{
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      "cell_type": "code",
      "source": [
        "# @title ###### Licensed to the Apache Software Foundation (ASF), Version 2.0 (the \"License\")\n",
        "\n",
        "# Licensed to the Apache Software Foundation (ASF) under one\n",
        "# or more contributor license agreements. See the NOTICE file\n",
        "# distributed with this work for additional information\n",
        "# regarding copyright ownership. The ASF licenses this file\n",
        "# to you under the Apache License, Version 2.0 (the\n",
        "# \"License\"); you may not use this file except in compliance\n",
        "# with the License. You may obtain a copy of the License at\n",
        "#\n",
        "#   http://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing,\n",
        "# software distributed under the License is distributed on an\n",
        "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
        "# KIND, either express or implied. See the License for the\n",
        "# specific language governing permissions and limitations\n",
        "# under the License"
      ],
      "metadata": {
        "cellView": "form",
        "id": "sARMhsXz8yR1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocessing with the Apache Beam DataFrames API\n",
        "\n",
        "[Pandas DataFrames](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html) is one of the most common tools used for data exploration and preprocessing. Pandas is popular because of its ease of use. It has intuitive methods to perform common analytical tasks and data preprocessing. \n",
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/dataframe_api_preprocessing.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/dataframe_api_preprocessing.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "</table>\n"
      ],
      "metadata": {
        "id": "A8xNRyZMW1yK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For rapid execution, Pandas loads all of the data into memory on a single machine (one node). This configuration works well when dealing with small-scale datasets. However, many projects involve datasets that are too big to fit in memory. These use cases generally require parallel data processing frameworks, such as Apache Beam.\n",
        "\n",
        "Beam DataFrames provide a Pandas-like\n",
        "API to declare and define Beam processing pipelines. It provides a familiar interface for machine learning practioners to build complex data-processing pipelines by only invoking standard pandas commands.\n",
        "\n",
        "To learn more about Apache Beam DataFrames, see the\n",
        "[Beam DataFrames overview](https://beam.apache.org/documentation/dsls/dataframes/overview) page.\n",
        "\n",
        "## Overview\n",
        "The goal of this example is to explore a dataset preprocessed with the Beam DataFrame API for machine learning model training.\n",
        "\n",
        "This example demonstrates the use of the Apache Beam DataFrames API to perform common data exploration as well as the preprocessing steps that are necessary to prepare your dataset for machine learning model training and inference. This example includes the following steps:  \n",
        "\n",
        "*   Removing unwanted columns.\n",
        "*   One-hot encoding categorical columns.\n",
        "*   Normalizing numerical columns.\n",
        "\n",
        "In this example, the first section demonstrates how to build and execute a pipeline locally using the interactive runner.\n",
        "The second section uses a distributed runner to demonstrate how to run the pipeline on the full dataset.\n"
      ],
      "metadata": {
        "id": "iFZC1inKuUCy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install Apache Beam\n",
        "\n",
        "To explore the elements within a `PCollection`, install Apache Beam with the `interactive` component to use the Interactive runner. The DataFrames API methods invoked in this example are available in Apache Beam SDK versions 2.43 and later.\n"
      ],
      "metadata": {
        "id": "A0f2HJ22D4lt"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pCjwrwNWnuqI"
      },
      "source": [
        "Install the latest Apache Beam SDK version."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-OJC0Xn5Um-C",
        "beam:comment": "TODO(https://github.com/apache/issues/23961): Just install 2.43.0 once it's released, [`issue 23276`](https://github.com/apache/beam/issues/23276)  is currently not implemented for Beam 2.42 (required fix for implementing `str.get_dummies()`"
      },
      "outputs": [],
      "source": [
        "!git clone https://github.com/apache/beam.git\n",
        "\n",
        "!cd beam/sdks/python && pip3 install -r build-requirements.txt \n",
        "\n",
        "%pip install -e beam/sdks/python/.[interactive,gcp]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Local exploration with the Interactive Beam runner\n",
        "Use the [Interactive Beam](https://beam.apache.org/releases/pydoc/2.20.0/apache_beam.runners.interactive.interactive_beam.html) runner to explore and develop your pipeline.\n",
        "This runner allows you to test the code interactively, progressively building out the pipeline before deploying it on a distributed runner. \n",
        "\n",
        "\n",
        "This section uses a subset of the original dataset, because the notebook instance has limited compute resources.\n"
      ],
      "metadata": {
        "id": "3NO6RgB7GkkE"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5I3G094hoB1P"
      },
      "source": [
        "### Load the data\n",
        "\n",
        "To read CSV files into DataFrames, Pandas has the\n",
        "[`pandas.read_csv`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)\n",
        "function.\n",
        "This notebook uses the Beam\n",
        "[`beam.dataframe.io.read_csv`](https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.io.html#apache_beam.dataframe.io.read_csv)\n",
        "function, which emulates `pandas.read_csv`. The main difference is that the Beam function returns a deferred Beam DataFrame, whereas the Pandas function returns a standard DataFrame.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X3_OB9cAULav"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd \n",
        "import apache_beam as beam\n",
        "import apache_beam.runners.interactive.interactive_beam as ib\n",
        "from apache_beam import dataframe\n",
        "from apache_beam.runners.interactive.interactive_runner import InteractiveRunner\n",
        "from apache_beam.runners.dataflow import DataflowRunner\n",
        "\n",
        "# Available options: [sample_1000, sample_10000, sample_100000, full], where\n",
        "# sample contains the entire dataset (around 1000000 samples).\n",
        "\n",
        "source_csv_file = 'gs://apache-beam-samples/nasa_jpl_asteroid/sample_10000.csv'\n",
        "\n",
        "# Initialize the pipeline.\n",
        "p = beam.Pipeline(InteractiveRunner())\n",
        "\n",
        "beam_df = p | beam.dataframe.io.read_csv(source_csv_file)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "paf7yf3YpCh8"
      },
      "source": [
        "### Preprocess the data\n",
        "\n",
        "This example uses the [NASA - Nearest Earth Objects dataset](https://cneos.jpl.nasa.gov/ca/).\n",
        "This dataset includes information about objects in outer space. Some objects are close enough to Earth to cause harm.\n",
        "This dataset compiles the list of NASA certified asteroids that are classified as the nearest earth objects to understand which objects pose a risk."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Inspect the dataset columns and their types."
