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Fix bug due to pandas release #359

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merged 2 commits into from
Jan 26, 2022
Merged

Fix bug due to pandas release #359

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aea7505
Change none no nan
fealho Jan 25, 2022
25f0278
Convert all data to np.nan
fealho Jan 25, 2022
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2 changes: 1 addition & 1 deletion tests/unit/transformers/test_categorical.py
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Expand Up @@ -156,7 +156,7 @@ def test__get_intervals_nans(self):
categorical value (start, end).
"""
# Setup
data = pd.Series(['foo', np.nan, None, 'foo', 'foo', 'tar'])
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Can we change the expected value instead of this part? I think it's good to know how this function handles None

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We can't really do it, since there are two distinct behaviors for this input, depending on what version of pandas we are running. For pandas 1.4.0, the existing code doesn't even work on this input. Notice that the categorical transformer does the following:

RDT/rdt/transformers/categorical.py

Lines 106 to 109 in 40d2667

if pd.isna(value):
value = np.nan
intervals[value] = (start, end, mean, std)

So we actually just overwrite the first np.nan with the None, which is not the intended behavior. If we do want to keep support for multiple types of nan's, we could cast all null objects no np.nan right at the start of the function.

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It's weird that value__counts doesn't treat None as np.nan, but pd.isna will still return True for it. My question is, is it possible for _get_intervals to receive data with different null types. If so, we have to handle that which means I think we should convert them all before doing the value_counts

data = pd.Series(['foo', np.nan, np.nan, 'foo', 'foo', 'tar'])

# Run
result = CategoricalTransformer._get_intervals(data)
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