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 5: when variable contains negative value, fit
with pytest.raises(ValueError):
transformer = LogTransformer()
transformer.fit(df_neg)
# test case 6: when variable contains negative value, transform
with pytest.raises(ValueError):
transformer = LogTransformer()
transformer.fit(df_vartypes)
transformer.transform(df_neg)
def test_transform_raises_error_if_na_in_df(df_vartypes, df_na):
# test case 4: when dataset contains na, transform method
with pytest.raises(ValueError):
transformer = LogTransformer()
transformer.fit(df_vartypes)
transformer.transform(df_na[["Name", "City", "Age", "Marks", "dob"]])
def test_fit_raises_error_if_na_in_df(df_na):
# test case 3: when dataset contains na, fit method
with pytest.raises(ValueError):
transformer = LogTransformer()
transformer.fit(df_na)