      ],
      "metadata": {
        "id": "cvAu5T0ENjuQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
        "id": "LwW77ixE-pjR",
        "outputId": "3dfba30d-165e-46a6-b0b9-f12519db1c27"
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              "spk_id                       int64\n",
              "full_name                   object\n",
              "near_earth_object           object\n",
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              "diameter                   float64\n",
              "albedo                     float64\n",
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              "semi_major_axis_au_unit    float64\n",
              "hazardous_flag              object\n",
              "dtype: object"
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          "execution_count": 27
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      "source": [
        "beam_df.dtypes"
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    {
      "cell_type": "markdown",
      "source": [
        "When using Interactive Beam, to bring a Beam DataFrame into local memory as a Pandas DataFrame, use `ib.collect()`."
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              "       spk_id                   full_name near_earth_object  \\\n",
              "0     2000001                     1 Ceres                 N   \n",
              "1     2000002                    2 Pallas                 N   \n",
              "2     2000003                      3 Juno                 N   \n",
              "3     2000004                     4 Vesta                 N   \n",
              "4     2000005                   5 Astraea                 N   \n",
              "...       ...                         ...               ...   \n",
              "9994  2009995    9995 Alouette (4805 P-L)                 N   \n",
              "9995  2009996         9996 ANS (9070 P-L)                 N   \n",
              "9996  2009997        9997 COBE (1217 T-1)                 N   \n",
              "9997  2009998         9998 ISO (1293 T-1)                 N   \n",
              "9998  2009999       9999 Wiles (4196 T-2)                 N   \n",
              "\n",
              "      absolute_magnitude  diameter  albedo  diameter_sigma  eccentricity  \\\n",
              "0                   3.40   939.400  0.0900           0.200      0.076009   \n",
              "1                   4.20   545.000  0.1010          18.000      0.229972   \n",
              "2                   5.33   246.596  0.2140          10.594      0.256936   \n",
              "3                   3.00   525.400  0.4228           0.200      0.088721   \n",
              "4                   6.90   106.699  0.2740           3.140      0.190913   \n",
              "...                  ...       ...     ...             ...           ...   \n",
              "9994               15.10     2.564  0.2450           0.550      0.160610   \n",
              "9995               13.60     8.978  0.1130           0.376      0.235174   \n",
              "9996               14.30       NaN     NaN             NaN      0.113059   \n",
              "9997               15.10     2.235  0.3880           0.373      0.093852   \n",
              "9998               13.00     7.148  0.2620           0.065      0.071351   \n",
              "\n",
              "      inclination     moid_ld object_class  semi_major_axis_au_unit  \\\n",
              "0       10.594067  620.640533          MBA                 2.769165   \n",
              "1       34.832932  480.348639          MBA                 2.773841   \n",
              "2       12.991043  402.514639          MBA                 2.668285   \n",
              "3        7.141771  443.451432          MBA                 2.361418   \n",
              "4        5.367427  426.433027          MBA                 2.574037   \n",
              "...           ...         ...          ...                      ...   \n",
              "9994     2.311731  388.723233          MBA                 2.390249   \n",
              "9995     7.657713  444.194746          MBA                 2.796605   \n",
              "9996     2.459643  495.460110          MBA                 2.545674   \n",
              "9997     3.912263  373.848377          MBA                 2.160961   \n",
              "9998     3.198839  632.144398          MBA                 2.839917   \n",
              "\n",
              "     hazardous_flag  \n",
              "0                 N  \n",
              "1                 N  \n",
              "2                 N  \n",
              "3                 N  \n",
              "4                 N  \n",
              "...             ...  \n",
              "9994              N  \n",
              "9995              N  \n",
              "9996              N  \n",
              "9997              N  \n",
              "9998              N  \n",
              "\n",
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              "      <th></th>\n",
              "      <th>spk_id</th>\n",
              "      <th>full_name</th>\n",
              "      <th>near_earth_object</th>\n",
              "      <th>absolute_magnitude</th>\n",
              "      <th>diameter</th>\n",
              "      <th>albedo</th>\n",
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              "      <th>0</th>\n",
              "      <td>2000001</td>\n",
              "      <td>1 Ceres</td>\n",
              "      <td>N</td>\n",
              "      <td>3.40</td>\n",
              "      <td>939.400</td>\n",
              "      <td>0.0900</td>\n",
              "      <td>0.200</td>\n",
              "      <td>0.076009</td>\n",
              "      <td>10.594067</td>\n",
              "      <td>620.640533</td>\n",
              "      <td>MBA</td>\n",
              "      <td>2.769165</td>\n",
              "      <td>N</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2000002</td>\n",
              "      <td>2 Pallas</td>\n",
              "      <td>N</td>\n",
              "      <td>4.20</td>\n",
              "      <td>545.000</td>\n",
              "      <td>0.1010</td>\n",
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              "      <td>0.229972</td>\n",
              "      <td>34.832932</td>\n",
              "      <td>480.348639</td>\n",
              "      <td>MBA</td>\n",
              "      <td>2.773841</td>\n",
              "      <td>N</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2000003</td>\n",
              "      <td>3 Juno</td>\n",
              "      <td>N</td>\n",
              "      <td>5.33</td>\n",
              "      <td>246.596</td>\n",
              "      <td>0.2140</td>\n",
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              "      <td>0.256936</td>\n",
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              "      <td>402.514639</td>\n",
              "      <td>MBA</td>\n",
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              "      <td>N</td>\n",
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              "      <th>3</th>\n",
              "      <td>2000004</td>\n",
