Yellow_Taxi_Data_for_2021.ipynb

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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ueYPQ4350uJX",
        "outputId": "1291d34b-44f5-4e4d-fdfd-1e38776f758f"
      },
      "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",
        "drive.mount('/content/drive',force_remount=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = \"/content/drive/MyDrive/2021-par/yellow_tripdata_2021-01.parquet\"\n",
        "data_dir = pathlib.Path(data_dir)\n",
        "df1 = pd.read_parquet(data_dir)"
      ],
      "metadata": {
        "id": "FzyaeS0y0xB1"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AptvL-Ffxa3z",
        "outputId": "b6151bdd-f00e-4c58-b832-fffb06eaa638"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1369769, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.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)"
      ],
      "metadata": {
        "id": "_NJGMmTqx6DA"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1[df1['trip_distance'] < 1.0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SWDXwPr_y58D",
        "outputId": "af27bb38-856a-4bf7-d8f5-e3b8d82e49e8"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(331342, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df1[df1['trip_distance'] >= 1.0]"
      ],
      "metadata": {
        "id": "MikVcIiEzjP4"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s9ShahTJzpTj",
        "outputId": "7692bfe9-e9d9-453c-b453-08134dfc5c1f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1038427, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1[df1['passenger_count'] == 0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pwP2H4OAzsjd",
        "outputId": "5dc68dc8-3654-4fab-d1d6-8a0ccd0c6f2a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(19603, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df1[df1['passenger_count'] != 0]"
      ],
      "metadata": {
        "id": "Lf1Y6Pv1zy8t"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ptVWOk8Xz60A",
        "outputId": "69172296-5635-4f03-e912-3f82e5c31df9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1018824, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = \"/content/drive/MyDrive/2021-par/yellow_tripdata_2021-02.parquet\"\n",
        "data_dir = pathlib.Path(data_dir)\n",
        "df2= pd.read_parquet(data_dir)"
      ],
      "metadata": {
        "id": "2k4mE9X80yMc"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p-ApwPIR0Pdm",
        "outputId": "943b5511-86e7-4c1a-8d58-d20212a0825a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1371709, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.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(df2[df2['trip_distance'] < 1.0].shape)\n",
        "df2 = df2[df2['trip_distance'] >= 1.0]\n",
        "print(df2.shape)\n",
        "print(df2[df2['passenger_count'] == 0].shape)\n",
        "df2 = df2[df2['passenger_count'] != 0]\n",
        "df2.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tessATyh0SGs",
        "outputId": "7f80b9b8-0f90-49bf-b70a-ea4f5bbe6b91"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(327533, 6)\n",
            "(1044176, 6)\n",
            "(19365, 6)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "(1024811, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = \"/content/drive/MyDrive/2021-par/yellow_tripdata_2021-03.parquet\"\n",
        "data_dir = pathlib.Path(data_dir)\n",
        "df3 = pd.read_parquet(data_dir)"
      ],
      "metadata": {
        "id": "fUpO58KzryGy"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df3.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QZqo-oGo1YKc",
        "outputId": "7b488bc8-4217-4c9f-c9c7-55c622e3e870"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1925152, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3.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(df3[df3['trip_distance'] < 1.0].shape)\n",
        "df3 = df3[df3['trip_distance'] >= 1.0]\n",
        "print(df3.shape)\n",
        "print(df3[df3['passenger_count'] == 0].shape)\n",
        "df3 = df3[df3['passenger_count'] != 0]\n",
        "df3.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "973DC0KF1PF3",
        "outputId": "6521bd1f-374d-42a6-e12b-72f2f60d0911"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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        {
          "output_type": "execute_result",
          "data": {
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            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = \"/content/drive/MyDrive/2021-par/yellow_tripdata_2021-04.parquet\"\n",
        "data_dir = pathlib.Path(data_dir)\n",
        "df4 = pd.read_parquet(data_dir)"
      ],
      "metadata": {
        "id": "O5mVCSYHr0O9"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df4.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OBV8hbpw3X3u",
