TheNeuralBit commented on a change in pull request #15600:
URL: https://github.com/apache/beam/pull/15600#discussion_r729968203
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File path: website/www/site/content/en/documentation/dsls/dataframes/overview.md
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@@ -55,32 +50,8 @@ To use the DataFrames API in a larger pipeline, you can
convert a PCollection to
Here’s an example that creates a schema-aware PCollection, converts it to a
DataFrame using `to_dataframe`, processes the DataFrame, and then converts the
DataFrame back to a PCollection using `to_pcollection`:
-<!-- TODO(BEAM-11480): Convert these examples to snippets -->
{{< highlight py >}}
-from apache_beam.dataframe.convert import to_dataframe
-from apache_beam.dataframe.convert import to_pcollection
-...
- # 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)
-
- # Do something with counted_pc
- ...
+{{< code_sample "sdks/python/apache_beam/examples/dataframe/wordcount.py"
DataFrame_wordcount >}}
Review comment:
Yes that's right, I guess I can just hardcode the imports here to keep
them in. Thanks for pointing this out.
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