25.3. `unittest` — Unit testing framework¶

New in version 2.1.

(If you are already familiar with the basic concepts of testing, you might want to skip to the list of assert methods.)

The Python unit testing framework, sometimes referred to as “PyUnit,” is a Python language version of JUnit, by Kent Beck and Erich Gamma. JUnit is, in turn, a Java version of Kent’s Smalltalk testing framework. Each is the de facto standard unit testing framework for its respective language.

unittest supports test automation, sharing of setup and shutdown code for tests, aggregation of tests into collections, and independence of the tests from the reporting framework. The unittest module provides classes that make it easy to support these qualities for a set of tests.

To achieve this, unittest supports some important concepts:

test fixture: A test fixture represents the preparation needed to perform one or more tests, and any associate cleanup actions. This may involve, for example, creating temporary or proxy databases, directories, or starting a server process.
test case: A test case is the smallest unit of testing. It checks for a specific response to a particular set of inputs. unittest provides a base class, TestCase, which may be used to create new test cases.
test suite: A test suite is a collection of test cases, test suites, or both. It is used to aggregate tests that should be executed together.
test runner: A test runner is a component which orchestrates the execution of tests and provides the outcome to the user. The runner may use a graphical interface, a textual interface, or return a special value to indicate the results of executing the tests.

The test case and test fixture concepts are supported through the TestCase and FunctionTestCase classes; the former should be used when creating new tests, and the latter can be used when integrating existing test code with a unittest-driven framework. When building test fixtures using TestCase, the setUp() and tearDown() methods can be overridden to provide initialization and cleanup for the fixture. With FunctionTestCase, existing functions can be passed to the constructor for these purposes. When the test is run, the fixture initialization is run first; if it succeeds, the cleanup method is run after the test has been executed, regardless of the outcome of the test. Each instance of the TestCase will only be used to run a single test method, so a new fixture is created for each test.

Test suites are implemented by the TestSuite class. This class allows individual tests and test suites to be aggregated; when the suite is executed, all tests added directly to the suite and in “child” test suites are run.

A test runner is an object that provides a single method, run(), which accepts a TestCase or TestSuite object as a parameter, and returns a result object. The class TestResult is provided for use as the result object. unittest provides the TextTestRunner as an example test runner which reports test results on the standard error stream by default. Alternate runners can be implemented for other environments (such as graphical environments) without any need to derive from a specific class.

25.3.1. Basic example¶

The unittest module provides a rich set of tools for constructing and running tests. This section demonstrates that a small subset of the tools suffice to meet the needs of most users.

Here is a short script to test three string methods:

import unittest

class TestStringMethods(unittest.TestCase):

    def test_upper(self):
        self.assertEqual('foo'.upper(), 'FOO')

    def test_isupper(self):
        self.assertTrue('FOO'.isupper())
        self.assertFalse('Foo'.isupper())

    def test_split(self):
        s = 'hello world'
        self.assertEqual(s.split(), ['hello', 'world'])
        # check that s.split fails when the separator is not a string
        with self.assertRaises(TypeError):
            s.split(2)

if __name__ == '__main__':
    unittest.main()

A testcase is created by subclassing unittest.TestCase. The three individual tests are defined with methods whose names start with the letters test. This naming convention informs the test runner about which methods represent tests.

The crux of each test is a call to assertEqual() to check for an expected result; assertTrue() or assertFalse() to verify a condition; or assertRaises() to verify that a specific exception gets raised. These methods are used instead of the assert statement so the test runner can accumulate all test results and produce a report.

The setUp() and tearDown() methods allow you to define instructions that will be executed before and after each test method. They are covered in more detail in the section Organizing test code.

The final block shows a simple way to run the tests. unittest.main() provides a command-line interface to the test script. When run from the command line, the above script produces an output that looks like this:

...
----------------------------------------------------------------------
Ran 3 tests in 0.000s

OK

Instead of unittest.main(), there are other ways to run the tests with a finer level of control, less terse output, and no requirement to be run from the command line. For example, the last two lines may be replaced with:

suite = unittest.TestLoader().loadTestsFromTestCase(TestStringMethods)
unittest.TextTestRunner(verbosity=2).run(suite)

Running the revised script from the interpreter or another script produces the following output:

test_isupper (__main__.TestStringMethods) ... ok
test_split (__main__.TestStringMethods) ... ok
test_upper (__main__.TestStringMethods) ... ok

----------------------------------------------------------------------
Ran 3 tests in 0.001s

OK

The above examples show the most commonly used unittest features which are sufficient to meet many everyday testing needs. The remainder of the documentation explores the full feature set from first principles.

