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)
