class typing.Protocol(Generic)
Base class for protocol classes. Protocol classes are defined like this: class Proto(Protocol):
    def meth(self) -> int:
        ...
 Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example: class C:
    def meth(self) -> int:
        return 0

def func(x: Proto) -> int:
    return x.meth()

func(C())  # Passes static type check
 See PEP 544 for details. Protocol classes decorated with runtime_checkable() (described later) act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures. Protocol classes can be generic, for example: class GenProto(Protocol[T]):
    def meth(self) -> T:
        ...
  New in version 3.8.


class typing.Reversible(Iterable[T_co])
A generic version of collections.abc.Reversible.  Deprecated since version 3.9: collections.abc.Reversible now supports []. See PEP 585 and Generic Alias Type.


@typing.runtime_checkable
Mark a protocol class as a runtime protocol. Such a protocol can be used with isinstance() and issubclass(). This raises TypeError when applied to a non-protocol class. This allows a simple-minded structural check, very similar to “one trick ponies” in collections.abc such as Iterable. For example: @runtime_checkable
class Closable(Protocol):
    def close(self): ...

assert isinstance(open('/some/file'), Closable)
  Note runtime_checkable() will check only the presence of the required methods, not their type signatures! For example, builtins.complex implements __float__(), therefore it passes an issubclass() check against SupportsFloat. However, the complex.__float__ method exists only to raise a TypeError with a more informative message.   New in version 3.8.


class typing.Sequence(Reversible[T_co], Collection[T_co])
A generic version of collections.abc.Sequence.  Deprecated since version 3.9: collections.abc.Sequence now supports []. See PEP 585 and Generic Alias Type.


class typing.Set(set, MutableSet[T])
A generic version of builtins.set. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as AbstractSet.  Deprecated since version 3.9: builtins.set now supports []. See PEP 585 and Generic Alias Type.


class typing.Sized
An alias to collections.abc.Sized


class typing.SupportsAbs
An ABC with one abstract method __abs__ that is covariant in its return type.


class typing.SupportsBytes
An ABC with one abstract method __bytes__.


class typing.SupportsComplex
An ABC with one abstract method __complex__.


class typing.SupportsFloat
An ABC with one abstract method __float__.


class typing.SupportsIndex
An ABC with one abstract method __index__.  New in version 3.8.


class typing.SupportsInt
An ABC with one abstract method __int__.


class typing.SupportsRound
An ABC with one abstract method __round__ that is covariant in its return type.


class typing.Text
Text is an alias for str. It is provided to supply a forward compatible path for Python 2 code: in Python 2, Text is an alias for unicode. Use Text to indicate that a value must contain a unicode string in a manner that is compatible with both Python 2 and Python 3: def add_unicode_checkmark(text: Text) -> Text:
    return text + u' \u2713'
  New in version 3.5.2.


class typing.IO
class typing.TextIO
class typing.BinaryIO
Generic type IO[AnyStr] and its subclasses TextIO(IO[str]) and BinaryIO(IO[bytes]) represent the types of I/O streams such as returned by open(). These types are also in the typing.io namespace.


typing.Tuple
Tuple type; Tuple[X, Y] is the type of a tuple of two items with the first item of type X and the second of type Y. The type of the empty tuple can be written as Tuple[()]. Example: Tuple[T1, T2] is a tuple of two elements corresponding to type variables T1 and T2. Tuple[int, float, str] is a tuple of an int, a float and a string. To specify a variable-length tuple of homogeneous type, use literal ellipsis, e.g. Tuple[int, ...]. A plain Tuple is equivalent to Tuple[Any, ...], and in turn to tuple.  Deprecated since version 3.9: builtins.tuple now supports []. See PEP 585 and Generic Alias Type.


class typing.Type(Generic[CT_co])
A variable annotated with C may accept a value of type C. In contrast, a variable annotated with Type[C] may accept values that are classes themselves – specifically, it will accept the class object of C. For example: a = 3         # Has type 'int'
b = int       # Has type 'Type[int]'
c = type(a)   # Also has type 'Type[int]'
 Note that Type[C] is covariant: class User: ...
class BasicUser(User): ...
class ProUser(User): ...
class TeamUser(User): ...

# Accepts User, BasicUser, ProUser, TeamUser, ...
def make_new_user(user_class: Type[User]) -> User:
    # ...
    return user_class()
 The fact that Type[C] is covariant implies that all subclasses of C should implement the same constructor signature and class method signatures as C. The type checker should flag violations of this, but should also allow constructor calls in subclasses that match the constructor calls in the indicated base class. How the type checker is required to handle this particular case may change in future revisions of PEP 484. The only legal parameters for Type are classes, Any, type variables, and unions of any of these types. For example: def new_non_team_user(user_class: Type[Union[BasicUser, ProUser]]): ...
 Type[Any] is equivalent to Type which in turn is equivalent to type, which is the root of Python’s metaclass hierarchy.  New in version 3.5.2.   Deprecated since version 3.9: builtins.type now supports []. See PEP 585 and Generic Alias Type.


class typing.TypedDict(dict)
Special construct to add type hints to a dictionary. At runtime it is a plain dict. TypedDict declares a dictionary type that expects all of its instances to have a certain set of keys, where each key is associated with a value of a consistent type. This expectation is not checked at runtime but is only enforced by type checkers. Usage: class Point2D(TypedDict):
    x: int
    y: int
    label: str

a: Point2D = {'x': 1, 'y': 2, 'label': 'good'}  # OK
b: Point2D = {'z': 3, 'label': 'bad'}           # Fails type check

assert Point2D(x=1, y=2, label='first') == dict(x=1, y=2, label='first')
 The type info for introspection can be accessed via Point2D.__annotations__ and Point2D.__total__. To allow using this feature with older versions of Python that do not support PEP 526, TypedDict supports two additional equivalent syntactic forms: Point2D = TypedDict('Point2D', x=int, y=int, label=str)
Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': str})
 By default, all keys must be present in a TypedDict. It is possible to override this by specifying totality. Usage: class point2D(TypedDict, total=False):
    x: int
    y: int
 This means that a point2D TypedDict can have any of the keys omitted. A type checker is only expected to support a literal False or True as the value of the total argument. True is the default, and makes all items defined in the class body be required. See PEP 589 for more examples and detailed rules of using TypedDict.  New in version 3.8.


class typing.TypeVar
Type variable. Usage: T = TypeVar('T')  # Can be anything
A = TypeVar('A', str, bytes)  # Must be str or bytes
 Type variables exist primarily for the benefit of static type checkers. They serve as the parameters for generic types as well as for generic function definitions. See Generic for more information on generic types. Generic functions work as follows: def repeat(x: T, n: int) -> Sequence[T]:
    """Return a list containing n references to x."""
    return [x]*n

def longest(x: A, y: A) -> A:
    """Return the longest of two strings."""
    return x if len(x) >= len(y) else y
 The latter example’s signature is essentially the overloading of (str, str) -> str and (bytes, bytes) -> bytes. Also note that if the arguments are instances of some subclass of str, the return type is still plain str. At runtime, isinstance(x, T) will raise TypeError. In general, isinstance() and issubclass() should not be used with types. Type variables may be marked covariant or contravariant by passing covariant=True or contravariant=True. See PEP 484 for more details. By default type variables are invariant. Alternatively, a type variable may specify an upper bound using bound=<type>. This means that an actual type substituted (explicitly or implicitly) for the type variable must be a subclass of the boundary type, see PEP 484.


typing.TYPE_CHECKING
A special constant that is assumed to be True by 3rd party static type checkers. It is False at runtime. Usage: if TYPE_CHECKING:
    import expensive_mod

def fun(arg: 'expensive_mod.SomeType') -> None:
    local_var: expensive_mod.AnotherType = other_fun()
 The first type annotation must be enclosed in quotes, making it a “forward reference”, to hide the expensive_mod reference from the interpreter runtime. Type annotations for local variables are not evaluated, so the second annotation does not need to be enclosed in quotes.  Note If from __future__ import annotations is used in Python 3.7 or later, annotations are not evaluated at function definition time. Instead, they are stored as strings in __annotations__, This makes it unnecessary to use quotes around the annotation. (see PEP 563).   New in version 3.5.2.


