Python学习之路——day03

九、collection系统

先做个练习

练习一：元素分类
有如下值集合 [11,22,33,44,55,66,77,88,99,90...]，将所有大于 66 的值保存至字典的第一个key中，将小于 66 的值保存至第二个key的值中。
即： {'k1': 大于66 , 'k2': 小于66}

#!/usr/bin/env python
# _*_ coding:utf-8 _*_
'''
将一个数字列表分割成字典，'k1'的值为小于66的值，'k2'的值为大于66的值
'''
li =  [11,22,33,44,55,66,77,88,99,90]
dic = {}
for ele in li:
    #先判断是否小于66
    if ele < 66:
        #判断是否有'k1'的键，如果有就追加
        if 'k1' in dic.keys():
            dic['k1'].append(ele)
        else:
            #创建只有一个元素的列表
            dic['k1'] = [ele,]
    else:
        if 'k2' in dic.keys():
            dic['k2'].append(ele);
        else:
            dic['k2'] = [ele,]

print dic

#打印出字典的键和值，值占4个字符，前面用’*‘填充，打印时要把数值转换成字符串形式
for k in dic.keys():
    print k
    if type(dic[k]) == list:
        for i in dic[k]:
            print str(i).rjust(4,'*')

练习二：将文件中读出的内容转换成字典

已知文件内容为

alex|123|1
eric|123|1
john|123|1

 转换成字典形式

dic = {
    'alex':[123,1]
    'eric':[123,1]
    'john':[123,1]
}

#!/usr/bin/env python
# _*_ coding:utf-8 _*_

'''
想要得到的效果
dic = {
    'alex':[123,1]
    'eric':[123,1]
    'john':[123,1]
}
'''

#打开文件，读取文件内容，用的with as，这样可以保证最后文件会被关闭
with open('log.txt') as f:
    #将文件读出来赋值给一个列表
    line_list = f.readlines();
print line_list
dic = {}
#遍历列表，将每行再分割成列表
for line in line_list:
    line = line.strip();
    ele_list = line.split('|')
    #把姓名做成字典的key，并把后面所有元素当做这个key的值，这里用的是[1:]，取出除了0外，所有的值
    dic[ele_list[0]] = ele_list[1:]
    
#下面是遍历字典的两种方法，推荐使用第一种，因为在字典内容少的时候两种方法都可以
#但当字典内容多的时候，第二种方法的字典会先值转换成列表，会消耗时间和占用内存
#方法一
for k in dic.keys():
    print k,dic[k]
    
for ele in dic:
    print ele,dic[ele];

#方法二    
for k,v in dic.items():
    print k,v

 

1、计数器（counter）

Counter是对字典类型的补充，用于追踪值的出现次数

ps:具备字典的所有功能+自己的功能

>>> import collections
NameError: name 'Counter' is not defined
>>> c = collections.Counter('asdfasdfasdfasdf')
>>> c
Counter({'a': 4, 's': 4, 'd': 4, 'f': 4})
>>> 

########################################################################
###  Counter
########################################################################

class Counter(dict):
    '''Dict subclass for counting hashable items.  Sometimes called a bag
    or multiset.  Elements are stored as dictionary keys and their counts
    are stored as dictionary values.

    >>> c = Counter('abcdeabcdabcaba')  # count elements from a string

    >>> c.most_common(3)                # three most common elements
    [('a', 5), ('b', 4), ('c', 3)]
    >>> sorted(c)                       # list all unique elements
    ['a', 'b', 'c', 'd', 'e']
    >>> ''.join(sorted(c.elements()))   # list elements with repetitions
    'aaaaabbbbcccdde'
    >>> sum(c.values())                 # total of all counts

    >>> c['a']                          # count of letter 'a'
    >>> for elem in 'shazam':           # update counts from an iterable
    ...     c[elem] += 1                # by adding 1 to each element's count
    >>> c['a']                          # now there are seven 'a'
    >>> del c['b']                      # remove all 'b'
    >>> c['b']                          # now there are zero 'b'

    >>> d = Counter('simsalabim')       # make another counter
    >>> c.update(d)                     # add in the second counter
    >>> c['a']                          # now there are nine 'a'

    >>> c.clear()                       # empty the counter
    >>> c
    Counter()

    Note:  If a count is set to zero or reduced to zero, it will remain
    in the counter until the entry is deleted or the counter is cleared:

