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https://issues.apache.org/jira/browse/ARROW-3652?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16731485#comment-16731485
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Matthew Rocklin commented on ARROW-3652:
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Right, I've just done a search on this topic and found a few other issues.
Happy to move conversation to one of them if desired.
In this particular case we already have pandas-specific metadata telling us
that we have a categorical column (this is in the `pandas_type` entry for the
column). As you say we don't know the category-mapping, but we could still
create a Categorical column and just let Pandas decide the mapping. I suspect
that it sorts the entries as a sane default.. This wouldn't necessarily
roundtrip the right category-to-code mapping, but we can at least roundtrip the
fact that these should be represented as categoricals.
Speaking from a Dask Dataframe perspective this would be desirable. We're
accustomed to having to remap mismatched categorical columns to make things
match up. Pandas generally handles this case well today. However, I can
understand if Arrow wants to play things safe here and resist the temptation to
let Pandas be magical.
So, the concrete ask here is that when converting a table to a Pandas dataframe
and using the `use_pandas_metadata=` keyword that we convert columns that
Pandas marked with `pandas_type='category'` to be categorical columns.
> [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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