yeandy commented on a change in pull request #16615:
URL: https://github.com/apache/beam/pull/16615#discussion_r806954398
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File path: sdks/python/apache_beam/dataframe/frames_test.py
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@@ -1950,9 +1950,55 @@ class BeamSpecificTest(unittest.TestCase):
"""Tests for functionality that's specific to the Beam DataFrame API.
These features don't exist in pandas so we must verify them independently."""
- def assert_frame_data_equivalent(self, actual, expected):
+ def assert_frame_data_equivalent(
+ self, actual, expected, check_subset=False, extra_col_value=0):
"""Verify that actual is the same as expected, ignoring the index and order
- of the data."""
+ of the data.
+
+ Note: In order for us to perform non-deferred operations in Beam, we have
+ to enumerate all possible categories of data, even if they are ultimately
+ unobserved. The default Pandas implementation on the other hand does not
+ produce unobserved columns. This means when conducting tests, we need to
+ account for the fact that the Beam result may be a superset of that of the
+ Pandas result.
+
+ If ``check_subset`` is `True`, we verify that all of the columns in the
+ Dataframe returned from the Pandas implementation is contained in the
+ Dataframe created from the Beam implementation.
+
+ We also check if all columns that exist in the Beam implementation but
+ not in the Pandas implementation are all equal to the ``extra_col_value``
+ to ensure that they were not erroneously populated.
+ """
+ if check_subset:
+ if isinstance(expected, pd.DataFrame):
+ expected_cols = set(expected.columns)
+ actual_cols = set(actual.columns)
+ # Verifying that expected columns is a subset of the actual columns
+ if not set(expected_cols).issubset(set(actual_cols)):
+ raise AssertionError(
+ f"Expected columns:\n{expected.columns}\n is not a"
+ f"subset of {actual.columns}.")
+
+ # Verifying that columns that don't exist in expected
+ # are all NaN in actual
Review comment:
No worries; I forgot to change the wording in this comment. The logic
I've added supports configurable values, with `0` as the default value to which
the extra columns should be compared.
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