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# [`unittest.mock`](unittest.mock.xhtml#module-unittest.mock "unittest.mock: Mock object library.") 上手指南
3\.3 新版功能.
## 使用 mock
### 模拟方法调用
使用 [`Mock`](unittest.mock.xhtml#unittest.mock.Mock "unittest.mock.Mock") 的常见场景：
- 模拟函数调用
- 记录“对象上的方法调用”
你可能需要替换一个对象上的方法，用于确认此方法被系统中的其他部分调用过，并且调用时使用了正确的参数。
```
>>> real = SomeClass()
>>> real.method = MagicMock(name='method')
>>> real.method(3, 4, 5, key='value')
<MagicMock name='method()' id='...'>
```
使用了 mock（本例中的 `real.method`）之后，它有方法和属性可以让你针对它是被如何使用的下断言。
注解
在多数示例中，[`Mock`](unittest.mock.xhtml#unittest.mock.Mock "unittest.mock.Mock") 与 [`MagicMock`](unittest.mock.xhtml#unittest.mock.MagicMock "unittest.mock.MagicMock") 两个类可以相互替换，而 `MagicMock` 是一个更适用的类，通常情况下，使用它就可以了。
如果 mock 被调用，它的 [`called`](unittest.mock.xhtml#unittest.mock.Mock.called "unittest.mock.Mock.called") 属性就会变成 `True`，更重要的是，我们可以使用 [`assert_called_with()`](unittest.mock.xhtml#unittest.mock.Mock.assert_called_with "unittest.mock.Mock.assert_called_with") 或者 [`assert_called_once_with()`](unittest.mock.xhtml#unittest.mock.Mock.assert_called_once_with "unittest.mock.Mock.assert_called_once_with") 方法来确认它在被调用时使用了正确的参数。
在如下的测试示例中，验证对于 `ProductionClass().method` 的调用会导致 `something` 的调用。
```
>>> class ProductionClass:
... def method(self):
... self.something(1, 2, 3)
... def something(self, a, b, c):
... pass
...
>>> real = ProductionClass()
>>> real.something = MagicMock()
>>> real.method()
>>> real.something.assert_called_once_with(1, 2, 3)
```
### 对象上的方法调用的 mock
In the last example we patched a method directly on an object to check that it was called correctly. Another common use case is to pass an object into a method (or some part of the system under test) and then check that it is used in the correct way.
The simple `ProductionClass` below has a `closer` method. If it is called with an object then it calls `close` on it.
```
>>> class ProductionClass:
... def closer(self, something):
... something.close()
...
```
So to test it we need to pass in an object with a `close` method and check that it was called correctly.
```
>>> real = ProductionClass()
>>> mock = Mock()
>>> real.closer(mock)
>>> mock.close.assert_called_with()
```
We don't have to do any work to provide the 'close' method on our mock. Accessing close creates it. So, if 'close' hasn't already been called then accessing it in the test will create it, but [`assert_called_with()`](unittest.mock.xhtml#unittest.mock.Mock.assert_called_with "unittest.mock.Mock.assert_called_with")will raise a failure exception.
### Mocking Classes
A common use case is to mock out classes instantiated by your code under test. When you patch a class, then that class is replaced with a mock. Instances are created by *calling the class*. This means you access the "mock instance" by looking at the return value of the mocked class.
In the example below we have a function `some_function` that instantiates `Foo`and calls a method on it. The call to [`patch()`](unittest.mock.xhtml#unittest.mock.patch "unittest.mock.patch") replaces the class `Foo` with a mock. The `Foo` instance is the result of calling the mock, so it is configured by modifying the mock [`return_value`](unittest.mock.xhtml#unittest.mock.Mock.return_value "unittest.mock.Mock.return_value").
```
>>> def some_function():
... instance = module.Foo()
... return instance.method()
...
>>> with patch('module.Foo') as mock:
... instance = mock.return_value
... instance.method.return_value = 'the result'
... result = some_function()
... assert result == 'the result'
```
### Naming your mocks
It can be useful to give your mocks a name. The name is shown in the repr of the mock and can be helpful when the mock appears in test failure messages. The name is also propagated to attributes or methods of the mock:
```
>>> mock = MagicMock(name='foo')
>>> mock
<MagicMock name='foo' id='...'>
>>> mock.method
<MagicMock name='foo.method' id='...'>
```
### Tracking all Calls
Often you want to track more than a single call to a method. The [`mock_calls`](unittest.mock.xhtml#unittest.mock.Mock.mock_calls "unittest.mock.Mock.mock_calls") attribute records all calls to child attributes of the mock - and also to their children.
```
>>> mock = MagicMock()
>>> mock.method()
<MagicMock name='mock.method()' id='...'>
>>> mock.attribute.method(10, x=53)
<MagicMock name='mock.attribute.method()' id='...'>
>>> mock.mock_calls
[call.method(), call.attribute.method(10, x=53)]
```
If you make an assertion about `mock_calls` and any unexpected methods have been called, then the assertion will fail. This is useful because as well as asserting that the calls you expected have been made, you are also checking that they were made in the right order and with no additional calls:
You use the [`call`](unittest.mock.xhtml#unittest.mock.call "unittest.mock.call") object to construct lists for comparing with `mock_calls`:
```
>>> expected = [call.method(), call.attribute.method(10, x=53)]
>>> mock.mock_calls == expected
True
```
However, parameters to calls that return mocks are not recorded, which means it is not possible to track nested calls where the parameters used to create ancestors are important:
```
>>> m = Mock()
>>> m.factory(important=True).deliver()
<Mock name='mock.factory().deliver()' id='...'>
>>> m.mock_calls[-1] == call.factory(important=False).deliver()
True
```
### Setting Return Values and Attributes
Setting the return values on a mock object is trivially easy:
```
>>> mock = Mock()
>>> mock.return_value = 3
>>> mock()
3
```
Of course you can do the same for methods on the mock:
```
>>> mock = Mock()
>>> mock.method.return_value = 3
>>> mock.method()
3
```
The return value can also be set in the constructor:
```
>>> mock = Mock(return_value=3)
>>> mock()
3
```
If you need an attribute setting on your mock, just do it:
```
>>> mock = Mock()
>>> mock.x = 3
>>> mock.x
3
```
Sometimes you want to mock up a more complex situation, like for example `mock.connection.cursor().execute("SELECT 1")`. If we wanted this call to return a list, then we have to configure the result of the nested call.
We can use [`call`](unittest.mock.xhtml#unittest.mock.call "unittest.mock.call") to construct the set of calls in a "chained call" like this for easy assertion afterwards:
```
>>> mock = Mock()
>>> cursor = mock.connection.cursor.return_value
>>> cursor.execute.return_value = ['foo']
>>> mock.connection.cursor().execute("SELECT 1")
['foo']
>>> expected = call.connection.cursor().execute("SELECT 1").call_list()
>>> mock.mock_calls
[call.connection.cursor(), call.connection.cursor().execute('SELECT 1')]
>>> mock.mock_calls == expected
True
```
It is the call to `.call_list()` that turns our call object into a list of calls representing the chained calls.
### Raising exceptions with mocks
A useful attribute is [`side_effect`](unittest.mock.xhtml#unittest.mock.Mock.side_effect "unittest.mock.Mock.side_effect"). If you set this to an exception class or instance then the exception will be raised when the mock is called.
```
>>> mock = Mock(side_effect=Exception('Boom!'))
>>> mock()
Traceback (most recent call last):
...
Exception: Boom!
```
### Side effect functions and iterables
`side_effect` can also be set to a function or an iterable. The use case for `side_effect` as an iterable is where your mock is going to be called several times, and you want each call to return a different value. When you set `side_effect` to an iterable every call to the mock returns the next value from the iterable:
```
>>> mock = MagicMock(side_effect=[4, 5, 6])
>>> mock()
4
>>> mock()
5
>>> mock()
6
```
For more advanced use cases, like dynamically varying the return values depending on what the mock is called with, `side_effect` can be a function. The function will be called with the same arguments as the mock. Whatever the function returns is what the call returns:
```
>>> vals = {(1, 2): 1, (2, 3): 2}
>>> def side_effect(*args):
... return vals[args]
...
>>> mock = MagicMock(side_effect=side_effect)
>>> mock(1, 2)
1
>>> mock(2, 3)
2
```
### Creating a Mock from an Existing Object
One problem with over use of mocking is that it couples your tests to the implementation of your mocks rather than your real code. Suppose you have a class that implements `some_method`. In a test for another class, you provide a mock of this object that *also* provides `some_method`. If later you refactor the first class, so that it no longer has `some_method` - then your tests will continue to pass even though your code is now broken!
[`Mock`](unittest.mock.xhtml#unittest.mock.Mock "unittest.mock.Mock") allows you to provide an object as a specification for the mock, using the *spec* keyword argument. Accessing methods / attributes on the mock that don't exist on your specification object will immediately raise an attribute error. If you change the implementation of your specification, then tests that use that class will start failing immediately without you having to instantiate the class in those tests.
```
>>> mock = Mock(spec=SomeClass)
>>> mock.old_method()
Traceback (most recent call last):
...
AttributeError: object has no attribute 'old_method'
```
Using a specification also enables a smarter matching of calls made to the mock, regardless of whether some parameters were passed as positional or named arguments:
```
>>> def f(a, b, c): pass
...
>>> mock = Mock(spec=f)
>>> mock(1, 2, 3)
<Mock name='mock()' id='140161580456576'>
>>> mock.assert_called_with(a=1, b=2, c=3)
```
If you want this smarter matching to also work with method calls on the mock, you can use [auto-speccing](unittest.mock.xhtml#auto-speccing).
If you want a stronger form of specification that prevents the setting of arbitrary attributes as well as the getting of them then you can use *spec\_set* instead of *spec*.
## Patch Decorators
注解
With [`patch()`](unittest.mock.xhtml#unittest.mock.patch "unittest.mock.patch") it matters that you patch objects in the namespace where they are looked up. This is normally straightforward, but for a quick guide read [where to patch](unittest.mock.xhtml#where-to-patch).
