def _from_sequence(cls,
data,
dtype=_TD_DTYPE,
copy=False,
freq=None,
unit=None):
if dtype:
_validate_td64_dtype(dtype)
freq, freq_infer = dtl.maybe_infer_freq(freq)
data, inferred_freq = sequence_to_td64ns(data, copy=copy, unit=unit)
freq, freq_infer = dtl.validate_inferred_freq(freq, inferred_freq,
freq_infer)
result = cls._simple_new(data, freq=freq)
if inferred_freq is None and freq is not None:
# this condition precludes `freq_infer`
cls._validate_frequency(result, freq)
elif freq_infer:
# Set _freq directly to bypass duplicative _validate_frequency
# check.
result._freq = to_offset(result.inferred_freq)
return result
def _from_sequence_not_strict(
cls,
data,
dtype=TD64NS_DTYPE,
copy: bool = False,
freq=lib.no_default,
unit=None,
) -> TimedeltaArray:
if dtype:
_validate_td64_dtype(dtype)
explicit_none = freq is None
freq = freq if freq is not lib.no_default else None
freq, freq_infer = dtl.maybe_infer_freq(freq)
data, inferred_freq = sequence_to_td64ns(data, copy=copy, unit=unit)
freq, freq_infer = dtl.validate_inferred_freq(freq, inferred_freq,
freq_infer)
if explicit_none:
freq = None
result = cls._simple_new(data, freq=freq)
if inferred_freq is None and freq is not None:
# this condition precludes `freq_infer`
cls._validate_frequency(result, freq)
elif freq_infer:
# Set _freq directly to bypass duplicative _validate_frequency
# check.
result._freq = to_offset(result.inferred_freq)
return result
def __init__(self,
values,
dtype=TD64NS_DTYPE,
freq=lib.no_default,
copy=False):
values = extract_array(values, extract_numpy=True)
if isinstance(values, IntegerArray):
values = values.to_numpy("int64", na_value=tslibs.iNaT)
inferred_freq = getattr(values, "_freq", None)
explicit_none = freq is None
freq = freq if freq is not lib.no_default else None
if isinstance(values, type(self)):
if explicit_none:
# dont inherit from values
pass
elif freq is None:
freq = values.freq
elif freq and values.freq:
freq = to_offset(freq)
freq, _ = dtl.validate_inferred_freq(freq, values.freq, False)
values = values._ndarray
if not isinstance(values, np.ndarray):
msg = (
f"Unexpected type '{type(values).__name__}'. 'values' must be a "
"TimedeltaArray, ndarray, or Series or Index containing one of those."
)
raise ValueError(msg)
if values.ndim not in [1, 2]:
raise ValueError("Only 1-dimensional input arrays are supported.")
if values.dtype == "i8":
# for compat with datetime/timedelta/period shared methods,
# we can sometimes get here with int64 values. These represent
# nanosecond UTC (or tz-naive) unix timestamps
values = values.view(TD64NS_DTYPE)
_validate_td64_dtype(values.dtype)
dtype = _validate_td64_dtype(dtype)
if freq == "infer":
msg = (
"Frequency inference not allowed in TimedeltaArray.__init__. "
"Use 'pd.array()' instead.")
raise ValueError(msg)
if copy:
values = values.copy()
if freq:
freq = to_offset(freq)
self._ndarray = values
self._dtype = dtype
self._freq = freq
if inferred_freq is None and freq is not None:
type(self)._validate_frequency(self, freq)
def _from_sequence(
cls, data, *, dtype=TD64NS_DTYPE, copy: bool = False
) -> TimedeltaArray:
if dtype:
_validate_td64_dtype(dtype)
data, inferred_freq = sequence_to_td64ns(data, copy=copy, unit=None)
freq, _ = dtl.validate_inferred_freq(None, inferred_freq, False)
return cls._simple_new(data, freq=freq)
def __init__(self, values, dtype=_TD_DTYPE, freq=None, copy=False):
if isinstance(values, (ABCSeries, ABCIndexClass)):
values = values._values
inferred_freq = getattr(values, "_freq", None)
if isinstance(values, type(self)):
if freq is None:
freq = values.freq
elif freq and values.freq:
freq = to_offset(freq)
freq, _ = dtl.validate_inferred_freq(freq, values.freq, False)
values = values._data
if not isinstance(values, np.ndarray):
msg = (
f"Unexpected type '{type(values).__name__}'. 'values' must be a "
"TimedeltaArray ndarray, or Series or Index containing one of those."
)
raise ValueError(msg)
if values.ndim not in [1, 2]:
raise ValueError("Only 1-dimensional input arrays are supported.")
if values.dtype == "i8":
# for compat with datetime/timedelta/period shared methods,
# we can sometimes get here with int64 values. These represent
# nanosecond UTC (or tz-naive) unix timestamps
values = values.view(_TD_DTYPE)
_validate_td64_dtype(values.dtype)
dtype = _validate_td64_dtype(dtype)
if freq == "infer":
msg = (
"Frequency inference not allowed in TimedeltaArray.__init__. "
"Use 'pd.array()' instead.")
raise ValueError(msg)
if copy:
values = values.copy()
if freq:
freq = to_offset(freq)
self._data = values
self._dtype = dtype
self._freq = freq
if inferred_freq is None and freq is not None:
type(self)._validate_frequency(self, freq)