[GitHub] [beam] robertwb commented on a change in pull request #10760: [BEAM-9545] Dataframe transforms
robertwb commented on a change in pull request #10760: [BEAM-9545] Dataframe
transforms
URL: https://github.com/apache/beam/pull/10760#discussion_r395900936
##
File path: sdks/python/apache_beam/dataframe/transforms.py
##
@@ -0,0 +1,246 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+#http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import pandas as pd
+
+import apache_beam as beam
+from apache_beam import transforms
+from apache_beam.dataframe import expressions
+from apache_beam.dataframe import frame_base
+from apache_beam.dataframe import frames # pylint: disable=unused-import
+
+
+class DataframeTransform(transforms.PTransform):
+ """A PTransform for applying function that takes and returns dataframes
+ to one or more PCollections.
+
+ For example, if pcoll is a PCollection of dataframes, one could write::
+
+ pcoll | DataframeTransform(lambda df: df.group_by('key').sum(),
proxy=...)
+
+ To pass multiple PCollections, pass a tuple of PCollections wich will be
+ passed to the callable as positional arguments, or a dictionary of
+ PCollections, in which case they will be passed as keyword arguments.
+ """
+ def __init__(self, func, proxy):
+self._func = func
+self._proxy = proxy
+
+ def expand(self, input_pcolls):
+def wrap_as_dict(values):
+ if isinstance(values, dict):
+return values
+ elif isinstance(values, tuple):
+return dict(enumerate(values))
+ else:
+return {None: values}
+
+# TODO: Infer the proxy from the input schema.
+def proxy(key):
+ if key is None:
+return self._proxy
+ else:
+return self._proxy[key]
+
+# The input can be a dictionary, tuple, or plain PCollection.
+# Wrap as a dict for homogeneity.
+# TODO: Possibly inject batching here.
+input_dict = wrap_as_dict(input_pcolls)
+placeholders = {
+key: frame_base.DeferredFrame.wrap(
+expressions.PlaceholderExpression(proxy(key)))
+for key in input_dict.keys()
+}
+
+# The calling convention of the user-supplied func varies according to the
+# type of the input.
+if isinstance(input_pcolls, dict):
+ result_frames = self._func(**placeholders)
+elif isinstance(input_pcolls, tuple):
+ result_frames = self._func(
+ *(value for _, value in sorted(placeholders.items(
+else:
+ result_frames = self._func(placeholders[None])
+
+# Likewise the output may be a dict, tuple, or raw (deferred) Dataframe.
+result_dict = wrap_as_dict(result_frames)
+
+result_pcolls = self._apply_deferred_ops(
+{placeholders[key]._expr: pcoll
+ for key, pcoll in input_dict.items()},
+{key: df._expr
+ for key, df in result_dict.items()})
+
+# Convert the result back into a set of PCollections.
+if isinstance(result_frames, dict):
+ return result_pcolls
+elif isinstance(result_frames, tuple):
+ return tuple((value for _, value in sorted(result_pcolls.items(
+else:
+ return result_pcolls[None]
+
+ def _apply_deferred_ops(
+ self,
+ inputs, # type: Dict[PlaceholderExpr, PCollection]
+ outputs, # type: Dict[Any, Expression]
+ ): # -> Dict[Any, PCollection]
+"""Construct a Beam graph that evaluates a set of expressions on a set of
+input PCollections.
+
+:param inputs: A mapping of placeholder expressions to PCollections.
+:param outputs: A mapping of keys to expressions defined in terms of the
+placeholders of inputs.
+
+Returns a dictionary whose keys are those of outputs, and whose values are
+PCollections corresponding to the values of outputs evaluated at the
+values of inputs.
+
+Logically, `_apply_deferred_ops({x: a, y: b}, {f: F(x, y), g: G(x, y)})`
+returns `{f: F(a, b), g: G(a, b)}`.
+"""
+class ComputeStage(beam.PTransform):
+ """A helper transform that computes a single stage of operations.
+ """
+ def __init__(self, stage):
+self.stage = stage
+
+ def default_label(self):
+return '%s:%s' % (self.stage.ops, id(self))
+
+ def expand(self, pcolls):
+if self.stage.is_grouping:
+ # Arrange such that
[GitHub] [beam] robertwb commented on a change in pull request #10760: [BEAM-9545] Dataframe transforms
robertwb commented on a change in pull request #10760: [BEAM-9545] Dataframe
transforms
URL: https://github.com/apache/beam/pull/10760#discussion_r395253978
##
File path: sdks/python/apache_beam/dataframe/transforms.py
##
@@ -0,0 +1,244 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+#http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import pandas as pd
+
+import apache_beam as beam
+from apache_beam import transforms
+from apache_beam.dataframe import expressions
+from apache_beam.dataframe import frame_base
+
+
+class DataframeTransform(transforms.PTransform):
+ """A PTransform for applying function that takes and returns dataframes
+ to one or more PCollections.