    >>> c = Counter('aaabbc')
    >>> c['b'] -= 2                     # reduce the count of 'b' by two
    >>> c.most_common()                 # 'b' is still in, but its count is zero
    [('a', 3), ('c', 1), ('b', 0)]

    '''
    # References:
    #   http://en.wikipedia.org/wiki/Multiset
    #   http://www.gnu.org/software/smalltalk/manual-base/html_node/Bag.html
    #   http://www.demo2s.com/Tutorial/Cpp/0380__set-multiset/Catalog0380__set-multiset.htm
    #   http://code.activestate.com/recipes/259174/
    #   Knuth, TAOCP Vol. II section 4.6.3

    def __init__(self, iterable=None, **kwds):
        '''Create a new, empty Counter object.  And if given, count elements
        from an input iterable.  Or, initialize the count from another mapping
        of elements to their counts.

        >>> c = Counter()                           # a new, empty counter
        >>> c = Counter('gallahad')                 # a new counter from an iterable
        >>> c = Counter({'a': 4, 'b': 2})           # a new counter from a mapping
        >>> c = Counter(a=4, b=2)                   # a new counter from keyword args

        '''
        super(Counter, self).__init__()
        self.update(iterable, **kwds)

    def __missing__(self, key):
        """ 对于不存在的元素，返回计数器为0 
        >>> c
        Counter({'a': 12, 'f': 10, 'd': 9, 's': 9, 'c': 3, 'g': 3, 'e': 1})
        >>> c['a']
        12
        >>> c['z']
        0
        #原生字典会抛异常
        >>> a = {1:2,3:4}
        >>> a[1]
        2
        >>> a[55]
        Traceback (most recent call last):
          File "<stdin>", line 1, in <module>
        KeyError: 55
        >>> 
        """
        'The count of elements not in the Counter is zero.'
        # Needed so that self[missing_item] does not raise KeyError
        return 0

    def most_common(self, n=None):
        """ 列出n个出现次数最多的元素 默认最从多到少排序的，所以看起来就像是截取了前几个元素
        >>> c = collections.Counter('asdfasdfasdfasdfaaaacccsdfefgasdfasdfgasdfasdgf')
        >>> c
        Counter({'a': 12, 'f': 10, 'd': 9, 's': 9, 'c': 3, 'g': 3, 'e': 1})
        >>> c.most_common(3)
        [('a', 12), ('f', 10), ('d', 9)]
        >>> 
        """
        '''List the n most common elements and their counts from the most
        common to the least.  If n is None, then list all element counts.

        >>> Counter('abcdeabcdabcaba').most_common(3)
        [('a', 5), ('b', 4), ('c', 3)]

        '''
        # Emulate Bag.sortedByCount from Smalltalk
        if n is None:
            return sorted(self.iteritems(), key=_itemgetter(1), reverse=True)
        return _heapq.nlargest(n, self.iteritems(), key=_itemgetter(1))

    def elements(self):
        """ 计数器中的所有元素，注：此处非所有元素集合，而是包含所有元素集合的迭代器 
        >>> c = collections.Counter('aaaabbbcc')
        >>> c
        Counter({'a': 4, 'b': 3, 'c': 2})
        #因为是迭代器，所以要用循环取出
        >>> c.elements()
        <itertools.chain object at 0x7fb491a27890>
        >>> for ele in c.elements():print ele;
        ... 
        a
        a
        a
        a
        c
        c
        b
        b
        b
        >>> 
        """
        '''Iterator over elements repeating each as many times as its count.

        >>> c = Counter('ABCABC')
        >>> sorted(c.elements())
        ['A', 'A', 'B', 'B', 'C', 'C']

        # Knuth's example for prime factors of 1836:  2**2 * 3**3 * 17**1
        >>> prime_factors = Counter({2: 2, 3: 3, 17: 1})
        >>> product = 1
        >>> for factor in prime_factors.elements():     # loop over factors
        ...     product *= factor                       # and multiply them
        >>> product

        Note, if an element's count has been set to zero or is a negative
        number, elements() will ignore it.

        '''
        # Emulate Bag.do from Smalltalk and Multiset.begin from C++.
        return _chain.from_iterable(_starmap(_repeat, self.iteritems()))

    # Override dict methods where necessary

    @classmethod
    def fromkeys(cls, iterable, v=None):
        # There is no equivalent method for counters because setting v=1
        # means that no element can have a count greater than one.
        raise NotImplementedError(
            'Counter.fromkeys() is undefined.  Use Counter(iterable) instead.')

    def update(self, iterable=None, **kwds):
        """ 更新计数器，其实就是增加；如果原来没有，则新建，如果有则加一 
        >>> c
        Counter({'a': 4, 'b': 3, 'c': 2})
        >>> c.update('a')
        >>> c
        Counter({'a': 5, 'b': 3, 'c': 2})
        >>> d = collections.Counter('ccddee')
        >>> c.update(d)
        >>> c
        Counter({'a': 5, 'c': 4, 'b': 3, 'e': 2, 'd': 2})
        >>> 
        """
        '''Like dict.update() but add counts instead of replacing them.

        Source can be an iterable, a dictionary, or another Counter instance.

        >>> c = Counter('which')
        >>> c.update('witch')           # add elements from another iterable
        >>> d = Counter('watch')
        >>> c.update(d)                 # add elements from another counter
        >>> c['h']                      # four 'h' in which, witch, and watch

        '''
        # The regular dict.update() operation makes no sense here because the
        # replace behavior results in the some of original untouched counts
        # being mixed-in with all of the other counts for a mismash that
        # doesn't have a straight-forward interpretation in most counting
        # contexts.  Instead, we implement straight-addition.  Both the inputs
        # and outputs are allowed to contain zero and negative counts.

        if iterable is not None:
            if isinstance(iterable, Mapping):
                if self:
                    self_get = self.get
                    for elem, count in iterable.iteritems():
                        self[elem] = self_get(elem, 0) + count
                else:
                    super(Counter, self).update(iterable) # fast path when counter is empty
            else:
                self_get = self.get
                for elem in iterable:
                    self[elem] = self_get(elem, 0) + 1
        if kwds:
            self.update(kwds)

    def subtract(self, iterable=None, **kwds):
        """ 相减，原来的计数器中的每一个元素的数量减去后添加的元素的数量 
        >>> c = collections.Counter('aabbcc')
        >>> c
        Counter({'a': 2, 'c': 2, 'b': 2})
        >>> c.subtract('a')
        >>> c
        Counter({'c': 2, 'b': 2, 'a': 1})
        >>> 
        """
        '''Like dict.update() but subtracts counts instead of replacing them.
        Counts can be reduced below zero.  Both the inputs and outputs are
        allowed to contain zero and negative counts.

        Source can be an iterable, a dictionary, or another Counter instance.

        >>> c = Counter('which')
        >>> c.subtract('witch')             # subtract elements from another iterable
        >>> c.subtract(Counter('watch'))    # subtract elements from another counter
        >>> c['h']                          # 2 in which, minus 1 in witch, minus 1 in watch
        >>> c['w']                          # 1 in which, minus 1 in witch, minus 1 in watch
        -1

        '''
        if iterable is not None:
            self_get = self.get
            if isinstance(iterable, Mapping):
                for elem, count in iterable.items():
                    self[elem] = self_get(elem, 0) - count
            else:
                for elem in iterable:
                    self[elem] = self_get(elem, 0) - 1
        if kwds:
            self.subtract(kwds)

    def copy(self):
        """ 拷贝 """
        'Return a shallow copy.'
        return self.__class__(self)

    def __reduce__(self):
        """ 返回一个元组（类型，元组） """
        return self.__class__, (dict(self),)

    def __delitem__(self, elem):
        """ 删除元素 """
        'Like dict.__delitem__() but does not raise KeyError for missing values.'
        if elem in self:
            super(Counter, self).__delitem__(elem)

    def __repr__(self):
        if not self:
            return '%s()' % self.__class__.__name__
        items = ', '.join(map('%r: %r'.__mod__, self.most_common()))
        return '%s({%s})' % (self.__class__.__name__, items)

    # Multiset-style mathematical operations discussed in:
    #       Knuth TAOCP Volume II section 4.6.3 exercise 19
    #       and at http://en.wikipedia.org/wiki/Multiset
    #
    # Outputs guaranteed to only include positive counts.
    #
    # To strip negative and zero counts, add-in an empty counter:
    #       c += Counter()

    def __add__(self, other):
        '''Add counts from two counters.

