[jira] [Assigned] (ARROW-9573) [Python] Parquet doesn't load when partitioned column starts with '_'

["Apache Arrow JIRA Bot (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/ARROW-9573?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Arrow JIRA Bot reassigned ARROW-9573:
--------------------------------------------

    Assignee: Ben Kietzman  (was: Apache Arrow JIRA Bot)

> [Python] Parquet doesn't load when partitioned column starts with '_'
> ---------------------------------------------------------------------
>
>                 Key: ARROW-9573
>                 URL: https://issues.apache.org/jira/browse/ARROW-9573
>             Project: Apache Arrow
>          Issue Type: Bug
>          Components: Python
>    Affects Versions: 1.0.0
>            Reporter: Tonnam Balankura
>            Assignee: Ben Kietzman
>            Priority: Major
>              Labels: pull-request-available
>          Time Spent: 10m
>  Remaining Estimate: 0h
>
> When the loading parquet with partitioned column that starts with an underscore '_', nothing is loaded. No exceptions are raised either. Loading this parquet have worked for me in pyarrow 0.17.1, but not working anymore in pyarrow 1.0.0.
> On the other hand, loading parquet with a partitioned column starting with '_' is possible by using the `use_legacy_dataset` option. Also, when the column that starts with an underscore is not a partitioned column, loading parquet seems to work as expected.
> {code:python}
> >>> import pyarrow as pa
> >>> import pyarrow.parquet as pq
> >>> import pandas as pd
> >>> df1 = pd.DataFrame(data={'_COL_1': [1, 2], 'COL_2': [3, 4], 'COL_3': [5, 6]})
> >>> table1 = pa.Table.from_pandas(df1)
> >>> pq.write_to_dataset(table1, partition_cols=['_COL_1', 'COL_2'], root_path='test_parquet1')
> >>> df_pq1 = pq.read_table('test_parquet1')
> >>> df_pq1
> pyarrow.Table
> >>> len(df_pq1)
> 0
> >>> df_pq1_legacy = pq.read_table('test_parquet1', use_legacy_dataset=True)
> pyarrow.Table
> COL_3: int64
> _COL_1: dictionary<values=int64, indices=int32, ordered=0>
> COL_2: dictionary<values=int64, indices=int32, ordered=0>
> >>> len(df_pq1_legacy)
> 2
> >>> df2 = pd.DataFrame(data={'COL_1': [1, 2], 'COL_2': [3, 4], '_COL_3': [5, 6]})
> >>> table2 = pa.Table.from_pandas(df2)
> >>> pq.write_to_dataset(table2, partition_cols=['COL_1', 'COL_2'], root_path='test_parquet2')
> >>> df_pq2 = pq.read_table('test_parquet2')
> >>> df_pq2
> pyarrow.Table
> _COL_3: int64
> COL_1: int32
> COL_2: int32
> >>> len(df_pq2)
> 2
> {code}



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