{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/documentation/transforms/python/elementwise/flatmap-py.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "view-the-docs-top"
   },
   "source": [
    "<table align=\"left\"><td><a target=\"_blank\" href=\"https://beam.apache.org/documentation/transforms/python/elementwise/flatmap\"><img src=\"https://beam.apache.org/images/logos/full-color/name-bottom/beam-logo-full-color-name-bottom-100.png\" width=\"32\" height=\"32\" />View the docs</a></td></table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "id": "_-code"
   },
   "outputs": [],
   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\")\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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "flatmap"
   },
   "source": [
    "# FlatMap\n",
    "\n",
    "<script type=\"text/javascript\">\n",
    "localStorage.setItem('language', 'language-py')\n",
    "</script>\n",
    "\n",
    "<table align=\"left\" style=\"margin-right:1em\">\n",
    "  <td>\n",
    "    <a class=\"button\" target=\"_blank\" href=\"https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.core.html#apache_beam.transforms.core.FlatMap\"><img src=\"https://beam.apache.org/images/logos/sdks/python.png\" width=\"32px\" height=\"32px\" alt=\"Pydoc\"/> Pydoc</a>\n",
    "  </td>\n",
    "</table>\n",
    "\n",
    "<br/><br/><br/>\n",
    "\n",
    "Applies a simple 1-to-many mapping function over each element in the collection.\n",
    "The many elements are flattened into the resulting collection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "setup"
   },
   "source": [
    "## Setup\n",
    "\n",
    "To run a code cell, you can click the **Run cell** button at the top left of the cell,\n",
    "or select it and press **`Shift+Enter`**.\n",
    "Try modifying a code cell and re-running it to see what happens.\n",
    "\n",
    "> To learn more about Colab, see\n",
    "> [Welcome to Colaboratory!](https://colab.sandbox.google.com/notebooks/welcome.ipynb).\n",
    "\n",
    "First, let's install the `apache-beam` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "setup-code"
   },
   "outputs": [],
   "source": [
    "!pip install --quiet -U apache-beam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "examples"
   },
   "source": [
    "## Examples\n",
    "\n",
    "In the following examples, we create a pipeline with a `PCollection` of produce with their icon, name, and duration.\n",
    "Then, we apply `FlatMap` in multiple ways to yield zero or more elements per each input element into the resulting `PCollection`.\n",
    "\n",
    "`FlatMap` accepts a function that returns an `iterable`,\n",
    "where each of the output `iterable`'s elements is an element of the resulting `PCollection`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "example-1-flatmap-with-a-predefined-function"
   },
   "source": [
    "### Example 1: FlatMap with a predefined function\n",
    "\n",
    "We use the function `str.split` which takes a single `str` element and outputs a `list` of `str`s.\n",
    "This pipeline splits the input element using whitespaces, creating a list of zero or more elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "example-1-flatmap-with-a-predefined-function-code"
   },
   "outputs": [],
   "source": [
    "import apache_beam as beam\n",
    "\n",
    "with beam.Pipeline() as pipeline:\n",
    "  plants = (\n",
    "      pipeline\n",
    "      | 'Gardening plants' >> beam.Create([\n",
    "          '🍓Strawberry 🥕Carrot 🍆Eggplant',\n",
    "          '🍅Tomato 🥔Potato',\n",
    "      ])\n",
    "      | 'Split words' >> beam.FlatMap(str.split)\n",
    "      | beam.Map(print))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "example-1-flatmap-with-a-predefined-function-2"
   },
   "source": [
    "<table align=\"left\" style=\"margin-right:1em\">\n",
    "  <td>\n",
    "    <a class=\"button\" target=\"_blank\" href=\"https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/elementwise/flatmap.py\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" width=\"32px\" height=\"32px\" alt=\"View source code\"/> View source code</a>\n",
