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]
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).