from __future__ import annotations
|
|
from datetime import timedelta
|
import operator
|
from typing import (
|
TYPE_CHECKING,
|
Iterator,
|
cast,
|
)
|
import warnings
|
|
import numpy as np
|
|
from pandas._libs import (
|
lib,
|
tslibs,
|
)
|
from pandas._libs.tslibs import (
|
BaseOffset,
|
NaT,
|
NaTType,
|
Tick,
|
Timedelta,
|
astype_overflowsafe,
|
get_supported_reso,
|
get_unit_from_dtype,
|
iNaT,
|
is_supported_unit,
|
npy_unit_to_abbrev,
|
periods_per_second,
|
to_offset,
|
)
|
from pandas._libs.tslibs.conversion import precision_from_unit
|
from pandas._libs.tslibs.fields import (
|
get_timedelta_days,
|
get_timedelta_field,
|
)
|
from pandas._libs.tslibs.timedeltas import (
|
array_to_timedelta64,
|
floordiv_object_array,
|
ints_to_pytimedelta,
|
parse_timedelta_unit,
|
truediv_object_array,
|
)
|
from pandas._typing import (
|
AxisInt,
|
DateTimeErrorChoices,
|
DtypeObj,
|
NpDtype,
|
npt,
|
)
|
from pandas.compat.numpy import function as nv
|
from pandas.util._validators import validate_endpoints
|
|
from pandas.core.dtypes.common import (
|
TD64NS_DTYPE,
|
is_dtype_equal,
|
is_extension_array_dtype,
|
is_float_dtype,
|
is_integer_dtype,
|
is_object_dtype,
|
is_scalar,
|
is_string_dtype,
|
is_timedelta64_dtype,
|
pandas_dtype,
|
)
|
from pandas.core.dtypes.missing import isna
|
|
from pandas.core import nanops
|
from pandas.core.array_algos import datetimelike_accumulations
|
from pandas.core.arrays import datetimelike as dtl
|
from pandas.core.arrays._ranges import generate_regular_range
|
import pandas.core.common as com
|
from pandas.core.ops import roperator
|
from pandas.core.ops.common import unpack_zerodim_and_defer
|
|
if TYPE_CHECKING:
|
from pandas import DataFrame
|
|
|
def _field_accessor(name: str, alias: str, docstring: str):
|
def f(self) -> np.ndarray:
|
values = self.asi8
|
if alias == "days":
|
result = get_timedelta_days(values, reso=self._creso)
|
else:
|
# error: Incompatible types in assignment (
|
# expression has type "ndarray[Any, dtype[signedinteger[_32Bit]]]",
|
# variable has type "ndarray[Any, dtype[signedinteger[_64Bit]]]
|
result = get_timedelta_field(values, alias, reso=self._creso) # type: ignore[assignment] # noqa: E501
|
if self._hasna:
|
result = self._maybe_mask_results(
|
result, fill_value=None, convert="float64"
|
)
|
|
return result
|
|
f.__name__ = name
|
f.__doc__ = f"\n{docstring}\n"
|
return property(f)
|
|
|
class TimedeltaArray(dtl.TimelikeOps):
|
"""
|
Pandas ExtensionArray for timedelta data.
|
|
.. warning::
|
|
TimedeltaArray is currently experimental, and its API may change
|
without warning. In particular, :attr:`TimedeltaArray.dtype` is
|
expected to change to be an instance of an ``ExtensionDtype``
|
subclass.
|
|
Parameters
|
----------
|
values : array-like
|
The timedelta data.
|
|
dtype : numpy.dtype
|
Currently, only ``numpy.dtype("timedelta64[ns]")`` is accepted.
|
freq : Offset, optional
|
copy : bool, default False
|
Whether to copy the underlying array of data.
|
|
Attributes
|
----------
|
None
|
|
Methods
|
-------
|
None
|
"""
|
|
_typ = "timedeltaarray"
|
_internal_fill_value = np.timedelta64("NaT", "ns")
|
_recognized_scalars = (timedelta, np.timedelta64, Tick)
|
_is_recognized_dtype = is_timedelta64_dtype
|
_infer_matches = ("timedelta", "timedelta64")
|
|
@property
|
def _scalar_type(self) -> type[Timedelta]:
|
return Timedelta
|
|
__array_priority__ = 1000
|
# define my properties & methods for delegation
|
_other_ops: list[str] = []
|
_bool_ops: list[str] = []
|
_object_ops: list[str] = ["freq"]
|
_field_ops: list[str] = ["days", "seconds", "microseconds", "nanoseconds"]
|
_datetimelike_ops: list[str] = _field_ops + _object_ops + _bool_ops + ["unit"]
|
_datetimelike_methods: list[str] = [
|
"to_pytimedelta",
|
"total_seconds",
|
"round",
|
"floor",
|
"ceil",
|
"as_unit",
|
]
