Merge branch 'master' of https://github.com/pandas-dev/pandas into cln-arith

Author: jbrockmendel

doc/source/whatsnew/v1.2.0.rst

@@ -268,6 +268,7 @@ Deprecations
 - Deprecated :meth:`Index.is_all_dates` (:issue:`27744`)
 - Deprecated automatic alignment on comparison operations between :class:`DataFrame` and :class:`Series`, do ``frame, ser = frame.align(ser, axis=1, copy=False)`` before e.g. ``frame == ser`` (:issue:`28759`)
 - :meth:`Rolling.count` with ``min_periods=None`` will default to the size of the window in a future version (:issue:`31302`)
+- :meth:`Index.ravel` returning a ``np.ndarray`` is deprecated, in the future this will return a view on the same index (:issue:`19956`)
 
 .. ---------------------------------------------------------------------------
 

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/arrays/interval.py

@@ -545,10 +545,10 @@ def __iter__(self):
     def __len__(self) -> int:
         return len(self._left)
 
-    def __getitem__(self, value):
-        value = check_array_indexer(self, value)
-        left = self._left[value]
-        right = self._right[value]
+    def __getitem__(self, key):
+        key = check_array_indexer(self, key)
+        left = self._left[key]
+        right = self._right[key]
 
         if not isinstance(left, (np.ndarray, ExtensionArray)):
             # scalar

pandas/core/indexes/base.py

@@ -659,6 +659,12 @@ def ravel(self, order="C"):
         --------
         numpy.ndarray.ravel
         """
+        warnings.warn(
+            "Index.ravel returning ndarray is deprecated; in a future version "
+            "this will return a view on self.",
+            FutureWarning,
+            stacklevel=2,
+        )
         values = self._get_engine_target()
         return values.ravel(order=order)
 

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/io/formats/format.py

@@ -1677,7 +1677,8 @@ def is_dates_only(
     values: Union[np.ndarray, DatetimeArray, Index, DatetimeIndex]
 ) -> bool:
     # return a boolean if we are only dates (and don't have a timezone)
-    values = values.ravel()
+    if not isinstance(values, Index):
+        values = values.ravel()
 
     values = DatetimeIndex(values)
     if values.tz is not None: