old_contents: #
  # Licensed to the Apache Software Foundation (ASF) under one or more
  # contributor license agreements.  See the NOTICE file distributed with
  # this work for additional information regarding copyright ownership.
  # The ASF licenses this file to You under the Apache License, Version 2.0
  # (the "License"); you may not use this file except in compliance with
  # the License.  You may obtain a copy of the License at
  #
  #    http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #
  
  """A word-counting workflow using dataframes."""
  
  # pytype: skip-file
  
  from __future__ import absolute_import
  
  import argparse
  import logging
  
  import apache_beam as beam
  from apache_beam.dataframe.convert import to_dataframe
  from apache_beam.io import ReadFromText
  from apache_beam.options.pipeline_options import PipelineOptions
  
  
  def run(argv=None):
    """Main entry point; defines and runs the wordcount pipeline."""
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--input',
        dest='input',
        default='gs://dataflow-samples/shakespeare/kinglear.txt',
        help='Input file to process.')
    parser.add_argument(
        '--output',
        dest='output',
        required=True,
        help='Output file to write results to.')
    known_args, pipeline_args = parser.parse_known_args(argv)
  
    # Import this here to avoid pickling the main session.
    import re
  
    # The pipeline will be run on exiting the with block.
    with beam.Pipeline(options=PipelineOptions(pipeline_args)) as p:
  
      # Read the text file[pattern] into a PCollection.
      lines = p | 'Read' >> ReadFromText(known_args.input)
  
      words = (
          lines
          | 'Split' >> beam.FlatMap(
              lambda line: re.findall(r'[\w]+', line)).with_output_types(str)
          # Map to Row objects to generate a schema suitable for conversion
          # to a dataframe.
          | 'ToRows' >> beam.Map(lambda word: beam.Row(word=word)))
  
      df = to_dataframe(words)
      df['count'] = 1
      counted = df.groupby('word').sum()
      counted.to_csv(known_args.output)
  
  
  if __name__ == '__main__':
    logging.getLogger().setLevel(logging.INFO)
    run()
  
new_contents: #
  # Licensed to the Apache Software Foundation (ASF) under one or more
  # contributor license agreements.  See the NOTICE file distributed with
  # this work for additional information regarding copyright ownership.
  # The ASF licenses this file to You under the Apache License, Version 2.0
  # (the "License"); you may not use this file except in compliance with
  # the License.  You may obtain a copy of the License at
  #
  #    http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #
  
  """A word-counting workflow using dataframes."""
  
  # pytype: skip-file
  
  from __future__ import absolute_import
  
  import argparse
  import logging
  
  import apache_beam as beam
  from apache_beam.dataframe.convert import to_dataframe
  from apache_beam.dataframe.convert import to_pcollection
  from apache_beam.io import ReadFromText
  from apache_beam.options.pipeline_options import PipelineOptions
  
  
  def run(argv=None):
    """Main entry point; defines and runs the wordcount pipeline."""
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--input',
        dest='input',
        default='gs://dataflow-samples/shakespeare/kinglear.txt',
        help='Input file to process.')
    parser.add_argument(
        '--output',
        dest='output',
        required=True,
        help='Output file to write results to.')
    known_args, pipeline_args = parser.parse_known_args(argv)
  
    # Import this here to avoid pickling the main session.
    import re
  
    # The pipeline will be run on exiting the with block.
    with beam.Pipeline(options=PipelineOptions(pipeline_args)) as p:
  
      # Read the text file[pattern] into a PCollection.
      lines = p | 'Read' >> ReadFromText(known_args.input)
  
      words = (
          lines
          | 'Split' >> beam.FlatMap(
              lambda line: re.findall(r'[\w]+', line)).with_output_types(str)
          # Map to Row objects to generate a schema suitable for conversion
          # to a dataframe.
          | 'ToRows' >> beam.Map(lambda word: beam.Row(word=word)))
  
      df = to_dataframe(words)
      df['count'] = 1
      counted = df.groupby('word').sum()
      counted.to_csv(known_args.output)
  
      # Deferred DataFrames can also be converted back to schema'd PCollections
      counted_pc = to_pcollection(counted, include_indexes=True)
  
      # Print out every word that occurred >50 times
      _ = (
          counted_pc
          | beam.Filter(lambda row: row.count > 50)
          | beam.Map(lambda row: f'{row.word}: {row.count}')
          | beam.Map(print))
  
  
  if __name__ == '__main__':
    logging.getLogger().setLevel(logging.INFO)
    run()
  
subject: Add to_pcollection example to wordcount_dataframe (#12923)