              "      <td>4 Vesta</td>\n",
              "      <td>N</td>\n",
              "      <td>3.00</td>\n",
              "      <td>525.400</td>\n",
              "      <td>0.4228</td>\n",
              "      <td>0.200</td>\n",
              "      <td>0.088721</td>\n",
              "      <td>7.141771</td>\n",
              "      <td>443.451432</td>\n",
              "      <td>MBA</td>\n",
              "      <td>2.361418</td>\n",
              "      <td>N</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2000005</td>\n",
              "      <td>5 Astraea</td>\n",
              "      <td>N</td>\n",
              "      <td>6.90</td>\n",
              "      <td>106.699</td>\n",
              "      <td>0.2740</td>\n",
              "      <td>3.140</td>\n",
              "      <td>0.190913</td>\n",
              "      <td>5.367427</td>\n",
              "      <td>426.433027</td>\n",
              "      <td>MBA</td>\n",
              "      <td>2.574037</td>\n",
              "      <td>N</td>\n",
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              "      <th>9994</th>\n",
              "      <td>2009995</td>\n",
              "      <td>9995 Alouette (4805 P-L)</td>\n",
              "      <td>N</td>\n",
              "      <td>15.10</td>\n",
              "      <td>2.564</td>\n",
              "      <td>0.2450</td>\n",
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              "      <td>388.723233</td>\n",
              "      <td>MBA</td>\n",
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              "      <td>N</td>\n",
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              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>2009996</td>\n",
              "      <td>9996 ANS (9070 P-L)</td>\n",
              "      <td>N</td>\n",
              "      <td>13.60</td>\n",
              "      <td>8.978</td>\n",
              "      <td>0.1130</td>\n",
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              "      <td>MBA</td>\n",
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              "      <td>N</td>\n",
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              "      <th>9996</th>\n",
              "      <td>2009997</td>\n",
              "      <td>9997 COBE (1217 T-1)</td>\n",
              "      <td>N</td>\n",
              "      <td>14.30</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>2009998</td>\n",
              "      <td>9998 ISO (1293 T-1)</td>\n",
              "      <td>N</td>\n",
              "      <td>15.10</td>\n",
              "      <td>2.235</td>\n",
              "      <td>0.3880</td>\n",
              "      <td>0.373</td>\n",
              "      <td>0.093852</td>\n",
              "      <td>3.912263</td>\n",
              "      <td>373.848377</td>\n",
              "      <td>MBA</td>\n",
              "      <td>2.160961</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>2009999</td>\n",
              "      <td>9999 Wiles (4196 T-2)</td>\n",
              "      <td>N</td>\n",
              "      <td>13.00</td>\n",
              "      <td>7.148</td>\n",
              "      <td>0.2620</td>\n",
              "      <td>0.065</td>\n",
              "      <td>0.071351</td>\n",
              "      <td>3.198839</td>\n",
              "      <td>632.144398</td>\n",
              "      <td>MBA</td>\n",
              "      <td>2.839917</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9999 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-064477d9-d4b6-44a6-a8fd-31f0cad2dcb5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\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",
              "    [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-064477d9-d4b6-44a6-a8fd-31f0cad2dcb5 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-064477d9-d4b6-44a6-a8fd-31f0cad2dcb5');\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": 28
        }
      ],
      "source": [
        "ib.collect(beam_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The datasets contain the following two types of columns:\n",
        "\n",
        "* **Numerical columns:** Use [normalization](https://developers.google.com/machine-learning/data-prep/transform/normalization) to transform these columns so that they can be used to train a machine learning model.\n",
        "\n",
        "* **Categorical columns:** Transform those columns with [one-hot encoding](https://developers.google.com/machine-learning/data-prep/transform/transform-categorical) to use them during training. \n"
      ],
      "metadata": {
        "id": "8jV9odKhNyF2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Use the standard pandas command `DataFrame.describe()` to generate descriptive statistics for the numerical columns, such as percentile, mean, std, and so on. "
      ],
      "metadata": {
        "id": "MGAErO0lAYws"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with dataframe.allow_non_parallel_operations():\n",
        "  beam_df_description = ib.collect(beam_df.describe())\n",
        "\n",
        "beam_df_description"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "id": "Befv697VBGM7",
        "outputId": "bb465020-94e4-4b3c-fda6-6e43da199be1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
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              "            <div id=\"progress_indicator_98687cb0060a8077a8abab6e464e4a75\">\n",
              "              <div class=\"spinner-border text-info\" role=\"status\"></div>\n",
              "              <span class=\"text-info\">Processing... collect</span>\n",
              "            </div>\n",
              "            "
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
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              "        if (typeof window.interactive_beam_jquery == 'undefined') {\n",
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              "          jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n",
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              "            datatableScript.type = 'text/javascript';\n",
              "            datatableScript.onload = function() {\n",
              "              window.interactive_beam_jquery = jQuery.noConflict(true);\n",
              "              window.interactive_beam_jquery(document).ready(function($){\n",
              "                \n",
              "            $(\"#progress_indicator_98687cb0060a8077a8abab6e464e4a75\").remove();\n",
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              "          document.head.appendChild(jqueryScript);\n",
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              "            $(\"#progress_indicator_98687cb0060a8077a8abab6e464e4a75\").remove();\n",
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             spk_id  absolute_magnitude     diameter       albedo  \\\n",
              "count  9.999000e+03         9999.000000  8688.000000  8672.000000   \n",
              "mean   2.005000e+06           12.675380    19.245446     0.197723   \n",
              "std    2.886607e+03            1.639609    30.190191     0.138819   \n",
