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https://issues.apache.org/jira/browse/ARROW-1743?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16224993#comment-16224993
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ASF GitHub Bot commented on ARROW-1743:
---------------------------------------
Licht-T commented on a change in pull request #1260: ARROW-1743: [Python] Avoid
non-array writeable-flag check
URL: https://github.com/apache/arrow/pull/1260#discussion_r147709151
##########
File path: python/pyarrow/pandas_compat.py
##########
@@ -404,7 +404,7 @@ def table_to_blockmanager(options, table, memory_pool,
nthreads=1):
index_name = None if is_unnamed_index_level(name) else name
col_pandas = col.to_pandas()
values = col_pandas.values
- if not values.flags.writeable:
+ if hasattr(values, 'flags') and not values.flags.writeable:
Review comment:
Thanks @xhochy, I got it. Should I leave a note about the problem on code?
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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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