术语表¶

>>>

Python默认的shell提示符。经常看到的代码示例，可以在解释器交互执行。

...

在输入缩进代码块的代码或一对匹配的左右分隔符（括号，方括号或花括号）时，交互式shell的默认Python提示。

2to3

尝试通过解析大多数不兼容性来尝试将Python 2.x代码转换为Python 3.x代码的工具，这些不兼容性可以通过解析源和遍历解析树来检测。

2to3 is available in the standard library as lib2to3; a standalone entry point is provided as Tools/scripts/2to3. See 2to3 - Automated Python 2 to 3 code translation.

abstract base class

Abstract base classes complement duck-typing by providing a way to define interfaces when other techniques like hasattr() would be clumsy or subtly wrong (for example with magic methods). ABCs introduce virtual subclasses, which are classes that don’t inherit from a class but are still recognized by isinstance() and issubclass(); see the abc module documentation. Python comes with many built-in ABCs for data structures (in the collections.abc module), numbers (in the numbers module), streams (in the io module), import finders and loaders (in the importlib.abc module). You can create your own ABCs with the abc module.

参数

A value passed to a function (or method) when calling the function. There are two kinds of argument:

keyword argument: an argument preceded by an identifier (e.g. name=) in a function call or passed as a value in a dictionary preceded by **. For example, 3 and 5 are both keyword arguments in the following calls to complex():
```
complex(real=3, imag=5)
complex(**{'real': 3, 'imag': 5})
```
positional argument: an argument that is not a keyword argument. Positional arguments can appear at the beginning of an argument list and/or be passed as elements of an iterable preceded by *. For example, 3 and 5 are both positional arguments in the following calls:
```
complex(3, 5)
complex(*(3, 5))
```

参数被分配给函数体中指定的局部变量。See the Calls section for the rules governing this assignment. Syntactically, any expression can be used to represent an argument; the evaluated value is assigned to the local variable.

See also the parameter glossary entry, the FAQ question on the difference between arguments and parameters, and PEP 362.

异步上下文管理器

An object which controls the environment seen in an async with statement by defining __aenter__() and __aexit__() methods. Introduced by PEP 492.

异步迭代

An object, that can be used in an async for statement. Must return an asynchronous iterator from its __aiter__() method. Introduced by PEP 492.

asynchronous iterator

An object that implements __aiter__() and __anext__() methods. __anext__ must return an awaitable object. async for resolves awaitable returned from asynchronous iterator’s __anext__() method until it raises StopAsyncIteration exception. Introduced by PEP 492.

attribute

A value associated with an object which is referenced by name using dotted expressions. For example, if an object o has an attribute a it would be referenced as o.a.

awaitable

An object that can be used in an await expression. Can be a coroutine or an object with an __await__() method. See also PEP 492.

BDFL

Benevolent Dictator For Life, a.k.a. Guido van Rossum, Python’s creator.

二进制文件

A file object able to read and write bytes-like objects.

See also

A text file reads and writes str objects.

bytes-like object

An object that supports the Buffer Protocol and can export a C-contiguous buffer. This includes all bytes, bytearray, and array.array objects, as well as many common memoryview objects. Bytes-like objects can be used for various operations that work with binary data; these include compression, saving to a binary file, and sending over a socket.

Some operations need the binary data to be mutable. The documentation often refers to these as “read-write bytes-like objects”. Example mutable buffer objects include bytearray and a memoryview of a bytearray. Other operations require the binary data to be stored in immutable objects (“read-only bytes-like objects”); examples of these include bytes and a memoryview of a bytes object.

bytecode

Python source code is compiled into bytecode, the internal representation of a Python program in the CPython interpreter. The bytecode is also cached in .pyc and .pyo files so that executing the same file is faster the second time (recompilation from source to bytecode can be avoided). This “intermediate language” is said to run on a virtual machine that executes the machine code corresponding to each bytecode. Do note that bytecodes are not expected to work between different Python virtual machines, nor to be stable between Python releases.

