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Python Mock Patch patches a used module for submodule under test and every other imported submodule in the submodule under test

Sorry for the rather confusing title, but find it really hard to get to a crisp headline.
My Problem:
I have a submodule under test lets call it backend. And I use random.choice in backend. I patch it in my test and that works. It is patched. backend imports pymongoto do some database stuff and pymongo uses random.choiceas well for selecting the right server in core.py.
Somehow my patch of backend.random.choice also is used for backend.pymongo.random.choice. I am not sure why. Is that the correct behavior? And if it is, what is the way to get around that? Preferebly without changing any code in backend and pymongo, but only in my tests.
Other investigation:
I set up a little test construct to see if that is something related to my project or a general thing.
I created some modules:
bar/bar.py
import random
def do_some_other_something():
return random.choice([10])
foo/foo.py
import random
from bar.bar import do_some_other_something
def do_something():
return random.choice([1])
def do_something_else():
return do_some_other_something()
And a test case:
from foo.foo import do_something, do_something_else
from unittest import mock
assert(do_something() == 1) # check
assert(do_something_else() == 10) # check
with mock.patch("foo.foo.random.choice") as mock_random_choice:
assert (do_something() != 1) # check (mocked as expected)
assert (do_something_else() == 10) # assertion! also mocked
So I am really confused about this. I explicitly mocked foo.foo's random.choice not bar.bar's. So why is that happening? Intended? Is there a way to fix that?
Thank you!

There's only one random module in the program. When foo.foo and bar.bar both import random, they share the random module. Patching the choice function on the random module modifies the one random module used by every part of the program, including both foo.foo and bar.bar.
Instead of patching foo.foo.random.choice, patch foo.foo.random. That replaces foo.foo's reference to the random module, but not bar.bar's reference. bar.bar will continue to access the real random module, but foo.foo will see a mock random.

Why can't Python's import work like C's #include?

I've literally been trying to understand Python imports for about a year now, and I've all but given up programming in Python because it just seems too obfuscated. I come from a C background, and I assumed that import worked like #include, yet if I try to import something, I invariably get errors.
If I have two files like this:
foo.py:
a = 1
bar.py:
import foo
print foo.a
input()
WHY do I need to reference the module name? Why not just be able to write import foo, print a? What is the point of this confusion? Why not just run the code and have stuff defined for you as if you wrote it in one big file? Why can't it work like C's #include directive where it basically copies and pastes your code? I don't have import problems in C.

To do what you want, you can use (not recommended, read further for explanation):
from foo import *
This will import everything to your current namespace, and you will be able to call print a.
However, the issue with this approach is the following. Consider the case when you have two modules, moduleA and moduleB, each having a function named GetSomeValue().
When you do:
from moduleA import *
from moduleB import *
you have a namespace resolution issue*, because what function are you actually calling with GetSomeValue(), the moduleA.GetSomeValue() or the moduleB.GetSomeValue()?
In addition to this, you can use the Import As feature:
from moduleA import GetSomeValue as AGetSomeValue
from moduleB import GetSomeValue as BGetSomeValue
Or
import moduleA.GetSomeValue as AGetSomeValue
import moduleB.GetSomeValue as BGetSomeValue
This approach resolves the conflict manually.
I am sure you can appreciate from these examples the need for explicit referencing.
* Python has its namespace resolution mechanisms, this is just a simplification for the purpose of the explanation.

Imagine you have your a function in your module which chooses some object from a list:
def choice(somelist):
...
Now imagine further that, either in that function or elsewhere in your module, you are using randint from the random library:
a = randint(1, x)
Therefore we
import random
You suggestion, that this does what is now accessed by from random import *, means that we now have two different functions called choice, as random includes one too. Only one will be accessible, but you have introduced ambiguity as to what choice() actually refers to elsewhere in your code.
This is why it is bad practice to import everything; either import what you need:
from random import randint
...
a = randint(1, x)
or the whole module:
import random
...
a = random.randint(1, x)
This has two benefits:
You minimise the risks of overlapping names (now and in future additions to your imported modules); and
When someone else reads your code, they can easily see where external functions come from.

