[jira] [Comment Edited] (ARROW-7782) Losing index information when using write_to_dataset with partition_cols

["Ludwik Bielczynski (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/ARROW-7782?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17031678#comment-17031678 ] 

Ludwik Bielczynski edited comment on ARROW-7782 at 2/6/20 3:32 PM:
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Maybe I was not clear. The index is moved from the index to the columns. That's the buggy behaviour I am describing. Version 0.9 before ARROW-2891 preserved the index as an index between writting and reading the data.

Does it make sense?


was (Author: ludwikb):
Maybe I was not clear. The index is moved from the index to the columns. That's the buggy behaviour I am describing. Version 0.9 before ARROW-2891 preserved the index as an index between writting and reading tha dataframe.

Does it make sense?

> Losing index information when using write_to_dataset with partition_cols
> ------------------------------------------------------------------------
>
>                 Key: ARROW-7782
>                 URL: https://issues.apache.org/jira/browse/ARROW-7782
>             Project: Apache Arrow
>          Issue Type: Bug
>         Environment: pyarrow==0.15.1
>            Reporter: Ludwik Bielczynski
>            Priority: Major
>
> One cannot save the index when using {{pyarrow.parquet.write_to_dataset()}} with given partition_cols arguments. Here I have created a minimal example which shows the issue:
> {code:java}
>  
> from pathlib import Path
> import pandas as pd
> from pyarrow import Table
> from pyarrow.parquet import write_to_dataset, read_table
> path = Path('/home/user/trials')
> file_name = 'local_database.parquet'
> df = pd.DataFrame({"A": [1, 2, 3], "B": ['a', 'a', 'b']}, 
>                   index=pd.Index(['a', 'b', 'c'], 
>                   name='idx'))
> table = Table.from_pandas(df)
> write_to_dataset(table, 
>                  str(path / file_name), 
>                  partition_cols=['B']
>                 )
> df_read = read_table(str(path / file_name))
> df_read.to_pandas()
> {code}
>  
> The issue is rather important for pandas and dask users.



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