Support callables in DataFrame.assign

python/cudf/cudf/core/dataframe.py

@@ -1390,10 +1390,21 @@ def _get_numeric_data(self):
         return self[columns]
 
     @_cudf_nvtx_annotate
-    def assign(self, **kwargs):
+    def assign(self, **kwargs: Union[Callable[[Self], Any], Any]):
         """
         Assign columns to DataFrame from keyword arguments.
 
+        Parameters
+        ----------
+        **kwargs: dict mapping string column names to values
+            The value for each key can either be a literal column (or
+            something that can be converted to a column), or
+            a callable of one argument that will be given the
+            dataframe as an argument and should return the new column
+            (without modifying the input argument).
+            Columns are added in-order, so callables can refer to
+            column names constructed in the assignment.
+
         Examples
         --------
         >>> import cudf
@@ -1405,15 +1416,9 @@ def assign(self, **kwargs):
         1  1  4
         2  2  5
         """
-        new_df = cudf.DataFrame(index=self.index.copy())
-        for name, col in self._data.items():
-            if name in kwargs:
-                new_df[name] = kwargs.pop(name)
-            else:
-                new_df._data[name] = col.copy()
-
+        new_df = self.copy(deep=False)
         for k, v in kwargs.items():
-            new_df[k] = v
+            new_df[k] = v(new_df) if callable(v) else v
         return new_df
 
     @classmethod

python/cudf/cudf/tests/test_dataframe.py

@@ -1327,6 +1327,25 @@ def test_assign():
     np.testing.assert_equal(gdf2.y.to_numpy(), [2, 3, 4])
 
 
+@pytest.mark.parametrize(
+    "mapping",
+    [
+        {"y": 1, "z": lambda df: df["x"] + df["y"]},
+        {
+            "x": lambda df: df["x"] * 2,
+            "y": lambda df: 2,
+            "z": lambda df: df["x"] / df["y"],
+        },
+    ],
+)
+def test_assign_callable(mapping):
+    df = pd.DataFrame({"x": [1, 2, 3]})
+    cdf = cudf.from_pandas(df)
+    expect = df.assign(**mapping)
+    actual = cdf.assign(**mapping)
+    assert_eq(expect, actual)
+
+
 @pytest.mark.parametrize("nrows", [1, 8, 100, 1000])
 @pytest.mark.parametrize("method", ["murmur3", "md5"])
 @pytest.mark.parametrize("seed", [None, 42])