There are a few good reasons. The module provides a sort of namespace for the objects in it, which allows you to use simple names without fear of collisions -- coming from a C background you have surely seen libraries with long, ugly function names to avoid colliding with anybody else.
Also, modules themselves are also objects. When a module is imported in more than one place in a python program, each actually gets the same reference. That way, changing foo.a changes it for everybody, not just the local module. This is in contrast to C where including a header is basically a copy+paste operation into the source file (obviously you can still share variables, but the mechanism is a bit different).
As mentioned, you can say from foo import * or better from foo import a, but understand that the underlying behavior is actually different, because you are taking a and binding it to your local module.
If you use something often, you can always use the from syntax to import it directly, or you can rename the module to something shorter, for example
import itertools as it

When you do import foo, a new module is created inside the current namespace named foo.
So, to use anything inside foo; you have to address it via the module.
However, if you use from from foo import something, you don't have use to prepend the module name, since it will load something from the module and assign to it the name something. (Not a recommended practice)

import importlib
# works like C's #include, you always call it with include(<path>, __name__)
def include(file, module_name):
spec = importlib.util.spec_from_file_location(module_name, file)
mod = importlib.util.module_from_spec(spec)
# spec.loader.exec_module(mod)
o = spec.loader.get_code(module_name)
exec(o, globals())
For example:
#### file a.py ####
a = 1
#### file b.py ####
b = 2
if __name__ == "__main__":
print("Hi, this is b.py")
#### file main.py ####
# assuming you have `include` in scope
include("a.py", __name__)
print(a)
include("b.py", __name__)
print(b)
the output will be:
1
Hi, this is b.py
2

Is there a reason why when importing python files, you still need to name the file.function_name?

I am currently doing a python tutorial, but they use IDLE, and I opted to use the interpreter on terminal. So I had to find out how to import a module I created. At first I tried
import my_file
then I tried calling the function inside the module by itself, and it failed. I looked around and doing
my_file.function
works. I am very confused why this needs to be done if it was imported. Also, is there a way around it so that I can just call the function? Can anyone point me in the right direction. Thanks in advance.

If you wanted to use my_file.function by just calling function, try using the from keyword.
Instead of import my_file try from my_file import *.
You can also do this to only import parts of a module like so :
from my_file import function1, function2, class1
To avoid clashes in names, you can import things with a different name:
from my_file import function as awesomePythonFunction
EDIT:
Be careful with this, if you import two modules (myfile, myfile2) that both have the same function inside, function will will point to the function in whatever module you imported last. This could make interesting things happen if you are unaware of it.

This is a central concept to python. It uses namespaces (see the last line of import this). The idea is that with thousands of people writing many different modules, the likelihood of a name collision is reasonably high. For example, I write module foo which provides function baz and Joe Smith writes module bar which provides a function baz. My baz is not the same as Joe Smiths, so in order to differentiate the two, we put them in a namespace (foo and bar) so mine can be called by foo.baz() and Joe's can be called by bar.baz().
Of course, typing foo.baz() all the time gets annoying if you just want baz() and are sure that none of your other modules imported will provide any problems... That is why python provides the from foo import * syntax, or even from foo import baz to only import the function/object/constant baz (as others have already noted).
Note that things can get even more complex:
Assume you have a module foo which provides function bar and baz, below are a few ways to import and then call the functions contained inside foo...
import foo # >>> foo.bar();foo.baz()
import foo as bar # >>> bar.bar();bar.baz()
from foo import bar,baz # >>> bar(); baz()
from foo import * # >>> bar(); baz()
from foo import bar as cow # >>> cow() # This calls bar(), baz() is not available
...

A basic import statement is an assignment of the module object (everything's an object in Python) to the specified name. I mean this literally: you can use an import anywhere in your program you can assign a value to a variable, because they're the same thing. Behind the scenes, Python is calling a built-in function called __import__() to do the import, then returning the result and assigning it to the variable name you provided.
import foo
means "import module foo and assign it the name foo in my namespace. This is the same as:
foo = __import__("foo")
Similarly, you can do:
import foo as f
which means "import module foo and assign it the name f in my namespace." This is the same as:
f = __import__("foo")
Since in this case, you have only a reference to the module object, referring to things contained by the module requires attribute access: foo.bar etc.
You can also do from foo import bar. This creates a variable named bar in your namespace that points to the bar function in the foo module. It's syntactic sugar for:
bar = __import__("foo").bar

