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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
### 模擬方法調用
使用 [`Mock`](unittest.mock.xhtml#unittest.mock.Mock "unittest.mock.Mock") 的常見場景:
- 模擬函數調用
- 記錄“對象上的方法調用”
你可能需要替換一個對象上的方法,用于確認此方法被系統中的其他部分調用過,并且調用時使用了正確的參數。
```
>>> 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,它有方法和屬性可以讓你針對它是被如何使用的下斷言。
注解
在多數示例中,[`Mock`](unittest.mock.xhtml#unittest.mock.Mock "unittest.mock.Mock") 與 [`MagicMock`](unittest.mock.xhtml#unittest.mock.MagicMock "unittest.mock.MagicMock") 兩個類可以相互替換,而 `MagicMock` 是一個更適用的類,通常情況下,使用它就可以了。
如果 mock 被調用,它的 [`called`](unittest.mock.xhtml#unittest.mock.Mock.called "unittest.mock.Mock.called") 屬性就會變成 `True`,更重要的是,我們可以使用 [`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") 方法來確認它在被調用時使用了正確的參數。
在如下的測試示例中,驗證對于 `ProductionClass().method` 的調用會導致 `something` 的調用。
```
>>> 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)
```
### 對象上的方法調用的 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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- PEP 3148: The concurrent.futures module
- PEP 3147: PYC Repository Directories
- PEP 3149: ABI Version Tagged .so Files
- PEP 3333: Python Web Server Gateway Interface v1.0.1
- 其他語言特性修改
- New, Improved, and Deprecated Modules
- 多線程
- 性能優化
- Unicode
- Codecs
- 文檔
- 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
- 其他語言特性修改
- New, Improved, and Deprecated Modules
- 性能優化
- 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
- 其他語言特性修改
- 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 有什么新變化
- 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
- 其他語言特性修改
- 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
- 其他語言特性修改
- 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
- 其他語言特性修改
- 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
- 其他語言特性修改
- 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
- 列表推導式
- 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 最終版
- Python 3.7.3 發布候選版 1
- Python 3.7.2 最終版
- Python 3.7.2 發布候選版 1
- Python 3.7.1 最終版
- Python 3.7.1 RC 2版本
- Python 3.7.1 發布候選版 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
- 課前甜點
- 使用 Python 解釋器
- 調用解釋器
- 解釋器的運行環境
- Python 的非正式介紹
- Python 作為計算器使用
- 走向編程的第一步
- 其他流程控制工具
- if 語句
- for 語句
- range() 函數
- break 和 continue 語句,以及循環中的 else 子句
- pass 語句
- 定義函數
- 函數定義的更多形式
- 小插曲:編碼風格
- 數據結構
- 列表的更多特性
- del 語句
- 元組和序列
- 集合
- 字典
- 循環的技巧
- 深入條件控制
- 序列和其它類型的比較
- 模塊
- 有關模塊的更多信息
- 標準模塊
- dir() 函數
- 包
- 輸入輸出
- 更漂亮的輸出格式
- 讀寫文件
- 錯誤和異常
- 語法錯誤
- 異常
- 處理異常
- 拋出異常
- 用戶自定義異常
- 定義清理操作
- 預定義的清理操作
- 類
- 名稱和對象
- Python 作用域和命名空間
- 初探類
- 補充說明
- 繼承
- 私有變量
- 雜項說明
- 迭代器
- 生成器
- 生成器表達式
- 標準庫簡介
- 操作系統接口
- 文件通配符
- 命令行參數
- 錯誤輸出重定向和程序終止
- 字符串模式匹配
- 數學
- 互聯網訪問
- 日期和時間
- 數據壓縮
- 性能測量
- 質量控制
- 自帶電池
- 標準庫簡介 —— 第二部分
- 格式化輸出
- 模板
- 使用二進制數據記錄格式
- 多線程
- 日志
- 弱引用
- 用于操作列表的工具
- 十進制浮點運算
- 虛擬環境和包
- 概述
- 創建虛擬環境
- 使用pip管理包
- 接下來?
