[GitHub] [beam] TheNeuralBit commented on a change in pull request #11980: [BEAM-9546] DataframeTransform can now consume a schema-aware PCollection

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TheNeuralBit commented on a change in pull request #11980:
URL: https://github.com/apache/beam/pull/11980#discussion_r471827891



##########
File path: sdks/python/apache_beam/dataframe/schemas_test.py
##########
@@ -0,0 +1,110 @@
+#
+# 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.
+#
+
+"""Tests for schemas."""
+
+# pytype: skip-file
+
+from __future__ import absolute_import
+
+import unittest
+from typing import NamedTuple
+
+import future.tests.base  # pylint: disable=unused-import
+# patches unittest.testcase to be python3 compatible
+import pandas as pd
+from past.builtins import unicode
+
+import apache_beam as beam
+from apache_beam.coders import RowCoder
+from apache_beam.coders.typecoders import registry as coders_registry
+from apache_beam.dataframe import schemas
+from apache_beam.dataframe import transforms
+from apache_beam.testing.test_pipeline import TestPipeline
+from apache_beam.testing.util import assert_that
+
+Simple = NamedTuple(
+    'Simple', [('name', unicode), ('id', int), ('height', float)])
+coders_registry.register_coder(Simple, RowCoder)
+Animal = NamedTuple('Animal', [('animal', unicode), ('max_speed', float)])
+coders_registry.register_coder(Animal, RowCoder)
+
+
+def matches_df(expected):
+  def check_df_pcoll_equal(actual):
+    actual = pd.concat(actual)
+    sorted_actual = actual.sort_values(by=list(actual.columns)).reset_index(
+        drop=True)
+    sorted_expected = expected.sort_values(
+        by=list(expected.columns)).reset_index(drop=True)
+    if not sorted_actual.equals(sorted_expected):
+      raise AssertionError(
+          'Dataframes not equal: \n\nActual:\n%s\n\nExpected:\n%s' %
+          (sorted_actual, sorted_expected))
+
+  return check_df_pcoll_equal
+
+
+class SchemasTest(unittest.TestCase):
+  def test_simple_df(self):
+    expected = pd.DataFrame({
+        'name': list(unicode(i) for i in range(5)),
+        'id': list(range(5)),
+        'height': list(float(i) for i in range(5))
+    },
+                            columns=['name', 'id', 'height'])
+
+    with TestPipeline() as p:
+      res = (
+          p
+          | beam.Create([
+              Simple(name=unicode(i), id=i, height=float(i)) for i in range(5)
+          ])
+          | schemas.BatchRowsAsDataFrame(min_batch_size=10, max_batch_size=10))
+      assert_that(res, matches_df(expected))
+
+  def test_generate_proxy(self):
+    expected = pd.DataFrame({
+        'animal': pd.Series(dtype=unicode), 'max_speed': pd.Series(dtype=float)
+    })
+
+    self.assertTrue(schemas.generate_proxy(Animal).equals(expected))
+
+  def test_batch_with_df_transform(self):
+    with TestPipeline() as p:
+      res = (
+          p
+          | beam.Create([
+              Animal('Falcon', 380.0),
+              Animal('Falcon', 370.0),
+              Animal('Parrot', 24.0),
+              Animal('Parrot', 26.0)
+          ])
+          | schemas.BatchRowsAsDataFrame()
+          | transforms.DataframeTransform(
+              lambda df: df.groupby('animal').mean(),
+              proxy=schemas.generate_proxy(Animal)))

Review comment:
       Added a TODO for now. I guess we'd need to store some type information on a PCollection[DataFrame], should we just store a proxy object when we know it?




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