I don't really understand your confusion. You've imported the name my_file, not anything underneath it, so that's how you reference it.
If you want to import functions or classes inside a module directly, you can use:
from my_file import function

I'm going to incorporate many of the comments already posted.
To have access to function without having to refer to the module my_file, you can do one of the following:
from my_file import function
or
from my_file import *
For a more in-depth description of how modules work, I would refer to the documentation on python modules.
The first is the preferred solution, and the second is not recommended for many reasons:
It pollutes your namespace
It is not a good practice for maintainability (it becomes more difficult to find where specific names reside.
You typically don't know exactly what is imported
You can't use tools such as pyflakes to statically detect errors in your code
Python imports work differently than the #includes/imports in a static language like C or Java, in that python executes the statements in a module. Thus if two modules need to import a specific name (or *) out of each other, you can run into circular referencing problems, such as an ImportError when importing a specific name, or simply not getting the expected names defined (in the case you from ... import *). When you don't request specific names, you don't run into the, risk of having circular references, as long as the name is defined by the time you actually want to use it.
The from ... import * also doesn't guarantee you get everything. As stated in the documentation on python modules, a module can defined the __all__ name, and cause from ... import * statements to miss importing all of the subpackages, except those listed by __all__.

How to mock an import

Module A includes import B at its top. However under test conditions I'd like to mock B in A (mock A.B) and completely refrain from importing B.
In fact, B isn't installed in the test environment on purpose.
A is the unit under test. I have to import A with all its functionality. B is the module I need to mock. But how can I mock B within A and stop A from importing the real B, if the first thing A does is import B?
(The reason B isn't installed is that I use pypy for quick testing and unfortunately B isn't compatible with pypy yet.)
How could this be done?

You can assign to sys.modules['B'] before importing A to get what you want:
test.py:
import sys
sys.modules['B'] = __import__('mock_B')
import A
print(A.B.__name__)
A.py:
import B
Note B.py does not exist, but when running test.py no error is returned and print(A.B.__name__) prints mock_B. You still have to create a mock_B.py where you mock B's actual functions/variables/etc. Or you can just assign a Mock() directly:
test.py:
import sys
sys.modules['B'] = Mock()
import A

The builtin __import__ can be mocked with the 'mock' library for more control:
# Store original __import__
orig_import = __import__
# This will be the B module
b_mock = mock.Mock()
def import_mock(name, *args):
if name == 'B':
return b_mock
return orig_import(name, *args)
with mock.patch('__builtin__.__import__', side_effect=import_mock):
import A
Say A looks like:
import B
def a():
return B.func()
A.a() returns b_mock.func() which can be mocked also.
b_mock.func.return_value = 'spam'
A.a() # returns 'spam'
Note for Python 3:
As stated in the changelog for 3.0, __builtin__ is now named builtins:
Renamed module __builtin__ to builtins (removing the underscores, adding an ‘s’).
The code in this answer works fine if you replace __builtin__ by builtins for Python 3.

How to mock an import, (mock A.B)?
Module A includes import B at its top.
Easy, just mock the library in sys.modules before it gets imported:
if wrong_platform():
sys.modules['B'] = mock.MagicMock()
and then, so long as A doesn't rely on specific types of data being returned from B's objects:
import A
should just work.
You can also mock import A.B:
This works even if you have submodules, but you'll want to mock each module. Say you have this:
from foo import This, That, andTheOtherThing
from foo.bar import Yada, YadaYada
from foo.baz import Blah, getBlah, boink
To mock, simply do the below before the module that contains the above is imported:
sys.modules['foo'] = MagicMock()
sys.modules['foo.bar'] = MagicMock()
sys.modules['foo.baz'] = MagicMock()
(My experience: I had a dependency that works on one platform, Windows, but didn't work on Linux, where we run our daily tests.
So I needed to mock the dependency for our tests. Luckily it was a black box, so I didn't need to set up a lot of interaction.)
Mocking Side Effects
Addendum: Actually, I needed to simulate a side-effect that took some time. So I needed an object's method to sleep for a second. That would work like this:
sys.modules['foo'] = MagicMock()
sys.modules['foo.bar'] = MagicMock()
sys.modules['foo.baz'] = MagicMock()
# setup the side-effect:
from time import sleep
def sleep_one(*args):
sleep(1)
# this gives us the mock objects that will be used
from foo.bar import MyObject
my_instance = MyObject()
# mock the method!
my_instance.method_that_takes_time = mock.MagicMock(side_effect=sleep_one)
And then the code takes some time to run, just like the real method.

