def prod(self, *, axis=None, skipna=True, min_count=0, **kwargs) -> Scalar: nv.validate_prod((), kwargs) result = nanops.nanprod(self._ndarray, axis=axis, skipna=skipna, min_count=min_count) return self._wrap_reduction_result(axis, result)
def prod(self, *, skipna=True, min_count=0, axis: int | None = 0, **kwargs): nv.validate_prod((), kwargs) return super()._reduce("prod", skipna=skipna, min_count=min_count, axis=axis)
def prod(self, *, skipna=True, min_count=0, axis: int | None = 0, **kwargs): nv.validate_prod((), kwargs) result = masked_reductions.prod( self._data, self._mask, skipna=skipna, min_count=min_count, axis=axis, ) return self._wrap_reduction_result( "prod", result, skipna=skipna, axis=axis, **kwargs )
def prod( self, axis=None, dtype=None, out=None, keepdims=False, initial=None, skipna=True, min_count=0, ): nv.validate_prod( (), dict(dtype=dtype, out=out, keepdims=keepdims, initial=initial) ) return nanops.nanprod( self._ndarray, axis=axis, skipna=skipna, min_count=min_count )
def prod(self, skipna=True, min_count=0, **kwargs): nv.validate_prod((), kwargs) return super()._reduce("prod", skipna=skipna, min_count=min_count)
def prod(self, axis=None, dtype=None, out=None, keepdims=False, initial=None, skipna=True, min_count=0): nv.validate_prod((), dict(dtype=dtype, out=out, keepdims=keepdims, initial=initial)) return nanops.nanprod(self._ndarray, axis=axis, skipna=skipna, min_count=min_count)
def prod(self, axis=None, skipna=True, min_count=0, **kwargs) -> Scalar: nv.validate_prod((), kwargs) return nanops.nanprod(self._ndarray, axis=axis, skipna=skipna, min_count=min_count)