def _homogenize(data, index, dtype=None):
oindex = None
homogenized = []
for val in data:
if isinstance(val, ABCSeries):
if dtype is not None:
val = val.astype(dtype)
if val.index is not index:
# Forces alignment. No need to copy data since we
# are putting it into an ndarray later
val = val.reindex(index, copy=False)
else:
if isinstance(val, dict):
if oindex is None:
oindex = index.astype('O')
if isinstance(index, (ABCDatetimeIndex, ABCTimedeltaIndex)):
val = com.dict_compat(val)
else:
val = dict(val)
val = lib.fast_multiget(val, oindex.values, default=np.nan)
val = sanitize_array(val,
index,
dtype=dtype,
copy=False,
raise_cast_failure=False)
homogenized.append(val)
return homogenized
def _homogenize(data, index, dtype=None):
oindex = None
homogenized = []
for val in data:
if isinstance(val, ABCSeries):
if dtype is not None:
val = val.astype(dtype)
if val.index is not index:
# Forces alignment. No need to copy data since we
# are putting it into an ndarray later
val = val.reindex(index, copy=False)
else:
if isinstance(val, dict):
if oindex is None:
oindex = index.astype('O')
if isinstance(index, (ABCDatetimeIndex, ABCTimedeltaIndex)):
val = com.dict_compat(val)
else:
val = dict(val)
val = lib.fast_multiget(val, oindex.values, default=np.nan)
val = sanitize_array(val, index, dtype=dtype, copy=False,
raise_cast_failure=False)
homogenized.append(val)
return homogenized
def _homogenize(data, index: Index, dtype: Optional[DtypeObj]):
oindex = None
homogenized = []
for val in data:
if isinstance(val, ABCSeries):
if dtype is not None:
val = val.astype(dtype)
if val.index is not index:
# Forces alignment. No need to copy data since we
# are putting it into an ndarray later
val = val.reindex(index, copy=False)
# TODO extract_array should be preferred, but that gives failures for
# `extension/test_numpy.py` (extract_array will convert numpy arrays
# to PandasArray), see https://github.com/pandas-dev/pandas/issues/40021
# val = extract_array(val, extract_numpy=True)
val = val._values
else:
if isinstance(val, dict):
if oindex is None:
oindex = index.astype("O")
if isinstance(index, (ABCDatetimeIndex, ABCTimedeltaIndex)):
val = dict_compat(val)
else:
val = dict(val)
val = lib.fast_multiget(val, oindex._values, default=np.nan)
val = sanitize_array(
val, index, dtype=dtype, copy=False, raise_cast_failure=False
)
homogenized.append(val)
return homogenized
def _homogenize(data, index: Index, dtype: DtypeObj | None) -> list[ArrayLike]:
oindex = None
homogenized = []
for val in data:
if isinstance(val, ABCSeries):
if dtype is not None:
val = val.astype(dtype, copy=False)
if val.index is not index:
# Forces alignment. No need to copy data since we
# are putting it into an ndarray later
val = val.reindex(index, copy=False)
val = val._values
else:
if isinstance(val, dict):
if oindex is None:
oindex = index.astype("O")
if isinstance(index, (DatetimeIndex, TimedeltaIndex)):
# see test_constructor_dict_datetime64_index
val = dict_compat(val)
else:
# see test_constructor_subclass_dict
val = dict(val)
val = lib.fast_multiget(val, oindex._values, default=np.nan)
val = sanitize_array(val,
index,
dtype=dtype,
copy=False,
raise_cast_failure=False)
homogenized.append(val)
return homogenized
