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From GitBox <...@apache.org>
Subject [GitHub] [beam] pcoet commented on a change in pull request #15470: [BEAM-12535] add dataframes notebook
Date Tue, 07 Sep 2021 19:37:11 GMT

pcoet commented on a change in pull request #15470:
URL: https://github.com/apache/beam/pull/15470#discussion_r703763952



##########
File path: examples/notebooks/tour-of-beam/dataframes.ipynb
##########
@@ -0,0 +1,820 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Beam DataFrames",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "cellView": "form",
+        "id": "rz2qIC9IL2rI"
+      },
+      "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."
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hDuXLLSZnI1D"
+      },
+      "source": [
+        "# Beam DataFrames\n",
+        "\n",
+        "<button>\n",
+        "  <a href=\"https://beam.apache.org/documentation/dsls/dataframes/overview/\">\n",
+        "    <img src=\"https://beam.apache.org/images/favicon.ico\" alt=\"Open the docs\" height=\"16\"/>\n",
+        "    Beam DataFrames overview\n",
+        "  </a>\n",
+        "</button>\n",
+        "\n",
+        "Beam DataFrames provide a pandas-like [DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html)\n",
+        "API to declare Beam pipelines.\n",
+        "\n",
+        "> ℹ️ To learn more about Beam DataFrames, take a look at the\n",
+        "[Beam DataFrames overview](https://beam.apache.org/documentation/dsls/dataframes/overview) page.\n",
+        "\n",
+        "First, we need to install Apache Beam with the `interactive` extra for the Interactive runner."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8QVByaWjkarZ"
+      },
+      "source": [
+        "%pip install --quiet apache-beam[interactive]"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aLqdbX4Mgipq"
+      },
+      "source": [
+        "Lets create a small data file of\n",
+        "[Comma-Separated Values (CSV)](https://en.wikipedia.org/wiki/Comma-separated_values).\n",
+        "It simply includes the dates of the\n",
+        "[equinoxes](https://en.wikipedia.org/wiki/Equinox) and\n",
+        "[solstices](https://en.wikipedia.org/wiki/Solstice)\n",
+        "of the year 2021."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hZjwAm7qotrJ"
+      },
+      "source": [
+        "%%writefile solar_events.csv\n",
+        "timestamp,event\n",
+        "2021-03-20 09:37:00,March Equinox\n",
+        "2021-06-21 03:32:00,June Solstice\n",
+        "2021-09-22 19:21:00,September Equinox\n",
+        "2021-12-21 15:59:00,December Solstice"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Hv_58JulleQ_"
+      },
+      "source": [
+        "# Interactive Beam\n",
+        "\n",
+        "Pandas has the\n",
+        "[`pandas.read_csv`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)\n",
+        "function to easily read CSV files into DataFrames.\n",
+        "Beam has the\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 that emulates `pandas.read_csv`, but returns us a deferred Beam DataFrame.\n",
+        "\n",
+        "If you’re using\n",
+        "[Interactive Beam](https://beam.apache.org/releases/pydoc/current/apache_beam.runners.interactive.interactive_beam.html),\n",
+        "you can use `collect` to bring a Beam DataFrame into local memory as a Pandas DataFrame."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 242
+        },
+        "id": "sKAMXD5ElhYP",
+        "outputId": "928d9ad7-ae75-42d7-8dc6-8c5afd730b11"
+      },
+      "source": [
+        "import apache_beam as beam\n",
+        "import apache_beam.runners.interactive.interactive_beam as ib\n",
+        "from apache_beam.runners.interactive.interactive_runner import InteractiveRunner\n",
+        "\n",
+        "pipeline = beam.Pipeline(InteractiveRunner())\n",
+        "\n",
+        "# Create a deferred Beam DataFrame with the contents of our csv file.\n",
+        "beam_df = pipeline | 'Read CSV' >> beam.dataframe.io.read_csv('solar_events.csv')\n",
+        "\n",
+        "# We can use `ib.collect` to view the contents of a Beam DataFrame.\n",
+        "ib.collect(beam_df)"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "\n",
+              "        if (typeof window.interactive_beam_jquery == 'undefined') {\n",
+              "          var jqueryScript = document.createElement('script');\n",
+              "          jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n",
+              "          jqueryScript.type = 'text/javascript';\n",
+              "          jqueryScript.onload = function() {\n",
+              "            var datatableScript = document.createElement('script');\n",
+              "            datatableScript.src = 'https://cdn.datatables.net/1.10.20/js/jquery.dataTables.min.js';\n",
+              "            datatableScript.type = 'text/javascript';\n",
+              "            datatableScript.onload = function() {\n",
+              "              window.interactive_beam_jquery = jQuery.noConflict(true);\n",
+              "              window.interactive_beam_jquery(document).ready(function($){\n",
+              "                \n",
+              "              });\n",
+              "            }\n",
+              "            document.head.appendChild(datatableScript);\n",
+              "          };\n",
+              "          document.head.appendChild(jqueryScript);\n",
+              "        } else {\n",
+              "          window.interactive_beam_jquery(document).ready(function($){\n",
+              "            \n",
+              "          });\n",
+              "        }"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "\n",
+              "            <link rel=\"stylesheet\" href=\"https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css\" integrity=\"sha384-Vkoo8x4CGsO3+Hhxv8T/Q5PaXtkKtu6ug5TOeNV6gBiFeWPGFN9MuhOf23Q9Ifjh\" crossorigin=\"anonymous\">\n",
