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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 <a href="https
Step1:  Filter
<script type="text/javascript">
localStorage.setItem('language', 'language-py')
</script>
<table align="left" style="margin-right
Step2:  Examples
In the following examples, we create a pipeline with a PCollection of produce with their icon, name, and duration.
Then, we apply Filter in multiple ways to filter out produce by their duration value.
Filter accepts a function that keeps elements that return True, and filters out the remaining elements.
Example 1
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Python Code:
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Explanation: <a href="https://colab.research.google.com/github/apache/beam/blob/master/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>
<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>
End of explanation
!pip install --quiet -U apache-beam
Explanation: Filter
<script type="text/javascript">
localStorage.setItem('language', 'language-py')
</script>
<table align="left" style="margin-right:1em">
  <td>
    <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>
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</table>
<br/><br/><br/>
Given a predicate, filter out all elements that don't satisfy that predicate.
May also be used to filter based on an inequality with a given value based
on the comparison ordering of the element.
Setup
To run a code cell, you can click the Run cell button at the top left of the cell,
or select it and press Shift+Enter.
Try modifying a code cell and re-running it to see what happens.
To learn more about Colab, see
Welcome to Colaboratory!.
First, let's install the apache-beam module.
End of explanation
import apache_beam as beam
def is_perennial(plant):
  return plant['duration'] == 'perennial'
with beam.Pipeline() as pipeline:
  perennials = (
      pipeline
      | 'Gardening plants' >> beam.Create([
          {
              'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'
          },
          {
              'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'
          },
          {
              'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'
          },
          {
              'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'
          },
          {
              'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'
          },
      ])
      | 'Filter perennials' >> beam.Filter(is_perennial)
      | beam.Map(print))
Explanation: Examples
In the following examples, we create a pipeline with a PCollection of produce with their icon, name, and duration.
Then, we apply Filter in multiple ways to filter out produce by their duration value.
Filter accepts a function that keeps elements that return True, and filters out the remaining elements.
Example 1: Filtering with a function
We define a function is_perennial which returns True if the element's duration equals 'perennial', and False otherwise.
End of explanation
import apache_beam as beam
with beam.Pipeline() as pipeline:
  perennials = (
      pipeline
      | 'Gardening plants' >> beam.Create([
          {
              'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'
          },
          {
              'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'
          },
          {
              'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'
          },
          {
              'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'
          },
          {
              'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'
          },
      ])
      | 'Filter perennials' >>
      beam.Filter(lambda plant: plant['duration'] == 'perennial')
      | beam.Map(print))
Explanation: <table align="left" style="margin-right:1em">
  <td>
    <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>
  </td>
</table>
<br/><br/><br/>
Example 2: Filtering with a lambda function
We can also use lambda functions to simplify Example 1.
End of explanation
import apache_beam as beam
def has_duration(plant, duration):
  return plant['duration'] == duration
with beam.Pipeline() as pipeline:
  perennials = (
      pipeline
      | 'Gardening plants' >> beam.Create([
          {
              'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'
          },
          {
              'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'
          },
          {
              'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'
          },
          {
              'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'
          },
          {
              'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'
          },
      ])
      | 'Filter perennials' >> beam.Filter(has_duration, 'perennial')
      | beam.Map(print))
Explanation: <table align="left" style="margin-right:1em">
  <td>
    <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>
  </td>
</table>
<br/><br/><br/>
Example 3: Filtering with multiple arguments
You can pass functions with multiple arguments to Filter.
They are passed as additional positional arguments or keyword arguments to the function.
In this example, has_duration takes plant and duration as arguments.
End of explanation
import apache_beam as beam
with beam.Pipeline() as pipeline:
  perennial = pipeline | 'Perennial' >> beam.Create(['perennial'])
  perennials = (
      pipeline
      | 'Gardening plants' >> beam.Create([
          {
              'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'
          },
          {
              'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'
          },
          {
              'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'
          },
          {
              'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'
          },
          {
              'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'
          },
      ])
      | 'Filter perennials' >> beam.Filter(
          lambda plant,
          duration: plant['duration'] == duration,
          duration=beam.pvalue.AsSingleton(perennial),
      )
      | beam.Map(print))
Explanation: <table align="left" style="margin-right:1em">
  <td>
    <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>
  </td>
</table>
<br/><br/><br/>
Example 4: Filtering with side inputs as singletons
If the PCollection has a single value, such as the average from another computation,
passing the PCollection as a singleton accesses that value.
In this example, we pass a PCollection the value 'perennial' as a singleton.
We then use that value to filter out perennials.
End of explanation
import apache_beam as beam
with beam.Pipeline() as pipeline:
  valid_durations = pipeline | 'Valid durations' >> beam.Create([
      'annual',
      'biennial',
      'perennial',
  ])
  valid_plants = (
      pipeline
      | 'Gardening plants' >> beam.Create([
          {
              'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'
          },
          {
              'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'
          },
          {
              'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'
          },
          {
              'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'
          },
          {
              'icon': '🥔', 'name': 'Potato', 'duration': 'PERENNIAL'
          },
      ])
      | 'Filter valid plants' >> beam.Filter(
          lambda plant,
          valid_durations: plant['duration'] in valid_durations,
          valid_durations=beam.pvalue.AsIter(valid_durations),
      )
      | beam.Map(print))
Explanation: <table align="left" style="margin-right:1em">
  <td>
    <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>
  </td>
</table>
<br/><br/><br/>
Example 5: Filtering with side inputs as iterators
If the PCollection has multiple values, pass the PCollection as an iterator.
This accesses elements lazily as they are needed,
so it is possible to iterate over large PCollections that won't fit into memory.
End of explanation
import apache_beam as beam
with beam.Pipeline() as pipeline:
  keep_duration = pipeline | 'Duration filters' >> beam.Create([
      ('annual', False),
      ('biennial', False),
      ('perennial', True),
  ])
  perennials = (
      pipeline
      | 'Gardening plants' >> beam.Create([
          {
              'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'
          },
          {
              'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'
          },
          {
              'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'
          },
          {
              'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'
          },
          {
              'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'
          },
      ])
      | 'Filter plants by duration' >> beam.Filter(
          lambda plant,
          keep_duration: keep_duration[plant['duration']],
          keep_duration=beam.pvalue.AsDict(keep_duration),
      )
      | beam.Map(print))
Explanation: <table align="left" style="margin-right:1em">
  <td>
    <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>
  </td>
</table>
<br/><br/><br/>
Note: You can pass the PCollection as a list with beam.pvalue.AsList(pcollection),
but this requires that all the elements fit into memory.
Example 6: Filtering with side inputs as dictionaries
If a PCollection is small enough to fit into memory, then that PCollection can be passed as a dictionary.
Each element must be a (key, value) pair.
Note that all the elements of the PCollection must fit into memory for this.
If the PCollection won't fit into memory, use beam.pvalue.AsIter(pcollection) instead.
End of explanation
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