25.3.2. Command-Line Interface¶

The unittest module can be used from the command line to run tests from modules, classes or even individual test methods:

python -m unittest test_module1 test_module2
python -m unittest test_module.TestClass
python -m unittest test_module.TestClass.test_method

You can pass in a list with any combination of module names, and fully qualified class or method names.

You can run tests with more detail (higher verbosity) by passing in the -v flag:

python -m unittest -v test_module

For a list of all the command-line options:

python -m unittest -h

Changed in version 2.7: In earlier versions it was only possible to run individual test methods and not modules or classes.

25.3.2.1. Command-line options¶

unittest supports these command-line options:

-b, --buffer¶: The standard output and standard error streams are buffered during the test run. Output during a passing test is discarded. Output is echoed normally on test fail or error and is added to the failure messages.

-c, --catch¶

Control-C during the test run waits for the current test to end and then reports all the results so far. A second Control-C raises the normal KeyboardInterrupt exception.

See Signal Handling for the functions that provide this functionality.

-f, --failfast¶: Stop the test run on the first error or failure.

New in version 2.7: The command-line options -b, -c and -f were added.

The command line can also be used for test discovery, for running all of the tests in a project or just a subset.

25.3.3. Test Discovery¶

New in version 2.7.

Unittest supports simple test discovery. In order to be compatible with test discovery, all of the test files must be modules or packages importable from the top-level directory of the project (this means that their filenames must be valid identifiers).

Test discovery is implemented in TestLoader.discover(), but can also be used from the command line. The basic command-line usage is:

cd project_directory
python -m unittest discover

The discover sub-command has the following options:

-v, --verbose¶: Verbose output

-s, --start-directory directory¶: Directory to start discovery (. default)

-p, --pattern pattern¶: Pattern to match test files (test*.py default)

-t, --top-level-directory directory¶: Top level directory of project (defaults to start directory)

The -s, -p, and -t options can be passed in as positional arguments in that order. The following two command lines are equivalent:

python -m unittest discover -s project_directory -p "*_test.py"
python -m unittest discover project_directory "*_test.py"

As well as being a path it is possible to pass a package name, for example myproject.subpackage.test, as the start directory. The package name you supply will then be imported and its location on the filesystem will be used as the start directory.

Caution

Test discovery loads tests by importing them. Once test discovery has found all the test files from the start directory you specify it turns the paths into package names to import. For example foo/bar/baz.py will be imported as foo.bar.baz.

If you have a package installed globally and attempt test discovery on a different copy of the package then the import could happen from the wrong place. If this happens test discovery will warn you and exit.

If you supply the start directory as a package name rather than a path to a directory then discover assumes that whichever location it imports from is the location you intended, so you will not get the warning.

Test modules and packages can customize test loading and discovery by through the load_tests protocol.

25.3.4. Organizing test code¶

The basic building blocks of unit testing are test cases — single scenarios that must be set up and checked for correctness. In unittest, test cases are represented by instances of unittest’s TestCase class. To make your own test cases you must write subclasses of TestCase, or use FunctionTestCase.

An instance of a TestCase-derived class is an object that can completely run a single test method, together with optional set-up and tidy-up code.

The testing code of a TestCase instance should be entirely self contained, such that it can be run either in isolation or in arbitrary combination with any number of other test cases.

The simplest TestCase subclass will simply override the runTest() method in order to perform specific testing code:

import unittest

class DefaultWidgetSizeTestCase(unittest.TestCase):
    def runTest(self):
        widget = Widget('The widget')
        self.assertEqual(widget.size(), (50, 50), 'incorrect default size')

Note that in order to test something, we use one of the assert*() methods provided by the TestCase base class. If the test fails, an exception will be raised, and unittest will identify the test case as a failure. Any other exceptions will be treated as errors. This helps you identify where the problem is: failures are caused by incorrect results - a 5 where you expected a 6. Errors are caused by incorrect code - e.g., a TypeError caused by an incorrect function call.