        "outputId": "d1f27f58-f449-406a-a5ba-f854a6329c69"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2171187, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df4.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(df4[df4['trip_distance'] < 1.0].shape)\n",
        "df4 = df4[df4['trip_distance'] >= 1.0]\n",
        "print(df4.shape)\n",
        "print(df4[df4['passenger_count'] == 0].shape)\n",
        "df4 = df4[df4['passenger_count'] != 0]\n",
        "df4.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QT0AI4q93aNJ",
        "outputId": "6c10c83a-2338-4255-ef38-2dd56fb37d2d"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(486776, 6)\n",
            "(1684411, 6)\n",
            "(31999, 6)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1652412, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = \"/content/drive/MyDrive/2021-par/yellow_tripdata_2021-05.parquet\"\n",
        "data_dir = pathlib.Path(data_dir)\n",
        "df5 = pd.read_parquet(data_dir)"
      ],
      "metadata": {
        "id": "AMxXzdGVr3Ok"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df5.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NPGlxAHX3nnP",
        "outputId": "9af29b74-f514-4247-a923-e90691a57b45"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2507109, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df5.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(df5[df5['trip_distance'] < 1.0].shape)\n",
        "df5 = df5[df5['trip_distance'] >= 1.0]\n",
        "print(df5.shape)\n",
        "print(df5[df5['passenger_count'] == 0].shape)\n",
        "df5 = df5[df5['passenger_count'] != 0]\n",
        "df5.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fJLSDHXe3pWK",
        "outputId": "a286f494-0cf5-4fa0-a22d-bf1bf93887ce"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(534270, 6)\n",
            "(1972839, 6)\n",
            "(37808, 6)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1935031, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = \"/content/drive/MyDrive/2021-par/yellow_tripdata_2021-06.parquet\"\n",
        "data_dir = pathlib.Path(data_dir)\n",
        "df6 = pd.read_parquet(data_dir)"
      ],
      "metadata": {
        "id": "o0hE4rw7r53t"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df6.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o54O6keB3zBA",
        "outputId": "ea01469b-b9a3-4168-e290-997f3033d054"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2834264, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df6.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(df6[df6['trip_distance'] < 1.0].shape)\n",
        "df6 = df6[df6['trip_distance'] >= 1.0]\n",
        "print(df6.shape)\n",
        "print(df6[df6['passenger_count'] == 0].shape)\n",
        "df6 = df6[df6['passenger_count'] != 0]\n",
        "df6.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DIRvXflx31VI",
        "outputId": "efb54707-ae6e-4149-ae68-15ba2fa3804c"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(591290, 6)\n",
            "(2242974, 6)\n",
            "(49533, 6)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2193441, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = \"/content/drive/MyDrive/2021-par/yellow_tripdata_2021-07.parquet\"\n",
        "data_dir = pathlib.Path(data_dir)\n",
        "df7 = pd.read_parquet(data_dir)"
      ],
      "metadata": {
        "id": "1D2WsWMir8dF"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df7.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9zf6-UF94DZY",
        "outputId": "5ad60c86-969d-4099-86e0-b410cea2ce7d"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2821746, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df7.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(df7[df7['trip_distance'] < 1.0].shape)\n",
        "df7 = df7[df7['trip_distance'] >= 1.0]\n",
        "print(df7.shape)\n",
        "print(df7[df7['passenger_count'] == 0].shape)\n",
        "df7 = df7[df7['passenger_count'] != 0]\n",
        "df7.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JTAUfIwg4GcS",
        "outputId": "e453a0be-73d2-4d3d-ea8e-e2903cc8c1a8"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(567763, 6)\n",
            "(2253983, 6)\n",
            "(49224, 6)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2204759, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = \"/content/drive/MyDrive/2021-par/yellow_tripdata_2021-08.parquet\"\n",
        "data_dir = pathlib.Path(data_dir)\n",
        "df8 = pd.read_parquet(data_dir)"
      ],
      "metadata": {
        "id": "P9OA1jqJr_FR"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df8.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZkxfjKThs6Z4",
        "outputId": "c78cf7f1-0d83-4c79-9665-5514866bcfb0"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2788757, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df8.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(df8[df8['trip_distance'] < 1.0].shape)\n",
