4. 深入了解流程控制
*******************

除了刚刚介绍过的 "while" 语句，Python 中也会使用其他语言中常见的流程控
制语句，只是稍有变化。


4.1. "if" 语句
==============

或許最常見的陳述式種類就是 "if" 了。舉例來說：

   >>> x = int(input("Please enter an integer: "))
   Please enter an integer: 42
   >>> if x < 0:
   ...     x = 0
   ...     print('Negative changed to zero')
   ... elif x == 0:
   ...     print('Zero')
   ... elif x == 1:
   ...     print('Single')
   ... else:
   ...     print('More')
   ...
   More

可以有零个或多个 "elif" 部分，以及一个可选的 "else" 部分。 关键字
'"elif"' 是 'else if' 的缩写，适合用于避免过多的缩进。 一个 "if" ...
"elif" ... "elif" ... 序列可以看作是其他语言中的 "switch" 或 "case" 语
句的替代。


4.2. "for" 语句
===============

Python 中的 "for" 语句与你在 C 或 Pascal 中可能用到的有所不同。 Python
中的 "for" 语句并不总是对算术递增的数值进行迭代（如同 Pascal），或是给
予用户定义迭代步骤和暂停条件的能力（如同 C），而是对任意序列进行迭代（
例如列表或字符串），条目的迭代顺序与它们在序列中出现的顺序一致。 例如
（此处英文为双关语）:

   >>> # Measure some strings:
   ... words = ['cat', 'window', 'defenestrate']
   >>> for w in words:
   ...     print(w, len(w))
   ...
   cat 3
   window 6
   defenestrate 12

在遍历同一个集合时修改该集合的代码可能很难获得正确的结果。通常，更直接
的做法是循环遍历该集合的副本或创建新集合：

   # Strategy:  Iterate over a copy
   for user, status in users.copy().items():
       if status == 'inactive':
           del users[user]

   # Strategy:  Create a new collection
   active_users = {}
   for user, status in users.items():
       if status == 'active':
           active_users[user] = status


4.3. "range()" 函式
===================

如果你需要疊代一個數列的話，使用內建 "range()" 函式就很方便。它可以生
成一等差級數：

   >>> for i in range(5):
   ...     print(i)
   ...
   0
   1
   2
   3
   4

給定的結束值永遠不會出現在生成的序列中；"range(10)" 生成的 10 個數值，
即對應存取一個長度為 10 的序列內每一個元素的索引值。也可以讓 range 從
其他數值計數，或者給定不同的級距（甚至為負；有時稱之為 step）：

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

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

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

欲疊代一個序列的索引值，你可以搭配使用 "range()" 和 "len()" 如下：

   >>> a = ['Mary', 'had', 'a', 'little', 'lamb']
   >>> for i in range(len(a)):
   ...     print(i, a[i])
   ...
   0 Mary
   1 had
   2 a
   3 little
   4 lamb

然而，在多數的情況，使用 "enumerate()" 函式將更為方便，詳見迴圈技巧。

如果直接印出一個 range 則會出現奇怪的輸出：

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

在很多情況下，由 "range()" 回傳的物件的行為如同一個 list，但實際上它並
不是。它是一個物件在你疊代時會回傳想要的序列的連續元素，並不會真正建出
這個序列的 list，以節省空間。

我们称这样的对象为 *iterable*，也就是说，适合作为这样的目标对象：函数
和结构期望从中获取连续的项直到所提供项全部耗尽。 我们已经看到 "for" 语
句就是这样一种结构，而接受可迭代对象的函数的一个例子是 "sum()":

   >>> sum(range(4))  # 0 + 1 + 2 + 3
   6

稍后我们将看到更多返回可迭代对象以及将可迭代对象作为参数的函数。 最后
，也许你会很好奇如何从一个指定范围内获取一个列表。 以下是解决方案：

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

在 資料結構 章节中，我们将讨论 "list()" 的更多细节。


4.4. "break" 和 "continue" 语句，以及循环中的 "else" 子句
=========================================================

"break" 陳述，如同 C 語言，終止包含其最內部的 "for" 或 "while" 迴圈。

循环语句可能带有 "else" 子句；它会在循环耗尽了可迭代对象 (使用 "for")
或循环条件变为假值 (使用 "while") 时被执行，但不会在循环被 "break" 语
句终止时被执行。 以下搜索素数的循环就是这样的一个例子:

   >>> for n in range(2, 10):
   ...     for x in range(2, n):
   ...         if n % x == 0:
   ...             print(n, 'equals', x, '*', n//x)
   ...             break
   ...     else:
   ...         # loop fell through without finding a factor
   ...         print(n, 'is a prime number')
   ...
   2 is a prime number
   3 is a prime number
   4 equals 2 * 2
   5 is a prime number
   6 equals 2 * 3
   7 is a prime number
   8 equals 2 * 4
   9 equals 3 * 3

（沒錯，這是正確的程式碼。請看仔細："else" 段落屬於 "for" 迴圈，**並非
** "if" 陳述。）

当和循环一起使用时，"else" 子句与 "try" 语句中的 "else" 子句的共同点多
于 "if" 语句中的同类子句: a "try" 语句中的 "else" 子句会在未发生异常时
执行，而循环中的 "else" 子句则会在未发生 "break" 时执行。 有关 "try"
语句和异常的更多信息，请参阅 處理例外。

"continue" 陳述，亦承襲於 C 語言，讓所屬的迴圈繼續執行下個疊代：

   >>> for num in range(2, 10):
   ...     if num % 2 == 0:
   ...         print("Found an even number", num)
   ...         continue
   ...     print("Found a number", num)
   Found an even number 2
   Found a number 3
   Found an even number 4
   Found a number 5
   Found an even number 6
   Found a number 7
   Found an even number 8
   Found a number 9


4.5. "pass" 语句
================

"pass" 陳述不執行任何動作。它用在語法上需要一個陳述但不需要執行任何動
作的時候。例如：

   >>> while True:
   ...     pass  # Busy-wait for keyboard interrupt (Ctrl+C)
   ...

這經常用於定義一個最簡單的類別：

   >>> class MyEmptyClass:
   ...     pass
   ...

"pass" 的另一个可以使用的场合是在你编写新的代码时作为一个函数或条件子
句体的占位符，允许你保持在更抽象的层次上进行思考。 "pass" 会被静默地忽
略:

   >>> def initlog(*args):
   ...     pass   # Remember to implement this!
   ...


4.6. 定義函式 (function)
========================

我們可以建立一個函式來產生費式數列到任何一個上界：

   >>> def fib(n):    # write Fibonacci series up to n
   ...     """Print a Fibonacci series up to n."""
   ...     a, b = 0, 1
   ...     while a < n:
   ...         print(a, end=' ')
   ...         a, b = b, a+b
   ...     print()
   ...
   >>> # Now call the function we just defined:
   ... fib(2000)
   0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597

關鍵字 "def" 帶入一個函式的*定義*。它之後必須連著該函式的名稱和置於括
號之中的參數。自下一行起，所有縮排的陳述成為該函式的主體。

一個函式的第一個陳述可以是一個字串值；此情況該字串值被視為該函式的說明
文件字串，即 *docstring*。（關於 docstring 的細節請參見說明文件字串段
落。）有些工具可以使用 docstring 來自動產生線上或可列印的文件，或讓使
用者能自由地自原始碼中瀏覽文件。在原始碼中加入 docstring 是個好慣例，
應該養成這樣的習慣。

函数的 *执行* 会引入一个用于函数局部变量的新符号表。 更确切地说，函数
中所有的变量赋值都将存储在局部符号表中；而变量引用会首先在局部符号表中
查找，然后是外层函数的局部符号表，最后是内置名称表。 因此，全局变量和
外层函数的变量不能在函数内部直接赋值（除非是在 "global" 语句中定义的全
局变量，或者是在 "nonlocal" 语句中定义的外层函数的变量），尽管它们可以
被引用。

在一個函式被呼叫的時候，實際傳入的參數（引數）會被加入至該函數的區域符
號表。因此，引數傳入的方式為*傳值呼叫 (call by value)*（這裡傳遞的「值
」永遠是一個物件的*參照（reference）*，而不是該物件的值）。[1] 當一個
函式呼叫別的函式時，在被呼叫的函式中會建立一個新的區域符號表。

一個函式定義會把該函式名稱加入至當前的符號表。該函式名稱的值帶有一個型
別，並被直譯器辨識為使用者自定函式（user-defined function）。該值可以
被賦予給別的變數名，而該變數也可以被當作函式使用。這即是常見的重新命名
方式：

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

如果你是來自別的語言，你可能不同意 "fib" 是個函式，而是個程序
(procedure)，因為它並沒有回傳值。實際上，即使一個函式缺少一個 "return"
陳述，它亦有一個固定的回傳值。這個值為 "None"（它是一個內建名稱）。在
直譯器中單獨使用 "None" 時，通常不會被顯示。你可以使用 "print()" 來看
到它：