The way to run a test case will be described later. For now, note that to construct an instance of such a test case, we call its constructor without arguments:

testCase = DefaultWidgetSizeTestCase()

Now, such test cases can be numerous, and their set-up can be repetitive. In the above case, constructing a Widget in each of 100 Widget test case subclasses would mean unsightly duplication.

Luckily, we can factor out such set-up code by implementing a method called setUp(), which the testing framework will automatically call for us when we run the test:

import unittest

class SimpleWidgetTestCase(unittest.TestCase):
    def setUp(self):
        self.widget = Widget('The widget')

class DefaultWidgetSizeTestCase(SimpleWidgetTestCase):
    def runTest(self):
        self.assertEqual(self.widget.size(), (50,50),
                         'incorrect default size')

class WidgetResizeTestCase(SimpleWidgetTestCase):
    def runTest(self):
        self.widget.resize(100,150)
        self.assertEqual(self.widget.size(), (100,150),
                         'wrong size after resize')

If the setUp() method raises an exception while the test is running, the framework will consider the test to have suffered an error, and the runTest() method will not be executed.

Similarly, we can provide a tearDown() method that tidies up after the runTest() method has been run:

import unittest

class SimpleWidgetTestCase(unittest.TestCase):
    def setUp(self):
        self.widget = Widget('The widget')

    def tearDown(self):
        self.widget.dispose()
        self.widget = None

If setUp() succeeded, the tearDown() method will be run whether runTest() succeeded or not.

Such a working environment for the testing code is called a fixture.

Often, many small test cases will use the same fixture. In this case, we would end up subclassing SimpleWidgetTestCase into many small one-method classes such as DefaultWidgetSizeTestCase. This is time-consuming and discouraging, so in the same vein as JUnit, unittest provides a simpler mechanism:

import unittest

class WidgetTestCase(unittest.TestCase):
    def setUp(self):
        self.widget = Widget('The widget')

    def tearDown(self):
        self.widget.dispose()
        self.widget = None

    def test_default_size(self):
        self.assertEqual(self.widget.size(), (50,50),
                         'incorrect default size')

    def test_resize(self):
        self.widget.resize(100,150)
        self.assertEqual(self.widget.size(), (100,150),
                         'wrong size after resize')

Here we have not provided a runTest() method, but have instead provided two different test methods. Class instances will now each run one of the test_*() methods, with self.widget created and destroyed separately for each instance. When creating an instance we must specify the test method it is to run. We do this by passing the method name in the constructor:

defaultSizeTestCase = WidgetTestCase('test_default_size')
resizeTestCase = WidgetTestCase('test_resize')

Test case instances are grouped together according to the features they test. unittest provides a mechanism for this: the test suite, represented by unittest’s TestSuite class:

widgetTestSuite = unittest.TestSuite()
widgetTestSuite.addTest(WidgetTestCase('test_default_size'))
widgetTestSuite.addTest(WidgetTestCase('test_resize'))

For the ease of running tests, as we will see later, it is a good idea to provide in each test module a callable object that returns a pre-built test suite:

def suite():
    suite = unittest.TestSuite()
    suite.addTest(WidgetTestCase('test_default_size'))
    suite.addTest(WidgetTestCase('test_resize'))
    return suite

or even:

def suite():
    tests = ['test_default_size', 'test_resize']

    return unittest.TestSuite(map(WidgetTestCase, tests))

Since it is a common pattern to create a TestCase subclass with many similarly named test functions, unittest provides a TestLoader class that can be used to automate the process of creating a test suite and populating it with individual tests. For example,

suite = unittest.TestLoader().loadTestsFromTestCase(WidgetTestCase)

will create a test suite that will run WidgetTestCase.test_default_size() and WidgetTestCase.test_resize. TestLoader uses the 'test' method name prefix to identify test methods automatically.

Note that the order in which the various test cases will be run is determined by sorting the test function names with respect to the built-in ordering for strings.

