4. 深入了解流程控制
*******************

除了剛才介紹的 "while"，Python 擁有在其他程式語言中常用的流程控制語法
，並有ㄧ些不一樣的改變。


4.1. "if" 陳述式
================

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

可以有零個或多個 "elif" 段落，且 "else" 段落可有可無。關鍵字 "elif" 只
是「else if」的縮寫，並且用來避免過多的縮排。一個 "if" ... "elif" ...
"elif" ... 序列可以用來替代其他語言中出現的 "switch" 或 "case" 陳述式
。


4.2. "for" 陳述式
=================

在 Python 中的 "for" 陳述式可能不同於在 C 或 Pascal 中所看到的使用方式
。與其只能疊代 (iterate) 一個等差級數（如 Pascal），或給與使用者定義疊
代步進方式與結束條件（如 C），Python 的 "for" 陳述疊代任何序列（list
或者字串）的元素，以他們出現在序列中的順序。例如（無意雙關）：

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

如果你在迴圈中需要修改一個你正在疊代的序列（例如重複一些選擇的元素），
那麼會建議你先建立一個序列的拷貝。疊代序列並不暗示建立新的拷貝。此時
slice 語法就讓這件事十分容易完成：

   >>> for w in words[:]:  # Loop over a slice copy of the entire list.
   ...     if len(w) > 6:
   ...         words.insert(0, w)
   ...
   >>> words
   ['defenestrate', 'cat', 'window', 'defenestrate']

在 "for w in words:" 的情況，這個例子會試著重覆不斷地插入
"defenestrate"，產生一個無限長的 list。


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。

我們稱這樣的物件為 *iterable*（可疊代的），意即能作為函式、陳述式中能
一直獲取連續元素直到用盡的部件。我們已經看過 "for" 陳述式可做為如此的
*iterator*（疊代器）。"list()" 函式為另一個例子，他可以自 iterable（可
疊代物件）建立 list：

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

待會我們可以看到更多函式回傳 iterable 和接受 iterable 為引數。


4.4. "break" 和 "continue" 陳述、迴圈內 "else" 段落
===================================================

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

迴圈可以帶有一個 "else" 段落。當迴圈歷遍疊代的 list （在 "for" 中）或
條件為偽（在 "while" 中）時，這個段落會被執行；但迴圈被 "break" 陳述終
止時則不會。底下尋找質數的迴圈即示範了這個行為：

   >>> 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。）

當 "else" 段落與迴圈使用時，相較於搭配 "if" 陳述使用，它的行為與搭配
"try" 陳述使用時更為相似："try" 的 "else" 段落在沒有任何例外
(exception) 時執行，而迴圈的 "else" 段落在沒有任何 "break" 時執行。更
多有關 "try" 陳述和例外的介紹，見處理例外。

"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" 陳述式
==================

"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" 亦可作為一個函式或條件判斷主體的預留位置，它可幫助你以更宏觀的
角度思考並撰寫新的程式碼。"pass" 可被忽略：

   >>> 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。

函式執行期間會建立一個新的符號表（symbol table）來儲存該函式內的區域變
數。更精確地說，所有在函式內的變數賦值都會把該值儲存在一個區域符號表。
然而，在讀取一個變數時，會先從區域符號表起搜尋，其次為所有包含其函式的
區域符號表，其次為全域符號表，最後為所有內建的名稱。因此，在函式中，全
域變數無法被直接賦值（除非該變數有被 "global" 陳述句提及），但它們可以
被讀取。

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" 陳述會讓一個函式回傳一個值。單獨使用 "return" 不外加一個
  表 達式作為引數會回傳 "None"。一個函式執行到結束也會回傳 "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'

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 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. 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'


4.7.4. 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))            # 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 !


4.7.5. 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 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')]


4.7.6. 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.


4.7.7. 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 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)".

* 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.

-[ 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).