   >>> fib(0)
   >>> print(fib(0))
   None

如果要寫一個函式回傳費式數列的 list 而不是直接印出它，這也很容易：

   >>> def fib2(n):  # return Fibonacci series up to n
   ...     """Return a list containing the Fibonacci series up to n."""
   ...     result = []
   ...     a, b = 0, 1
   ...     while a < n:
   ...         result.append(a)    # see below
   ...         a, b = b, a+b
   ...     return result
   ...
   >>> f100 = fib2(100)    # call it
   >>> f100                # write the result
   [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]

這個例子一樣示範了一些新的 Python 特性：

* "return" 语句会从函数内部返回一个值。 不带表达式参数的 "return" 会
  返 回 "None"。 函数执行完毕退出也会返回 "None"。

* "result.append(a)" 陳述呼叫了一個 list 物件的 "result" *method（方
  法 ）*。method 為「屬於」一個物件的函式，命名規則為 "obj.methodname"
  ， 其中 "obj" 為某個物件（亦可為一表達式），而 "methodname" 為該
  method 的名稱，並由該物件的型別所定義。不同的型別代表不同的 method。
  不同型 別的 method 可以擁有一樣的名稱而不會讓 Python 混淆。（你可以
  使用 *class* 定義自己的物件型別和 method，見 Classes）這裡
  "append()" method 定義在 list 物件中；它會加入一個新的元素在該 list
  的末端。這 個例子等同於 "result = result + [a]"，但更有效率。


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 ask_ok(prompt, retries=4, reminder='Please try again!'):
       while True:
           ok = input(prompt)
           if ok in ('y', 'ye', 'yes'):
               return True
           if ok in ('n', 'no', 'nop', 'nope'):
               return False
           retries = retries - 1
           if retries < 0:
               raise ValueError('invalid user response')
           print(reminder)

This function can be called in several ways:

* giving only the mandatory argument: "ask_ok('Do you really want to
  quit?')"

* giving one of the optional arguments: "ask_ok('OK to overwrite the
  file?', 2)"

* or even giving all arguments: "ask_ok('OK to overwrite the file?',
  2, 'Come on, only yes or 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

   i = 5

   def f(arg=i):
       print(arg)

   i = 6
   f()

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 f(a, L=[]):
       L.append(a)
       return L

   print(f(1))
   print(f(2))
   print(f(3))

This will print

   [1]
   [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 f(a, L=None):
       if L is None:
           L = []
       L.append(a)
       return L


4.7.2. Keyword Arguments
------------------------

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

   def parrot(voltage, state='a stiff', action='voom', type='Norwegian Blue'):
       print("-- This parrot wouldn't", action, end=' ')
       print("if you put", voltage, "volts through it.")
       print("-- Lovely plumage, the", type)
       print("-- It's", state, "!")

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

   parrot(1000)                                          # 1 positional argument
   parrot(voltage=1000)                                  # 1 keyword argument
   parrot(voltage=1000000, action='VOOOOOM')             # 2 keyword arguments
   parrot(action='VOOOOOM', voltage=1000000)             # 2 keyword arguments
   parrot('a million', 'bereft of life', 'jump')         # 3 positional arguments
   parrot('a thousand', state='pushing up the daisies')  # 1 positional, 1 keyword

but all the following calls would be invalid:

   parrot()                     # required argument missing
   parrot(voltage=5.0, 'dead')  # non-keyword argument after a keyword argument
   parrot(110, voltage=220)     # duplicate value for the same argument
   parrot(actor='John Cleese')  # unknown keyword argument

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

当存在一个形式为 "**name" 的正式形参时，它会接收一个字典 (参见 Mapping
Types --- dict)，其中包含除了与正式形参相对应的关键字参数以外的所有关
键字参数。 这可以与一个形式为 "*name"，接收一个包含除了正式形参列表以
外的位置参数的 元组 的正式形参 (将在下一小节介绍) 组合使用 ("*name" 必
须出现在 "**name" 之前。) 例如，如果我们这样定义一个函数:

   def cheeseshop(kind, *arguments, **keywords):
       print("-- Do you have any", kind, "?")
       print("-- I'm sorry, we're all out of", kind)
       for arg in arguments:
           print(arg)
       print("-" * 40)
       for kw in keywords:
           print(kw, ":", keywords[kw])