@typing.type_check_only
Decorator to mark a class or function to be unavailable at runtime. This decorator is itself not available at runtime. It is mainly intended to mark classes that are defined in type stub files if an implementation returns an instance of a private class: @type_check_only
class Response:  # private or not available at runtime
    code: int
    def get_header(self, name: str) -> str: ...

def fetch_response() -> Response: ...
 Note that returning instances of private classes is not recommended. It is usually preferable to make such classes public.


typing.Union
Union type; Union[X, Y] means either X or Y. To define a union, use e.g. Union[int, str]. Details:  The arguments must be types and there must be at least one.
Unions of unions are flattened, e.g.: Union[Union[int, str], float] == Union[int, str, float]

Unions of a single argument vanish, e.g.: Union[int] == int  # The constructor actually returns int

Redundant arguments are skipped, e.g.: Union[int, str, int] == Union[int, str]

When comparing unions, the argument order is ignored, e.g.: Union[int, str] == Union[str, int]
  You cannot subclass or instantiate a union. You cannot write Union[X][Y]. You can use Optional[X] as a shorthand for Union[X, None].   Changed in version 3.7: Don’t remove explicit subclasses from unions at runtime.


class typing.ValuesView(MappingView[VT_co])
A generic version of collections.abc.ValuesView.  Deprecated since version 3.9: collections.abc.ValuesView now supports []. See PEP 585 and Generic Alias Type.


exception UnboundLocalError
Raised when a reference is made to a local variable in a function or method, but no value has been bound to that variable. This is a subclass of NameError.


unicodedata — Unicode Database This module provides access to the Unicode Character Database (UCD) which defines character properties for all Unicode characters. The data contained in this database is compiled from the UCD version 13.0.0. The module uses the same names and symbols as defined by Unicode Standard Annex #44, “Unicode Character Database”. It defines the following functions:
unicodedata.lookup(name)
Look up character by name. If a character with the given name is found, return the corresponding character. If not found, KeyError is raised.  Changed in version 3.3: Support for name aliases 1 and named sequences 2 has been added.

unicodedata.name(chr[, default])
Returns the name assigned to the character chr as a string. If no name is defined, default is returned, or, if not given, ValueError is raised.

unicodedata.decimal(chr[, default])
Returns the decimal value assigned to the character chr as integer. If no such value is defined, default is returned, or, if not given, ValueError is raised.

unicodedata.digit(chr[, default])
Returns the digit value assigned to the character chr as integer. If no such value is defined, default is returned, or, if not given, ValueError is raised.

unicodedata.numeric(chr[, default])
Returns the numeric value assigned to the character chr as float. If no such value is defined, default is returned, or, if not given, ValueError is raised.

unicodedata.category(chr)
Returns the general category assigned to the character chr as string.

unicodedata.bidirectional(chr)
Returns the bidirectional class assigned to the character chr as string. If no such value is defined, an empty string is returned.

unicodedata.combining(chr)
Returns the canonical combining class assigned to the character chr as integer. Returns 0 if no combining class is defined.

unicodedata.east_asian_width(chr)
Returns the east asian width assigned to the character chr as string.

unicodedata.mirrored(chr)
Returns the mirrored property assigned to the character chr as integer. Returns 1 if the character has been identified as a “mirrored” character in bidirectional text, 0 otherwise.

unicodedata.decomposition(chr)
Returns the character decomposition mapping assigned to the character chr as string. An empty string is returned in case no such mapping is defined.

unicodedata.normalize(form, unistr)
Return the normal form form for the Unicode string unistr. Valid values for form are ‘NFC’, ‘NFKC’, ‘NFD’, and ‘NFKD’. The Unicode standard defines various normalization forms of a Unicode string, based on the definition of canonical equivalence and compatibility equivalence. In Unicode, several characters can be expressed in various way. For example, the character U+00C7 (LATIN CAPITAL LETTER C WITH CEDILLA) can also be expressed as the sequence U+0043 (LATIN CAPITAL LETTER C) U+0327 (COMBINING CEDILLA). For each character, there are two normal forms: normal form C and normal form D. Normal form D (NFD) is also known as canonical decomposition, and translates each character into its decomposed form. Normal form C (NFC) first applies a canonical decomposition, then composes pre-combined characters again. In addition to these two forms, there are two additional normal forms based on compatibility equivalence. In Unicode, certain characters are supported which normally would be unified with other characters. For example, U+2160 (ROMAN NUMERAL ONE) is really the same thing as U+0049 (LATIN CAPITAL LETTER I). However, it is supported in Unicode for compatibility with existing character sets (e.g. gb2312). The normal form KD (NFKD) will apply the compatibility decomposition, i.e. replace all compatibility characters with their equivalents. The normal form KC (NFKC) first applies the compatibility decomposition, followed by the canonical composition. Even if two unicode strings are normalized and look the same to a human reader, if one has combining characters and the other doesn’t, they may not compare equal.

unicodedata.is_normalized(form, unistr)
Return whether the Unicode string unistr is in the normal form form. Valid values for form are ‘NFC’, ‘NFKC’, ‘NFD’, and ‘NFKD’.  New in version 3.8.
 In addition, the module exposes the following constant:
unicodedata.unidata_version
The version of the Unicode database used in this module.

unicodedata.ucd_3_2_0
This is an object that has the same methods as the entire module, but uses the Unicode database version 3.2 instead, for applications that require this specific version of the Unicode database (such as IDNA).
 Examples: >>> import unicodedata
>>> unicodedata.lookup('LEFT CURLY BRACKET')
'{'
>>> unicodedata.name('/')
'SOLIDUS'
>>> unicodedata.decimal('9')
9
>>> unicodedata.decimal('a')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: not a decimal
>>> unicodedata.category('A')  # 'L'etter, 'u'ppercase
'Lu'
>>> unicodedata.bidirectional('\u0660') # 'A'rabic, 'N'umber
'AN'
 Footnotes
1
https://www.unicode.org/Public/13.0.0/ucd/NameAliases.txt
2
https://www.unicode.org/Public/13.0.0/ucd/NamedSequences.txt


unicodedata.bidirectional(chr)
Returns the bidirectional class assigned to the character chr as string. If no such value is defined, an empty string is returned.


unicodedata.category(chr)
Returns the general category assigned to the character chr as string.


unicodedata.combining(chr)
Returns the canonical combining class assigned to the character chr as integer. Returns 0 if no combining class is defined.


unicodedata.decimal(chr[, default])
Returns the decimal value assigned to the character chr as integer. If no such value is defined, default is returned, or, if not given, ValueError is raised.


unicodedata.decomposition(chr)
Returns the character decomposition mapping assigned to the character chr as string. An empty string is returned in case no such mapping is defined.


unicodedata.digit(chr[, default])
Returns the digit value assigned to the character chr as integer. If no such value is defined, default is returned, or, if not given, ValueError is raised.


unicodedata.east_asian_width(chr)
Returns the east asian width assigned to the character chr as string.


unicodedata.is_normalized(form, unistr)
Return whether the Unicode string unistr is in the normal form form. Valid values for form are ‘NFC’, ‘NFKC’, ‘NFD’, and ‘NFKD’.  New in version 3.8.


unicodedata.lookup(name)
Look up character by name. If a character with the given name is found, return the corresponding character. If not found, KeyError is raised.  Changed in version 3.3: Support for name aliases 1 and named sequences 2 has been added.


unicodedata.mirrored(chr)
Returns the mirrored property assigned to the character chr as integer. Returns 1 if the character has been identified as a “mirrored” character in bidirectional text, 0 otherwise.


unicodedata.name(chr[, default])
Returns the name assigned to the character chr as a string. If no name is defined, default is returned, or, if not given, ValueError is raised.