|
|
# Note: ndim must be defined to ensure NaT.__richcmp__(TimedeltaArray)
|
# operates pointwise.
|
|
def _box_func(self, x: np.timedelta64) -> Timedelta | NaTType:
|
y = x.view("i8")
|
if y == NaT._value:
|
return NaT
|
return Timedelta._from_value_and_reso(y, reso=self._creso)
|
|
@property
|
# error: Return type "dtype" of "dtype" incompatible with return type
|
# "ExtensionDtype" in supertype "ExtensionArray"
|
def dtype(self) -> np.dtype: # type: ignore[override]
|
"""
|
The dtype for the TimedeltaArray.
|
|
.. warning::
|
|
A future version of pandas will change dtype to be an instance
|
of a :class:`pandas.api.extensions.ExtensionDtype` subclass,
|
not a ``numpy.dtype``.
|
|
Returns
|
-------
|
numpy.dtype
|
"""
|
return self._ndarray.dtype
|
|
# ----------------------------------------------------------------
|
# Constructors
|
|
_freq = None
|
_default_dtype = TD64NS_DTYPE # used in TimeLikeOps.__init__
|
|
@classmethod
|
def _validate_dtype(cls, values, dtype):
|
# used in TimeLikeOps.__init__
|
_validate_td64_dtype(values.dtype)
|
dtype = _validate_td64_dtype(dtype)
|
return dtype
|
|
# error: Signature of "_simple_new" incompatible with supertype "NDArrayBacked"
|
@classmethod
|
def _simple_new( # type: ignore[override]
|
cls, values: np.ndarray, freq: BaseOffset | None = None, dtype=TD64NS_DTYPE
|
) -> TimedeltaArray:
|
# Require td64 dtype, not unit-less, matching values.dtype
|
assert isinstance(dtype, np.dtype) and dtype.kind == "m"
|
assert not tslibs.is_unitless(dtype)
|
assert isinstance(values, np.ndarray), type(values)
|
assert dtype == values.dtype
|
|
result = super()._simple_new(values=values, dtype=dtype)
|
result._freq = freq
|
return result
|
|
@classmethod
|
def _from_sequence(cls, data, *, dtype=None, copy: bool = False) -> TimedeltaArray:
|
if dtype:
|
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)
|
|
if dtype is not None:
|
data = astype_overflowsafe(data, dtype=dtype, copy=False)
|
|
return cls._simple_new(data, dtype=data.dtype, freq=freq)
|
|
@classmethod
|
def _from_sequence_not_strict(
|
cls,
|
data,
|
*,
|
dtype=None,
|
copy: bool = False,
|
freq=lib.no_default,
|
unit=None,
|
) -> TimedeltaArray:
|
"""
|
A non-strict version of _from_sequence, called from TimedeltaIndex.__new__.
