Apply Ruff and Ruff-format auto-fixes

doc/source/whatsnew/v3.0.0.rst

@@ -811,11 +811,11 @@ ExtensionArray
 - Bug in :class:`Categorical` when constructing with an :class:`Index` with :class:`ArrowDtype` (:issue:`60563`)
 - Bug in :meth:`.arrays.ArrowExtensionArray.__setitem__` which caused wrong behavior when using an integer array with repeated values as a key (:issue:`58530`)
 - Bug in :meth:`ArrowExtensionArray.factorize` where NA values were dropped when input was dictionary-encoded even when dropna was set to False(:issue:`60567`)
+- Bug in :meth:`Series.map` and :meth:`Series.apply` where applying functions to a Series with an :class:`Int32Dtype` or other :class:`ExtensionDtype` would convert elements to float and ``pd.NA`` to ``np.nan``, instead of preserving the original types (:issue:`60766`)
 - Bug in :meth:`api.types.is_datetime64_any_dtype` where a custom :class:`ExtensionDtype` would return ``False`` for array-likes (:issue:`57055`)
 - Bug in comparison between object with :class:`ArrowDtype` and incompatible-dtyped (e.g. string vs bool) incorrectly raising instead of returning all-``False`` (for ``==``) or all-``True`` (for ``!=``) (:issue:`59505`)
 - Bug in constructing pandas data structures when passing into ``dtype`` a string of the type followed by ``[pyarrow]`` while PyArrow is not installed would raise ``NameError`` rather than ``ImportError`` (:issue:`57928`)
 - Bug in various :class:`DataFrame` reductions for pyarrow temporal dtypes returning incorrect dtype when result was null (:issue:`59234`)
-- Bug in :meth:`Series.map` and :meth:`Series.apply` where applying functions to a Series with an :class:`Int32Dtype` or other :class:`ExtensionDtype` would convert elements to float and ``pd.NA`` to ``np.nan``, instead of preserving the original types (:issue:`60766`)
 
 
 Styler

pandas/tests/arrays/masked/test_basemaskedarray_map.py

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

pandas/tests/extension/test_masked.py

@@ -176,7 +176,7 @@ def test_map(self, data_missing, na_action):
                 if result[i] is pd.NA:
                     result[i] = "nan"
             result = result.astype("float64")
-            
+
         if data_missing.dtype == Float32Dtype():
             # map roundtrips through objects, which converts to float64
             expected = data_missing.to_numpy(dtype="float64", na_value=np.nan)
@@ -187,7 +187,7 @@ 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")
-        
+
         if data_missing_for_sorting.dtype.kind == "b":
             expected = np.array([False, pd.NA, False], dtype=object)
         else: