author Carsten "Tomcat" Book <>
Tue, 30 Jun 2015 10:30:51 +0200
changeset 274663 79800010be78122db7c36d4b5833814fdbf28495
parent 112588 5981ff9d3f45de802d956b2f8e064f49b74cb7a6
permissions -rw-r--r--
Backed out changeset f41a2121425f (bug 1171931) for bustage

 Mock - Mocking and Testing Library

.. currentmodule:: mock

:Author: `Michael Foord
:Version: |release|
:Date: 2012/10/07
:Homepage: `Mock Homepage`_
:Download: `Mock on PyPI`_
:Documentation: `PDF Documentation
:License: `BSD License`_
:Support: `Mailing list (
:Issue tracker: `Google code project

.. _Mock Homepage:
.. _BSD License:

.. currentmodule:: mock

.. module:: mock
   :synopsis: Mock object and testing library.

.. index:: introduction

mock is a library for testing in Python. It allows you to replace parts of
your system under test with mock objects and make assertions about how they
have been used.

mock is now part of the Python standard library, available as `unittest.mock
in Python 3.3 onwards.

mock provides a core :class:`Mock` class removing the need to create a host
of stubs throughout your test suite. After performing an action, you can make
assertions about which methods / attributes were used and arguments they were
called with. You can also specify return values and set needed attributes in
the normal way.

Additionally, mock provides a :func:`patch` decorator that handles patching
module and class level attributes within the scope of a test, along with
:const:`sentinel` for creating unique objects. See the `quick guide`_ for
some examples of how to use :class:`Mock`, :class:`MagicMock` and

Mock is very easy to use and is designed for use with
`unittest <>`_. Mock is based on
the 'action -> assertion' pattern instead of `'record -> replay'` used by many
mocking frameworks.

mock is tested on Python versions 2.4-2.7, Python 3 plus the latest versions of
Jython and PyPy.

.. testsetup::

   class ProductionClass(object):
      def method(self, *args):

   module = sys.modules['module'] = ProductionClass
   ProductionClass.ClassName1 = ProductionClass
   ProductionClass.ClassName2 = ProductionClass

API Documentation

.. toctree::
   :maxdepth: 2


User Guide

.. toctree::
   :maxdepth: 2


.. index:: installing


The current version is |release|. Mock is stable and widely used. If you do
find any bugs, or have suggestions for improvements / extensions
then please contact us.

* `mock on PyPI <>`_
* `mock documentation as PDF
* `Google Code Home & Mercurial Repository <>`_

.. index:: repository
.. index:: hg

You can checkout the latest development version from the Google Code Mercurial
repository with the following command:

    ``hg clone mock``

.. index:: pip
.. index:: easy_install
.. index:: setuptools

If you have pip, setuptools or distribute you can install mock with:

    | ``easy_install -U mock``
    | ``pip install -U mock``

Alternatively you can download the mock distribution from PyPI and after
unpacking run:

   ``python install``

Quick Guide

:class:`Mock` and :class:`MagicMock` objects create all attributes and
methods as you access them and store details of how they have been used. You
can configure them, to specify return values or limit what attributes are
available, and then make assertions about how they have been used:

.. doctest::

    >>> from mock import MagicMock
    >>> thing = ProductionClass()
    >>> thing.method = MagicMock(return_value=3)
    >>> thing.method(3, 4, 5, key='value')
    >>> thing.method.assert_called_with(3, 4, 5, key='value')

:attr:`side_effect` allows you to perform side effects, including raising an
exception when a mock is called:

.. doctest::

   >>> mock = Mock(side_effect=KeyError('foo'))
   >>> mock()
   Traceback (most recent call last):
   KeyError: 'foo'

   >>> values = {'a': 1, 'b': 2, 'c': 3}
   >>> def side_effect(arg):
   ...     return values[arg]
   >>> mock.side_effect = side_effect
   >>> mock('a'), mock('b'), mock('c')
   (1, 2, 3)
   >>> mock.side_effect = [5, 4, 3, 2, 1]
   >>> mock(), mock(), mock()
   (5, 4, 3)

Mock has many other ways you can configure it and control its behaviour. For
example the `spec` argument configures the mock to take its specification
from another object. Attempting to access attributes or methods on the mock
that don't exist on the spec will fail with an `AttributeError`.

The :func:`patch` decorator / context manager makes it easy to mock classes or
objects in a module under test. The object you specify will be replaced with a
mock (or other object) during the test and restored when the test ends:

.. doctest::

    >>> from mock import patch
    >>> @patch('module.ClassName2')
    ... @patch('module.ClassName1')
    ... def test(MockClass1, MockClass2):
    ...     module.ClassName1()
    ...     module.ClassName2()

    ...     assert MockClass1 is module.ClassName1
    ...     assert MockClass2 is module.ClassName2
    ...     assert MockClass1.called
    ...     assert MockClass2.called
    >>> test()

.. note::

   When you nest patch decorators the mocks are passed in to the decorated
   function in the same order they applied (the normal *python* order that
   decorators are applied). This means from the bottom up, so in the example
   above the mock for `module.ClassName1` is passed in first.

   With `patch` it matters that you patch objects in the namespace where they
   are looked up. This is normally straightforward, but for a quick guide
   read :ref:`where to patch <where-to-patch>`.