A common need in tests is to patch a class attribute or a module attribute, for example patching a builtin or patching a class in a module to test that it is instantiated. Modules and classes are effectively global, so patching on them has to be undone after the test or the patch will persist into other tests and cause hard to diagnose problems.
mock provides three convenient decorators for this: [`patch()`](unittest.mock.xhtml#unittest.mock.patch "unittest.mock.patch"), [`patch.object()`](unittest.mock.xhtml#unittest.mock.patch.object "unittest.mock.patch.object") and [`patch.dict()`](unittest.mock.xhtml#unittest.mock.patch.dict "unittest.mock.patch.dict"). `patch` takes a single string, of the form `package.module.Class.attribute` to specify the attribute you are patching. It also optionally takes a value that you want the attribute (or class or whatever) to be replaced with. 'patch.object' takes an object and the name of the attribute you would like patched, plus optionally the value to patch it with.
`patch.object`:
```
>>> original = SomeClass.attribute
>>> @patch.object(SomeClass, 'attribute', sentinel.attribute)
... def test():
... assert SomeClass.attribute == sentinel.attribute
...
>>> test()
>>> assert SomeClass.attribute == original
```
```
>>> @patch('package.module.attribute', sentinel.attribute)
... def test():
... from package.module import attribute
... assert attribute is sentinel.attribute
...
>>> test()
```
If you are patching a module (including [`builtins`](builtins.xhtml#module-builtins "builtins: The module that provides the built-in namespace.")) then use [`patch()`](unittest.mock.xhtml#unittest.mock.patch "unittest.mock.patch")instead of [`patch.object()`](unittest.mock.xhtml#unittest.mock.patch.object "unittest.mock.patch.object"):
```
>>> mock = MagicMock(return_value=sentinel.file_handle)
>>> with patch('builtins.open', mock):
... handle = open('filename', 'r')
...
>>> mock.assert_called_with('filename', 'r')
>>> assert handle == sentinel.file_handle, "incorrect file handle returned"
```
The module name can be 'dotted', in the form `package.module` if needed:
```
>>> @patch('package.module.ClassName.attribute', sentinel.attribute)
... def test():
... from package.module import ClassName
... assert ClassName.attribute == sentinel.attribute
...
>>> test()
```
A nice pattern is to actually decorate test methods themselves:
```
>>> class MyTest(unittest.TestCase):
... @patch.object(SomeClass, 'attribute', sentinel.attribute)
... def test_something(self):
... self.assertEqual(SomeClass.attribute, sentinel.attribute)
...
>>> original = SomeClass.attribute
>>> MyTest('test_something').test_something()
>>> assert SomeClass.attribute == original
```
If you want to patch with a Mock, you can use [`patch()`](unittest.mock.xhtml#unittest.mock.patch "unittest.mock.patch") with only one argument (or [`patch.object()`](unittest.mock.xhtml#unittest.mock.patch.object "unittest.mock.patch.object") with two arguments). The mock will be created for you and passed into the test function / method:
```
>>> class MyTest(unittest.TestCase):
... @patch.object(SomeClass, 'static_method')
... def test_something(self, mock_method):
... SomeClass.static_method()
... mock_method.assert_called_with()
...
>>> MyTest('test_something').test_something()
```
You can stack up multiple patch decorators using this pattern:
```
>>> class MyTest(unittest.TestCase):
... @patch('package.module.ClassName1')
... @patch('package.module.ClassName2')
... def test_something(self, MockClass2, MockClass1):
... self.assertIs(package.module.ClassName1, MockClass1)
... self.assertIs(package.module.ClassName2, MockClass2)
...
>>> MyTest('test_something').test_something()
```
When you nest patch decorators the mocks are passed in to the decorated function in the same order they applied (the normal *Python* order that decorators are applied). This means from the bottom up, so in the example above the mock for `test_module.ClassName2` is passed in first.
There is also [`patch.dict()`](unittest.mock.xhtml#unittest.mock.patch.dict "unittest.mock.patch.dict") for setting values in a dictionary just during a scope and restoring the dictionary to its original state when the test ends:
```
>>> foo = {'key': 'value'}
>>> original = foo.copy()
>>> with patch.dict(foo, {'newkey': 'newvalue'}, clear=True):
... assert foo == {'newkey': 'newvalue'}
...
>>> assert foo == original
```
`patch`, `patch.object` and `patch.dict` can all be used as context managers.
Where you use [`patch()`](unittest.mock.xhtml#unittest.mock.patch "unittest.mock.patch") to create a mock for you, you can get a reference to the mock using the "as" form of the with statement:
```
>>> class ProductionClass:
... def method(self):
... pass
...
>>> with patch.object(ProductionClass, 'method') as mock_method:
... mock_method.return_value = None
... real = ProductionClass()
... real.method(1, 2, 3)
...
>>> mock_method.assert_called_with(1, 2, 3)
```
As an alternative `patch`, `patch.object` and `patch.dict` can be used as class decorators. When used in this way it is the same as applying the decorator individually to every method whose name starts with "test".
## Further Examples
Here are some more examples for some slightly more advanced scenarios.
### Mocking chained calls
Mocking chained calls is actually straightforward with mock once you understand the [`return_value`](unittest.mock.xhtml#unittest.mock.Mock.return_value "unittest.mock.Mock.return_value") attribute. When a mock is called for the first time, or you fetch its `return_value` before it has been called, a new [`Mock`](unittest.mock.xhtml#unittest.mock.Mock "unittest.mock.Mock") is created.
This means that you can see how the object returned from a call to a mocked object has been used by interrogating the `return_value` mock:
```
>>> mock = Mock()
>>> mock().foo(a=2, b=3)
<Mock name='mock().foo()' id='...'>
>>> mock.return_value.foo.assert_called_with(a=2, b=3)
```
From here it is a simple step to configure and then make assertions about chained calls. Of course another alternative is writing your code in a more testable way in the first place...
So, suppose we have some code that looks a little bit like this:
```
>>> class Something:
... def __init__(self):
... self.backend = BackendProvider()
... def method(self):
... response = self.backend.get_endpoint('foobar').create_call('spam', 'eggs').start_call()
... # more code
```
Assuming that `BackendProvider` is already well tested, how do we test `method()`? Specifically, we want to test that the code section
```
# more
code
```
uses the response object in the correct way.
As this chain of calls is made from an instance attribute we can monkey patch the `backend` attribute on a `Something` instance. In this particular case we are only interested in the return value from the final call to `start_call` so we don't have much configuration to do. Let's assume the object it returns is 'file-like', so we'll ensure that our response object uses the builtin [`open()`](functions.xhtml#open "open") as its `spec`.
To do this we create a mock instance as our mock backend and create a mock response object for it. To set the response as the return value for that final `start_call` we could do this:
```
mock_backend.get_endpoint.return_value.create_call.return_value.start_call.return_value = mock_response
```
We can do that in a slightly nicer way using the [`configure_mock()`](unittest.mock.xhtml#unittest.mock.Mock.configure_mock "unittest.mock.Mock.configure_mock")method to directly set the return value for us:
```
>>> something = Something()
>>> mock_response = Mock(spec=open)
>>> mock_backend = Mock()
>>> config = {'get_endpoint.return_value.create_call.return_value.start_call.return_value': mock_response}
>>> mock_backend.configure_mock(**config)
```
With these we monkey patch the "mock backend" in place and can make the real call:
```
>>> something.backend = mock_backend
>>> something.method()
```
Using [`mock_calls`](unittest.mock.xhtml#unittest.mock.Mock.mock_calls "unittest.mock.Mock.mock_calls") we can check the chained call with a single assert. A chained call is several calls in one line of code, so there will be several entries in `mock_calls`. We can use [`call.call_list()`](unittest.mock.xhtml#unittest.mock.call.call_list "unittest.mock.call.call_list") to create this list of calls for us:
```
>>> chained = call.get_endpoint('foobar').create_call('spam', 'eggs').start_call()
>>> call_list = chained.call_list()
>>> assert mock_backend.mock_calls == call_list
```
### Partial mocking
In some tests I wanted to mock out a call to [`datetime.date.today()`](datetime.xhtml#datetime.date.today "datetime.date.today")to return a known date, but I didn't want to prevent the code under test from creating new date objects. Unfortunately [`datetime.date`](datetime.xhtml#datetime.date "datetime.date") is written in C, and so I couldn't just monkey-patch out the static `date.today()` method.
I found a simple way of doing this that involved effectively wrapping the date class with a mock, but passing through calls to the constructor to the real class (and returning real instances).
The [`patch decorator`](unittest.mock.xhtml#unittest.mock.patch "unittest.mock.patch") is used here to mock out the `date` class in the module under test. The `side_effect`attribute on the mock date class is then set to a lambda function that returns a real date. When the mock date class is called a real date will be constructed and returned by `side_effect`.
```
>>> from datetime import date
>>> with patch('mymodule.date') as mock_date:
... mock_date.today.return_value = date(2010, 10, 8)
... mock_date.side_effect = lambda *args, **kw: date(*args, **kw)
...
... assert mymodule.date.today() == date(2010, 10, 8)
... assert mymodule.date(2009, 6, 8) == date(2009, 6, 8)
...
```
Note that we don't patch [`datetime.date`](datetime.xhtml#datetime.date "datetime.date") globally, we patch `date` in the module that *uses* it. See [where to patch](unittest.mock.xhtml#where-to-patch).
When `date.today()` is called a known date is returned, but calls to the `date(...)` constructor still return normal dates. Without this you can find yourself having to calculate an expected result using exactly the same algorithm as the code under test, which is a classic testing anti-pattern.
Calls to the date constructor are recorded in the `mock_date` attributes (`call_count` and friends) which may also be useful for your tests.
An alternative way of dealing with mocking dates, or other builtin classes, is discussed in [this blog entry](https://williambert.online/2011/07/how-to-unit-testing-in-django-with-mocking-and-patching/) \[https://williambert.online/2011/07/how-to-unit-testing-in-django-with-mocking-and-patching/\].
### Mocking a Generator Method
A Python generator is a function or method that uses the [`yield`](../reference/simple_stmts.xhtml#yield) statement to return a series of values when iterated over [1](#id3).