              "min    2.000001e+06            3.000000     0.300000     0.008000   \n",
              "25%    2.002500e+06           11.900000     5.614000     0.074000   \n",
              "50%    2.005000e+06           12.900000     9.814000     0.187000   \n",
              "75%    2.007500e+06           13.700000    19.156750     0.283000   \n",
              "max    2.009999e+06           20.700000   939.400000     1.000000   \n",
              "\n",
              "       diameter_sigma  eccentricity  inclination      moid_ld  \\\n",
              "count     8591.000000   9999.000000  9999.000000  9999.000000   \n",
              "mean         0.454072      0.148716     7.890742   509.805237   \n",
              "std          1.093676      0.083803     6.336244   205.046582   \n",
              "min          0.006000      0.001003     0.042716     0.131028   \n",
              "25%          0.120000      0.093780     3.220137   377.829197   \n",
              "50%          0.201000      0.140335     6.018836   470.650523   \n",
              "75%          0.375000      0.187092    10.918176   636.010802   \n",
              "max         39.297000      0.889831    68.018875  4241.524913   \n",
              "\n",
              "       semi_major_axis_au_unit  \n",
              "count              9999.000000  \n",
              "mean                  2.689836  \n",
              "std                   0.607190  \n",
              "min                   0.832048  \n",
              "25%                   2.340816  \n",
              "50%                   2.614468  \n",
              "75%                   3.005449  \n",
              "max                  24.667968  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>spk_id</th>\n",
              "      <th>absolute_magnitude</th>\n",
              "      <th>diameter</th>\n",
              "      <th>albedo</th>\n",
              "      <th>diameter_sigma</th>\n",
              "      <th>eccentricity</th>\n",
              "      <th>inclination</th>\n",
              "      <th>moid_ld</th>\n",
              "      <th>semi_major_axis_au_unit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9.999000e+03</td>\n",
              "      <td>9999.000000</td>\n",
              "      <td>8688.000000</td>\n",
              "      <td>8672.000000</td>\n",
              "      <td>8591.000000</td>\n",
              "      <td>9999.000000</td>\n",
              "      <td>9999.000000</td>\n",
              "      <td>9999.000000</td>\n",
              "      <td>9999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.005000e+06</td>\n",
              "      <td>12.675380</td>\n",
              "      <td>19.245446</td>\n",
              "      <td>0.197723</td>\n",
              "      <td>0.454072</td>\n",
              "      <td>0.148716</td>\n",
              "      <td>7.890742</td>\n",
              "      <td>509.805237</td>\n",
              "      <td>2.689836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.886607e+03</td>\n",
              "      <td>1.639609</td>\n",
              "      <td>30.190191</td>\n",
              "      <td>0.138819</td>\n",
              "      <td>1.093676</td>\n",
              "      <td>0.083803</td>\n",
              "      <td>6.336244</td>\n",
              "      <td>205.046582</td>\n",
              "      <td>0.607190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.000001e+06</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.008000</td>\n",
              "      <td>0.006000</td>\n",
              "      <td>0.001003</td>\n",
              "      <td>0.042716</td>\n",
              "      <td>0.131028</td>\n",
              "      <td>0.832048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.002500e+06</td>\n",
              "      <td>11.900000</td>\n",
              "      <td>5.614000</td>\n",
              "      <td>0.074000</td>\n",
              "      <td>0.120000</td>\n",
              "      <td>0.093780</td>\n",
              "      <td>3.220137</td>\n",
              "      <td>377.829197</td>\n",
              "      <td>2.340816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.005000e+06</td>\n",
              "      <td>12.900000</td>\n",
              "      <td>9.814000</td>\n",
              "      <td>0.187000</td>\n",
              "      <td>0.201000</td>\n",
              "      <td>0.140335</td>\n",
              "      <td>6.018836</td>\n",
              "      <td>470.650523</td>\n",
              "      <td>2.614468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.007500e+06</td>\n",
              "      <td>13.700000</td>\n",
              "      <td>19.156750</td>\n",
              "      <td>0.283000</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>0.187092</td>\n",
              "      <td>10.918176</td>\n",
              "      <td>636.010802</td>\n",
              "      <td>3.005449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.009999e+06</td>\n",
              "      <td>20.700000</td>\n",
              "      <td>939.400000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>39.297000</td>\n",
              "      <td>0.889831</td>\n",
              "      <td>68.018875</td>\n",
              "      <td>4241.524913</td>\n",
              "      <td>24.667968</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "      </button>\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-238f6456-dbfb-4707-a725-c607c847f522');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D9uJtHLSSAMC"
      },
      "source": [
        "Before running any transformations, verify that all of the columns need to be used for model training. Start by looking at the column description provided by the [JPL website](https://ssd.jpl.nasa.gov/sbdb_query.cgi):\n",
        "\n",
        "* **spk_id:** Object primary SPK-ID.\n",
        "* **full_name:** Asteroid name.\n",
        "* **near_earth_object:** Near-earth object flag.\n",
        "* **absolute_magnitude:** The apparent magnitude an object would have if it were located at a distance of 10 parsecs.\n",
        "* **diameter:** Object diameter (from equivalent sphere) km unit.\n",
        "* **albedo:** A measure of the diffuse reflection of solar radiation out of the total solar radiation, measured on a scale from 0 to 1.\n",
        "* **diameter_sigma:** 1-sigma uncertainty in object diameter km unit.\n",
        "* **eccentricity:** A value between 0 and 1 that refers to how flat or round the asteroid is.\n",
        "* **inclination:** The angle with respect to the x-y ecliptic plane.\n",
        "* **moid_ld:** Earth Minimum Orbit Intersection Distance au unit.\n",
        "* **object_class:** The classification of the asteroid. For a more detailed description, see [NASA object classifications](https://pdssbn.astro.umd.edu/data_other/objclass.shtml).\n",
        "* **Semi-major axis au Unit:** The length of half of the long axis in AU unit.\n",
        "* **hazardous_flag:** Identifies hazardous asteroids."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DzYVKbwTp72d"
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        "The **spk_id** and **full_name** columns are unique for each row. You can remove these columns, because they are not needed for model training."
