def sem(self, axis=None, dtype=None, out=None, ddof=1, keepdims=False,
skipna=True):
nv.validate_stat_ddof_func((), dict(dtype=dtype, out=out,
keepdims=keepdims),
fname='sem')
return nanops.nansem(self._ndarray, axis=axis, skipna=skipna,
ddof=ddof)
def std(
self,
*,
axis: int | None = None,
dtype: NpDtype | None = None,
out=None,
ddof=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)
return self._wrap_reduction_result(axis, result)
def std(
self,
axis=None,
dtype=None,
out=None,
ddof: int = 1,
keepdims: bool = False,
skipna: bool = True,
):
nv.validate_stat_ddof_func((),
dict(dtype=dtype,
out=out,
keepdims=keepdims),
fname="std")
if not len(self):
return NaT
if not skipna and self._hasnans:
return NaT
result = nanops.nanstd(self._data, axis=axis, skipna=skipna, ddof=ddof)
return Timedelta(result)
def sem(
self,
*,
axis=None,
dtype: Optional[NpDtype] = None,
out=None,
ddof=1,
keepdims=False,
skipna=True,
):
nv.validate_stat_ddof_func((), {
"dtype": dtype,
"out": out,
"keepdims": keepdims
},
fname="sem")
result = nanops.nansem(self._ndarray,
axis=axis,
skipna=skipna,
ddof=ddof)
return self._wrap_reduction_result(axis, result)
def std(
self,
axis=None,
dtype=None,
out=None,
ddof: int = 1,
keepdims: bool = False,
skipna: bool = True,
):
nv.validate_stat_ddof_func((),
dict(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)
def std(
self,
*,
axis=None,
dtype: Optional[NpDtype] = 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)
def skew(self, axis=None, dtype=None, out=None, keepdims=False, skipna=True):
nv.validate_stat_ddof_func(
(), dict(dtype=dtype, out=out, keepdims=keepdims), fname="skew"
)
return nanops.nanskew(self._ndarray, axis=axis, skipna=skipna)
def skew(self, *, axis=None, dtype=None, out=None, keepdims=False, skipna=True):
nv.validate_stat_ddof_func(
(), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="skew"
)
result = nanops.nanskew(self._ndarray, axis=axis, skipna=skipna)
return self._wrap_reduction_result(axis, result)