examples/notebooks/documentation/transforms/python/elementwise/filter-py.ipynb - beam - Git at Google

 {
  "cells": [
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    "source": [
     "<a href=\"https://colab.research.google.com/github/apache/beam/blob/master//Users/dcavazos/src/beam/examples/notebooks/documentation/transforms/python/elementwise/filter-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/filter\"><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"
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    "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": "filter"
    },
    "source": [
     "# Filter\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.Filter\"><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",
     "Given a predicate, filter out all elements that don't satisfy that predicate.\n",
     "May also be used to filter based on an inequality with a given value based\n",
     "on the comparison ordering of the element."
    ]
   },
   {
    "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 `Filter` in multiple ways to filter out produce by their duration value.\n",
     "\n",
     "`Filter` accepts a function that keeps elements that return `True`, and filters out the remaining elements."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "id": "example-1-filtering-with-a-function"
    },
    "source": [
     "### Example 1: Filtering with a function\n",
     "\n",
     "We define a function `is_perennial` which returns `True` if the element's duration equals `'perennial'`, and `False` otherwise."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "id": "example-1-filtering-with-a-function-code"
    },
    "outputs": [],
    "source": [
     "import apache_beam as beam\n",
     "\n",
     "def is_perennial(plant):\n",
     "  return plant['duration'] == 'perennial'\n",
     "\n",
     "with beam.Pipeline() as pipeline:\n",
     "  perennials = (\n",
     "      pipeline\n",
     "      | 'Gardening plants' >> beam.Create([\n",
     "          {'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'},\n",
     "          {'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'},\n",
     "          {'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'},\n",
     "          {'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'},\n",
     "          {'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'},\n",
     "      ])\n",
     "      | 'Filter perennials' >> beam.Filter(is_perennial)\n",
     "      | beam.Map(print)\n",
     "  )"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "id": "example-1-filtering-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/filter.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-filtering-with-a-lambda-function"
    },
    "source": [
     "### Example 2: Filtering with a lambda function\n",
     "\n",
     "We can also use lambda functions to simplify **Example 1**."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "id": "example-2-filtering-with-a-lambda-function-code"
    },
    "outputs": [],
    "source": [
     "import apache_beam as beam\n",
     "\n",
     "with beam.Pipeline() as pipeline:\n",
     "  perennials = (\n",
     "      pipeline\n",
     "      | 'Gardening plants' >> beam.Create([\n",
     "          {'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'},\n",
     "          {'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'},\n",
     "          {'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'},\n",
     "          {'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'},\n",
     "          {'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'},\n",
     "      ])\n",
     "      | 'Filter perennials' >> beam.Filter(\n",
     "          lambda plant: plant['duration'] == 'perennial')\n",
     "      | beam.Map(print)\n",
     "  )"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "id": "example-2-filtering-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/filter.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-filtering-with-multiple-arguments"
    },
    "source": [
     "### Example 3: Filtering with multiple arguments\n",
     "\n",
     "You can pass functions with multiple arguments to `Filter`.\n",
     "They are passed as additional positional arguments or keyword arguments to the function.\n",
     "\n",
     "In this example, `has_duration` takes `plant` and `duration` as arguments."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "id": "example-3-filtering-with-multiple-arguments-code"
    },
    "outputs": [],
    "source": [
     "import apache_beam as beam\n",
     "\n",
     "def has_duration(plant, duration):\n",
     "  return plant['duration'] == duration\n",
     "\n",
     "with beam.Pipeline() as pipeline:\n",
     "  perennials = (\n",
     "      pipeline\n",
     "      | 'Gardening plants' >> beam.Create([\n",
     "          {'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'},\n",
     "          {'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'},\n",
     "          {'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'},\n",
     "          {'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'},\n",
     "          {'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'},\n",
     "      ])\n",
     "      | 'Filter perennials' >> beam.Filter(has_duration, 'perennial')\n",
     "      | beam.Map(print)\n",
     "  )"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "id": "example-3-filtering-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/filter.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-filtering-with-side-inputs-as-singletons"
    },
    "source": [
     "### Example 4: Filtering 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 `'perennial'` as a singleton.\n",
     "We then use that value to filter out perennials."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "id": "example-4-filtering-with-side-inputs-as-singletons-code"
    },
    "outputs": [],
    "source": [
     "import apache_beam as beam\n",
     "\n",
     "with beam.Pipeline() as pipeline:\n",
     "  perennial = pipeline | 'Perennial' >> beam.Create(['perennial'])\n",
     "\n",
     "  perennials = (\n",
     "      pipeline\n",
     "      | 'Gardening plants' >> beam.Create([\n",
     "          {'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'},\n",
     "          {'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'},\n",