Aaron Hall's answer works for me.
Just want to mention one important thing,
if in A.py you do
from B.C.D import E
then in test.py you must mock every module along the path, otherwise you get ImportError
sys.modules['B'] = mock.MagicMock()
sys.modules['B.C'] = mock.MagicMock()
sys.modules['B.C.D'] = mock.MagicMock()

I realize I'm a bit late to the party here, but here's a somewhat insane way to automate this with the mock library:
(here's an example usage)
import contextlib
import collections
import mock
import sys
def fake_module(**args):
return (collections.namedtuple('module', args.keys())(**args))
def get_patch_dict(dotted_module_path, module):
patch_dict = {}
module_splits = dotted_module_path.split('.')
# Add our module to the patch dict
patch_dict[dotted_module_path] = module
# We add the rest of the fake modules in backwards
while module_splits:
# This adds the next level up into the patch dict which is a fake
# module that points at the next level down
patch_dict['.'.join(module_splits[:-1])] = fake_module(
**{module_splits[-1]: patch_dict['.'.join(module_splits)]}
)
module_splits = module_splits[:-1]
return patch_dict
with mock.patch.dict(
sys.modules,
get_patch_dict('herp.derp', fake_module(foo='bar'))
):
import herp.derp
# prints bar
print herp.derp.foo
The reason this is so ridiculously complicated is when an import occurs python basically does this (take for example from herp.derp import foo)
Does sys.modules['herp'] exist? Else import it. If still not ImportError
Does sys.modules['herp.derp'] exist? Else import it. If still not ImportError
Get attribute foo of sys.modules['herp.derp']. Else ImportError
foo = sys.modules['herp.derp'].foo
There are some downsides to this hacked together solution: If something else relies on other stuff in the module path this kind of screws it over. Also this only works for stuff that is being imported inline such as
def foo():
import herp.derp
or
def foo():
__import__('herp.derp')

I found fine way to mock the imports in Python. It's Eric's Zaadi solution found here which I just use inside my Django application.
I've got class SeatInterface which is interface to Seat model class.
So inside my seat_interface module I have such an import:
from ..models import Seat
class SeatInterface(object):
(...)
I wanted to create isolated tests for SeatInterface class with mocked Seat class as FakeSeat. The problem was - how tu run tests offline, where Django application is down. I had below error:
ImproperlyConfigured: Requested setting BASE_DIR, but settings are not
configured. You must either define the environment variable
DJANGO_SETTINGS_MODULE or call settings.configure() before accessing
settings.
Ran 1 test in 0.078s
FAILED (errors=1)
The solution was:
import unittest
from mock import MagicMock, patch
class FakeSeat(object):
pass
class TestSeatInterface(unittest.TestCase):
def setUp(self):
models_mock = MagicMock()
models_mock.Seat.return_value = FakeSeat
modules = {'app.app.models': models_mock}
patch.dict('sys.modules', modules).start()
def test1(self):
from app.app.models_interface.seat_interface import SeatInterface
And then test magically runs OK :)
.
Ran 1 test in 0.002s
OK

If you do an import ModuleB you are really calling the builtin method __import__ as:
ModuleB = __import__('ModuleB', globals(), locals(), [], -1)
You could overwrite this method by importing the __builtin__ module and make a wrapper around the __builtin__.__import__method. Or you could play with the NullImporter hook from the imp module. Catching the exception and Mock your module/class in the except-block.
Pointer to the relevant docs:
docs.python.org: __import__
Accessing Import internals with the imp Module
I hope this helps. Be HIGHLY adviced that you step into the more arcane perimeters of python programming and that a) solid understanding what you really want to achieve and b)thorough understanding of the implications is important.

