[GitHub] [beam] TheNeuralBit commented on a change in pull request #15833: [WIP] BEAM-12561 method truncate on series and dataframe

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

TheNeuralBit commented on a change in pull request #15833:
URL: https://github.com/apache/beam/pull/15833#discussion_r743869147



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File path: sdks/python/apache_beam/dataframe/frames.py
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@@ -922,6 +922,19 @@ def mask(self, cond, **kwargs):
     """mask is not parallelizable when ``errors="ignore"`` is specified."""
     return self.where(~cond, **kwargs)
 
+  @frame_base.with_docs_from(pd.DataFrame)
+  @frame_base.args_to_kwargs(pd.DataFrame)
+  @frame_base.populate_defaults(pd.DataFrame)
+  def truncate(self, before, after, axis):
+    return frame_base.DeferredFrame.wrap(
+        expressions.ComputedExpression(
+            'truncate',
+            lambda df: df.truncate(before=before, after=after, axis=axis),

Review comment:
       Hey @AlikRodriguez sorry I missed your question!
   
   > I'm struggling with an error with df['A'].truncate(before=2, after=4) on pandas_doctest_test, and the error message is ValueError: truncate requires a sorted index, How can I fix this?
   
   The issue here seems to be that each individual partitioned dataframe (i.e. `df` in this method) is not necessarily sorted by index. I'd suggest just making this method call `sort_index` before it calls truncate, e.g. `df.sort_index().truncate(...)`. We should only do that when axis is 'index' though, in the axis='columns` case the user is in control of the order of the columns, so raising that error would be appropriate.




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