|
"""
|
if dtype:
|
dtype = _validate_td64_dtype(dtype)
|
|
assert unit not in ["Y", "y", "M"] # caller is responsible for checking
|
|
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
|
|
if dtype is not None:
|
data = astype_overflowsafe(data, dtype=dtype, copy=False)
|
|
result = cls._simple_new(data, dtype=data.dtype, 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
|
|
# Signature of "_generate_range" incompatible with supertype
|
# "DatetimeLikeArrayMixin"
|
@classmethod
|
def _generate_range( # type: ignore[override]
|
cls, start, end, periods, freq, closed=None, *, unit: str | None = None
|
):
|
periods = dtl.validate_periods(periods)
|
if freq is None and any(x is None for x in [periods, start, end]):
|
raise ValueError("Must provide freq argument if no data is supplied")
|
|
if com.count_not_none(start, end, periods, freq) != 3:
|
raise ValueError(
|
"Of the four parameters: start, end, periods, "
|
"and freq, exactly three must be specified"
|
)
|
|
if start is not None:
|
start = Timedelta(start).as_unit("ns")
|
|
if end is not None:
|
end = Timedelta(end).as_unit("ns")
|
|
if unit is not None:
|
if unit not in ["s", "ms", "us", "ns"]:
|
raise ValueError("'unit' must be one of 's', 'ms', 'us', 'ns'")
|
else:
|
unit = "ns"
|
|
if start is not None and unit is not None:
|
start = start.as_unit(unit, round_ok=False)
|
if end is not None and unit is not None:
|
end = end.as_unit(unit, round_ok=False)
|
|
left_closed, right_closed = validate_endpoints(closed)
|
|
if freq is not None:
|
index = generate_regular_range(start, end, periods, freq, unit=unit)
|
else:
|
index = np.linspace(start._value, end._value, periods).astype("i8")
|
|
if not left_closed:
|
index = index[1:]
|
if not right_closed:
|
index = index[:-1]
|
|
td64values = index.view(f"m8[{unit}]")
|
return cls._simple_new(td64values, dtype=td64values.dtype, freq=freq)
|
|
# ----------------------------------------------------------------
|
# DatetimeLike Interface
|
|
def _unbox_scalar(self, value) -> np.timedelta64:
|
if not isinstance(value, self._scalar_type) and value is not NaT:
|
raise ValueError("'value' should be a Timedelta.")
|
self._check_compatible_with(value)
|
if value is NaT:
|
return np.timedelta64(value._value, self.unit)
|
else:
|
return value.as_unit(self.unit).asm8
|
|
def _scalar_from_string(self, value) -> Timedelta | NaTType:
|
return Timedelta(value)
|
|
def _check_compatible_with(self, other) -> None:
|
# we don't have anything to validate.
|
pass
|
|
# ----------------------------------------------------------------
|
# Array-Like / EA-Interface Methods
|
|
def astype(self, dtype, copy: bool = True):
|
# We handle
|
# --> timedelta64[ns]
|
# --> timedelta64
|
# DatetimeLikeArrayMixin super call handles other cases
|
dtype = pandas_dtype(dtype)
|
|
if isinstance(dtype, np.dtype) and dtype.kind == "m":
|
if dtype == self.dtype:
|
if copy:
|
return self.copy()
|
return self
|
|
if is_supported_unit(get_unit_from_dtype(dtype)):
|
# unit conversion e.g. timedelta64[s]
|
res_values = astype_overflowsafe(self._ndarray, dtype, copy=False)
|
return type(self)._simple_new(
|
res_values, dtype=res_values.dtype, freq=self.freq
|
)
|
else:
|
raise ValueError(
|
f"Cannot convert from {self.dtype} to {dtype}. "
|
"Supported resolutions are 's', 'ms', 'us', 'ns'"
|
)
|
|
return dtl.DatetimeLikeArrayMixin.astype(self, dtype, copy=copy)
|
|
def __iter__(self) -> Iterator:
|
if self.ndim > 1:
|
for i in range(len(self)):
|
yield self[i]
|
else:
|
# convert in chunks of 10k for efficiency
|
data = self._ndarray
|
length = len(self)
|
chunksize = 10000
|
chunks = (length // chunksize) + 1
|
for i in range(chunks):
|
start_i = i * chunksize
|
end_i = min((i + 1) * chunksize, length)
|
converted = ints_to_pytimedelta(data[start_i:end_i], box=True)
|
yield from converted
|
|
# ----------------------------------------------------------------
|
# Reductions
|
|
def sum(
|
self,
|
*,
|
axis: AxisInt | None = None,
|
dtype: NpDtype | None = None,
|
out=None,
|
keepdims: bool = False,
|
initial=None,
|
skipna: bool = True,
|
min_count: int = 0,
|
):
|
nv.validate_sum(
|
(), {"dtype": dtype, "out": out, "keepdims": keepdims, "initial": initial}
|
)
|
|
result = nanops.nansum(
|
self._ndarray, axis=axis, skipna=skipna, min_count=min_count
|
)
|
return self._wrap_reduction_result(axis, result)
|
|
def std(
|
self,
|
*,
|
axis: AxisInt | None = None,
|
dtype: NpDtype | None = None,
|
out=None,
|
ddof: int = 1,
|
keepdims: bool = False,
|
skipna: bool = True,
|
):
|
nv.validate_stat_ddof_func(
|
(), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="std"
|
)
|
|
result = nanops.nanstd(self._ndarray, axis=axis, skipna=skipna, ddof=ddof)
|
if axis is None or self.ndim == 1:
|
return self._box_func(result)
|
return self._from_backing_data(result)
|
|
# ----------------------------------------------------------------
|
# Accumulations
|
|
def _accumulate(self, name: str, *, skipna: bool = True, **kwargs):
|
if name == "cumsum":
|
op = getattr(datetimelike_accumulations, name)
|
result = op(self._ndarray.copy(), skipna=skipna, **kwargs)
|
|
return type(self)._simple_new(result, freq=None, dtype=self.dtype)
|
elif name == "cumprod":
|
raise TypeError("cumprod not supported for Timedelta.")