        "df8 = df8[df8['trip_distance'] >= 1.0]\n",
        "print(df8.shape)\n",
        "print(df8[df8['passenger_count'] == 0].shape)\n",
        "df8 = df8[df8['passenger_count'] != 0]\n",
        "df8.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P5kUEQJU4QsU",
        "outputId": "900cafae-a68a-4aff-e15d-ed3a9692e6d1"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "(52751, 6)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2184962, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = \"/content/drive/MyDrive/2021-par/yellow_tripdata_2021-09.parquet\"\n",
        "data_dir = pathlib.Path(data_dir)\n",
        "df9 = pd.read_parquet(data_dir)"
      ],
      "metadata": {
        "id": "WBV5STVqsA9-"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df9.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VacltdBm4dQs",
        "outputId": "2206af03-5e34-4e26-a7a9-40144bfde944"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2963793, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y1ZPEAB-4e2G",
        "outputId": "425115aa-bcca-4c15-9774-781ea1f38e86"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(591095, 6)\n",
            "(2372698, 6)\n",
            "(51536, 6)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2321162, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = \"/content/drive/MyDrive/2021-par/yellow_tripdata_2021-10.parquet\"\n",
        "data_dir = pathlib.Path(data_dir)\n",
        "df10 = pd.read_parquet(data_dir)"
      ],
      "metadata": {
        "id": "wjbVC5UAsCpy"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df10.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k82PJNBF4yGs",
        "outputId": "0fe961bc-e11a-48c3-9faa-0ac01ef26d29"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3463504, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "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/"
        },
        "id": "AgJFOdOM41fP",
        "outputId": "bf11e004-55ed-47d1-ecc2-df1ad1e3095c"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(696358, 6)\n",
            "(2767146, 6)\n",
            "(61113, 6)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2706033, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = \"/content/drive/MyDrive/2021-par/yellow_tripdata_2021-11.parquet\"\n",
        "data_dir = pathlib.Path(data_dir)\n",
        "df11 = pd.read_parquet(data_dir)"
      ],
      "metadata": {
        "id": "4uXmwra0sExX"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df11.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "55E8D0S35az4",
        "outputId": "6dd4d234-9146-4347-d09b-e6c6d9f1806a"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3472949, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "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": "0444eb45-2a22-4fe9-e45d-cdf8d226b904"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(705241, 6)\n",
            "(2767708, 6)\n",
            "(62526, 6)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2705182, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = \"/content/drive/MyDrive/2021-par/yellow_tripdata_2021-12.parquet\"\n",
        "data_dir = pathlib.Path(data_dir)\n",
        "df12 = pd.read_parquet(data_dir)"
      ],
      "metadata": {
        "id": "i1zpLE9lsHJK"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df12.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SbjP94QJ5r_C",
        "outputId": "b5328450-8d04-4f62-c9ba-bacf995ce674"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3214369, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "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": "610cfa39-3682-49ea-db91-8442dcad9fe5"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(663463, 6)\n",
            "(2550906, 6)\n",
            "(54940, 6)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2495966, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "frames = [df1, df2, df3, df4, df5, df6, df7, df8, df9, df10, df11, df12]"
      ],
      "metadata": {
        "id": "UEazLVsUsMs5"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2021 = 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",
        "gc.collect()\n",
        "yelloTaxiData2021"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "wqYkkaMh6-gk",
        "outputId": "37256847-edc2-4ba4-b5a5-911de0268e08"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
              "0        2021-01-01 00:30:10   2021-01-01 00:36:12              1.0   \n",
              "2        2021-01-01 00:43:30   2021-01-01 01:11:06              1.0   \n",
              "4        2021-01-01 00:31:49   2021-01-01 00:48:21              1.0   \n",
              "5        2021-01-01 00:16:29   2021-01-01 00:24:30              1.0   \n",
