"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize=(20,10))\n",
        "plt.title(\"Visualize Outliers\")\n",
        "yelloTaxiData2020.boxplot(column=['ride_duration'], showfliers=False, notch=True);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "0nEO2WRgi0tZ",
        "outputId": "e1b44235-26a5-45be-dec1-070eefa6d49f"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2020[yelloTaxiData2020['ride_duration']<=0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YwBIqsxHi_05",
        "outputId": "acb83895-6d85-4de6-f458-77c6d2521c99"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(18967, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2020 = yelloTaxiData2020[yelloTaxiData2020['ride_duration']>0]"
      ],
      "metadata": {
        "id": "GLQp8V2PjKJ7"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2020.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YojGRcEGjQDP",
        "outputId": "d09c069d-55da-4eb8-ec84-0133710c1b01"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4444779, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2020[yelloTaxiData2020['ride_duration']>2500].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oCaGqbqrjTek",
        "outputId": "83a5b649-7f4d-4bd2-fbd8-f9dd6e516b14"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(136222, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2020 = yelloTaxiData2020[yelloTaxiData2020['ride_duration']<=2500]"
      ],
      "metadata": {
        "id": "C2En3scHjtd9"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "yelloTaxiData2020 = yelloTaxiData2020[yelloTaxiData2020['ride_duration'].between(np.mean(yelloTaxiData2020['ride_duration']) - 2*np.std(yelloTaxiData2020['ride_duration']), np.mean(yelloTaxiData2020['ride_duration']) + 2*np.std(yelloTaxiData2020['ride_duration']), inclusive = True)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pqjz2iYSP-t_",
        "outputId": "a6a48a57-9bbe-4ecc-f341-5ad387accc86"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: Boolean inputs to the `inclusive` argument are deprecated infavour of `both` or `neither`.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2020.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cJtRl24RRUr9",
        "outputId": "963b454b-c280-4310-ebba-842de2d03b27"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4069192, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2020.loc[:, 'pickup_date'] = yelloTaxiData2020['tpep_pickup_datetime'].dt.date\n",
        "yelloTaxiData2020.loc[:, 'pickup_time'] = yelloTaxiData2020['tpep_pickup_datetime'].dt.time\n",
        "yelloTaxiData2020.loc[:, 'pickup_weekday'] = yelloTaxiData2020['tpep_pickup_datetime'].dt.weekday\n",
        "yelloTaxiData2020.loc[:, 'pickup_weekofyear'] = yelloTaxiData2020['tpep_pickup_datetime'].dt.weekofyear\n",
        "yelloTaxiData2020.loc[:, 'pickup_hour'] = yelloTaxiData2020['tpep_pickup_datetime'].dt.hour\n",
        "yelloTaxiData2020.loc[:, 'pickup_minute'] = yelloTaxiData2020['tpep_pickup_datetime'].dt.minute\n",
        "yelloTaxiData2020.loc[:, 'pickup_dt'] = (yelloTaxiData2020['tpep_pickup_datetime'] - yelloTaxiData2020['tpep_pickup_datetime'].min()).dt.total_seconds()\n",
        "yelloTaxiData2020.loc[:, 'pickup_week_hour'] = yelloTaxiData2020['pickup_weekday'] * 24 + yelloTaxiData2020['pickup_hour']\n",
        "yelloTaxiData2020.loc[:, 'pickup_dayofyear'] = yelloTaxiData2020['tpep_pickup_datetime'].dt.dayofyear\n",
        "yelloTaxiData2020.loc[:, 'pickup_month'] = yelloTaxiData2020['tpep_pickup_datetime'].dt.month\n",
        "yelloTaxiData2020.loc[:, 'dropoff_date'] = yelloTaxiData2020['tpep_dropoff_datetime'].dt.date\n",
        "yelloTaxiData2020.loc[:, 'dropoff_time'] = yelloTaxiData2020['tpep_dropoff_datetime'].dt.time\n",
        "yelloTaxiData2020.loc[:, 'dropoff_hour'] = yelloTaxiData2020['tpep_dropoff_datetime'].dt.hour\n",
        "yelloTaxiData2020.loc[:, 'dropoff_weekday'] = yelloTaxiData2020['tpep_dropoff_datetime'].dt.weekday\n",
        "yelloTaxiData2020.loc[:, 'dropoff_month'] = yelloTaxiData2020['tpep_dropoff_datetime'].dt.month\n",
        "holidayCalendar = USFederalHolidayCalendar()\n",
        "holidays = holidayCalendar.holidays()\n",
        "yelloTaxiData2020['pickup_holiday'] = pd.to_datetime(yelloTaxiData2020.tpep_pickup_datetime.dt.date).isin(holidays)\n",
        "yelloTaxiData2020['pickup_holiday'] = yelloTaxiData2020.pickup_holiday.map(lambda x: 1 if x == True else 0)\n",
        "yelloTaxiData2020['pickup_near_holiday'] = (pd.to_datetime(yelloTaxiData2020.tpep_pickup_datetime.dt.date).isin(holidays + timedelta(days=1)) | pd.to_datetime(yelloTaxiData2020.tpep_pickup_datetime.dt.date).isin(holidays - timedelta(days=1)))\n",
        "yelloTaxiData2020['pickup_near_holiday'] = yelloTaxiData2020.pickup_near_holiday.map(lambda x: 1 if x == True else 0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jVCLW_yiObP3",
        "outputId": "5ff1de45-8c6b-40f7-860d-f2b9268a6882"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated.  Please use Series.dt.isocalendar().week instead.\n",
            "  after removing the cwd from sys.path.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2020.head()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "mNJHl6j3RszI",
        "outputId": "39c1ff54-dc7f-4adf-d420-995551a263f4"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  trip_distance  \\\n",
              "1  2020-09-01 00:55:26   2020-09-01 01:15:34              1.0           6.37   \n",
              "2  2020-09-01 00:00:40   2020-09-01 00:10:25              5.0           2.32   \n",
              "3  2020-09-01 00:26:01   2020-09-01 00:35:50              1.0           1.82   \n",
              "5  2020-09-01 00:09:50   2020-09-01 00:12:38              3.0           1.03   \n",