     "          {'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'},\n",
     "          {'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'},\n",
     "          {'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'},\n",
     "      ])\n",
     "      | 'Filter perennials' >> beam.Filter(\n",
     "          lambda plant, duration: plant['duration'] == duration,\n",
     "          duration=beam.pvalue.AsSingleton(perennial),\n",
     "      )\n",
     "      | beam.Map(print)\n",
     "  )"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "id": "example-4-filtering-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/filter.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-filtering-with-side-inputs-as-iterators"
    },
    "source": [
     "### Example 5: Filtering 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-5-filtering-with-side-inputs-as-iterators-code"
    },
    "outputs": [],
    "source": [
     "import apache_beam as beam\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",
     "          {'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'},\n",
     "          {'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'},\n",
     "          {'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'},\n",
     "          {'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'},\n",
     "          {'icon': '🥔', 'name': 'Potato', 'duration': 'PERENNIAL'},\n",
     "      ])\n",
     "      | 'Filter valid plants' >> beam.Filter(\n",
     "          lambda plant, valid_durations: plant['duration'] in valid_durations,\n",
     "          valid_durations=beam.pvalue.AsIter(valid_durations),\n",
     "      )\n",
     "      | beam.Map(print)\n",
     "  )"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "id": "example-5-filtering-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/filter.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-6-filtering-with-side-inputs-as-dictionaries"
    },
    "source": [
     "### Example 6: Filtering 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-6-filtering-with-side-inputs-as-dictionaries-code"
    },
    "outputs": [],
    "source": [
     "import apache_beam as beam\n",
     "\n",
     "with beam.Pipeline() as pipeline:\n",
     "  keep_duration = pipeline | 'Duration filters' >> beam.Create([\n",
     "      ('annual', False),\n",
     "      ('biennial', False),\n",
     "      ('perennial', True),\n",
     "  ])\n",
     "\n",
     "  perennials = (\n",
     "      pipeline\n",
     "      | 'Gardening plants' >> beam.Create([\n",
     "          {'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'},\n",
     "          {'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'},\n",
     "          {'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'},\n",
     "          {'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'},\n",
     "          {'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'},\n",
     "      ])\n",
     "      | 'Filter plants by duration' >> beam.Filter(\n",
     "          lambda plant, keep_duration: keep_duration[plant['duration']],\n",
     "          keep_duration=beam.pvalue.AsDict(keep_duration),\n",
     "      )\n",
     "      | beam.Map(print)\n",
     "  )"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "id": "example-6-filtering-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/filter.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",
     "* [FlatMap](https://beam.apache.org/documentation/transforms/python/elementwise/flatmap) behaves the same as `Map`, but for\n",
     "  each input it might produce zero or more outputs.\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",
     "\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.Filter\"><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/filter\"><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>"
    ]
   }
  ],
  "metadata": {
   "colab": {
    "name": "Filter - element-wise transform",
    "toc_visible": true
   },
   "kernelspec": {
    "display_name": "python3",
    "name": "python3"
   }
  },
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  "nbformat_minor": 2
 }
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	"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": "filter"
	},
	"source": [
	"# Filter\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.Filter\"><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",
	"Given a predicate, filter out all elements that don't satisfy that predicate.\n",
	"May also be used to filter based on an inequality with a given value based\n",
	"on the comparison ordering of the element."
	]
	},
	{
	"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 `Filter` in multiple ways to filter out produce by their duration value.\n",
	"\n",
	"`Filter` accepts a function that keeps elements that return `True`, and filters out the remaining elements."
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {
	"id": "example-1-filtering-with-a-function"
	},
	"source": [
	"### Example 1: Filtering with a function\n",
	"\n",
	"We define a function `is_perennial` which returns `True` if the element's duration equals `'perennial'`, and `False` otherwise."
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"id": "example-1-filtering-with-a-function-code"
	},
	"outputs": [],
	"source": [
	"import apache_beam as beam\n",
	"\n",
	"def is_perennial(plant):\n",
	" return plant['duration'] == 'perennial'\n",
	"\n",
	"with beam.Pipeline() as pipeline:\n",
	" perennials = (\n",
	" pipeline\n",
	" \| 'Gardening plants' >> beam.Create([\n",
	" {'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'},\n",
	" {'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'},\n",
	" {'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'},\n",
	" {'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'},\n",
	" {'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'},\n",
	" ])\n",
	" \| 'Filter perennials' >> beam.Filter(is_perennial)\n",
	" \| beam.Map(print)\n",
	" )"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {
	"id": "example-1-filtering-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/filter.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-filtering-with-a-lambda-function"
	},
	"source": [
	"### Example 2: Filtering with a lambda function\n",
	"\n",
	"We can also use lambda functions to simplify Example 1."