Often it is desirable to group suites of test cases together, so as to run tests for the whole system at once. This is easy, since TestSuite instances can be added to a TestSuite just as TestCase instances can be added to a TestSuite:

suite1 = module1.TheTestSuite()
suite2 = module2.TheTestSuite()
alltests = unittest.TestSuite([suite1, suite2])

You can place the definitions of test cases and test suites in the same modules as the code they are to test (such as widget.py), but there are several advantages to placing the test code in a separate module, such as test_widget.py:

The test module can be run standalone from the command line.
The test code can more easily be separated from shipped code.
There is less temptation to change test code to fit the code it tests without a good reason.
Test code should be modified much less frequently than the code it tests.
Tested code can be refactored more easily.
Tests for modules written in C must be in separate modules anyway, so why not be consistent?
If the testing strategy changes, there is no need to change the source code.

25.3.5. Re-using old test code¶

Some users will find that they have existing test code that they would like to run from unittest, without converting every old test function to a TestCase subclass.

For this reason, unittest provides a FunctionTestCase class. This subclass of TestCase can be used to wrap an existing test function. Set-up and tear-down functions can also be provided.

Given the following test function:

def testSomething():
    something = makeSomething()
    assert something.name is not None
    # ...

one can create an equivalent test case instance as follows:

testcase = unittest.FunctionTestCase(testSomething)

If there are additional set-up and tear-down methods that should be called as part of the test case’s operation, they can also be provided like so:

testcase = unittest.FunctionTestCase(testSomething,
                                     setUp=makeSomethingDB,
                                     tearDown=deleteSomethingDB)

To make migrating existing test suites easier, unittest supports tests raising AssertionError to indicate test failure. However, it is recommended that you use the explicit TestCase.fail*() and TestCase.assert*() methods instead, as future versions of unittest may treat AssertionError differently.

Note

Even though FunctionTestCase can be used to quickly convert an existing test base over to a unittest-based system, this approach is not recommended. Taking the time to set up proper TestCase subclasses will make future test refactorings infinitely easier.

In some cases, the existing tests may have been written using the doctest module. If so, doctest provides a DocTestSuite class that can automatically build unittest.TestSuite instances from the existing doctest-based tests.

25.3.6. Skipping tests and expected failures¶

New in version 2.7.

Unittest supports skipping individual test methods and even whole classes of tests. In addition, it supports marking a test as an “expected failure,” a test that is broken and will fail, but shouldn’t be counted as a failure on a TestResult.

Skipping a test is simply a matter of using the skip() decorator or one of its conditional variants.

Basic skipping looks like this:

class MyTestCase(unittest.TestCase):

    @unittest.skip("demonstrating skipping")
    def test_nothing(self):
        self.fail("shouldn't happen")

    @unittest.skipIf(mylib.__version__ < (1, 3),
                     "not supported in this library version")
    def test_format(self):
        # Tests that work for only a certain version of the library.
        pass

    @unittest.skipUnless(sys.platform.startswith("win"), "requires Windows")
    def test_windows_support(self):
        # windows specific testing code
        pass

This is the output of running the example above in verbose mode:

test_format (__main__.MyTestCase) ... skipped 'not supported in this library version'
test_nothing (__main__.MyTestCase) ... skipped 'demonstrating skipping'
test_windows_support (__main__.MyTestCase) ... skipped 'requires Windows'

----------------------------------------------------------------------
Ran 3 tests in 0.005s

OK (skipped=3)

Classes can be skipped just like methods:

@unittest.skip("showing class skipping")
class MySkippedTestCase(unittest.TestCase):
    def test_not_run(self):
        pass

TestCase.setUp() can also skip the test. This is useful when a resource that needs to be set up is not available.

