4. More Control Flow Tools

Besides the while statement just introduced, Python knows the usual control flow statements known from other languages, with some twists.

4.1. if Statements

Perhaps the most well-known statement type is the if statement. For example:

>>>  = int(input("请输入整数: "))
请输入整数: 42
>>> if  < 0:
...      = 0
...     print('负数置为零')
... elif  == 0:
...     print('零')
... elif  == 1:
...     print('一')
... else:
...     print('更大')

There can be zero or more elif parts, and the else part is optional. The keyword 'elif' is short for 'else if', and is useful to avoid excessive indentation. An if ... elif ... elif ... sequence is a substitute for the switch or case statements found in other languages.

4.2. for Statements

The for statement in Python differs a bit from what you may be used to in C or Pascal. Rather than always iterating over an arithmetic progression of numbers (like in Pascal), or giving the user the ability to define both the iteration step and halting condition (as C), Python's for statement iterates over the items of any sequence (a list or a string), in the order that they appear in the sequence. For example (no pun intended):

>>> # 丈量字符串:
... 词表 = ['猫', '窗户', '丢出窗户']
>>> for  in 词表:
...     print(, len())
猫 1
窗户 2
丢出窗户 4

If you need to modify the sequence you are iterating over while inside the loop (for example to duplicate selected items), it is recommended that you first make a copy. Iterating over a sequence does not implicitly make a copy. The slice notation makes this especially convenient:

>>> for  in 词表[:]:  # 遍历截取了整个列表的拷贝.
...     if len() > 2:
...         词表.insert(0, )
>>> 词表
['丢出窗户', '猫', '窗户', '丢出窗户']

With for w in 词表:, the example would attempt to create an infinite list, inserting 丢出窗户 over and over again.

4.3. The range() Function

If you do need to iterate over a sequence of numbers, the built-in function range() comes in handy. It generates arithmetic progressions:

>>> for  in range(5):
...     print()

The given end point is never part of the generated sequence; range(10) generates 10 values, the legal indices for items of a sequence of length 10. It is possible to let the range start at another number, or to specify a different increment (even negative; sometimes this is called the 'step'):

range(5, 10)
   5, 6, 7, 8, 9

range(0, 10, 3)
   0, 3, 6, 9

range(-10, -100, -30)
  -10, -40, -70

To iterate over the indices of a sequence, you can combine range() and len() as follows:

>>>  = ['玛丽', '有', '只', '小', '羊羔']
>>> for 索引 in range(len()):
...     print(索引, [索引])
0 玛丽
1 有
2 只
3 小
4 羊羔

In most such cases, however, it is convenient to use the enumerate() function, see Looping Techniques.

A strange thing happens if you just print a range:

>>> print(range(10))
range(0, 10)

In many ways the object returned by range() behaves as if it is a list, but in fact it isn't. It is an object which returns the successive items of the desired sequence when you iterate over it, but it doesn't really make the list, thus saving space.

We say such an object is iterable, that is, suitable as a target for functions and constructs that expect something from which they can obtain successive items until the supply is exhausted. We have seen that the for statement is such an iterator. The function list() is another; it creates lists from iterables:

>>> list(range(5))
[0, 1, 2, 3, 4]

Later we will see more functions that return iterables and take iterables as argument.

4.4. break and continue Statements, and else Clauses on Loops

The break statement, like in C, breaks out of the innermost enclosing for or while loop.

Loop statements may have an else clause; it is executed when the loop terminates through exhaustion of the list (with for) or when the condition becomes false (with while), but not when the loop is terminated by a break statement. This is exemplified by the following loop, which searches for prime numbers:

>>> for 被除数 in range(2, 10):
...     for 除数 in range(2, 被除数):
...         if 被除数 % 除数 == 0:
...             print(被除数, '等于', 除数, '*', 被除数//除数)
...             break
...     else:
...         # 之前的循环没有找到约数
...         print(被除数, '是质数')
2 是质数
3 是质数
4 等于 2 * 2
5 是质数
6 等于 2 * 3
7 是质数
8 等于 2 * 4
9 等于 3 * 3

(Yes, this is the correct code. Look closely: the else clause belongs to the for loop, not the if statement.)