    "  </td>\n",
    "</table>\n",
    "\n",
    "<br/><br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "example-2-flatmap-with-a-function"
   },
   "source": [
    "### Example 2: FlatMap with a function\n",
    "\n",
    "We define a function `split_words` which splits an input `str` element using the delimiter `','` and outputs a `list` of `str`s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "example-2-flatmap-with-a-function-code"
   },
   "outputs": [],
   "source": [
    "import apache_beam as beam\n",
    "\n",
    "def split_words(text):\n",
    "  return text.split(',')\n",
    "\n",
    "with beam.Pipeline() as pipeline:\n",
    "  plants = (\n",
    "      pipeline\n",
    "      | 'Gardening plants' >> beam.Create([\n",
    "          '🍓Strawberry,🥕Carrot,🍆Eggplant',\n",
    "          '🍅Tomato,🥔Potato',\n",
    "      ])\n",
    "      | 'Split words' >> beam.FlatMap(split_words)\n",
    "      | beam.Map(print))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "example-2-flatmap-with-a-function-2"
   },
   "source": [
    "<table align=\"left\" style=\"margin-right:1em\">\n",
    "  <td>\n",
    "    <a class=\"button\" target=\"_blank\" href=\"https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/elementwise/flatmap.py\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" width=\"32px\" height=\"32px\" alt=\"View source code\"/> View source code</a>\n",
    "  </td>\n",
    "</table>\n",
    "\n",
    "<br/><br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "example-3-flatmap-with-a-lambda-function"
   },
   "source": [
    "### Example 3: FlatMap with a lambda function\n",
    "\n",
    "For this example, we want to flatten a `PCollection` of lists of `str`s into a `PCollection` of `str`s.\n",
    "Each input element is already an `iterable`, where each element is what we want in the resulting `PCollection`.\n",
    "We use a lambda function that returns the same input element it received."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "example-3-flatmap-with-a-lambda-function-code"
   },
   "outputs": [],
   "source": [
    "import apache_beam as beam\n",
    "\n",
    "with beam.Pipeline() as pipeline:\n",
    "  plants = (\n",
    "      pipeline\n",
    "      | 'Gardening plants' >> beam.Create([\n",
    "          ['🍓Strawberry', '🥕Carrot', '🍆Eggplant'],\n",
    "          ['🍅Tomato', '🥔Potato'],\n",
    "      ])\n",
    "      | 'Flatten lists' >> beam.FlatMap(lambda elements: elements)\n",
    "      | beam.Map(print))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "example-3-flatmap-with-a-lambda-function-2"
   },
   "source": [
    "<table align=\"left\" style=\"margin-right:1em\">\n",
    "  <td>\n",
    "    <a class=\"button\" target=\"_blank\" href=\"https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/elementwise/flatmap.py\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" width=\"32px\" height=\"32px\" alt=\"View source code\"/> View source code</a>\n",
    "  </td>\n",
    "</table>\n",
    "\n",
    "<br/><br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "example-4-flatmap-with-a-generator"
   },
   "source": [
    "### Example 4: FlatMap with a generator\n",
    "\n",
    "For this example, we want to flatten a `PCollection` of lists of `str`s into a `PCollection` of `str`s.\n",
    "We use a generator to iterate over the input list and yield each of the elements.\n",
    "Each yielded result in the generator is an element in the resulting `PCollection`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "example-4-flatmap-with-a-generator-code"
   },
   "outputs": [],
   "source": [
    "import apache_beam as beam\n",
    "\n",
    "def generate_elements(elements):\n",
    "  for element in elements:\n",
    "    yield element\n",
    "\n",
    "with beam.Pipeline() as pipeline:\n",
    "  plants = (\n",
    "      pipeline\n",
    "      | 'Gardening plants' >> beam.Create([\n",
    "          ['🍓Strawberry', '🥕Carrot', '🍆Eggplant'],\n",
    "          ['🍅Tomato', '🥔Potato'],\n",
    "      ])\n",
    "      | 'Flatten lists' >> beam.FlatMap(generate_elements)\n",