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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    {
      "cell_type": "markdown",
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              "near_earth_object           0.000000\n",
              "absolute_magnitude          0.000000\n",
              "diameter                   13.111311\n",
              "albedo                     13.271327\n",
              "diameter_sigma             14.081408\n",
              "eccentricity                0.000000\n",
              "inclination                 0.000000\n",
              "moid_ld                     0.000000\n",
              "object_class                0.000000\n",
              "semi_major_axis_au_unit     0.000000\n",
              "hazardous_flag              0.000000\n",
              "dtype: float64"
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          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "ib.collect(beam_df.isnull().mean() * 100)"
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        "Most of the columns do not have missing values. However, the columns **diameter**, **albedo**, and **diameter_sigma** have many missing values. Because these values cannot be measured or derived and aren't needed for training the ML model, remove the columns."
      ]
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          "data": {
            "text/plain": [
              "     near_earth_object  absolute_magnitude  eccentricity  inclination  \\\n",
              "0                    N                3.40      0.076009    10.594067   \n",
              "1                    N                4.20      0.229972    34.832932   \n",
              "2                    N                5.33      0.256936    12.991043   \n",
              "3                    N                3.00      0.088721     7.141771   \n",
              "4                    N                6.90      0.190913     5.367427   \n",
              "...                ...                 ...           ...          ...   \n",
              "9994                 N               15.10      0.160610     2.311731   \n",
              "9995                 N               13.60      0.235174     7.657713   \n",
              "9996                 N               14.30      0.113059     2.459643   \n",
              "9997                 N               15.10      0.093852     3.912263   \n",
              "9998                 N               13.00      0.071351     3.198839   \n",
              "\n",
              "         moid_ld object_class  semi_major_axis_au_unit hazardous_flag  \n",
              "0     620.640533          MBA                 2.769165              N  \n",
              "1     480.348639          MBA                 2.773841              N  \n",
              "2     402.514639          MBA                 2.668285              N  \n",
              "3     443.451432          MBA                 2.361418              N  \n",
              "4     426.433027          MBA                 2.574037              N  \n",
              "...          ...          ...                      ...            ...  \n",
              "9994  388.723233          MBA                 2.390249              N  \n",
              "9995  444.194746          MBA                 2.796605              N  \n",
              "9996  495.460110          MBA                 2.545674              N  \n",
              "9997  373.848377          MBA                 2.160961              N  \n",
              "9998  632.144398          MBA                 2.839917              N  \n",
              "\n",
              "[9999 rows x 8 columns]"
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          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "beam_df = beam_df.drop(['diameter', 'albedo', 'diameter_sigma'], axis='columns', inplace=False)\n",
        "ib.collect(beam_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v03ABuXJKEmv"
      },
      "source": [
        "### Normalize the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a3PojL3WBqgE"
      },
      "source": [
        "Normalize the numerical columns so that they can be used to train a model. To standarize the data, you can subtract the mean and divide by the standard deviation. This process is also known as finding the [z-score](https://developers.google.com/machine-learning/data-prep/transform/normalization#z-score).\n",
        "This step improves the performance and training stability of the model during training and inference.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sZ2_gB8wENF1"
      },
      "source": [
        "First, retrieve both the numerical columns and the categorical columns."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vsWY8xW5d_Wn"
      },
      "outputs": [],
      "source": [
        "numerical_cols = beam_df.select_dtypes(include=np.number).columns.tolist()\n",
        "categorical_cols = list(set(beam_df.columns) - set(numerical_cols))"
      ]
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        "outputId": "16fede03-f67e-4c26-8714-fd3fc6892109"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/content/beam/sdks/python/apache_beam/dataframe/frame_base.py:145: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  lambda left, right: getattr(left, op)(right), name=op, args=[other])\n"
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              "      absolute_magnitude  eccentricity  inclination   moid_ld  \\\n",
              "306            -1.570727     -0.062543    -0.278518  0.373194   \n",
              "310            -1.631718     -1.724526    -0.736389  1.087833   \n",
              "546            -1.753698      1.028793     1.415303 -0.339489   \n",
              "635            -1.875678      0.244869     0.005905  0.214107   \n",
              "701            -3.278451     -1.570523     2.006145  1.542754   \n",
              "...                  ...           ...          ...       ...   \n",
              "9697            0.807888     -1.151809    -0.082944 -0.129556   \n",
              "9813            1.722740      0.844551    -0.583247 -1.006447   \n",
              "9868            0.807888     -0.207399    -0.784665 -0.462136   \n",
              "9903            0.868878      0.460086     0.092258 -0.107597   \n",
              "9956            0.746898     -0.234132    -0.161116 -0.601379   \n",
              "\n",
              "      semi_major_axis_au_unit  \n",
              "306                  0.357201  \n",
              "310                  0.344233  \n",
              "546                  0.139080  \n",
              "635                  0.367559  \n",
              "701                  0.829337  \n",
              "...                       ...  \n",
              "9697                -0.533538  \n",
              "9813                -0.677961  \n",
              "9868                -0.539794  \n",
              "9903                 0.071794  \n",
              "9956                -0.664887  \n",
              "\n",
              "[9999 rows x 5 columns]"
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              "      <th>absolute_magnitude</th>\n",
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              "      <th>306</th>\n",
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              "      <td>0.373194</td>\n",
              "      <td>0.357201</td>\n",
