betodealmeida commented on code in PR #21002:
URL: https://github.com/apache/superset/pull/21002#discussion_r942564689
##########
tests/unit_tests/pandas_postprocessing/test_contribution.py:
##########
@@ -74,7 +74,7 @@ def test_contribution():
rename_columns=["pct_a"],
)
assert processed_df.columns.tolist() == ["a", "b", "c", "pct_a"]
- assert_array_equal(processed_df["a"].tolist(), [1, 3, nan])
- assert_array_equal(processed_df["b"].tolist(), [1, 9, nan])
- assert_array_equal(processed_df["c"].tolist(), [nan, nan, nan])
+ assert_array_equal(processed_df["a"].tolist(), [1, 3, 0])
+ assert_array_equal(processed_df["b"].tolist(), [1, 9, 0])
+ assert_array_equal(processed_df["c"].tolist(), [0, 0, 0])
Review Comment:
@villebro in this case this is the result of the `contribution` post
processing function, so I think it makes sense — if something is not present
it's contribution is zero. But I agree, we definitely don't want nans
everywhere to become zeros... it would be nice to have more coverage on tests
that manipulate data to ensure we're not returning wrong results (the worst
kind of bug!).
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