unicodedata.normalize(form, unistr)
Return the normal form form for the Unicode string unistr. Valid values for form are ‘NFC’, ‘NFKC’, ‘NFD’, and ‘NFKD’. The Unicode standard defines various normalization forms of a Unicode string, based on the definition of canonical equivalence and compatibility equivalence. In Unicode, several characters can be expressed in various way. For example, the character U+00C7 (LATIN CAPITAL LETTER C WITH CEDILLA) can also be expressed as the sequence U+0043 (LATIN CAPITAL LETTER C) U+0327 (COMBINING CEDILLA). For each character, there are two normal forms: normal form C and normal form D. Normal form D (NFD) is also known as canonical decomposition, and translates each character into its decomposed form. Normal form C (NFC) first applies a canonical decomposition, then composes pre-combined characters again. In addition to these two forms, there are two additional normal forms based on compatibility equivalence. In Unicode, certain characters are supported which normally would be unified with other characters. For example, U+2160 (ROMAN NUMERAL ONE) is really the same thing as U+0049 (LATIN CAPITAL LETTER I). However, it is supported in Unicode for compatibility with existing character sets (e.g. gb2312). The normal form KD (NFKD) will apply the compatibility decomposition, i.e. replace all compatibility characters with their equivalents. The normal form KC (NFKC) first applies the compatibility decomposition, followed by the canonical composition. Even if two unicode strings are normalized and look the same to a human reader, if one has combining characters and the other doesn’t, they may not compare equal.


unicodedata.numeric(chr[, default])
Returns the numeric value assigned to the character chr as float. If no such value is defined, default is returned, or, if not given, ValueError is raised.


unicodedata.ucd_3_2_0
This is an object that has the same methods as the entire module, but uses the Unicode database version 3.2 instead, for applications that require this specific version of the Unicode database (such as IDNA).


unicodedata.unidata_version
The version of the Unicode database used in this module.


exception UnicodeDecodeError
Raised when a Unicode-related error occurs during decoding. It is a subclass of UnicodeError.


exception UnicodeEncodeError
Raised when a Unicode-related error occurs during encoding. It is a subclass of UnicodeError.


exception UnicodeError
Raised when a Unicode-related encoding or decoding error occurs. It is a subclass of ValueError. UnicodeError has attributes that describe the encoding or decoding error. For example, err.object[err.start:err.end] gives the particular invalid input that the codec failed on.
encoding
The name of the encoding that raised the error.

reason
A string describing the specific codec error.

object
The object the codec was attempting to encode or decode.

start
The first index of invalid data in object.

end
The index after the last invalid data in object.


encoding
The name of the encoding that raised the error.


end
The index after the last invalid data in object.


object
The object the codec was attempting to encode or decode.


reason
A string describing the specific codec error.


start
The first index of invalid data in object.


exception UnicodeTranslateError
Raised when a Unicode-related error occurs during translating. It is a subclass of UnicodeError.


exception UnicodeWarning
Base class for warnings related to Unicode.


unittest — Unit testing framework Source code: Lib/unittest/__init__.py (If you are already familiar with the basic concepts of testing, you might want to skip to the list of assert methods.) The unittest unit testing framework was originally inspired by JUnit and has a similar flavor as major unit testing frameworks in other languages. It 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. To achieve this, unittest supports some important concepts in an object-oriented way:  test fixture

A test fixture represents the preparation needed to perform one or more tests, and any associated 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 individual 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.    See also
Module doctest

Another test-support module with a very different flavor.  Simple Smalltalk Testing: With Patterns

Kent Beck’s original paper on testing frameworks using the pattern shared by unittest.  pytest

Third-party unittest framework with a lighter-weight syntax for writing tests. For example, assert func(10) == 42.  The Python Testing Tools Taxonomy

An extensive list of Python testing tools including functional testing frameworks and mock object libraries.  Testing in Python Mailing List

A special-interest-group for discussion of testing, and testing tools, in Python.   The script Tools/unittestgui/unittestgui.py in the Python source distribution is a GUI tool for test discovery and execution. This is intended largely for ease of use for those new to unit testing. For production environments it is recommended that tests be driven by a continuous integration system such as Buildbot, Jenkins or Travis-CI, or AppVeyor.  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
 Passing the -v option to your test script will instruct unittest.main() to enable a higher level of verbosity, and produce 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. 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. Test modules can be specified by file path as well: python -m unittest tests/test_something.py
 This allows you to use the shell filename completion to specify the test module. The file specified must still be importable as a module. The path is converted to a module name by removing the ‘.py’ and converting path separators into ‘.’. If you want to execute a test file that isn’t importable as a module you should execute the file directly instead. You can run tests with more detail (higher verbosity) by passing in the -v flag: python -m unittest -v test_module
 When executed without arguments Test Discovery is started: python -m unittest
 For a list of all the command-line options: python -m unittest -h
  Changed in version 3.2: In earlier versions it was only possible to run individual test methods and not modules or classes.  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.

-k
Only run test methods and classes that match the pattern or substring. This option may be used multiple times, in which case all test cases that match of the given patterns are included. Patterns that contain a wildcard character (*) are matched against the test name using fnmatch.fnmatchcase(); otherwise simple case-sensitive substring matching is used. Patterns are matched against the fully qualified test method name as imported by the test loader. For example, -k foo matches foo_tests.SomeTest.test_something, bar_tests.SomeTest.test_foo, but not bar_tests.FooTest.test_something.

--locals
Show local variables in tracebacks.
  New in version 3.2: The command-line options -b, -c and -f were added.   New in version 3.5: The command-line option --locals.   New in version 3.7: The command-line option -k.  The command line can also be used for test discovery, for running all of the tests in a project or just a subset. Test Discovery  New in version 3.2.  Unittest supports simple test discovery. In order to be compatible with test discovery, all of the test files must be modules or packages (including namespace 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
  Note As a shortcut, python -m unittest is the equivalent of python -m unittest discover. If you want to pass arguments to test discovery the discover sub-command must be used explicitly.  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.  Changed in version 3.4: Test discovery supports namespace packages for start directory. Note that you need to the top level directory too. (e.g. python -m unittest discover -s root/namespace -t root).  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 unittest.TestCase instances. To make your own test cases you must write subclasses of TestCase or use FunctionTestCase. 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 implement a test method (i.e. a method whose name starts with test) in order to perform specific testing code: import unittest

class DefaultWidgetSizeTestCase(unittest.TestCase):
    def test_default_widget_size(self):
        widget = Widget('The widget')
        self.assertEqual(widget.size(), (50, 50))
 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 with an explanatory message, and unittest will identify the test case as a failure. Any other exceptions will be treated as errors. Tests can be numerous, and their set-up can be repetitive. Luckily, we can factor out set-up code by implementing a method called setUp(), which the testing framework will automatically call for every single test we run: import unittest

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

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

    def test_widget_resize(self):
        self.widget.resize(100,150)
        self.assertEqual(self.widget.size(), (100,150),
                         'wrong size after resize')
  Note The order in which the various tests will be run is determined by sorting the test method names with respect to the built-in ordering for strings.  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 test method will not be executed. Similarly, we can provide a tearDown() method that tidies up after the test method has been run: import unittest

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

    def tearDown(self):
        self.widget.dispose()
 If setUp() succeeded, tearDown() will be run whether the test method succeeded or not. Such a working environment for the testing code is called a test fixture. A new TestCase instance is created as a unique test fixture used to execute each individual test method. Thus setUp(), tearDown(), and __init__() will be called once per test. It is recommended that you use TestCase implementations to group tests together according to the features they test. unittest provides a mechanism for this: the test suite, represented by unittest’s TestSuite class. In most cases, calling unittest.main() will do the right thing and collect all the module’s test cases for you and execute them. However, should you want to customize the building of your test suite, you can do it yourself: def suite():
    suite = unittest.TestSuite()
    suite.addTest(WidgetTestCase('test_default_widget_size'))
    suite.addTest(WidgetTestCase('test_widget_resize'))
    return suite

if __name__ == '__main__':
    runner = unittest.TextTestRunner()
    runner.run(suite())
 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.  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, with optional set-up and tear-down methods: testcase = unittest.FunctionTestCase(testSomething,
                                     setUp=makeSomethingDB,
                                     tearDown=deleteSomethingDB)
  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. Skipping tests and expected failures  New in version 3.1.  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, calling TestCase.skipTest() within a setUp() or test method, or raising SkipTest directly. 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

    def test_maybe_skipped(self):
        if not external_resource_available():
            self.skipTest("external resource not available")
        # test code that depends on the external resource
        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_maybe_skipped (__main__.MyTestCase) ... skipped 'external resource not available'
test_windows_support (__main__.MyTestCase) ... skipped 'requires Windows'

----------------------------------------------------------------------
Ran 4 tests in 0.005s

OK (skipped=4)
 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 and exception 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 or error. If the test fails or errors it will be considered a success. If the test passes, it will be considered 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. Skipped modules will not have setUpModule() or tearDownModule() run. Distinguishing test iterations using subtests  New in version 3.4.  When there are very small differences among your tests, for instance some parameters, unittest allows you to distinguish them inside the body of a test method using the subTest() context manager. For example, the following test: class NumbersTest(unittest.TestCase):

    def test_even(self):
        """
        Test that numbers between 0 and 5 are all even.
        """
        for i in range(0, 6):
            with self.subTest(i=i):
                self.assertEqual(i % 2, 0)
 will produce the following output: ======================================================================
FAIL: test_even (__main__.NumbersTest) (i=1)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "subtests.py", line 32, in test_even
    self.assertEqual(i % 2, 0)
AssertionError: 1 != 0

======================================================================
FAIL: test_even (__main__.NumbersTest) (i=3)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "subtests.py", line 32, in test_even
    self.assertEqual(i % 2, 0)
AssertionError: 1 != 0

======================================================================
FAIL: test_even (__main__.NumbersTest) (i=5)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "subtests.py", line 32, in test_even
    self.assertEqual(i % 2, 0)
AssertionError: 1 != 0
 Without using a subtest, execution would stop after the first failure, and the error would be less easy to diagnose because the value of i wouldn’t be displayed: ======================================================================
FAIL: test_even (__main__.NumbersTest)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "subtests.py", line 32, in test_even
    self.assertEqual(i % 2, 0)
AssertionError: 1 != 0
 Classes and functions This section describes in depth the API of unittest. Test cases
class unittest.TestCase(methodName='runTest')
Instances of the TestCase class represent the logical test 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 tests, 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 base method: the method named methodName. In most uses of TestCase, you will neither change the methodName nor reimplement the default runTest() method.  Changed in version 3.2: TestCase can be instantiated successfully without providing a methodName. This makes it easier to experiment with TestCase from the interactive interpreter.  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 3.2.

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 3.2.

run(result=None)
Run the test, collecting the result into the TestResult 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 returned to run()’s caller. The same effect may be had by simply calling the TestCase instance.  Changed in version 3.3: Previous versions of run did not return the result. Neither did calling an 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 3.1.

subTest(msg=None, **params)
Return a context manager which executes the enclosed code block as a subtest. msg and params are optional, arbitrary values which are displayed whenever a subtest fails, allowing you to identify them clearly. A test case can contain any number of subtest declarations, and they can be arbitrarily nested. See Distinguishing test iterations using subtests for more information.  New in version 3.4.

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 3.1
assertIsNot(a, b) a is not b 3.1
assertIsNone(x) x is None 3.1
assertIsNotNone(x) x is not None 3.1
assertIn(a, b) a in b 3.1
assertNotIn(a, b) a not in b 3.1
assertIsInstance(a, b) isinstance(a, b) 3.2
assertNotIsInstance(a, b) not isinstance(a, b) 3.2   All the assert methods accept a msg argument that, if specified, is used as the error message on failure (see also longMessage). Note that the msg keyword argument can be passed to assertRaises(), assertRaisesRegex(), assertWarns(), assertWarnsRegex() only when they are used as a context manager.
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 str 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 3.1: Added the automatic calling of type-specific equality function.   Changed in version 3.2: assertMultiLineEqual() added as the default type equality function for comparing strings.

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 are (or are not) the same object.  New in version 3.1.

assertIsNone(expr, msg=None)
assertIsNotNone(expr, msg=None)
Test that expr is (or is not) None.  New in version 3.1.

assertIn(member, container, msg=None)
assertNotIn(member, container, msg=None)
Test that member is (or is not) in container.  New in version 3.1.

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 3.2.
 It is also possible to check the production of exceptions, warnings, and log messages using the following methods:
Method Checks that New in
assertRaises(exc, fun, *args, **kwds) fun(*args, **kwds) raises exc
assertRaisesRegex(exc, r, fun, *args, **kwds) fun(*args, **kwds) raises exc and the message matches regex r 3.1
assertWarns(warn, fun, *args, **kwds) fun(*args, **kwds) raises warn 3.2
assertWarnsRegex(warn, r, fun, *args, **kwds) fun(*args, **kwds) raises warn and the message matches regex r 3.2
assertLogs(logger, level) The with block logs on logger with minimum level 3.4
assertRaises(exception, callable, *args, **kwds)
assertRaises(exception, *, msg=None)
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 and possibly the msg arguments are given, return a context manager so that the code under test can be written inline rather than as a function: with self.assertRaises(SomeException):
    do_something()
 When used as a context manager, assertRaises() accepts the additional keyword argument msg. 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 3.1: Added the ability to use assertRaises() as a context manager.   Changed in version 3.2: Added the exception attribute.   Changed in version 3.3: Added the msg keyword argument when used as a context manager.

assertRaisesRegex(exception, regex, callable, *args, **kwds)
assertRaisesRegex(exception, regex, *, msg=None)
Like assertRaises() but also tests that regex matches on the string representation of the raised exception. regex may be a regular expression object or a string containing a regular expression suitable for use by re.search(). Examples: self.assertRaisesRegex(ValueError, "invalid literal for.*XYZ'$",
                       int, 'XYZ')
 or: with self.assertRaisesRegex(ValueError, 'literal'):
   int('XYZ')
  New in version 3.1: Added under the name assertRaisesRegexp.   Changed in version 3.2: Renamed to assertRaisesRegex().   Changed in version 3.3: Added the msg keyword argument when used as a context manager.

assertWarns(warning, callable, *args, **kwds)
assertWarns(warning, *, msg=None)
Test that a warning is triggered when callable is called with any positional or keyword arguments that are also passed to assertWarns(). The test passes if warning is triggered and fails if it isn’t. Any exception is an error. To catch any of a group of warnings, a tuple containing the warning classes may be passed as warnings. If only the warning and possibly the msg arguments are given, return a context manager so that the code under test can be written inline rather than as a function: with self.assertWarns(SomeWarning):
    do_something()
 When used as a context manager, assertWarns() accepts the additional keyword argument msg. The context manager will store the caught warning object in its warning attribute, and the source line which triggered the warnings in the filename and lineno attributes. This can be useful if the intention is to perform additional checks on the warning caught: with self.assertWarns(SomeWarning) as cm:
    do_something()

self.assertIn('myfile.py', cm.filename)
self.assertEqual(320, cm.lineno)
 This method works regardless of the warning filters in place when it is called.  New in version 3.2.   Changed in version 3.3: Added the msg keyword argument when used as a context manager.

assertWarnsRegex(warning, regex, callable, *args, **kwds)
assertWarnsRegex(warning, regex, *, msg=None)
Like assertWarns() but also tests that regex matches on the message of the triggered warning. regex may be a regular expression object or a string containing a regular expression suitable for use by re.search(). Example: self.assertWarnsRegex(DeprecationWarning,
                      r'legacy_function\(\) is deprecated',
                      legacy_function, 'XYZ')
 or: with self.assertWarnsRegex(RuntimeWarning, 'unsafe frobnicating'):
    frobnicate('/etc/passwd')
  New in version 3.2.   Changed in version 3.3: Added the msg keyword argument when used as a context manager.

assertLogs(logger=None, level=None)
A context manager to test that at least one message is logged on the logger or one of its children, with at least the given level. If given, logger should be a logging.Logger object or a str giving the name of a logger. The default is the root logger, which will catch all messages that were not blocked by a non-propagating descendent logger. If given, level should be either a numeric logging level or its string equivalent (for example either "ERROR" or logging.ERROR). The default is logging.INFO. The test passes if at least one message emitted inside the with block matches the logger and level conditions, otherwise it fails. The object returned by the context manager is a recording helper which keeps tracks of the matching log messages. It has two attributes:
records
A list of logging.LogRecord objects of the matching log messages.