A generator method / function is called to return the generator object. It is the generator object that is then iterated over. The protocol method for iteration is [`__iter__()`](stdtypes.xhtml#container.__iter__ "container.__iter__"), so we can mock this using a [`MagicMock`](unittest.mock.xhtml#unittest.mock.MagicMock "unittest.mock.MagicMock").
Here's an example class with an "iter" method implemented as a generator:
```
>>> class Foo:
... def iter(self):
... for i in [1, 2, 3]:
... yield i
...
>>> foo = Foo()
>>> list(foo.iter())
[1, 2, 3]
```
How would we mock this class, and in particular its "iter" method?
To configure the values returned from the iteration (implicit in the call to [`list`](stdtypes.xhtml#list "list")), we need to configure the object returned by the call to `foo.iter()`.
```
>>> mock_foo = MagicMock()
>>> mock_foo.iter.return_value = iter([1, 2, 3])
>>> list(mock_foo.iter())
[1, 2, 3]
```
[1](#id2)There are also generator expressions and more [advanced uses](http://www.dabeaz.com/coroutines/index.html) \[http://www.dabeaz.com/coroutines/index.html\] of generators, but we aren't concerned about them here. A very good introduction to generators and how powerful they are is: [Generator Tricks for Systems Programmers](http://www.dabeaz.com/generators/) \[http://www.dabeaz.com/generators/\].
### Applying the same patch to every test method
If you want several patches in place for multiple test methods the obvious way is to apply the patch decorators to every method. This can feel like unnecessary repetition. For Python 2.6 or more recent you can use [`patch()`](unittest.mock.xhtml#unittest.mock.patch "unittest.mock.patch") (in all its various forms) as a class decorator. This applies the patches to all test methods on the class. A test method is identified by methods whose names start with `test`:
```
>>> @patch('mymodule.SomeClass')
... class MyTest(TestCase):
...
... def test_one(self, MockSomeClass):
... self.assertIs(mymodule.SomeClass, MockSomeClass)
...
... def test_two(self, MockSomeClass):
... self.assertIs(mymodule.SomeClass, MockSomeClass)
...
... def not_a_test(self):
... return 'something'
...
>>> MyTest('test_one').test_one()
>>> MyTest('test_two').test_two()
>>> MyTest('test_two').not_a_test()
'something'
```
An alternative way of managing patches is to use the [patch methods: start and stop](unittest.mock.xhtml#start-and-stop). These allow you to move the patching into your `setUp` and `tearDown` methods.
```
>>> class MyTest(TestCase):
... def setUp(self):
... self.patcher = patch('mymodule.foo')
... self.mock_foo = self.patcher.start()
...
... def test_foo(self):
... self.assertIs(mymodule.foo, self.mock_foo)
...
... def tearDown(self):
... self.patcher.stop()
...
>>> MyTest('test_foo').run()
```
If you use this technique you must ensure that the patching is "undone" by calling `stop`. This can be fiddlier than you might think, because if an exception is raised in the setUp then tearDown is not called. [`unittest.TestCase.addCleanup()`](unittest.xhtml#unittest.TestCase.addCleanup "unittest.TestCase.addCleanup") makes this easier:
```
>>> class MyTest(TestCase):
... def setUp(self):
... patcher = patch('mymodule.foo')
... self.addCleanup(patcher.stop)
... self.mock_foo = patcher.start()
...
... def test_foo(self):
... self.assertIs(mymodule.foo, self.mock_foo)
...
>>> MyTest('test_foo').run()
```
### Mocking Unbound Methods
Whilst writing tests today I needed to patch an *unbound method* (patching the method on the class rather than on the instance). I needed self to be passed in as the first argument because I want to make asserts about which objects were calling this particular method. The issue is that you can't patch with a mock for this, because if you replace an unbound method with a mock it doesn't become a bound method when fetched from the instance, and so it doesn't get self passed in. The workaround is to patch the unbound method with a real function instead. The [`patch()`](unittest.mock.xhtml#unittest.mock.patch "unittest.mock.patch") decorator makes it so simple to patch out methods with a mock that having to create a real function becomes a nuisance.
If you pass `autospec=True` to patch then it does the patching with a *real* function object. This function object has the same signature as the one it is replacing, but delegates to a mock under the hood. You still get your mock auto-created in exactly the same way as before. What it means though, is that if you use it to patch out an unbound method on a class the mocked function will be turned into a bound method if it is fetched from an instance. It will have `self` passed in as the first argument, which is exactly what I wanted:
```
>>> class Foo:
... def foo(self):
... pass
...
>>> with patch.object(Foo, 'foo', autospec=True) as mock_foo:
... mock_foo.return_value = 'foo'
... foo = Foo()
... foo.foo()
...
'foo'
>>> mock_foo.assert_called_once_with(foo)
```
If we don't use `autospec=True` then the unbound method is patched out with a Mock instance instead, and isn't called with `self`.
### Checking multiple calls with mock
mock has a nice API for making assertions about how your mock objects are used.
```
>>> mock = Mock()
>>> mock.foo_bar.return_value = None
>>> mock.foo_bar('baz', spam='eggs')
>>> mock.foo_bar.assert_called_with('baz', spam='eggs')
```
If your mock is only being called once you can use the `assert_called_once_with()` method that also asserts that the `call_count` is one.
```
>>> mock.foo_bar.assert_called_once_with('baz', spam='eggs')
>>> mock.foo_bar()
>>> mock.foo_bar.assert_called_once_with('baz', spam='eggs')
Traceback (most recent call last):
...
AssertionError: Expected to be called once. Called 2 times.
```
Both `assert_called_with` and `assert_called_once_with` make assertions about the *most recent* call. If your mock is going to be called several times, and you want to make assertions about *all* those calls you can use [`call_args_list`](unittest.mock.xhtml#unittest.mock.Mock.call_args_list "unittest.mock.Mock.call_args_list"):
```
>>> mock = Mock(return_value=None)
>>> mock(1, 2, 3)
>>> mock(4, 5, 6)
>>> mock()
>>> mock.call_args_list
[call(1, 2, 3), call(4, 5, 6), call()]
```
The [`call`](unittest.mock.xhtml#unittest.mock.call "unittest.mock.call") helper makes it easy to make assertions about these calls. You can build up a list of expected calls and compare it to `call_args_list`. This looks remarkably similar to the repr of the `call_args_list`:
```
>>> expected = [call(1, 2, 3), call(4, 5, 6), call()]
>>> mock.call_args_list == expected
True
```
### Coping with mutable arguments
Another situation is rare, but can bite you, is when your mock is called with mutable arguments. `call_args` and `call_args_list` store *references* to the arguments. If the arguments are mutated by the code under test then you can no longer make assertions about what the values were when the mock was called.
Here's some example code that shows the problem. Imagine the following functions defined in 'mymodule':
```
def frob(val):
pass
def grob(val):
"First frob and then clear val"
frob(val)
val.clear()
```
When we try to test that `grob` calls `frob` with the correct argument look what happens:
```
>>> with patch('mymodule.frob') as mock_frob:
... val = {6}
... mymodule.grob(val)
...
>>> val
set()
>>> mock_frob.assert_called_with({6})
Traceback (most recent call last):
...
AssertionError: Expected: (({6},), {})
Called with: ((set(),), {})
```
One possibility would be for mock to copy the arguments you pass in. This could then cause problems if you do assertions that rely on object identity for equality.
Here's one solution that uses the `side_effect`functionality. If you provide a `side_effect` function for a mock then `side_effect` will be called with the same args as the mock. This gives us an opportunity to copy the arguments and store them for later assertions. In this example I'm using *another* mock to store the arguments so that I can use the mock methods for doing the assertion. Again a helper function sets this up for me.
```
>>> from copy import deepcopy
>>> from unittest.mock import Mock, patch, DEFAULT
>>> def copy_call_args(mock):
... new_mock = Mock()
... def side_effect(*args, **kwargs):
... args = deepcopy(args)
... kwargs = deepcopy(kwargs)
... new_mock(*args, **kwargs)
... return DEFAULT
... mock.side_effect = side_effect
... return new_mock
...
>>> with patch('mymodule.frob') as mock_frob:
... new_mock = copy_call_args(mock_frob)
... val = {6}
... mymodule.grob(val)
...
>>> new_mock.assert_called_with({6})
>>> new_mock.call_args
call({6})
```
`copy_call_args` is called with the mock that will be called. It returns a new mock that we do the assertion on. The `side_effect` function makes a copy of the args and calls our `new_mock` with the copy.
注解
If your mock is only going to be used once there is an easier way of checking arguments at the point they are called. You can simply do the checking inside a `side_effect` function.
```
>>> def side_effect(arg):
... assert arg == {6}
...
>>> mock = Mock(side_effect=side_effect)
>>> mock({6})
>>> mock(set())
Traceback (most recent call last):
...
AssertionError
```
An alternative approach is to create a subclass of [`Mock`](unittest.mock.xhtml#unittest.mock.Mock "unittest.mock.Mock") or [`MagicMock`](unittest.mock.xhtml#unittest.mock.MagicMock "unittest.mock.MagicMock") that copies (using [`copy.deepcopy()`](copy.xhtml#copy.deepcopy "copy.deepcopy")) the arguments. Here's an example implementation:
```
>>> from copy import deepcopy
>>> class CopyingMock(MagicMock):
... def __call__(self, *args, **kwargs):
... args = deepcopy(args)
... kwargs = deepcopy(kwargs)
... return super(CopyingMock, self).__call__(*args, **kwargs)
...
>>> c = CopyingMock(return_value=None)
>>> arg = set()
>>> c(arg)
>>> arg.add(1)
>>> c.assert_called_with(set())
>>> c.assert_called_with(arg)
Traceback (most recent call last):
...
AssertionError: Expected call: mock({1})
Actual call: mock(set())
>>> c.foo
<CopyingMock name='mock.foo' id='...'>
```
When you subclass `Mock` or `MagicMock` all dynamically created attributes, and the `return_value` will use your subclass automatically. That means all children of a `CopyingMock` will also have the type `CopyingMock`.
### Nesting Patches
Using patch as a context manager is nice, but if you do multiple patches you can end up with nested with statements indenting further and further to the right:
```
>>> class MyTest(TestCase):
...
