[jira] [Updated] (ARROW-3861) [Python] ParquetDataset().read columns argument always returns partition column

["Antoine Pitrou (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/ARROW-3861?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Antoine Pitrou updated ARROW-3861:
----------------------------------
    Component/s: Python

> [Python] ParquetDataset().read columns argument always returns partition column
> -------------------------------------------------------------------------------
>
>                 Key: ARROW-3861
>                 URL: https://issues.apache.org/jira/browse/ARROW-3861
>             Project: Apache Arrow
>          Issue Type: Bug
>          Components: Python
>            Reporter: Christian Thiel
>            Priority: Major
>              Labels: pyarrow, python
>             Fix For: 0.14.0
>
>
> I just noticed that no matter which columns are specified on load of a dataset, the partition column is always returned. This might lead to strange behaviour, as the resulting dataframe has more than the expected columns:
> {code}
> import dask as da
> import pyarrow as pa
> import pyarrow.parquet as pq
> import pandas as pd
> import os
> import numpy as np
> import shutil
> PATH_PYARROW_MANUAL = '/tmp/pyarrow_manual.pa/'
> if os.path.exists(PATH_PYARROW_MANUAL):
>     shutil.rmtree(PATH_PYARROW_MANUAL)
> os.mkdir(PATH_PYARROW_MANUAL)
> arrays = np.array([np.array([0, 1, 2]), np.array([3, 4]), np.nan, np.nan])
> strings = np.array([np.nan, np.nan, 'a', 'b'])
> df = pd.DataFrame([0, 0, 1, 1], columns=['partition_column'])
> df.index.name='DPRD_ID'
> df['arrays'] = pd.Series(arrays)
> df['strings'] = pd.Series(strings)
> my_schema = pa.schema([('DPRD_ID', pa.int64()),
>                        ('partition_column', pa.int32()),
>                        ('arrays', pa.list_(pa.int32())),
>                        ('strings', pa.string()),
>                        ('new_column', pa.string())])
> table = pa.Table.from_pandas(df, schema=my_schema)
> pq.write_to_dataset(table, root_path=PATH_PYARROW_MANUAL, partition_cols=['partition_column'])
> df_pq = pq.ParquetDataset(PATH_PYARROW_MANUAL).read(columns=['DPRD_ID', 'strings']).to_pandas()
> # pd.read_parquet(PATH_PYARROW_MANUAL, columns=['DPRD_ID', 'strings'], engine='pyarrow')
> df_pq
> {code}
> df_pq has column `partition_column`



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