When used with a loop, the else clause has more in common with the else clause of a try statement than it does that of if statements: a try statement's else clause runs when no exception occurs, and a loop's else clause runs when no break occurs. For more on the try statement and exceptions, see Handling Exceptions.

The continue statement, also borrowed from C, continues with the next iteration of the loop:

>>> for  in range(2, 10):
...     if  % 2 == 0:
...         print("找到偶数", )
...         continue
...     print("找到数", )
找到偶数 2
找到数 3
找到偶数 4
找到数 5
找到偶数 6
找到数 7
找到偶数 8
找到数 9

4.5. pass Statements

The pass statement does nothing. It can be used when a statement is required syntactically but the program requires no action. For example:

>>> while True:
...     pass  # 忙于等待键盘输入 (Ctrl+C)

This is commonly used for creating minimal classes:

>>> class 空类:
...     pass

Another place pass can be used is as a place-holder for a function or conditional body when you are working on new code, allowing you to keep thinking at a more abstract level. The pass is silently ignored:

>>> def 初始化日志(*参数):
...     pass   # 记着实现!

4.6. Defining Functions

We can create a function that writes the Fibonacci series to an arbitrary boundary:

>>> def 斐波那契(n):    # 打印小于n的斐波那契数列
...     """打印小于n的斐波那契数列."""
...     前数, 后数 = 0, 1
...     while 前数 < n:
...         print(前数, end=' ')
...         前数, 后数 = 后数, 前数+后数
...     print()
>>> # 现在调用刚才定义的函数:
... 斐波那契(2000)
0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597

The keyword def introduces a function definition. It must be followed by the function name and the parenthesized list of formal parameters. The statements that form the body of the function start at the next line, and must be indented.

The first statement of the function body can optionally be a string literal; this string literal is the function's documentation string, or docstring. (More about docstrings can be found in the section Documentation Strings.) There are tools which use docstrings to automatically produce online or printed documentation, or to let the user interactively browse through code; it's good practice to include docstrings in code that you write, so make a habit of it.

The execution of a function introduces a new symbol table used for the local variables of the function. More precisely, all variable assignments in a function store the value in the local symbol table; whereas variable references first look in the local symbol table, then in the local symbol tables of enclosing functions, then in the global symbol table, and finally in the table of built-in names. Thus, global variables cannot be directly assigned a value within a function (unless named in a global statement), although they may be referenced.

The actual parameters (arguments) to a function call are introduced in the local symbol table of the called function when it is called; thus, arguments are passed using call by value (where the value is always an object reference, not the value of the object). [1] When a function calls another function, a new local symbol table is created for that call.

A function definition introduces the function name in the current symbol table. The value of the function name has a type that is recognized by the interpreter as a user-defined function. This value can be assigned to another name which can then also be used as a function. This serves as a general renaming mechanism:

>>> 斐波那契
<function 斐波那契 at 10042ed0>
>>> f = 斐波那契
>>> f(100)
0 1 1 2 3 5 8 13 21 34 55 89

Coming from other languages, you might object that fib is not a function but a procedure since it doesn't return a value. In fact, even functions without a return statement do return a value, albeit a rather boring one. This value is called None (it's a built-in name). Writing the value None is normally suppressed by the interpreter if it would be the only value written. You can see it if you really want to using print():

>>> 斐波那契(0)
>>> print(斐波那契(0))

It is simple to write a function that returns a list of the numbers of the Fibonacci series, instead of printing it:

>>> def 斐波那契2(n):  # 返回小于n的斐波那契数列
...     """返回一个包含小于n的斐波那契数列的列表."""
...     结果 = []
...     前数, 后数 = 0, 1
...     while 前数 < n:
...         结果.append(前数)    # 见下
...         前数, 后数 = 后数, 前数+后数
...     return 结果
>>> f100 = 斐波那契2(100)    # 调用它
>>> f100                # 输出结果
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]