It could be called like this:

   cheeseshop("Limburger", "It's very runny, sir.",
              "It's really very, VERY runny, sir.",
              shopkeeper="Michael Palin",
              client="John Cleese",
              sketch="Cheese Shop Sketch")

and of course it would print:

   -- Do you have any Limburger ?
   -- I'm sorry, we're all out of Limburger
   It's very runny, sir.
   It's really very, VERY runny, sir.
   ----------------------------------------
   shopkeeper : Michael Palin
   client : John Cleese
   sketch : Cheese Shop Sketch

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. 特殊参数
---------------

默认情况下，函数的参数传递形式可以是位置参数或是显式的关键字参数。 为
了确保可读性和运行效率，限制允许的参数传递形式是有意义的，这样开发者只
需查看函数定义即可确定参数项是仅按位置、按位置也按关键字，还是仅按关键
字传递。

函数的定义看起来可以像是这样：

   def f(pos1, pos2, /, pos_or_kwd, *, kwd1, kwd2):
         -----------    ----------     ----------
           |             |                  |
           |        Positional or keyword   |
           |                                - Keyword only
            -- Positional only

在这里 "/" 和 "*" 是可选的。 如果使用这些符号则表明可以通过何种形参将
参数值传递给函数：仅限位置、位置或关键字，以及仅限关键字。 关键字形参
也被称为命名形参。


4.7.3.1. 位置或关键字参数
~~~~~~~~~~~~~~~~~~~~~~~~~

如果函数定义中未使用 "/" 和 "*"，则参数可以按位置或按关键字传递给函数
。


4.7.3.2. 仅限位置参数
~~~~~~~~~~~~~~~~~~~~~

在这里还可以发现更多细节，特定形参可以被标记为 *仅限位置*。 如果是 *仅
限位置* 的形参，则其位置是重要的，并且该形参不能作为关键字传入。 仅限
位置形参要放在 "/" (正斜杠) 之前。 这个 "/" 被用来从逻辑上分隔仅限位置
形参和其它形参。 如果函数定义中没有 "/"，则表示没有仅限位置形参。

在 "/" 之后的形参可以为 *位置或关键字* 或 *仅限关键字*。


4.7.3.3. 仅限关键字参数
~~~~~~~~~~~~~~~~~~~~~~~

要将形参标记为 *仅限关键字*，即指明该形参必须以关键字参数的形式传入，
应在参数列表的第一个 *keyword-only* 形参之前放置一个 "*"。


4.7.3.4. 函数示例
~~~~~~~~~~~~~~~~~

请考虑以下示例函数定义并特别注意 "/" 和 "*" 标记:

   >>> def standard_arg(arg):
   ...     print(arg)
   ...
   >>> def pos_only_arg(arg, /):
   ...     print(arg)
   ...
   >>> def kwd_only_arg(*, arg):
   ...     print(arg)
   ...
   >>> def combined_example(pos_only, /, standard, *, kwd_only):
   ...     print(pos_only, standard, kwd_only)

第一个函数定义 "standard_arg" 是最常见的形式，对调用方式没有任何限制，
参数可以按位置也可以按关键字传入:

   >>> standard_arg(2)
   2

   >>> standard_arg(arg=2)
   2

第二个函数 "pos_only_arg" 在函数定义中带有 "/"，限制仅使用位置形参。:

   >>> pos_only_arg(1)
   1

   >>> pos_only_arg(arg=1)
   Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
   TypeError: pos_only_arg() got an unexpected keyword argument 'arg'

第三个函数 "kwd_only_args" 在函数定义中通过 "*" 指明仅允许关键字参数:

   >>> kwd_only_arg(3)
   Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
   TypeError: kwd_only_arg() takes 0 positional arguments but 1 was given

   >>> kwd_only_arg(arg=3)
   3

而最后一个则在同一函数定义中使用了全部三种调用方式:

   >>> combined_example(1, 2, 3)
   Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
   TypeError: combined_example() takes 2 positional arguments but 3 were given

   >>> combined_example(1, 2, kwd_only=3)
   1 2 3

   >>> combined_example(1, standard=2, kwd_only=3)
   1 2 3

   >>> combined_example(pos_only=1, standard=2, kwd_only=3)
   Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
   TypeError: combined_example() got an unexpected keyword argument 'pos_only'

