I always wanted to have this. The cool part of me, of course, wanted me to be the one who writes it, the pragmatic part just wanted to have access to a list like this and the hedonic part of me made me ignore the whole topic by telling me to chase after greater pleasures of life, at least greater than this blog post, no matter how magnificent it might eventually become, could ever be. But here I am, some years later, in the wrath of the epidemic lockdown, re-running Python tests in an infinite loop until I figure out which nobs and settings of this mock library I have to turn and set to get it to mock the damn remote calls.

The situation makes you wonder. Did it have to take a nationwide lockdown for me to start composing this list? Is it the dullness of the mock library that is the problem? Or is the culprit just a general aversion to spending any more time on tests than absolutely necessary? .. Who knows. But here we are, so let’s get on with it.

mock is a great library. There are people out there, who are absolutely against mock and who will tell you that you should not mock anything ever. You should handle their stance just like you would the stance of any other absolutist: don’t trust them. Writing tests is less about who has the moral upper ground and more about preventing bugs.

## Level 1: Creating a new Mock object

First, let’s see what all the ways are we can create and configure a Mock object.

Attribute/Function we wish to mock
Possible Mock objects Result of calling attr/func on Mock
bool(book)
book = Mock() True
book = None False
book.title
book = Mock(title="Russian Literature") "Russian Literature"
book = Mock(title="") ""
book = Mock() <Mock name='mock.title' id='14039307'>
book.author.first_name
book = Mock(author=Mock(first_name="Tatjana")) "Tatjana"
book = Mock()
book.author.first_name = "Evgenij"
"Evgenij"
book = Mock() <Mock name='mock.author.first_name' id='140393072216336'>
book.author.get_full_name()
book = Mock()
book.author.get_full_name.return_value = ""
""
book = Mock(
author=Mock(
get_full_name=Mock(return_value="Aleksandr Pushkin")
)
)
"Aleksandr Pushkin"

⚠ One special situation is the parameter name. The Mock class has a few input arguments, most of them (like return_value) are easy to remember. But then there is name, which nobody ever remembers. Add to this the fact that practically every class in the computer world has either a title or a name attribute and you have got yourself a perfect programmer trap.

 author.name ❤ author = Mock()author.name="Pushkin" "Pushkin" ❤ 💀 author = Mock(name="Pushkin") 💀

Next to Mock there is also MagicMock. MagicMock can do all the same things that Mock can do, but it can do a smidgen more. MagicMock has some dunder (or magic) methods already defined: __lt__, __gt__, __int__, __len__, __iter__, … . This means we can do this: len(MagicMock()) and receive 0. Or this: float(MagicMock()) and receive 1.0. Or this: MagicMock()[1] and receive a new MagicMock object.

#### Implicitly creating sub-Mocks

When we are mocking a deeply nested attribute, we don’t need to explicitly create sub-Mocks at every level. As soon as we access an attribute/function/property, a new Mock will automatically be created, if none exists. And because calling the same attribute will return the same sub-Mock every time, we can use this to our advantage and simply say mock_object.author.country().title = "Lichtenstein" instead of mock_object.author = Mock(country=Mock(return_value=...)). But the rule of thumb is that the path must consist solely of functions and attributes. The MagicMock can handle a few more things in the path, like [0].

Examples of paths thatare ok:

• book.get_review(sort="date", order="desc").reviewer.get_country().short_name
• book()()()

As soon as a non-function comes along, we get an Exception. These paths are not ok:

• book.reviews[0]
• len(book.reviews)
• book.year > 1950
 book.data["reviews"][0].reviewer.date book = Mock(   data={     "reviews":[       Mock(reviewer=Mock(         date=datetime.now()       ))     ]   } ) datetime.datetime(2020, 5, 17, ....) book = Mock() TypeError: 'Mock' object is not subscriptable The problem is with the '["reviews"]'-part. book = MagicMock() book = MagicMock() book.data["reviews"][0].reviewer.date = datetime.now() datetime.datetime(2020, 5, 17, ....) len(book.data["reviews"]) book = MagicMock()book.data["reviews"].__len__.return_value = 120 120
 book.get_review(type="oldest").reviewer.get("name", "unknown") book = Mock() book.get_review.return_value.reviewer = {"name": "Natalia"} "Natalia" book = Mock()book.get_review.return_value.reviewer = {} "unknown" book = Mock(   **{"get_review.return_value.reviewer": {}} ) "unknown" book.all_reviews[-1].get_country(locale="en").reviewer.get("name") book = MagicMock() book.all_reviews[-1].get_country(locale="en").reviewer = {"name": "Natalia"} "Natalia"

If you don’t know the exact path you have to take to get to the value you wish to mock, just create a Mock() object, call the attribute/function and print out the mocked value, that is produced.