+
+ For example, if pcoll is a PCollection of dataframes, one could write::
+
+ pcoll | DataframeTransform(lambda df: df.group_by('key').sum())
+
+ To pass multiple PCollections, pass a tuple of PCollections wich will be
+ passed to the callable as positional arguments, or a dictionary of
+ PCollections, in which case they will be passed as keyword arguments.
+ """
+ def __init__(self, func, proxy):
+self._func = func
+self._proxy = proxy
+
+ def expand(self, input_pcolls):
+def wrap_as_dict(values):
+ if isinstance(values, dict):
+return dict
+ elif isinstance(values, tuple):
+return dict(enumerate(values))
+ else:
+return {None: values}
+
+# TODO: Infer the proxy from the input schema.
+def proxy(key):
+ if key is None:
+return self._proxy
+ else:
+return self._proxy[key]
+
+# The input can be a dictionary, tuple, or plain PCollection.
+# Wrap as a dict for homogeneity.
+# TODO: Possibly inject batching here.
+input_dict = wrap_as_dict(input_pcolls)
+placeholders = {
+key: frame_base.DeferredFrame.wrap(
+expressions.PlaceholderExpression(proxy(key)))
+for key in input_dict.keys()
+}
+
+# The calling convention of the user-supplied func varies according to the
+# type of the input.
+if isinstance(input_pcolls, dict):
+ result_frames = self._func(**placeholders)
+elif isinstance(input_pcolls, tuple):
+ self._func(*(value for _, value in sorted(placeholders.items(
+else:
+ result_frames = self._func(placeholders[None])
+
+# Likewise the output may be a dict, tuple, or raw (deferred) Dataframe.
+result_dict = wrap_as_dict(result_frames)
+
+result_pcolls = self._apply_deferred_ops(
+{placeholders[key]._expr: pcoll
+ for key, pcoll in input_dict.items()},
+{key: df._expr
+ for key, df in result_dict.items()})
+
+# Convert the result back into a set of PCollections.
+if isinstance(result_frames, dict):
+ return result_pcolls
+elif isinstance(result_frames, tuple):
+ return tuple(*(value for _, value in sorted(result_pcolls.items(
+else:
+ return result_pcolls[None]
+
+ def _apply_deferred_ops(
+ self,
+ inputs, # type: Dict[PlaceholderExpr, PCollection]
+ outputs, # type: Dict[Any, Expression]
+ ): # -> Dict[Any, PCollection]
+"""Construct a Beam graph that evaluates a set of expressions on a set of
+input PCollections.
+
+:param inputs: A mapping of placeholder expressions to PCollections.
+:param outputs: A mapping of keys to expressions defined in terms of the
+placeholders of inputs.
+
+Returns a dictionary whose keys are those of outputs, and whose values are
+PCollections corresponding to the values of outputs evaluated at the
+values of inputs.
+
+Logically, `_apply_deferred_ops({x: a, y: b}, {f: F(x, y), g: G(x, y)})`
+returns `{f: F(a, b), g: G(a, b)}`.
+"""
+class ComputeStage(beam.PTransform):
+ """A helper transform that computes a single stage of operations.
+ """
+ def __init__(self, stage):
+self.stage = stage
+
+ def default_label(self):
+return '%s:%s' % (self.stage.ops, id(self))
+
+ def expand(self, pcolls):
+if self.stage.is_grouping:
+ # Arrange such that partitioned_pcoll is properly partitioned.
+ input_pcolls = {
+ k: pcoll | 'Flat%s' % k >>
[GitHub] [beam] robertwb commented on a change in pull request #10760: [BEAM-9545] Dataframe transforms
robertwb commented on a change in pull request #10760: [BEAM-9545] Dataframe
transforms
URL: https://github.com/apache/beam/pull/10760#discussion_r395254391
##
File path: sdks/python/apache_beam/dataframe/transforms.py
##
@@ -0,0 +1,244 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+#http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import pandas as pd
+
+import apache_beam as beam
+from apache_beam import transforms
+from apache_beam.dataframe import expressions
+from apache_beam.dataframe import frame_base
+
+
+class DataframeTransform(transforms.PTransform):
+ """A PTransform for applying function that takes and returns dataframes
+ to one or more PCollections.
+
+ For example, if pcoll is a PCollection of dataframes, one could write::
+
+ pcoll | DataframeTransform(lambda df: df.group_by('key').sum())
+
+ To pass multiple PCollections, pass a tuple of PCollections wich will be
+ passed to the callable as positional arguments, or a dictionary of
+ PCollections, in which case they will be passed as keyword arguments.