              "6        2021-01-01 00:00:28   2021-01-01 00:17:28              1.0   \n",
              "...                      ...                   ...              ...   \n",
              "3214364  2021-12-31 23:18:02   2021-12-31 23:32:19              NaN   \n",
              "3214365  2021-12-31 23:46:00   2022-01-01 00:07:00              NaN   \n",
              "3214366  2021-12-31 23:46:00   2022-01-01 00:04:00              NaN   \n",
              "3214367  2021-12-31 23:53:00   2022-01-01 00:02:00              NaN   \n",
              "3214368  2021-12-31 23:19:28   2021-12-31 23:26:30              NaN   \n",
              "\n",
              "         trip_distance  PULocationID  DOLocationID  \n",
              "0                 2.10           142            43  \n",
              "2                14.70           132           165  \n",
              "4                 4.94            68            33  \n",
              "5                 1.60           224            68  \n",
              "6                 4.10            95           157  \n",
              "...                ...           ...           ...  \n",
              "3214364           3.99           145           107  \n",
              "3214365           5.96           236           158  \n",
              "3214366           4.99            48            13  \n",
              "3214367           1.36            75            75  \n",
              "3214368           1.99           170           237  \n",
              "\n",
              "[23891211 rows x 6 columns]"
            ],
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              "\n",
              "  <div id=\"df-164c53b1-b7bc-410f-b432-cc0b5794ebd8\">\n",
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              "<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>2021-01-01 00:30:10</td>\n",
              "      <td>2021-01-01 00:36:12</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.10</td>\n",
              "      <td>142</td>\n",
              "      <td>43</td>\n",
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              "      <th>2</th>\n",
              "      <td>2021-01-01 00:43:30</td>\n",
              "      <td>2021-01-01 01:11:06</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14.70</td>\n",
              "      <td>132</td>\n",
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              "      <th>4</th>\n",
              "      <td>2021-01-01 00:31:49</td>\n",
              "      <td>2021-01-01 00:48:21</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>2021-01-01 00:24:30</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.60</td>\n",
              "      <td>224</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2021-01-01 00:00:28</td>\n",
              "      <td>2021-01-01 00:17:28</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>3214364</th>\n",
              "      <td>2021-12-31 23:18:02</td>\n",
              "      <td>2021-12-31 23:32:19</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.99</td>\n",
              "      <td>145</td>\n",
              "      <td>107</td>\n",
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              "    <tr>\n",
              "      <th>3214365</th>\n",
              "      <td>2021-12-31 23:46:00</td>\n",
              "      <td>2022-01-01 00:07:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.96</td>\n",
              "      <td>236</td>\n",
              "      <td>158</td>\n",
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              "      <th>3214366</th>\n",
              "      <td>2021-12-31 23:46:00</td>\n",
              "      <td>2022-01-01 00:04:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.99</td>\n",
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              "      <td>13</td>\n",
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              "      <td>2021-12-31 23:53:00</td>\n",
              "      <td>2022-01-01 00:02:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.36</td>\n",
              "      <td>75</td>\n",
              "      <td>75</td>\n",
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              "    <tr>\n",
              "      <th>3214368</th>\n",
              "      <td>2021-12-31 23:19:28</td>\n",
              "      <td>2021-12-31 23:26:30</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.99</td>\n",
              "      <td>170</td>\n",
              "      <td>237</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>23891211 rows × 6 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-164c53b1-b7bc-410f-b432-cc0b5794ebd8')\"\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",
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              "      </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",
