# This would silently produce meaningless results
FeatureUnion([
    ("a", FunctionTransformer(lambda df: df["a"])),  # Returns 1D
    ("b", FunctionTransformer(lambda df: df["b"])),  # Returns 1D
]).fit_transform(data)
# Output: array([0, 1, 2, 0, 1, 2])  # Meaningless concatenation

# This now raises an informative error (please check the test_feature_union_xs_dims() in tests/test_pipeline.py
FeatureUnion([
    ("a", FunctionTransformer(lambda df: df["a"])),
    ("b", FunctionTransformer(lambda df: df["b"])),
]).fit_transform(data)
# Raises: ValueError: Transformer 'a' returned an array with 1 dimensions, 
#         but expected 2 dimensions (n_samples, n_features).