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CLN: remove unused arg from _ensure_data #40071

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Merged
merged 1 commit into from
Feb 26, 2021
Merged

CLN: remove unused arg from _ensure_data #40071

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36 changes: 11 additions & 25 deletions pandas/core/algorithms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand All @@ -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
Expand All @@ -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
Expand All @@ -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")
Expand Down