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              "      border-radius: 50%;\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "            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",
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          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2021.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jxs6j1Fb71d4",
        "outputId": "1bd282fc-82b5-48e0-b703-c3a4c85e92ef"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23891211, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize=(20,10))\n",
        "plt.title(\"Visualize Outliers\")\n",
        "yelloTaxiData2021.boxplot();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 664
        },
        "id": "JTmyZGiLl5_8",
        "outputId": "44bdaed7-3952-40ff-e433-61f677d51ae4"
      },
      "execution_count": 47,
      "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": [
        "yelloTaxiData2021[yelloTaxiData2021['trip_distance'] > 345000].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dw9QHkQimkfA",
        "outputId": "1e171532-dc75-4f90-ecbd-5ec110b61836"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2021 = yelloTaxiData2021[yelloTaxiData2021['trip_distance'] < 345000]"
      ],
      "metadata": {
        "id": "Tn0kSEBWmyZD"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the ride duration from the pickup and dropoff date time\n",
        "yelloTaxiData2021['tpep_dropoff_datetime'] = pd.to_datetime(yelloTaxiData2021.tpep_dropoff_datetime)\n",
        "yelloTaxiData2021['tpep_pickup_datetime'] = pd.to_datetime(yelloTaxiData2021.tpep_pickup_datetime)\n",
        "yelloTaxiData2021.loc[:, 'ride_duration'] = (yelloTaxiData2021['tpep_dropoff_datetime'] - yelloTaxiData2021['tpep_pickup_datetime']).astype('timedelta64[s]')"
      ],
      "metadata": {
        "id": "OAgPA33pWicc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d405c39c-63cf-4c88-dfd3-1cd8c08a1f83"
      },
      "execution_count": 50,
      "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",
        "yelloTaxiData2021.boxplot(column=['ride_duration']);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "ZQM5O2kim8rz",
        "outputId": "d842eb70-0732-4c3e-e0b5-eb5e92fee729"
      },
      "execution_count": 51,
      "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",
        "yelloTaxiData2021.boxplot(column=['ride_duration'], showfliers=False, notch=True);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "noyhKRTnm_Kt",
        "outputId": "a5aba80e-2e6d-41fd-bccd-f7213af7162b"
      },
      "execution_count": 52,
      "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": [
        "yelloTaxiData2021[yelloTaxiData2021['ride_duration']<=0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AdObUPxinPHS",
        "outputId": "252f07cd-2d91-4bc1-a896-f4b397ca4afe"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(36582, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2021 = yelloTaxiData2021[yelloTaxiData2021['ride_duration']>0]"
      ],
      "metadata": {
        "id": "u7ElpEFNnUWK"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2021.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yEF-Uq_indZR",
        "outputId": "8f5fc7fd-6d1b-47cb-ce9b-d1615b384e72"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23854627, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2021[yelloTaxiData2021['ride_duration']>2500].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vRsPeDBXnjxF",
        "outputId": "42189fbd-e35f-4306-8137-2f7716f99398"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1064341, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2021 = yelloTaxiData2021[yelloTaxiData2021['ride_duration']<=2500]"
      ],
      "metadata": {
        "id": "8KZ2ove7nncZ"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2021 = yelloTaxiData2021[yelloTaxiData2021['ride_duration'].between(np.mean(yelloTaxiData2021['ride_duration']) - 2*np.std(yelloTaxiData2021['ride_duration']), np.mean(yelloTaxiData2021['ride_duration']) + 2*np.std(yelloTaxiData2021['ride_duration']), inclusive = True)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hzsl7EetR2h4",
        "outputId": "c289b6ee-cba7-49e2-db59-b1ca75463ee1"
      },
      "execution_count": 58,
      "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": [
        "yelloTaxiData2021.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aHVrn49YnxcP",
        "outputId": "05ac81da-b0cc-47db-bbdb-b298cf0cc0a6"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(21507479, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2021.loc[:, 'pickup_date'] = yelloTaxiData2021['tpep_pickup_datetime'].dt.date\n",
        "yelloTaxiData2021.loc[:, 'pickup_time'] = yelloTaxiData2021['tpep_pickup_datetime'].dt.time\n",
        "yelloTaxiData2021.loc[:, 'pickup_weekday'] = yelloTaxiData2021['tpep_pickup_datetime'].dt.weekday\n",
