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https://issues.apache.org/jira/browse/BEAM-12563?focusedWorklogId=692746&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-692746
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ASF GitHub Bot logged work on BEAM-12563:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 08/Dec/21 17:31
Start Date: 08/Dec/21 17:31
Worklog Time Spent: 10m
Work Description: TheNeuralBit commented on a change in pull request
#15944:
URL: https://github.com/apache/beam/pull/15944#discussion_r765077294
##########
File path: sdks/python/apache_beam/dataframe/frames.py
##########
@@ -228,6 +228,16 @@ def droplevel(self, level, axis):
preserves_partition_by=partitionings.Arbitrary()
if axis in (1, 'column') else partitionings.Singleton()))
+ @frame_base.with_docs_from(pd.DataFrame)
+ @frame_base.args_to_kwargs(pd.DataFrame)
+ def swaplevel(self, **kwargs):
+ return frame_base.DeferredFrame.wrap(
+ expressions.ComputedExpression(
+ 'swaplevel',
+ lambda df: df.swaplevel(**kwargs), [self._expr],
+ requires_partition_by=partitionings.Arbitrary(),
+ preserves_partition_by=partitionings.Arbitrary()))
Review comment:
I wouldn't think this should preserve Index partitioning, since it
modifies the index. But our test framework does verify this.
It turns out this expression _does_ preserve index partitioning, since we
generate hashes by summing hashes on the individual index levels, which is
independent of the order of the index levels.
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Issue Time Tracking
-------------------
Worklog Id: (was: 692746)
Time Spent: 3h 40m (was: 3.5h)
> Implement swaplevel for Series and DataFrame
> --------------------------------------------
>
> Key: BEAM-12563
> URL: https://issues.apache.org/jira/browse/BEAM-12563
> Project: Beam
> Issue Type: Improvement
> Components: dsl-dataframe
> Reporter: Brian Hulette
> Assignee: Irwin Alejandro Rodirguez Ramirez
> Priority: P3
> Time Spent: 3h 40m
> Remaining Estimate: 0h
>
> Add an implementation of
> [swaplevel|https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.swaplevel.html]
> for DeferredDataFrame, DeferredSeries, and DeferredGroupBy. It should be
> fully unit tested with some combination of pandas_doctests_test.py and
> frames_test.py.
> https://github.com/apache/beam/pull/14274 is an example of a typical PR that
> adds new operations. See
> https://lists.apache.org/thread.html/r8ffe96d756054610dfdb4e849ffc6a741e826d15ba7e5bdeee1b4266%40%3Cdev.beam.apache.org%3E
> for background on the DataFrame API.
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