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              "      <th>310</th>\n",
              "      <td>-1.631718</td>\n",
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              "      <td>1.087833</td>\n",
              "      <td>0.344233</td>\n",
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              "      <th>546</th>\n",
              "      <td>-1.753698</td>\n",
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              "      <td>-0.339489</td>\n",
              "      <td>0.139080</td>\n",
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              "    <tr>\n",
              "      <th>635</th>\n",
              "      <td>-1.875678</td>\n",
              "      <td>0.244869</td>\n",
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              "      <td>0.214107</td>\n",
              "      <td>0.367559</td>\n",
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              "      <th>701</th>\n",
              "      <td>-3.278451</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>9697</th>\n",
              "      <td>0.807888</td>\n",
              "      <td>-1.151809</td>\n",
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              "      <td>-0.129556</td>\n",
              "      <td>-0.533538</td>\n",
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              "    <tr>\n",
              "      <th>9813</th>\n",
              "      <td>1.722740</td>\n",
              "      <td>0.844551</td>\n",
              "      <td>-0.583247</td>\n",
              "      <td>-1.006447</td>\n",
              "      <td>-0.677961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9868</th>\n",
              "      <td>0.807888</td>\n",
              "      <td>-0.207399</td>\n",
              "      <td>-0.784665</td>\n",
              "      <td>-0.462136</td>\n",
              "      <td>-0.539794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9903</th>\n",
              "      <td>0.868878</td>\n",
              "      <td>0.460086</td>\n",
              "      <td>0.092258</td>\n",
              "      <td>-0.107597</td>\n",
              "      <td>0.071794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9956</th>\n",
              "      <td>0.746898</td>\n",
              "      <td>-0.234132</td>\n",
              "      <td>-0.161116</td>\n",
              "      <td>-0.601379</td>\n",
              "      <td>-0.664887</td>\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "# Get the numerical columns.\n",
        "beam_df_numericals = beam_df.filter(items=numerical_cols)\n",
        "\n",
        "# Standarize DataFrames with only the numerical columns.\n",
        "beam_df_numericals = (beam_df_numericals - beam_df_numericals.mean())/beam_df_numericals.std()\n",
        "\n",
        "ib.collect(beam_df_numericals)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qdNILsajFvex"
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      "source": [
        "Next, convert the categorical columns into one-hot encoded variables to use during training.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ngoxg0rSywVd"
      },
      "outputs": [],
      "source": [
        "def get_one_hot_encoding(df: pd.DataFrame, categorical_col:list) -> pd.DataFrame:\n",
        "  beam_df_categorical= beam_df[categorical_col]\n",
        "  # Get unique values.\n",
        "  with dataframe.allow_non_parallel_operations():\n",
        "    unique_classes = pd.CategoricalDtype(ib.collect(beam_df_categorical.unique(as_series=True)))\n",
        "  # Use `str.get_dummies()` to get the one-hot encoded representation of the categorical columns.\n",
        "  beam_df_categorical = beam_df_categorical.astype(unique_classes).str.get_dummies()\n",
        "  # Add a column name prefix to the newly created categorical columns.\n",
        "  beam_df_categorical = beam_df_categorical.add_prefix(f'{categorical_col}_')\n",
        "\n",
        "  return beam_df_categorical"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for categorical_col in categorical_cols:\n",
        "  beam_df_categorical = get_one_hot_encoding(df=beam_df, categorical_col=categorical_col)\n",
        "  beam_df_numericals = beam_df_numericals.merge(beam_df_categorical, left_index = True, right_index = True)\n",
        "ib.collect(beam_df_numericals)"
      ],
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          "height": 602
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        "id": "k9rvtWqHf6Qw",
        "outputId": "b8d8ae57-6dba-45b4-e7ae-e4b14084eede"
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      "execution_count": null,
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              "      absolute_magnitude  eccentricity  inclination   moid_ld  \\\n",
              "0              -5.657067     -0.867596     0.426645  0.540537   \n",
              "12             -3.583402     -0.756931     1.364340  0.238610   \n",
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              "...                  ...           ...          ...       ...   \n",
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              "\n",
              "      semi_major_axis_au_unit  near_earth_object_N  near_earth_object_Y  \\\n",
              "0                    0.130649                    1                    0   \n",
              "12                  -0.187375                    1                    0   \n",
              "47                   0.691182                    1                    0   \n",
              "381                  0.712755                    1                    0   \n",
              "515                 -0.014654                    1                    0   \n",
              "...                       ...                  ...                  ...   \n",
              "9146                -0.820638                    1                    0   \n",
              "9657                -0.468778                    1                    0   \n",
              "9704                 0.587128                    1                    0   \n",
              "9879                -0.662602                    1                    0   \n",
              "9980                -0.022726                    1                    0   \n",
              "\n",
              "      near_earth_object_nan  object_class_AMO  object_class_APO  ...  \\\n",
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              "\n",
              "      object_class_OMB  object_class_TJN  object_class_nan  hazardous_flag_N  \\\n",
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              "9980                 0                 0                 0                 1   \n",
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              "      hazardous_flag_Y  hazardous_flag_nan  \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",
              "            + ' 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": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rVdSIyCB0spw"
      },
      "source": [
        "### Run the pipeline\n",
        "\n",
        "This section combines the previous steps into a full pipeline implementation, and then visualizes the preprocessed data.\n",
        "\n",
        "Note that the only standard Apache Beam method invoked here is the `pipeline` instance. The rest of the preprocessing commands are based on native pandas methods that are integrated with the Apache Beam DataFrame API."