+ """
+ def __init__(self, func, proxy):
+self._func = func
+self._proxy = proxy
+
+ def expand(self, input_pcolls):
+def wrap_as_dict(values):
+ if isinstance(values, dict):
+return dict
+ elif isinstance(values, tuple):
+return dict(enumerate(values))
+ else:
+return {None: values}
+
+# TODO: Infer the proxy from the input schema.
+def proxy(key):
+ if key is None:
+return self._proxy
+ else:
+return self._proxy[key]
+
+# The input can be a dictionary, tuple, or plain PCollection.
+# Wrap as a dict for homogeneity.
+# TODO: Possibly inject batching here.
+input_dict = wrap_as_dict(input_pcolls)
+placeholders = {
+key: frame_base.DeferredFrame.wrap(
+expressions.PlaceholderExpression(proxy(key)))
+for key in input_dict.keys()
+}
+
+# The calling convention of the user-supplied func varies according to the
+# type of the input.
+if isinstance(input_pcolls, dict):
+ result_frames = self._func(**placeholders)
+elif isinstance(input_pcolls, tuple):
+ self._func(*(value for _, value in sorted(placeholders.items(
+else:
+ result_frames = self._func(placeholders[None])
+
+# Likewise the output may be a dict, tuple, or raw (deferred) Dataframe.
+result_dict = wrap_as_dict(result_frames)
+
+result_pcolls = self._apply_deferred_ops(
+{placeholders[key]._expr: pcoll
+ for key, pcoll in input_dict.items()},
+{key: df._expr
+ for key, df in result_dict.items()})
+
+# Convert the result back into a set of PCollections.
+if isinstance(result_frames, dict):
+ return result_pcolls
+elif isinstance(result_frames, tuple):
+ return tuple(*(value for _, value in sorted(result_pcolls.items(
+else:
+ return result_pcolls[None]
+
+ def _apply_deferred_ops(
+ self,
+ inputs, # type: Dict[PlaceholderExpr, PCollection]
+ outputs, # type: Dict[Any, Expression]
+ ): # -> Dict[Any, PCollection]
+"""Construct a Beam graph that evaluates a set of expressions on a set of
+input PCollections.
+
+:param inputs: A mapping of placeholder expressions to PCollections.
+:param outputs: A mapping of keys to expressions defined in terms of the
+placeholders of inputs.
+
+Returns a dictionary whose keys are those of outputs, and whose values are
+PCollections corresponding to the values of outputs evaluated at the
+values of inputs.
+
+Logically, `_apply_deferred_ops({x: a, y: b}, {f: F(x, y), g: G(x, y)})`
+returns `{f: F(a, b), g: G(a, b)}`.
+"""
+class ComputeStage(beam.PTransform):
+ """A helper transform that computes a single stage of operations.
+ """
+ def __init__(self, stage):
+self.stage = stage
+
+ def default_label(self):
+return '%s:%s' % (self.stage.ops, id(self))
+
+ def expand(self, pcolls):
+if self.stage.is_grouping:
+ # Arrange such that partitioned_pcoll is properly partitioned.
+ input_pcolls = {
+ k: pcoll | 'Flat%s' % k >>
[GitHub] [beam] robertwb commented on a change in pull request #10760: [BEAM-9545] Dataframe transforms
robertwb commented on a change in pull request #10760: [BEAM-9545] Dataframe
transforms
URL: https://github.com/apache/beam/pull/10760#discussion_r395226313
##
File path: sdks/python/apache_beam/dataframe/transforms.py
##
@@ -0,0 +1,244 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+#http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import pandas as pd
+
+import apache_beam as beam
+from apache_beam import transforms
+from apache_beam.dataframe import expressions
+from apache_beam.dataframe import frame_base
+
+
+class DataframeTransform(transforms.PTransform):
+ """A PTransform for applying function that takes and returns dataframes
+ to one or more PCollections.
+
+ For example, if pcoll is a PCollection of dataframes, one could write::
+
+ pcoll | DataframeTransform(lambda df: df.group_by('key').sum())
+
+ To pass multiple PCollections, pass a tuple of PCollections wich will be
+ passed to the callable as positional arguments, or a dictionary of
+ PCollections, in which case they will be passed as keyword arguments.
+ """
+ def __init__(self, func, proxy):
+self._func = func
+self._proxy = proxy
+
+ def expand(self, input_pcolls):
+def wrap_as_dict(values):
+ if isinstance(values, dict):
+return dict
+ elif isinstance(values, tuple):
+return dict(enumerate(values))
+ else:
+return {None: values}
+
+# TODO: Infer the proxy from the input schema.
+def proxy(key):
+ if key is None:
+return self._proxy
+ else:
+return self._proxy[key]
+
+# The input can be a dictionary, tuple, or plain PCollection.
+# Wrap as a dict for homogeneity.
+# TODO: Possibly inject batching here.