+              "            <div id=\"progress_indicator_1516f4062e4fc6d4e58f33cf44c41c1d\" class=\"spinner-border text-info\" role=\"status\">\n",
+              "            </div>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "\n",
+              "        if (typeof window.interactive_beam_jquery == 'undefined') {\n",
+              "          var jqueryScript = document.createElement('script');\n",
+              "          jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n",
+              "          jqueryScript.type = 'text/javascript';\n",
+              "          jqueryScript.onload = function() {\n",
+              "            var datatableScript = document.createElement('script');\n",
+              "            datatableScript.src = 'https://cdn.datatables.net/1.10.20/js/jquery.dataTables.min.js';\n",
+              "            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",
+              "            $(\"#progress_indicator_1516f4062e4fc6d4e58f33cf44c41c1d\").remove();\n",
+              "              });\n",
+              "            }\n",
+              "            document.head.appendChild(datatableScript);\n",
+              "          };\n",
+              "          document.head.appendChild(jqueryScript);\n",
+              "        } else {\n",
+              "          window.interactive_beam_jquery(document).ready(function($){\n",
+              "            \n",
+              "            $(\"#progress_indicator_1516f4062e4fc6d4e58f33cf44c41c1d\").remove();\n",
+              "          });\n",
+              "        }"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>timestamp</th>\n",
+              "      <th>event</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>solar_events.csv:0</th>\n",
+              "      <td>2021-03-20 09:37:00</td>\n",
+              "      <td>March Equinox</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>solar_events.csv:1</th>\n",
+              "      <td>2021-06-21 03:32:00</td>\n",
+              "      <td>June Solstice</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>solar_events.csv:2</th>\n",
+              "      <td>2021-09-22 19:21:00</td>\n",
+              "      <td>September Equinox</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>solar_events.csv:3</th>\n",
+              "      <td>2021-12-21 15:59:00</td>\n",
+              "      <td>December Solstice</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                              timestamp              event\n",
+              "solar_events.csv:0  2021-03-20 09:37:00      March Equinox\n",
+              "solar_events.csv:1  2021-06-21 03:32:00      June Solstice\n",
+              "solar_events.csv:2  2021-09-22 19:21:00  September Equinox\n",
+              "solar_events.csv:3  2021-12-21 15:59:00  December Solstice"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "t3Is6dArtN_Z"
+      },
+      "source": [
+        "Collecting a Beam DataFrame into a Pandas DataFrame is useful to perform\n",
+        "[operations not supported by Beam DataFrames](https://beam.apache.org/documentation/dsls/dataframes/differences-from-pandas#classes-of-unsupported-operations).\n",
+        "\n",
+        "For example, lets say we want to take only the first two events in chronological order.\n",

Review comment:
       lets -> let's

##########
File path: examples/notebooks/tour-of-beam/dataframes.ipynb
##########
@@ -0,0 +1,820 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Beam DataFrames",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "cellView": "form",
+        "id": "rz2qIC9IL2rI"
+      },
+      "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."
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hDuXLLSZnI1D"
+      },
+      "source": [
+        "# Beam DataFrames\n",
+        "\n",
+        "<button>\n",
+        "  <a href=\"https://beam.apache.org/documentation/dsls/dataframes/overview/\">\n",
+        "    <img src=\"https://beam.apache.org/images/favicon.ico\" alt=\"Open the docs\" height=\"16\"/>\n",
+        "    Beam DataFrames overview\n",
+        "  </a>\n",
+        "</button>\n",
+        "\n",
+        "Beam DataFrames provide a pandas-like [DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html)\n",
+        "API to declare Beam pipelines.\n",

Review comment:
       I'm not sure if "declare" is exactly right here. The API doesn't provide a declarative syntax for creating a pipeline, right? You create the pipeline using standard Beam syntax, and then read/write data into a DataFrame.

##########
File path: examples/notebooks/tour-of-beam/dataframes.ipynb
##########
@@ -0,0 +1,820 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Beam DataFrames",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "cellView": "form",
+        "id": "rz2qIC9IL2rI"
+      },
+      "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."
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hDuXLLSZnI1D"
+      },
+      "source": [
+        "# Beam DataFrames\n",
+        "\n",
+        "<button>\n",
+        "  <a href=\"https://beam.apache.org/documentation/dsls/dataframes/overview/\">\n",
+        "    <img src=\"https://beam.apache.org/images/favicon.ico\" alt=\"Open the docs\" height=\"16\"/>\n",
+        "    Beam DataFrames overview\n",
+        "  </a>\n",
+        "</button>\n",
+        "\n",
+        "Beam DataFrames provide a pandas-like [DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html)\n",
+        "API to declare Beam pipelines.\n",

Review comment:
       We should have @TheNeuralBit review this too.

##########
File path: examples/notebooks/tour-of-beam/dataframes.ipynb
##########
@@ -0,0 +1,820 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Beam DataFrames",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "cellView": "form",
+        "id": "rz2qIC9IL2rI"
+      },
+      "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."