A list of bytecode instructions can be found in the documentation for the dis module.

class

A template for creating user-defined objects. Class definitions normally contain method definitions which operate on instances of the class.

coercion

The implicit conversion of an instance of one type to another during an operation which involves two arguments of the same type. For example, int(3.15) converts the floating point number to the integer 3, but in 3+4.5, each argument is of a different type (one int, one float), and both must be converted to the same type before they can be added or it will raise a TypeError. Without coercion, all arguments of even compatible types would have to be normalized to the same value by the programmer, e.g., float(3)+4.5 rather than just 3+4.5.

complex number

An extension of the familiar real number system in which all numbers are expressed as a sum of a real part and an imaginary part. Imaginary numbers are real multiples of the imaginary unit (the square root of -1), often written i in mathematics or j in engineering. Python has built-in support for complex numbers, which are written with this latter notation; the imaginary part is written with a j suffix, e.g., 3+1j. To get access to complex equivalents of the math module, use cmath. Use of complex numbers is a fairly advanced mathematical feature. If you’re not aware of a need for them, it’s almost certain you can safely ignore them.

context manager

通过定义__ enter __()和__ exit __()方法控制在中使用语句看到的环境的对象。See PEP 343.

contiguous

A buffer is considered contiguous exactly if it is either C-contiguous or Fortran contiguous. Zero-dimensional buffers are C and Fortran contiguous. In one-dimensional arrays, the items must be laid out in memory next to each other, in order of increasing indexes starting from zero. In multidimensional C-contiguous arrays, the last index varies the fastest when visiting items in order of memory address. However, in Fortran contiguous arrays, the first index varies the fastest.

coroutine

Coroutines is a more generalized form of subroutines. Subroutines are entered at one point and exited at another point. Coroutines can be entered, exited, and resumed at many different points. They can be implemented with the async def statement. See also PEP 492.

coroutine function

A function which returns a coroutine object. A coroutine function may be defined with the async def statement, and may contain await, async for, and async with keywords. These were introduced by PEP 492.

CPython

The canonical implementation of the Python programming language, as distributed on python.org. The term “CPython” is used when necessary to distinguish this implementation from others such as Jython or IronPython.

decorator

A function returning another function, usually applied as a function transformation using the @wrapper syntax. Common examples for decorators are classmethod() and staticmethod().

The decorator syntax is merely syntactic sugar, the following two function definitions are semantically equivalent:

def f(...):
    ...
f = staticmethod(f)

@staticmethod
def f(...):
    ...

The same concept exists for classes, but is less commonly used there. See the documentation for function definitions and class definitions for more about decorators.

descriptor

定义方法__ get __()，__ set __()或__ delete __()的任何对象。当类属性是描述符时，其特殊绑定行为在属性查找时触发。通常，使用a.b来获取，设置或删除属性会在a的类字典中查找名为b的对象，但如果b是一个描述器，相应的描述器方法被调用。Understanding descriptors is a key to a deep understanding of Python because they are the basis for many features including functions, methods, properties, class methods, static methods, and reference to super classes.

For more information about descriptors’ methods, see Implementing Descriptors.

dictionary

An associative array, where arbitrary keys are mapped to values. The keys can be any object with __hash__() and __eq__() methods. Called a hash in Perl.

dictionary view

The objects returned from dict.keys(), dict.values(), and dict.items() are called dictionary views. They provide a dynamic view on the dictionary’s entries, which means that when the dictionary changes, the view reflects these changes. To force the dictionary view to become a full list use list(dictview). See Dictionary view objects.

docstring

A string literal which appears as the first expression in a class, function or module. While ignored when the suite is executed, it is recognized by the compiler and put into the __doc__ attribute of the enclosing class, function or module. Since it is available via introspection, it is the canonical place for documentation of the object.

鸭子类型

A programming style which does not look at an object’s type to determine if it has the right interface; instead, the method or attribute is simply called or used (“If it looks like a duck and quacks like a duck, it must be a duck.”) By emphasizing interfaces rather than specific types, well-designed code improves its flexibility by allowing polymorphic substitution. Duck-typing avoids tests using type() or isinstance(). (Note, however, that duck-typing can be complemented with abstract base classes.) Instead, it typically employs hasattr() tests or EAFP programming.