- 交互式編輯和編輯歷史
- Tab 補全和編輯歷史
- 默認交互式解釋器的替代品
- 浮點算術:爭議和限制
- 表示性錯誤
- 附錄
- 交互模式
- 安裝和使用 Python
- 命令行與環境
- 命令行
- 環境變量
- 在Unix平臺中使用Python
- 獲取最新版本的Python
- 構建Python
- 與Python相關的路徑和文件
- 雜項
- 編輯器和集成開發環境
- 在Windows上使用 Python
- 完整安裝程序
- Microsoft Store包
- nuget.org 安裝包
- 可嵌入的包
- 替代捆綁包
- 配置Python
- 適用于Windows的Python啟動器
- 查找模塊
- 附加模塊
- 在Windows上編譯Python
- 其他平臺
- 在蘋果系統上使用 Python
- 獲取和安裝 MacPython
- IDE
- 安裝額外的 Python 包
- Mac 上的圖形界面編程
- 在 Mac 上分發 Python 應用程序
- 其他資源
- Python 語言參考
- 概述
- 其他實現
- 標注
- 詞法分析
- 行結構
- 其他形符
- 標識符和關鍵字
- 字面值
- 運算符
- 分隔符
- 數據模型
- 對象、值與類型
- 標準類型層級結構
- 特殊方法名稱
- 協程
- 執行模型
- 程序的結構
- 命名與綁定
- 異常
- 導入系統
- importlib
- 包
- 搜索
- 加載
- 基于路徑的查找器
- 替換標準導入系統
- Package Relative Imports
- 有關 main 的特殊事項
- 開放問題項
- 參考文獻
- 表達式
- 算術轉換
- 原子
- 原型
- await 表達式
- 冪運算符
- 一元算術和位運算
- 二元算術運算符
- 移位運算
- 二元位運算
- 比較運算
- 布爾運算
- 條件表達式
- lambda 表達式
- 表達式列表
- 求值順序
- 運算符優先級
- 簡單語句
- 表達式語句
- 賦值語句
- assert 語句
- pass 語句
- del 語句
- return 語句
- yield 語句
- raise 語句
- break 語句
- continue 語句
- import 語句
- global 語句
- nonlocal 語句
- 復合語句
- if 語句
- while 語句
- for 語句
- try 語句
- with 語句
- 函數定義
- 類定義
- 協程
- 最高層級組件
- 完整的 Python 程序
- 文件輸入
- 交互式輸入
- 表達式輸入
- 完整的語法規范
- Python 標準庫
- 概述
- 可用性注釋
- 內置函數
- 內置常量
- 由 site 模塊添加的常量
- 內置類型
- 邏輯值檢測
- 布爾運算 — and, or, not
- 比較
- 數字類型 — int, float, complex
- 迭代器類型
- 序列類型 — list, tuple, range
- 文本序列類型 — str
- 二進制序列類型 — bytes, bytearray, memoryview
- 集合類型 — set, frozenset
- 映射類型 — dict
- 上下文管理器類型
- 其他內置類型
- 特殊屬性
- 內置異常
- 基類
- 具體異常
- 警告
- 異常層次結構
- 文本處理服務
- string — 常見的字符串操作
- re — 正則表達式操作
- 模塊 difflib 是一個計算差異的助手
- textwrap — Text wrapping and filling
- unicodedata — Unicode 數據庫
- stringprep — Internet String Preparation
- readline — GNU readline interface
- rlcompleter — GNU readline的完成函數
- 二進制數據服務
- struct — Interpret bytes as packed binary data
- codecs — Codec registry and base classes
- 數據類型
- datetime — 基礎日期/時間數據類型
- calendar — General calendar-related functions
- collections — 容器數據類型
- collections.abc — 容器的抽象基類
- heapq — 堆隊列算法
- 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 — 淺層 (shallow) 和深層 (deep) 復制操作
- pprint — 數據美化輸出
- reprlib — Alternate repr() implementation
- enum — Support for enumerations
- 數字和數學模塊
- numbers — 數字的抽象基類
- math — 數學函數
- cmath — Mathematical functions for complex numbers
- decimal — 十進制定點和浮點運算
- fractions — 分數
- random — 生成偽隨機數
- statistics — Mathematical statistics functions
- 函數式編程模塊
- itertools — 為高效循環而創建迭代器的函數
- functools — 高階函數和可調用對象上的操作
- operator — 標準運算符替代函數
- 文件和目錄訪問
- pathlib — 面向對象的文件系統路徑
- os.path — 常見路徑操作
- 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 路徑操作函數
- 數據持久化
- pickle —— Python 對象序列化
- copyreg — Register pickle support functions
