def test_error_if_df_contains_negative_values(df_vartypes): # test error when data contains negative values df_neg = df_vartypes.copy() df_neg.loc[1, "Age"] = -1 # test case 4: when variable contains negative value, fit with pytest.raises(ValueError): transformer = BoxCoxTransformer() transformer.fit(df_neg) # test case 5: when variable contains negative value, transform with pytest.raises(ValueError): transformer = BoxCoxTransformer() transformer.fit(df_vartypes) transformer.transform(df_neg)
def test_non_fitted_error(df_vartypes): with pytest.raises(NotFittedError): transformer = BoxCoxTransformer() transformer.transform(df_vartypes)
def test_transform_raises_error_if_df_contains_na(df_vartypes, df_na): # test case 3: when dataset contains na, transform method with pytest.raises(ValueError): transformer = BoxCoxTransformer() transformer.fit(df_vartypes) transformer.transform(df_na[["Name", "City", "Age", "Marks", "dob"]])