def __new__(
cls,
data=None,
freq=lib.no_default,
tz=None,
normalize: bool = False,
closed=None,
ambiguous="raise",
dayfirst: bool = False,
yearfirst: bool = False,
dtype: Dtype | None = None,
copy: bool = False,
name: Hashable = None,
) -> DatetimeIndex:
if is_scalar(data):
raise cls._scalar_data_error(data)
# - Cases checked above all return/raise before reaching here - #
name = maybe_extract_name(name, data, cls)
dtarr = DatetimeArray._from_sequence_not_strict(
data,
dtype=dtype,
copy=copy,
tz=tz,
freq=freq,
dayfirst=dayfirst,
yearfirst=yearfirst,
ambiguous=ambiguous,
)
subarr = cls._simple_new(dtarr, name=name)
return subarr
def __new__(
cls,
data=None,
freq: str | BaseOffset | lib.NoDefault = lib.no_default,
tz=None,
normalize: bool = False,
closed=None,
ambiguous="raise",
dayfirst: bool = False,
yearfirst: bool = False,
dtype: Dtype | None = None,
copy: bool = False,
name: Hashable = None,
) -> DatetimeIndex:
if is_scalar(data):
raise cls._scalar_data_error(data)
# - Cases checked above all return/raise before reaching here - #
name = maybe_extract_name(name, data, cls)
if (
isinstance(data, DatetimeArray)
and freq is lib.no_default
and tz is None
and dtype is None
):
# fastpath, similar logic in TimedeltaIndex.__new__;
# Note in this particular case we retain non-nano.
if copy:
data = data.copy()
return cls._simple_new(data, name=name)
elif (
isinstance(data, DatetimeArray)
and freq is lib.no_default
and tz is None
and is_dtype_equal(data.dtype, dtype)
):
# Reached via Index.__new__ when we call .astype
# TODO(2.0): special casing can be removed once _from_sequence_not_strict
# no longer chokes on non-nano
if copy:
data = data.copy()
return cls._simple_new(data, name=name)
dtarr = DatetimeArray._from_sequence_not_strict(
data,
dtype=dtype,
copy=copy,
tz=tz,
freq=freq,
dayfirst=dayfirst,
yearfirst=yearfirst,
ambiguous=ambiguous,
)
subarr = cls._simple_new(dtarr, name=name)
return subarr
def __new__(
cls,
data=None,
freq=lib.no_default,
tz=None,
normalize=False,
closed=None,
ambiguous="raise",
dayfirst=False,
yearfirst=False,
dtype=None,
copy=False,
name=None,
):
if is_scalar(data):
raise TypeError(
f"{cls.__name__}() must be called with a "
f"collection of some kind, {repr(data)} was passed"
)
# - Cases checked above all return/raise before reaching here - #
name = maybe_extract_name(name, data, cls)
dtarr = DatetimeArray._from_sequence_not_strict(
data,
dtype=dtype,
copy=copy,
tz=tz,
freq=freq,
dayfirst=dayfirst,
yearfirst=yearfirst,
ambiguous=ambiguous,
)
subarr = cls._simple_new(dtarr, name=name)
return subarr
