def mean(self, axis=None, dtype=None, out=None, keepdims=False, skipna=True): nv.validate_mean((), dict(dtype=dtype, out=out, keepdims=keepdims)) return nanops.nanmean(self._ndarray, axis=axis, skipna=skipna)
def mean(self, *, axis=None, dtype=None, out=None, keepdims=False, skipna=True): nv.validate_mean((), dict(dtype=dtype, out=out, keepdims=keepdims)) result = nanops.nanmean(self._ndarray, axis=axis, skipna=skipna) return self._wrap_reduction_result(axis, result)
def mean( self, *, axis=None, dtype: Optional[NpDtype] = None, out=None, keepdims=False, skipna=True, ): nv.validate_mean((), {"dtype": dtype, "out": out, "keepdims": keepdims}) result = nanops.nanmean(self._ndarray, axis=axis, skipna=skipna) return self._wrap_reduction_result(axis, result)
def mean(self, axis=0, *args, **kwargs): """ Mean of non-NA/null values Returns ------- mean : float """ nv.validate_mean(args, kwargs) valid_vals = self._valid_sp_values sp_sum = valid_vals.sum() ct = len(valid_vals) if self._null_fill_value: return sp_sum / ct else: nsparse = self.sp_index.ngaps return (sp_sum + self.fill_value * nsparse) / (ct + nsparse)