+input_dict = wrap_as_dict(input_pcolls)
+placeholders = {
+key: frame_base.DeferredFrame.wrap(
+expressions.PlaceholderExpression(proxy(key)))
+for key in input_dict.keys()
+}
+
+# The calling convention of the user-supplied func varies according to the
+# type of the input.
+if isinstance(input_pcolls, dict):
+ result_frames = self._func(**placeholders)
+elif isinstance(input_pcolls, tuple):
+ self._func(*(value for _, value in sorted(placeholders.items(
Review comment:
Yeah. And now it's tested.
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[GitHub] [beam] robertwb commented on a change in pull request #10760: [BEAM-9545] Dataframe transforms
robertwb commented on a change in pull request #10760: [BEAM-9545] Dataframe
transforms
URL: https://github.com/apache/beam/pull/10760#discussion_r395255358
##
File path: sdks/python/apache_beam/dataframe/transforms_test.py
##
@@ -0,0 +1,87 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+#http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import unittest
+
+import pandas as pd
+
+import apache_beam as beam
+from apache_beam.dataframe import expressions
+from apache_beam.dataframe import frame_base
+from apache_beam.dataframe import transforms
+from apache_beam.testing.util import assert_that
+
+
+class TransformTest(unittest.TestCase):
+ def run_test(self, input, func):
+expected = func(input)
+
+empty = input[0:0]
+input_placeholder = expressions.PlaceholderExpression(empty)
+input_deferred = frame_base.DeferredFrame.wrap(input_placeholder)
+actual_deferred = func(input_deferred)._expr.evaluate_at(
+expressions.Session({input_placeholder: input}))
+
+def check_correct(actual):
+ if actual is None:
+raise AssertionError('Empty frame but expected: \n\n%s' % (expected))
+ sorted_actual = actual.sort_index()
+ sorted_expected = expected.sort_index()
+ if not sorted_actual.equals(sorted_expected):
+raise AssertionError(
+'Dataframes not equal: \n\n%s\n\n%s' %
+(sorted_actual, sorted_expected))
+
+check_correct(actual_deferred)
+
+with beam.Pipeline() as p:
+ input_pcoll = p | beam.Create([input[::2], input[1::2]])
+ output_pcoll = input_pcoll | transforms.DataframeTransform(
+ func, proxy=empty)
+ assert_that(
+ output_pcoll,
+ lambda actual: check_correct(pd.concat(actual) if actual else None))
+
+ def test_identity(self):
+df = pd.DataFrame({
+'Animal': ['Falcon', 'Falcon', 'Parrot', 'Parrot'],
+'Speed': [380., 370., 24., 26.]
+})
+self.run_test(df, lambda x: x)
+
+ def test_sum_mean(self):
+df = pd.DataFrame({
+'Animal': ['Falcon', 'Falcon', 'Parrot', 'Parrot'],
+'Speed': [380., 370., 24., 26.]
+})
+self.run_test(df, lambda df: df.groupby('Animal').sum())
+self.run_test(df, lambda df: df.groupby('Animal').mean())
+
+ def test_filter(self):
+df = pd.DataFrame({
+'Animal': ['Aardvark', 'Ant', 'Elephant', 'Zebra'],
+'Speed': [5, 2, 35, 40]
+})
+self.run_test(df, lambda df: df.filter(items=['Animal']))
+self.run_test(df, lambda df: df.filter(regex='A.*'))
+self.run_test(
+df, lambda df: df.set_index('Animal').filter(regex='F.*',
axis='index'))
+
+
+if __name__ == '__main__':
+ unittest.main()
Review comment:
I actually have some code that does just that: it books into the doctest
framework to run vanilla pandas tests through these functions as beam
pipelines. I've been meaning to get it into a PR, but didn't want to overwhelm
you.
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[GitHub] [beam] robertwb commented on a change in pull request #10760: [BEAM-9545] Dataframe transforms
robertwb commented on a change in pull request #10760: [BEAM-9545] Dataframe
transforms
URL: https://github.com/apache/beam/pull/10760#discussion_r395225830
##
File path: sdks/python/apache_beam/dataframe/transforms.py
##
@@ -0,0 +1,244 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+#http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import pandas as pd
+
+import apache_beam as beam
+from apache_beam import transforms
+from apache_beam.dataframe import expressions
+from apache_beam.dataframe import frame_base
+
+
+class DataframeTransform(transforms.PTransform):
+ """A PTransform for applying function that takes and returns dataframes
+ to one or more PCollections.
+
+ For example, if pcoll is a PCollection of dataframes, one could write::
+
+ pcoll | DataframeTransform(lambda df: df.group_by('key').sum())
Review comment:
This is aspirational, hoping we can infer the proxy from the pcollection
schema. Want to take that up :)?
Updated the doc for now.
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