Let’s say I have: a.b(c, d).e.f.g().h(). When I create a = Mock() and call the previous code I receive this Mock object: <Mock name='mock.b().e.f.g().h()' id='14050'>. This tells me the exact path I have to mock. I copy the path and just replace every () with .return_value and get: a.b.return_value.e.f.g.return_value.h.return_value = "My Result".

## Level 2: Missing attributes

 item.slug -> should raise AttributeError book = Mock(spec=[]) AttributeError: Mock object has no attribute 'slug' getattr(item, "slug", None) or getattr(item, "uid", None) item = Mock(uid="12345", spec=["uid"]) Making sure there is no 'slug' attribute. "12345"

What if we have a common utility function, which extracts the correct title from any object. It might look like this:

def extract_title(item: Union[Book, Author, Review]) -> str:
if isinstance(item, Book):
return item.title

if hasattr(item, "get_full_title"):
return item.get_full_title()

if hasattr(item, "summary"):
return item.summary

raise Exception("Don't know how to get title")

# ---- models: ---------------

@dataclass
class Book:
title: str

class Author:
def get_full_title() -> str:
pass

class Review:
@property
def summary() -> str:
pass


To test extract_title with objects mocking all 3 classes (Book, Author, Review), we can resort to Mock’s spec attribute. spec can be a list of attributes as shown above or it can be a class or a class instance or anything really as long as Mock can call dir(spec_obj) on it.

Calling Mock(spec=Author) is the same as: Mock(spec=[attr for attr in dir(Author) if not attr.startswith("__")]. If we try to access any attribute not on the spec-list, an AttributeError is raised.

 extract_title(item) default_mock_obj = Mock() Will alwas return True for hasattr(item, "get_full_title"): mock_book = Mock(spec=Book("")) mock_book = Mock(   spec=Book(""),   title="Russian Literature" ) "Russian Literature" mock_author = Mock(spec=Author) mock_book = Mock(spec=Author) mock_author.get_full_title.return_value = "Aleksandr Pushkin" "Aleksandr Pushkin" mock_review = Mock(spec=Review) mock_review = Mock(spec=Review) mock_review.summary = "Oh, bitter destiny" "Oh, bitter destiny"

## Level 3: The attribute with the very unfortunate name - side_effect

Every Mock object can be instantiated with a side_effect attribute. This attribute is useful in 3 types of situations and only one of them can honestly be called a side effect. Unfortunately, the lamentable name makes developers miss how versatile and convenient its underlying functionality actually is.

#### side_effect as an iterator

We have a remote call to Slack where we fetch all channels of some Slack workspace. But Slack’s API is in its Nth version and has thus the annoying, but very sensible, limitation built-in which permits us to only fetch 100 channels at a time. To get the 101st channel we have to make a new GET request with the pagination cursor we received in the last request. Here is the code:

def fetch_one_page_of_slack_channels(cursor=None):
....<channels: List[str]>....
return channels, next_page_cursor

def fetch_all_slack_channels():
all_channels: List[str] = []
fetched_all_pages = False
cursor = None
while not fetched_all_pages:
new_channels, cursor = fetch_one_page_of_slack_channels(cursor)
all_channels.extend(new_channels)
fetched_all_pages = bool(cursor is None)

return all_channels


Now we want a test making sure that fetched_all_pages doesn’t stop after the first page.

 fetch_all_slack_channels() fetch_one_page_of_slack_channels = Mock() TypeError: cannot unpack non-iterable Mock object Explanation: Mock will return a new Mock object for every call, but 1 Mock cannot be unpacked into 2 items: new_channels and cursor:  ----> new_channels, cursor = fetch_one_page_of_slack_channels(cursor)  fetch_one_page_of_slack_channels = Mock(   side_effect=[     (["channel_1","channel_2"], "__NEXT__PAGE__"),     (["channel_3"], None),   ]) ['channel_1', 'channel_2', 'channel_3']

#### side_effect as an Exception

This one is for testing Exception-handling code.

