def sum(self, skipna=True, min_count=0, **kwargs): nv.validate_sum((), kwargs) result = masked_reductions.sum(values=self._data, mask=self._mask, skipna=skipna, min_count=min_count) return result
def sum( self, axis=None, dtype=None, out=None, keepdims: bool = False, initial=None, skipna: bool = True, min_count: int = 0, ): nv.validate_sum((), dict(dtype=dtype, out=out, keepdims=keepdims, initial=initial)) if not len(self): return NaT if not skipna and self._hasnans: return NaT result = nanops.nansum(self._data, axis=axis, skipna=skipna, min_count=min_count) return Timedelta(result)
def sum( self, axis=None, dtype=None, out=None, keepdims: bool = False, initial=None, skipna: bool = True, min_count: int = 0, ): nv.validate_sum((), dict(dtype=dtype, out=out, keepdims=keepdims, initial=initial)) if not self.size and (self.ndim == 1 or axis is None): return NaT result = nanops.nansum(self._data, axis=axis, skipna=skipna, min_count=min_count) if is_scalar(result): return Timedelta(result) return self._from_backing_data(result)
def sum(self, *, axis=None, skipna=True, min_count=0, **kwargs) -> Scalar: nv.validate_sum((), kwargs) result = nanops.nansum(self._ndarray, axis=axis, skipna=skipna, min_count=min_count) return self._wrap_reduction_result(axis, result)
def sum(self, axis: int = 0, min_count: int = 0, *args, **kwargs) -> Scalar: """ Sum of non-NA/null values Parameters ---------- axis : int, default 0 Not Used. NumPy compatibility. min_count : int, default 0 The required number of valid values to perform the summation. If fewer than ``min_count`` valid values are present, the result will be the missing value indicator for subarray type. *args, **kwargs Not Used. NumPy compatibility. Returns ------- scalar """ nv.validate_sum(args, kwargs) valid_vals = self._valid_sp_values sp_sum = valid_vals.sum() if self._null_fill_value: if check_below_min_count(valid_vals.shape, None, min_count): return na_value_for_dtype(self.dtype.subtype, compat=False) return sp_sum else: nsparse = self.sp_index.ngaps if check_below_min_count(valid_vals.shape, None, min_count - nsparse): return na_value_for_dtype(self.dtype.subtype, compat=False) return sp_sum + self.fill_value * nsparse
def sum(self, axis=0, *args, **kwargs): """ Sum of non-NA/null values Returns ------- sum : float """ nv.validate_sum(args, kwargs) valid_vals = self._valid_sp_values sp_sum = valid_vals.sum() if self._null_fill_value: return sp_sum else: nsparse = self.sp_index.ngaps return sp_sum + self.fill_value * nsparse
def sum( self, axis=None, dtype=None, out=None, keepdims=False, initial=None, skipna=True, min_count=0, ): nv.validate_sum( (), dict(dtype=dtype, out=out, keepdims=keepdims, initial=initial) ) return nanops.nansum( self._ndarray, axis=axis, skipna=skipna, min_count=min_count )
def sum( self, *, axis: int | 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 sum( self, *, axis=None, dtype=None, out=None, keepdims: bool = False, initial=None, skipna: bool = True, min_count: int = 0, ): nv.validate_sum( (), dict(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 sum(self, *, skipna=True, min_count=0, axis: int | None = 0, **kwargs): nv.validate_sum((), kwargs) # TODO: do this in validate_sum? if "out" in kwargs: # np.sum; test_floating_array_numpy_sum if kwargs["out"] is not None: raise NotImplementedError kwargs.pop("out") result = masked_reductions.sum( self._data, self._mask, skipna=skipna, min_count=min_count, axis=axis, ) return self._wrap_reduction_result( "sum", result, skipna=skipna, axis=axis, **kwargs )
def sum( self, axis=None, dtype=None, out=None, keepdims: bool = False, initial=None, skipna: bool = True, min_count: int = 0, ): nv.validate_sum((), dict(dtype=dtype, out=out, keepdims=keepdims, initial=initial)) result = nanops.nansum(self._ndarray, axis=axis, skipna=skipna, min_count=min_count) if axis is None or self.ndim == 1: return self._box_func(result) return self._from_backing_data(result)
def sum(self, *, skipna=True, min_count=0, **kwargs): nv.validate_sum((), kwargs) return super()._reduce("sum", skipna=skipna, min_count=min_count)
def sum(self, axis=None, dtype=None, out=None, keepdims=False, initial=None, skipna=True, min_count=0): nv.validate_sum((), dict(dtype=dtype, out=out, keepdims=keepdims, initial=initial)) return nanops.nansum(self._ndarray, axis=axis, skipna=skipna, min_count=min_count)
def sum(self, axis=None, skipna=True, min_count=0, **kwargs) -> Scalar: nv.validate_sum((), kwargs) return nanops.nansum(self._ndarray, axis=axis, skipna=skipna, min_count=min_count)