|
|
else:
|
return super()._accumulate(name, skipna=skipna, **kwargs)
|
|
# ----------------------------------------------------------------
|
# Rendering Methods
|
|
def _formatter(self, boxed: bool = False):
|
from pandas.io.formats.format import get_format_timedelta64
|
|
return get_format_timedelta64(self, box=True)
|
|
def _format_native_types(
|
self, *, na_rep: str | float = "NaT", date_format=None, **kwargs
|
) -> npt.NDArray[np.object_]:
|
from pandas.io.formats.format import get_format_timedelta64
|
|
# Relies on TimeDelta._repr_base
|
formatter = get_format_timedelta64(self._ndarray, na_rep)
|
# equiv: np.array([formatter(x) for x in self._ndarray])
|
# but independent of dimension
|
return np.frompyfunc(formatter, 1, 1)(self._ndarray)
|
|
# ----------------------------------------------------------------
|
# Arithmetic Methods
|
|
def _add_offset(self, other):
|
assert not isinstance(other, Tick)
|
raise TypeError(
|
f"cannot add the type {type(other).__name__} to a {type(self).__name__}"
|
)
|
|
@unpack_zerodim_and_defer("__mul__")
|
def __mul__(self, other) -> TimedeltaArray:
|
if is_scalar(other):
|
# numpy will accept float and int, raise TypeError for others
|
result = self._ndarray * other
|
freq = None
|
if self.freq is not None and not isna(other):
|
freq = self.freq * other
|
return type(self)._simple_new(result, dtype=result.dtype, freq=freq)
|
|
if not hasattr(other, "dtype"):
|
# list, tuple
|
other = np.array(other)
|
if len(other) != len(self) and not is_timedelta64_dtype(other.dtype):
|
# Exclude timedelta64 here so we correctly raise TypeError
|
# for that instead of ValueError
|
raise ValueError("Cannot multiply with unequal lengths")
|
|
if is_object_dtype(other.dtype):
|
# this multiplication will succeed only if all elements of other
|
# are int or float scalars, so we will end up with
|
# timedelta64[ns]-dtyped result
|
arr = self._ndarray
|
result = [arr[n] * other[n] for n in range(len(self))]
|
result = np.array(result)
|
return type(self)._simple_new(result, dtype=result.dtype)
|
|
# numpy will accept float or int dtype, raise TypeError for others
|
result = self._ndarray * other
|
return type(self)._simple_new(result, dtype=result.dtype)
|
|
__rmul__ = __mul__
|
|
def _scalar_divlike_op(self, other, op):
|
"""
|
Shared logic for __truediv__, __rtruediv__, __floordiv__, __rfloordiv__
|
with scalar 'other'.
|
"""
|
if isinstance(other, self._recognized_scalars):
|
other = Timedelta(other)
|
# mypy assumes that __new__ returns an instance of the class
|
# github.com/python/mypy/issues/1020
|
if cast("Timedelta | NaTType", other) is NaT:
|
# specifically timedelta64-NaT
|
result = np.empty(self.shape, dtype=np.float64)
|
result.fill(np.nan)
|
return result
|
|
# otherwise, dispatch to Timedelta implementation
|
return op(self._ndarray, other)
|
|
else:
|
# caller is responsible for checking lib.is_scalar(other)
|
# assume other is numeric, otherwise numpy will raise
|
|
if op in [roperator.rtruediv, roperator.rfloordiv]:
|
raise TypeError(
|
f"Cannot divide {type(other).__name__} by {type(self).__name__}"
|
)
|
|
result = op(self._ndarray, other)
|
freq = None
|
|
if self.freq is not None:
|
# Note: freq gets division, not floor-division, even if op
|
# is floordiv.
|
freq = self.freq / other
|
|
# TODO: 2022-12-24 test_ufunc_coercions, test_tdi_ops_attributes
|
# get here for truediv, no tests for floordiv
|
|
if op is operator.floordiv:
|
if freq.nanos == 0 and self.freq.nanos != 0:
|
# e.g. if self.freq is Nano(1) then dividing by 2
|
# rounds down to zero
|
# TODO: 2022-12-24 should implement the same check
|
# for truediv case
|
freq = None
|
|
return type(self)._simple_new(result, dtype=result.dtype, freq=freq)
|
|
def _cast_divlike_op(self, other):
|
if not hasattr(other, "dtype"):
|
# e.g. list, tuple
|
other = np.array(other)
|
|
if len(other) != len(self):
|
raise ValueError("Cannot divide vectors with unequal lengths")
|
return other
|
|
def _vector_divlike_op(self, other, op) -> np.ndarray | TimedeltaArray:
|
"""
|
Shared logic for __truediv__, __floordiv__, and their reversed versions
|
with timedelta64-dtype ndarray other.
|
"""
|
# Let numpy handle it
|
result = op(self._ndarray, np.asarray(other))
|
|
if (is_integer_dtype(other.dtype) or is_float_dtype(other.dtype)) and op in [
|
operator.truediv,
|
operator.floordiv,
|
]:
|
return type(self)._simple_new(result, dtype=result.dtype)
|
|
if op in [operator.floordiv, roperator.rfloordiv]:
|
mask = self.isna() | isna(other)
|
if mask.any():
|
result = result.astype(np.float64)
|
np.putmask(result, mask, np.nan)
|
|
return result
|
|
@unpack_zerodim_and_defer("__truediv__")
|
def __truediv__(self, other):
|
# timedelta / X is well-defined for timedelta-like or numeric X
|
op = operator.truediv
|
if is_scalar(other):
|
return self._scalar_divlike_op(other, op)
|
|
other = self._cast_divlike_op(other)
|
if (
|
is_timedelta64_dtype(other.dtype)
|
or is_integer_dtype(other.dtype)
|
or is_float_dtype(other.dtype)
|
):
|
return self._vector_divlike_op(other, op)
|
|
if is_object_dtype(other.dtype):
|
other = np.asarray(other)
|
if self.ndim > 1:
|
res_cols = [left / right for left, right in zip(self, other)]
|
res_cols2 = [x.reshape(1, -1) for x in res_cols]
|
result = np.concatenate(res_cols2, axis=0)
|
else:
|
result = truediv_object_array(self._ndarray, other)
|
|
return result
|
|
else:
|
return NotImplemented
|
|
@unpack_zerodim_and_defer("__rtruediv__")
|
def __rtruediv__(self, other):
|
# X / timedelta is defined only for timedelta-like X
|
op = roperator.rtruediv
|
if is_scalar(other):
|
return self._scalar_divlike_op(other, op)
|
|
other = self._cast_divlike_op(other)
|
if is_timedelta64_dtype(other.dtype):
|
return self._vector_divlike_op(other, op)
|
|
elif is_object_dtype(other.dtype):
|
# Note: unlike in __truediv__, we do not _need_ to do type
|
# inference on the result. It does not raise, a numeric array
|
# is returned. GH#23829
|
result_list = [other[n] / self[n] for n in range(len(self))]
|
return np.array(result_list)
|
|
else:
|
return NotImplemented
|
|
@unpack_zerodim_and_defer("__floordiv__")
|
def __floordiv__(self, other):
|
op = operator.floordiv
|
if is_scalar(other):
|
return self._scalar_divlike_op(other, op)
|
|
other = self._cast_divlike_op(other)
|
if (
|
is_timedelta64_dtype(other.dtype)
|
or is_integer_dtype(other.dtype)
|
or is_float_dtype(other.dtype)
|
):
|
return self._vector_divlike_op(other, op)
|
|
elif is_object_dtype(other.dtype):
|
other = np.asarray(other)
|
if self.ndim > 1:
|
res_cols = [left // right for left, right in zip(self, other)]
|
res_cols2 = [x.reshape(1, -1) for x in res_cols]
|
result = np.concatenate(res_cols2, axis=0)
|
else:
|
result = floordiv_object_array(self._ndarray, other)
|
|
assert result.dtype == object
|
return result
|
|
else:
|
return NotImplemented
|
|
@unpack_zerodim_and_defer("__rfloordiv__")
|
def __rfloordiv__(self, other):
|
op = roperator.rfloordiv
|