EAFP

Easier to ask for forgiveness than permission. This common Python coding style assumes the existence of valid keys or attributes and catches exceptions if the assumption proves false. This clean and fast style is characterized by the presence of many try and except statements. The technique contrasts with the LBYL style common to many other languages such as C.

表达式

A piece of syntax which can be evaluated to some value. In other words, an expression is an accumulation of expression elements like literals, names, attribute access, operators or function calls which all return a value. In contrast to many other languages, not all language constructs are expressions. There are also statements which cannot be used as expressions, such as if. Assignments are also statements, not expressions.

extension module

A module written in C or C++, using Python’s C API to interact with the core and with user code.

file object

An object exposing a file-oriented API (with methods such as read() or write()) to an underlying resource. Depending on the way it was created, a file object can mediate access to a real on-disk file or to another type of storage or communication device (for example standard input/output, in-memory buffers, sockets, pipes, etc.). File objects are also called file-like objects or streams.

There are actually three categories of file objects: raw binary files, buffered binary files and text files. Their interfaces are defined in the io module. The canonical way to create a file object is by using the open() function.

file-like object

A synonym for file object.

finder

试图为正在导入的模块找到loader的对象。

Since Python 3.3, there are two types of finder: meta path finders for use with sys.meta_path, and path entry finders for use with sys.path_hooks.

See PEP 302, PEP 420 and PEP 451 for much more detail.

floor division

Mathematical division that rounds down to nearest integer. The floor division operator is //. For example, the expression 11 // 4 evaluates to 2 in contrast to the 2.75 returned by float true division. Note that (-11) // 4 is -3 because that is -2.75 rounded downward. See PEP 238.

function

A series of statements which returns some value to a caller. It can also be passed zero or more arguments which may be used in the execution of the body. See also parameter, method, and the Function definitions section.

function annotation

An arbitrary metadata value associated with a function parameter or return value. Its syntax is explained in section Function definitions. Annotations may be accessed via the __annotations__ special attribute of a function object.

Python itself does not assign any particular meaning to function annotations. They are intended to be interpreted by third-party libraries or tools. See PEP 3107, which describes some of their potential uses.

__future__

A pseudo-module which programmers can use to enable new language features which are not compatible with the current interpreter.

By importing the __future__ module and evaluating its variables, you can see when a new feature was first added to the language and when it becomes the default:

>>> import __future__
>>> __future__.division
_Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192)

garbage collection

The process of freeing memory when it is not used anymore. Python performs garbage collection via reference counting and a cyclic garbage collector that is able to detect and break reference cycles.

generator

返回一个 generator iterator 的函数。它看上去很像普通的函数，只是它包含 yield 表达式用于生成一系列的值，这些值可以用于 for 循环或通过 next() 函数一次一个地取出来。

通常是指一个 generator 函数，但在某些上下文中可能是指一个 generator iterator。如果意欲达到的含义不明确，则使用完整术语可避免含糊不清。

generator iterator

由一个 generator 函数创建的对象。

每个 yield 临时挂起当前的运行，记住执行的位置和状态（包括局部变量和等待的 try 语句）。当 generator iterator 恢复时，它从离开的位置重新开始（与函数不同，函数每次调用从起始开始）。

generator expression

一个返回 iterator 的表达式。它看起来像一个普通的表达式后跟一个定义循环变量的for表达式、range和一个可选的if表达式。这个组合表达式为闭包函数生成值：

>>> sum(i*i for i in range(10))         # sum of squares 0, 1, 4, ... 81
285

generic function

A function composed of multiple functions implementing the same operation for different types. Which implementation should be used during a call is determined by the dispatch algorithm.

See also the single dispatch glossary entry, the functools.singledispatch() decorator, and PEP 443.