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hDuXLLSZnI1D"
+      },
+      "source": [
+        "# Beam DataFrames\n",
+        "\n",
+        "<button>\n",
+        "  <a href=\"https://beam.apache.org/documentation/dsls/dataframes/overview/\">\n",
+        "    <img src=\"https://beam.apache.org/images/favicon.ico\" alt=\"Open the docs\" height=\"16\"/>\n",
+        "    Beam DataFrames overview\n",
+        "  </a>\n",
+        "</button>\n",
+        "\n",
+        "Beam DataFrames provide a pandas-like [DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html)\n",
+        "API to declare Beam pipelines.\n",
+        "\n",
+        "> ℹ️ To learn more about Beam DataFrames, take a look at the\n",
+        "[Beam DataFrames overview](https://beam.apache.org/documentation/dsls/dataframes/overview) page.\n",
+        "\n",
+        "First, we need to install Apache Beam with the `interactive` extra for the Interactive runner."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8QVByaWjkarZ"
+      },
+      "source": [
+        "%pip install --quiet apache-beam[interactive]"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aLqdbX4Mgipq"
+      },
+      "source": [
+        "Lets create a small data file of\n",
+        "[Comma-Separated Values (CSV)](https://en.wikipedia.org/wiki/Comma-separated_values).\n",
+        "It simply includes the dates of the\n",
+        "[equinoxes](https://en.wikipedia.org/wiki/Equinox) and\n",
+        "[solstices](https://en.wikipedia.org/wiki/Solstice)\n",
+        "of the year 2021."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hZjwAm7qotrJ"
+      },
+      "source": [
+        "%%writefile solar_events.csv\n",
+        "timestamp,event\n",
+        "2021-03-20 09:37:00,March Equinox\n",
+        "2021-06-21 03:32:00,June Solstice\n",
+        "2021-09-22 19:21:00,September Equinox\n",
+        "2021-12-21 15:59:00,December Solstice"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Hv_58JulleQ_"
+      },
+      "source": [
+        "# Interactive Beam\n",
+        "\n",
+        "Pandas has the\n",
+        "[`pandas.read_csv`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)\n",
+        "function to easily read CSV files into DataFrames.\n",
+        "Beam has the\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 that emulates `pandas.read_csv`, but returns us a deferred Beam DataFrame.\n",

Review comment:
       You don't need "us" here.

##########
File path: examples/notebooks/tour-of-beam/dataframes.ipynb
##########
@@ -0,0 +1,820 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Beam DataFrames",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "cellView": "form",
+        "id": "rz2qIC9IL2rI"
+      },
+      "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."
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hDuXLLSZnI1D"
+      },
+      "source": [
+        "# Beam DataFrames\n",
+        "\n",
+        "<button>\n",
+        "  <a href=\"https://beam.apache.org/documentation/dsls/dataframes/overview/\">\n",
+        "    <img src=\"https://beam.apache.org/images/favicon.ico\" alt=\"Open the docs\" height=\"16\"/>\n",
+        "    Beam DataFrames overview\n",
+        "  </a>\n",
+        "</button>\n",
+        "\n",
+        "Beam DataFrames provide a pandas-like [DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html)\n",
+        "API to declare Beam pipelines.\n",
+        "\n",
+        "> ℹ️ To learn more about Beam DataFrames, take a look at the\n",
+        "[Beam DataFrames overview](https://beam.apache.org/documentation/dsls/dataframes/overview) page.\n",
+        "\n",
+        "First, we need to install Apache Beam with the `interactive` extra for the Interactive runner."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8QVByaWjkarZ"
+      },
+      "source": [
+        "%pip install --quiet apache-beam[interactive]"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aLqdbX4Mgipq"
+      },
+      "source": [
+        "Lets create a small data file of\n",
+        "[Comma-Separated Values (CSV)](https://en.wikipedia.org/wiki/Comma-separated_values).\n",
+        "It simply includes the dates of the\n",
+        "[equinoxes](https://en.wikipedia.org/wiki/Equinox) and\n",
+        "[solstices](https://en.wikipedia.org/wiki/Solstice)\n",
+        "of the year 2021."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hZjwAm7qotrJ"
+      },
+      "source": [
+        "%%writefile solar_events.csv\n",
+        "timestamp,event\n",
+        "2021-03-20 09:37:00,March Equinox\n",
+        "2021-06-21 03:32:00,June Solstice\n",
+        "2021-09-22 19:21:00,September Equinox\n",
+        "2021-12-21 15:59:00,December Solstice"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Hv_58JulleQ_"
+      },
+      "source": [
+        "# Interactive Beam\n",
+        "\n",
+        "Pandas has the\n",
+        "[`pandas.read_csv`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)\n",
+        "function to easily read CSV files into DataFrames.\n",
+        "Beam has the\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 that emulates `pandas.read_csv`, but returns us a deferred Beam DataFrame.\n",
+        "\n",
+        "If you’re using\n",
+        "[Interactive Beam](https://beam.apache.org/releases/pydoc/current/apache_beam.runners.interactive.interactive_beam.html),\n",
+        "you can use `collect` to bring a Beam DataFrame into local memory as a Pandas DataFrame."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 242
+        },
+        "id": "sKAMXD5ElhYP",
+        "outputId": "928d9ad7-ae75-42d7-8dc6-8c5afd730b11"
+      },
+      "source": [