output
A list of str objects with the formatted output of matching messages.
 Example: with self.assertLogs('foo', level='INFO') as cm:
   logging.getLogger('foo').info('first message')
   logging.getLogger('foo.bar').error('second message')
self.assertEqual(cm.output, ['INFO:foo:first message',
                             'ERROR:foo.bar:second message'])
  New in version 3.4.
 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 3.1
assertGreaterEqual(a, b) a >= b 3.1
assertLess(a, b) a < b 3.1
assertLessEqual(a, b) a <= b 3.1
assertRegex(s, r) r.search(s) 3.1
assertNotRegex(s, r) not r.search(s) 3.2
assertCountEqual(a, b) a and b have the same elements in the same number, regardless of their order. 3.2
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 3.2: 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 3.1.

assertRegex(text, regex, msg=None)
assertNotRegex(text, regex, msg=None)
Test that a regex search matches (or does not match) 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). regex may be a regular expression object or a string containing a regular expression suitable for use by re.search().  New in version 3.1: Added under the name assertRegexpMatches.   Changed in version 3.2: The method assertRegexpMatches() has been renamed to assertRegex().   New in version 3.2: assertNotRegex().   New in version 3.5: The name assertNotRegexpMatches is a deprecated alias for assertNotRegex().

assertCountEqual(first, second, msg=None)
Test that sequence first contains the same elements as second, 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 first and second. It verifies whether each element has the same count in both sequences. Equivalent to: assertEqual(Counter(list(first)), Counter(list(second))) but works with sequences of unhashable objects as well.  New in 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 3.1.
 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 3.1
assertSequenceEqual(a, b) sequences 3.1
assertListEqual(a, b) lists 3.1
assertTupleEqual(a, b) tuples 3.1
assertSetEqual(a, b) sets or frozensets 3.1
assertDictEqual(a, b) dicts 3.1
assertMultiLineEqual(first, second, msg=None)
Test that the multiline string first is equal to the string second. When not equal a diff of the two strings highlighting the differences will be included in the error message. This method is used by default when comparing strings with assertEqual().  New in version 3.1.

assertSequenceEqual(first, second, msg=None, seq_type=None)
Tests that two sequences are equal. If a seq_type is supplied, both first and second must be instances of seq_type or a failure will be raised. If the sequences are different an error message is constructed that shows the difference between the two. This method is not called directly by assertEqual(), but it’s used to implement assertListEqual() and assertTupleEqual().  New in version 3.1.

assertListEqual(first, second, msg=None)
assertTupleEqual(first, second, msg=None)
Tests that two lists or tuples are equal. If not, an error message is constructed that shows only the differences between the two. An error is also raised if either of the parameters are of the wrong type. These methods are used by default when comparing lists or tuples with assertEqual().  New in version 3.1.

assertSetEqual(first, second, msg=None)
Tests that two sets are equal. If not, an error message is constructed that lists the differences between the sets. This method is used by default when comparing sets or frozensets with assertEqual(). Fails if either of first or second does not have a set.difference() method.  New in version 3.1.

assertDictEqual(first, second, msg=None)
Test that two dictionaries are equal. If not, an error message is constructed that shows the differences in the dictionaries. This method will be used by default to compare dictionaries in calls to assertEqual().  New in version 3.1.
 Finally the TestCase provides the following methods and attributes:
fail(msg=None)
Signals a test failure unconditionally, with msg or None for the error message.

failureException
This class attribute gives the exception raised by the test method. If a test framework needs to use a specialized exception, possibly to carry additional information, it must subclass this exception in order to “play fair” with the framework. The initial value of this attribute is AssertionError.

longMessage
This class attribute determines what happens when a custom failure message is passed as the msg argument to an assertXYY call that fails. True is the default value. In this case, the custom message is appended to the end of the standard failure message. When set to False, the custom message replaces the standard message. The class setting can be overridden in individual test methods by assigning an instance attribute, self.longMessage, to True or False before calling the assert methods. The class setting gets reset before each test call.  New in version 3.1.

maxDiff
This attribute controls the maximum length of diffs output by assert methods that report diffs on failure. It defaults to 80*8 characters. Assert methods affected by this attribute are assertSequenceEqual() (including all the sequence comparison methods that delegate to it), assertDictEqual() and assertMultiLineEqual(). Setting maxDiff to None means that there is no maximum length of diffs.  New in version 3.2.
 Testing frameworks can use the following methods to collect information on the test:
countTestCases()
Return the number of tests represented by this test object. For TestCase instances, this will always be 1.

defaultTestResult()
Return an instance of the test result class that should be used for this test case class (if no other result instance is provided to the run() method). For TestCase instances, this will always be an instance of TestResult; subclasses of TestCase should override this as necessary.

id()
Return a string identifying the specific test case. This is usually the full name of the test method, including the module and class name.

shortDescription()
Returns a description of the test, or None if no description has been provided. The default implementation of this method returns the first line of the test method’s docstring, if available, or None.  Changed in version 3.1: In 3.1 this was changed to add the test name to the short description even in the presence of a docstring. This caused compatibility issues with unittest extensions and adding the test name was moved to the TextTestResult in Python 3.2.

addCleanup(function, /, *args, **kwargs)
Add a function to be called after tearDown() to cleanup resources used during the test. Functions will be called in reverse order to the order they are added (LIFO). They are called with any arguments and keyword arguments passed into addCleanup() when they are added. If setUp() fails, meaning that tearDown() is not called, then any cleanup functions added will still be called.  New in version 3.1.

doCleanups()
This method is called unconditionally after tearDown(), or after setUp() if setUp() raises an exception. It is responsible for calling all the cleanup functions added by addCleanup(). If you need cleanup functions to be called prior to tearDown() then you can call doCleanups() yourself. doCleanups() pops methods off the stack of cleanup functions one at a time, so it can be called at any time.  New in version 3.1.

classmethod addClassCleanup(function, /, *args, **kwargs)
Add a function to be called after tearDownClass() to cleanup resources used during the test class. Functions will be called in reverse order to the order they are added (LIFO). They are called with any arguments and keyword arguments passed into addClassCleanup() when they are added. If setUpClass() fails, meaning that tearDownClass() is not called, then any cleanup functions added will still be called.  New in version 3.8.

classmethod doClassCleanups()
This method is called unconditionally after tearDownClass(), or after setUpClass() if setUpClass() raises an exception. It is responsible for calling all the cleanup functions added by addClassCleanup(). If you need cleanup functions to be called prior to tearDownClass() then you can call doClassCleanups() yourself. doClassCleanups() pops methods off the stack of cleanup functions one at a time, so it can be called at any time.  New in version 3.8.

class unittest.IsolatedAsyncioTestCase(methodName='runTest')
This class provides an API similar to TestCase and also accepts coroutines as test functions.  New in version 3.8.
coroutine asyncSetUp()
Method called to prepare the test fixture. This is called after setUp(). 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.

coroutine asyncTearDown()
Method called immediately after the test method has been called and the result recorded. This is called before tearDown(). 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 asyncSetUp() succeeds, regardless of the outcome of the test method. The default implementation does nothing.

addAsyncCleanup(function, /, *args, **kwargs)
This method accepts a coroutine that can be used as a cleanup function.

run(result=None)
Sets up a new event loop to run the test, collecting the result into the TestResult 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 returned to run()’s caller. At the end of the test all the tasks in the event loop are cancelled.
 An example illustrating the order: from unittest import IsolatedAsyncioTestCase

events = []

class Test(IsolatedAsyncioTestCase):

    def setUp(self):
        events.append("setUp")

    async def asyncSetUp(self):
        self._async_connection = await AsyncConnection()
        events.append("asyncSetUp")

    async def test_response(self):
        events.append("test_response")
        response = await self._async_connection.get("https://example.com")
        self.assertEqual(response.status_code, 200)
        self.addAsyncCleanup(self.on_cleanup)

    def tearDown(self):
        events.append("tearDown")

    async def asyncTearDown(self):
        await self._async_connection.close()
        events.append("asyncTearDown")

    async def on_cleanup(self):
        events.append("cleanup")

if __name__ == "__main__":
    unittest.main()
 After running the test, events would contain ["setUp", "asyncSetUp", "test_response", "asyncTearDown", "tearDown", "cleanup"].