GIL

See global interpreter lock.

global interpreter lock

The mechanism used by the CPython interpreter to assure that only one thread executes Python bytecode at a time. This simplifies the CPython implementation by making the object model (including critical built-in types such as dict) implicitly safe against concurrent access. Locking the entire interpreter makes it easier for the interpreter to be multi-threaded, at the expense of much of the parallelism afforded by multi-processor machines.

However, some extension modules, either standard or third-party, are designed so as to release the GIL when doing computationally-intensive tasks such as compression or hashing. Also, the GIL is always released when doing I/O.

Past efforts to create a “free-threaded” interpreter (one which locks shared data at a much finer granularity) have not been successful because performance suffered in the common single-processor case. It is believed that overcoming this performance issue would make the implementation much more complicated and therefore costlier to maintain.

hashable

如果对象具有hashable，则其哈希值在其生命周期内不会改变（它需要__ hash __()方法），并且可以与其他对象进行比较（它需要一个__ eq __()方法）。比较相等的可哈希对象必须具有相同的哈希值。

散列性使对象可用作字典键和set成员，因为这些数据结构在内部使用散列值。

Python的所有不可变内置对象都是可哈希的，而没有可变容器（例如列表或字典）是可哈希的。作为用户定义类实例的对象默认情况下可哈希化；它们都比较不相等（除了它们本身），并且其哈希值是从其id()派生的。

IDLE

An Integrated Development Environment for Python. IDLE is a basic editor and interpreter environment which ships with the standard distribution of Python.

immutable

An object with a fixed value. Immutable objects include numbers, strings and tuples. Such an object cannot be altered. A new object has to be created if a different value has to be stored. They play an important role in places where a constant hash value is needed, for example as a key in a dictionary.

import path

A list of locations (or path entries) that are searched by the path based finder for modules to import. During import, this list of locations usually comes from sys.path, but for subpackages it may also come from the parent package’s __path__ attribute.

importing

The process by which Python code in one module is made available to Python code in another module.

importer

An object that both finds and loads a module; both a finder and loader object.

interactive

Python has an interactive interpreter which means you can enter statements and expressions at the interpreter prompt, immediately execute them and see their results. Just launch python with no arguments (possibly by selecting it from your computer’s main menu). It is a very powerful way to test out new ideas or inspect modules and packages (remember help(x)).

interpreted

Python is an interpreted language, as opposed to a compiled one, though the distinction can be blurry because of the presence of the bytecode compiler. This means that source files can be run directly without explicitly creating an executable which is then run. Interpreted languages typically have a shorter development/debug cycle than compiled ones, though their programs generally also run more slowly. See also interactive.

interpreter shutdown

When asked to shut down, the Python interpreter enters a special phase where it gradually releases all allocated resources, such as modules and various critical internal structures. It also makes several calls to the garbage collector. This can trigger the execution of code in user-defined destructors or weakref callbacks. Code executed during the shutdown phase can encounter various exceptions as the resources it relies on may not function anymore (common examples are library modules or the warnings machinery).

The main reason for interpreter shutdown is that the __main__ module or the script being run has finished executing.

iterable

一个对象，能够一次返回其一个成员。Iterables 的例子包括所有的序列类型（例如 list、str 和 tuple）以及一些非序列类型如 dict、file 对象和任何定义了__iter__() 或 __getitem__() 方法的对象。 Iterables 可用于 for 循环以及其它许多需要序列的地方（zip()、map()，...）。当一个 iterable 对象作为参数传递给内置函数 iter() 时，它返回该对象的一个 iterator。这个 iterator 适用于一组值的一次传递。使用iterables时，通常不需要自己调用 iter() 或处理 iterator 对象。for 语句会自动为你执行此操作，它会创建一个临时的未命名变量以在循环期间保存 iterator。参见 iterator、sequence 和 generator。

