CLN: remove unused arg from _ensure_data (#40071)

Author: jbrockmendel

pandas/core/algorithms.py

@@ -108,9 +108,7 @@
 # --------------- #
 # dtype access    #
 # --------------- #
-def _ensure_data(
-    values: ArrayLike, dtype: Optional[DtypeObj] = None
-) -> Tuple[np.ndarray, DtypeObj]:
+def _ensure_data(values: ArrayLike) -> Tuple[np.ndarray, DtypeObj]:
     """
     routine to ensure that our data is of the correct
     input dtype for lower-level routines
@@ -126,43 +124,33 @@ def _ensure_data(
     Parameters
     ----------
     values : array-like
-    dtype : pandas_dtype, optional
-        coerce to this dtype
 
     Returns
     -------
     values : ndarray
     pandas_dtype : np.dtype or ExtensionDtype
     """
 
-    if dtype is not None:
-        # We only have non-None dtype when called from `isin`, and
-        #  both Datetimelike and Categorical dispatch before getting here.
-        assert not needs_i8_conversion(dtype)
-        assert not is_categorical_dtype(dtype)
-
     if not isinstance(values, ABCMultiIndex):
         # extract_array would raise
         values = extract_array(values, extract_numpy=True)
 
     # we check some simple dtypes first
-    if is_object_dtype(dtype):
-        return ensure_object(np.asarray(values)), np.dtype("object")
-    elif is_object_dtype(values) and dtype is None:
+    if is_object_dtype(values):
         return ensure_object(np.asarray(values)), np.dtype("object")
 
     try:
-        if is_bool_dtype(values) or is_bool_dtype(dtype):
+        if is_bool_dtype(values):
             # we are actually coercing to uint64
             # until our algos support uint8 directly (see TODO)
             return np.asarray(values).astype("uint64"), np.dtype("bool")
-        elif is_signed_integer_dtype(values) or is_signed_integer_dtype(dtype):
+        elif is_signed_integer_dtype(values):
             return ensure_int64(values), np.dtype("int64")
-        elif is_unsigned_integer_dtype(values) or is_unsigned_integer_dtype(dtype):
+        elif is_unsigned_integer_dtype(values):
             return ensure_uint64(values), np.dtype("uint64")
-        elif is_float_dtype(values) or is_float_dtype(dtype):
+        elif is_float_dtype(values):
             return ensure_float64(values), np.dtype("float64")
-        elif is_complex_dtype(values) or is_complex_dtype(dtype):
+        elif is_complex_dtype(values):
 
             # ignore the fact that we are casting to float
             # which discards complex parts
@@ -177,12 +165,12 @@ def _ensure_data(
         return ensure_object(values), np.dtype("object")
 
     # datetimelike
-    if needs_i8_conversion(values.dtype) or needs_i8_conversion(dtype):
-        if is_period_dtype(values.dtype) or is_period_dtype(dtype):
+    if needs_i8_conversion(values.dtype):
+        if is_period_dtype(values.dtype):
             from pandas import PeriodIndex
 
             values = PeriodIndex(values)._data
-        elif is_timedelta64_dtype(values.dtype) or is_timedelta64_dtype(dtype):
+        elif is_timedelta64_dtype(values.dtype):
             from pandas import TimedeltaIndex
 
             values = TimedeltaIndex(values)._data
@@ -202,9 +190,7 @@ def _ensure_data(
         dtype = values.dtype
         return values.asi8, dtype
 
-    elif is_categorical_dtype(values.dtype) and (
-        is_categorical_dtype(dtype) or dtype is None
-    ):
+    elif is_categorical_dtype(values.dtype):
         values = cast("Categorical", values)
         values = values.codes
         dtype = pandas_dtype("category")