              "6  2020-09-01 00:40:30   2020-09-01 00:52:16              5.0           3.43   \n",
              "\n",
              "   PULocationID  DOLocationID  ride_duration pickup_date pickup_time  \\\n",
              "1            48           129         1208.0  2020-09-01    00:55:26   \n",
              "2           102            37          585.0  2020-09-01    00:00:40   \n",
              "3           137           246          589.0  2020-09-01    00:26:01   \n",
              "5           263           141          168.0  2020-09-01    00:09:50   \n",
              "6           170           263          706.0  2020-09-01    00:40:30   \n",
              "\n",
              "   pickup_weekday  ...  pickup_week_hour  pickup_dayofyear  pickup_month  \\\n",
              "1               1  ...                24               245             9   \n",
              "2               1  ...                24               245             9   \n",
              "3               1  ...                24               245             9   \n",
              "5               1  ...                24               245             9   \n",
              "6               1  ...                24               245             9   \n",
              "\n",
              "   dropoff_date  dropoff_time  dropoff_hour  dropoff_weekday dropoff_month  \\\n",
              "1    2020-09-01      01:15:34             1                1             9   \n",
              "2    2020-09-01      00:10:25             0                1             9   \n",
              "3    2020-09-01      00:35:50             0                1             9   \n",
              "5    2020-09-01      00:12:38             0                1             9   \n",
              "6    2020-09-01      00:52:16             0                1             9   \n",
              "\n",
              "  pickup_holiday  pickup_near_holiday  \n",
              "1              0                    0  \n",
              "2              0                    0  \n",
              "3              0                    0  \n",
              "5              0                    0  \n",
              "6              0                    0  \n",
              "\n",
              "[5 rows x 24 columns]"
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              "\n",
              "  <div id=\"df-ae469723-ce01-4549-90e9-e10ce7bfd3d8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tpep_pickup_datetime</th>\n",
              "      <th>tpep_dropoff_datetime</th>\n",
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              "      <th>...</th>\n",
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              "      <th>1</th>\n",
              "      <td>2020-09-01 00:55:26</td>\n",
              "      <td>2020-09-01 01:15:34</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.37</td>\n",
              "      <td>48</td>\n",
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              "      <td>1208.0</td>\n",
              "      <td>2020-09-01</td>\n",
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              "      <td>1</td>\n",
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              "      <td>2020-09-01</td>\n",
              "      <td>01:15:34</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-09-01 00:00:40</td>\n",
              "      <td>2020-09-01 00:10:25</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.32</td>\n",
              "      <td>102</td>\n",
              "      <td>37</td>\n",
              "      <td>585.0</td>\n",
              "      <td>2020-09-01</td>\n",
              "      <td>00:00:40</td>\n",
              "      <td>1</td>\n",
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              "      <td>9</td>\n",
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              "      <td>2020-09-01 00:26:01</td>\n",
              "      <td>2020-09-01 00:35:50</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.82</td>\n",
              "      <td>137</td>\n",
              "      <td>246</td>\n",
              "      <td>589.0</td>\n",
              "      <td>2020-09-01</td>\n",
              "      <td>00:26:01</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>24</td>\n",
              "      <td>245</td>\n",
              "      <td>9</td>\n",
              "      <td>2020-09-01</td>\n",
              "      <td>00:35:50</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2020-09-01 00:09:50</td>\n",
              "      <td>2020-09-01 00:12:38</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.03</td>\n",
              "      <td>263</td>\n",
              "      <td>141</td>\n",
              "      <td>168.0</td>\n",
              "      <td>2020-09-01</td>\n",
              "      <td>00:09:50</td>\n",
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              "      <td>9</td>\n",
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              "      <td>2020-09-01 00:40:30</td>\n",
              "      <td>2020-09-01 00:52:16</td>\n",
              "      <td>5.0</td>\n",
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              "      <td>170</td>\n",
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              "      <td>706.0</td>\n",
              "      <td>2020-09-01</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2020.shape"
      ],
      "metadata": {
        "id": "gcmU2WCFTHBU",
        "outputId": "69a505a8-07ce-46a3-a04c-4a29ff951ad9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4069192, 24)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2020.to_parquet('/content/drive/MyDrive/yellow_tripdata_2020_data.parquet')"
      ],
      "metadata": {
        "id": "8YMOrFBY0z1C"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "nUvZUWNY0wy6"
      }
    }
  ]
}