+        "import apache_beam as beam\n",
+        "import apache_beam.runners.interactive.interactive_beam as ib\n",
+        "from apache_beam.runners.interactive.interactive_runner import InteractiveRunner\n",
+        "\n",
+        "pipeline = beam.Pipeline(InteractiveRunner())\n",
+        "\n",
+        "# Create a deferred Beam DataFrame with the contents of our csv file.\n",
+        "beam_df = pipeline | 'Read CSV' >> beam.dataframe.io.read_csv('solar_events.csv')\n",
+        "\n",
+        "# We can use `ib.collect` to view the contents of a Beam DataFrame.\n",
+        "ib.collect(beam_df)"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "\n",
+              "        if (typeof window.interactive_beam_jquery == 'undefined') {\n",
+              "          var jqueryScript = document.createElement('script');\n",
+              "          jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n",
+              "          jqueryScript.type = 'text/javascript';\n",
+              "          jqueryScript.onload = function() {\n",
+              "            var datatableScript = document.createElement('script');\n",
+              "            datatableScript.src = 'https://cdn.datatables.net/1.10.20/js/jquery.dataTables.min.js';\n",
+              "            datatableScript.type = 'text/javascript';\n",
+              "            datatableScript.onload = function() {\n",
+              "              window.interactive_beam_jquery = jQuery.noConflict(true);\n",
+              "              window.interactive_beam_jquery(document).ready(function($){\n",
+              "                \n",
+              "              });\n",
+              "            }\n",
+              "            document.head.appendChild(datatableScript);\n",
+              "          };\n",
+              "          document.head.appendChild(jqueryScript);\n",
+              "        } else {\n",
+              "          window.interactive_beam_jquery(document).ready(function($){\n",
+              "            \n",
+              "          });\n",
+              "        }"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "\n",
+              "            <link rel=\"stylesheet\" href=\"https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css\" integrity=\"sha384-Vkoo8x4CGsO3+Hhxv8T/Q5PaXtkKtu6ug5TOeNV6gBiFeWPGFN9MuhOf23Q9Ifjh\" crossorigin=\"anonymous\">\n",
+              "            <div id=\"progress_indicator_1516f4062e4fc6d4e58f33cf44c41c1d\" class=\"spinner-border text-info\" role=\"status\">\n",
+              "            </div>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "\n",
+              "        if (typeof window.interactive_beam_jquery == 'undefined') {\n",
+              "          var jqueryScript = document.createElement('script');\n",
+              "          jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n",
+              "          jqueryScript.type = 'text/javascript';\n",
+              "          jqueryScript.onload = function() {\n",
+              "            var datatableScript = document.createElement('script');\n",
+              "            datatableScript.src = 'https://cdn.datatables.net/1.10.20/js/jquery.dataTables.min.js';\n",
+              "            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",
+              "            $(\"#progress_indicator_1516f4062e4fc6d4e58f33cf44c41c1d\").remove();\n",
+              "              });\n",
+              "            }\n",
+              "            document.head.appendChild(datatableScript);\n",
+              "          };\n",
+              "          document.head.appendChild(jqueryScript);\n",
+              "        } else {\n",
+              "          window.interactive_beam_jquery(document).ready(function($){\n",
+              "            \n",
+              "            $(\"#progress_indicator_1516f4062e4fc6d4e58f33cf44c41c1d\").remove();\n",
+              "          });\n",
+              "        }"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>timestamp</th>\n",
+              "      <th>event</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>solar_events.csv:0</th>\n",
+              "      <td>2021-03-20 09:37:00</td>\n",
+              "      <td>March Equinox</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>solar_events.csv:1</th>\n",
+              "      <td>2021-06-21 03:32:00</td>\n",
+              "      <td>June Solstice</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>solar_events.csv:2</th>\n",
+              "      <td>2021-09-22 19:21:00</td>\n",
+              "      <td>September Equinox</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>solar_events.csv:3</th>\n",
+              "      <td>2021-12-21 15:59:00</td>\n",
+              "      <td>December Solstice</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                              timestamp              event\n",
+              "solar_events.csv:0  2021-03-20 09:37:00      March Equinox\n",
+              "solar_events.csv:1  2021-06-21 03:32:00      June Solstice\n",
+              "solar_events.csv:2  2021-09-22 19:21:00  September Equinox\n",
+              "solar_events.csv:3  2021-12-21 15:59:00  December Solstice"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "t3Is6dArtN_Z"
+      },
+      "source": [
+        "Collecting a Beam DataFrame into a Pandas DataFrame is useful to perform\n",
+        "[operations not supported by Beam DataFrames](https://beam.apache.org/documentation/dsls/dataframes/differences-from-pandas#classes-of-unsupported-operations).\n",
+        "\n",
+        "For example, lets say we want to take only the first two events in chronological order.\n",