... def test_foo(self):
... with patch('mymodule.Foo') as mock_foo:
... with patch('mymodule.Bar') as mock_bar:
... with patch('mymodule.Spam') as mock_spam:
... assert mymodule.Foo is mock_foo
... assert mymodule.Bar is mock_bar
... assert mymodule.Spam is mock_spam
...
>>> original = mymodule.Foo
>>> MyTest('test_foo').test_foo()
>>> assert mymodule.Foo is original
```
With unittest `cleanup` functions and the [patch methods: start and stop](unittest.mock.xhtml#start-and-stop) we can achieve the same effect without the nested indentation. A simple helper method, `create_patch`, puts the patch in place and returns the created mock for us:
```
>>> class MyTest(TestCase):
...
... def create_patch(self, name):
... patcher = patch(name)
... thing = patcher.start()
... self.addCleanup(patcher.stop)
... return thing
...
... def test_foo(self):
... mock_foo = self.create_patch('mymodule.Foo')
... mock_bar = self.create_patch('mymodule.Bar')
... mock_spam = self.create_patch('mymodule.Spam')
...
... assert mymodule.Foo is mock_foo
... assert mymodule.Bar is mock_bar
... assert mymodule.Spam is mock_spam
...
>>> original = mymodule.Foo
>>> MyTest('test_foo').run()
>>> assert mymodule.Foo is original
```
### Mocking a dictionary with MagicMock
You may want to mock a dictionary, or other container object, recording all access to it whilst having it still behave like a dictionary.
We can do this with [`MagicMock`](unittest.mock.xhtml#unittest.mock.MagicMock "unittest.mock.MagicMock"), which will behave like a dictionary, and using [`side_effect`](unittest.mock.xhtml#unittest.mock.Mock.side_effect "unittest.mock.Mock.side_effect") to delegate dictionary access to a real underlying dictionary that is under our control.
When the [`__getitem__()`](../reference/datamodel.xhtml#object.__getitem__ "object.__getitem__") and [`__setitem__()`](../reference/datamodel.xhtml#object.__setitem__ "object.__setitem__") methods of our `MagicMock` are called (normal dictionary access) then `side_effect` is called with the key (and in the case of `__setitem__` the value too). We can also control what is returned.
After the `MagicMock` has been used we can use attributes like [`call_args_list`](unittest.mock.xhtml#unittest.mock.Mock.call_args_list "unittest.mock.Mock.call_args_list") to assert about how the dictionary was used:
```
>>> my_dict = {'a': 1, 'b': 2, 'c': 3}
>>> def getitem(name):
... return my_dict[name]
...
>>> def setitem(name, val):
... my_dict[name] = val
...
>>> mock = MagicMock()
>>> mock.__getitem__.side_effect = getitem
>>> mock.__setitem__.side_effect = setitem
```
注解
An alternative to using `MagicMock` is to use `Mock` and *only* provide the magic methods you specifically want:
```
>>> mock = Mock()
>>> mock.__getitem__ = Mock(side_effect=getitem)
>>> mock.__setitem__ = Mock(side_effect=setitem)
```
A *third* option is to use `MagicMock` but passing in `dict` as the *spec*(or *spec\_set*) argument so that the `MagicMock` created only has dictionary magic methods available:
```
>>> mock = MagicMock(spec_set=dict)
>>> mock.__getitem__.side_effect = getitem
>>> mock.__setitem__.side_effect = setitem
```
With these side effect functions in place, the `mock` will behave like a normal dictionary but recording the access. It even raises a [`KeyError`](exceptions.xhtml#KeyError "KeyError") if you try to access a key that doesn't exist.
```
>>> mock['a']
1
>>> mock['c']
3
>>> mock['d']
Traceback (most recent call last):
...
KeyError: 'd'
>>> mock['b'] = 'fish'
>>> mock['d'] = 'eggs'
>>> mock['b']
'fish'
>>> mock['d']
'eggs'
```
After it has been used you can make assertions about the access using the normal mock methods and attributes:
```
>>> mock.__getitem__.call_args_list
[call('a'), call('c'), call('d'), call('b'), call('d')]
>>> mock.__setitem__.call_args_list
[call('b', 'fish'), call('d', 'eggs')]
>>> my_dict
{'a': 1, 'c': 3, 'b': 'fish', 'd': 'eggs'}
```
### Mock subclasses and their attributes
There are various reasons why you might want to subclass [`Mock`](unittest.mock.xhtml#unittest.mock.Mock "unittest.mock.Mock"). One reason might be to add helper methods. Here's a silly example:
```
>>> class MyMock(MagicMock):
... def has_been_called(self):
... return self.called
...
>>> mymock = MyMock(return_value=None)
>>> mymock
<MyMock id='...'>
>>> mymock.has_been_called()
False
>>> mymock()
>>> mymock.has_been_called()
True
```
The standard behaviour for `Mock` instances is that attributes and the return value mocks are of the same type as the mock they are accessed on. This ensures that `Mock` attributes are `Mocks` and `MagicMock` attributes are `MagicMocks`[2](#id5). So if you're subclassing to add helper methods then they'll also be available on the attributes and return value mock of instances of your subclass.
```
>>> mymock.foo
<MyMock name='mock.foo' id='...'>
>>> mymock.foo.has_been_called()
False
>>> mymock.foo()
<MyMock name='mock.foo()' id='...'>
>>> mymock.foo.has_been_called()
True
```
Sometimes this is inconvenient. For example, [one user](https://code.google.com/archive/p/mock/issues/105) \[https://code.google.com/archive/p/mock/issues/105\] is subclassing mock to created a [Twisted adaptor](https://twistedmatrix.com/documents/11.0.0/api/twisted.python.components.html) \[https://twistedmatrix.com/documents/11.0.0/api/twisted.python.components.html\]. Having this applied to attributes too actually causes errors.
`Mock` (in all its flavours) uses a method called `_get_child_mock` to create these "sub-mocks" for attributes and return values. You can prevent your subclass being used for attributes by overriding this method. The signature is that it takes arbitrary keyword arguments (`**kwargs`) which are then passed onto the mock constructor:
```
>>> class Subclass(MagicMock):
... def _get_child_mock(self, **kwargs):
... return MagicMock(**kwargs)
...
>>> mymock = Subclass()
>>> mymock.foo
<MagicMock name='mock.foo' id='...'>
>>> assert isinstance(mymock, Subclass)
>>> assert not isinstance(mymock.foo, Subclass)
>>> assert not isinstance(mymock(), Subclass)
```
[2](#id4)An exception to this rule are the non-callable mocks. Attributes use the callable variant because otherwise non-callable mocks couldn't have callable methods.
### Mocking imports with patch.dict
One situation where mocking can be hard is where you have a local import inside a function. These are harder to mock because they aren't using an object from the module namespace that we can patch out.
Generally local imports are to be avoided. They are sometimes done to prevent circular dependencies, for which there is *usually* a much better way to solve the problem (refactor the code) or to prevent "up front costs" by delaying the import. This can also be solved in better ways than an unconditional local import (store the module as a class or module attribute and only do the import on first use).
That aside there is a way to use `mock` to affect the results of an import. Importing fetches an *object* from the [`sys.modules`](sys.xhtml#sys.modules "sys.modules") dictionary. Note that it fetches an *object*, which need not be a module. Importing a module for the first time results in a module object being put in sys.modules, so usually when you import something you get a module back. This need not be the case however.
This means you can use [`patch.dict()`](unittest.mock.xhtml#unittest.mock.patch.dict "unittest.mock.patch.dict") to *temporarily* put a mock in place in [`sys.modules`](sys.xhtml#sys.modules "sys.modules"). Any imports whilst this patch is active will fetch the mock. When the patch is complete (the decorated function exits, the with statement body is complete or `patcher.stop()` is called) then whatever was there previously will be restored safely.
Here's an example that mocks out the 'fooble' module.
```
>>> mock = Mock()
>>> with patch.dict('sys.modules', {'fooble': mock}):
... import fooble
... fooble.blob()
...
<Mock name='mock.blob()' id='...'>
>>> assert 'fooble' not in sys.modules
>>> mock.blob.assert_called_once_with()
```
As you can see the `import fooble` succeeds, but on exit there is no 'fooble' left in [`sys.modules`](sys.xhtml#sys.modules "sys.modules").
This also works for the `from module import name` form:
```
>>> mock = Mock()
>>> with patch.dict('sys.modules', {'fooble': mock}):
... from fooble import blob
... blob.blip()
...
<Mock name='mock.blob.blip()' id='...'>
>>> mock.blob.blip.assert_called_once_with()
```
With slightly more work you can also mock package imports:
```
>>> mock = Mock()
>>> modules = {'package': mock, 'package.module': mock.module}
>>> with patch.dict('sys.modules', modules):
... from package.module import fooble
... fooble()
...
<Mock name='mock.module.fooble()' id='...'>
>>> mock.module.fooble.assert_called_once_with()
```
### Tracking order of calls and less verbose call assertions
The [`Mock`](unittest.mock.xhtml#unittest.mock.Mock "unittest.mock.Mock") class allows you to track the *order* of method calls on your mock objects through the [`method_calls`](unittest.mock.xhtml#unittest.mock.Mock.method_calls "unittest.mock.Mock.method_calls") attribute. This doesn't allow you to track the order of calls between separate mock objects, however we can use [`mock_calls`](unittest.mock.xhtml#unittest.mock.Mock.mock_calls "unittest.mock.Mock.mock_calls") to achieve the same effect.
Because mocks track calls to child mocks in `mock_calls`, and accessing an arbitrary attribute of a mock creates a child mock, we can create our separate mocks from a parent one. Calls to those child mock will then all be recorded, in order, in the `mock_calls` of the parent:
```
>>> manager = Mock()
>>> mock_foo = manager.foo
>>> mock_bar = manager.bar
```
```
>>> mock_foo.something()
<Mock name='mock.foo.something()' id='...'>
>>> mock_bar.other.thing()
<Mock name='mock.bar.other.thing()' id='...'>
```
```
>>> manager.mock_calls
[call.foo.something(), call.bar.other.thing()]
```
We can then assert about the calls, including the order, by comparing with the `mock_calls` attribute on the manager mock:
```
>>> expected_calls = [call.foo.something(), call.bar.other.thing()]
>>> manager.mock_calls == expected_calls
True
```
If `patch` is creating, and putting in place, your mocks then you can attach them to a manager mock using the [`attach_mock()`](unittest.mock.xhtml#unittest.mock.Mock.attach_mock "unittest.mock.Mock.attach_mock") method. After attaching calls will be recorded in `mock_calls` of the manager.