Let’s say you are letting your users log in with any social account they choose. But how do you handle the event where 2 of your users want to connect the same GitHub account? We want to catch this event and transform it into a friendly error message for the user.

Here is the code:

def connect_github_account(user, github_uid):
try:
logger.log('...', exc_info=exc)
return False, "Sorry, we could not connect you"
...


How do we test for this?

 user.social_accounts.add("github", github_uid) -> Must raise SocialAlreadyClaimedException user = Mock() user.social_accounts.add.side_effect = SocialAlreadyClaimedException (False, 'Sorry, we could not connect you') user = Mock() user.social_accounts.add.side_effect = SocialAlreadyClaimedException(   "GitHub account XXX already connected to user AAA" ) (False, 'Sorry, we could not connect you') The return value for the function is the same. The difference is in the message of the exception. In the first example, the exception is raised without any message and in the second our message is supplied. We could check this by mocking the logger and looking at the calls that were made to it: logger.call_args -> call('...', exc_info=SocialAlreadyClaimedException('GitHub account XXX already connected to user AAA')) 

#### side_effect as a substitute class/function (not a Mock object)

The last and most awesome use for side_effect is to use it to replace the contents of functions or classes.You can specify a new function/class which will be used instead of the original function/class. But it must be a function or a class not a different type of object and it must accept the same variables as the original function/class.

For example, we want to write a unit test for our menu. The links that we show on the menu depend on who is logged in and what organization they belong to. The code looks like this:

def menu_urls(user):
org_settings_url = create_url(endpoint="org_settings", org_slug=user.org.slug)
dashboard_url = create_url(endpoint="dashboard")
user_settings_url = create_url(endpoint="user_settings", org_slug=user.org.slug, user_slug=user.slug)
...

return [org_settings_url, dashboard_url, user_settings_url, ... ]


We don’t want to set up the whole app for this test, we want to write the simplest unit test, just making sure that the links are created with the correct arguments.

One solution is to mock create_url with the side_effect option.

 menu_urls(user) def substitue_create_url(endpoint, **kwargs):   return f"{endpoint} WITH {kwargs}" create_url = Mock(side_effect=substitue_create_url) user = Mock(**{   "slug": "__USER__SLUG__",   "org.slug": "__ORG__SLUG__" }) [   "org_settings WITH {'org_slug': '__ORG__SLUG__'}",   'dashboard WITH {}',   "user_settings WITH {'org_slug': '__ORG__SLUG__', 'user_slug': '__USER__SLUG__'}" ] Each time that create_url is called inside menu_urls, what is actually called is our substitue_create_url. It is of course called with the same input arguments, which we can modify or store in any way. def substitue_create_url(**kwargs):   return "_SOME_URL_" create_url = Mock(side_effect=substitue_create_url) user = Mock() ['_SOME_URL_', '_SOME_URL_', '_SOME_URL_'] This is the same as if we had set up create_url = Mock(return_value="_SOME__URL_").

The same thing can be done with classes. Let’s say I store some user configuration in a class and I build it up step by step. The code looks like this:

def create_config(user):
config = Configuration()
config.set("literate", True)
if user.can_jump():
config.set("springy", True)
...
return config


One possible way to write a test for this would be to mock the Configuration class.

 create_config(user_1)create_config(user_2) class SubstituteConfiguration:   def __init__(self):     self.config = {}   def set(self, key, value):     self.config[key] = value   def __repr__(self):     return str(self.config) Configuration = Mock(side_effect=SubstituteConfiguration) user_1 = Mock(**{   "can_read.return_value": False, }) user_2 = Mock() {'springy': True}{'literate': True, 'springy': True}

When using side_effect in this way, be careful. Don’t go overboard with mocking. These tests can be very unstable. Even non-functional changes to the source like replacing a positional argument with a keyword argument can make such a test fail.

#### side_effect vs return_value

With side_effect you define a function/class (or iterator or exception), which should be called instead of the original function/class. With return_value you define the result, what the function/class is supposed to return so that we don’t need to call it.

## Level 4: Tracking calls

Every Mock remembers all the ways it was called. Sometimes we want to control what a mocked function/class returns, other times we want to inspect how it was called.