This example, as usual, demonstrates some new Python features:

  • The return statement returns with a value from a function. return without an expression argument returns None. Falling off the end of a function also returns None.
  • The statement result.append(a) calls a method of the list object result. A method is a function that 'belongs' to an object and is named obj.methodname, where obj is some object (this may be an expression), and methodname is the name of a method that is defined by the object's type. Different types define different methods. Methods of different types may have the same name without causing ambiguity. (It is possible to define your own object types and methods, using classes, see Classes) The method append() shown in the example is defined for list objects; it adds a new element at the end of the list. In this example it is equivalent to result = result + [a], but more efficient.

4.7. More on Defining Functions

It is also possible to define functions with a variable number of arguments. There are three forms, which can be combined.

4.7.1. Default Argument Values

The most useful form is to specify a default value for one or more arguments. This creates a function that can be called with fewer arguments than it is defined to allow. For example:

def 请求同意(提示, 重试次数=4, 提醒='请重试!'):
    while True:
         = input(提示)
        if  in ('y', 'ye', 'yes'):
            return True
        if  in ('n', 'no', 'nop', 'nope'):
            return False
        重试次数 = 重试次数 - 1
        if 重试次数 < 0:
            raise ValueError('无效的用户输入')

This function can be called in several ways:

  • giving only the mandatory argument: 请求同意('确认退出?')
  • giving one of the optional arguments: 请求同意('确认覆盖文件?', 2)
  • or even giving all arguments: 请求同意('确认覆盖文件?', 2, '快, 就说yes还是no!')

This example also introduces the in keyword. This tests whether or not a sequence contains a certain value.

The default values are evaluated at the point of function definition in the defining scope, so that

 = 5

def 函数(参数=):

 = 6

will print 5.

Important warning: The default value is evaluated only once. This makes a difference when the default is a mutable object such as a list, dictionary, or instances of most classes. For example, the following function accumulates the arguments passed to it on subsequent calls:

def 函数(, 列表=[]):
    return 列表


This will print

[1, 2]
[1, 2, 3]

If you don't want the default to be shared between subsequent calls, you can write the function like this instead:

def 函数(, 列表=None):
    if 列表 is None:
        列表 = []
    return 列表

4.7.2. Keyword Arguments

Functions can also be called using keyword arguments of the form kwarg=value. For instance, the following function:

def 鹦鹉(电压, 状态='死透了', 行为='轰隆隆', 种类='挪威蓝'):
    print("-- 这只鹦鹉不会", 行为, end=' ')
    print("即使你用", 电压, "伏的电压电它.")
    print("-- 多漂亮的羽毛, 这", 种类)
    print("-- 它", 状态, "!")

accepts one required argument (voltage) and three optional arguments (state, action, and type). This function can be called in any of the following ways:

鹦鹉(1000)                                          # 1个位置参数
鹦鹉(电压=1000)                                      # 1个关键词参数
鹦鹉(电压=1000000, 行为='轰隆隆隆隆')                  # 2个关键词参数
鹦鹉(行为='轰隆隆隆隆', 电压=1000000)                  # 2个关键词参数
鹦鹉('一百万', '没命了', '跳')                         # 3个位置参数
鹦鹉('一千', 状态='坟头长草了')                         # 1个位置参数, 1个关键词参数

but all the following calls would be invalid:

鹦鹉()                     # 欠缺必需的参数
鹦鹉(电压=5.0, '死了')      # 非关键词参数在关键词参数之后
鹦鹉(110, 电压=220)         # 同一参数的重复赋值
鹦鹉(演员='John Cleese')    # 未知的关键词参数

In a function call, keyword arguments must follow positional arguments. All the keyword arguments passed must match one of the arguments accepted by the function (e.g. actor is not a valid argument for the parrot function), and their order is not important. This also includes non-optional arguments (e.g. parrot(voltage=1000) is valid too). No argument may receive a value more than once. Here's an example that fails due to this restriction:

>>> def 函数(参数):
...     pass
>>> 函数(0, 参数=0)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: 函数() got multiple values for keyword argument '参数'