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"id": "example-2-filtering-with-a-lambda-function-code"
	},
	"outputs": [],
	"source": [
	"import apache_beam as beam\n",
	"\n",
	"with beam.Pipeline() as pipeline:\n",
	" perennials = (\n",
	" pipeline\n",
	" \| 'Gardening plants' >> beam.Create([\n",
	" {'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'},\n",
	" {'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'},\n",
	" {'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'},\n",
	" {'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'},\n",
	" {'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'},\n",
	" ])\n",
	" \| 'Filter perennials' >> beam.Filter(\n",
	" lambda plant: plant['duration'] == 'perennial')\n",
	" \| beam.Map(print)\n",
	" )"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {
	"id": "example-2-filtering-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/filter.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-filtering-with-multiple-arguments"
	},
	"source": [
	"### Example 3: Filtering with multiple arguments\n",
	"\n",
	"You can pass functions with multiple arguments to `Filter`.\n",
	"They are passed as additional positional arguments or keyword arguments to the function.\n",
	"\n",
	"In this example, `has_duration` takes `plant` and `duration` as arguments."
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"id": "example-3-filtering-with-multiple-arguments-code"
	},
	"outputs": [],
	"source": [
	"import apache_beam as beam\n",
	"\n",
	"def has_duration(plant, duration):\n",
	" return plant['duration'] == duration\n",
	"\n",
	"with beam.Pipeline() as pipeline:\n",
	" perennials = (\n",
	" pipeline\n",
	" \| 'Gardening plants' >> beam.Create([\n",
	" {'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'},\n",
	" {'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'},\n",
	" {'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'},\n",
	" {'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'},\n",
	" {'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'},\n",
	" ])\n",
	" \| 'Filter perennials' >> beam.Filter(has_duration, 'perennial')\n",
	" \| beam.Map(print)\n",
	" )"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {
	"id": "example-3-filtering-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/filter.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-filtering-with-side-inputs-as-singletons"
	},
	"source": [
	"### Example 4: Filtering 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 `'perennial'` as a singleton.\n",
	"We then use that value to filter out perennials."
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"id": "example-4-filtering-with-side-inputs-as-singletons-code"
	},
	"outputs": [],
	"source": [
	"import apache_beam as beam\n",
	"\n",
	"with beam.Pipeline() as pipeline:\n",
	" perennial = pipeline \| 'Perennial' >> beam.Create(['perennial'])\n",
	"\n",
	" perennials = (\n",
	" pipeline\n",
	" \| 'Gardening plants' >> beam.Create([\n",
	" {'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'},\n",
	" {'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'},\n",
	" {'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'},\n",
	" {'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'},\n",
	" {'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'},\n",
	" ])\n",
	" \| 'Filter perennials' >> beam.Filter(\n",
	" lambda plant, duration: plant['duration'] == duration,\n",
	" duration=beam.pvalue.AsSingleton(perennial),\n",
	" )\n",
	" \| beam.Map(print)\n",
	" )"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {
	"id": "example-4-filtering-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/filter.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-filtering-with-side-inputs-as-iterators"
	},
	"source": [
	"### Example 5: Filtering 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-5-filtering-with-side-inputs-as-iterators-code"
	},
	"outputs": [],
	"source": [
	"import apache_beam as beam\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",
	" {'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'},\n",
	" {'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'},\n",
	" {'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'},\n",
	" {'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'},\n",
	" {'icon': '🥔', 'name': 'Potato', 'duration': 'PERENNIAL'},\n",
	" ])\n",
	" \| 'Filter valid plants' >> beam.Filter(\n",
	" lambda plant, valid_durations: plant['duration'] in valid_durations,\n",
	" valid_durations=beam.pvalue.AsIter(valid_durations),\n",
	" )\n",
	" \| beam.Map(print)\n",
	" )"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {
	"id": "example-5-filtering-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/filter.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-6-filtering-with-side-inputs-as-dictionaries"
	},
	"source": [
	"### Example 6: Filtering 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-6-filtering-with-side-inputs-as-dictionaries-code"
	},
	"outputs": [],
	"source": [
	"import apache_beam as beam\n",
	"\n",
	"with beam.Pipeline() as pipeline:\n",
	" keep_duration = pipeline \| 'Duration filters' >> beam.Create([\n",
	" ('annual', False),\n",
	" ('biennial', False),\n",
	" ('perennial', True),\n",
	" ])\n",
	"\n",
	" perennials = (\n",
	" pipeline\n",
	" \| 'Gardening plants' >> beam.Create([\n",
	" {'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'},\n",
	" {'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'},\n",
	" {'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'},\n",
	" {'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'},\n",
	" {'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'},\n",
	" ])\n",
	" \| 'Filter plants by duration' >> beam.Filter(\n",
	" lambda plant, keep_duration: keep_duration[plant['duration']],\n",
	" keep_duration=beam.pvalue.AsDict(keep_duration),\n",
	" )\n",
	" \| beam.Map(print)\n",
	" )"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {
	"id": "example-6-filtering-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/filter.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",
	"* [FlatMap](https://beam.apache.org/documentation/transforms/python/elementwise/flatmap) behaves the same as `Map`, but for\n",
	" each input it might produce zero or more outputs.\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",
	"\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.Filter\"><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/filter\"><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>"
	]
	}
	],
	"metadata": {
	"colab": {
	"name": "Filter - element-wise transform",
	"toc_visible": true
	},
	"kernelspec": {
	"display_name": "python3",
	"name": "python3"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}