TheNeuralBit commented on a change in pull request #15729:
URL: https://github.com/apache/beam/pull/15729#discussion_r730083674
##########
File path: sdks/python/apache_beam/dataframe/frames.py
##########
@@ -1098,6 +1098,23 @@ def fn(s):
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Arbitrary())
+ @property # type: ignore
+ @frame_base.with_docs_from(pd.Series)
+ def hasnans(self):
+ has_nans = expressions.ComputedExpression(
+ 'has_nans',
+ lambda s: pd.Series(s.hasnans), [self._expr],
+ requires_partition_by=partitionings.Index(),
+ preserves_partition_by=partitionings.Singleton())
+
+ with expressions.allow_non_parallel_operations():
+ return frame_base.DeferredFrame.wrap(
+ expressions.ComputedExpression(
+ 'combine',
+ lambda s: s.all(), [has_nans],
Review comment:
Is `all()` correct here? I think this should be `s.any()` (it should
return True if any of the partitions is True, meaning there's a NaN)
It's surprising this wasn't caught in testing
##########
File path: sdks/python/apache_beam/dataframe/frames.py
##########
@@ -1098,6 +1098,23 @@ def fn(s):
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Arbitrary())
+ @property # type: ignore
+ @frame_base.with_docs_from(pd.Series)
+ def hasnans(self):
+ has_nans = expressions.ComputedExpression(
+ 'has_nans',
+ lambda s: pd.Series(s.hasnans), [self._expr],
+ requires_partition_by=partitionings.Index(),
+ preserves_partition_by=partitionings.Singleton())
+
+ with expressions.allow_non_parallel_operations():
+ return frame_base.DeferredFrame.wrap(
+ expressions.ComputedExpression(
+ 'combine',
Review comment:
nit:
```suggestion
'combine_hasnans',
```
##########
File path: sdks/python/apache_beam/dataframe/frames.py
##########
@@ -1098,6 +1098,23 @@ def fn(s):
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Arbitrary())
+ @property # type: ignore
+ @frame_base.with_docs_from(pd.Series)
+ def hasnans(self):
+ has_nans = expressions.ComputedExpression(
+ 'has_nans',
+ lambda s: pd.Series(s.hasnans), [self._expr],
+ requires_partition_by=partitionings.Index(),
Review comment:
```suggestion
requires_partition_by=partitionings.Arbitrary(),
```
This expression doesn't have a particular partitioning requirement.
Partitioning by Index is for when the expression need to co-locate data for
some reason (e.g. a join operation, or to group by some key for an aggregation).
##########
File path: sdks/python/apache_beam/dataframe/frames.py
##########
@@ -1098,6 +1098,23 @@ def fn(s):
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Arbitrary())
+ @property # type: ignore
+ @frame_base.with_docs_from(pd.Series)
+ def hasnans(self):
+ has_nans = expressions.ComputedExpression(
+ 'has_nans',
Review comment:
nit:
```suggestion
'hasnans',
```
##########
File path: sdks/python/apache_beam/dataframe/frames.py
##########
@@ -1098,6 +1098,23 @@ def fn(s):
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Arbitrary())
+ @property # type: ignore
+ @frame_base.with_docs_from(pd.Series)
+ def hasnans(self):
+ has_nans = expressions.ComputedExpression(
+ 'has_nans',
+ lambda s: pd.Series(s.hasnans), [self._expr],
+ requires_partition_by=partitionings.Index(),
+ preserves_partition_by=partitionings.Singleton())
+
+ with expressions.allow_non_parallel_operations():
+ return frame_base.DeferredFrame.wrap(
+ expressions.ComputedExpression(
+ 'combine',
+ lambda s: s.all(), [has_nans],
Review comment:
This looks like a bug in the `frames_test` framework.
https://github.com/apache/beam/pull/15734 seems to fix it.
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