- shelve — Python object persistence
- marshal — Internal Python object serialization
- dbm — Interfaces to Unix “databases”
- sqlite3 — SQLite 數據庫 DB-API 2.0 接口模塊
- 數據壓縮和存檔
- zlib — 與 gzip 兼容的壓縮
- gzip — 對 gzip 格式的支持
- bz2 — 對 bzip2 壓縮算法的支持
- lzma — 用 LZMA 算法壓縮
- zipfile — 在 ZIP 歸檔中工作
- tarfile — Read and write tar archive files
- 文件格式
- csv — CSV 文件讀寫
- configparser — Configuration file parser
- netrc — netrc file processing
- xdrlib — Encode and decode XDR data
- plistlib — Generate and parse Mac OS X .plist files
- 加密服務
- hashlib — 安全哈希與消息摘要
- hmac — 基于密鑰的消息驗證
- secrets — Generate secure random numbers for managing secrets
- 通用操作系統服務
- os — 操作系統接口模塊
- io — 處理流的核心工具
- time — 時間的訪問和轉換
- argparse — 命令行選項、參數和子命令解析器
- getopt — C-style parser for command line options
- 模塊 logging — Python 的日志記錄工具
- logging.config — 日志記錄配置
- logging.handlers — Logging handlers
- getpass — 便攜式密碼輸入工具
- curses — 終端字符單元顯示的處理
- 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 的外部函數庫
- 并發執行
- threading — 基于線程的并行
- multiprocessing — 基于進程的并行
- concurrent 包
- concurrent.futures — 啟動并行任務
- subprocess — 子進程管理
- sched — 事件調度器
- queue — 一個同步的隊列類
- _thread — 底層多線程 API
- _dummy_thread — _thread 的替代模塊
- dummy_threading — 可直接替代 threading 模塊。
- contextvars — Context Variables
- Context Variables
- Manual Context Management
- asyncio support
- 網絡和進程間通信
- asyncio — 異步 I/O
- socket — 底層網絡接口
- ssl — TLS/SSL wrapper for socket objects
- select — Waiting for I/O completion
- selectors — 高級 I/O 復用庫
- asyncore — 異步socket處理器
- asynchat — 異步 socket 指令/響應 處理器
- signal — Set handlers for asynchronous events
- mmap — Memory-mapped file support
- 互聯網數據處理
- email — 電子郵件與 MIME 處理包
- json — JSON 編碼和解碼器
- mailcap — Mailcap file handling
- mailbox — Manipulate mailboxes in various formats
- mimetypes — Map filenames to MIME types
- base64 — Base16, Base32, Base64, Base85 數據編碼
- binhex — 對binhex4文件進行編碼和解碼
- binascii — 二進制和 ASCII 碼互轉
- quopri — Encode and decode MIME quoted-printable data
- uu — Encode and decode uuencode files
- 結構化標記處理工具
- html — 超文本標記語言支持
- html.parser — 簡單的 HTML 和 XHTML 解析器
- html.entities — HTML 一般實體的定義
- XML處理模塊
- 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
- 互聯網協議和支持
- webbrowser — 方便的Web瀏覽器控制器
- cgi — Common Gateway Interface support
- cgitb — Traceback manager for CGI scripts
- wsgiref — WSGI Utilities and Reference Implementation
- urllib — URL 處理模塊
- urllib.request — 用于打開 URL 的可擴展庫
- 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 模塊
- http.client — HTTP協議客戶端
- ftplib — FTP protocol client
- poplib — POP3 protocol client
- imaplib — IMAP4 protocol client
