Add basic backend dispatching to dask-expr

dask_expr/__init__.py

@@ -1,5 +1,6 @@
 from dask_expr import _version, datasets
 from dask_expr._collection import *
+from dask_expr._dispatch import get_collection_type
 from dask_expr._dummies import get_dummies
 from dask_expr.io._delayed import from_delayed
 from dask_expr.io.bag import to_bag

dask_expr/_backends.py

@@ -0,0 +1,77 @@
+from __future__ import annotations
+
+import pandas as pd
+from dask.backends import CreationDispatch
+from dask.dataframe.backends import DataFrameBackendEntrypoint
+
+from dask_expr._dispatch import get_collection_type
+
+dataframe_creation_dispatch = CreationDispatch(
+    module_name="dataframe",
+    default="pandas",
+    entrypoint_root="dask_expr",
+    entrypoint_class=DataFrameBackendEntrypoint,
+    name="dataframe_creation_dispatch",
+)
+
+
+class PandasBackendEntrypoint(DataFrameBackendEntrypoint):
+    """Pandas-Backend Entrypoint Class for Dask-Expressions
+
+    Note that all DataFrame-creation functions are defined
+    and registered 'in-place'.
+    """
+
+    @classmethod
+    def to_backend_dispatch(cls):
+        from dask.dataframe.dispatch import to_pandas_dispatch
+
+        return to_pandas_dispatch
+
+    @classmethod
+    def to_backend(cls, data, **kwargs):
+        if isinstance(data._meta, (pd.DataFrame, pd.Series, pd.Index)):
+            # Already a pandas-backed collection
+            return data
+        return data.map_partitions(cls.to_backend_dispatch(), **kwargs)
+
+
+dataframe_creation_dispatch.register_backend("pandas", PandasBackendEntrypoint())
+
+
+@get_collection_type.register(pd.Series)
+def get_collection_type_series(_):
+    from dask_expr._collection import Series
+
+    return Series
+
+
+@get_collection_type.register(pd.DataFrame)
+def get_collection_type_dataframe(_):
+    from dask_expr._collection import DataFrame
+
+    return DataFrame
+
+
+@get_collection_type.register(pd.Index)
+def get_collection_type_index(_):
+    from dask_expr._collection import Index
+
+    return Index
+
+
+@get_collection_type.register(object)
+def get_collection_type_object(_):
+    from dask_expr._collection import Scalar
+
+    return Scalar
+
+
+######################################
+# cuDF: Pandas Dataframes on the GPU #
+######################################
+
+
+@get_collection_type.register_lazy("cudf")
+def _register_cudf():
+    import dask_cudf  # noqa: F401

dask_expr/_collection.py

@@ -26,7 +26,6 @@
     has_parallel_type,
     is_arraylike,
     is_dataframe_like,
-    is_index_like,
     is_series_like,
     meta_warning,
     new_dd_object,
@@ -57,12 +56,15 @@
 from pandas.api.types import is_timedelta64_dtype
 from tlz import first
 
+import dask_expr._backends  # noqa: F401
 from dask_expr import _expr as expr
 from dask_expr._align import AlignPartitions
+from dask_expr._backends import dataframe_creation_dispatch
 from dask_expr._categorical import CategoricalAccessor, Categorize, GetCategories
 from dask_expr._concat import Concat
 from dask_expr._datetime import DatetimeAccessor
 from dask_expr._describe import DescribeNonNumeric, DescribeNumeric
+from dask_expr._dispatch import get_collection_type
 from dask_expr._expr import (
     BFill,
     Diff,
@@ -1578,9 +1580,14 @@ def to_backend(self, backend: str | None = None, **kwargs):
         -------
         DataFrame, Series or Index
         """
-        from dask.dataframe.io import to_backend
+        from dask_expr._backends import dataframe_creation_dispatch
 
-        return to_backend(self.to_dask_dataframe(), backend=backend, **kwargs)
+        # Get desired backend
+        backend = backend or dataframe_creation_dispatch.backend
+        # Check that "backend" has a registered entrypoint
+        backend_entrypoint = dataframe_creation_dispatch.dispatch(backend)
+        # Call `DataFrameBackendEntrypoint.to_backend`
+        return backend_entrypoint.to_backend(self, **kwargs)
 
     def dot(self, other, meta=no_default):
         if not isinstance(other, FrameBase):
@@ -1981,7 +1988,7 @@ def __getattr__(self, key):
                 # Check if key is in columns if key
                 # is not a normal attribute
                 if key in self.expr._meta.columns:
-                    return Series(self.expr[key])
+                    return new_collection(self.expr[key])
                 raise err
             except AttributeError:
                 # Fall back to `BaseFrame.__getattr__`
@@ -3156,17 +3163,9 @@ def __array__(self):
 
 def new_collection(expr):
     """Create new collection from an expr"""
-
     meta = expr._meta
     expr._name  # Ensure backend is imported
-    if is_dataframe_like(meta):
-        return DataFrame(expr)
-    elif is_series_like(meta):
-        return Series(expr)
-    elif is_index_like(meta):
-        return Index(expr)
-    else:
-        return Scalar(expr)
+    return get_collection_type(meta)(expr)
 
 
 def optimize(collection, fuse=True):
@@ -3233,6 +3232,7 @@ def from_graph(*args, **kwargs):
     return new_collection(FromGraph(*args, **kwargs))
 
 
+@dataframe_creation_dispatch.register_inplace("pandas")
 def from_dict(
     data,
     npartitions,

dask_expr/_dispatch.py

@@ -0,0 +1,5 @@
+from __future__ import annotations
+
+from dask.utils import Dispatch
+
+get_collection_type = Dispatch("get_collection_type")