Pandas get_dummies validate columns input

doc/source/whatsnew/v1.0.0.rst

@@ -278,6 +278,8 @@ Reshaping
 
 - Bug in :meth:`DataFrame.apply` that caused incorrect output with empty :class:`DataFrame` (:issue:`28202`, :issue:`21959`)
 - Bug in :meth:`DataFrame.stack` not handling non-unique indexes correctly when creating MultiIndex (:issue: `28301`)
+- Better error message in :func:`get_dummies` when `columns` isn't a list-like value (:issue:`28383`)
+- 
 
 Sparse
 ^^^^^^

pandas/core/reshape/reshape.py

@@ -863,6 +863,8 @@ def get_dummies(
         # determine columns being encoded
         if columns is None:
             data_to_encode = data.select_dtypes(include=dtypes_to_encode)
+        elif not is_list_like(columns):
+            raise TypeError("Input must be a list-like for parameter `columns`")
         else:
             data_to_encode = data[columns]
 

pandas/tests/reshape/test_reshape.py

@@ -607,6 +607,23 @@ def test_get_dummies_all_sparse(self):
         )
         tm.assert_frame_equal(result, expected)
 
+    @pytest.mark.parametrize("values", ["baz", "zoo"])
+    def test_get_dummies_with_string_values(self, values):
+        # issue #28383
+        df = pd.DataFrame(
+            {
+                "bar": [1, 2, 3, 4, 5, 6],
+                "foo": ["one", "one", "one", "two", "two", "two"],
+                "baz": ["A", "B", "C", "A", "B", "C"],
+                "zoo": ["x", "y", "z", "q", "w", "t"],
+            }
+        )
+
+        msg = "Input must be a list-like for parameter `columns`"
+
+        with pytest.raises(TypeError, match=msg):
+            pd.get_dummies(df, columns=values)
+
 
 class TestCategoricalReshape:
     def test_reshaping_multi_index_categorical(self):