    "      | beam.Map(print))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "example-4-flatmap-with-a-generator-2"
   },
   "source": [
    "<table align=\"left\" style=\"margin-right:1em\">\n",
    "  <td>\n",
    "    <a class=\"button\" target=\"_blank\" href=\"https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/elementwise/flatmap.py\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" width=\"32px\" height=\"32px\" alt=\"View source code\"/> View source code</a>\n",
    "  </td>\n",
    "</table>\n",
    "\n",
    "<br/><br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "example-5-flatmaptuple-for-key-value-pairs"
   },
   "source": [
    "### Example 5: FlatMapTuple for key-value pairs\n",
    "\n",
    "If your `PCollection` consists of `(key, value)` pairs,\n",
    "you can use `FlatMapTuple` to unpack them into different function arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "example-5-flatmaptuple-for-key-value-pairs-code"
   },
   "outputs": [],
   "source": [
    "import apache_beam as beam\n",
    "\n",
    "def format_plant(icon, plant):\n",
    "  if icon:\n",
    "    yield '{}{}'.format(icon, plant)\n",
    "\n",
    "with beam.Pipeline() as pipeline:\n",
    "  plants = (\n",
    "      pipeline\n",
    "      | 'Gardening plants' >> beam.Create([\n",
    "          ('🍓', 'Strawberry'),\n",
    "          ('🥕', 'Carrot'),\n",
    "          ('🍆', 'Eggplant'),\n",
    "          ('🍅', 'Tomato'),\n",
    "          ('🥔', 'Potato'),\n",
    "          (None, 'Invalid'),\n",
    "      ])\n",
    "      | 'Format' >> beam.FlatMapTuple(format_plant)\n",
    "      | beam.Map(print))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "example-5-flatmaptuple-for-key-value-pairs-2"
   },
   "source": [
    "<table align=\"left\" style=\"margin-right:1em\">\n",
    "  <td>\n",
    "    <a class=\"button\" target=\"_blank\" href=\"https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/elementwise/flatmap.py\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" width=\"32px\" height=\"32px\" alt=\"View source code\"/> View source code</a>\n",
    "  </td>\n",
    "</table>\n",
    "\n",
    "<br/><br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "example-6-flatmap-with-multiple-arguments"
   },
   "source": [
    "### Example 6: FlatMap with multiple arguments\n",
    "\n",
    "You can pass functions with multiple arguments to `FlatMap`.\n",
    "They are passed as additional positional arguments or keyword arguments to the function.\n",
    "\n",
    "In this example, `split_words` takes `text` and `delimiter` as arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "example-6-flatmap-with-multiple-arguments-code"
   },
   "outputs": [],
   "source": [
    "import apache_beam as beam\n",
    "\n",
    "def split_words(text, delimiter=None):\n",
    "  return text.split(delimiter)\n",
    "\n",
    "with beam.Pipeline() as pipeline:\n",
    "  plants = (\n",
    "      pipeline\n",
    "      | 'Gardening plants' >> beam.Create([\n",
    "          '🍓Strawberry,🥕Carrot,🍆Eggplant',\n",
    "          '🍅Tomato,🥔Potato',\n",
    "      ])\n",
    "      | 'Split words' >> beam.FlatMap(split_words, delimiter=',')\n",
    "      | beam.Map(print))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "example-6-flatmap-with-multiple-arguments-2"
   },
   "source": [
    "<table align=\"left\" style=\"margin-right:1em\">\n",
    "  <td>\n",
    "    <a class=\"button\" target=\"_blank\" href=\"https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/elementwise/flatmap.py\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" width=\"32px\" height=\"32px\" alt=\"View source code\"/> View source code</a>\n",
    "  </td>\n",
    "</table>\n",
    "\n",
    "<br/><br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "example-7-flatmap-with-side-inputs-as-singletons"
   },
   "source": [
    "### Example 7: FlatMap with side inputs as singletons\n",
    "\n",
    "If the `PCollection` has a single value, such as the average from another computation,\n",
    "passing the `PCollection` as a *singleton* accesses that value.\n",
    "\n",
    "In this example, we pass a `PCollection` the value `','` as a singleton.\n",
