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)