diff --git a/python/cudf/cudf/core/column/column.py b/python/cudf/cudf/core/column/column.py
index 7bc036587af..f1c7d5f5a3a 100644
--- a/python/cudf/cudf/core/column/column.py
+++ b/python/cudf/cudf/core/column/column.py
@@ -2037,6 +2037,8 @@ def as_column(
             np_type = None
             try:
                 if dtype is not None:
+                    if is_categorical_dtype(dtype) or is_interval_dtype(dtype):
+                        raise TypeError
                     if is_list_dtype(dtype):
                         data = pa.array(arbitrary)
                         if type(data) not in (pa.ListArray, pa.NullArray):
@@ -2044,6 +2046,11 @@ def as_column(
                                 "Cannot create list column from given data"
                             )
                         return as_column(data, nan_as_null=nan_as_null)
+                    elif isinstance(
+                        dtype, cudf.StructDtype
+                    ) and not isinstance(dtype, cudf.IntervalDtype):
+                        data = pa.array(arbitrary, type=dtype.to_arrow())
+                        return as_column(data, nan_as_null=nan_as_null)
                     if isinstance(dtype, cudf.core.dtypes.Decimal64Dtype):
                         data = pa.array(
                             arbitrary,
@@ -2065,14 +2072,11 @@ def as_column(
                             data
                         )
                     dtype = pd.api.types.pandas_dtype(dtype)
-                    if is_categorical_dtype(dtype) or is_interval_dtype(dtype):
-                        raise TypeError
+                    np_type = np.dtype(dtype).type
+                    if np_type == np.bool_:
+                        pa_type = pa.bool_()
                     else:
-                        np_type = np.dtype(dtype).type
-                        if np_type == np.bool_:
-                            pa_type = pa.bool_()
-                        else:
-                            pa_type = np_to_pa_dtype(np.dtype(dtype))
+                        pa_type = np_to_pa_dtype(np.dtype(dtype))
                 data = as_column(
                     pa.array(
                         arbitrary,
diff --git a/python/cudf/cudf/core/dataframe.py b/python/cudf/cudf/core/dataframe.py
index c02bf3d11a4..275a0d59fc3 100644
--- a/python/cudf/cudf/core/dataframe.py
+++ b/python/cudf/cudf/core/dataframe.py
@@ -7477,6 +7477,25 @@ def to_dict(self, orient="dict", into=dict):
             "`.to_pandas().to_dict()` to construct a Python dictionary."
         )
 
+    def to_struct(self, name=None):
+        """
+        Return a struct Series composed of the columns of the DataFrame.
+        Note that no copies of the data are made.
+
+        Parameters
+        ----------
+        name: optional
+            Name of the resulting Series
+        """
+        col = cudf.core.column.build_struct_column(
+            names=self._data.names, children=self._data.columns, size=len(self)
+        )
+        return cudf.Series._from_data(
+            cudf.core.column_accessor.ColumnAccessor({name: col}),
+            index=self.index,
+            name=name,
+        )
+
     def keys(self):
         """
         Get the columns.
diff --git a/python/cudf/cudf/tests/test_struct.py b/python/cudf/cudf/tests/test_struct.py
index b38fd3a5b6c..8c226ef1680 100644
--- a/python/cudf/cudf/tests/test_struct.py
+++ b/python/cudf/cudf/tests/test_struct.py
@@ -164,3 +164,17 @@ def test_struct_scalar_host_construction(data):
 def test_struct_scalar_null():
     slr = cudf.Scalar(cudf.NA, dtype=StructDtype)
     assert slr.device_value.value is cudf.NA
+
+
+def test_dataframe_to_struct():
+    df = cudf.DataFrame()
+    expect = cudf.Series(dtype=cudf.StructDtype({}))
+    got = df.to_struct()
+    assert_eq(expect, got)
+
+    df = cudf.DataFrame({"a": [1, 2, 3], "b": ["x", "y", "z"]})
+    expect = cudf.Series(
+        [{"a": 1, "b": "x"}, {"a": 2, "b": "y"}, {"a": 3, "b": "z"}]
+    )
+    got = df.to_struct()
+    assert_eq(expect, got)