每天学点python（1）

　　通过这段时间的学习，发现很有必要精通一门动态语言，选择了比较火也较好入门的Python。这次学习这门语言，准备通过官方文档的tutorial先了解下与之前的c++和java有什么不同，然后找一本比较好的教材。

　　在这个系列的博客里，我通过阅读tutorial，记录一些我没有见过的新特性，比较有代表性的代码会放在博客里，如果知识点比较重要，我也会把英文放在上边，以供查阅。另外，如果一个知识点我看tutorial看不懂，会上网搜一些资料，理解之后会把自己的理解放在里边。

　　博客的结构只是按照我学习的顺序做一些记录，每篇博客有我记录的十个知识点，在博客开始的位置标明知识点，具体。

　　我始终认为学习是一个坚持的过程，希望我这次能坚持下去。

　　成功贵在坚持不懈------------------------与大家共勉

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1. for: words[:]

2. list等做函数参数重复调用

3. keyword Argument

4. Arbitrary Argument Lists

5. Unpacking Argument Lists

6. Lambda

7. Documentation Strings

8. Lists作为stack和queue

9. filter(), map(), and reduce()

10. List Comprehension

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1. for循环里：

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

 注意words[:]，如果是words，就成为死循环

2. 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)

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

 原因尚未得知，如果不想得到这样的结果，应该这么写：

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

 3. keyword Arguments

　　从下面的例子可以看出，keyword argument出现之后，后边必须要用keyword argument的形式。keyword arguments must follow positional arguments

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

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

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

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
    keys = sorted(keywords.keys())
    for kw in keys:
        print kw, ":", keywords[kw]

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

-- 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.
----------------------------------------
client : John Cleese
shopkeeper : Michael Palin
sketch : Cheese Shop Sketch

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 and 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))

5. Unpacking Argument Lists

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

>>> def parrot(voltage, state='a stiff', action='voom'):
...     print "-- This parrot wouldn't", action,
...     print "if you put", voltage, "volts through it.",
...     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 !

6. Lambda Forms

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

7. Documentation Strings

在三个引号里添加函数的说明以及参数的意义，然后调用function_name.__doc__可以查看文档说明

>>> 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.

 8. Lists作为stack

>>> stack = [3, 4, 5]
>>> stack.append(6)
>>> stack.append(7)
>>> stack
[3, 4, 5, 6, 7]
>>> stack.pop()
7
>>> stack
[3, 4, 5, 6]
>>> stack.pop()
6
>>> stack.pop()
5
>>> stack
[3, 4]

Lists作为queue

>>> from collections import deque
>>> queue = deque(["Eric", "John", "Michael"])
>>> queue.append("Terry")           # Terry arrives
>>> queue.append("Graham")          # Graham arrives
>>> queue.popleft()                 # The first to arrive now leaves
'Eric'
>>> queue.popleft()                 # The second to arrive now leaves
'John'
>>> queue                           # Remaining queue in order of arrival
deque(['Michael', 'Terry', 'Graham'])

9. Functional Programming Tools

There are three built-in functions that are very useful when used with lists: filter(), map(), and reduce().

filter(function, sequence) returns a sequence consisting of those items from the sequence for which function(item) is true. If sequence is a string ortuple, the result will be of the same type; otherwise, it is always a list. For example, to compute a sequence of numbers not divisible by 2 and 3:

>>> def f(x): return x % 2 != 0 and x % 3 != 0
...
>>> filter(f, range(2, 25))
[5, 7, 11, 13, 17, 19, 23]

map(function, sequence) calls function(item) for each of the sequence’s items and returns a list of the return values. For example, to compute some cubes:

>>> def cube(x): return x*x*x
...
>>> map(cube, range(1, 11))
[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]

More than one sequence may be passed; the function must then have as many arguments as there are sequences and is called with the corresponding item from each sequence (or None if some sequence is shorter than another). For example:

>>> seq = range(8)
>>> def add(x, y): return x+y
...
>>> map(add, seq, seq)
[0, 2, 4, 6, 8, 10, 12, 14]

reduce(function, sequence) returns a single value constructed by calling the binary function function on the first two items of the sequence, then on the result and the next item, and so on. For example, to compute the sum of the numbers 1 through 10:

>>> def add(x,y): return x+y
...
>>> reduce(add, range(1, 11))
55

A third argument can be passed to indicate the starting value. In this case the starting value is returned for an empty sequence, and the function is first applied to the starting value and the first sequence item, then to the result and the next item, and so on. For example,

>>> def sum(seq):
...     def add(x,y): return x+y
...     return reduce(add, seq, 0)
...
>>> sum(range(1, 11))
55
>>> sum([])
0

对于这三个函数，不甚了解，这是我接触过的语言中没有出现过的。对于这三个函数，形式都是func_name(function, sequeues)，都作用在一个函数参数是一个集合（List）上，对于其用法，参考其他资料，有

filter() 函数:filter 函数的功能相当于过滤器。调用一个布尔函数bool_func来迭代遍历每个seq中的元素；返回一个使bool_seq返回值为true的元素的序列。

>>>a=[1,2,3,4,5,6,7]
>>>b=filter(lambda x:x>5, a)
>>>print b
>>>[6,7]

如果filter参数值为None，就使用identity（）函数，list参数中所有为假的元 素都将被删除。如下所示：

>>>a=[0,1,2,3,4,5,6,7]
>>>b=filter(None, a)
>>>print b
>>>[1,2,3,4,5,6,7]

map() 函数：map函数func作用于给定序列的每个元素，并用一个列表来提供返回值。

>>>map(lambda x:x+3, a) #这里的a同上
>>>[3,4,5,6,7,8,9,10]

#另一个例子
>>>a=[1,2,3]
>>>b=[4,5,6]
>>>map(lambda x,y:x+y, a,b)
>>>[5,7,9]

reduce() 函数：reduce(func,seq[,init]),用二元函数func对序列seq中的元素进行处理，每次处理两个数据项(一个是前次处理的结果，一个是序列中的下一个元素)，如此反复的递归处理，最后对整个序列求出一个单一的返回值。

>>>a = [1,2,3,4,5]
>>>reduce(lambda x,y:x+y,a)
15

 10. List Comprehensions

>>> squares = []
>>> for x in range(10):
...     squares.append(x**2)
...
>>> squares
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

squares = [x**2 for x in range(10)]

>>> [(x, y) for x in [1,2,3] for y in [3,1,4] if x != y]
[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]

>>> vec = [-4, -2, 0, 2, 4]
>>> # create a new list with the values doubled
>>> [x*2 for x in vec]
[-8, -4, 0, 4, 8]
>>> # filter the list to exclude negative numbers
>>> [x for x in vec if x >= 0]
[0, 2, 4]
>>> # apply a function to all the elements
>>> [abs(x) for x in vec]
[4, 2, 0, 2, 4]
>>> # call a method on each element
>>> freshfruit = ['  banana', '  loganberry ', 'passion fruit  ']
>>> [weapon.strip() for weapon in freshfruit]
['banana', 'loganberry', 'passion fruit']
>>> # create a list of 2-tuples like (number, square)
>>> [(x, x**2) for x in range(6)]
[(0, 0), (1, 1), (2, 4), (3, 9), (4, 16), (5, 25)]
>>> # the tuple must be parenthesized, otherwise an error is raised
>>> [x, x**2 for x in range(6)]
  File "<stdin>", line 1
    [x, x**2 for x in range(6)]
               ^
SyntaxError: invalid syntax
>>> # flatten a list using a listcomp with two 'for'
>>> vec = [[1,2,3], [4,5,6], [7,8,9]]
>>> [num for elem in vec for num in elem]
[1, 2, 3, 4, 5, 6, 7, 8, 9]

嵌套的：

>>> matrix = [
...     [1, 2, 3, 4],
...     [5, 6, 7, 8],
...     [9, 10, 11, 12],
... ]

>>> [[row[i] for row in matrix] for i in range(4)]
[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]