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Posted to github@beam.apache.org by GitBox <gi...@apache.org> on 2021/03/31 21:51:43 UTC
[GitHub] [beam] pabloem commented on a change in pull request #14274: [BEAM-9547] Initial implementation for where, mask
pabloem commented on a change in pull request #14274:
URL: https://github.com/apache/beam/pull/14274#discussion_r605241660
##########
File path: sdks/python/apache_beam/dataframe/frames.py
##########
@@ -344,6 +344,57 @@ def equals(self, other):
requires_partition_by=partitionings.Singleton(),
preserves_partition_by=partitionings.Singleton()))
+ @frame_base.args_to_kwargs(pd.DataFrame)
+ @frame_base.populate_defaults(pd.DataFrame)
+ @frame_base.maybe_inplace
+ def where(self, cond, other, errors, **kwargs):
+ requires = partitionings.Arbitrary()
+ deferred_args = {}
+ actual_args = {}
+
+ # TODO(bhulette): This is very similar to the logic in
+ # frame_base.elementwise_method, can we unify it?
+ if isinstance(cond, frame_base.DeferredFrame):
+ deferred_args['cond'] = cond
+ requires = partitionings.Index()
+ else:
+ actual_args['cond'] = cond
+
+ if isinstance(other, frame_base.DeferredFrame):
+ deferred_args['other'] = other
+ requires = partitionings.Index()
+ else:
+ actual_args['other'] = other
+
+ if errors == "ignore":
+ # We need all data in order to ignore errors and propagate the original
+ # data.
+ requires = partitionings.Singleton()
Review comment:
are errors related to the whole DF or Series? can there be errors related to individual elements in the series, and ignored individually?
##########
File path: sdks/python/apache_beam/dataframe/frames.py
##########
@@ -344,6 +344,57 @@ def equals(self, other):
requires_partition_by=partitionings.Singleton(),
preserves_partition_by=partitionings.Singleton()))
+ @frame_base.args_to_kwargs(pd.DataFrame)
Review comment:
It's not a must for now, but I'd be curious to see more pydoc on the elements from the framework so that I can wrap my head around them more easily : )
##########
File path: sdks/python/apache_beam/dataframe/frames.py
##########
@@ -344,6 +344,57 @@ def equals(self, other):
requires_partition_by=partitionings.Singleton(),
preserves_partition_by=partitionings.Singleton()))
+ @frame_base.args_to_kwargs(pd.DataFrame)
+ @frame_base.populate_defaults(pd.DataFrame)
+ @frame_base.maybe_inplace
+ def where(self, cond, other, errors, **kwargs):
+ requires = partitionings.Arbitrary()
+ deferred_args = {}
+ actual_args = {}
+
+ # TODO(bhulette): This is very similar to the logic in
+ # frame_base.elementwise_method, can we unify it?
+ if isinstance(cond, frame_base.DeferredFrame):
+ deferred_args['cond'] = cond
+ requires = partitionings.Index()
+ else:
+ actual_args['cond'] = cond
+
+ if isinstance(other, frame_base.DeferredFrame):
+ deferred_args['other'] = other
+ requires = partitionings.Index()
+ else:
+ actual_args['other'] = other
+
+ if errors == "ignore":
+ # We need all data in order to ignore errors and propagate the original
+ # data.
+ requires = partitionings.Singleton()
+
+ actual_args['errors'] = errors
+
+ def where(self, *args):
+ runtime_values = {
+ name: value
+ for (name, value) in zip(deferred_args.keys(), args)
+ }
+ return self.where(**runtime_values, **actual_args, **kwargs)
Review comment:
I feel a little confused. What is `self` in this where fn call? Is this called at pipeline runtime by a dataframe/series?
##########
File path: sdks/python/apache_beam/dataframe/frames.py
##########
@@ -344,6 +344,57 @@ def equals(self, other):
requires_partition_by=partitionings.Singleton(),
preserves_partition_by=partitionings.Singleton()))
+ @frame_base.args_to_kwargs(pd.DataFrame)
+ @frame_base.populate_defaults(pd.DataFrame)
+ @frame_base.maybe_inplace
+ def where(self, cond, other, errors, **kwargs):
+ requires = partitionings.Arbitrary()
+ deferred_args = {}
+ actual_args = {}
+
+ # TODO(bhulette): This is very similar to the logic in
+ # frame_base.elementwise_method, can we unify it?
+ if isinstance(cond, frame_base.DeferredFrame):
+ deferred_args['cond'] = cond
+ requires = partitionings.Index()
+ else:
+ actual_args['cond'] = cond
+
+ if isinstance(other, frame_base.DeferredFrame):
+ deferred_args['other'] = other
+ requires = partitionings.Index()
+ else:
+ actual_args['other'] = other
+
+ if errors == "ignore":
+ # We need all data in order to ignore errors and propagate the original
+ # data.
+ requires = partitionings.Singleton()
Review comment:
I guess my question is - what is an example of an error here?
##########
File path: sdks/python/apache_beam/dataframe/frames.py
##########
@@ -344,6 +344,57 @@ def equals(self, other):
requires_partition_by=partitionings.Singleton(),
preserves_partition_by=partitionings.Singleton()))
+ @frame_base.args_to_kwargs(pd.DataFrame)
+ @frame_base.populate_defaults(pd.DataFrame)
+ @frame_base.maybe_inplace
+ def where(self, cond, other, errors, **kwargs):
+ requires = partitionings.Arbitrary()
+ deferred_args = {}
+ actual_args = {}
+
+ # TODO(bhulette): This is very similar to the logic in
+ # frame_base.elementwise_method, can we unify it?
+ if isinstance(cond, frame_base.DeferredFrame):
+ deferred_args['cond'] = cond
+ requires = partitionings.Index()
+ else:
+ actual_args['cond'] = cond
+
+ if isinstance(other, frame_base.DeferredFrame):
+ deferred_args['other'] = other
+ requires = partitionings.Index()
+ else:
+ actual_args['other'] = other
+
+ if errors == "ignore":
+ # We need all data in order to ignore errors and propagate the original
+ # data.
+ requires = partitionings.Singleton()
+
+ actual_args['errors'] = errors
+
+ def where(self, *args):
+ runtime_values = {
+ name: value
+ for (name, value) in zip(deferred_args.keys(), args)
+ }
+ return self.where(**runtime_values, **actual_args, **kwargs)
+
+ return frame_base.DeferredFrame.wrap(
+ expressions.ComputedExpression(
+ "where",
+ where,
+ [self._expr] + [df._expr for df in deferred_args.values()],
+ requires_partition_by=requires,
+ preserves_partition_by=partitionings.Index(),
Review comment:
In this case, IIUC, output partitioning will be by Index (or by Singleton for `errors='ignore'`), right?
Am I correct understanding that Singleton partitioning is a special case of Index partitioning, and thus it's included?
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