        "yelloTaxiData2021.loc[:, 'pickup_weekofyear'] = yelloTaxiData2021['tpep_pickup_datetime'].dt.weekofyear\n",
        "yelloTaxiData2021.loc[:, 'pickup_hour'] = yelloTaxiData2021['tpep_pickup_datetime'].dt.hour\n",
        "yelloTaxiData2021.loc[:, 'pickup_minute'] = yelloTaxiData2021['tpep_pickup_datetime'].dt.minute\n",
        "yelloTaxiData2021.loc[:, 'pickup_dt'] = (yelloTaxiData2021['tpep_pickup_datetime'] - yelloTaxiData2021['tpep_pickup_datetime'].min()).dt.total_seconds()\n",
        "yelloTaxiData2021.loc[:, 'pickup_week_hour'] = yelloTaxiData2021['pickup_weekday'] * 24 + yelloTaxiData2021['pickup_hour']\n",
        "yelloTaxiData2021.loc[:, 'pickup_dayofyear'] = yelloTaxiData2021['tpep_pickup_datetime'].dt.dayofyear\n",
        "yelloTaxiData2021.loc[:, 'pickup_month'] = yelloTaxiData2021['tpep_pickup_datetime'].dt.month\n",
        "yelloTaxiData2021.loc[:, 'dropoff_date'] = yelloTaxiData2021['tpep_dropoff_datetime'].dt.date\n",
        "yelloTaxiData2021.loc[:, 'dropoff_time'] = yelloTaxiData2021['tpep_dropoff_datetime'].dt.time\n",
        "yelloTaxiData2021.loc[:, 'dropoff_hour'] = yelloTaxiData2021['tpep_dropoff_datetime'].dt.hour\n",
        "yelloTaxiData2021.loc[:, 'dropoff_weekday'] = yelloTaxiData2021['tpep_dropoff_datetime'].dt.weekday\n",
        "yelloTaxiData2021.loc[:, 'dropoff_month'] = yelloTaxiData2021['tpep_dropoff_datetime'].dt.month\n",
        "holidayCalendar = USFederalHolidayCalendar()\n",
        "holidays = holidayCalendar.holidays()\n",
        "yelloTaxiData2021['pickup_holiday'] = pd.to_datetime(yelloTaxiData2021.tpep_pickup_datetime.dt.date).isin(holidays)\n",
        "yelloTaxiData2021['pickup_holiday'] = yelloTaxiData2021.pickup_holiday.map(lambda x: 1 if x == True else 0)\n",
        "yelloTaxiData2021['pickup_near_holiday'] = (pd.to_datetime(yelloTaxiData2021.tpep_pickup_datetime.dt.date).isin(holidays + timedelta(days=1)) | pd.to_datetime(yelloTaxiData2021.tpep_pickup_datetime.dt.date).isin(holidays - timedelta(days=1)))\n",
        "yelloTaxiData2021['pickup_near_holiday'] = yelloTaxiData2021.pickup_near_holiday.map(lambda x: 1 if x == True else 0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kZn3TWwXWqPF",
        "outputId": "62f455d5-14df-4f34-c555-ddcaf571ff2f"
      },
      "execution_count": 60,
      "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": [
        "yelloTaxiData2021.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "JIjfV7DFYkSp",
        "outputId": "5fff9e81-d20a-4e67-d049-3638cc7b889e"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  trip_distance  \\\n",
              "0  2021-01-01 00:30:10   2021-01-01 00:36:12              1.0           2.10   \n",
              "2  2021-01-01 00:43:30   2021-01-01 01:11:06              1.0          14.70   \n",
              "4  2021-01-01 00:31:49   2021-01-01 00:48:21              1.0           4.94   \n",
              "5  2021-01-01 00:16:29   2021-01-01 00:24:30              1.0           1.60   \n",
              "6  2021-01-01 00:00:28   2021-01-01 00:17:28              1.0           4.10   \n",
              "\n",
              "   PULocationID  DOLocationID  ride_duration pickup_date pickup_time  \\\n",
              "0           142            43          362.0  2021-01-01    00:30:10   \n",
              "2           132           165         1656.0  2021-01-01    00:43:30   \n",
              "4            68            33          992.0  2021-01-01    00:31:49   \n",
              "5           224            68          481.0  2021-01-01    00:16:29   \n",
              "6            95           157         1020.0  2021-01-01    00:00:28   \n",
              "\n",
              "   pickup_weekday  ...  pickup_week_hour  pickup_dayofyear  pickup_month  \\\n",
              "0               4  ...                96                 1             1   \n",
              "2               4  ...                96                 1             1   \n",
              "4               4  ...                96                 1             1   \n",
              "5               4  ...                96                 1             1   \n",
              "6               4  ...                96                 1             1   \n",
              "\n",
              "   dropoff_date  dropoff_time  dropoff_hour  dropoff_weekday dropoff_month  \\\n",
              "0    2021-01-01      00:36:12             0                4             1   \n",
              "2    2021-01-01      01:11:06             1                4             1   \n",
              "4    2021-01-01      00:48:21             0                4             1   \n",
              "5    2021-01-01      00:24:30             0                4             1   \n",
              "6    2021-01-01      00:17:28             0                4             1   \n",
              "\n",
              "  pickup_holiday  pickup_near_holiday  \n",