When a final formal parameter of the form **name is present, it receives a dictionary (see Mapping Types --- dict) containing all keyword arguments except for those corresponding to a formal parameter. This may be combined with a formal parameter of the form *name (described in the next subsection) which receives a tuple containing the positional arguments beyond the formal parameter list. (*name must occur before **name.) For example, if we define a function like this:

def 奶酪店(种类, *所有参数, **所有关键词):
    print("-- 你有没有", 种类, "?")
    print("-- 抱歉, 我们已经卖光了", 种类)
    for 参数 in 所有参数:
    print("-" * 40)
    for 索引 in 所有关键词:
        print(索引, ":", 所有关键词[索引])

It could be called like this:

奶酪店("林堡奶酪", "它滑溜着呢, 先生.",
           "它真的非常, 非常滑溜, 先生.",
           经理="Michael Palin",
           客人="John Cleese",

and of course it would print:

-- 你有没有林堡奶酪 ?
-- 抱歉, 我们已经卖光了 林堡奶酪
它滑溜着呢, 先生.
它真的非常, 非常滑溜, 先生.
经理 : Michael Palin
客人 : John Cleese
脚本 : 奶酪店脚本

Note that the order in which the keyword arguments are printed is guaranteed to match the order in which they were provided in the function call.

4.7.3. Arbitrary Argument Lists

Finally, the least frequently used option is to specify that a function can be called with an arbitrary number of arguments. These arguments will be wrapped up in a tuple (see Tuples and Sequences). Before the variable number of arguments, zero or more normal arguments may occur.

def 写入几个东西(文件, 分隔符, *参数):

Normally, these variadic arguments will be last in the list of formal parameters, because they scoop up all remaining input arguments that are passed to the function. Any formal parameters which occur after the *args parameter are 'keyword-only' arguments, meaning that they can only be used as keywords rather than positional arguments.

>>> def 联结(*参数, 分隔符="/"):
...     return 分隔符.join(参数)
>>> 联结("地球", "土星", "金星")
>>> 联结("地球", "土星", "金星", 分隔符=".")

4.7.4. Unpacking Argument Lists

The reverse situation occurs when the arguments are already in a list or tuple but need to be unpacked for a function call requiring separate positional arguments. For instance, the built-in range() function expects separate start and stop arguments. If they are not available separately, write the function call with the *-operator to unpack the arguments out of a list or tuple:

>>> list(range(3, 6))            # 一般调用时使用分开的参数
[3, 4, 5]
>>> 参数 = [3, 6]
>>> list(range(*参数))            # 调用时可以用列表分解成的参数
[3, 4, 5]

In the same fashion, dictionaries can deliver keyword arguments with the **-operator:

>>> def 鹦鹉(电压, 状态='死透了', 行为='轰隆隆'):
...     print("-- 这只鹦鹉不会", 行为, end=' ')
...     print("即使你用", 电压, "伏的电压电它.", end=' ')
...     print("这货", 状态, "!")
>>> 字典 = {"电压": "四百万", "状态": "死翘翘了", "行为": "轰隆隆隆隆"}
>>> 鹦鹉(**字典)
-- 这只鹦鹉不会 轰隆隆隆隆 即使你用 四百万 伏的电压电它. 这货 死翘翘了 !

4.7.5. Lambda Expressions

Small anonymous functions can be created with the lambda keyword. This function returns the sum of its two arguments: lambda a, b: a+b. Lambda functions can be used wherever function objects are required. They are syntactically restricted to a single expression. Semantically, they are just syntactic sugar for a normal function definition. Like nested function definitions, lambda functions can reference variables from the containing scope:

>>> def 生成递增器(增量):
...     return lambda x: x + 增量
>>> f = 生成递增器(42)
>>> f(0)
>>> f(1)

The above example uses a lambda expression to return a function. Another use is to pass a small function as an argument:

>>> 所有对 = [(1, 'one'), (2, 'two'), (3, 'three'), (4, 'four')]
>>> 所有对.sort(key=lambda : [1])
>>> 所有对
[(4, 'four'), (1, 'one'), (3, 'three'), (2, 'two')]

4.7.6. Documentation Strings

Here are some conventions about the content and formatting of documentation strings.

