CLN: remove ensure_categorical

pandas/core/dtypes/common.py

@@ -111,27 +111,6 @@ def ensure_str(value: Union[bytes, Any]) -> str:
     return value
 
 
-def ensure_categorical(arr):
-    """
-    Ensure that an array-like object is a Categorical (if not already).
-
-    Parameters
-    ----------
-    arr : array-like
-        The array that we want to convert into a Categorical.
-
-    Returns
-    -------
-    cat_arr : The original array cast as a Categorical. If it already
-              is a Categorical, we return as is.
-    """
-    if not is_categorical_dtype(arr.dtype):
-        from pandas import Categorical
-
-        arr = Categorical(arr)
-    return arr
-
-
 def ensure_int_or_float(arr: ArrayLike, copy: bool = False) -> np.array:
     """
     Ensure that an dtype array of some integer dtype

pandas/core/groupby/grouper.py

@@ -11,7 +11,6 @@
 from pandas.util._decorators import cache_readonly
 
 from pandas.core.dtypes.common import (
-    ensure_categorical,
     is_categorical_dtype,
     is_datetime64_dtype,
     is_list_like,
@@ -418,7 +417,7 @@ def indices(self):
         if isinstance(self.grouper, ops.BaseGrouper):
             return self.grouper.indices
 
-        values = ensure_categorical(self.grouper)
+        values = Categorical(self.grouper)
         return values._reverse_indexer()
 
     @property

pandas/core/indexes/base.py

@@ -24,7 +24,6 @@
     validate_numeric_casting,
 )
 from pandas.core.dtypes.common import (
-    ensure_categorical,
     ensure_int64,
     ensure_object,
     ensure_platform_int,
@@ -68,7 +67,7 @@
 from pandas.core import ops
 from pandas.core.accessor import CachedAccessor
 import pandas.core.algorithms as algos
-from pandas.core.arrays import ExtensionArray
+from pandas.core.arrays import Categorical, ExtensionArray
 from pandas.core.arrays.datetimes import tz_to_dtype, validate_tz_from_dtype
 from pandas.core.base import IndexOpsMixin, PandasObject
 import pandas.core.common as com
@@ -4727,8 +4726,8 @@ def groupby(self, values) -> PrettyDict[Hashable, np.ndarray]:
         # TODO: if we are a MultiIndex, we can do better
         # that converting to tuples
         if isinstance(values, ABCMultiIndex):
-            values = values.values
-        values = ensure_categorical(values)
+            values = values._values
+        values = Categorical(values)
         result = values._reverse_indexer()
 
         # map to the label

pandas/tests/dtypes/test_inference.py

@@ -21,7 +21,6 @@
 
 from pandas.core.dtypes import inference
 from pandas.core.dtypes.common import (
-    ensure_categorical,
     ensure_int32,
     is_bool,
     is_datetime64_any_dtype,
@@ -1502,13 +1501,3 @@ def test_ensure_int32():
     values = np.arange(10, dtype=np.int64)
     result = ensure_int32(values)
     assert result.dtype == np.int32
-
-
-def test_ensure_categorical():
-    values = np.arange(10, dtype=np.int32)
-    result = ensure_categorical(values)
-    assert result.dtype == "category"
-
-    values = Categorical(values)
-    result = ensure_categorical(values)
-    tm.assert_categorical_equal(result, values)