              "0              1                    0  \n",
              "2              1                    0  \n",
              "4              1                    0  \n",
              "5              1                    0  \n",
              "6              1                    0  \n",
              "\n",
              "[5 rows x 24 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3c08d806-c965-41d4-96f0-ec606dd61423\">\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",
              "      <th>passenger_count</th>\n",
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              "      <th>pickup_weekday</th>\n",
              "      <th>...</th>\n",
              "      <th>pickup_week_hour</th>\n",
              "      <th>pickup_dayofyear</th>\n",
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              "      <th>0</th>\n",
              "      <td>2021-01-01 00:30:10</td>\n",
              "      <td>2021-01-01 00:36:12</td>\n",
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              "      <td>362.0</td>\n",
              "      <td>2021-01-01</td>\n",
              "      <td>00:30:10</td>\n",
              "      <td>4</td>\n",
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              "      <td>1</td>\n",
              "      <td>2021-01-01</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-01-01 00:43:30</td>\n",
              "      <td>2021-01-01 01:11:06</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14.70</td>\n",
              "      <td>132</td>\n",
              "      <td>165</td>\n",
              "      <td>1656.0</td>\n",
              "      <td>2021-01-01</td>\n",
              "      <td>00:43:30</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>96</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2021-01-01</td>\n",
              "      <td>01:11:06</td>\n",
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              "      <td>4</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021-01-01 00:31:49</td>\n",
              "      <td>2021-01-01 00:48:21</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.94</td>\n",
              "      <td>68</td>\n",
              "      <td>33</td>\n",
              "      <td>992.0</td>\n",
              "      <td>2021-01-01</td>\n",
              "      <td>00:31:49</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>96</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2021-01-01</td>\n",
              "      <td>00:48:21</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2021-01-01 00:16:29</td>\n",
              "      <td>2021-01-01 00:24:30</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.60</td>\n",
              "      <td>224</td>\n",
              "      <td>68</td>\n",
              "      <td>481.0</td>\n",
              "      <td>2021-01-01</td>\n",
              "      <td>00:16:29</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>96</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2021-01-01</td>\n",
              "      <td>00:24:30</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2021-01-01 00:00:28</td>\n",
              "      <td>2021-01-01 00:17:28</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.10</td>\n",
              "      <td>95</td>\n",
              "      <td>157</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>2021-01-01</td>\n",
              "      <td>00:00:28</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>96</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2021-01-01</td>\n",
              "      <td>00:17:28</td>\n",
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              "<p>5 rows × 24 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3c08d806-c965-41d4-96f0-ec606dd61423')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
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              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3c08d806-c965-41d4-96f0-ec606dd61423 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-3c08d806-c965-41d4-96f0-ec606dd61423');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
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              "          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",
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            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2021.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eQ6bUfeAoRuB",
        "outputId": "6113e7cf-bb1c-487c-faac-0d32db42e399"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(21507479, 24)"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yelloTaxiData2021.to_parquet('/content/drive/MyDrive/yellow_tripdata_2021_data.parquet')"
      ],
      "metadata": {
        "id": "8YMOrFBY0z1C"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "nUvZUWNY0wy6"
      }
    }
  ]
}