def _prep_ndarray(values, copy: bool = True) -> np.ndarray:
if isinstance(values,
TimedeltaArray) or (isinstance(values, DatetimeArray)
and values.tz is None):
# On older numpy, np.asarray below apparently does not call __array__,
# so nanoseconds get dropped.
values = values._ndarray
if not isinstance(values, (np.ndarray, ABCSeries, Index)):
if len(values) == 0:
return np.empty((0, 0), dtype=object)
elif isinstance(values, range):
arr = range_to_ndarray(values)
return arr[..., np.newaxis]
def convert(v):
if not is_list_like(v) or isinstance(v, ABCDataFrame):
return v
elif not hasattr(v, "dtype") and not isinstance(
v, (list, tuple, range)):
# TODO: should we cast these to list?
return v
v = extract_array(v, extract_numpy=True)
res = maybe_convert_platform(v)
return res
# we could have a 1-dim or 2-dim list here
# this is equiv of np.asarray, but does object conversion
# and platform dtype preservation
if is_list_like(values[0]):
values = np.array([convert(v) for v in values])
elif isinstance(values[0], np.ndarray) and values[0].ndim == 0:
# GH#21861
values = np.array([convert(v) for v in values])
else:
values = convert(values)
else:
# drop subclass info
values = np.array(values, copy=copy)
if values.ndim == 1:
values = values.reshape((values.shape[0], 1))
elif values.ndim != 2:
raise ValueError(f"Must pass 2-d input. shape={values.shape}")
return values
def _prep_ndarraylike(
values, copy: bool = True
) -> np.ndarray | DatetimeArray | TimedeltaArray:
if isinstance(values, TimedeltaArray) or (
isinstance(values, DatetimeArray) and values.tz is None
):
# By retaining DTA/TDA instead of unpacking, we end up retaining non-nano
pass
elif not isinstance(values, (np.ndarray, ABCSeries, Index)):
if len(values) == 0:
return np.empty((0, 0), dtype=object)
elif isinstance(values, range):
arr = range_to_ndarray(values)
return arr[..., np.newaxis]
def convert(v):
if not is_list_like(v) or isinstance(v, ABCDataFrame):
return v
v = extract_array(v, extract_numpy=True)
res = maybe_convert_platform(v)
return res
# we could have a 1-dim or 2-dim list here
# this is equiv of np.asarray, but does object conversion
# and platform dtype preservation
if is_list_like(values[0]):
values = np.array([convert(v) for v in values])
elif isinstance(values[0], np.ndarray) and values[0].ndim == 0:
# GH#21861 see test_constructor_list_of_lists
values = np.array([convert(v) for v in values])
else:
values = convert(values)
else:
# drop subclass info
values = np.array(values, copy=copy)
if values.ndim == 1:
values = values.reshape((values.shape[0], 1))
elif values.ndim != 2:
raise ValueError(f"Must pass 2-d input. shape={values.shape}")
return values