    "We then use that value as the delimiter for the `str.split` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "example-7-flatmap-with-side-inputs-as-singletons-code"
   },
   "outputs": [],
   "source": [
    "import apache_beam as beam\n",
    "\n",
    "with beam.Pipeline() as pipeline:\n",
    "  delimiter = pipeline | 'Create delimiter' >> beam.Create([','])\n",
    "\n",
    "  plants = (\n",
    "      pipeline\n",
    "      | 'Gardening plants' >> beam.Create([\n",
    "          '🍓Strawberry,🥕Carrot,🍆Eggplant',\n",
    "          '🍅Tomato,🥔Potato',\n",
    "      ])\n",
    "      | 'Split words' >> beam.FlatMap(\n",
    "          lambda text,\n",
    "          delimiter: text.split(delimiter),\n",
    "          delimiter=beam.pvalue.AsSingleton(delimiter),\n",
    "      )\n",
    "      | beam.Map(print))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "example-7-flatmap-with-side-inputs-as-singletons-2"
   },
   "source": [
    "<table align=\"left\" style=\"margin-right:1em\">\n",
    "  <td>\n",
    "    <a class=\"button\" target=\"_blank\" href=\"https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/elementwise/flatmap.py\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" width=\"32px\" height=\"32px\" alt=\"View source code\"/> View source code</a>\n",
    "  </td>\n",
    "</table>\n",
    "\n",
    "<br/><br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "example-8-flatmap-with-side-inputs-as-iterators"
   },
   "source": [
    "### Example 8: FlatMap with side inputs as iterators\n",
    "\n",
    "If the `PCollection` has multiple values, pass the `PCollection` as an *iterator*.\n",
    "This accesses elements lazily as they are needed,\n",
    "so it is possible to iterate over large `PCollection`s that won't fit into memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "example-8-flatmap-with-side-inputs-as-iterators-code"
   },
   "outputs": [],
   "source": [
    "import apache_beam as beam\n",
    "\n",
    "def normalize_and_validate_durations(plant, valid_durations):\n",
    "  plant['duration'] = plant['duration'].lower()\n",
    "  if plant['duration'] in valid_durations:\n",
    "    yield plant\n",
    "\n",
    "with beam.Pipeline() as pipeline:\n",
    "  valid_durations = pipeline | 'Valid durations' >> beam.Create([\n",
    "      'annual',\n",
    "      'biennial',\n",
    "      'perennial',\n",
    "  ])\n",
    "\n",
    "  valid_plants = (\n",
    "      pipeline\n",
    "      | 'Gardening plants' >> beam.Create([\n",
    "          {\n",
    "              'icon': '🍓', 'name': 'Strawberry', 'duration': 'Perennial'\n",
    "          },\n",
    "          {\n",
    "              'icon': '🥕', 'name': 'Carrot', 'duration': 'BIENNIAL'\n",
    "          },\n",
    "          {\n",
    "              'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'\n",
    "          },\n",
    "          {\n",
    "              'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'\n",
    "          },\n",
    "          {\n",
    "              'icon': '🥔', 'name': 'Potato', 'duration': 'unknown'\n",
    "          },\n",
    "      ])\n",
    "      | 'Normalize and validate durations' >> beam.FlatMap(\n",
    "          normalize_and_validate_durations,\n",
    "          valid_durations=beam.pvalue.AsIter(valid_durations),\n",
    "      )\n",
    "      | beam.Map(print))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "example-8-flatmap-with-side-inputs-as-iterators-2"
   },
   "source": [
    "<table align=\"left\" style=\"margin-right:1em\">\n",
    "  <td>\n",
    "    <a class=\"button\" target=\"_blank\" href=\"https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/elementwise/flatmap.py\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" width=\"32px\" height=\"32px\" alt=\"View source code\"/> View source code</a>\n",
    "  </td>\n",
    "</table>\n",
    "\n",
    "<br/><br/><br/>\n",
    "\n",
    "> **Note**: You can pass the `PCollection` as a *list* with `beam.pvalue.AsList(pcollection)`,\n",
    "> but this requires that all the elements fit into memory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "example-9-flatmap-with-side-inputs-as-dictionaries"
