As in here, apart from using side_effect
in unittest.mock.Mock you can also use @mock.patch.object
with new_callable
, which allows you to patch an attribute of an object with a mock object.
Let's say a module my_module.py
uses pandas
to read from a database and we would like to test this module by mocking pd.read_sql_table
method (which takes table_name
as argument).
What you can do is to create (inside your test) a db_mock
method that returns different objects depending on the argument provided:
def db_mock(**kwargs):
if kwargs['table_name'] == 'table_1':
# return some DataFrame
elif kwargs['table_name'] == 'table_2':
# return some other DataFrame
In your test function you then do:
import my_module as my_module_imported
@mock.patch.object(my_module_imported.pd, "read_sql_table", new_callable=lambda: db_mock)
def test_my_module(mock_read_sql_table):
# You can now test any methods from `my_module`, e.g. `foo` and any call this
# method does to `read_sql_table` will be mocked by `db_mock`, e.g.
ret = my_module_imported.foo(table_name='table_1')
# `ret` is some DataFrame returned by `db_mock`