Replace na_action by skipna

Author: datapythonista

pandas/core/algorithms.py

@@ -1629,9 +1629,7 @@ def union_with_duplicates(
 
 
 def map_array(
-    arr: ArrayLike,
-    mapper,
-    na_action: Literal["ignore"] | None = None,
+    arr: ArrayLike, mapper, skipna: bool = False
 ) -> np.ndarray | ExtensionArray | Index:
     """
     Map values using an input mapping or function.
@@ -1640,8 +1638,8 @@ def map_array(
     ----------
     mapper : function, dict, or Series
         Mapping correspondence.
-    na_action : {None, 'ignore'}, default None
-        If 'ignore', propagate NA values, without passing them to the
+    skipna : bool, default False
+        If ``True``, propagate NA values, without passing them to the
         mapping correspondence.
 
     Returns
@@ -1653,10 +1651,6 @@ def map_array(
     """
     from pandas import Index
 
-    if na_action not in (None, "ignore"):
-        msg = f"na_action must either be 'ignore' or None, {na_action} was passed"
-        raise ValueError(msg)
-
     # we can fastpath dict/Series to an efficient map
     # as we know that we are not going to have to yield
     # python types
@@ -1690,7 +1684,7 @@ def map_array(
                 mapper = Series(mapper)
 
     if isinstance(mapper, ABCSeries):
-        if na_action == "ignore":
+        if skipna:
             mapper = mapper[mapper.index.notna()]
 
         # Since values were input this means we came from either
@@ -1705,7 +1699,7 @@ def map_array(
 
     # we must convert to python types
     values = arr.astype(object, copy=False)
-    if na_action is None:
+    if not skipna:
         return lib.map_infer(values, mapper)
     else:
         return lib.map_infer_mask(values, mapper, mask=isna(values).view(np.uint8))

pandas/core/apply.py

@@ -126,8 +126,7 @@ def map(
             executor the user wants to use.
         skip_na : bool
             Whether the function should be called for missing values or not.
-            This is specified by the pandas user as ``map(na_action=None)``
-            or ``map(na_action='ignore')``.
+            This is specified by the pandas user as ``map(skipna=)``.
         """
 
     @staticmethod

pandas/core/arrays/arrow/array.py

@@ -1477,11 +1477,11 @@ def to_numpy(
             result[~mask] = data[~mask]._pa_array.to_numpy()
         return result
 
-    def map(self, mapper, na_action: Literal["ignore"] | None = None):
+    def map(self, mapper, skipna: bool = False):
         if is_numeric_dtype(self.dtype):
-            return map_array(self.to_numpy(), mapper, na_action=na_action)
+            return map_array(self.to_numpy(), mapper, skipna=skipna)
         else:
-            return super().map(mapper, na_action)
+            return super().map(mapper, skipna)
 
     @doc(ExtensionArray.duplicated)
     def duplicated(

pandas/core/arrays/base.py

@@ -2541,17 +2541,17 @@ def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
 
         return arraylike.default_array_ufunc(self, ufunc, method, *inputs, **kwargs)
 
-    def map(self, mapper, na_action: Literal["ignore"] | None = None):
+    def map(self, mapper, skipna: bool = False):
         """
         Map values using an input mapping or function.
 
         Parameters
         ----------
         mapper : function, dict, or Series
             Mapping correspondence.
-        na_action : {None, 'ignore'}, default None
-            If 'ignore', propagate NA values, without passing them to the
-            mapping correspondence. If 'ignore' is not supported, a
+        skipna : bool, default False
+            If ``True``, propagate NA values, without passing them to the
+            mapping correspondence. If ``True`` is not supported, a
             ``NotImplementedError`` should be raised.
 