+        "Since a deferred Beam DataFrame does not have any ordering guarantees,\n",
+        "first we need to sort the values.\n",
+        "In Pandas, we could first\n",
+        "[`df.sort_values(by='timestamp')`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_values.html) and then\n",
+        "[`df.head(2)`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.head.html) to achieve this.\n",
+        "\n",
+        "However, these are\n",
+        "[order-sensitive operations](https://beam.apache.org/documentation/dsls/dataframes/differences-from-pandas#order-sensitive-operations)\n",
+        "so using them in a Beam DataFrame raises a\n",
+        "[`WontImplementError`](https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.frame_base.html#apache_beam.dataframe.frame_base.WontImplementError).\n",
+        "We can work around this by using `collect` to convert the Beam DataFrame into a Pandas DataFrame."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 138
+        },
+        "id": "8haEu6_9iTi7",
+        "outputId": "a1e07bdc-c66d-45e5-efff-90b93219c648"
+      },
+      "source": [
+        "import apache_beam.runners.interactive.interactive_beam as ib\n",
+        "\n",
+        "# Collect the Beam DataFrame into a Pandas DataFrame.\n",
+        "df = ib.collect(beam_df)\n",
+        "\n",
+        "# We can now use any Pandas transforms with our data.\n",
+        "df.sort_values(by='timestamp').head(2)"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "\n",
+              "            <link rel=\"stylesheet\" href=\"https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css\" integrity=\"sha384-Vkoo8x4CGsO3+Hhxv8T/Q5PaXtkKtu6ug5TOeNV6gBiFeWPGFN9MuhOf23Q9Ifjh\" crossorigin=\"anonymous\">\n",
+              "            <div id=\"progress_indicator_4486e01c01f75e7a68a4a5fefa9ecd2c\" class=\"spinner-border text-info\" role=\"status\">\n",
+              "            </div>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "\n",
+              "        if (typeof window.interactive_beam_jquery == 'undefined') {\n",
+              "          var jqueryScript = document.createElement('script');\n",
+              "          jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n",
+              "          jqueryScript.type = 'text/javascript';\n",
+              "          jqueryScript.onload = function() {\n",
+              "            var datatableScript = document.createElement('script');\n",
+              "            datatableScript.src = 'https://cdn.datatables.net/1.10.20/js/jquery.dataTables.min.js';\n",
+              "            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",
+              "            $(\"#progress_indicator_4486e01c01f75e7a68a4a5fefa9ecd2c\").remove();\n",
+              "              });\n",
+              "            }\n",
+              "            document.head.appendChild(datatableScript);\n",
+              "          };\n",
+              "          document.head.appendChild(jqueryScript);\n",
+              "        } else {\n",
+              "          window.interactive_beam_jquery(document).ready(function($){\n",
+              "            \n",
+              "            $(\"#progress_indicator_4486e01c01f75e7a68a4a5fefa9ecd2c\").remove();\n",
+              "          });\n",
+              "        }"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>timestamp</th>\n",
+              "      <th>event</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>solar_events.csv:0</th>\n",
+              "      <td>2021-03-20 09:37:00</td>\n",
+              "      <td>March Equinox</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>solar_events.csv:1</th>\n",
+              "      <td>2021-06-21 03:32:00</td>\n",
+              "      <td>June Solstice</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                              timestamp          event\n",
+              "solar_events.csv:0  2021-03-20 09:37:00  March Equinox\n",
+              "solar_events.csv:1  2021-06-21 03:32:00  June Solstice"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZkthQ13pwpm0"
+      },
+      "source": [
+        "> ℹ️ Note that `collect` is _only_ accessible if you’re using\n",
+        "[Interactive Beam](https://beam.apache.org/releases/pydoc/current/apache_beam.runners.interactive.interactive_beam.html)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ujRm4K0iP8SX"
+      },
+      "source": [
+        "# Beam DataFrames to PCollections\n",
+        "\n",
+        "If you have your data as a Beam DataFrame, you can convert it into a regular PCollection with\n",
+        "[`to_pcollection`](https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.convert.html#apache_beam.dataframe.convert.to_pcollection).\n",
+        "\n",
+        "Converting a Beam DataFrame into a PCollection gives us each element as a\n",
+        "[`namedtuple`](https://docs.python.org/3/library/collections.html#collections.namedtuple)\n",
+        "instance.\n",
+        "This allows us to easily access each property, for example `element.event` and `element.timestamp`.\n",
+        "\n",
+        "Sometimes it's more convenient to convert the named tuples to Python dictionaries,\n",

Review comment:
       Grammar nit (sorry): "dictionaries, we" -> "dictionaries. We"

##########
File path: examples/notebooks/tour-of-beam/dataframes.ipynb
##########
@@ -0,0 +1,820 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Beam DataFrames",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "cellView": "form",
+        "id": "rz2qIC9IL2rI"
+      },
+      "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."
