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Posted to issues@arrow.apache.org by "ASF GitHub Bot (JIRA)" <ji...@apache.org> on 2017/11/07 03:27:00 UTC

[jira] [Commented] (ARROW-1743) [Python] Table to_pandas fails when index contains categorical column

    [ https://issues.apache.org/jira/browse/ARROW-1743?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16241429#comment-16241429 ] 

ASF GitHub Bot commented on ARROW-1743:
---------------------------------------

wesm commented on issue #1260: ARROW-1743: [Python] Avoid non-array writeable-flag check
URL: https://github.com/apache/arrow/pull/1260#issuecomment-342364593
 
 
   @Licht-T what's the status on this one? I can also take a look

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> [Python] Table to_pandas fails when index contains categorical column
> ---------------------------------------------------------------------
>
>                 Key: ARROW-1743
>                 URL: https://issues.apache.org/jira/browse/ARROW-1743
>             Project: Apache Arrow
>          Issue Type: Bug
>          Components: Python
>    Affects Versions: 0.7.1
>            Reporter: Brian Pendleton
>            Assignee: Licht Takeuchi
>              Labels: pull-request-available
>             Fix For: 0.8.0
>
>
> Categorical columns in the index of a dataframe are causing a roundtrip failure.  
> {code}
> >>> df = pd.DataFrame({'a': [1, 2, 3], 'b': [1, 2, 3]})
> >>> df['a'] = df.a.astype('category')
> >>> df = df.set_index('a')
> >>> tbl = pa.Table.from_pandas(df)
> >>> tbl.to_pandas()
> Traceback (most recent call last):
>   File "<stdin>", line 1, in <module>
>   File "table.pxi", line 881, in pyarrow.lib.Table.to_pandas
>   File "C:\Users\bpendlet\Miniconda3\envs\panpy3\lib\site-packages\pyarrow\pandas_compat.py", line 303, in table_to_blockmanager
>     if not values.flags.writeable:
> AttributeError: 'Categorical' object has no attribute 'flags'
> {code}
> Works as expected when you don't change have the categorical:
> {code}
> >>> df = pd.DataFrame({'a': [1, 2, 3], 'b': [1, 2, 3]})
> >>> df = df.set_index('a')
> >>> tbl = pa.Table.from_pandas(df)
> >>> tbl.to_pandas()
>    b
> a
> 1  1
> 2  2
> 3  3
> {code}



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