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https://issues.apache.org/jira/browse/ARROW-3246?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Wes McKinney resolved ARROW-3246.
---------------------------------
Resolution: Fixed
Fix Version/s: (was: 1.0.0)
0.15.0
Issue resolved by pull request 5077
[https://github.com/apache/arrow/pull/5077]
> [Python][Parquet] direct reading/writing of pandas categoricals in parquet
> --------------------------------------------------------------------------
>
> Key: ARROW-3246
> URL: https://issues.apache.org/jira/browse/ARROW-3246
> Project: Apache Arrow
> Issue Type: Improvement
> Components: Python
> Reporter: Martin Durant
> Assignee: Wes McKinney
> Priority: Minor
> Labels: parquet, pull-request-available
> Fix For: 0.15.0
>
> Time Spent: 9h 40m
> Remaining Estimate: 0h
>
> Parquet supports "dictionary encoding" of column data in a manner very
> similar to the concept of Categoricals in pandas. It is natural to use this
> encoding for a column which originated as a categorical. Conversely, when
> loading, if the file metadata says that a given column came from a pandas (or
> arrow) categorical, then we can trust that the whole of the column is
> dictionary-encoded and load the data directly into a categorical column,
> rather than expanding the labels upon load and recategorising later.
> If the data does not have the pandas metadata, then the guarantee cannot
> hold, and we cannot assume either that the whole column is dictionary encoded
> or that the labels are the same throughout. In this case, the current
> behaviour is fine.
>
> (please forgive that some of this has already been mentioned elsewhere; this
> is one of the entries in the list at
> [https://github.com/dask/fastparquet/issues/374] as a feature that is useful
> in fastparquet)
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