Repository: dask/dask
Issue category: Feature Request

Issue:
Support bag.to_dataframe when query planning is enabled
As far as I can tell, dask bags can only be converted to dask dataframes when query planning is disabled. It would be great to support both query planning, and the flexibility of turning bags into dataframes. Currently calling to_dataframe with query planning enabled throws the following error.

>    df = bag.to_dataframe(columns=['line'])
  File ".../python3.10/site-packages/dask/bag/core.py", line 1624, in to_dataframe
    return dd.DataFrame(dsk, dfs.name, meta, divisions)
TypeError: FrameBase.__init__() takes 2 positional arguments but 5 were given



Hints:

Patch:
diff --git a/dask/bag/core.py b/dask/bag/core.py
index e3a6f675def..31f640b5841 100644
--- a/dask/bag/core.py
+++ b/dask/bag/core.py
@@ -1621,7 +1621,15 @@ def to_dataframe(self, meta=None, columns=None, optimize_graph=True):
             dsk = dfs.dask
 
         divisions = [None] * (self.npartitions + 1)
-        return dd.DataFrame(dsk, dfs.name, meta, divisions)
+        if not dd._dask_expr_enabled():
+            return dd.DataFrame(dsk, dfs.name, meta, divisions)
+        else:
+            from dask_expr import from_dask_dataframe
+
+            from dask.dataframe.core import DataFrame
+
+            df = DataFrame(dsk, dfs.name, meta, divisions)
+            return from_dask_dataframe(df)
 
     def to_delayed(self, optimize_graph=True):
         """Convert into a list of ``dask.delayed`` objects, one per partition.


Test patch:
diff --git a/dask/bag/tests/test_bag.py b/dask/bag/tests/test_bag.py
index c27f8bed2fb..d00addb5dc9 100644
--- a/dask/bag/tests/test_bag.py
+++ b/dask/bag/tests/test_bag.py
@@ -739,9 +739,7 @@ def test_from_long_sequence():
 
 
 def test_from_empty_sequence():
-    dd = pytest.importorskip("dask.dataframe")
-    if dd._dask_expr_enabled():
-        pytest.skip("conversion not supported")
+    pytest.importorskip("dask.dataframe")
     b = db.from_sequence([])
     assert b.npartitions == 1
     df = b.to_dataframe(meta={"a": "int"}).compute()
@@ -846,8 +844,6 @@ def test_args():
 def test_to_dataframe():
     dd = pytest.importorskip("dask.dataframe")
     pd = pytest.importorskip("pandas")
-    if dd._dask_expr_enabled():
-        pytest.skip("conversion not supported")
 
     def check_parts(df, sol):
         assert all(
@@ -1629,8 +1625,6 @@ def test_dask_layers_to_delayed(optimize):
 
 def test_to_dataframe_optimize_graph():
     dd = pytest.importorskip("dask.dataframe")
-    if dd._dask_expr_enabled():
-        pytest.skip("conversion not supported")
 
     from dask.dataframe.utils import assert_eq as assert_eq_df
     from dask.dataframe.utils import pyarrow_strings_enabled
@@ -1652,19 +1646,24 @@ def test_to_dataframe_optimize_graph():
     d = y.to_dataframe()
 
     # All the `map` tasks have been fused
-    assert len(d.dask) < len(y.dask) + d.npartitions * int(pyarrow_strings_enabled())
+    if not dd._dask_expr_enabled():
+        assert len(d.dask) < len(y.dask) + d.npartitions * int(
+            pyarrow_strings_enabled()
+        )
 
     # no optimizations
     d2 = y.to_dataframe(optimize_graph=False)
 
     # Graph hasn't been fused. It contains all the original tasks,
     # plus one extra layer converting to DataFrame
-    assert len(d2.dask.keys() - y.dask.keys()) == d.npartitions * (
-        1 + int(pyarrow_strings_enabled())
-    )
+    if not dd._dask_expr_enabled():
+        assert len(d2.dask.keys() - y.dask.keys()) == d.npartitions * (
+            1 + int(pyarrow_strings_enabled())
+        )
 
     # Annotations are still there
-    assert hlg_layer_topological(d2.dask, 1).annotations == {"foo": True}
+    if not dd._dask_expr_enabled():
+        assert hlg_layer_topological(d2.dask, 1).annotations == {"foo": True}
 
     assert_eq_df(d, d2)