class unittest.FunctionTestCase(testFunc, setUp=None, tearDown=None, description=None)
This class implements the portion of the TestCase interface which allows the test runner to drive the test, but does not provide the methods which test code can use to check and report errors. This is used to create test cases using legacy test code, allowing it to be integrated into a unittest-based test framework.
 Deprecated aliases For historical reasons, some of the TestCase methods had one or more aliases that are now deprecated. The following table lists the correct names along with their deprecated aliases:
Method Name Deprecated alias Deprecated alias
assertEqual() failUnlessEqual assertEquals
assertNotEqual() failIfEqual assertNotEquals
assertTrue() failUnless assert_
assertFalse() failIf
assertRaises() failUnlessRaises
assertAlmostEqual() failUnlessAlmostEqual assertAlmostEquals
assertNotAlmostEqual() failIfAlmostEqual assertNotAlmostEquals
assertRegex()  assertRegexpMatches
assertNotRegex()  assertNotRegexpMatches
assertRaisesRegex()  assertRaisesRegexp    Deprecated since version 3.1: The fail* aliases listed in the second column have been deprecated.   Deprecated since version 3.2: The assert* aliases listed in the third column have been deprecated.   Deprecated since version 3.2: assertRegexpMatches and assertRaisesRegexp have been renamed to assertRegex() and assertRaisesRegex().   Deprecated since version 3.5: The assertNotRegexpMatches name is deprecated in favor of assertNotRegex().  Grouping tests
class unittest.TestSuite(tests=())
This class represents an aggregation of individual test cases and test suites. The class presents the interface needed by the test runner to allow it to be run as any other test case. Running a TestSuite instance is the same as iterating over the suite, running each test individually. If tests is given, it must be an iterable of individual test cases or other test suites that will be used to build the suite initially. Additional methods are provided to add test cases and suites to the collection later on. TestSuite objects behave much like TestCase objects, except they do not actually implement a test. Instead, they are used to aggregate tests into groups of tests that should be run together. Some additional methods are available to add tests to TestSuite instances:
addTest(test)
Add a TestCase or TestSuite to the suite.

addTests(tests)
Add all the tests from an iterable of TestCase and TestSuite instances to this test suite. This is equivalent to iterating over tests, calling addTest() for each element.
 TestSuite shares the following methods with TestCase:
run(result)
Run the tests associated with this suite, collecting the result into the test result object passed as result. Note that unlike TestCase.run(), TestSuite.run() requires the result object to be passed in.

debug()
Run the tests associated with this suite 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.

countTestCases()
Return the number of tests represented by this test object, including all individual tests and sub-suites.

__iter__()
Tests grouped by a TestSuite are always accessed by iteration. Subclasses can lazily provide tests by overriding __iter__(). Note that this method may be called several times on a single suite (for example when counting tests or comparing for equality) so the tests returned by repeated iterations before TestSuite.run() must be the same for each call iteration. After TestSuite.run(), callers should not rely on the tests returned by this method unless the caller uses a subclass that overrides TestSuite._removeTestAtIndex() to preserve test references.  Changed in version 3.2: In earlier versions the TestSuite accessed tests directly rather than through iteration, so overriding __iter__() wasn’t sufficient for providing tests.   Changed in version 3.4: In earlier versions the TestSuite held references to each TestCase after TestSuite.run(). Subclasses can restore that behavior by overriding TestSuite._removeTestAtIndex().
 In the typical usage of a TestSuite object, the run() method is invoked by a TestRunner rather than by the end-user test harness.
 Loading and running tests
class unittest.TestLoader
The TestLoader class is used to create test suites from classes and modules. Normally, there is no need to create an instance of this class; the unittest module provides an instance that can be shared as unittest.defaultTestLoader. Using a subclass or instance, however, allows customization of some configurable properties. TestLoader objects have the following attributes:
errors
A list of the non-fatal errors encountered while loading tests. Not reset by the loader at any point. Fatal errors are signalled by the relevant a method raising an exception to the caller. Non-fatal errors are also indicated by a synthetic test that will raise the original error when run.  New in version 3.5.
 TestLoader objects have the following methods:
loadTestsFromTestCase(testCaseClass)
Return a suite of all test cases contained in the TestCase-derived testCaseClass. A test case instance is created for each method named by getTestCaseNames(). By default these are the method names beginning with test. If getTestCaseNames() returns no methods, but the runTest() method is implemented, a single test case is created for that method instead.

loadTestsFromModule(module, pattern=None)
Return a suite of all test cases contained in the given module. This method searches module for classes derived from TestCase and creates an instance of the class for each test method defined for the class.  Note While using a hierarchy of TestCase-derived classes can be convenient in sharing fixtures and helper functions, defining test methods on base classes that are not intended to be instantiated directly does not play well with this method. Doing so, however, can be useful when the fixtures are different and defined in subclasses.  If a module provides a load_tests function it will be called to load the tests. This allows modules to customize test loading. This is the load_tests protocol. The pattern argument is passed as the third argument to load_tests.  Changed in version 3.2: Support for load_tests added.   Changed in version 3.5: The undocumented and unofficial use_load_tests default argument is deprecated and ignored, although it is still accepted for backward compatibility. The method also now accepts a keyword-only argument pattern which is passed to load_tests as the third argument.

loadTestsFromName(name, module=None)
Return a suite of all test cases given a string specifier. The specifier name is a “dotted name” that may resolve either to a module, a test case class, a test method within a test case class, a TestSuite instance, or a callable object which returns a TestCase or TestSuite instance. These checks are applied in the order listed here; that is, a method on a possible test case class will be picked up as “a test method within a test case class”, rather than “a callable object”. For example, if you have a module SampleTests containing a TestCase-derived class SampleTestCase with three test methods (test_one(), test_two(), and test_three()), the specifier 'SampleTests.SampleTestCase' would cause this method to return a suite which will run all three test methods. Using the specifier 'SampleTests.SampleTestCase.test_two' would cause it to return a test suite which will run only the test_two() test method. The specifier can refer to modules and packages which have not been imported; they will be imported as a side-effect. The method optionally resolves name relative to the given module.  Changed in version 3.5: If an ImportError or AttributeError occurs while traversing name then a synthetic test that raises that error when run will be returned. These errors are included in the errors accumulated by self.errors.

loadTestsFromNames(names, module=None)
Similar to loadTestsFromName(), but takes a sequence of names rather than a single name. The return value is a test suite which supports all the tests defined for each name.

getTestCaseNames(testCaseClass)
Return a sorted sequence of method names found within testCaseClass; this should be a subclass of TestCase.

discover(start_dir, pattern='test*.py', top_level_dir=None)
Find all the test modules by recursing into subdirectories from the specified start directory, and return a TestSuite object containing them. Only test files that match pattern will be loaded. (Using shell style pattern matching.) Only module names that are importable (i.e. are valid Python identifiers) will be loaded. All test modules must be importable from the top level of the project. If the start directory is not the top level directory then the top level directory must be specified separately. If importing a module fails, for example due to a syntax error, then this will be recorded as a single error and discovery will continue. If the import failure is due to SkipTest being raised, it will be recorded as a skip instead of an error. If a package (a directory containing a file named __init__.py) is found, the package will be checked for a load_tests function. If this exists then it will be called package.load_tests(loader, tests, pattern). Test discovery takes care to ensure that a package is only checked for tests once during an invocation, even if the load_tests function itself calls loader.discover. If load_tests exists then discovery does not recurse into the package, load_tests is responsible for loading all tests in the package. The pattern is deliberately not stored as a loader attribute so that packages can continue discovery themselves. top_level_dir is stored so load_tests does not need to pass this argument in to loader.discover(). start_dir can be a dotted module name as well as a directory.  New in version 3.2.   Changed in version 3.4: Modules that raise SkipTest on import are recorded as skips, not errors.   Changed in version 3.4: start_dir can be a namespace packages.   Changed in version 3.4: Paths are sorted before being imported so that execution order is the same even if the underlying file system’s ordering is not dependent on file name.   Changed in version 3.5: Found packages are now checked for load_tests regardless of whether their path matches pattern, because it is impossible for a package name to match the default pattern.
 The following attributes of a TestLoader can be configured either by subclassing or assignment on an instance:
testMethodPrefix
String giving the prefix of method names which will be interpreted as test methods. The default value is 'test'. This affects getTestCaseNames() and all the loadTestsFrom*() methods.