The first line should always be a short, concise summary of the object's purpose. For brevity, it should not explicitly state the object's name or type, since these are available by other means (except if the name happens to be a verb describing a function's operation). This line should begin with a capital letter and end with a period.

If there are more lines in the documentation string, the second line should be blank, visually separating the summary from the rest of the description. The following lines should be one or more paragraphs describing the object's calling conventions, its side effects, etc.

The Python parser does not strip indentation from multi-line string literals in Python, so tools that process documentation have to strip indentation if desired. This is done using the following convention. The first non-blank line after the first line of the string determines the amount of indentation for the entire documentation string. (We can't use the first line since it is generally adjacent to the string's opening quotes so its indentation is not apparent in the string literal.) Whitespace "equivalent" to this indentation is then stripped from the start of all lines of the string. Lines that are indented less should not occur, but if they occur all their leading whitespace should be stripped. Equivalence of whitespace should be tested after expansion of tabs (to 8 spaces, normally).

Here is an example of a multi-line docstring:

>>> def 我的函数():
...     """无作为, 仅文档.
...     真的不做任何事.
...     """
...     pass
>>> print(我的函数.__doc__)
无作为, 仅文档.


4.7.7. Function Annotations

Function annotations are completely optional metadata information about the types used by user-defined functions (see PEP 3107 and PEP 484 for more information).

Annotations are stored in the __annotations__ attribute of the function as a dictionary and have no effect on any other part of the function. Parameter annotations are defined by a colon after the parameter name, followed by an expression evaluating to the value of the annotation. Return annotations are defined by a literal ->, followed by an expression, between the parameter list and the colon denoting the end of the def statement. The following example has a positional argument, a keyword argument, and the return value annotated:

>>> def 函数(火腿: str, 鸡蛋: str = '好鸡蛋') -> str:
...     print("注解:", 函数.__annotations__)
...     print("参数:", 火腿, 鸡蛋)
...     return 火腿 + '和' + 鸡蛋
>>> 函数('午餐肉')
注解: {'火腿': <class 'str'>, '鸡蛋': <class 'str'>, 'return': <class 'str'>}
参数: 午餐肉 好鸡蛋

4.8. Intermezzo: Coding Style

Now that you are about to write longer, more complex pieces of Python, it is a good time to talk about coding style. Most languages can be written (or more concise, formatted) in different styles; some are more readable than others. Making it easy for others to read your code is always a good idea, and adopting a nice coding style helps tremendously for that.

For Python, PEP 8 has emerged as the style guide that most projects adhere to; it promotes a very readable and eye-pleasing coding style. Every Python developer should read it at some point; here are the most important points extracted for you:

  • Use 4-space indentation, and no tabs.

    4 spaces are a good compromise between small indentation (allows greater nesting depth) and large indentation (easier to read). Tabs introduce confusion, and are best left out.

  • Wrap lines so that they don't exceed 79 characters.

    This helps users with small displays and makes it possible to have several code files side-by-side on larger displays.

  • Use blank lines to separate functions and classes, and larger blocks of code inside functions.

  • When possible, put comments on a line of their own.

  • Use docstrings.

  • Use spaces around operators and after commas, but not directly inside bracketing constructs: a = f(1, 2) + g(3, 4).

  • Name your classes and functions consistently; the convention is to use CamelCase for classes and lower_case_with_underscores for functions and methods. Always use self as the name for the first method argument (see A First Look at Classes for more on classes and methods).

  • Don't use fancy encodings if your code is meant to be used in international environments. Python's default, UTF-8, or even plain ASCII work best in any case.

  • Likewise, don't use non-ASCII characters in identifiers if there is only the slightest chance people speaking a different language will read or maintain the code.


[1]Actually, call by object reference would be a better description, since if a mutable object is passed, the caller will see any changes the callee makes to it (items inserted into a list).