iterator

一个表示数据流的对象。对 iterator 的 __next__() 方法的调用（或者将它传递给内建的函数 next()）返回流中连续的条目。当没有更多数据时，会引发一个StopIteration异常。此时，该 iterator 对象已耗尽，并且对其 __next__() 方法的任何进一步调用再次引发StopIteration。Iterator 需要有一个 __iter__() 方法，该方法返回 iterator 对象本身，因此每个 iterator 也是可迭代的，并且可以在大多数接受其它可迭代对象的地方使用。一个值得注意的例外是尝试多次迭代传递的代码。容器对象（例如 list）在每次将它传递给 iter() 函数或在 for 循环中使用它时都会生成一个全新的 iterator。对 iterator 尝试这样的操作只会返回上一次迭代过程中使用的相同的已耗尽的 iterator 对象，使其看起来像一个空容器。

更多信息可以在Iterator 类型中找到。

key function

A key function or collation function is a callable that returns a value used for sorting or ordering. For example, locale.strxfrm() is used to produce a sort key that is aware of locale specific sort conventions.

A number of tools in Python accept key functions to control how elements are ordered or grouped. They include min(), max(), sorted(), list.sort(), heapq.merge(), heapq.nsmallest(), heapq.nlargest(), and itertools.groupby().

There are several ways to create a key function. For example. the str.lower() method can serve as a key function for case insensitive sorts. Alternatively, a key function can be built from a lambda expression such as lambda r: (r[0], r[2]). Also, the operator module provides three key function constructors: attrgetter(), itemgetter(), and methodcaller(). See the Sorting HOW TO for examples of how to create and use key functions.

keyword argument

See argument.

lambda

一个由单个表达式组成的匿名内联函数，当这个函数被调用时开始计算。创建 lambda 函数的语法是 lambda [arguments]: expression

LBYL

Look before you leap. This coding style explicitly tests for pre-conditions before making calls or lookups. This style contrasts with the EAFP approach and is characterized by the presence of many if statements.

In a multi-threaded environment, the LBYL approach can risk introducing a race condition between “the looking” and “the leaping”. For example, the code, if key in mapping: return mapping[key] can fail if another thread removes key from mapping after the test, but before the lookup. This issue can be solved with locks or by using the EAFP approach.

list

A built-in Python sequence. 尽管它叫做列表，但它看上去更像是其它语言中的数组，而不是列表。它通过O(1)来访问子元素。

list comprehension

处理序列中所有或部分元素并返回包含结果的列表的紧凑方法。result = ['{:#04x}'.format(x) for x in range(256) if x % 2 == 0] generates a list of strings containing even hex numbers (0x..) in the range from 0 to 255. The if clause is optional. If omitted, all elements in range(256) are processed.

loader

An object that loads a module. It must define a method named load_module(). A loader is typically returned by a finder. See PEP 302 for details and importlib.abc.Loader for an abstract base class.

mapping

A container object that supports arbitrary key lookups and implements the methods specified in the Mapping or MutableMapping abstract base classes. Examples include dict, collections.defaultdict, collections.OrderedDict and collections.Counter.

meta path finder

A finder returned by a search of sys.meta_path. Meta path finders are related to, but different from path entry finders.

See importlib.abc.MetaPathFinder for the methods that meta path finders implement.

metaclass

The class of a class. Class definitions create a class name, a class dictionary, and a list of base classes. The metaclass is responsible for taking those three arguments and creating the class. Most object oriented programming languages provide a default implementation. What makes Python special is that it is possible to create custom metaclasses. Most users never need this tool, but when the need arises, metaclasses can provide powerful, elegant solutions. They have been used for logging attribute access, adding thread-safety, tracking object creation, implementing singletons, and many other tasks.

More information can be found in Customizing class creation.

method

A function which is defined inside a class body. If called as an attribute of an instance of that class, the method will get the instance object as its first argument (which is usually called self). See function and nested scope.

method resolution order

Method Resolution Order is the order in which base classes are searched for a member during lookup. See The Python 2.3 Method Resolution Order for details of the algorithm used by the Python interpreter since the 2.3 release.

module

An object that serves as an organizational unit of Python code. Modules have a namespace containing arbitrary Python objects. Modules are loaded into Python by the process of importing.

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