if is_scalar(other):
|
return self._scalar_divlike_op(other, op)
|
|
other = self._cast_divlike_op(other)
|
if is_timedelta64_dtype(other.dtype):
|
return self._vector_divlike_op(other, op)
|
|
elif is_object_dtype(other.dtype):
|
result_list = [other[n] // self[n] for n in range(len(self))]
|
result = np.array(result_list)
|
return result
|
|
else:
|
return NotImplemented
|
|
@unpack_zerodim_and_defer("__mod__")
|
def __mod__(self, other):
|
# Note: This is a naive implementation, can likely be optimized
|
if isinstance(other, self._recognized_scalars):
|
other = Timedelta(other)
|
return self - (self // other) * other
|
|
@unpack_zerodim_and_defer("__rmod__")
|
def __rmod__(self, other):
|
# Note: This is a naive implementation, can likely be optimized
|
if isinstance(other, self._recognized_scalars):
|
other = Timedelta(other)
|
return other - (other // self) * self
|
|
@unpack_zerodim_and_defer("__divmod__")
|
def __divmod__(self, other):
|
# Note: This is a naive implementation, can likely be optimized
|
if isinstance(other, self._recognized_scalars):
|
other = Timedelta(other)
|
|
res1 = self // other
|
res2 = self - res1 * other
|
return res1, res2
|
|
@unpack_zerodim_and_defer("__rdivmod__")
|
def __rdivmod__(self, other):
|
# Note: This is a naive implementation, can likely be optimized
|
if isinstance(other, self._recognized_scalars):
|
other = Timedelta(other)
|
|
res1 = other // self
|
res2 = other - res1 * self
|
return res1, res2
|
|
def __neg__(self) -> TimedeltaArray:
|
freq = None
|
if self.freq is not None:
|
freq = -self.freq
|
return type(self)._simple_new(-self._ndarray, dtype=self.dtype, freq=freq)
|
|
def __pos__(self) -> TimedeltaArray:
|
return type(self)(self._ndarray.copy(), freq=self.freq)
|
|
def __abs__(self) -> TimedeltaArray:
|
# Note: freq is not preserved
|
return type(self)(np.abs(self._ndarray))
|
|
# ----------------------------------------------------------------
|
# Conversion Methods - Vectorized analogues of Timedelta methods
|
|
def total_seconds(self) -> npt.NDArray[np.float64]:
|
"""
|
Return total duration of each element expressed in seconds.
|
|
This method is available directly on TimedeltaArray, TimedeltaIndex
|
and on Series containing timedelta values under the ``.dt`` namespace.
|
|
Returns
|
-------
|
ndarray, Index or Series
|
When the calling object is a TimedeltaArray, the return type
|
is ndarray. When the calling object is a TimedeltaIndex,
|
the return type is an Index with a float64 dtype. When the calling object
|
is a Series, the return type is Series of type `float64` whose
|
index is the same as the original.
|
|
See Also
|
--------
|
datetime.timedelta.total_seconds : Standard library version
|
of this method.
|
TimedeltaIndex.components : Return a DataFrame with components of
|
each Timedelta.
|
|
Examples
|
--------
|
**Series**
|
|
>>> s = pd.Series(pd.to_timedelta(np.arange(5), unit='d'))
|
>>> s
|
0 0 days
|
1 1 days
|
2 2 days
|
3 3 days
|
4 4 days
|
dtype: timedelta64[ns]
|
|
>>> s.dt.total_seconds()
|
0 0.0
|
1 86400.0
|
2 172800.0
|
3 259200.0
|
4 345600.0
|
dtype: float64
|
|
**TimedeltaIndex**
|
|
>>> idx = pd.to_timedelta(np.arange(5), unit='d')
|
>>> idx
|
TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
|
dtype='timedelta64[ns]', freq=None)
|
|
>>> idx.total_seconds()
|
Index([0.0, 86400.0, 172800.0, 259200.0, 345600.0], dtype='float64')
|
"""
|
pps = periods_per_second(self._creso)
|
return self._maybe_mask_results(self.asi8 / pps, fill_value=None)
|
|
def to_pytimedelta(self) -> npt.NDArray[np.object_]:
|
"""
|
Return an ndarray of datetime.timedelta objects.
|
|
Returns
|
-------
|
numpy.ndarray
|
"""
|
return ints_to_pytimedelta(self._ndarray)
|
|
days = _field_accessor("days", "days", "Number of days for each element.")
