TheNeuralBit commented on a change in pull request #13853:
URL: https://github.com/apache/beam/pull/13853#discussion_r572202678
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File path: sdks/python/apache_beam/dataframe/frames_test.py
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@@ -182,14 +194,53 @@ def test_groupby_project(self):
self._run_test(lambda df: df.groupby('group')['foo'].median(), df)
self._run_test(lambda df: df.groupby('group')['baz'].median(), df)
self._run_test(lambda df: df.groupby('group')[['bar', 'baz']].median(), df)
+
+ def test_groupby_errors(self):
+ df = pd.DataFrame({
+ 'group': ['a' if i % 5 == 0 or i % 3 == 0 else 'b' for i in
range(100)],
+ 'foo': [None if i % 11 == 0 else i for i in range(100)],
+ 'bar': [None if i % 7 == 0 else 99 - i for i in range(100)],
+ 'baz': [None if i % 13 == 0 else i * 2 for i in range(100)],
+ })
+
+ # non-existent projection column
self._run_test(
lambda df: df.groupby('group')[['bar', 'baz']].bar.median(),
df,
expect_error=True)
self._run_test(
- lambda df: df.groupby('group')[['bat']].median(), df,
expect_error=True)
+ lambda df: df.groupby('group')[['bad']].median(), df,
expect_error=True)
+
+ self._run_test(
+ lambda df: df.groupby('group').bad.median(), df, expect_error=True)
+
+ # non-existent grouping label
+ self._run_test(
+ lambda df: df.groupby(['really_bad', 'foo', 'bad']).foo.sum(),
+ df,
+ expect_error=True)
+ self._run_test(
+ lambda df: df.groupby('bad').foo.sum(), df, expect_error=True)
+
+ def test_set_index(self):
+ df = pd.DataFrame({
+ # Generate some unique columns to use for indexes
Review comment:
Thanks these are good questions :)
Partitioning is done by hashing the index:
https://github.com/apache/beam/blob/befcc3d780d561e81f23512742862a65c0ae3b69/sdks/python/apache_beam/dataframe/partitionings.py#L107-L116
So depending on what happens with the
[hashing](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.util.hash_array.html)
we could get lucky or unlucky with the partitioning. A larger input doesn't
even remove the risk, just reduces it.
Another (better?) guard might be to run tests multiple times with different
hash keys (or a single randomized hash key, but that would introduce flakiness).
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