```
>>> manager = MagicMock()
>>> with patch('mymodule.Class1') as MockClass1:
... with patch('mymodule.Class2') as MockClass2:
... manager.attach_mock(MockClass1, 'MockClass1')
... manager.attach_mock(MockClass2, 'MockClass2')
... MockClass1().foo()
... MockClass2().bar()
...
<MagicMock name='mock.MockClass1().foo()' id='...'>
<MagicMock name='mock.MockClass2().bar()' id='...'>
>>> manager.mock_calls
[call.MockClass1(),
call.MockClass1().foo(),
call.MockClass2(),
call.MockClass2().bar()]
```
If many calls have been made, but you're only interested in a particular sequence of them then an alternative is to use the [`assert_has_calls()`](unittest.mock.xhtml#unittest.mock.Mock.assert_has_calls "unittest.mock.Mock.assert_has_calls") method. This takes a list of calls (constructed with the [`call`](unittest.mock.xhtml#unittest.mock.call "unittest.mock.call") object). If that sequence of calls are in [`mock_calls`](unittest.mock.xhtml#unittest.mock.Mock.mock_calls "unittest.mock.Mock.mock_calls") then the assert succeeds.
```
>>> m = MagicMock()
>>> m().foo().bar().baz()
<MagicMock name='mock().foo().bar().baz()' id='...'>
>>> m.one().two().three()
<MagicMock name='mock.one().two().three()' id='...'>
>>> calls = call.one().two().three().call_list()
>>> m.assert_has_calls(calls)
```
Even though the chained call `m.one().two().three()` aren't the only calls that have been made to the mock, the assert still succeeds.
Sometimes a mock may have several calls made to it, and you are only interested in asserting about *some* of those calls. You may not even care about the order. In this case you can pass `any_order=True` to `assert_has_calls`:
```
>>> m = MagicMock()
>>> m(1), m.two(2, 3), m.seven(7), m.fifty('50')
(...)
>>> calls = [call.fifty('50'), call(1), call.seven(7)]
>>> m.assert_has_calls(calls, any_order=True)
```
### More complex argument matching
Using the same basic concept as [`ANY`](unittest.mock.xhtml#unittest.mock.ANY "unittest.mock.ANY") we can implement matchers to do more complex assertions on objects used as arguments to mocks.
Suppose we expect some object to be passed to a mock that by default compares equal based on object identity (which is the Python default for user defined classes). To use [`assert_called_with()`](unittest.mock.xhtml#unittest.mock.Mock.assert_called_with "unittest.mock.Mock.assert_called_with") we would need to pass in the exact same object. If we are only interested in some of the attributes of this object then we can create a matcher that will check these attributes for us.
You can see in this example how a 'standard' call to `assert_called_with` isn't sufficient:
```
>>> class Foo:
... def __init__(self, a, b):
... self.a, self.b = a, b
...
>>> mock = Mock(return_value=None)
>>> mock(Foo(1, 2))
>>> mock.assert_called_with(Foo(1, 2))
Traceback (most recent call last):
...
AssertionError: Expected: call(<__main__.Foo object at 0x...>)
Actual call: call(<__main__.Foo object at 0x...>)
```
A comparison function for our `Foo` class might look something like this:
```
>>> def compare(self, other):
... if not type(self) == type(other):
... return False
... if self.a != other.a:
... return False
... if self.b != other.b:
... return False
... return True
...
```
And a matcher object that can use comparison functions like this for its equality operation would look something like this:
```
>>> class Matcher:
... def __init__(self, compare, some_obj):
... self.compare = compare
... self.some_obj = some_obj
... def __eq__(self, other):
... return self.compare(self.some_obj, other)
...
```
Putting all this together:
```
>>> match_foo = Matcher(compare, Foo(1, 2))
>>> mock.assert_called_with(match_foo)
```
The `Matcher` is instantiated with our compare function and the `Foo` object we want to compare against. In `assert_called_with` the `Matcher` equality method will be called, which compares the object the mock was called with against the one we created our matcher with. If they match then `assert_called_with` passes, and if they don't an [`AssertionError`](exceptions.xhtml#AssertionError "AssertionError") is raised:
```
>>> match_wrong = Matcher(compare, Foo(3, 4))
>>> mock.assert_called_with(match_wrong)
Traceback (most recent call last):
...
AssertionError: Expected: ((<Matcher object at 0x...>,), {})
Called with: ((<Foo object at 0x...>,), {})
```
With a bit of tweaking you could have the comparison function raise the [`AssertionError`](exceptions.xhtml#AssertionError "AssertionError") directly and provide a more useful failure message.
As of version 1.5, the Python testing library [PyHamcrest](https://pyhamcrest.readthedocs.io/) \[https://pyhamcrest.readthedocs.io/\] provides similar functionality, that may be useful here, in the form of its equality matcher ([hamcrest.library.integration.match\_equality](https://pyhamcrest.readthedocs.io/en/release-1.8/integration/#module-hamcrest.library.integration.match_equality) \[https://pyhamcrest.readthedocs.io/en/release-1.8/integration/#module-hamcrest.library.integration.match\_equality\]).
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- 性能优化
- Unicode
- Codecs
- 文档
- IDLE
- Code Repository
- Build and C API Changes
- Porting to Python 3.2
- What's New In Python 3.1
- PEP 372: Ordered Dictionaries
- PEP 378: Format Specifier for Thousands Separator
- 其他语言特性修改
- New, Improved, and Deprecated Modules
- 性能优化
- IDLE
- Build and C API Changes
- Porting to Python 3.1
- What's New In Python 3.0
- Common Stumbling Blocks
- Overview Of Syntax Changes
- Changes Already Present In Python 2.6
- Library Changes
- PEP 3101: A New Approach To String Formatting
- Changes To Exceptions
- Miscellaneous Other Changes
- Build and C API Changes
- 性能
- Porting To Python 3.0
- What's New in Python 2.7
- The Future for Python 2.x
- Changes to the Handling of Deprecation Warnings
- Python 3.1 Features
- PEP 372: Adding an Ordered Dictionary to collections
- PEP 378: Format Specifier for Thousands Separator
- PEP 389: The argparse Module for Parsing Command Lines
- PEP 391: Dictionary-Based Configuration For Logging
- PEP 3106: Dictionary Views
- PEP 3137: The memoryview Object
- 其他语言特性修改
- New and Improved Modules
- Build and C API Changes
- Other Changes and Fixes
- Porting to Python 2.7
- New Features Added to Python 2.7 Maintenance Releases
- Acknowledgements
- Python 2.6 有什么新变化
- Python 3.0
- Changes to the Development Process
- PEP 343: The 'with' statement
- PEP 366: Explicit Relative Imports From a Main Module
- PEP 370: Per-user site-packages Directory
- PEP 371: The multiprocessing Package
- PEP 3101: Advanced String Formatting
- PEP 3105: print As a Function
- PEP 3110: Exception-Handling Changes
- PEP 3112: Byte Literals
- PEP 3116: New I/O Library
- PEP 3118: Revised Buffer Protocol
- PEP 3119: Abstract Base Classes
- PEP 3127: Integer Literal Support and Syntax
- PEP 3129: Class Decorators
- PEP 3141: A Type Hierarchy for Numbers
- 其他语言特性修改
- New and Improved Modules
- Deprecations and Removals
- Build and C API Changes
- Porting to Python 2.6
- Acknowledgements
- What's New in Python 2.5
- PEP 308: Conditional Expressions
- PEP 309: Partial Function Application
- PEP 314: Metadata for Python Software Packages v1.1
- PEP 328: Absolute and Relative Imports
- PEP 338: Executing Modules as Scripts
- PEP 341: Unified try/except/finally
- PEP 342: New Generator Features
- PEP 343: The 'with' statement
- PEP 352: Exceptions as New-Style Classes
- PEP 353: Using ssize_t as the index type
- PEP 357: The 'index' method
- 其他语言特性修改
- New, Improved, and Removed Modules
- Build and C API Changes
- Porting to Python 2.5
- Acknowledgements
- What's New in Python 2.4
- PEP 218: Built-In Set Objects
- PEP 237: Unifying Long Integers and Integers
- PEP 289: Generator Expressions
- PEP 292: Simpler String Substitutions
- PEP 318: Decorators for Functions and Methods
- PEP 322: Reverse Iteration
- PEP 324: New subprocess Module
- PEP 327: Decimal Data Type
- PEP 328: Multi-line Imports
- PEP 331: Locale-Independent Float/String Conversions
- 其他语言特性修改
- New, Improved, and Deprecated Modules
- Build and C API Changes
- Porting to Python 2.4
- Acknowledgements
- What's New in Python 2.3
- PEP 218: A Standard Set Datatype
- PEP 255: Simple Generators
- PEP 263: Source Code Encodings
- PEP 273: Importing Modules from ZIP Archives
- PEP 277: Unicode file name support for Windows NT
- PEP 278: Universal Newline Support
- PEP 279: enumerate()
- PEP 282: The logging Package
- PEP 285: A Boolean Type
- PEP 293: Codec Error Handling Callbacks
- PEP 301: Package Index and Metadata for Distutils
- PEP 302: New Import Hooks
- PEP 305: Comma-separated Files
- PEP 307: Pickle Enhancements
- Extended Slices
- 其他语言特性修改
- New, Improved, and Deprecated Modules
- Pymalloc: A Specialized Object Allocator
- Build and C API Changes
- Other Changes and Fixes
- Porting to Python 2.3
- Acknowledgements
- What's New in Python 2.2
- 概述
- PEPs 252 and 253: Type and Class Changes
- PEP 234: Iterators
- PEP 255: Simple Generators
- PEP 237: Unifying Long Integers and Integers
- PEP 238: Changing the Division Operator
- Unicode Changes
- PEP 227: Nested Scopes
- New and Improved Modules
- Interpreter Changes and Fixes
- Other Changes and Fixes
- Acknowledgements
- What's New in Python 2.1
- 概述
- PEP 227: Nested Scopes
- PEP 236: future Directives
- PEP 207: Rich Comparisons
- PEP 230: Warning Framework
- PEP 229: New Build System
- PEP 205: Weak References
- PEP 232: Function Attributes
- PEP 235: Importing Modules on Case-Insensitive Platforms
- PEP 217: Interactive Display Hook
- PEP 208: New Coercion Model
- PEP 241: Metadata in Python Packages
- New and Improved Modules
- Other Changes and Fixes
- Acknowledgements
- What's New in Python 2.0
- 概述
- What About Python 1.6?