|
seconds = _field_accessor(
|
"seconds",
|
"seconds",
|
"Number of seconds (>= 0 and less than 1 day) for each element.",
|
)
|
microseconds = _field_accessor(
|
"microseconds",
|
"microseconds",
|
"Number of microseconds (>= 0 and less than 1 second) for each element.",
|
)
|
nanoseconds = _field_accessor(
|
"nanoseconds",
|
"nanoseconds",
|
"Number of nanoseconds (>= 0 and less than 1 microsecond) for each element.",
|
)
|
|
@property
|
def components(self) -> DataFrame:
|
"""
|
Return a DataFrame of the individual resolution components of the Timedeltas.
|
|
The components (days, hours, minutes seconds, milliseconds, microseconds,
|
nanoseconds) are returned as columns in a DataFrame.
|
|
Returns
|
-------
|
DataFrame
|
"""
|
from pandas import DataFrame
|
|
columns = [
|
"days",
|
"hours",
|
"minutes",
|
"seconds",
|
"milliseconds",
|
"microseconds",
|
"nanoseconds",
|
]
|
hasnans = self._hasna
|
if hasnans:
|
|
def f(x):
|
if isna(x):
|
return [np.nan] * len(columns)
|
return x.components
|
|
else:
|
|
def f(x):
|
return x.components
|
|
result = DataFrame([f(x) for x in self], columns=columns)
|
if not hasnans:
|
result = result.astype("int64")
|
return result
|
|
|
# ---------------------------------------------------------------------
|
# Constructor Helpers
|
|
|
def sequence_to_td64ns(
|
data,
|
copy: bool = False,
|
unit=None,
|
errors: DateTimeErrorChoices = "raise",
|
) -> tuple[np.ndarray, Tick | None]:
|
"""
|
Parameters
|
----------
|
data : list-like
|
copy : bool, default False
|
unit : str, optional
|
The timedelta unit to treat integers as multiples of. For numeric
|
data this defaults to ``'ns'``.
|
Must be un-specified if the data contains a str and ``errors=="raise"``.
|
errors : {"raise", "coerce", "ignore"}, default "raise"
|
How to handle elements that cannot be converted to timedelta64[ns].
|
See ``pandas.to_timedelta`` for details.
|
|
Returns
|
-------
|
converted : numpy.ndarray
|
The sequence converted to a numpy array with dtype ``timedelta64[ns]``.
|
inferred_freq : Tick or None
|
The inferred frequency of the sequence.
|
|
Raises
|
------
|
ValueError : Data cannot be converted to timedelta64[ns].
|
|
Notes
|
-----
|
Unlike `pandas.to_timedelta`, if setting ``errors=ignore`` will not cause
|
errors to be ignored; they are caught and subsequently ignored at a
|
higher level.
|
"""
|
assert unit not in ["Y", "y", "M"] # caller is responsible for checking
|
|
inferred_freq = None
|
if unit is not None:
|
unit = parse_timedelta_unit(unit)
|
|
data, copy = dtl.ensure_arraylike_for_datetimelike(
|
data, copy, cls_name="TimedeltaArray"
|
)
|
|
if isinstance(data, TimedeltaArray):
|
inferred_freq = data.freq
|
|
# Convert whatever we have into timedelta64[ns] dtype
|
if is_object_dtype(data.dtype) or is_string_dtype(data.dtype):
|
# no need to make a copy, need to convert if string-dtyped
|
data = _objects_to_td64ns(data, unit=unit, errors=errors)
|
copy = False
|
|
elif is_integer_dtype(data.dtype):
|
# treat as multiples of the given unit
|
data, copy_made = _ints_to_td64ns(data, unit=unit)
|
copy = copy and not copy_made
|
|
elif is_float_dtype(data.dtype):
|
# cast the unit, multiply base/frac separately
|
# to avoid precision issues from float -> int
|
if is_extension_array_dtype(data):
|
mask = data._mask
|
data = data._data
|
else:
|
mask = np.isnan(data)
|
# The next few lines are effectively a vectorized 'cast_from_unit'
|
m, p = precision_from_unit(unit or "ns")
|
with warnings.catch_warnings():
|
# Suppress RuntimeWarning about All-NaN slice
|
warnings.filterwarnings(
|
"ignore", "invalid value encountered in cast", RuntimeWarning
|
)
|
