svetakvsundhar commented on a change in pull request #15809:
URL: https://github.com/apache/beam/pull/15809#discussion_r741295029
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File path: sdks/python/apache_beam/dataframe/frames.py
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@@ -1430,6 +1430,72 @@ def corr(self, other, method, min_periods):
[self._expr, other._expr],
requires_partition_by=partitionings.Singleton(reason=reason)))
+ @frame_base.with_docs_from(pd.Series)
+ @frame_base.args_to_kwargs(pd.Series)
+ @frame_base.populate_defaults(pd.Series)
+ def skew(self, axis, skipna, level, numeric_only, **kwargs):
+ if level is not None:
+ raise NotImplementedError("per-level aggregation")
+ if skipna is None or skipna:
+ self = self.dropna() # pylint: disable=self-cls-assignment
+ # See the online, numerically stable formulae at
+ #
https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Higher-order_statistics
+ def compute_moments(x):
+ n = len(x)
+ if n == 0:
+ m, s, third_moment = 0, 0, 0
+ elif n < 3:
+ m = x.std(ddof=0)**2 * n
+ s = x.sum()
+ third_moment = (((x - x.mean())**3).sum())
+ else:
+ m = x.std(ddof=0)**2 * n
+ s = x.sum()
+ third_moment = (((x - x.mean())**3).sum())
Review comment:
I ran some tests to time this. using the explicit formula (Sum of the
expectations), makes sense here based on the numbers i saw even for rather
large sizes of the dataset.
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