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hDuXLLSZnI1D"
+      },
+      "source": [
+        "# Beam DataFrames\n",
+        "\n",
+        "<button>\n",
+        "  <a href=\"https://beam.apache.org/documentation/dsls/dataframes/overview/\">\n",
+        "    <img src=\"https://beam.apache.org/images/favicon.ico\" alt=\"Open the docs\" height=\"16\"/>\n",
+        "    Beam DataFrames overview\n",
+        "  </a>\n",
+        "</button>\n",
+        "\n",
+        "Beam DataFrames provide a pandas-like [DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html)\n",
+        "API to declare Beam pipelines.\n",
+        "\n",
+        "> ℹ️ To learn more about Beam DataFrames, take a look at the\n",
+        "[Beam DataFrames overview](https://beam.apache.org/documentation/dsls/dataframes/overview) page.\n",
+        "\n",
+        "First, we need to install Apache Beam with the `interactive` extra for the Interactive runner."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8QVByaWjkarZ"
+      },
+      "source": [
+        "%pip install --quiet apache-beam[interactive]"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aLqdbX4Mgipq"
+      },
+      "source": [
+        "Lets create a small data file of\n",
+        "[Comma-Separated Values (CSV)](https://en.wikipedia.org/wiki/Comma-separated_values).\n",
+        "It simply includes the dates of the\n",
+        "[equinoxes](https://en.wikipedia.org/wiki/Equinox) and\n",
+        "[solstices](https://en.wikipedia.org/wiki/Solstice)\n",
+        "of the year 2021."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hZjwAm7qotrJ"
+      },
+      "source": [
+        "%%writefile solar_events.csv\n",
+        "timestamp,event\n",
+        "2021-03-20 09:37:00,March Equinox\n",
+        "2021-06-21 03:32:00,June Solstice\n",
+        "2021-09-22 19:21:00,September Equinox\n",
+        "2021-12-21 15:59:00,December Solstice"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Hv_58JulleQ_"
+      },
+      "source": [
+        "# Interactive Beam\n",
+        "\n",
+        "Pandas has the\n",
+        "[`pandas.read_csv`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)\n",
+        "function to easily read CSV files into DataFrames.\n",
+        "Beam has the\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 that emulates `pandas.read_csv`, but returns us a deferred Beam DataFrame.\n",
+        "\n",
+        "If you’re using\n",
+        "[Interactive Beam](https://beam.apache.org/releases/pydoc/current/apache_beam.runners.interactive.interactive_beam.html),\n",
+        "you can use `collect` to bring a Beam DataFrame into local memory as a Pandas DataFrame."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 242
+        },
+        "id": "sKAMXD5ElhYP",
+        "outputId": "928d9ad7-ae75-42d7-8dc6-8c5afd730b11"
+      },
+      "source": [
+        "import apache_beam as beam\n",
+        "import apache_beam.runners.interactive.interactive_beam as ib\n",
+        "from apache_beam.runners.interactive.interactive_runner import InteractiveRunner\n",
+        "\n",
+        "pipeline = beam.Pipeline(InteractiveRunner())\n",
+        "\n",
+        "# Create a deferred Beam DataFrame with the contents of our csv file.\n",
+        "beam_df = pipeline | 'Read CSV' >> beam.dataframe.io.read_csv('solar_events.csv')\n",
+        "\n",
+        "# We can use `ib.collect` to view the contents of a Beam DataFrame.\n",
+        "ib.collect(beam_df)"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "\n",
+              "        if (typeof window.interactive_beam_jquery == 'undefined') {\n",
+              "          var jqueryScript = document.createElement('script');\n",
+              "          jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n",
+              "          jqueryScript.type = 'text/javascript';\n",
+              "          jqueryScript.onload = function() {\n",
+              "            var datatableScript = document.createElement('script');\n",
+              "            datatableScript.src = 'https://cdn.datatables.net/1.10.20/js/jquery.dataTables.min.js';\n",
+              "            datatableScript.type = 'text/javascript';\n",
+              "            datatableScript.onload = function() {\n",
+              "              window.interactive_beam_jquery = jQuery.noConflict(true);\n",
+              "              window.interactive_beam_jquery(document).ready(function($){\n",
+              "                \n",
+              "              });\n",
+              "            }\n",
+              "            document.head.appendChild(datatableScript);\n",
+              "          };\n",
+              "          document.head.appendChild(jqueryScript);\n",
+              "        } else {\n",
+              "          window.interactive_beam_jquery(document).ready(function($){\n",
+              "            \n",
+              "          });\n",
+              "        }"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "\n",
+              "            <link rel=\"stylesheet\" href=\"https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css\" integrity=\"sha384-Vkoo8x4CGsO3+Hhxv8T/Q5PaXtkKtu6ug5TOeNV6gBiFeWPGFN9MuhOf23Q9Ifjh\" crossorigin=\"anonymous\">\n",
+              "            <div id=\"progress_indicator_1516f4062e4fc6d4e58f33cf44c41c1d\" class=\"spinner-border text-info\" role=\"status\">\n",
+              "            </div>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "\n",
+              "        if (typeof window.interactive_beam_jquery == 'undefined') {\n",
+              "          var jqueryScript = document.createElement('script');\n",
+              "          jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n",
+              "          jqueryScript.type = 'text/javascript';\n",
+              "          jqueryScript.onload = function() {\n",
+              "            var datatableScript = document.createElement('script');\n",