base = data.astype(np.int64)
|
frac = data - base
|
if p:
|
frac = np.round(frac, p)
|
with warnings.catch_warnings():
|
warnings.filterwarnings(
|
"ignore", "invalid value encountered in cast", RuntimeWarning
|
)
|
data = (base * m + (frac * m).astype(np.int64)).view("timedelta64[ns]")
|
data[mask] = iNaT
|
copy = False
|
|
elif is_timedelta64_dtype(data.dtype):
|
data_unit = get_unit_from_dtype(data.dtype)
|
if not is_supported_unit(data_unit):
|
# cast to closest supported unit, i.e. s or ns
|
new_reso = get_supported_reso(data_unit)
|
new_unit = npy_unit_to_abbrev(new_reso)
|
new_dtype = np.dtype(f"m8[{new_unit}]")
|
data = astype_overflowsafe(data, dtype=new_dtype, copy=False)
|
copy = False
|
|
else:
|
# This includes datetime64-dtype, see GH#23539, GH#29794
|
raise TypeError(f"dtype {data.dtype} cannot be converted to timedelta64[ns]")
|
|
data = np.array(data, copy=copy)
|
|
assert data.dtype.kind == "m"
|
assert data.dtype != "m8" # i.e. not unit-less
|
|
return data, inferred_freq
|
|
|
def _ints_to_td64ns(data, unit: str = "ns"):
|
"""
|
Convert an ndarray with integer-dtype to timedelta64[ns] dtype, treating
|
the integers as multiples of the given timedelta unit.
|
|
Parameters
|
----------
|
data : numpy.ndarray with integer-dtype
|
unit : str, default "ns"
|
The timedelta unit to treat integers as multiples of.
|
|
Returns
|
-------
|
numpy.ndarray : timedelta64[ns] array converted from data
|
bool : whether a copy was made
|
"""
|
copy_made = False
|
unit = unit if unit is not None else "ns"
|
|
if data.dtype != np.int64:
|
# converting to int64 makes a copy, so we can avoid
|
# re-copying later
|
data = data.astype(np.int64)
|
copy_made = True
|
|
if unit != "ns":
|
dtype_str = f"timedelta64[{unit}]"
|
data = data.view(dtype_str)
|
|
data = astype_overflowsafe(data, dtype=TD64NS_DTYPE)
|
|
# the astype conversion makes a copy, so we can avoid re-copying later
|
copy_made = True
|
|
else:
|
data = data.view("timedelta64[ns]")
|
|
return data, copy_made
|
|
|
def _objects_to_td64ns(data, unit=None, errors: DateTimeErrorChoices = "raise"):
|
"""
|
Convert a object-dtyped or string-dtyped array into an
|
timedelta64[ns]-dtyped array.
|
|
Parameters
|
----------
|
data : ndarray or Index
|
unit : str, default "ns"
|
The timedelta unit to treat integers as multiples of.
|
Must not be specified if the data contains a str.
|
errors : {"raise", "coerce", "ignore"}, default "raise"
|
How to handle elements that cannot be converted to timedelta64[ns].
|
See ``pandas.to_timedelta`` for details.
|
|
Returns
|
-------
|
numpy.ndarray : timedelta64[ns] array converted from data
|
|
Raises
|
------
|
ValueError : Data cannot be converted to timedelta64[ns].
|
|
Notes
|
-----
|
Unlike `pandas.to_timedelta`, if setting `errors=ignore` will not cause
|
errors to be ignored; they are caught and subsequently ignored at a
|
higher level.
|
"""
|
# coerce Index to np.ndarray, converting string-dtype if necessary
|
values = np.array(data, dtype=np.object_, copy=False)
|
|
result = array_to_timedelta64(values, unit=unit, errors=errors)
|
return result.view("timedelta64[ns]")
|
|
|
def _validate_td64_dtype(dtype) -> DtypeObj:
|
dtype = pandas_dtype(dtype)
|
if is_dtype_equal(dtype, np.dtype("timedelta64")):
|
# no precision disallowed GH#24806
|
msg = (
|
"Passing in 'timedelta' dtype with no precision is not allowed. "
|
"Please pass in 'timedelta64[ns]' instead."
|
)
|
raise ValueError(msg)
|
|
if (
|
not isinstance(dtype, np.dtype)
|
or dtype.kind != "m"
|
or not is_supported_unit(get_unit_from_dtype(dtype))
|
):
|
raise ValueError(f"dtype {dtype} cannot be converted to timedelta64[ns]")
|
|
return dtype
|
