def test_fit_and_transform_raise_error_if_df_contains_na(df_normal_dist):
df_na = df_normal_dist.copy()
df_na.loc[1, "var"] = np.nan
# test case 5: when dataset contains na, fit method
with pytest.raises(ValueError):
transformer = ArbitraryOutlierCapper(
min_capping_dict={"var": -0.17486039103044})
transformer.fit(df_na)
# test case 6: when dataset contains na, transform method
with pytest.raises(ValueError):
transformer = ArbitraryOutlierCapper(
min_capping_dict={"var": -0.17486039103044})
transformer.fit(df_normal_dist)
transformer.transform(df_na)
def test_non_fitted_error(df_vartypes):
with pytest.raises(NotFittedError):
transformer = ArbitraryOutlierCapper(
min_capping_dict={"var": -0.17486039103044})
transformer.transform(df_vartypes)