make_meta_dispatch.register((pd.Series, pd.DataFrame))

concat_dispatch.register_lazy("cudf")

get_collection_type.register_lazy("cudf")

tolist_dispatch.register_lazy("cupy")

array_creation_dispatch.register_inplace("numpy")

categorical_dtype_dispatch.register((pd.DataFrame, pd.Series, pd.Index))

concat_dispatch.register((pd.DataFrame, pd.Series, pd.Index))

config.command(name="get")

to_numpy_dispatch.register(cupy.ndarray)

md.dispatch(int, int, int)

as_gufunc("(i)->()", axis=-1, keepdims=False, output_dtypes=float, vectorize=True)

dataframe_creation_dispatch.register_inplace("pandas")

get_collection_type.register(np.ndarray)

get_collection_type.register(CompositeScalarMeta)

get_collection_type.register(pd.DataFrame)

get_collection_type.register(pd.Index)

get_collection_type.register(object)

get_collection_type.register(pd.Series)

grouper_dispatch.register((pd.DataFrame, pd.Series))

from_pyarrow_table_dispatch.register((pd.DataFrame,))

get_parallel_type.register(pd.DataFrame)

get_parallel_type.register(FrameBase)

get_parallel_type.register(pd.Index)

get_parallel_type.register(object)

get_parallel_type.register(pd.Series)

pyarrow_schema_dispatch.register((cudf.DataFrame,))

pyarrow_schema_dispatch.register((pd.DataFrame,))

to_pyarrow_table_dispatch.register((pd.DataFrame,))

group_split_dispatch.register((pd.DataFrame, pd.Series, pd.Index))

da.as_gufunc(signature="(n, n)->(n, n)", output_dtypes=float, vectorize=True)

hash_object_dispatch.register((pd.DataFrame, pd.Series, pd.Index))

is_categorical_dtype_dispatch.register( (pd.Series, pd.Index, pd.api.extensions.ExtensionDtype, np.dtype)

safe_wraps(getattr(np, "isin", None))

make_meta_dispatch.register(pd.DatetimeTZDtype)

to_cupy_dispatch.register(np.ndarray)

partd_encode_dispatch.register(pd.DataFrame)

dataframe_creation_dispatch.register_inplace("pandas")

dataframe_creation_dispatch.register_inplace("pandas")

dataframe_creation_dispatch.register_inplace("pandas")

dataframe_creation_dispatch.register_inplace("pandas")

to_numpy_dispatch.register_lazy("cupy")

pytest.mark.parametrize( "func1d_name, func1d, specify_output_props", [ ["ndim", lambda x: x.n

pytest.mark.parametrize("output_dtypes", [int, (int,)])

pytest.mark.parametrize("target_dtype", [None, int, float])

pytest.mark.parametrize("target_dtype", [None, int, float])

pytest.mark.parametrize( "func", [ lambda x: np.append(x, x), lambda x: np.concatenate

pytest.mark.parametrize("get", [get_threaded, get_sync])

mock.patch.object(da.core, "_concatenate2", wraps=da.core._concatenate2)

pytest.mark.parametrize( "computation", [ None, "compute_as_if_collection", "d

pytest.mark.parametrize( "include,exclude,percentiles,subset", [ (None, None, None, ["c", "d"]

pytest.mark.skip( reason="Requires a postgres server. Sqlite does not support multiple schemas." )

pytest.mark.parametrize( "filename,format,target,expected_result_type", [ ("mydaskpdf", "svg",

pytest.mark.parametrize( "arr, result", [ (WrappedArray(np.arange(4)), False), (da.fro

pytest.mark.parametrize("use_dask_input", [True, False])

pytest.mark.parametrize("use_dask_input", [True, False])

pytest.mark.parametrize("use_dask_input", [True, False])

xfail_gpu("compute_hll_array doesn't work for cudf")

pytest.mark.parametrize( "shape,chunks,bs,expected", [ (100, 1, 10, (10,) * 10), (100,

pytest.mark.parametrize( "inputs,expected", [ # output name, output index, task, input indices

pytest.mark.parametrize( "include, exclude", [ ([int], None), (None, [int]), (

pytest.mark.parametrize( "input,expected", [ ( # Add -> Mul lambda fn:

pytest.mark.parametrize( "output_chunks, window_shape, axis", [ (((25, 21), (9, 8, 8, 9, 8, 8)

pytest.mark.parametrize( "input_sizes", [ { "x_shape": (4, 8), "y_shap

pytest.mark.parametrize("delayed_target", [False, True])

pytest.mark.parametrize("compute", [False, True])

pytest.mark.parametrize("compute", [False, True])

pytest.mark.parametrize( "func, kwargs", [ ("len", {}), ("capitalize", {}), ("

pytest.mark.parametrize( "input_dtype, output_dtype", [(np.float32, np.float32), (np.float64, np.float64)]

pytest.mark.parametrize("compute", [False, True])

pytest.mark.parametrize( "pandas_input", [ pd.Series(np.random.randint(1, 100, size=20)),

pytest.mark.parametrize( "make_pandas_input", [ lambda: pd.Series(np.random.randint(1, 100, si

pytest.mark.parametrize("compute", [True, False])

pytest.mark.parametrize("compute", [False, True])

to_numpy_dispatch.register(np.ndarray)

to_pandas_dispatch.register((pd.DataFrame, pd.Series, pd.Index))

tolist_dispatch.register(cupy.ndarray)

tolist_dispatch.register((np.ndarray, pd.Series, pd.Index, pd.Categorical))

union_categoricals_dispatch.register( (pd.DataFrame, pd.Series, pd.Index, pd.Categorical) )