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https://issues.apache.org/jira/browse/BEAM-11777?focusedWorklogId=594724&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-594724
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ASF GitHub Bot logged work on BEAM-11777:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 11/May/21 19:12
Start Date: 11/May/21 19:12
Worklog Time Spent: 10m
Work Description: TheNeuralBit commented on a change in pull request
#14438:
URL: https://github.com/apache/beam/pull/14438#discussion_r630452260
##########
File path: sdks/python/apache_beam/dataframe/frames.py
##########
@@ -948,41 +949,87 @@ def dropna(self, **kwargs):
to_string = frame_base.wont_implement_method(
pd.Series, 'to_string', reason="non-deferred-result")
- def aggregate(self, func, axis=0, *args, **kwargs):
+ @frame_base.args_to_kwargs(pd.Series)
+ @frame_base.populate_defaults(pd.Series)
+ def aggregate(self, func, axis, *args, **kwargs):
+ if kwargs.get('skipna', False):
+ # Eagerly generate a proxy to make sure skipna is a valid argument
+ # for this aggregation method
+ _ = self._expr.proxy().aggregate(func, axis, *args, **kwargs)
+ kwargs.pop('skipna')
+ self.dropna().aggregate(func, axis, *args, **kwargs)
Review comment:
Yes, fixed
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Issue Time Tracking
-------------------
Worklog Id: (was: 594724)
Time Spent: 14h 50m (was: 14h 40m)
> 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
> Time Spent: 14h 50m
> Remaining Estimate: 0h
>
> {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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