[GitHub] [beam] TheNeuralBit commented on a change in pull request #15944: [BEAM-12563] swaplevel general function for dataframe and series

[GitBox <gi...@apache.org>]

TheNeuralBit commented on a change in pull request #15944:
URL: https://github.com/apache/beam/pull/15944#discussion_r765077294



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File path: sdks/python/apache_beam/dataframe/frames.py
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@@ -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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