- New Development Process
- Unicode
- 列表推导式
- Augmented Assignment
- 字符串的方法
- Garbage Collection of Cycles
- Other Core Changes
- Porting to 2.0
- Extending/Embedding Changes
- Distutils: Making Modules Easy to Install
- XML Modules
- Module changes
- New modules
- IDLE Improvements
- Deleted and Deprecated Modules
- Acknowledgements
- 更新日志
- Python 下一版
- Python 3.7.3 最终版
- Python 3.7.3 发布候选版 1
- Python 3.7.2 最终版
- Python 3.7.2 发布候选版 1
- Python 3.7.1 最终版
- Python 3.7.1 RC 2版本
- Python 3.7.1 发布候选版 1
- Python 3.7.0 正式版
- Python 3.7.0 release candidate 1
- Python 3.7.0 beta 5
- Python 3.7.0 beta 4
- Python 3.7.0 beta 3
- Python 3.7.0 beta 2
- Python 3.7.0 beta 1
- Python 3.7.0 alpha 4
- Python 3.7.0 alpha 3
- Python 3.7.0 alpha 2
- Python 3.7.0 alpha 1
- Python 3.6.6 final
- Python 3.6.6 RC 1
- Python 3.6.5 final
- Python 3.6.5 release candidate 1
- Python 3.6.4 final
- Python 3.6.4 release candidate 1
- Python 3.6.3 final
- Python 3.6.3 release candidate 1
- Python 3.6.2 final
- Python 3.6.2 release candidate 2
- Python 3.6.2 release candidate 1
- Python 3.6.1 final
- Python 3.6.1 release candidate 1
- Python 3.6.0 final
- Python 3.6.0 release candidate 2
- Python 3.6.0 release candidate 1
- Python 3.6.0 beta 4
- Python 3.6.0 beta 3
- Python 3.6.0 beta 2
- Python 3.6.0 beta 1
- Python 3.6.0 alpha 4
- Python 3.6.0 alpha 3
- Python 3.6.0 alpha 2
- Python 3.6.0 alpha 1
- Python 3.5.5 final
- Python 3.5.5 release candidate 1
- Python 3.5.4 final
- Python 3.5.4 release candidate 1
- Python 3.5.3 final
- Python 3.5.3 release candidate 1
- Python 3.5.2 final
- Python 3.5.2 release candidate 1
- Python 3.5.1 final
- Python 3.5.1 release candidate 1
- Python 3.5.0 final
- Python 3.5.0 release candidate 4
- Python 3.5.0 release candidate 3
- Python 3.5.0 release candidate 2
- Python 3.5.0 release candidate 1
- Python 3.5.0 beta 4
- Python 3.5.0 beta 3
- Python 3.5.0 beta 2
- Python 3.5.0 beta 1
- Python 3.5.0 alpha 4
- Python 3.5.0 alpha 3
- Python 3.5.0 alpha 2
- Python 3.5.0 alpha 1
- Python 教程
- 课前甜点
- 使用 Python 解释器
- 调用解释器
- 解释器的运行环境
- Python 的非正式介绍
- Python 作为计算器使用
- 走向编程的第一步
- 其他流程控制工具
- if 语句
- for 语句
- range() 函数
- break 和 continue 语句，以及循环中的 else 子句
- pass 语句
- 定义函数
- 函数定义的更多形式
- 小插曲：编码风格
- 数据结构
- 列表的更多特性
- del 语句
- 元组和序列
- 集合
- 字典
- 循环的技巧
- 深入条件控制
- 序列和其它类型的比较
- 模块
- 有关模块的更多信息
- 标准模块
- dir() 函数
- 包
- 输入输出
- 更漂亮的输出格式
- 读写文件
- 错误和异常
- 语法错误
- 异常
- 处理异常
- 抛出异常
- 用户自定义异常
- 定义清理操作
- 预定义的清理操作
- 类
- 名称和对象
- Python 作用域和命名空间
- 初探类
- 补充说明
- 继承
- 私有变量
- 杂项说明
- 迭代器
- 生成器
- 生成器表达式
- 标准库简介
- 操作系统接口
- 文件通配符
- 命令行参数
- 错误输出重定向和程序终止
- 字符串模式匹配
- 数学
- 互联网访问
- 日期和时间
- 数据压缩
- 性能测量
- 质量控制
- 自带电池
- 标准库简介 —— 第二部分
- 格式化输出
- 模板
- 使用二进制数据记录格式
- 多线程
- 日志
- 弱引用
- 用于操作列表的工具
- 十进制浮点运算
- 虚拟环境和包
- 概述
- 创建虚拟环境
- 使用pip管理包
- 接下来？
- 交互式编辑和编辑历史
- Tab 补全和编辑历史
- 默认交互式解释器的替代品
- 浮点算术：争议和限制
- 表示性错误
- 附录
- 交互模式
- 安装和使用 Python
- 命令行与环境
- 命令行
- 环境变量
- 在Unix平台中使用Python
- 获取最新版本的Python
- 构建Python
- 与Python相关的路径和文件
- 杂项
- 编辑器和集成开发环境
- 在Windows上使用 Python
- 完整安装程序
- Microsoft Store包
- nuget.org 安装包
- 可嵌入的包
- 替代捆绑包
- 配置Python
- 适用于Windows的Python启动器
- 查找模块
- 附加模块
- 在Windows上编译Python
- 其他平台
- 在苹果系统上使用 Python
- 获取和安装 MacPython
- IDE
- 安装额外的 Python 包
- Mac 上的图形界面编程
- 在 Mac 上分发 Python 应用程序
- 其他资源
- Python 语言参考
- 概述
- 其他实现
- 标注
- 词法分析
- 行结构
- 其他形符
- 标识符和关键字
- 字面值
- 运算符
- 分隔符
- 数据模型
- 对象、值与类型
- 标准类型层级结构
- 特殊方法名称
- 协程
- 执行模型
- 程序的结构
- 命名与绑定
- 异常
- 导入系统
- importlib
- 包
- 搜索
- 加载
- 基于路径的查找器
- 替换标准导入系统
- Package Relative Imports
- 有关 main 的特殊事项
- 开放问题项
- 参考文献
- 表达式
- 算术转换
- 原子
- 原型
- await 表达式
- 幂运算符
- 一元算术和位运算
- 二元算术运算符
- 移位运算
- 二元位运算
- 比较运算
- 布尔运算
- 条件表达式
- lambda 表达式
- 表达式列表
- 求值顺序
- 运算符优先级
- 简单语句
- 表达式语句
- 赋值语句
- assert 语句
- pass 语句
- del 语句
- return 语句
- yield 语句
- raise 语句
- break 语句
- continue 语句
- import 语句
- global 语句
- nonlocal 语句
- 复合语句
- if 语句
- while 语句
- for 语句
- try 语句
- with 语句
- 函数定义
- 类定义
- 协程
- 最高层级组件
- 完整的 Python 程序
- 文件输入
- 交互式输入
- 表达式输入
- 完整的语法规范
- Python 标准库
- 概述
- 可用性注释
- 内置函数
- 内置常量
- 由 site 模块添加的常量
- 内置类型
- 逻辑值检测
- 布尔运算 — and, or, not
- 比较
- 数字类型 — int, float, complex
- 迭代器类型
- 序列类型 — list, tuple, range
- 文本序列类型 — str
- 二进制序列类型 — bytes, bytearray, memoryview
- 集合类型 — set, frozenset
- 映射类型 — dict
- 上下文管理器类型
- 其他内置类型
- 特殊属性
- 内置异常
- 基类
- 具体异常
- 警告
- 异常层次结构
- 文本处理服务
- string — 常见的字符串操作
- re — 正则表达式操作
- 模块 difflib 是一个计算差异的助手
- textwrap — Text wrapping and filling
- unicodedata — Unicode 数据库
- stringprep — Internet String Preparation
- readline — GNU readline interface
- rlcompleter — GNU readline的完成函数
- 二进制数据服务
- struct — Interpret bytes as packed binary data
- codecs — Codec registry and base classes
- 数据类型
- datetime — 基础日期/时间数据类型
- calendar — General calendar-related functions
- collections — 容器数据类型
- collections.abc — 容器的抽象基类
- heapq — 堆队列算法
- bisect — Array bisection algorithm
- array — Efficient arrays of numeric values
- weakref — 弱引用
- types — Dynamic type creation and names for built-in types
- copy — 浅层 (shallow) 和深层 (deep) 复制操作
- pprint — 数据美化输出
- reprlib — Alternate repr() implementation
- enum — Support for enumerations
- 数字和数学模块
- numbers — 数字的抽象基类
- math — 数学函数
- cmath — Mathematical functions for complex numbers
- decimal — 十进制定点和浮点运算
- fractions — 分数
- random — 生成伪随机数
- statistics — Mathematical statistics functions
- 函数式编程模块
- itertools — 为高效循环而创建迭代器的函数
- functools — 高阶函数和可调用对象上的操作
- operator — 标准运算符替代函数
- 文件和目录访问
- pathlib — 面向对象的文件系统路径
- os.path — 常见路径操作
- fileinput — Iterate over lines from multiple input streams
- stat — Interpreting stat() results
- filecmp — File and Directory Comparisons
- tempfile — Generate temporary files and directories
- glob — Unix style pathname pattern expansion
- fnmatch — Unix filename pattern matching
- linecache — Random access to text lines
- shutil — High-level file operations
- macpath — Mac OS 9 路径操作函数
- 数据持久化
- pickle —— Python 对象序列化
- copyreg — Register pickle support functions
- shelve — Python object persistence
- marshal — Internal Python object serialization
- dbm — Interfaces to Unix “databases”
- sqlite3 — SQLite 数据库 DB-API 2.0 接口模块
- 数据压缩和存档
- zlib — 与 gzip 兼容的压缩
- gzip — 对 gzip 格式的支持
- bz2 — 对 bzip2 压缩算法的支持
- lzma — 用 LZMA 算法压缩
- zipfile — 在 ZIP 归档中工作
- tarfile — Read and write tar archive files
- 文件格式
- csv — CSV 文件读写
- configparser — Configuration file parser
- netrc — netrc file processing
- xdrlib — Encode and decode XDR data
- plistlib — Generate and parse Mac OS X .plist files
- 加密服务
- hashlib — 安全哈希与消息摘要
- hmac — 基于密钥的消息验证
- secrets — Generate secure random numbers for managing secrets
- 通用操作系统服务
- os — 操作系统接口模块
- io — 处理流的核心工具
- time — 时间的访问和转换
- argparse — 命令行选项、参数和子命令解析器
- getopt — C-style parser for command line options
- 模块 logging — Python 的日志记录工具
- logging.config — 日志记录配置
- logging.handlers — Logging handlers
- getpass — 便携式密码输入工具
- curses — 终端字符单元显示的处理
- curses.textpad — Text input widget for curses programs
- curses.ascii — Utilities for ASCII characters
- curses.panel — A panel stack extension for curses
- platform — Access to underlying platform's identifying data
- errno — Standard errno system symbols
- ctypes — Python 的外部函数库
- 并发执行
- threading — 基于线程的并行
- multiprocessing — 基于进程的并行
- concurrent 包
- concurrent.futures — 启动并行任务
- subprocess — 子进程管理
- sched — 事件调度器
- queue — 一个同步的队列类
- _thread — 底层多线程 API
- _dummy_thread — _thread 的替代模块
- dummy_threading — 可直接替代 threading 模块。
- contextvars — Context Variables
- Context Variables
- Manual Context Management
- asyncio support
- 网络和进程间通信
- asyncio — 异步 I/O
- socket — 底层网络接口
- ssl — TLS/SSL wrapper for socket objects
- select — Waiting for I/O completion