Expected failures use the expectedFailure() decorator.

class ExpectedFailureTestCase(unittest.TestCase):
    @unittest.expectedFailure
    def test_fail(self):
        self.assertEqual(1, 0, "broken")

It’s easy to roll your own skipping decorators by making a decorator that calls skip() on the test when it wants it to be skipped. This decorator skips the test unless the passed object has a certain attribute:

def skipUnlessHasattr(obj, attr):
    if hasattr(obj, attr):
        return lambda func: func
    return unittest.skip("{!r} doesn't have {!r}".format(obj, attr))

The following decorators implement test skipping and expected failures:

unittest.skip(reason)¶: Unconditionally skip the decorated test. reason should describe why the test is being skipped.

unittest.skipIf(condition, reason)¶: Skip the decorated test if condition is true.

unittest.skipUnless(condition, reason)¶: Skip the decorated test unless condition is true.

unittest.expectedFailure()¶: Mark the test as an expected failure. If the test fails when run, the test is not counted as a failure.

exception unittest.SkipTest(reason)¶

This exception is raised to skip a test.

Usually you can use TestCase.skipTest() or one of the skipping decorators instead of raising this directly.

Skipped tests will not have setUp() or tearDown() run around them. Skipped classes will not have setUpClass() or tearDownClass() run.

25.3.7. Classes and functions¶

This section describes in depth the API of unittest.

25.3.7.1. Test cases¶

class unittest.TestCase(methodName='runTest')¶

Instances of the TestCase class represent the smallest testable units in the unittest universe. This class is intended to be used as a base class, with specific tests being implemented by concrete subclasses. This class implements the interface needed by the test runner to allow it to drive the test, and methods that the test code can use to check for and report various kinds of failure.

Each instance of TestCase will run a single test method: the method named methodName. If you remember, we had an earlier example that went something like this:

def suite():
    suite = unittest.TestSuite()
    suite.addTest(WidgetTestCase('test_default_size'))
    suite.addTest(WidgetTestCase('test_resize'))
    return suite

Here, we create two instances of WidgetTestCase, each of which runs a single test.

methodName defaults to runTest().

TestCase instances provide three groups of methods: one group used to run the test, another used by the test implementation to check conditions and report failures, and some inquiry methods allowing information about the test itself to be gathered.

Methods in the first group (running the test) are:

setUp()¶: Method called to prepare the test fixture. This is called immediately before calling the test method; other than AssertionError or SkipTest, any exception raised by this method will be considered an error rather than a test failure. The default implementation does nothing.

tearDown()¶: Method called immediately after the test method has been called and the result recorded. This is called even if the test method raised an exception, so the implementation in subclasses may need to be particularly careful about checking internal state. Any exception, other than AssertionError or SkipTest, raised by this method will be considered an additional error rather than a test failure (thus increasing the total number of reported errors). This method will only be called if the setUp() succeeds, regardless of the outcome of the test method. The default implementation does nothing.

setUpClass()¶

A class method called before tests in an individual class are run. setUpClass is called with the class as the only argument and must be decorated as a classmethod():

@classmethod
def setUpClass(cls):
    ...

See Class and Module Fixtures for more details.

New in version 2.7.

tearDownClass()¶

A class method called after tests in an individual class have run. tearDownClass is called with the class as the only argument and must be decorated as a classmethod():

@classmethod
def tearDownClass(cls):
    ...

See Class and Module Fixtures for more details.

New in version 2.7.

run(result=None)¶

Run the test, collecting the result into the test result object passed as result. If result is omitted or None, a temporary result object is created (by calling the defaultTestResult() method) and used. The result object is not returned to run()’s caller.

The same effect may be had by simply calling the TestCase instance.

skipTest(reason)¶: Calling this during a test method or setUp() skips the current test. See Skipping tests and expected failures for more information.

New in version 2.7.

debug()¶: Run the test without collecting the result. This allows exceptions raised by the test to be propagated to the caller, and can be used to support running tests under a debugger.

The TestCase class provides several assert methods to check for and report failures. The following table lists the most commonly used methods (see the tables below for more assert methods):

Method	Checks that	New in
`assertEqual(a, b)`	`a == b`
`assertNotEqual(a, b)`	`a != b`
`assertTrue(x)`	`bool(x) is True`
`assertFalse(x)`	`bool(x) is False`
`assertIs(a, b)`	`a is b`	2.7
`assertIsNot(a, b)`	`a is not b`	2.7
`assertIsNone(x)`	`x is None`	2.7
`assertIsNotNone(x)`	`x is not None`	2.7
`assertIn(a, b)`	`a in b`	2.7
`assertNotIn(a, b)`	`a not in b`	2.7
`assertIsInstance(a, b)`	`isinstance(a, b)`	2.7
`assertNotIsInstance(a, b)`	`not isinstance(a, b)`	2.7

All the assert methods (except assertRaises(), assertRaisesRegexp()) accept a msg argument that, if specified, is used as the error message on failure (see also longMessage).

assertEqual(first, second, msg=None)¶

Test that first and second are equal. If the values do not compare equal, the test will fail.

