Standardize cast_str behavior in all datetimelike fill_value validators (pandas-dev#36746)

* Standardize cast_str behavior in all datetimelike fill_value validators

* CLN: remove cast_str kwarg

Author: jbrockmendel

pandas/core/arrays/datetimelike.py

@@ -765,9 +765,7 @@ def _validate_shift_value(self, fill_value):
 
         return self._unbox(fill_value)
 
-    def _validate_scalar(
-        self, value, msg: Optional[str] = None, cast_str: bool = False
-    ):
+    def _validate_scalar(self, value, msg: Optional[str] = None):
         """
         Validate that the input value can be cast to our scalar_type.
 
@@ -778,14 +776,12 @@ def _validate_scalar(
             Message to raise in TypeError on invalid input.
             If not provided, `value` is cast to a str and used
             as the message.
-        cast_str : bool, default False
-            Whether to try to parse string input to scalar_type.
 
         Returns
         -------
         self._scalar_type or NaT
         """
-        if cast_str and isinstance(value, str):
+        if isinstance(value, str):
             # NB: Careful about tzawareness
             try:
                 value = self._scalar_from_string(value)
@@ -807,9 +803,7 @@ def _validate_scalar(
 
         return value
 
-    def _validate_listlike(
-        self, value, opname: str, cast_str: bool = False, allow_object: bool = False
-    ):
+    def _validate_listlike(self, value, opname: str, allow_object: bool = False):
         if isinstance(value, type(self)):
             return value
 
@@ -818,7 +812,7 @@ def _validate_listlike(
         value = array(value)
         value = extract_array(value, extract_numpy=True)
 
-        if cast_str and is_dtype_equal(value.dtype, "string"):
+        if is_dtype_equal(value.dtype, "string"):
             # We got a StringArray
             try:
                 # TODO: Could use from_sequence_of_strings if implemented
@@ -848,9 +842,9 @@ def _validate_listlike(
     def _validate_searchsorted_value(self, value):
         msg = "searchsorted requires compatible dtype or scalar"
         if not is_list_like(value):
-            value = self._validate_scalar(value, msg, cast_str=True)
+            value = self._validate_scalar(value, msg)
         else:
-            value = self._validate_listlike(value, "searchsorted", cast_str=True)
+            value = self._validate_listlike(value, "searchsorted")
 
         rv = self._unbox(value)
         return self._rebox_native(rv)
@@ -861,15 +855,15 @@ def _validate_setitem_value(self, value):
             f"or array of those. Got '{type(value).__name__}' instead."
         )
         if is_list_like(value):
-            value = self._validate_listlike(value, "setitem", cast_str=True)
+            value = self._validate_listlike(value, "setitem")
         else:
-            value = self._validate_scalar(value, msg, cast_str=True)
+            value = self._validate_scalar(value, msg)
 
         return self._unbox(value, setitem=True)
 
     def _validate_insert_value(self, value):
         msg = f"cannot insert {type(self).__name__} with incompatible label"
-        value = self._validate_scalar(value, msg, cast_str=False)
+        value = self._validate_scalar(value, msg)
 
         self._check_compatible_with(value, setitem=True)
         # TODO: if we dont have compat, should we raise or astype(object)?

pandas/core/indexes/datetimelike.py

@@ -646,7 +646,7 @@ def _wrap_joined_index(self, joined: np.ndarray, other):
     def _convert_arr_indexer(self, keyarr):
         try:
             return self._data._validate_listlike(
-                keyarr, "convert_arr_indexer", cast_str=True, allow_object=True
+                keyarr, "convert_arr_indexer", allow_object=True
             )
         except (ValueError, TypeError):
             return com.asarray_tuplesafe(keyarr)

pandas/core/indexes/timedeltas.py

@@ -217,7 +217,7 @@ def get_loc(self, key, method=None, tolerance=None):
             raise InvalidIndexError(key)
 
         try:
-            key = self._data._validate_scalar(key, cast_str=True)
+            key = self._data._validate_scalar(key)
         except TypeError as err:
             raise KeyError(key) from err
 

pandas/tests/arrays/test_datetimelike.py

@@ -160,6 +160,16 @@ def test_take_fill(self):
         result = arr.take([-1, 1], allow_fill=True, fill_value=pd.NaT)
         assert result[0] is pd.NaT
 
+    def test_take_fill_str(self, arr1d):
+        # Cast str fill_value matching other fill_value-taking methods
+        result = arr1d.take([-1, 1], allow_fill=True, fill_value=str(arr1d[-1]))
+        expected = arr1d[[-1, 1]]
+        tm.assert_equal(result, expected)
+
+        msg = r"'fill_value' should be a <.*>\. Got 'foo'"
+        with pytest.raises(ValueError, match=msg):
+            arr1d.take([-1, 1], allow_fill=True, fill_value="foo")
+
     def test_concat_same_type(self):
         data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9
 

pandas/tests/indexes/datetimelike.py

@@ -115,3 +115,24 @@ def test_not_equals_numeric(self):
         assert not index.equals(pd.Index(index.asi8))
         assert not index.equals(pd.Index(index.asi8.astype("u8")))
         assert not index.equals(pd.Index(index.asi8).astype("f8"))
+
+    def test_where_cast_str(self):
+        index = self.create_index()
+
+        mask = np.ones(len(index), dtype=bool)
+        mask[-1] = False
+
+        result = index.where(mask, str(index[0]))
+        expected = index.where(mask, index[0])
+        tm.assert_index_equal(result, expected)
+
+        result = index.where(mask, [str(index[0])])
+        tm.assert_index_equal(result, expected)
+
+        msg = "Where requires matching dtype, not foo"
+        with pytest.raises(TypeError, match=msg):
+            index.where(mask, "foo")
+
+        msg = r"Where requires matching dtype, not \['foo'\]"
+        with pytest.raises(TypeError, match=msg):
+            index.where(mask, ["foo"])