   },
   "source": [
    "### Example 9: FlatMap with side inputs as dictionaries\n",
    "\n",
    "If a `PCollection` is small enough to fit into memory, then that `PCollection` can be passed as a *dictionary*.\n",
    "Each element must be a `(key, value)` pair.\n",
    "Note that all the elements of the `PCollection` must fit into memory for this.\n",
    "If the `PCollection` won't fit into memory, use `beam.pvalue.AsIter(pcollection)` instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "example-9-flatmap-with-side-inputs-as-dictionaries-code"
   },
   "outputs": [],
   "source": [
    "import apache_beam as beam\n",
    "\n",
    "def replace_duration_if_valid(plant, durations):\n",
    "  if plant['duration'] in durations:\n",
    "    plant['duration'] = durations[plant['duration']]\n",
    "    yield plant\n",
    "\n",
    "with beam.Pipeline() as pipeline:\n",
    "  durations = pipeline | 'Durations dict' >> beam.Create([\n",
    "      (0, 'annual'),\n",
    "      (1, 'biennial'),\n",
    "      (2, 'perennial'),\n",
    "  ])\n",
    "\n",
    "  valid_plants = (\n",
    "      pipeline\n",
    "      | 'Gardening plants' >> beam.Create([\n",
    "          {\n",
    "              'icon': '🍓', 'name': 'Strawberry', 'duration': 2\n",
    "          },\n",
    "          {\n",
    "              'icon': '🥕', 'name': 'Carrot', 'duration': 1\n",
    "          },\n",
    "          {\n",
    "              'icon': '🍆', 'name': 'Eggplant', 'duration': 2\n",
    "          },\n",
    "          {\n",
    "              'icon': '🍅', 'name': 'Tomato', 'duration': 0\n",
    "          },\n",
    "          {\n",
    "              'icon': '🥔', 'name': 'Potato', 'duration': -1\n",
    "          },\n",
    "      ])\n",
    "      | 'Replace duration if valid' >> beam.FlatMap(\n",
    "          replace_duration_if_valid,\n",
    "          durations=beam.pvalue.AsDict(durations),\n",
    "      )\n",
    "      | beam.Map(print))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "example-9-flatmap-with-side-inputs-as-dictionaries-2"
   },
   "source": [
    "<table align=\"left\" style=\"margin-right:1em\">\n",
    "  <td>\n",
    "    <a class=\"button\" target=\"_blank\" href=\"https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/elementwise/flatmap.py\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" width=\"32px\" height=\"32px\" alt=\"View source code\"/> View source code</a>\n",
    "  </td>\n",
    "</table>\n",
    "\n",
    "<br/><br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "related-transforms"
   },
   "source": [
    "## Related transforms\n",
    "\n",
    "* [Filter](https://beam.apache.org/documentation/transforms/python/elementwise/filter) is useful if the function is just\n",
    "  deciding whether to output an element or not.\n",
    "* [ParDo](https://beam.apache.org/documentation/transforms/python/elementwise/pardo) is the most general elementwise mapping\n",
    "  operation, and includes other abilities such as multiple output collections and side-inputs.\n",
    "* [Map](https://beam.apache.org/documentation/transforms/python/elementwise/map) behaves the same, but produces exactly one output for each input.\n",
    "\n",
    "<table align=\"left\" style=\"margin-right:1em\">\n",
    "  <td>\n",
    "    <a class=\"button\" target=\"_blank\" href=\"https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.core.html#apache_beam.transforms.core.FlatMap\"><img src=\"https://beam.apache.org/images/logos/sdks/python.png\" width=\"32px\" height=\"32px\" alt=\"Pydoc\"/> Pydoc</a>\n",
    "  </td>\n",
    "</table>\n",
    "\n",
    "<br/><br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "view-the-docs-bottom"
   },
   "source": [
    "<table align=\"left\"><td><a target=\"_blank\" href=\"https://beam.apache.org/documentation/transforms/python/elementwise/flatmap\"><img src=\"https://beam.apache.org/images/logos/full-color/name-bottom/beam-logo-full-color-name-bottom-100.png\" width=\"32\" height=\"32\" />View the docs</a></td></table>"
   ]
  }
 ],
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