diff --git a/CHANGES.md b/CHANGES.md
index 7fdca4e6914b..049ef1a1e227 100644
--- a/CHANGES.md
+++ b/CHANGES.md
@@ -64,6 +64,9 @@
 ## New Features / Improvements
 
 * The Flink runner now supports Flink 1.16.x ([#25046](https://github.com/apache/beam/issues/25046)).
+* Schema'd PTransforms can now be directly applied to Beam dataframes just like PCollections.
+  (Note that when doing multiple operations, it may be more efficient to explicitly chain the operations
+  like `df | (Transform1 | Transform2 | ...)` to avoid excessive conversions.)
 
 ## Breaking Changes
 
diff --git a/sdks/python/apache_beam/dataframe/convert_test.py b/sdks/python/apache_beam/dataframe/convert_test.py
index 152c0f00d18b..b00ce0e51fa8 100644
--- a/sdks/python/apache_beam/dataframe/convert_test.py
+++ b/sdks/python/apache_beam/dataframe/convert_test.py
@@ -184,6 +184,26 @@ def test_convert_memoization_clears_cache(self):
       gc.enable()
       logging.disable(logging.NOTSET)
 
+  def test_auto_convert(self):
+    class MySchemaTransform(beam.PTransform):
+      def expand(self, pcoll):
+        return pcoll | beam.Map(
+            lambda x: beam.Row(
+                a=x.n**2 - x.m**2, b=2 * x.m * x.n, c=x.n**2 + x.m**2))
+
+    with beam.Pipeline() as p:
+      pc_mn = p | beam.Create([
+          (1, 2), (2, 3), (3, 10)
+      ]) | beam.MapTuple(lambda m, n: beam.Row(m=m, n=n))
+
+      df_mn = convert.to_dataframe(pc_mn)
+
+      # Apply a transform directly to a dataframe to get another dataframe.
+      df_abc = df_mn | MySchemaTransform()
+
+      pc_abc = convert.to_pcollection(df_abc) | beam.Map(tuple)
+      assert_that(pc_abc, equal_to([(3, 4, 5), (5, 12, 13), (91, 60, 109)]))
+
 
 if __name__ == '__main__':
   unittest.main()
diff --git a/sdks/python/apache_beam/dataframe/frames.py b/sdks/python/apache_beam/dataframe/frames.py
index ac3dd36c98ad..3020eecbaeb5 100644
--- a/sdks/python/apache_beam/dataframe/frames.py
+++ b/sdks/python/apache_beam/dataframe/frames.py
@@ -49,10 +49,12 @@
 from pandas.api.types import is_list_like
 from pandas.core.groupby.generic import DataFrameGroupBy
 
+from apache_beam.dataframe import convert
 from apache_beam.dataframe import expressions
 from apache_beam.dataframe import frame_base
 from apache_beam.dataframe import io
 from apache_beam.dataframe import partitionings
+from apache_beam.transforms import PTransform
 
 __all__ = [
     'DeferredSeries',
@@ -5394,6 +5396,24 @@ def func(df, *args, **kwargs):
         frame_base._elementwise_method(inplace_name, inplace=True,
                                        base=pd.DataFrame))
 
+# Allow dataframe | SchemaTransform
+def _create_maybe_elementwise_or(base):
+  elementwise = frame_base._elementwise_method(
+      '__or__', restrictions={'level': None}, base=base)
+
+  def _maybe_elementwise_or(self, right):
+    if isinstance(right, PTransform):
+      return convert.to_dataframe(convert.to_pcollection(self) | right)
+    else:
+      return elementwise(self, right)
+
+  return _maybe_elementwise_or
+
+
+DeferredSeries.__or__ = _create_maybe_elementwise_or(pd.Series)  # type: ignore
+DeferredDataFrame.__or__ = _create_maybe_elementwise_or(pd.DataFrame)  # type: ignore
+
+
 for name in ['lt', 'le', 'gt', 'ge', 'eq', 'ne']:
   for p in '%s', '__%s__':
     # Note that non-underscore name is used for both as the __xxx__ methods are