- nntplib — NNTP protocol client
- smtplib —SMTP協議客戶端
- smtpd — SMTP Server
- telnetlib — Telnet client
- uuid — UUID objects according to RFC 4122
- socketserver — A framework for network servers
- http.server — HTTP 服務器
- http.cookies — HTTP state management
- http.cookiejar — Cookie handling for HTTP clients
- xmlrpc — XMLRPC 服務端與客戶端模塊
- xmlrpc.client — XML-RPC client access
- xmlrpc.server — Basic XML-RPC servers
- ipaddress — IPv4/IPv6 manipulation library
- 多媒體服務
- audioop — Manipulate raw audio data
- aifc — Read and write AIFF and AIFC files
- sunau — 讀寫 Sun AU 文件
- wave — 讀寫WAV格式文件
- chunk — Read IFF chunked data
- colorsys — Conversions between color systems
- imghdr — 推測圖像類型
- sndhdr — 推測聲音文件的類型
- ossaudiodev — Access to OSS-compatible audio devices
- 國際化
- gettext — 多語種國際化服務
- locale — 國際化服務
- 程序框架
- turtle — 海龜繪圖
- cmd — 支持面向行的命令解釋器
- shlex — Simple lexical analysis
- Tk圖形用戶界面(GUI)
- tkinter — Tcl/Tk的Python接口
- tkinter.ttk — Tk themed widgets
- tkinter.tix — Extension widgets for Tk
- tkinter.scrolledtext — 滾動文字控件
- IDLE
- 其他圖形用戶界面(GUI)包
- 開發工具
- typing — 類型標注支持
- pydoc — Documentation generator and online help system
- doctest — Test interactive Python examples
- unittest — 單元測試框架
- unittest.mock — mock object library
- unittest.mock 上手指南
- 2to3 - 自動將 Python 2 代碼轉為 Python 3 代碼
- test — Regression tests package for Python
- test.support — Utilities for the Python test suite
- test.support.script_helper — Utilities for the Python execution tests
- 調試和分析
- bdb — Debugger framework
- faulthandler — Dump the Python traceback
- pdb — The Python Debugger
- The Python Profilers
- timeit — 測量小代碼片段的執行時間
- trace — Trace or track Python statement execution
- tracemalloc — Trace memory allocations
- 軟件打包和分發
- distutils — 構建和安裝 Python 模塊
- ensurepip — Bootstrapping the pip installer
- venv — 創建虛擬環境
- zipapp — Manage executable Python zip archives
- Python運行時服務
- sys — 系統相關的參數和函數
- sysconfig — Provide access to Python's configuration information
- builtins — 內建對象
- main — 頂層腳本環境
- warnings — Warning control
- dataclasses — 數據類
- contextlib — Utilities for with-statement contexts
- abc — 抽象基類
- atexit — 退出處理器
- traceback — Print or retrieve a stack traceback
- future — Future 語句定義
- gc — 垃圾回收器接口
- inspect — 檢查對象
- site — Site-specific configuration hook
- 自定義 Python 解釋器
- code — Interpreter base classes
- codeop — Compile Python code
- 導入模塊
- zipimport — Import modules from Zip archives
- pkgutil — Package extension utility
- modulefinder — 查找腳本使用的模塊
- runpy — Locating and executing Python modules
- importlib — The implementation of import
- Python 語言服務
