Parallel_processing_in_Pandas_using_pandarallel.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/SatadruMukherjee/Data-Preprocessing-Models/blob/main/Parallel_processing_in_Pandas_using_pandarallel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install boto3 pandas"
      ],
      "metadata": {
        "id": "NOdpB4KUjeMK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import boto3\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "YwwBio5ckoJ3"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_file_list(bucket, prefix):\n",
        "    s3 = boto3.client('s3',aws_access_key_id='{}',aws_secret_access_key='{}')\n",
        "    keys = []\n",
        "    list_objects_v2_paginator = s3.get_paginator('list_objects_v2')\n",
        "    for s3_list_page in list_objects_v2_paginator.paginate(Bucket=bucket, Prefix=prefix):\n",
        "        keys.extend(s3_list_page['Contents'])\n",
        "    list_objects=[]\n",
        "    for key in keys:\n",
        "        if not key['Key'].endswith('/'):\n",
        "          list_objects.append([bucket,key['Key']])\n",
        "    column_names = ['input_bucket', 'input_key']\n",
        "    df = pd.DataFrame(list_objects, columns=column_names)\n",
        "    return df"
      ],
      "metadata": {
        "id": "2Ss9uhVvknpn"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=get_file_list('{}', '{}')\n",
        "df.head()"
      ],
      "metadata": {
        "id": "NPe7g4KLmEtG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PNhf6tl6pv_M",
        "outputId": "51709174-7cb7-4b75-d57a-3024ee10818a"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "850"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "uNMUbnSShr_r"
      },
      "outputs": [],
      "source": [
        "def copy_file_s3(source_bucket, source_key, destination_bucket,toggle):\n",
        "    try:\n",
        "      s3 = boto3.client('s3',aws_access_key_id='{}',aws_secret_access_key='{}')\n",
        "      copy_source = {'Bucket': source_bucket, 'Key': source_key}\n",
        "      source_file_name=source_key.split('/')[-1]\n",
        "      if toggle ==0:\n",
        "        destination_key=f'copy_folder/{source_file_name}'\n",
        "      else:\n",
        "        destination_key=f'copy_parallel/{source_file_name}'\n",
        "      s3.copy_object(CopySource=copy_source, Bucket=destination_bucket, Key=destination_key)\n",
        "      return \"File copied successfully\"\n",
        "    except Exception as e:\n",
        "        return f\"Failed: {str(e)}\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "start_time = time.time()\n",
        "df['copy_status']=df.apply(lambda row: copy_file_s3(row['input_bucket'], row['input_key'], '{}',0), axis=1)\n",
        "end_time = time.time()\n",
        "\n",
        "duration = end_time - start_time\n",
        "print(\"Time taken:\", duration, \"seconds\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wun7qSe1ka0p",
        "outputId": "d4eb213f-cacf-46fb-d958-a19ba39405f9"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time taken: 297.6148874759674 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "id": "HBLxFiBLp4DY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['copy_status']!='File copied successfully'].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "ogIxuOmUsZSE",
        "outputId": "dce97e02-73e3-46c6-d9df-211e9c3ad7b0"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [input_bucket, input_key, copy_status]\n",
              "Index: []"
            ],
            "text/html": [
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              "    <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",
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              "\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>input_bucket</th>\n",
              "      <th>input_key</th>\n",
              "      <th>copy_status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "</table>\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc29ee5f-3eef-4d0c-be09-871128a534b7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "      width: 32px;\n",
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              "\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bc29ee5f-3eef-4d0c-be09-871128a534b7 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-bc29ee5f-3eef-4d0c-be09-871128a534b7');\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",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "Out of range float values are not JSON compliant: nan"
            }
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandarallel"
      ],
      "metadata": {
        "id": "0z8nz4jLmley"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pandarallel import pandarallel\n",
        "pandarallel.initialize(progress_bar=True,nb_workers=8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PxgWL1VOmpD_",
        "outputId": "d38a4a18-a7b7-4886-e4eb-42f1231ad84e"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO: Pandarallel will run on 8 workers.\n",
            "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1=get_file_list('deloittesalarybucket', 'multiplesmalldataset')"
      ],
      "metadata": {
        "id": "I2P1LcyUp1bO"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.head()"
      ],
      "metadata": {
        "id": "FWE9Nj8Oz1Fg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "start_time = time.time()\n",
        "df1['copy_status']=df1.parallel_apply(lambda row: copy_file_s3(row['input_bucket'], row['input_key'], '{}',1), axis=1)\n",
        "end_time = time.time()\n",
        "\n",
        "duration = end_time - start_time\n",
        "print(\"Time taken:\", duration, \"seconds\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290,
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      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=107), Label(value='0 / 107'))), HB…"
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          "output_type": "stream",
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          "text": [
            "Time taken: 78.95515441894531 seconds\n"
          ]
        }
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    },
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      "cell_type": "code",
      "source": [
        "df1.head()"
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      "metadata": {
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        "id": "y6DgAw-xsLPJ",
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              "Empty DataFrame\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "        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>input_bucket</th>\n",
              "      <th>input_key</th>\n",
              "      <th>copy_status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6398208d-db7a-4d53-ac77-9e22092dbfaf')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-6398208d-db7a-4d53-ac77-9e22092dbfaf 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-6398208d-db7a-4d53-ac77-9e22092dbfaf');\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",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "Out of range float values are not JSON compliant: nan"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    }
  ]
}