+              "            datatableScript.src = 'https://cdn.datatables.net/1.10.20/js/jquery.dataTables.min.js';\n",
+              "            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",
+              "            $(\"#progress_indicator_1516f4062e4fc6d4e58f33cf44c41c1d\").remove();\n",
+              "              });\n",
+              "            }\n",
+              "            document.head.appendChild(datatableScript);\n",
+              "          };\n",
+              "          document.head.appendChild(jqueryScript);\n",
+              "        } else {\n",
+              "          window.interactive_beam_jquery(document).ready(function($){\n",
+              "            \n",
+              "            $(\"#progress_indicator_1516f4062e4fc6d4e58f33cf44c41c1d\").remove();\n",
+              "          });\n",
+              "        }"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>timestamp</th>\n",
+              "      <th>event</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>solar_events.csv:0</th>\n",
+              "      <td>2021-03-20 09:37:00</td>\n",
+              "      <td>March Equinox</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>solar_events.csv:1</th>\n",
+              "      <td>2021-06-21 03:32:00</td>\n",
+              "      <td>June Solstice</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>solar_events.csv:2</th>\n",
+              "      <td>2021-09-22 19:21:00</td>\n",
+              "      <td>September Equinox</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>solar_events.csv:3</th>\n",
+              "      <td>2021-12-21 15:59:00</td>\n",
+              "      <td>December Solstice</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                              timestamp              event\n",
+              "solar_events.csv:0  2021-03-20 09:37:00      March Equinox\n",
+              "solar_events.csv:1  2021-06-21 03:32:00      June Solstice\n",
+              "solar_events.csv:2  2021-09-22 19:21:00  September Equinox\n",
+              "solar_events.csv:3  2021-12-21 15:59:00  December Solstice"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "t3Is6dArtN_Z"
+      },
+      "source": [
+        "Collecting a Beam DataFrame into a Pandas DataFrame is useful to perform\n",
+        "[operations not supported by Beam DataFrames](https://beam.apache.org/documentation/dsls/dataframes/differences-from-pandas#classes-of-unsupported-operations).\n",
+        "\n",
+        "For example, lets say we want to take only the first two events in chronological order.\n",
+        "Since a deferred Beam DataFrame does not have any ordering guarantees,\n",
+        "first we need to sort the values.\n",
+        "In Pandas, we could first\n",
+        "[`df.sort_values(by='timestamp')`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_values.html) and then\n",
+        "[`df.head(2)`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.head.html) to achieve this.\n",
+        "\n",
+        "However, these are\n",
+        "[order-sensitive operations](https://beam.apache.org/documentation/dsls/dataframes/differences-from-pandas#order-sensitive-operations)\n",
+        "so using them in a Beam DataFrame raises a\n",
+        "[`WontImplementError`](https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.frame_base.html#apache_beam.dataframe.frame_base.WontImplementError).\n",
+        "We can work around this by using `collect` to convert the Beam DataFrame into a Pandas DataFrame."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 138
+        },
+        "id": "8haEu6_9iTi7",
+        "outputId": "a1e07bdc-c66d-45e5-efff-90b93219c648"
+      },
+      "source": [
+        "import apache_beam.runners.interactive.interactive_beam as ib\n",
+        "\n",
+        "# Collect the Beam DataFrame into a Pandas DataFrame.\n",
+        "df = ib.collect(beam_df)\n",
+        "\n",
+        "# We can now use any Pandas transforms with our data.\n",
+        "df.sort_values(by='timestamp').head(2)"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "\n",
+              "            <link rel=\"stylesheet\" href=\"https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css\" integrity=\"sha384-Vkoo8x4CGsO3+Hhxv8T/Q5PaXtkKtu6ug5TOeNV6gBiFeWPGFN9MuhOf23Q9Ifjh\" crossorigin=\"anonymous\">\n",
+              "            <div id=\"progress_indicator_4486e01c01f75e7a68a4a5fefa9ecd2c\" class=\"spinner-border text-info\" role=\"status\">\n",
+              "            </div>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "\n",
+              "        if (typeof window.interactive_beam_jquery == 'undefined') {\n",
+              "          var jqueryScript = document.createElement('script');\n",
+              "          jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n",
+              "          jqueryScript.type = 'text/javascript';\n",
+              "          jqueryScript.onload = function() {\n",
+              "            var datatableScript = document.createElement('script');\n",
+              "            datatableScript.src = 'https://cdn.datatables.net/1.10.20/js/jquery.dataTables.min.js';\n",
+              "            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",
+              "            $(\"#progress_indicator_4486e01c01f75e7a68a4a5fefa9ecd2c\").remove();\n",
+              "              });\n",
+              "            }\n",
+              "            document.head.appendChild(datatableScript);\n",
+              "          };\n",
+              "          document.head.appendChild(jqueryScript);\n",
+              "        } else {\n",
+              "          window.interactive_beam_jquery(document).ready(function($){\n",
+              "            \n",
+              "            $(\"#progress_indicator_4486e01c01f75e7a68a4a5fefa9ecd2c\").remove();\n",
+              "          });\n",
+              "        }"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>timestamp</th>\n",