         Returns
@@ -2561,7 +2561,7 @@ def map(self, mapper, na_action: Literal["ignore"] | None = None):
             If the function returns a tuple with more than one element
             a MultiIndex will be returned.
         """
-        return map_array(self, mapper, na_action=na_action)
+        return map_array(self, mapper, skipna=skipna)
 
     # ------------------------------------------------------------------------
     # GroupBy Methods

pandas/core/arrays/categorical.py

@@ -1497,7 +1497,7 @@ def remove_unused_categories(self) -> Self:
     def map(
         self,
         mapper,
-        na_action: Literal["ignore"] | None = None,
+        skipna: bool = False,
     ):
         """
         Map categories using an input mapping or function.
@@ -1515,8 +1515,8 @@ def map(
         ----------
         mapper : function, dict, or Series
             Mapping correspondence.
-        na_action : {None, 'ignore'}, default None
-            If 'ignore', propagate NaN values, without passing them to the
+        skipna : bool, default False
+            If ``True``, propagate NA values, without passing them to the
             mapping correspondence.
 
         Returns
@@ -1541,10 +1541,10 @@ def map(
         >>> cat
         ['a', 'b', 'c']
         Categories (3, object): ['a', 'b', 'c']
-        >>> cat.map(lambda x: x.upper(), na_action=None)
+        >>> cat.map(lambda x: x.upper(), skipna=False)
         ['A', 'B', 'C']
         Categories (3, object): ['A', 'B', 'C']
-        >>> cat.map({"a": "first", "b": "second", "c": "third"}, na_action=None)
+        >>> cat.map({"a": "first", "b": "second", "c": "third"}, skipna=False)
         ['first', 'second', 'third']
         Categories (3, object): ['first', 'second', 'third']
 
@@ -1555,19 +1555,19 @@ def map(
         >>> cat
         ['a', 'b', 'c']
         Categories (3, object): ['a' < 'b' < 'c']
-        >>> cat.map({"a": 3, "b": 2, "c": 1}, na_action=None)
+        >>> cat.map({"a": 3, "b": 2, "c": 1}, skipna=False)
         [3, 2, 1]
         Categories (3, int64): [3 < 2 < 1]
 
         If the mapping is not one-to-one an :class:`~pandas.Index` is returned:
 
-        >>> cat.map({"a": "first", "b": "second", "c": "first"}, na_action=None)
+        >>> cat.map({"a": "first", "b": "second", "c": "first"}, skipna=False)
         Index(['first', 'second', 'first'], dtype='object')
 
         If a `dict` is used, all unmapped categories are mapped to `NaN` and
         the result is an :class:`~pandas.Index`:
 
-        >>> cat.map({"a": "first", "b": "second"}, na_action=None)
+        >>> cat.map({"a": "first", "b": "second"}, skipna=False)
         Index(['first', 'second', nan], dtype='object')
         """
         assert callable(mapper) or is_dict_like(mapper)
@@ -1577,7 +1577,7 @@ def map(
         has_nans = np.any(self._codes == -1)
 
         na_val = np.nan
-        if na_action is None and has_nans:
+        if not skipna and has_nans:
             na_val = mapper(np.nan) if callable(mapper) else mapper.get(np.nan, np.nan)
 
         if new_categories.is_unique and not new_categories.hasnans and na_val is np.nan:

pandas/core/arrays/datetimelike.py

@@ -743,10 +743,10 @@ def _unbox(self, other) -> np.int64 | np.datetime64 | np.timedelta64 | np.ndarra
     #  pandas assumes they're there.
 
     @ravel_compat
-    def map(self, mapper, na_action: Literal["ignore"] | None = None):
+    def map(self, mapper, skipna: bool = False):
         from pandas import Index
 
-        result = map_array(self, mapper, na_action=na_action)
+        result = map_array(self, mapper, skipna=skipna)
         result = Index(result)
 
         if isinstance(result, ABCMultiIndex):

pandas/core/arrays/masked.py

@@ -1324,8 +1324,8 @@ 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)
+    def map(self, mapper, skipna: bool = False):
+        return map_array(self.to_numpy(), mapper, skipna=skipna)
 
     @overload
     def any(

pandas/core/arrays/sparse/array.py

@@ -1309,16 +1309,16 @@ def astype(self, dtype: AstypeArg | None = None, copy: bool = True):
 
         return self._simple_new(sp_values, self.sp_index, dtype)
 
-    def map(self, mapper, na_action: Literal["ignore"] | None = None) -> Self:
+    def map(self, mapper, skipna: bool = False) -> Self:
         """
         Map categories using an input mapping or function.
 