As well as a decorator `patch` can be used as a context manager in a with

.. doctest::

    >>> with patch.object(ProductionClass, 'method', return_value=None) as mock_method:
    ...     thing = ProductionClass()
    ...     thing.method(1, 2, 3)
    >>> mock_method.assert_called_once_with(1, 2, 3)

There is also :func:`patch.dict` for setting values in a dictionary just
during a scope and restoring the dictionary to its original state when the test

.. doctest::

   >>> foo = {'key': 'value'}
   >>> original = foo.copy()
   >>> with patch.dict(foo, {'newkey': 'newvalue'}, clear=True):
   ...     assert foo == {'newkey': 'newvalue'}
   >>> assert foo == original

Mock supports the mocking of Python :ref:`magic methods <magic-methods>`. The
easiest way of using magic methods is with the :class:`MagicMock` class. It
allows you to do things like:

.. doctest::

    >>> mock = MagicMock()
    >>> mock.__str__.return_value = 'foobarbaz'
    >>> str(mock)
    >>> mock.__str__.assert_called_with()

Mock allows you to assign functions (or other Mock instances) to magic methods
and they will be called appropriately. The `MagicMock` class is just a Mock
variant that has all of the magic methods pre-created for you (well, all the
useful ones anyway).

The following is an example of using magic methods with the ordinary Mock

.. doctest::

    >>> mock = Mock()
    >>> mock.__str__ = Mock(return_value='wheeeeee')
    >>> str(mock)

For ensuring that the mock objects in your tests have the same api as the
objects they are replacing, you can use :ref:`auto-speccing <auto-speccing>`.
Auto-speccing can be done through the `autospec` argument to patch, or the
:func:`create_autospec` function. Auto-speccing creates mock objects that
have the same attributes and methods as the objects they are replacing, and
any functions and methods (including constructors) have the same call
signature as the real object.

This ensures that your mocks will fail in the same way as your production
code if they are used incorrectly:

.. doctest::

   >>> from mock import create_autospec
   >>> def function(a, b, c):
   ...     pass
   >>> mock_function = create_autospec(function, return_value='fishy')
   >>> mock_function(1, 2, 3)
   >>> mock_function.assert_called_once_with(1, 2, 3)
   >>> mock_function('wrong arguments')
   Traceback (most recent call last):
   TypeError: <lambda>() takes exactly 3 arguments (1 given)

`create_autospec` can also be used on classes, where it copies the signature of
the `__init__` method, and on callable objects where it copies the signature of
the `__call__` method.

.. index:: references
.. index:: articles


Articles, blog entries and other stuff related to testing with Mock:

* `Imposing a No DB Discipline on Django unit tests
* `mock-django: tools for mocking the Django ORM and models
* `PyCon 2011 Video: Testing with mock <>`_
* `Mock objects in Python
* `Python: Injecting Mock Objects for Powerful Testing
* `Python Mock: How to assert a substring of logger output
* `Mocking Django <>`_
* `Mocking dates and other classes that can't be modified
* `Mock recipes <>`_
* `Mockity mock mock - some love for the mock module
* `Coverage and Mock (with django)
* `Python Unit Testing with Mock <>`_
* `Getting started with Python Mock
* `Smart Parameter Checks with mock
* `Python mock testing techniques and tools
* `How To Test Django Template Tags
* `A presentation on Unit Testing with Mock
* `Mocking with Django and Google AppEngine

.. index:: tests
.. index:: unittest2


Mock uses `unittest2 <>`_ for its own
test suite. In order to run it, use the `unit2` script that comes with
`unittest2` module on a checkout of the source repository:

   `unit2 discover`

If you have `setuptools <>`_ as well as
unittest2 you can run:

   ``python test``

On Python 3.2 you can use ``unittest`` module from the standard library.

   ``python3.2 -m unittest discover``

.. index:: Python 3

On Python 3 the tests for unicode are skipped as they are not relevant. On
Python 2.4 tests that use the with statements are skipped as the with statement
is invalid syntax on Python 2.4.

.. index:: older versions

Older Versions

Documentation for older versions of mock:

* `mock 0.8 <>`_
* `mock 0.7 <>`_
* `mock 0.6 <>`_

Docs from the in-development version of `mock` can be found at
` <>`_.


Terminology for objects used to replace other ones can be confusing. Terms
like double, fake, mock, stub, and spy are all used with varying meanings.

In `classic mock terminology
:class:`mock.Mock` is a `spy <>`_ that
allows for *post-mortem* examination. This is what I call the "action ->
assertion" [#]_ pattern of testing.

I'm not however a fan of this "statically typed mocking terminology"
promulgated by `Martin Fowler
<>`_. It confuses usage
patterns with implementation and prevents you from using natural terminology
when discussing mocking.

I much prefer duck typing, if an object used in your test suite looks like a
mock object and quacks like a mock object then it's fine to call it a mock, no
matter what the implementation looks like.

This terminology is perhaps more useful in less capable languages where
different usage patterns will *require* different implementations.
`mock.Mock()` is capable of being used in most of the different roles
described by Fowler, except (annoyingly / frustratingly / ironically) a Mock

How about a simpler definition: a "mock object" is an object used to replace a
real one in a system under test.

.. [#] This pattern is called "AAA" by some members of the testing community;
   "Arrange - Act - Assert".