+              "      <th>event</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>solar_events.csv:0</th>\n",
+              "      <td>2021-03-20 09:37:00</td>\n",
+              "      <td>March Equinox</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>solar_events.csv:1</th>\n",
+              "      <td>2021-06-21 03:32:00</td>\n",
+              "      <td>June Solstice</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                              timestamp          event\n",
+              "solar_events.csv:0  2021-03-20 09:37:00  March Equinox\n",
+              "solar_events.csv:1  2021-06-21 03:32:00  June Solstice"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZkthQ13pwpm0"
+      },
+      "source": [
+        "> ℹ️ Note that `collect` is _only_ accessible if you’re using\n",
+        "[Interactive Beam](https://beam.apache.org/releases/pydoc/current/apache_beam.runners.interactive.interactive_beam.html)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ujRm4K0iP8SX"
+      },
+      "source": [
+        "# Beam DataFrames to PCollections\n",
+        "\n",
+        "If you have your data as a Beam DataFrame, you can convert it into a regular PCollection with\n",
+        "[`to_pcollection`](https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.convert.html#apache_beam.dataframe.convert.to_pcollection).\n",
+        "\n",
+        "Converting a Beam DataFrame into a PCollection gives us each element as a\n",
+        "[`namedtuple`](https://docs.python.org/3/library/collections.html#collections.namedtuple)\n",
+        "instance.\n",
+        "This allows us to easily access each property, for example `element.event` and `element.timestamp`.\n",
+        "\n",
+        "Sometimes it's more convenient to convert the named tuples to Python dictionaries,\n",
+        "we can do that with the\n",
+        "[`_asdict`](https://docs.python.org/3/library/collections.html#collections.somenamedtuple._asdict)\n",
+        "method."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "g22op8rZPvB3",
+        "outputId": "bba88b0b-4d19-4d61-dac7-2c168998a2e4"
+      },
+      "source": [
+        "import apache_beam as beam\n",
+        "from apache_beam.dataframe import convert\n",
+        "\n",
+        "with beam.Pipeline() as pipeline:\n",
+        "  beam_df = pipeline | 'Read CSV' >> beam.dataframe.io.read_csv('solar_events.csv')\n",
+        "\n",
+        "  (\n",
+        "      # Convert the Beam DataFrame to a PCollection.\n",
+        "      convert.to_pcollection(beam_df)\n",
+        "\n",
+        "      # We get named tuples, we can convert them to dictionaries like this.\n",
+        "      | 'To dictionaries' >> beam.Map(lambda x: dict(x._asdict()))\n",
+        "\n",
+        "      # Print the elements in the PCollection.\n",
+        "      | 'Print' >> beam.Map(print)\n",
+        "  )"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'timestamp': '2021-03-20 09:37:00', 'event': 'March Equinox'}\n",
+            "{'timestamp': '2021-06-21 03:32:00', 'event': 'June Solstice'}\n",
+            "{'timestamp': '2021-09-22 19:21:00', 'event': 'September Equinox'}\n",
+            "{'timestamp': '2021-12-21 15:59:00', 'event': 'December Solstice'}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "t6xNIO0iPwtn"
+      },
+      "source": [
+        "# Pandas DataFrames to PCollections\n",
+        "\n",
+        "If you have your data as a Pandas DataFrame, you can convert it into a regular PCollection with\n",
+        "[`to_pcollection`](https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.convert.html#apache_beam.dataframe.convert.to_pcollection).\n",
+        "\n",
+        "Since Pandas DataFrames are not part of any Beam pipeline, we must provide the `pipeline` explicitly."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YWYVFkvFuksz",
+        "outputId": "a3e3e6fa-85ce-4891-95a0-389fba4461a6"
+      },
+      "source": [
+        "import pandas as pd\n",
+        "import apache_beam as beam\n",
+        "from apache_beam.dataframe import convert\n",
+        "\n",
+        "with beam.Pipeline() as pipeline:\n",
+        "  df = pd.read_csv('solar_events.csv')\n",
+        "\n",
+        "  (\n",
+        "      # Convert the Pandas DataFrame to a PCollection.\n",
+        "      convert.to_pcollection(df, pipeline=pipeline)\n",
+        "\n",
+        "      # We get named tuples, we can convert them to dictionaries like this.\n",
+        "      | 'To dictionaries' >> beam.Map(lambda x: dict(x._asdict()))\n",
+        "\n",
+        "      # Print the elements in the PCollection.\n",
+        "      | 'Print' >> beam.Map(print)\n",
+        "  )"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'timestamp': '2021-03-20 09:37:00', 'event': 'March Equinox'}\n",
+            "{'timestamp': '2021-06-21 03:32:00', 'event': 'June Solstice'}\n",
+            "{'timestamp': '2021-09-22 19:21:00', 'event': 'September Equinox'}\n",
+            "{'timestamp': '2021-12-21 15:59:00', 'event': 'December Solstice'}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "z6Q_tyWszkMC"
+      },
+      "source": [
+        "If you have your data as a PCollection of Pandas DataFrames, you can convert them into a PCollection with\n",
+        "[`FlatMap`](https://beam.apache.org/documentation/transforms/python/elementwise/flatmap).\n",
+        "\n",
+        "> ℹ️ If the number of elements in each DataFrame can be very different, that is that some DataFrames contain thousands of elements while others only contain a handful of elements, it might be a good idea to\n",

Review comment:
       "different, that is that some DataFrames contain thousands of elements while others only contain a handful of elements, it" -> "different (that is, some DataFrames might contain thousands of elements while others contain only a handful of elements), it"




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