         Parameters
         ----------
         mapper : dict, Series, callable
             The correspondence from old values to new.
-        na_action : {None, 'ignore'}, default None
-            If 'ignore', propagate NA values, without passing them to the
+        skipna : bool, default False
+            If ``True``, propagate NA values, without passing them to the
             mapping correspondence.
 
         Returns
@@ -1353,7 +1353,7 @@ def map(self, mapper, na_action: Literal["ignore"] | None = None) -> Self:
 
         fill_val = self.fill_value
 
-        if na_action is None or notna(fill_val):
+        if not skipna or notna(fill_val):
             fill_val = mapper.get(fill_val, fill_val) if is_map else mapper(fill_val)
 
         def func(sp_val):

pandas/core/base.py

@@ -945,7 +945,7 @@ def hasnans(self) -> bool:
         return bool(isna(self).any())  # type: ignore[union-attr]
 
     @final
-    def _map_values(self, mapper, na_action=None):
+    def _map_values(self, mapper, skipna=False):
         """
         An internal function that maps values using the input
         correspondence (which can be a dict, Series, or function).
@@ -954,8 +954,8 @@ def _map_values(self, mapper, na_action=None):
         ----------
         mapper : function, dict, or Series
             The input correspondence object
-        na_action : {None, 'ignore'}
-            If 'ignore', propagate NA values, without passing them to the
+        skipna : bool, default False
+            If ``True``, propagate NA values, without passing them to the
             mapping function
 
         Returns
@@ -968,9 +968,9 @@ def _map_values(self, mapper, na_action=None):
         arr = self._values
 
         if isinstance(arr, ExtensionArray):
-            return arr.map(mapper, na_action=na_action)
+            return arr.map(mapper, skipna=skipna)
 
-        return algorithms.map_array(arr, mapper, na_action=na_action)
+        return algorithms.map_array(arr, mapper, skipna=skipna)
 
     def value_counts(
         self,

pandas/core/frame.py

@@ -10641,7 +10641,11 @@ def apply(
             raise ValueError(f"Unknown engine {engine}")
 
     def map(
-        self, func: PythonFuncType, na_action: Literal["ignore"] | None = None, **kwargs
+        self,
+        func: PythonFuncType,
+        skipna: bool = False,
+        na_action: Literal["ignore"] | None = None,
+        **kwargs,
     ) -> DataFrame:
         """
         Apply a function to a Dataframe elementwise.
@@ -10657,6 +10661,8 @@ def map(
         ----------
         func : callable
             Python function, returns a single value from a single value.
+        skipna : bool = False
+            If ``True``, propagate missing values without passing them to ``func``.
         na_action : {None, 'ignore'}, default None
             If 'ignore', propagate NaN values, without passing them to func.
         **kwargs
@@ -10691,7 +10697,7 @@ def map(
 
         >>> df_copy = df.copy()
         >>> df_copy.iloc[0, 0] = pd.NA
-        >>> df_copy.map(lambda x: len(str(x)), na_action="ignore")
+        >>> df_copy.map(lambda x: len(str(x)), skipna=True)
              0  1
         0  NaN  4
         1  5.0  5
@@ -10719,16 +10725,22 @@ def map(
         0   1.000000   4.494400
         1  11.262736  20.857489
         """
-        if na_action not in {"ignore", None}:
-            raise ValueError(f"na_action must be 'ignore' or None. Got {na_action!r}")
+        if na_action == "ignore":
+            warnings.warn(
+                "The ``na_action`` parameter has been deprecated and it will be "
+                "removed in a future version of pandas. Use ``skipna`` instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            skipna = True
 
         if self.empty:
             return self.copy()
 
         func = functools.partial(func, **kwargs)
 
         def infer(x):
-            return x._map_values(func, na_action=na_action)
+            return x._map_values(func, skipna=skipna)
 
         return self.apply(infer).__finalize__(self, "map")