最后，请考虑这个函数定义，它的位置参数 "name"  和 "**kwds" 之间由于存
在关键字名称 "name" 而可能产生潜在冲突:

   def foo(name, **kwds):
       return 'name' in kwds

任何调用都不可能让它返回 "True"，因为关键字 "'name'" 将总是绑定到第一
个形参。 例如:

   >>> foo(1, **{'name': 2})
   Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
   TypeError: foo() got multiple values for argument 'name'
   >>>

但使用 "/" (仅限位置参数) 就可能做到，因为它允许 "name" 作为位置参数，
也允许 "'name'" 作为关键字参数的关键字名称:

   def foo(name, /, **kwds):
       return 'name' in kwds
   >>> foo(1, **{'name': 2})
   True

换句话说，仅限位置形参的名称可以在 "**kwds" 中使用而不产生歧义。


4.7.3.5. 概括
~~~~~~~~~~~~~

用例将确定要在函数定义中使用的参数:

   def f(pos1, pos2, /, pos_or_kwd, *, kwd1, kwd2):

作为指导：

* 如果你希望形参名称对用户来说不可用，则使用仅限位置形参。 这适用于
  形 参名称没有实际意义，以及当你希望强制规定调用时的参数顺序，或是需
  要同 时收受一些位置形参和任意关键字形参等情况。

* 当形参名称有实际意义，以及显式指定形参名称可使函数定义更易理解，或
  者 当你想要防止用户过于依赖传入参数的位置时，则使用仅限关键字形参。

* 对于 API 来说，使用仅限位置形参可以防止形参名称在未来被修改时造成
  破 坏性的 API 变动。


4.7.4. 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 和序列
(Sequences)).  Before the variable number of arguments, zero or more
normal arguments may occur.

   def write_multiple_items(file, separator, *args):
       file.write(separator.join(args))

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 concat(*args, sep="/"):
   ...     return sep.join(args)
   ...
   >>> concat("earth", "mars", "venus")
   'earth/mars/venus'
   >>> concat("earth", "mars", "venus", sep=".")
   'earth.mars.venus'


4.7.5. 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))            # normal call with separate arguments
   [3, 4, 5]
   >>> args = [3, 6]
   >>> list(range(*args))            # call with arguments unpacked from a list
   [3, 4, 5]

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

   >>> def parrot(voltage, state='a stiff', action='voom'):
   ...     print("-- This parrot wouldn't", action, end=' ')
   ...     print("if you put", voltage, "volts through it.", end=' ')
   ...     print("E's", state, "!")
   ...
   >>> d = {"voltage": "four million", "state": "bleedin' demised", "action": "VOOM"}
   >>> parrot(**d)
   -- This parrot wouldn't VOOM if you put four million volts through it. E's bleedin' demised !


4.7.6. 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 make_incrementor(n):
   ...     return lambda x: x + n
   ...
   >>> f = make_incrementor(42)
   >>> f(0)
   42
   >>> f(1)
   43

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

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


4.7.7. 說明文件字串
-------------------

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 my_function():
   ...     """Do nothing, but document it.
   ...
   ...     No, really, it doesn't do anything.
   ...     """
   ...     pass
   ...
   >>> print(my_function.__doc__)
   Do nothing, but document it.

       No, really, it doesn't do anything.


4.7.8. 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).

*函数标注* 以字典的形式存放在函数的 "__annotations__" 属性中，并且不会
影响函数的任何其他部分。 形参标注的定义方式是在形参名称后加上冒号，后
面跟一个表达式，该表达式会被求值为标注的值。 返回值标注的定义方式是加
上一个组合符号 "->"，后面跟一个表达式，该标注位于形参列表和表示 "def"
语句结束的冒号之间。 下面的示例有一个位置参数，一个关键字参数以及返回
值带有相应标注:

   >>> def f(ham: str, eggs: str = 'eggs') -> str:
   ...     print("Annotations:", f.__annotations__)
   ...     print("Arguments:", ham, eggs)
   ...     return ham + ' and ' + eggs
   ...
   >>> f('spam')
   Annotations: {'ham': <class 'str'>, 'return': <class 'str'>, 'eggs': <class 'str'>}
   Arguments: spam eggs
   'spam and eggs'


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)".

* 以一致的规则为你的类和函数命名；按照惯例应使用 "UpperCamelCase" 来
  命 名类，而以 "lowercase_with_underscores" 来命名函数和方法。 始终应
  使 用 "self" 来命名第一个方法参数 (有关类和方法的更多信息请参阅 A
  First Look at Classes)。

* 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.

-[ 註解 ]-

[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).