      ]
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      "cell_type": "code",
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      "metadata": {
        "id": "ndaSNond0v8Q",
        "colab": {
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          "height": 651
        },
        "outputId": "b265e915-e649-44e4-a31a-95ac85c0ebf6"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/content/beam/sdks/python/apache_beam/dataframe/frame_base.py:145: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  lambda left, right: getattr(left, op)(right), name=op, args=[other])\n"
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            "text/plain": [
              "      absolute_magnitude  eccentricity  inclination   moid_ld  \\\n",
              "0              -5.657067     -0.867596     0.426645  0.540537   \n",
              "12             -3.583402     -0.756931     1.364340  0.238610   \n",
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              "515            -2.729540      1.469775     0.799915 -0.602881   \n",
              "...                  ...           ...          ...       ...   \n",
              "9146            0.563927     -0.508757    -0.327512 -0.637391   \n",
              "9657            1.478779      0.487849    -0.637779 -0.648240   \n",
              "9704            0.380957     -0.238383     0.443053  0.670490   \n",
              "9879            1.295809     -0.442966    -0.698505 -0.494818   \n",
              "9980            0.746898     -1.455992    -0.849144  0.592902   \n",
              "\n",
              "      semi_major_axis_au_unit  near_earth_object_N  near_earth_object_Y  \\\n",
              "0                    0.130649                    1                    0   \n",
              "12                  -0.187375                    1                    0   \n",
              "47                   0.691182                    1                    0   \n",
              "381                  0.712755                    1                    0   \n",
              "515                 -0.014654                    1                    0   \n",
              "...                       ...                  ...                  ...   \n",
              "9146                -0.820638                    1                    0   \n",
              "9657                -0.468778                    1                    0   \n",
              "9704                 0.587128                    1                    0   \n",
              "9879                -0.662602                    1                    0   \n",
              "9980                -0.022726                    1                    0   \n",
              "\n",
              "      near_earth_object_nan  object_class_AMO  object_class_APO  ...  \\\n",
              "0                         0                 0                 0  ...   \n",
              "12                        0                 0                 0  ...   \n",
              "47                        0                 0                 0  ...   \n",
              "381                       0                 0                 0  ...   \n",
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              "9657                      0                 0                 0  ...   \n",
              "9704                      0                 0                 0  ...   \n",
              "9879                      0                 0                 0  ...   \n",
              "9980                      0                 0                 0  ...   \n",
              "\n",
              "      object_class_CEN  object_class_IMB  object_class_MBA  object_class_MCA  \\\n",
              "0                    0                 0                 1                 0   \n",
              "12                   0                 0                 1                 0   \n",
              "47                   0                 0                 1                 0   \n",
              "381                  0                 0                 1                 0   \n",
              "515                  0                 0                 1                 0   \n",
              "...                ...               ...               ...               ...   \n",
              "9146                 0                 0                 1                 0   \n",
              "9657                 0                 0                 1                 0   \n",
              "9704                 0                 0                 1                 0   \n",
              "9879                 0                 0                 1                 0   \n",
              "9980                 0                 0                 1                 0   \n",
              "\n",
              "      object_class_OMB  object_class_TJN  object_class_nan  hazardous_flag_N  \\\n",
              "0                    0                 0                 0                 1   \n",
              "12                   0                 0                 0                 1   \n",
              "47                   0                 0                 0                 1   \n",
              "381                  0                 0                 0                 1   \n",
              "515                  0                 0                 0                 1   \n",
              "...                ...               ...               ...               ...   \n",
              "9146                 0                 0                 0                 1   \n",
              "9657                 0                 0                 0                 1   \n",
              "9704                 0                 0                 0                 1   \n",
              "9879                 0                 0                 0                 1   \n",
              "9980                 0                 0                 0                 1   \n",
              "\n",
              "      hazardous_flag_Y  hazardous_flag_nan  \n",
              "0                    0                   0  \n",
              "12                   0                   0  \n",
              "47                   0                   0  \n",
              "381                  0                   0  \n",
              "515                  0                   0  \n",
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              "9657                 0                   0  \n",
              "9704                 0                   0  \n",
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              "9980                 0                   0  \n",
              "\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9146</th>\n",
              "      <td>0.563927</td>\n",
              "      <td>-0.508757</td>\n",
              "      <td>-0.327512</td>\n",
              "      <td>-0.637391</td>\n",
              "      <td>-0.820638</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9657</th>\n",
              "      <td>1.478779</td>\n",
              "      <td>0.487849</td>\n",
              "      <td>-0.637779</td>\n",
              "      <td>-0.648240</td>\n",
              "      <td>-0.468778</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9704</th>\n",
              "      <td>0.380957</td>\n",
              "      <td>-0.238383</td>\n",
              "      <td>0.443053</td>\n",
              "      <td>0.670490</td>\n",
              "      <td>0.587128</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9879</th>\n",
              "      <td>1.295809</td>\n",
              "      <td>-0.442966</td>\n",
              "      <td>-0.698505</td>\n",
              "      <td>-0.494818</td>\n",
              "      <td>-0.662602</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9980</th>\n",
              "      <td>0.746898</td>\n",
              "      <td>-1.455992</td>\n",
              "      <td>-0.849144</td>\n",
              "      <td>0.592902</td>\n",
              "      <td>-0.022726</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9999 rows × 22 columns</p>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \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",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-43e93125-8ade-4324-bff6-6c06d8f90ea1 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-43e93125-8ade-4324-bff6-6c06d8f90ea1');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "# Specify the location of the source CSV file.\n",
        "source_csv_file = 'gs://apache-beam-samples/nasa_jpl_asteroid/sample_10000.csv'\n",
        "\n",
        "# Initialize the pipeline.\n",
        "p = beam.Pipeline(InteractiveRunner())\n",
        "\n",
        "# Create a deferred Apache Beam DataFrame with the contents of the CSV file.\n",
        "beam_df = p | beam.dataframe.io.read_csv(source_csv_file)\n",
        "\n",
        "# Drop irrelevant columns and columns with missing values.\n",
        "beam_df = beam_df.drop(['spk_id', 'full_name','diameter', 'albedo', 'diameter_sigma'], axis='columns', inplace=False)\n",
        "\n",
        "# Get numerical columns and columns with categorical values.\n",
        "numerical_cols = beam_df.select_dtypes(include=np.number).columns.tolist()\n",
        "categorical_cols = list(set(beam_df.columns) - set(numerical_cols))\n",
        "\n",
        "# Normalize the numerical values.\n",
        "beam_df_numericals = beam_df.filter(items=numerical_cols)\n",
        "beam_df_numericals = (beam_df_numericals - beam_df_numericals.mean())/beam_df_numericals.std()\n",
        "\n",
        "\n",
        "# One-hot encode the categorical values.\n",
        "for categorical_col in categorical_cols:\n",
        "  beam_df_categorical= get_one_hot_encoding(df=beam_df, categorical_col=categorical_col)\n",
        "  beam_df_numericals = beam_df_numericals.merge(beam_df_categorical, left_index = True, right_index = True)\n",
        "\n",
        "ib.collect(beam_df_numericals)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xZvJTqa3XKI_"
      },
      "source": [
        "## Process the full dataset with the distributed runner\n",
        "The previous section demonstrates how to build and execute the pipeline locally using the interactive runner.\n",
        "This section demonstrates how to run the pipeline on the full dataset by switching to a distributed runner. For this example, the pipeline runs on [Dataflow](https://cloud.google.com/dataflow/docs/guides/deploying-a-pipeline)."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "PROJECT_ID = \"<my-gcp-project>\"\n",
        "REGION = \"us-central1\"\n",
        "TEMP_DIR = \"gs://<my-bucket>/tmp\"\n",
        "OUTPUT_DIR = \"gs://<my-bucket>/dataframe-result\""
      ],
      "metadata": {
        "id": "dDBYbMEWbL4t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "These steps process the full dataset, `full.csv`, which contains approximately one million rows. To materialize the deferred DataFrame, these steps also write the results to a CSV file instead of using `ib.collect()`.\n",
        "\n",
        "To switch from an interactive runner to a distributed runner, update the pipeline options. The rest of the pipeline steps don't change."