Mock objects come with built-in assert functions. The simplest one is probably mock_obj.assert_called() and the most commonly used might be mock_obj.assert_called_once_with(...). With the help of assert-functions and only occasionally by inspecting the attributes mock_obj.call_args_list and mock_call_args we can write tests verifying how our objects are accessed.

 All useful functions: mock_obj.assert_called() mock_obj.assert_called_once() mock_obj.assert_called_with(100, "Natalia") mock_obj.assert_called_once_with(100, "Natalia") ❤ from mock import ANYmock_obj.assert_called_once_with(ANY, "Natalia") When we don't care to know all function parameters or don't care to set them all, we can use ANY as a placeholder.
A call on a Mock object
Assert calls, which will not raise an error
mock_obj(100, "Natalia")
mock_obj.assert_called()
mock_obj.assert_called_once()
mock_obj.assert_called_with(100, "Natalia")
mock_obj.assert_called_once_with(100, "Natalia")
from mock import ANY
mock_obj.assert_called_once_with(ANY, "Natalia")

When we don't care to know all function parameters or don't care to set them all, we can use ANY as a placeholder.

What about when the mocked function is called more than once:

 mock_obj(100, "Natalia")mock_obj(None, "Evgenij") mock_obj.assert_called() mock_obj.assert_called_with(None, "Evgenij")The assert_called_with compares only to the last call. Had we checked for the "Natalia"-call it would raise an error. mock_obj.assert_any_call(100, "Natalia") from mock import callmock_obj.assert_has_calls([   call(None, "Evgenij"),   call(100, "Natalia"), ], any_order=True)

For when we want to make sure that something didn’t happen we can use assert_not_called(). Example: we send Slack messages to those users, who have opted-in to receiving Slack messages and not to others.

def send_slack_msg(user):
if user.has_slack_disabled():
return

slack_post(...)

 user=Mock(**{"has_slack_disabled.return_value": True})slack_post = Mock()send_slack_msg(user) slack_post.assert_not_called()

Should we want to make more calls to the same mock function, we can call reset_mock in between calls.

 send_slack_msg(user_1)send_slack_msg(user_2) slack_post.assert_called_once_with(...)slack_post.reset_mock()slack_post_assert_called_once_with(...)

## Level 5: Mocking imports with patch

The last piece in our puzzle of mocking is patch. Until now we’ve pretended that we have complete access to all functions and variables we are testing, that the test and the source code live in the same context, the same file, but this is never true. So, if the source file sending.py looks like this:

#  sending.py
def send_post(...):
...

def send_slack_msg(user):
if user.has_slack_disabled():
return

slack_post(...)


and we want to write a test in the file test_sending.py, how can we mock the function send_post?

By using patch .. and giving it the full path to the relative location/target of the thing we want to mock.

Generally speaking, the target is constructed like this: <prefix><suffix><optional suffix>.
The prefix is: the path to the module, which will import the function/class/module we are mocking.
The suffix is: the last part of the from .. import.. statement which imports the object we are mocking, everything after import.
The optional suffix is: If the suffix is the name of a module or class, then the optional suffix can the a class in this module or a function in this class. This way we can mock only 1 function in a class or 1 class in a module.

patch can be used as a decorator for a function, a decorator for a class or a context manager. In each case, it produces a MagicMock (exception: AsyncMock) variable, which it passes either to the function it mocks, to all functions of the class it mocks or to the with statement when it is a context manager.

#### Patch target - Examples of prefix-suffix-optional_suffix combinations

Here is a layered series of calls: slack/sending.py calls aaa.py, which calls bbb.py, which calls ccc.py, which calls slack_api.py.

In a diagram:

and in code:

#  slack/sending.py
from a_lib.aaa import A_slack_post

def send_slack_msg(user):
print(f"send_slack_msg will call {A_slack_post}")
A_slack_post(user)

#  a_lib/aaa.py
from b_lib.bbb import B_slack_post

def A_slack_post(user):
print(f"A will call {B_slack_post}")
B_slack_post(user)

# b_lib/bbb.py
from c_lib.ccc import C_slack_post

def B_slack_post(user):
print(f"B will call {C_slack_post}")
C_slack_post(user)

# c_lib/ccc.py
from slack_lib.slack_api import slack_post

def C_slack_post(user):
print(f"C will call {slack_post}")
slack_post(user)

# slack_lib/slack_api.py
def slack_post(user):
print(f"send Slack msg")



What is the patch target if we want to only mock what is inside slack_post? What if we want to mock everything from B_slack_post on?

