udim commented on a change in pull request #14568:
URL: https://github.com/apache/beam/pull/14568#discussion_r626090138
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File path: sdks/python/apache_beam/dataframe/schemas_test.py
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@@ -203,19 +223,32 @@ def test_batch_with_df_transform(self):
proxy=schemas.generate_proxy(Animal)))
assert_that(res, equal_to([('Falcon', 375.), ('Parrot', 25.)]))
+ def assert_typehints_equal(self, left, right):
+ left = typehints.normalize(left)
+ right = typehints.normalize(right)
+
+ if _match_is_named_tuple(left):
+ self.assertTrue(_match_is_named_tuple(right))
+ self.assertEqual(left.__annotations__, right.__annotations__)
+ else:
+ self.assertEqual(left, right)
+
@parameterized.expand(SERIES_TESTS + NOINDEX_DF_TESTS)
- def test_unbatch_no_index(self, df_or_series, rows):
+ def test_unbatch_no_index(self, df_or_series, rows, beam_type):
proxy = df_or_series[:0]
with TestPipeline() as p:
res = (
p | beam.Create([df_or_series[::2], df_or_series[1::2]])
| schemas.UnbatchPandas(proxy))
+ # Verify that the unbatched PCollection has the expected typehint
+ self.assert_typehints_equal(res.element_type, beam_type)
Review comment:
There shouldn't be `typing` module types during type checking. They
should get converted to internal types or Any.
The solution would be to write an internal type for typing.NamedTuple, and
use that type internally.
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