One of the first things that should stick out is that we’re using the mock.patch method decorator to mock an object located at mymodule.os, and injecting that mock into our test case method. Wouldn’t it make more sense to just mock os itself, rather than the reference to it at mymodule.os?
Well, Python is somewhat of a sneaky snake when it comes to imports and managing modules. At runtime, the mymodule module has its own os which is imported into its own local scope in the module. Thus, if we mock os, we won’t see the effects of the mock in the mymodule module.
The mantra to keep repeating is this:
> Mock an item where it is used, not where it came from.
If you need to mock the tempfile module for myproject.app.MyElaborateClass, you probably need to apply the mock to myproject.app.tempfile, as each module keeps its own imports.
With that pitfall out of the way, let’s keep mocking.
### Adding Validation to ‘rm’
The rm method defined earlier is quite oversimplified. We’d like to have it validate that a path exists and is a file before just blindly attempting to remove it. Let’s refactor rm to be a bit smarter:
```
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import os.path
def rm(filename):
if os.path.isfile(filename):
os.remove(filename)
```
Great. Now, let’s adjust our test case to keep coverage up.
Our testing paradigm has completely changed. We now can verify and validate internal functionality of methods without any side-effects.
### File-Removal as a Service
So far, we’ve only been working with supplying mocks for functions, but not for methods on objects or cases where mocking is necessary for sending parameters. Let’s cover object methods first.
We’ll begin with a refactor of the rm method into a service class. There really isn’t a justifiable need, per se, to encapsulate such a simple function into an object, but it will at the very least help us demonstrate key concepts in mock. Let’s refactor:
```
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import os.path
class RemovalService(object):
"""A service for removing objects from the filesystem."""
def rm(filename):
if os.path.isfile(filename):
os.remove(filename)
```
### You’ll notice that not much has changed in our test case:
Since we already have test coverage on the RemovalService, we’re not going to validate internal functionality of the rm method in our tests of UploadService. Rather, we’ll simply test (without side-effects, of course) that UploadService calls the RemovalService.rm method, which we know “just works™” from our previous test case.
There are two ways to go about this:
1. Mock out the RemovalService.rm method itself.
2. Supply a mocked instance in the constructor of UploadService.
As both methods are often important in unit-testing, we’ll review both.
### Option 1: Mocking Instance Methods
The mock library has a special method decorator for mocking object instance methods and properties, the @mock.patch.object decorator:
```
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from mymodule import RemovalService, UploadService
Great! We’ve validated that the UploadService successfully calls our instance’s rm method. Notice anything interesting in there? The patching mechanism actually replaced the rm method of all RemovalService instances in our test method. That means that we can actually inspect the instances themselves. If you want to see more, try dropping in a breakpoint in your mocking code to get a good feel for how the patching mechanism works.
### Pitfall: Decorator Order
When using multiple decorators on your test methods, order is important, and it’s kind of confusing. Basically, when mapping decorators to method parameters, [work backwards][3]. Consider this example:
Notice how our parameters are matched to the reverse order of the decorators? That’s partly because of [the way that Python works][4]. With multiple method decorators, here’s the order of execution in pseudocode:
Since the patch to sys is the outermost patch, it will be executed last, making it the last parameter in the actual test method arguments. Take note of this well and use a debugger when running your tests to make sure that the right parameters are being injected in the right order.
### Option 2: Creating Mock Instances
Instead of mocking the specific instance method, we could instead just supply a mocked instance to UploadService with its constructor. I prefer option 1 above, as it’s a lot more precise, but there are many cases where option 2 might be efficient or necessary. Let’s refactor our test again:
```
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from mymodule import RemovalService, UploadService
In this example, we haven’t even had to patch any functionality, we simply create an auto-spec for the RemovalService class, and then inject this instance into our UploadService to validate the functionality.
The [mock.create_autospec][5] method creates a functionally equivalent instance to the provided class. What this means, practically speaking, is that when the returned instance is interacted with, it will raise exceptions if used in illegal ways. More specifically, if a method is called with the wrong number of arguments, an exception will be raised. This is extremely important as refactors happen. As a library changes, tests break and that is expected. Without using an auto-spec, our tests will still pass even though the underlying implementation is broken.
### Pitfall: The mock.Mock and mock.MagicMock Classes
The mock library also includes two important classes upon which most of the internal functionality is built upon: [mock.Mock][6] and mock.MagicMock. When given a choice to use a mock.Mock instance, a mock.MagicMock instance, or an auto-spec, always favor using an auto-spec, as it helps keep your tests sane for future changes. This is because mock.Mock and mock.MagicMock accept all method calls and property assignments regardless of the underlying API. Consider the following use case:
```
class Target(object):
def apply(value):
return value
def method(target, value):
return target.apply(value)
```
We can test this with a mock.Mock instance like this:
```
class MethodTestCase(unittest.TestCase):
def test_method(self):
target = mock.Mock()
method(target, "value")
target.apply.assert_called_with("value")
```
This logic seems sane, but let’s modify the Target.apply method to take more parameters:
```
class Target(object):
def apply(value, are_you_sure):
if are_you_sure:
return value
else:
return None
```
Re-run your test, and you’ll find that it still passes. That’s because it isn’t built against your actual API. This is why you should always use the create_autospec method and the autospec parameter with the @patch and @patch.object decorators.
### Real-World Example: Mocking a Facebook API Call
To finish up, let’s write a more applicable real-world example, one which we mentioned in the introduction: posting a message to Facebook. We’ll write a nice wrapper class and a corresponding test case.
As we’ve seen so far, it’s really simple to start writing smarter tests with mock in Python.
### Mocking in python Conclusion
Python’s mock library, if a little confusing to work with, is a game-changer for [unit-testing][7]. We’ve demonstrated common use-cases for getting started using mock in unit-testing, and hopefully this article will help [Python developers][8] overcome the initial hurdles and write excellent, tested code.