sortTestMethodsUsing
Function to be used to compare method names when sorting them in getTestCaseNames() and all the loadTestsFrom*() methods.

suiteClass
Callable object that constructs a test suite from a list of tests. No methods on the resulting object are needed. The default value is the TestSuite class. This affects all the loadTestsFrom*() methods.

testNamePatterns
List of Unix shell-style wildcard test name patterns that test methods have to match to be included in test suites (see -v option). If this attribute is not None (the default), all test methods to be included in test suites must match one of the patterns in this list. Note that matches are always performed using fnmatch.fnmatchcase(), so unlike patterns passed to the -v option, simple substring patterns will have to be converted using * wildcards. This affects all the loadTestsFrom*() methods.  New in version 3.7.

class unittest.TestResult
This class is used to compile information about which tests have succeeded and which have failed. A TestResult object stores the results of a set of tests. The TestCase and TestSuite classes ensure that results are properly recorded; test authors do not need to worry about recording the outcome of tests. Testing frameworks built on top of unittest may want access to the TestResult object generated by running a set of tests for reporting purposes; a TestResult instance is returned by the TestRunner.run() method for this purpose. TestResult instances have the following attributes that will be of interest when inspecting the results of running a set of tests:
errors
A list containing 2-tuples of TestCase instances and strings holding formatted tracebacks. Each tuple represents a test which raised an unexpected exception.

failures
A list containing 2-tuples of TestCase instances and strings holding formatted tracebacks. Each tuple represents a test where a failure was explicitly signalled using the TestCase.assert*() methods.

skipped
A list containing 2-tuples of TestCase instances and strings holding the reason for skipping the test.  New in version 3.1.

expectedFailures
A list containing 2-tuples of TestCase instances and strings holding formatted tracebacks. Each tuple represents an expected failure or error of the test case.

unexpectedSuccesses
A list containing TestCase instances that were marked as expected failures, but succeeded.

shouldStop
Set to True when the execution of tests should stop by stop().

testsRun
The total number of tests run so far.

buffer
If set to true, sys.stdout and sys.stderr will be buffered in between startTest() and stopTest() being called. Collected output will only be echoed onto the real sys.stdout and sys.stderr if the test fails or errors. Any output is also attached to the failure / error message.  New in version 3.2.

failfast
If set to true stop() will be called on the first failure or error, halting the test run.  New in version 3.2.

tb_locals
If set to true then local variables will be shown in tracebacks.  New in version 3.5.

wasSuccessful()
Return True if all tests run so far have passed, otherwise returns False.  Changed in version 3.4: Returns False if there were any unexpectedSuccesses from tests marked with the expectedFailure() decorator.

stop()
This method can be called to signal that the set of tests being run should be aborted by setting the shouldStop attribute to True. TestRunner objects should respect this flag and return without running any additional tests. For example, this feature is used by the TextTestRunner class to stop the test framework when the user signals an interrupt from the keyboard. Interactive tools which provide TestRunner implementations can use this in a similar manner.
 The following methods of the TestResult class are used to maintain the internal data structures, and may be extended in subclasses to support additional reporting requirements. This is particularly useful in building tools which support interactive reporting while tests are being run.
startTest(test)
Called when the test case test is about to be run.

stopTest(test)
Called after the test case test has been executed, regardless of the outcome.

startTestRun()
Called once before any tests are executed.  New in version 3.1.

stopTestRun()
Called once after all tests are executed.  New in version 3.1.

addError(test, err)
Called when the test case test raises an unexpected exception. err is a tuple of the form returned by sys.exc_info(): (type, value,
traceback). The default implementation appends a tuple (test, formatted_err) to the instance’s errors attribute, where formatted_err is a formatted traceback derived from err.

addFailure(test, err)
Called when the test case test signals a failure. err is a tuple of the form returned by sys.exc_info(): (type, value, traceback). The default implementation appends a tuple (test, formatted_err) to the instance’s failures attribute, where formatted_err is a formatted traceback derived from err.

addSuccess(test)
Called when the test case test succeeds. The default implementation does nothing.

addSkip(test, reason)
Called when the test case test is skipped. reason is the reason the test gave for skipping. The default implementation appends a tuple (test, reason) to the instance’s skipped attribute.

addExpectedFailure(test, err)
Called when the test case test fails or errors, but was marked with the expectedFailure() decorator. The default implementation appends a tuple (test, formatted_err) to the instance’s expectedFailures attribute, where formatted_err is a formatted traceback derived from err.

addUnexpectedSuccess(test)
Called when the test case test was marked with the expectedFailure() decorator, but succeeded. The default implementation appends the test to the instance’s unexpectedSuccesses attribute.

addSubTest(test, subtest, outcome)
Called when a subtest finishes. test is the test case corresponding to the test method. subtest is a custom TestCase instance describing the subtest. If outcome is None, the subtest succeeded. Otherwise, it failed with an exception where outcome is a tuple of the form returned by sys.exc_info(): (type, value, traceback). The default implementation does nothing when the outcome is a success, and records subtest failures as normal failures.  New in version 3.4.

class unittest.TextTestResult(stream, descriptions, verbosity)
A concrete implementation of TestResult used by the TextTestRunner.  New in version 3.2: This class was previously named _TextTestResult. The old name still exists as an alias but is deprecated.

unittest.defaultTestLoader
Instance of the TestLoader class intended to be shared. If no customization of the TestLoader is needed, this instance can be used instead of repeatedly creating new instances.

class unittest.TextTestRunner(stream=None, descriptions=True, verbosity=1, failfast=False, buffer=False, resultclass=None, warnings=None, *, tb_locals=False)
A basic test runner implementation that outputs results to a stream. If stream is None, the default, sys.stderr is used as the output stream. This class has a few configurable parameters, but is essentially very simple. Graphical applications which run test suites should provide alternate implementations. Such implementations should accept **kwargs as the interface to construct runners changes when features are added to unittest. By default this runner shows DeprecationWarning, PendingDeprecationWarning, ResourceWarning and ImportWarning even if they are ignored by default. Deprecation warnings caused by deprecated unittest methods are also special-cased and, when the warning filters are 'default' or 'always', they will appear only once per-module, in order to avoid too many warning messages. This behavior can be overridden using Python’s -Wd or -Wa options (see Warning control) and leaving warnings to None.  Changed in version 3.2: Added the warnings argument.   Changed in version 3.2: The default stream is set to sys.stderr at instantiation time rather than import time.   Changed in version 3.5: Added the tb_locals parameter.
_makeResult()
This method returns the instance of TestResult used by run(). It is not intended to be called directly, but can be overridden in subclasses to provide a custom TestResult. _makeResult() instantiates the class or callable passed in the TextTestRunner constructor as the resultclass argument. It defaults to TextTestResult if no resultclass is provided. The result class is instantiated with the following arguments: stream, descriptions, verbosity

run(test)
This method is the main public interface to the TextTestRunner. This method takes a TestSuite or TestCase instance. A TestResult is created by calling _makeResult() and the test(s) are run and the results printed to stdout.