In addition, if first and second are the exact same type and one of list, tuple, dict, set, frozenset or unicode or any type that a subclass registers with addTypeEqualityFunc() the type-specific equality function will be called in order to generate a more useful default error message (see also the list of type-specific methods).

Changed in version 2.7: Added the automatic calling of type-specific equality function.

assertNotEqual(first, second, msg=None)¶: Test that first and second are not equal. If the values do compare equal, the test will fail.

assertTrue(expr, msg=None)¶

assertFalse(expr, msg=None)¶

Test that expr is true (or false).

Note that this is equivalent to bool(expr) is True and not to expr is True (use assertIs(expr, True) for the latter). This method should also be avoided when more specific methods are available (e.g. assertEqual(a, b) instead of assertTrue(a == b)), because they provide a better error message in case of failure.

assertIs(first, second, msg=None)¶
assertIsNot(first, second, msg=None)¶: Test that first and second evaluate (or don’t evaluate) to the same object.

New in version 2.7.

assertIsNone(expr, msg=None)¶
assertIsNotNone(expr, msg=None)¶: Test that expr is (or is not) None.

New in version 2.7.

assertIn(first, second, msg=None)¶
assertNotIn(first, second, msg=None)¶: Test that first is (or is not) in second.

New in version 2.7.

assertIsInstance(obj, cls, msg=None)¶
assertNotIsInstance(obj, cls, msg=None)¶: Test that obj is (or is not) an instance of cls (which can be a class or a tuple of classes, as supported by isinstance()). To check for the exact type, use assertIs(type(obj), cls).

New in version 2.7.

It is also possible to check that exceptions and warnings are raised using the following methods:

Method	Checks that	New in
`assertRaises(exc, fun, args, *kwds)`	`fun(args, kwds)` raises exc*
`assertRaisesRegexp(exc, r, fun, args, *kwds)`	`fun(args, kwds)` raises exc* and the message matches regex r	2.7

assertRaises(exception, callable, *args, **kwds)¶

assertRaises(exception)

Test that an exception is raised when callable is called with any positional or keyword arguments that are also passed to assertRaises(). The test passes if exception is raised, is an error if another exception is raised, or fails if no exception is raised. To catch any of a group of exceptions, a tuple containing the exception classes may be passed as exception.

If only the exception argument is given, returns a context manager so that the code under test can be written inline rather than as a function:

with self.assertRaises(SomeException):
    do_something()

The context manager will store the caught exception object in its exception attribute. This can be useful if the intention is to perform additional checks on the exception raised:

with self.assertRaises(SomeException) as cm:
    do_something()

the_exception = cm.exception
self.assertEqual(the_exception.error_code, 3)

Changed in version 2.7: Added the ability to use assertRaises() as a context manager.

assertRaisesRegexp(exception, regexp, callable, *args, **kwds)¶

assertRaisesRegexp(exception, regexp)

Like assertRaises() but also tests that regexp matches on the string representation of the raised exception. regexp may be a regular expression object or a string containing a regular expression suitable for use by re.search(). Examples:

self.assertRaisesRegexp(ValueError, "invalid literal for.*XYZ'$",
                        int, 'XYZ')

or:

with self.assertRaisesRegexp(ValueError, 'literal'):
   int('XYZ')

New in version 2.7.