- parser — Access Python parse trees
- ast — 抽象語法樹
- symtable — Access to the compiler's symbol tables
- symbol — 與 Python 解析樹一起使用的常量
- token — 與Python解析樹一起使用的常量
- keyword — 檢驗Python關鍵字
- tokenize — Tokenizer for Python source
- tabnanny — 模糊縮進檢測
- pyclbr — Python class browser support
- py_compile — Compile Python source files
- compileall — Byte-compile Python libraries
- dis — Python 字節碼反匯編器
- pickletools — Tools for pickle developers
- 雜項服務
- formatter — Generic output formatting
- Windows系統相關模塊
- msilib — Read and write Microsoft Installer files
- msvcrt — Useful routines from the MS VC++ runtime
- winreg — Windows 注冊表訪問
- winsound — Sound-playing interface for Windows
- Unix 專有服務
- posix — The most common POSIX system calls
- pwd — 用戶密碼數據庫
- spwd — The shadow password database
- grp — The group database
- crypt — Function to check Unix passwords
- termios — POSIX style tty control
- tty — 終端控制功能
- 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 庫例程
- 被取代的模塊
- optparse — Parser for command line options
- imp — Access the import internals
- 未創建文檔的模塊
- 平臺特定模塊
- 擴展和嵌入 Python 解釋器
- 推薦的第三方工具
- 不使用第三方工具創建擴展
- 使用 C 或 C++ 擴展 Python
- 自定義擴展類型:教程
- 定義擴展類型:已分類主題
- 構建C/C++擴展
- 在Windows平臺編譯C和C++擴展
- 在更大的應用程序中嵌入 CPython 運行時
- Embedding Python in Another Application
- Python/C API 參考手冊
- 概述
- 代碼標準
- 包含文件
- 有用的宏
- 對象、類型和引用計數
- 異常
- 嵌入Python
- 調試構建
- 穩定的應用程序二進制接口
- The Very High Level Layer
- Reference Counting
- 異常處理
- Printing and clearing
- 拋出異常
- Issuing warnings
- Querying the error indicator
- Signal Handling
- Exception Classes
- Exception Objects
- Unicode Exception Objects
- Recursion Control
- 標準異常
- 標準警告類別
- 工具
- 操作系統實用程序
- 系統功能
- 過程控制
- 導入模塊
- Data marshalling support
- 語句解釋及變量編譯
- 字符串轉換與格式化
- 反射
- 編解碼器注冊與支持功能
- 抽象對象層
- Object Protocol
- 數字協議
- Sequence Protocol
- Mapping Protocol
- 迭代器協議
- 緩沖協議
- Old Buffer Protocol
- 具體的對象層
- 基本對象
- 數值對象
- 序列對象
- 容器對象
- 函數對象
- 其他對象
- Initialization, Finalization, and Threads
- 在Python初始化之前
- 全局配置變量
- 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
- 內存管理
- 概述
- 原始內存接口
- Memory Interface
- 對象分配器
- 默認內存分配器
- Customize Memory Allocators
- The pymalloc allocator
- tracemalloc C API
- 示例
- 對象實現支持
- 在堆中分配對象
- Common Object Structures
- Type 對象
- Number Object Structures
- Mapping Object Structures
- Sequence Object Structures
- Buffer Object Structures
- Async Object Structures
- 使對象類型支持循環垃圾回收
- API 和 ABI 版本管理
- 分發 Python 模塊
- 關鍵術語
- 開源許可與協作
- 安裝工具
- 閱讀指南
- 我該如何...?
- ...為我的項目選擇一個名字?
- ...創建和分發二進制擴展?
- 安裝 Python 模塊
- 關鍵術語
- 基本使用
- 我應如何 ...?
- ... 在 Python 3.4 之前的 Python 版本中安裝 pip ?
- ... 只為當前用戶安裝軟件包?
- ... 安裝科學計算類 Python 軟件包?
