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Posted to github@beam.apache.org by GitBox <gi...@apache.org> on 2022/07/06 18:18:32 UTC
[GitHub] [beam] TheNeuralBit opened a new pull request, #22174: Parallelizable mean
TheNeuralBit opened a new pull request, #22174:
URL: https://github.com/apache/beam/pull/22174
(Depends on #22173, see 23884a3280663f46523449623e3f6d750ce8a2a3 for changes relevant to this PR)
Fixes #22171
This adds a parallelizable custom implementation of `DeferredSeries.mean`, which uses `sum()/count()`. In addition:
- `DeferredDataFrame` leverages this implementation through `_agg_method`
- Updates tests
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[GitHub] [beam] TheNeuralBit commented on pull request #22174: Parallelizable mean
Posted by GitBox <gi...@apache.org>.
TheNeuralBit commented on PR #22174:
URL: https://github.com/apache/beam/pull/22174#issuecomment-1176776191
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[GitHub] [beam] TheNeuralBit commented on pull request #22174: Parallelizable DataFrame/Series mean
Posted by GitBox <gi...@apache.org>.
TheNeuralBit commented on PR #22174:
URL: https://github.com/apache/beam/pull/22174#issuecomment-1176847000
Run Python PreCommit
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[GitHub] [beam] github-actions[bot] commented on pull request #22174: Parallelizable mean
Posted by GitBox <gi...@apache.org>.
github-actions[bot] commented on PR #22174:
URL: https://github.com/apache/beam/pull/22174#issuecomment-1176655642
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[GitHub] [beam] codecov[bot] commented on pull request #22174: Parallelizable mean
Posted by GitBox <gi...@apache.org>.
codecov[bot] commented on PR #22174:
URL: https://github.com/apache/beam/pull/22174#issuecomment-1176554037
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[GitHub] [beam] TheNeuralBit commented on a diff in pull request #22174: Parallelizable DataFrame/Series mean
Posted by GitBox <gi...@apache.org>.
TheNeuralBit commented on code in PR #22174:
URL: https://github.com/apache/beam/pull/22174#discussion_r917413791
##########
sdks/python/apache_beam/dataframe/frames.py:
##########
@@ -1577,6 +1578,17 @@ def std(self, *args, **kwargs):
# Compute variance (deferred scalar) with same args, then sqrt it
return self.var(*args, **kwargs).apply(lambda var: math.sqrt(var))
+ @frame_base.with_docs_from(pd.Series)
+ @frame_base.args_to_kwargs(pd.Series)
+ @frame_base.populate_defaults(pd.Series)
+ def mean(self, skipna, **kwargs):
Review Comment:
That's actually what the decorators do - populate_defaults pulls the default values from the panda implementation of the function, so we don't have to duplicate them here.
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[GitHub] [beam] TheNeuralBit merged pull request #22174: Parallelizable DataFrame/Series mean
Posted by GitBox <gi...@apache.org>.
TheNeuralBit merged PR #22174:
URL: https://github.com/apache/beam/pull/22174
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[GitHub] [beam] AnandInguva commented on a diff in pull request #22174: Parallelizable DataFrame/Series mean
Posted by GitBox <gi...@apache.org>.
AnandInguva commented on code in PR #22174:
URL: https://github.com/apache/beam/pull/22174#discussion_r917328278
##########
sdks/python/apache_beam/dataframe/frames.py:
##########
@@ -1577,6 +1578,17 @@ def std(self, *args, **kwargs):
# Compute variance (deferred scalar) with same args, then sqrt it
return self.var(*args, **kwargs).apply(lambda var: math.sqrt(var))
+ @frame_base.with_docs_from(pd.Series)
+ @frame_base.args_to_kwargs(pd.Series)
+ @frame_base.populate_defaults(pd.Series)
+ def mean(self, skipna, **kwargs):
Review Comment:
Can we make `skipna` as keyword argument?
```suggestion
def mean(self, skipna=True, **kwargs):
```
##########
sdks/python/apache_beam/dataframe/frames.py:
##########
@@ -1577,6 +1578,17 @@ def std(self, *args, **kwargs):
# Compute variance (deferred scalar) with same args, then sqrt it
return self.var(*args, **kwargs).apply(lambda var: math.sqrt(var))
+ @frame_base.with_docs_from(pd.Series)
+ @frame_base.args_to_kwargs(pd.Series)
+ @frame_base.populate_defaults(pd.Series)
+ def mean(self, skipna, **kwargs):
Review Comment:
Following this https://pandas.pydata.org/docs/reference/api/pandas.Series.mean.html#pandas.Series.mean
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