There are also other methods used to perform more specific checks, such as:

Method	Checks that	New in
`assertAlmostEqual(a, b)`	`round(a-b, 7) == 0`
`assertNotAlmostEqual(a, b)`	`round(a-b, 7) != 0`
`assertGreater(a, b)`	`a > b`	2.7
`assertGreaterEqual(a, b)`	`a >= b`	2.7
`assertLess(a, b)`	`a < b`	2.7
`assertLessEqual(a, b)`	`a <= b`	2.7
`assertRegexpMatches(s, r)`	`r.search(s)`	2.7
`assertNotRegexpMatches(s, r)`	`not r.search(s)`	2.7
`assertItemsEqual(a, b)`	sorted(a) == sorted(b) and works with unhashable objs	2.7
`assertDictContainsSubset(a, b)`	all the key/value pairs in a exist in b	2.7

assertAlmostEqual(first, second, places=7, msg=None, delta=None)¶

assertNotAlmostEqual(first, second, places=7, msg=None, delta=None)¶

Test that first and second are approximately (or not approximately) equal by computing the difference, rounding to the given number of decimal places (default 7), and comparing to zero. Note that these methods round the values to the given number of decimal places (i.e. like the round() function) and not significant digits.

If delta is supplied instead of places then the difference between first and second must be less or equal to (or greater than) delta.

Supplying both delta and places raises a TypeError.

Changed in version 2.7: assertAlmostEqual() automatically considers almost equal objects that compare equal. assertNotAlmostEqual() automatically fails if the objects compare equal. Added the delta keyword argument.

assertGreater(first, second, msg=None)¶

assertGreaterEqual(first, second, msg=None)¶

assertLess(first, second, msg=None)¶

assertLessEqual(first, second, msg=None)¶

Test that first is respectively >, >=, < or <= than second depending on the method name. If not, the test will fail:

>>> self.assertGreaterEqual(3, 4)
AssertionError: "3" unexpectedly not greater than or equal to "4"

New in version 2.7.

assertRegexpMatches(text, regexp, msg=None)¶: Test that a regexp search matches text. In case of failure, the error message will include the pattern and the text (or the pattern and the part of text that unexpectedly matched). regexp may be a regular expression object or a string containing a regular expression suitable for use by re.search().

New in version 2.7.

assertNotRegexpMatches(text, regexp, msg=None)¶: Verifies that a regexp search does not match text. Fails with an error message including the pattern and the part of text that matches. regexp may be a regular expression object or a string containing a regular expression suitable for use by re.search().

New in version 2.7.

assertItemsEqual(actual, expected, msg=None)¶

Test that sequence expected contains the same elements as actual, regardless of their order. When they don’t, an error message listing the differences between the sequences will be generated.

Duplicate elements are not ignored when comparing actual and expected. It verifies if each element has the same count in both sequences. It is the equivalent of assertEqual(sorted(expected), sorted(actual)) but it works with sequences of unhashable objects as well.

In Python 3, this method is named assertCountEqual.

New in version 2.7.

assertDictContainsSubset(expected, actual, msg=None)¶: Tests whether the key/value pairs in dictionary actual are a superset of those in expected. If not, an error message listing the missing keys and mismatched values is generated.

New in version 2.7.

Deprecated since version 3.2.

The assertEqual() method dispatches the equality check for objects of the same type to different type-specific methods. These methods are already implemented for most of the built-in types, but it’s also possible to register new methods using addTypeEqualityFunc():

addTypeEqualityFunc(typeobj, function)¶: Registers a type-specific method called by assertEqual() to check if two objects of exactly the same typeobj (not subclasses) compare equal. function must take two positional arguments and a third msg=None keyword argument just as assertEqual() does. It must raise self.failureException(msg) when inequality between the first two parameters is detected – possibly providing useful information and explaining the inequalities in details in the error message.

New in version 2.7.

The list of type-specific methods automatically used by assertEqual() are summarized in the following table. Note that it’s usually not necessary to invoke these methods directly.

Method	Used to compare	New in
`assertMultiLineEqual(a, b)`	strings	2.7
`assertSequenceEqual(a, b)`	sequences	2.7

25.3. unittest — Unit testing framework¶

25.3.1. Basic example¶

25.3.2. Command-Line Interface¶

25.3.2.1. Command-line options¶

25.3.3. Test Discovery¶

25.3.4. Organizing test code¶

25.3.5. Re-using old test code¶

25.3.6. Skipping tests and expected failures¶

25.3.7. Classes and functions¶

25.3.7.1. Test cases¶

25.3. `unittest` — Unit testing framework¶