- ... 使用并行安裝的多個 Python 版本?
- 常見的安裝問題
- 在 Linux 的系統 Python 版本上安裝
- 未安裝 pip
- 安裝二進制編譯擴展
- Python 常用指引
- 將 Python 2 代碼遷移到 Python 3
- 簡要說明
- 詳情
- 將擴展模塊移植到 Python 3
- 條件編譯
- 對象API的更改
- 模塊初始化和狀態
- CObject 替換為 Capsule
- 其他選項
- Curses Programming with Python
- What is curses?
- Starting and ending a curses application
- Windows and Pads
- Displaying Text
- User Input
- For More Information
- 實現描述器
- 摘要
- 定義和簡介
- 描述器協議
- 發起調用描述符
- 描述符示例
- Properties
- 函數和方法
- Static Methods and Class Methods
- 函數式編程指引
- 概述
- 迭代器
- 生成器表達式和列表推導式
- 生成器
- 內置函數
- itertools 模塊
- The functools module
- Small functions and the lambda expression
- Revision History and Acknowledgements
- 引用文獻
- 日志 HOWTO
- 日志基礎教程
- 進階日志教程
- 日志級別
- 有用的處理程序
- 記錄日志中引發的異常
- 使用任意對象作為消息
- 優化
- 日志操作手冊
- 在多個模塊中使用日志
- 在多線程中使用日志
- 使用多個日志處理器和多種格式化
- 在多個地方記錄日志
- 日志服務器配置示例
- 處理日志處理器的阻塞
- 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
- 正則表達式HOWTO
- 概述
- 簡單模式
- 使用正則表達式
- 更多模式能力
- 修改字符串
- 常見問題
- 反饋
- 套接字編程指南
- 套接字
- 創建套接字
- 使用一個套接字
- 斷開連接
- 非阻塞的套接字
- 排序指南
- 基本排序
- 關鍵函數
- Operator 模塊函數
- 升序和降序
- 排序穩定性和排序復雜度
- 使用裝飾-排序-去裝飾的舊方法
- 使用 cmp 參數的舊方法
- 其它
- Unicode 指南
- Unicode 概述
- Python's Unicode Support
- Reading and Writing Unicode Data
- Acknowledgements
- 如何使用urllib包獲取網絡資源
- 概述
- Fetching URLs
- 處理異常
- info and geturl
- Openers and Handlers
- Basic Authentication
- Proxies
- Sockets and Layers
- 腳注
- Argparse 教程
- 概念
- 基礎
- 位置參數介紹
- Introducing Optional arguments
- Combining Positional and Optional arguments
- Getting a little more advanced
- Conclusion
- ipaddress模塊介紹
- 創建 Address/Network/Interface 對象
- 審查 Address/Network/Interface 對象
- Network 作為 Address 列表
- 比較
- 將IP地址與其他模塊一起使用
- 實例創建失敗時獲取更多詳細信息
- Argument Clinic How-To
- The Goals Of Argument Clinic
- Basic Concepts And Usage
- Converting Your First Function
- Advanced Topics
- 使用 DTrace 和 SystemTap 檢測CPython
- Enabling the static markers
- Static DTrace probes
- Static SystemTap markers
- Available static markers
- SystemTap Tapsets
- 示例
- Python 常見問題
- Python常見問題
- 一般信息
- 現實世界中的 Python
- 編程常見問題
- 一般問題
- 核心語言
- 數字和字符串
- 性能
- 序列(元組/列表)
- 對象
- 模塊
- 設計和歷史常見問題
- 為什么Python使用縮進來分組語句?
- 為什么簡單的算術運算得到奇怪的結果?
- 為什么浮點計算不準確?
- 為什么Python字符串是不可變的?
- 為什么必須在方法定義和調用中顯式使用“self”?
- 為什么不能在表達式中賦值?
- 為什么Python對某些功能(例如list.index())使用方法來實現,而其他功能(例如len(List))使用函數實現?