unittest.main(module='__main__', defaultTest=None, argv=None, testRunner=None, testLoader=unittest.defaultTestLoader, exit=True, verbosity=1, failfast=None, catchbreak=None, buffer=None, warnings=None)
A command-line program that loads a set of tests from module and runs them; this is primarily for making test modules conveniently executable. The simplest use for this function is to include the following line at the end of a test script: if __name__ == '__main__':
    unittest.main()
 You can run tests with more detailed information by passing in the verbosity argument: if __name__ == '__main__':
    unittest.main(verbosity=2)
 The defaultTest argument is either the name of a single test or an iterable of test names to run if no test names are specified via argv. If not specified or None and no test names are provided via argv, all tests found in module are run. The argv argument can be a list of options passed to the program, with the first element being the program name. If not specified or None, the values of sys.argv are used. The testRunner argument can either be a test runner class or an already created instance of it. By default main calls sys.exit() with an exit code indicating success or failure of the tests run. The testLoader argument has to be a TestLoader instance, and defaults to defaultTestLoader. main supports being used from the interactive interpreter by passing in the argument exit=False. This displays the result on standard output without calling sys.exit(): >>> from unittest import main
>>> main(module='test_module', exit=False)
 The failfast, catchbreak and buffer parameters have the same effect as the same-name command-line options. The warnings argument specifies the warning filter that should be used while running the tests. If it’s not specified, it will remain None if a -W option is passed to python (see Warning control), otherwise it will be set to 'default'. Calling main actually returns an instance of the TestProgram class. This stores the result of the tests run as the result attribute.  Changed in version 3.1: The exit parameter was added.   Changed in version 3.2: The verbosity, failfast, catchbreak, buffer and warnings parameters were added.   Changed in version 3.4: The defaultTest parameter was changed to also accept an iterable of test names.
 load_tests Protocol  New in version 3.2.  Modules or packages can customize how tests are loaded from them during normal test runs or test discovery by implementing a function called load_tests. If a test module defines load_tests it will be called by TestLoader.loadTestsFromModule() with the following arguments: load_tests(loader, standard_tests, pattern)
 where pattern is passed straight through from loadTestsFromModule. It defaults to None. It should return a TestSuite. loader is the instance of TestLoader doing the loading. standard_tests are the tests that would be loaded by default from the module. It is common for test modules to only want to add or remove tests from the standard set of tests. The third argument is used when loading packages as part of test discovery. A typical load_tests function that loads tests from a specific set of TestCase classes may look like: test_cases = (TestCase1, TestCase2, TestCase3)

def load_tests(loader, tests, pattern):
    suite = TestSuite()
    for test_class in test_cases:
        tests = loader.loadTestsFromTestCase(test_class)
        suite.addTests(tests)
    return suite
 If discovery is started in a directory containing a package, either from the command line or by calling TestLoader.discover(), then the package __init__.py will be checked for load_tests. If that function does not exist, discovery will recurse into the package as though it were just another directory. Otherwise, discovery of the package’s tests will be left up to load_tests which is called with the following arguments: load_tests(loader, standard_tests, pattern)
 This should return a TestSuite representing all the tests from the package. (standard_tests will only contain tests collected from __init__.py.) Because the pattern is passed into load_tests the package is free to continue (and potentially modify) test discovery. A ‘do nothing’ load_tests function for a test package would look like: def load_tests(loader, standard_tests, pattern):
    # top level directory cached on loader instance
    this_dir = os.path.dirname(__file__)
    package_tests = loader.discover(start_dir=this_dir, pattern=pattern)
    standard_tests.addTests(package_tests)
    return standard_tests
  Changed in version 3.5: Discovery no longer checks package names for matching pattern due to the impossibility of package names matching the default pattern.  Class and Module Fixtures Class and module level fixtures are implemented in TestSuite. When the test suite encounters a test from a new class then tearDownClass() from the previous class (if there is one) is called, followed by setUpClass() from the new class. Similarly if a test is from a different module from the previous test then tearDownModule from the previous module is run, followed by setUpModule from the new module. After all the tests have run the final tearDownClass and tearDownModule are run. Note that shared fixtures do not play well with [potential] features like test parallelization and they break test isolation. They should be used with care. The default ordering of tests created by the unittest test loaders is to group all tests from the same modules and classes together. This will lead to setUpClass / setUpModule (etc) being called exactly once per class and module. If you randomize the order, so that tests from different modules and classes are adjacent to each other, then these shared fixture functions may be called multiple times in a single test run. Shared fixtures are not intended to work with suites with non-standard ordering. A BaseTestSuite still exists for frameworks that don’t want to support shared fixtures. If there are any exceptions raised during one of the shared fixture functions the test is reported as an error. Because there is no corresponding test instance an _ErrorHolder object (that has the same interface as a TestCase) is created to represent the error. If you are just using the standard unittest test runner then this detail doesn’t matter, but if you are a framework author it may be relevant. setUpClass and tearDownClass These must be implemented as class methods: import unittest

class Test(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls._connection = createExpensiveConnectionObject()

    @classmethod
    def tearDownClass(cls):
        cls._connection.destroy()
 If you want the setUpClass and tearDownClass on base classes called then you must call up to them yourself. The implementations in TestCase are empty. If an exception is raised during a setUpClass then the tests in the class are not run and the tearDownClass is not run. Skipped classes will not have setUpClass or tearDownClass run. If the exception is a SkipTest exception then the class will be reported as having been skipped instead of as an error. setUpModule and tearDownModule These should be implemented as functions: def setUpModule():
    createConnection()

def tearDownModule():
    closeConnection()
 If an exception is raised in a setUpModule then none of the tests in the module will be run and the tearDownModule will not be run. If the exception is a SkipTest exception then the module will be reported as having been skipped instead of as an error. To add cleanup code that must be run even in the case of an exception, use addModuleCleanup:
unittest.addModuleCleanup(function, /, *args, **kwargs)
Add a function to be called after tearDownModule() to cleanup resources used during the test class. Functions will be called in reverse order to the order they are added (LIFO). They are called with any arguments and keyword arguments passed into addModuleCleanup() when they are added. If setUpModule() fails, meaning that tearDownModule() is not called, then any cleanup functions added will still be called.  New in version 3.8.

unittest.doModuleCleanups()
This function is called unconditionally after tearDownModule(), or after setUpModule() if setUpModule() raises an exception. It is responsible for calling all the cleanup functions added by addCleanupModule(). If you need cleanup functions to be called prior to tearDownModule() then you can call doModuleCleanups() yourself. doModuleCleanups() pops methods off the stack of cleanup functions one at a time, so it can be called at any time.  New in version 3.8.
 Signal Handling  New in version 3.2.  The -c/--catch command-line option to unittest, along with the catchbreak parameter to unittest.main(), provide more friendly handling of control-C during a test run. With catch break behavior enabled control-C will allow the currently running test to complete, and the test run will then end and report all the results so far. A second control-c will raise a KeyboardInterrupt in the usual way. The control-c handling signal handler attempts to remain compatible with code or tests that install their own signal.SIGINT handler. If the unittest handler is called but isn’t the installed signal.SIGINT handler, i.e. it has been replaced by the system under test and delegated to, then it calls the default handler. This will normally be the expected behavior by code that replaces an installed handler and delegates to it. For individual tests that need unittest control-c handling disabled the removeHandler() decorator can be used. There are a few utility functions for framework authors to enable control-c handling functionality within test frameworks.
unittest.installHandler()
Install the control-c handler. When a signal.SIGINT is received (usually in response to the user pressing control-c) all registered results have stop() called.

unittest.registerResult(result)
Register a TestResult object for control-c handling. Registering a result stores a weak reference to it, so it doesn’t prevent the result from being garbage collected. Registering a TestResult object has no side-effects if control-c handling is not enabled, so test frameworks can unconditionally register all results they create independently of whether or not handling is enabled.

unittest.removeResult(result)
Remove a registered result. Once a result has been removed then stop() will no longer be called on that result object in response to a control-c.

unittest.removeHandler(function=None)
When called without arguments this function removes the control-c handler if it has been installed. This function can also be used as a test decorator to temporarily remove the handler while the test is being executed: @unittest.removeHandler
def test_signal_handling(self):
    ...


unittest.addModuleCleanup(function, /, *args, **kwargs)
Add a function to be called after tearDownModule() to cleanup resources used during the test class. Functions will be called in reverse order to the order they are added (LIFO). They are called with any arguments and keyword arguments passed into addModuleCleanup() when they are added. If setUpModule() fails, meaning that tearDownModule() is not called, then any cleanup functions added will still be called.  New in version 3.8.


unittest.defaultTestLoader
Instance of the TestLoader class intended to be shared. If no customization of the TestLoader is needed, this instance can be used instead of repeatedly creating new instances.