Fix pandas-dev#60766:.map,.apply would convert element type for extension 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.

Author: pedromfdiogo

pandas/core/arrays/masked.py

@@ -1325,7 +1325,7 @@ def max(self, *, skipna: bool = True, axis: AxisInt | None = 0, **kwargs):
         return self._wrap_reduction_result("max", result, skipna=skipna, axis=axis)
 
     def map(self, mapper, na_action: Literal["ignore"] | None = None):
-        return map_array(self.to_numpy(), mapper, na_action=na_action)
+        return map_array(self, mapper, na_action=na_action)
 
     @overload
     def any(

pandas/tests/arrays/masked/test_basemaskedarray_map.py

@@ -0,0 +1,18 @@
+import pandas as pd
+
+def test_basemaskedarray_map():
+    for dtype, data, expected_data in [
+        ("Int32", [1, 2, None, 4], [2, 3, pd.NA, 5]),  
+          
+    ]:
+        s = pd.Series(data, dtype=dtype)
+
+        def transform(x):
+            if x is None:  
+                return x
+            return x + 1
+
+        result = s.map(transform)
+        expected = pd.Series(expected_data, dtype=result.dtype) 
+
+        assert result.tolist() == expected.tolist()