I know this is a fairly old question, but I have found myself returning to it a few times recently, and wanted to share a concise solution to this.
import sys
from unittest import mock
def mock_module_import(module):
"""Source: https://stackoverflow.com/a/63584866/3972558"""
def _outer_wrapper(func):
def _inner_wrapper(*args, **kwargs):
orig = sys.modules.get(module) # get the original module, if present
sys.modules[module] = mock.MagicMock() # patch it
try:
return func(*args, **kwargs)
finally:
if orig is not None: # if the module was installed, restore patch
sys.modules[module] = orig
else: # if the module never existed, remove the key
del sys.modules[module]
return _inner_wrapper
return _outer_wrapper
It works by temporarily patching the key for the module in sys.modules, and then restoring the original module after the decorated function is called. This can be used in scenarios where a package may not be installed in the testing environment, or a more complex scenario where the patched module might actually perform some of its own internal monkey-patching (which was the case I was facing).
Here's an example of use:
#mock_module_import("some_module")
def test_foo():
# use something that relies upon "some_module" here
assert True

I found myself facing a similar problem today, and I've decided to solve it a bit differently. Rather than hacking on top of Python's import machinery, you can simply add the mocked module into sys.path, and have Python prefer it over the original module.
Create the replacement module in a subdirectory, e.g.:
mkdir -p test/mocked-lib
${EDITOR} test/mocked-lib/B.py
Before A is imported, insert this directory to sys.path. I'm using pytest, so in my test/conftest.py, I've simply done:
import os.path
import sys
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "mocked-lib"))
Now, when the test suite is run, the mocked-lib subdirectory is prepended into sys.path and import A uses B from mocked-lib.

Python (and Django) best import practices

Out of the various ways to import code, are there some ways that are preferable to use, compared to others? This link http://effbot.org/zone/import-confusion.htm in short
states that
from foo.bar import MyClass
is not the preferred way to import MyClass under normal circumstances or unless you know what you are doing. (Rather, a better way would like:
import foo.bar as foobaralias
and then in the code, to access MyClass use
foobaralias.MyClass
)
In short, it seems that the above-referenced link is saying it is usually better to import everything from a module, rather than just parts of the module.
However, that article I linked is really old.
I've also heard that it is better, at least in the context of Django projects, to instead only import the classes you want to use, rather than the whole module. It has been said that this form helps avoid circular import errors or at least makes the django import system less fragile. It was pointed out that Django's own code seems to prefer "from x import y" over "import x".
Assuming the project I am working on doesn't use any special features of __init__.py ... (all of our __init__.py files are empty), what import method should I favor, and why?

First, and primary, rule of imports: never ever use from foo import *.
The article is discussing the issue of cyclical imports, which still exists today in poorly-structured code. I dislike cyclical imports; their presence is a strong sign that some module is doing too much, and needs to be split up. If for whatever reason you need to work with code with cyclical imports which cannot be re-arranged, import foo is the only option.
For most cases, there's not much difference between import foo and from foo import MyClass. I prefer the second, because there's less typing involved, but there's a few reasons why I might use the first:
The module and class/value have different names. It can be difficult for readers to remember where a particular import is coming from, when the imported value's name is unrelated to the module.
Good: import myapp.utils as utils; utils.frobnicate()
Good: import myapp.utils as U; U.frobnicate()
Bad: from myapp.utils import frobnicate
You're importing a lot of values from one module. Save your fingers, and reader's eyes.
Bad: from myapp.utils import frobnicate, foo, bar, baz, MyClass, SomeOtherClass, # yada yada

For me, it's dependent on the situation. If it's a uniquely named method/class (i.e., not process() or something like that), and you're going to use it a lot, then save typing and just do from foo import MyClass.
If you're importing multiple things from one module, it's probably better to just import the module, and do module.bar, module.foo, module.baz, etc., to keep the namespace clean.
You also said
It has been said that this form helps avoid circular import errors or at least makes the django import system less fragile. It was pointed out that Django's own code seems to prefer "from x import y" over "import x".
I don't see how one way or the other would help prevent circular imports. The reason is that even when you do from x import y, ALL of x is imported. Only y is brought into the current namespace, but the entire module x is processed. Try out this example:
In test.py, put the following:
def a():
print "a"
print "hi"
def b():
print "b"
print "bye"
Then in 'runme.py', put:
from test import b
b()
Then just do python runme.py
You'll see the following output:
hi
bye
b
So everything in test.py was run, even though you only imported b

The advantage of the latter is that the origin of MyClass is more explicit. The former puts MyClass in the current namespace so the code can just use MyClass unqualified. So it's less obvious to someone reading the code where MyClass is defined.