________________________ test_groupby_reduction_split[tasks-split_out] _________________________
[gw5] linux -- Python 3.9.13 /home/julia/conda/envs/dask-upstream/bin/python

keyword = 'split_out'

    @pytest.mark.filterwarnings(
        "ignore:The default value of numeric_only:FutureWarning",
    )
    @pytest.mark.parametrize("keyword", ["split_every", "split_out"])
    def test_groupby_reduction_split(keyword):
        pdf = pd.DataFrame(
            {"a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 100, "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 100}
        )
        ddf = dd.from_pandas(pdf, npartitions=15)
    
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        # DataFrame
        for m in AGG_FUNCS:
            # nunique is not implemented for DataFrameGroupBy
            # covariance/correlation is not a series aggregation
            if m in ("nunique", "cov", "corr"):
                continue
            res = call(ddf.groupby("b"), m, **{keyword: 2})
>           sol = call(pdf.groupby("b"), m)

dask/dataframe/tests/test_groupby.py:774: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
dask/dataframe/tests/test_groupby.py:765: in call
    return getattr(g, m)(**kwargs)
../conda/envs/dask-upstream/lib/python3.9/site-packages/pandas/core/groupby/groupby.py:2423: in prod
    return self._agg_general(
../conda/envs/dask-upstream/lib/python3.9/site-packages/pandas/core/groupby/groupby.py:1662: in _agg_general
    result = self._cython_agg_general(
../conda/envs/dask-upstream/lib/python3.9/site-packages/pandas/core/groupby/groupby.py:1756: in _cython_agg_general
    new_mgr = data.grouped_reduce(array_func, ignore_failures=ignore_failures)
../conda/envs/dask-upstream/lib/python3.9/site-packages/pandas/core/internals/managers.py:1334: in grouped_reduce
    applied = blk.apply(func)
../conda/envs/dask-upstream/lib/python3.9/site-packages/pandas/core/internals/blocks.py:351: in apply
    result = func(self.values, **kwargs)
../conda/envs/dask-upstream/lib/python3.9/site-packages/pandas/core/groupby/groupby.py:1742: in array_func
    result = self.grouper._cython_operation(
../conda/envs/dask-upstream/lib/python3.9/site-packages/pandas/core/groupby/ops.py:1008: in _cython_operation
    return cy_op.cython_operation(
../conda/envs/dask-upstream/lib/python3.9/site-packages/pandas/core/groupby/ops.py:676: in cython_operation
    return self._cython_op_ndim_compat(
../conda/envs/dask-upstream/lib/python3.9/site-packages/pandas/core/groupby/ops.py:508: in _cython_op_ndim_compat
    return self._call_cython_op(
../conda/envs/dask-upstream/lib/python3.9/site-packages/pandas/core/groupby/ops.py:630: in _call_cython_op
    op_result = maybe_downcast_to_dtype(result, res_dtype)
../conda/envs/dask-upstream/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:294: in maybe_downcast_to_dtype
    converted = maybe_downcast_numeric(result, dtype, do_round)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

result = array([[5.41065251e+185, 4.10018609e+114, 2.58224988e+120,
        1.00000000e+000, 6.53318624e+077]])
dtype = dtype('int64'), do_round = False

    def maybe_downcast_numeric(
        result: ArrayLike, dtype: DtypeObj, do_round: bool = False
    ) -> ArrayLike:
        """
        Subset of maybe_downcast_to_dtype restricted to numeric dtypes.
    
        Parameters
        ----------
        result : ndarray or ExtensionArray
        dtype : np.dtype or ExtensionDtype
        do_round : bool
    
        Returns
        -------
        ndarray or ExtensionArray
        """
        if not isinstance(dtype, np.dtype) or not isinstance(result.dtype, np.dtype):
            # e.g. SparseDtype has no itemsize attr
            return result
    
        def trans(x):
            if do_round:
                return x.round()
            return x
    
        if dtype.kind == result.dtype.kind:
            # don't allow upcasts here (except if empty)
            if result.dtype.itemsize <= dtype.itemsize and result.size:
                return result
    
        if is_bool_dtype(dtype) or is_integer_dtype(dtype):
    
            if not result.size:
                # if we don't have any elements, just astype it
                return trans(result).astype(dtype)
    
            # do a test on the first element, if it fails then we are done
            r = result.ravel()
            arr = np.array([r[0]])
    
            if isna(arr).any():
                # if we have any nulls, then we are done
                return result
    
            elif not isinstance(r[0], (np.integer, np.floating, int, float, bool)):
                # a comparable, e.g. a Decimal may slip in here
                return result
    
            if (
                issubclass(result.dtype.type, (np.object_, np.number))
                and notna(result).all()
            ):
>               new_result = trans(result).astype(dtype)
E               RuntimeWarning: invalid value encountered in cast

../conda/envs/dask-upstream/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:380: RuntimeWarning