 we will call send_slack_msg(user)and mock slack_lib.slack_api.slack_post @patch("c_lib.ccc.slack_post") def test(mock_slack_post):   send_slack_msg(...)   mock_slack_post.assert_called() Prefix: the path to the file, where we want to start mocking = c_lib/ccc.py. Suffix: name of our function = slack_post. Our printout will be: send_slack_msg will call A will call B will call C will call we will call send_slack_msg(user)and mock b_lib.bbb.B_slack_post @patch("a_lib.aaa.B_slack_post") def test(mock_B_slack_post):   mock_B_slack_post.return_value = True   send_slack_msg(...) Prefix: the path to the file, where we want to start mocking = a_lib/aaa.py. Suffix: name of our function = B_slack_post. Our printout will be: send_slack_msg will call A will call

For reference, when nothing is mocked and we call send_slack_msg(user), the following is printed:

send_slack_msg will call <function A_slack_post at 0x7f25>
A will call <function B_slack_post at 0x7f25>
B will call <function C_slack_post at 0x7f25>
C will call <function send_slack_msg at 0x7f25>
send Slack msg

#### Patching only one function in a class

#  slack_lib/slack_api.py
from slack import slack_api

def send_slack_msg(user):
slack_instance = slack_api.SlackAPI(user)
print(f"send_slack_msg will call {slack_instance.post} on {slack_instance}")
slack_instance.post()

#  slack_lib/slack_api.py
class SlackAPI:
def __init__(self, user):
self.user = user

def post(self):
print(f"send from class to user: {self.user}")

 we will call send_slack_msg and mock only SlackAPI.post: @patch("slack.sending.slack_api.SlackAPI.post") def test(mock_slack_api_post):   mock_slack_api_post = lambda :"Mock was called"   send_slack_msg(Mock()) Prefix: path to module where send_slack_msg lives = slack.sending. Suffix: what is after import in from slack import slack_api = slack_api. Optional suffix: a class inside slack_api + a function inside the class = SlackAPI.post Our printout will be: send_slack_msg will call on Mock was called

#### Patch object

As far as I can see patch.object() works exactly the same as patch(). The only difference is that patch takes a string as the target while patch.object needs a reference. patch.object is thus used for patching individual functions of a class.

For the above situation the pather would look like this:

 we will call send_slack_msg and mock only SlackAPI.post: from slack_lib.slack_api import SlackAPI @patch.object(SlackAPI, "post") def test(mock_slack_post):

## Knicks-knacks A: PropertyMock

For when you need to mock a @property:

# models/user.py
class User:
@property
def name(self):
return f"{self.first_name} {self.last_name}"
@name.setter
def name(self, value):
self.first_name = value

# service/user.py
def create_user(name):
user = User()
user.name = name
return user.name

 mock the property name: from mock import PropertyMock from models.user import User from services.user import create_user @patch.object(User, "name", new_callable=PropertyMock) def test(mock_user):   mock_user.return_value = "Jane Doe"   assert create_user("Janette") == "Jane Doe"   mock_user.assert_any_call("Janette") Whenever we will call user.name, we will get the string "Jane Doe". But the mock also remembers all the values it was called with.

## Knicks-knacks B: Patching a constant

How do we mock a constant? With patch’s new-attribute:

# constants.py
MSG_LIMIT = 7

# code.py
from constants import MSG_LIMIT

def get_latest_msgs():
return messages.limit(MSG_LIMIT).all()

 mock MSG_LIMIT and set it to 3 @patch("code.MSG_LIMIT", new=3)

## Knicks-knacks C: Having many patch-ers

Each patcher can pass a MagicMock variable to the calling function. Because this is Python, the decorators are applied bottom up:

@patch("path_1")
@patch("path_2")
@patch("path_3")
def test(mock_path_3, mock_path_2, mock_path_1):
...


## More!

This was supposed to be a list of essential mock functionalities. It covers 99% of everything I ever needed or saw mocked. Certainly, this is not the limit of the mock library, but I’m already looking forward to utilizing this summarized version of how Mocks should be constructed instead of reading through the longer (and more precise) official documentation or googling various StackOverflow answers. And who knows, maybe it will someday be of use to some other mock-frustrated programmer soul, who will be gladdened to have found this post.