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https://issues.apache.org/jira/browse/ARROW-3652?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16731479#comment-16731479
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Wes McKinney commented on ARROW-3652:
-------------------------------------
As has been discussed in other issues, this is a somewhat complex topic:
* Parquet does not have a categorical type
* Dictionary encoding is used in general for compression only
* A single RowGroup may stop using dictionary encoding mid-stream if the
dictionary grows large
* The dictionary will be different from row group to roup group in general
In the case where both the writer and the reader is pandas, we can add
pandas-specific metadata to indicate that the same dictionary has been written
in each row group, and that all the data pages are dictionary encoded, to avoid
the issues above. It's a doable project but with a steep learning curve,
requiring comfort level with multiple layers of the C++ library and the Arrow
conversion layer.
> [Python] CategoricalIndex is lost after reading back
> ----------------------------------------------------
>
> Key: ARROW-3652
> URL: https://issues.apache.org/jira/browse/ARROW-3652
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Affects Versions: 0.11.1
> Reporter: Armin Berres
> Priority: Major
> Labels: parquet
>
> When a {{CategoricalIndex}} is written and read back the resulting index is
> not more categorical.
> {code}
> df = pd.DataFrame([['a', 'b'], ['c', 'd']], columns=['c1', 'c2'])
> df['c1'] = df['c1'].astype('category')
> df = df.set_index(['c1'])
> table = pa.Table.from_pandas(df)
> pq.write_table(table, 'test.parquet')
> ref_df = pq.read_pandas('test.parquet').to_pandas()
> print(df.index)
> # CategoricalIndex(['a', 'c'], categories=['a', 'c'], ordered=False,
> name='c1', dtype='category')
> print(ref_df.index)
> # Index(['a', 'c'], dtype='object', name='c1')
> {code}
> In the metadata the information is correctly contained:
> {code:java}
> {"name": "c1", "field_name": "c1", "p'
> b'andas_type": "categorical", "numpy_type": "int8", "metadata":
> {"'
> b'num_categories": 2, "ordered": false}
> {code}
>
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