      ],
      "metadata": {
        "id": "Qk1GaYoSc9-1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify the location of the source CSV file (the full dataset).\n",
        "source_csv_file = 'gs://apache-beam-samples/nasa_jpl_asteroid/full.csv'\n",
        "\n",
        "# Build a new pipeline that runs on Dataflow.\n",
        "p = beam.Pipeline(DataflowRunner(),\n",
        "                  options=beam.options.pipeline_options.PipelineOptions(\n",
        "                      project=PROJECT_ID,\n",
        "                      region=REGION,\n",
        "                      temp_location=TEMP_DIR,\n",
        "                      # To speed up the demo, disable autoscaling.\n",
        "                      autoscaling_algorithm='NONE',\n",
        "                      num_workers=10))\n",
        "\n",
        "# Create a deferred Apache Beam DataFrame with the contents of the CSV file.\n",
        "beam_df = p | beam.dataframe.io.read_csv(source_csv_file)\n",
        "\n",
        "# Drop irrelevant columns and columns with missing values.\n",
        "beam_df = beam_df.drop(['spk_id', 'full_name','diameter', 'albedo', 'diameter_sigma'], axis='columns', inplace=False)\n",
        "\n",
        "# Get numerical columns and columns with categorical values.\n",
        "numerical_cols = beam_df.select_dtypes(include=np.number).columns.tolist()\n",
        "categorical_cols = list(set(beam_df.columns) - set(numerical_cols))\n",
        "\n",
        "# Normalize the numerical values. \n",
        "beam_df_numericals = beam_df.filter(items=numerical_cols)\n",
        "beam_df_numericals = (beam_df_numericals - beam_df_numericals.mean())/beam_df_numericals.std()\n",
        "\n",
        "\n",
        "# One-hot encode the categorical values. \n",
        "for categorical_col in categorical_cols:\n",
        "  beam_df_categorical= get_one_hot_encoding(df=beam_df, categorical_col=categorical_col)\n",
        "  beam_df_numericals = beam_df_numericals.merge(beam_df_categorical, left_index = True, right_index = True\n",
        "\n",
        "# Write the preprocessed dataset to a CSV file.\n",
        "beam_df_numericals.to_csv(os.path.join(OUTPUT_DIR, \"preprocessed_data.csv\"))"
      ],
      "metadata": {
        "id": "1XovR0gKbMlK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Submit and run the pipeline."
      ],
      "metadata": {
        "id": "a789u4Yecs_g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "p.run().wait_until_finish()"
      ],
      "metadata": {
        "id": "pbUlC102bPaZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Wait while the pipeline job runs."
      ],
      "metadata": {
        "id": "dzdqmzKzTOng"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## What's next \n",
        "\n",
        "This tutorial demonstrated how to analyze and preprocess a large-scale dataset with the Apache Beam DataFrames API. You can now train a model on a classification task using the preprocessed dataset.\n",
        "\n",
        "To learn more about how to get started with classifying structured data, see \n",
        "[Structured data classification from scratch](https://keras.io/examples/structured_data/structured_data_classification_from_scratch/).\n",
        "\n",
        "To continue learning, find another dataset to use with the Apache Beam DataFrames API processing. Think carefully about which features to include in your model and how to represent them.\n"
      ],
      "metadata": {
        "id": "UOLr6YgOOSVQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Resources\n",
        "\n",
        "* [Beam DataFrames overview](https://beam.apache.org/documentation/dsls/dataframes/overview) - An overview of the Apache Beam DataFrames API.\n",
        "* [Differences from pandas](https://beam.apache.org/documentation/dsls/dataframes/differences-from-pandas) - Reviews the differences between Apache Beam DataFrames and Pandas DataFrames, as well as some of the workarounds for unsupported operations.\n",
        "* [10 minutes to Pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html) - A quickstart guide to the Pandas DataFrames.\n",
        "* [Pandas DataFrame API](https://pandas.pydata.org/pandas-docs/stable/reference/frame.html) - The API reference for the Pandas DataFrames.\n",
        "* [Data preparation and feature training in ML](https://developers.google.com/machine-learning/data-prep) - A guideline about data transformation for ML training."
      ],
      "metadata": {
        "id": "nG9WXXVcMCe_"
      }
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
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    "kernelspec": {
      "display_name": "Python 3",
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    },
    "language_info": {
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