[jira] [Comment Edited] (BEAM-11777) Support correct kwargs in aggregation methods on DataFrame, Series

["Brian Hulette (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/BEAM-11777?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17298400#comment-17298400 ] 

Brian Hulette edited comment on BEAM-11777 at 3/9/21, 10:24 PM:
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This may be incorrect, it looks like agg passes kwargs back down to the underlying implementation, for example df.agg('count', level='Person') seems to work, even though agg doesn't document a level= kwarg. To close this out lets just make sure there are tests verifying the various arguments.

We should also consider how these impact partitioning specs.


was (Author: bhulette):
This may be incorrect, it looks like agg passes kwargs back down to the underlying implementation, for example df.agg('count', level='Person') seems to work, even though agg doesn't document a level= kwarg. To close this out lets just make sure there are tests verifying the various arguments.

> Support correct kwargs in aggregation methods on DataFrame, Series
> ------------------------------------------------------------------
>
>                 Key: BEAM-11777
>                 URL: https://issues.apache.org/jira/browse/BEAM-11777
>             Project: Beam
>          Issue Type: Improvement
>          Components: sdk-py-core
>            Reporter: Brian Hulette
>            Priority: P2
>              Labels: dataframe-api
>
> {DataFrame,Series}.{all, any, max, min, prod, mean, median, sum} are all implemented via frame_base._agg_method, which just re-uses {DataFrame,Series}.agg}. However the pandas operations have some different kwargs that are not supported by agg. Some are universal (level=, skip_na=), others are unique to each operation (numeric_only= or bool_only=).



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