- selectors — 高级 I/O 复用库
- asyncore — 异步socket处理器
- asynchat — 异步 socket 指令/响应 处理器
- signal — Set handlers for asynchronous events
- mmap — Memory-mapped file support
- 互联网数据处理
- email — 电子邮件与 MIME 处理包
- json — JSON 编码和解码器
- mailcap — Mailcap file handling
- mailbox — Manipulate mailboxes in various formats
- mimetypes — Map filenames to MIME types
- base64 — Base16, Base32, Base64, Base85 数据编码
- binhex — 对binhex4文件进行编码和解码
- binascii — 二进制和 ASCII 码互转
- quopri — Encode and decode MIME quoted-printable data
- uu — Encode and decode uuencode files
- 结构化标记处理工具
- html — 超文本标记语言支持
- html.parser — 简单的 HTML 和 XHTML 解析器
- html.entities — HTML 一般实体的定义
- XML处理模块
- xml.etree.ElementTree — The ElementTree XML API
- xml.dom — The Document Object Model API
- xml.dom.minidom — Minimal DOM implementation
- xml.dom.pulldom — Support for building partial DOM trees
- xml.sax — Support for SAX2 parsers
- xml.sax.handler — Base classes for SAX handlers
- xml.sax.saxutils — SAX Utilities
- xml.sax.xmlreader — Interface for XML parsers
- xml.parsers.expat — Fast XML parsing using Expat
- 互联网协议和支持
- webbrowser — 方便的Web浏览器控制器
- cgi — Common Gateway Interface support
- cgitb — Traceback manager for CGI scripts
- wsgiref — WSGI Utilities and Reference Implementation
- urllib — URL 处理模块
- urllib.request — 用于打开 URL 的可扩展库
- urllib.response — Response classes used by urllib
- urllib.parse — Parse URLs into components
- urllib.error — Exception classes raised by urllib.request
- urllib.robotparser — Parser for robots.txt
- http — HTTP 模块
- http.client — HTTP协议客户端
- ftplib — FTP protocol client
- poplib — POP3 protocol client
- imaplib — IMAP4 protocol client
- nntplib — NNTP protocol client
- smtplib —SMTP协议客户端
- smtpd — SMTP Server
- telnetlib — Telnet client
- uuid — UUID objects according to RFC 4122
- socketserver — A framework for network servers
- http.server — HTTP 服务器
- http.cookies — HTTP state management
- http.cookiejar — Cookie handling for HTTP clients
- xmlrpc — XMLRPC 服务端与客户端模块
- xmlrpc.client — XML-RPC client access
- xmlrpc.server — Basic XML-RPC servers
- ipaddress — IPv4/IPv6 manipulation library
- 多媒体服务
- audioop — Manipulate raw audio data
- aifc — Read and write AIFF and AIFC files
- sunau — 读写 Sun AU 文件
- wave — 读写WAV格式文件
- chunk — Read IFF chunked data
- colorsys — Conversions between color systems
- imghdr — 推测图像类型
- sndhdr — 推测声音文件的类型
- ossaudiodev — Access to OSS-compatible audio devices
- 国际化
- gettext — 多语种国际化服务
- locale — 国际化服务
- 程序框架
- turtle — 海龟绘图
- cmd — 支持面向行的命令解释器
- shlex — Simple lexical analysis
- Tk图形用户界面(GUI)
- tkinter — Tcl/Tk的Python接口
- tkinter.ttk — Tk themed widgets
- tkinter.tix — Extension widgets for Tk
- tkinter.scrolledtext — 滚动文字控件
- IDLE
- 其他图形用户界面（GUI）包
- 开发工具
- typing — 类型标注支持
- pydoc — Documentation generator and online help system
- doctest — Test interactive Python examples
- unittest — 单元测试框架
- unittest.mock — mock object library
- unittest.mock 上手指南
- 2to3 - 自动将 Python 2 代码转为 Python 3 代码
- test — Regression tests package for Python
- test.support — Utilities for the Python test suite
- test.support.script_helper — Utilities for the Python execution tests
- 调试和分析
- bdb — Debugger framework
- faulthandler — Dump the Python traceback
- pdb — The Python Debugger
- The Python Profilers
- timeit — 测量小代码片段的执行时间
- trace — Trace or track Python statement execution
- tracemalloc — Trace memory allocations
- 软件打包和分发
- distutils — 构建和安装 Python 模块
- ensurepip — Bootstrapping the pip installer
- venv — 创建虚拟环境
- zipapp — Manage executable Python zip archives
- Python运行时服务
- sys — 系统相关的参数和函数
- sysconfig — Provide access to Python's configuration information
- builtins — 内建对象
- main — 顶层脚本环境
- warnings — Warning control
- dataclasses — 数据类
- contextlib — Utilities for with-statement contexts
- abc — 抽象基类
- atexit — 退出处理器
- traceback — Print or retrieve a stack traceback
- future — Future 语句定义
- gc — 垃圾回收器接口
- inspect — 检查对象
- site — Site-specific configuration hook
- 自定义 Python 解释器
- code — Interpreter base classes
- codeop — Compile Python code
- 导入模块
- zipimport — Import modules from Zip archives
- pkgutil — Package extension utility
- modulefinder — 查找脚本使用的模块
- runpy — Locating and executing Python modules
- importlib — The implementation of import
- Python 语言服务
- parser — Access Python parse trees
- ast — 抽象语法树
- symtable — Access to the compiler's symbol tables
- symbol — 与 Python 解析树一起使用的常量
- token — 与Python解析树一起使用的常量
- keyword — 检验Python关键字
- tokenize — Tokenizer for Python source
- tabnanny — 模糊缩进检测
- pyclbr — Python class browser support
- py_compile — Compile Python source files
- compileall — Byte-compile Python libraries
- dis — Python 字节码反汇编器
- pickletools — Tools for pickle developers
- 杂项服务
- formatter — Generic output formatting
- Windows系统相关模块
- msilib — Read and write Microsoft Installer files
- msvcrt — Useful routines from the MS VC++ runtime
- winreg — Windows 注册表访问
- winsound — Sound-playing interface for Windows
- Unix 专有服务
- posix — The most common POSIX system calls
- pwd — 用户密码数据库
- spwd — The shadow password database
- grp — The group database
- crypt — Function to check Unix passwords
- termios — POSIX style tty control
- tty — 终端控制功能
- pty — Pseudo-terminal utilities
- fcntl — The fcntl and ioctl system calls
- pipes — Interface to shell pipelines
- resource — Resource usage information
- nis — Interface to Sun's NIS (Yellow Pages)
- Unix syslog 库例程
- 被取代的模块
- optparse — Parser for command line options
- imp — Access the import internals
- 未创建文档的模块
- 平台特定模块
- 扩展和嵌入 Python 解释器
- 推荐的第三方工具
- 不使用第三方工具创建扩展
- 使用 C 或 C++ 扩展 Python
- 自定义扩展类型：教程
- 定义扩展类型：已分类主题
- 构建C/C++扩展
- 在Windows平台编译C和C++扩展
- 在更大的应用程序中嵌入 CPython 运行时
- Embedding Python in Another Application
- Python/C API 参考手册
- 概述
- 代码标准
- 包含文件
- 有用的宏
- 对象、类型和引用计数
- 异常
- 嵌入Python
- 调试构建
- 稳定的应用程序二进制接口
- The Very High Level Layer
- Reference Counting
- 异常处理
- Printing and clearing
- 抛出异常
- Issuing warnings
- Querying the error indicator
- Signal Handling
- Exception Classes
- Exception Objects
- Unicode Exception Objects
- Recursion Control
- 标准异常
- 标准警告类别
- 工具
- 操作系统实用程序
- 系统功能
- 过程控制
- 导入模块
- Data marshalling support
- 语句解释及变量编译
- 字符串转换与格式化
- 反射
- 编解码器注册与支持功能
- 抽象对象层
- Object Protocol
- 数字协议
- Sequence Protocol
- Mapping Protocol
- 迭代器协议
- 缓冲协议
- Old Buffer Protocol
- 具体的对象层
- 基本对象
- 数值对象
- 序列对象
- 容器对象
- 函数对象
- 其他对象
- Initialization, Finalization, and Threads
- 在Python初始化之前
- 全局配置变量
- Initializing and finalizing the interpreter
- Process-wide parameters
- Thread State and the Global Interpreter Lock
- Sub-interpreter support
- Asynchronous Notifications
- Profiling and Tracing
- Advanced Debugger Support
- Thread Local Storage Support
- 内存管理
- 概述
- 原始内存接口
- Memory Interface
- 对象分配器
- 默认内存分配器
- Customize Memory Allocators
- The pymalloc allocator
- tracemalloc C API
- 示例
- 对象实现支持
- 在堆中分配对象
- Common Object Structures
- Type 对象
- Number Object Structures
- Mapping Object Structures
- Sequence Object Structures
- Buffer Object Structures
- Async Object Structures
- 使对象类型支持循环垃圾回收
- API 和 ABI 版本管理
- 分发 Python 模块
- 关键术语
- 开源许可与协作
- 安装工具
- 阅读指南
- 我该如何...？
- ...为我的项目选择一个名字？
- ...创建和分发二进制扩展？
- 安装 Python 模块
- 关键术语
- 基本使用
- 我应如何 ...？
- ... 在 Python 3.4 之前的 Python 版本中安装 pip ？
- ... 只为当前用户安装软件包？
- ... 安装科学计算类 Python 软件包？
- ... 使用并行安装的多个 Python 版本？
- 常见的安装问题
- 在 Linux 的系统 Python 版本上安装
- 未安装 pip
- 安装二进制编译扩展
- Python 常用指引
- 将 Python 2 代码迁移到 Python 3
- 简要说明
- 详情
- 将扩展模块移植到 Python 3
- 条件编译
- 对象API的更改
- 模块初始化和状态
- CObject 替换为 Capsule
- 其他选项
- Curses Programming with Python
- What is curses?