- 為什么 join()是一個字符串方法而不是列表或元組方法?
- 異常有多快?
- 為什么Python中沒有switch或case語句?
- 難道不能在解釋器中模擬線程,而非得依賴特定于操作系統的線程實現嗎?
- 為什么lambda表達式不能包含語句?
- 可以將Python編譯為機器代碼,C或其他語言嗎?
- Python如何管理內存?
- 為什么CPython不使用更傳統的垃圾回收方案?
- CPython退出時為什么不釋放所有內存?
- 為什么有單獨的元組和列表數據類型?
- 列表是如何在CPython中實現的?
- 字典是如何在CPython中實現的?
- 為什么字典key必須是不可變的?
- 為什么 list.sort() 沒有返回排序列表?
- 如何在Python中指定和實施接口規范?
- 為什么沒有goto?
- 為什么原始字符串(r-strings)不能以反斜杠結尾?
- 為什么Python沒有屬性賦值的“with”語句?
- 為什么 if/while/def/class語句需要冒號?
- 為什么Python在列表和元組的末尾允許使用逗號?
- 代碼庫和插件 FAQ
- 通用的代碼庫問題
- 通用任務
- 線程相關
- 輸入輸出
- 網絡 / Internet 編程
- 數據庫
- 數學和數字
- 擴展/嵌入常見問題
- 可以使用C語言中創建自己的函數嗎?
- 可以使用C++語言中創建自己的函數嗎?
- C很難寫,有沒有其他選擇?
- 如何從C執行任意Python語句?
- 如何從C中評估任意Python表達式?
- 如何從Python對象中提取C的值?
- 如何使用Py_BuildValue()創建任意長度的元組?
- 如何從C調用對象的方法?
- 如何捕獲PyErr_Print()(或打印到stdout / stderr的任何內容)的輸出?
- 如何從C訪問用Python編寫的模塊?
- 如何從Python接口到C ++對象?
- 我使用Setup文件添加了一個模塊,為什么make失敗了?
- 如何調試擴展?
- 我想在Linux系統上編譯一個Python模塊,但是缺少一些文件。為什么?
- 如何區分“輸入不完整”和“輸入無效”?
- 如何找到未定義的g++符號__builtin_new或__pure_virtual?
- 能否創建一個對象類,其中部分方法在C中實現,而其他方法在Python中實現(例如通過繼承)?
- Python在Windows上的常見問題
- 我怎樣在Windows下運行一個Python程序?
- 我怎么讓 Python 腳本可執行?
- 為什么有時候 Python 程序會啟動緩慢?
- 我怎樣使用Python腳本制作可執行文件?
- *.pyd 文件和DLL文件相同嗎?
- 我怎樣將Python嵌入一個Windows程序?
- 如何讓編輯器不要在我的 Python 源代碼中插入 tab ?
- 如何在不阻塞的情況下檢查按鍵?
- 圖形用戶界面(GUI)常見問題
- 圖形界面常見問題
- Python 是否有平臺無關的圖形界面工具包?
- 有哪些Python的GUI工具是某個平臺專用的?
- 有關Tkinter的問題
- “為什么我的電腦上安裝了 Python ?”
- 什么是Python?
- 為什么我的電腦上安裝了 Python ?
- 我能刪除 Python 嗎?
- 術語對照表
- 文檔說明
- Python 文檔貢獻者
- 解決 Bug
- 文檔錯誤
- 使用 Python 的錯誤追蹤系統
- 開始為 Python 貢獻您的知識
- 版權
- 歷史和許可證
- 軟件歷史
- 訪問Python或以其他方式使用Python的條款和條件
- Python 3.7.3 的 PSF 許可協議
- Python 2.0 的 BeOpen.com 許可協議
- Python 1.6.1 的 CNRI 許可協議
- Python 0.9.0 至 1.2 的 CWI 許可協議
- 集成軟件的許可和認可
- 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