Fix #60766:.map,.apply would convert element type for extension array #61396
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…sion array. The Int32Dtype type allows representing integers with support for null values (pd.NA). However, when using .map(f) or .apply(f), the elements passed to f are converted to float64, and pd.NA is transformed into np.nan. This happens because .map() and .apply() internally use numpy, which automatically converts the data to float64, even when the original type is Int32Dtype. The fix (just remove the method to_numpy()) ensures that when using .map() or .apply(), the elements in the series retain their original type (Int32, Float64, boolean, etc.), preventing unnecessary conversions to float64 and ensuring that pd.NA remains correctly handled.
| @@ -181,10 +187,15 @@ def test_map(self, data_missing, na_action): | |||
| def test_map_na_action_ignore(self, data_missing_for_sorting): | |||
| zero = data_missing_for_sorting[2] | |||
| result = data_missing_for_sorting.map(lambda x: zero, na_action="ignore") | |||
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.map&.applywould convert element type for extension array. #60766doc/source/whatsnew/v3.0.0.rstfile if fixing a bug or adding a new feature.The Int32Dtype type allows representing integers with support for null values (pd.NA). However, when using .map(f) or .apply(f), the elements passed to f are converted to float64, and pd.NA is transformed into np.nan.
This happens because .map() and .apply() internally use numpy, which automatically converts the data to float64, even when the original type is Int32Dtype.
The fix (just remove the method to_numpy()) ensures that when using .map() or .apply(), the elements in the series retain their original type (Int32, Float64, boolean, etc.), preventing unnecessary conversions to float64 and ensuring that pd.NA remains correctly handled.