- Starting and ending a curses application
- Windows and Pads
- Displaying Text
- User Input
- For More Information
- 实现描述器
- 摘要
- 定义和简介
- 描述器协议
- 发起调用描述符
- 描述符示例
- Properties
- 函数和方法
- Static Methods and Class Methods
- 函数式编程指引
- 概述
- 迭代器
- 生成器表达式和列表推导式
- 生成器
- 内置函数
- itertools 模块
- The functools module
- Small functions and the lambda expression
- Revision History and Acknowledgements
- 引用文献
- 日志 HOWTO
- 日志基础教程
- 进阶日志教程
- 日志级别
- 有用的处理程序
- 记录日志中引发的异常
- 使用任意对象作为消息
- 优化
- 日志操作手册
- 在多个模块中使用日志
- 在多线程中使用日志
- 使用多个日志处理器和多种格式化
- 在多个地方记录日志
- 日志服务器配置示例
- 处理日志处理器的阻塞
- Sending and receiving logging events across a network
- Adding contextual information to your logging output
- Logging to a single file from multiple processes
- Using file rotation
- Use of alternative formatting styles
- Customizing LogRecord
- Subclassing QueueHandler - a ZeroMQ example
- Subclassing QueueListener - a ZeroMQ example
- An example dictionary-based configuration
- Using a rotator and namer to customize log rotation processing
- A more elaborate multiprocessing example
- Inserting a BOM into messages sent to a SysLogHandler
- Implementing structured logging
- Customizing handlers with dictConfig()
- Using particular formatting styles throughout your application
- Configuring filters with dictConfig()
- Customized exception formatting
- Speaking logging messages
- Buffering logging messages and outputting them conditionally
- Formatting times using UTC (GMT) via configuration
- Using a context manager for selective logging
- 正则表达式HOWTO
- 概述
- 简单模式
- 使用正则表达式
- 更多模式能力
- 修改字符串
- 常见问题
- 反馈
- 套接字编程指南
- 套接字
- 创建套接字
- 使用一个套接字
- 断开连接
- 非阻塞的套接字
- 排序指南
- 基本排序
- 关键函数
- Operator 模块函数
- 升序和降序
- 排序稳定性和排序复杂度
- 使用装饰-排序-去装饰的旧方法
- 使用 cmp 参数的旧方法
- 其它
- Unicode 指南
- Unicode 概述
- Python's Unicode Support
- Reading and Writing Unicode Data
- Acknowledgements
- 如何使用urllib包获取网络资源
- 概述
- Fetching URLs
- 处理异常
- info and geturl
- Openers and Handlers
- Basic Authentication
- Proxies
- Sockets and Layers
- 脚注
- Argparse 教程
- 概念
- 基础
- 位置参数介绍
- Introducing Optional arguments
- Combining Positional and Optional arguments
- Getting a little more advanced
- Conclusion
- ipaddress模块介绍
- 创建 Address/Network/Interface 对象
- 审查 Address/Network/Interface 对象
- Network 作为 Address 列表
- 比较
- 将IP地址与其他模块一起使用
- 实例创建失败时获取更多详细信息
- Argument Clinic How-To
- The Goals Of Argument Clinic
- Basic Concepts And Usage
- Converting Your First Function
- Advanced Topics
- 使用 DTrace 和 SystemTap 检测CPython
- Enabling the static markers
- Static DTrace probes
- Static SystemTap markers
- Available static markers
- SystemTap Tapsets
- 示例
- Python 常见问题
- Python常见问题
- 一般信息
- 现实世界中的 Python
- 编程常见问题
- 一般问题
- 核心语言
- 数字和字符串
- 性能
- 序列（元组/列表）
- 对象
- 模块
- 设计和历史常见问题
- 为什么Python使用缩进来分组语句？
- 为什么简单的算术运算得到奇怪的结果？
- 为什么浮点计算不准确？
- 为什么Python字符串是不可变的？
- 为什么必须在方法定义和调用中显式使用“self”？
- 为什么不能在表达式中赋值？
- 为什么Python对某些功能（例如list.index()）使用方法来实现，而其他功能（例如len(List)）使用函数实现？
- 为什么 join()是一个字符串方法而不是列表或元组方法？
- 异常有多快？
- 为什么Python中没有switch或case语句？
- 难道不能在解释器中模拟线程，而非得依赖特定于操作系统的线程实现吗？
- 为什么lambda表达式不能包含语句？
- 可以将Python编译为机器代码，C或其他语言吗？
- Python如何管理内存？
- 为什么CPython不使用更传统的垃圾回收方案？
- CPython退出时为什么不释放所有内存？
- 为什么有单独的元组和列表数据类型？
- 列表是如何在CPython中实现的？
- 字典是如何在CPython中实现的？
- 为什么字典key必须是不可变的？
- 为什么 list.sort() 没有返回排序列表？
- 如何在Python中指定和实施接口规范？
- 为什么没有goto？
- 为什么原始字符串（r-strings）不能以反斜杠结尾？
- 为什么Python没有属性赋值的“with”语句？
- 为什么 if/while/def/class语句需要冒号？
- 为什么Python在列表和元组的末尾允许使用逗号？
- 代码库和插件 FAQ
- 通用的代码库问题
- 通用任务
- 线程相关
- 输入输出
- 网络 / Internet 编程
- 数据库
- 数学和数字
- 扩展/嵌入常见问题
- 可以使用C语言中创建自己的函数吗？
- 可以使用C++语言中创建自己的函数吗？
- C很难写，有没有其他选择？
- 如何从C执行任意Python语句？
- 如何从C中评估任意Python表达式？
- 如何从Python对象中提取C的值？
- 如何使用Py_BuildValue()创建任意长度的元组？
- 如何从C调用对象的方法？
- 如何捕获PyErr_Print()（或打印到stdout / stderr的任何内容）的输出？
- 如何从C访问用Python编写的模块？
- 如何从Python接口到C ++对象？
- 我使用Setup文件添加了一个模块，为什么make失败了？
- 如何调试扩展？
- 我想在Linux系统上编译一个Python模块，但是缺少一些文件。为什么?
- 如何区分“输入不完整”和“输入无效”？
- 如何找到未定义的g++符号__builtin_new或__pure_virtual？
- 能否创建一个对象类，其中部分方法在C中实现，而其他方法在Python中实现（例如通过继承）？
- Python在Windows上的常见问题
- 我怎样在Windows下运行一个Python程序？
- 我怎么让 Python 脚本可执行？
- 为什么有时候 Python 程序会启动缓慢？
- 我怎样使用Python脚本制作可执行文件？
- *.pyd 文件和DLL文件相同吗？
- 我怎样将Python嵌入一个Windows程序？
- 如何让编辑器不要在我的 Python 源代码中插入 tab ？
- 如何在不阻塞的情况下检查按键？
- 图形用户界面（GUI）常见问题
- 图形界面常见问题
- Python 是否有平台无关的图形界面工具包？
- 有哪些Python的GUI工具是某个平台专用的？
- 有关Tkinter的问题
- “为什么我的电脑上安装了 Python ？”
- 什么是Python？
- 为什么我的电脑上安装了 Python ？
- 我能删除 Python 吗？
- 术语对照表
- 文档说明
- Python 文档贡献者
- 解决 Bug
- 文档错误
- 使用 Python 的错误追踪系统
- 开始为 Python 贡献您的知识
- 版权
- 历史和许可证
- 软件历史
- 访问Python或以其他方式使用Python的条款和条件
- Python 3.7.3 的 PSF 许可协议
- Python 2.0 的 BeOpen.com 许可协议
- Python 1.6.1 的 CNRI 许可协议
- Python 0.9.0 至 1.2 的 CWI 许可协议
- 集成软件的许可和认可
- Mersenne Twister
- 套接字
- Asynchronous socket services
- Cookie management
- Execution tracing
- UUencode and UUdecode functions
- XML Remote Procedure Calls
- test_epoll
- Select kqueue
- SipHash24
- strtod and dtoa
- OpenSSL
- expat
- libffi
- zlib
- cfuhash
- libmpdec