davidcavazos commented on a change in pull request #15470:
URL: https://github.com/apache/beam/pull/15470#discussion_r704522951
##########
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:
Good catch! Done
##########
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:
Thanks, done
##########
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 =
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+ " datatableScript.src =
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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",
+ " });\n",
+ " }\n",
+ " document.head.appendChild(datatableScript);\n",
+ " };\n",
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+ " } else {\n",
+ "
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+ " });\n",
+ " }"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "\n",
+ " <link rel=\"stylesheet\"
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+ " </div>"
+ ],
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+ "<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 =
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+ " 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 =
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+ "
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+ " \n",
+ "
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+ " }\n",
+ " document.head.appendChild(datatableScript);\n",
+ " };\n",
+ " document.head.appendChild(jqueryScript);\n",
+ " } else {\n",
+ "
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+ " \n",
+ "
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+ " });\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>"
+ ],
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+ "<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 =
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+ "
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+ " \n",
+ "
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+ " });\n",
+ " }\n",
+ " document.head.appendChild(datatableScript);\n",
+ " };\n",
+ " document.head.appendChild(jqueryScript);\n",
+ " } else {\n",
+ "
window.interactive_beam_jquery(document).ready(function($){\n",
+ " \n",
+ "
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+ " });\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:
Thank you, I greatly appreciate this, I'm always looking forward to
improving my writing :)
##########
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",
+ "
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+ " });\n",
+ " }\n",
+ " document.head.appendChild(datatableScript);\n",
+ " };\n",
+ " document.head.appendChild(jqueryScript);\n",
+ " } else {\n",
+ "
window.interactive_beam_jquery(document).ready(function($){\n",
+ " \n",
+ "
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+ " });\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:
Thanks, done
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