        >>> Counter('abbb') + Counter('bcc')
        Counter({'b': 4, 'c': 2, 'a': 1})

        '''
        if not isinstance(other, Counter):
            return NotImplemented
        result = Counter()
        for elem, count in self.items():
            newcount = count + other[elem]
            if newcount > 0:
                result[elem] = newcount
        for elem, count in other.items():
            if elem not in self and count > 0:
                result[elem] = count
        return result

    def __sub__(self, other):
        ''' Subtract count, but keep only results with positive counts.

        >>> Counter('abbbc') - Counter('bccd')
        Counter({'b': 2, 'a': 1})

        '''
        if not isinstance(other, Counter):
            return NotImplemented
        result = Counter()
        for elem, count in self.items():
            newcount = count - other[elem]
            if newcount > 0:
                result[elem] = newcount
        for elem, count in other.items():
            if elem not in self and count < 0:
                result[elem] = 0 - count
        return result

    def __or__(self, other):
        '''Union is the maximum of value in either of the input counters.

        >>> Counter('abbb') | Counter('bcc')
        Counter({'b': 3, 'c': 2, 'a': 1})

        '''
        if not isinstance(other, Counter):
            return NotImplemented
        result = Counter()
        for elem, count in self.items():
            other_count = other[elem]
            newcount = other_count if count < other_count else count
            if newcount > 0:
                result[elem] = newcount
        for elem, count in other.items():
            if elem not in self and count > 0:
                result[elem] = count
        return result

    def __and__(self, other):
        ''' Intersection is the minimum of corresponding counts.

        >>> Counter('abbb') & Counter('bcc')
        Counter({'b': 1})

        '''
        if not isinstance(other, Counter):
            return NotImplemented
        result = Counter()
        for elem, count in self.items():
            other_count = other[elem]
            newcount = count if count < other_count else other_count
            if newcount > 0:
                result[elem] = newcount
        return result

也可以传入列表或元组

>>> li = [11,22,33,44,11,22,22]
>>> c1 = collections.Counter(li)
>>> c1
Counter({22: 3, 11: 2, 33: 1, 44: 1})
>>> 

 2、有序字典（orderedDict）

orderedDict是对字典类型的补充，他记住了字典元素添加的顺序

有序字典和原生字典的使用完全一样

唯一的区别，orderedDict在内部做了一个排序，它其实是在内部维护一个列表，因为列表是有序的

class OrderedDict(dict):
    'Dictionary that remembers insertion order'
    # An inherited dict maps keys to values.
    # The inherited dict provides __getitem__, __len__, __contains__, and get.
    # The remaining methods are order-aware.
    # Big-O running times for all methods are the same as regular dictionaries.

    # The internal self.__map dict maps keys to links in a doubly linked list.
    # The circular doubly linked list starts and ends with a sentinel element.
    # The sentinel element never gets deleted (this simplifies the algorithm).
    # Each link is stored as a list of length three:  [PREV, NEXT, KEY].

    def __init__(self, *args, **kwds):
        '''Initialize an ordered dictionary.  The signature is the same as
        regular dictionaries, but keyword arguments are not recommended because
        their insertion order is arbitrary.

        '''
        if len(args) > 1:
            raise TypeError('expected at most 1 arguments, got %d' % len(args))
        try:
            self.__root
        except AttributeError:
            self.__root = root = []                     # sentinel node
            root[:] = [root, root, None]
            self.__map = {}
        self.__update(*args, **kwds)

    def __setitem__(self, key, value, dict_setitem=dict.__setitem__):
        'od.__setitem__(i, y) <==> od[i]=y'
        # Setting a new item creates a new link at the end of the linked list,
        # and the inherited dictionary is updated with the new key/value pair.
        if key not in self:
            root = self.__root
            last = root[0]
            last[1] = root[0] = self.__map[key] = [last, root, key]
        return dict_setitem(self, key, value)

    def __delitem__(self, key, dict_delitem=dict.__delitem__):
        'od.__delitem__(y) <==> del od[y]'
        # Deleting an existing item uses self.__map to find the link which gets
        # removed by updating the links in the predecessor and successor nodes.
        dict_delitem(self, key)
        link_prev, link_next, _ = self.__map.pop(key)
        link_prev[1] = link_next                        # update link_prev[NEXT]
        link_next[0] = link_prev                        # update link_next[PREV]

    def __iter__(self):
        'od.__iter__() <==> iter(od)'
        # Traverse the linked list in order.
        root = self.__root
        curr = root[1]                                  # start at the first node
        while curr is not root:
            yield curr[2]                               # yield the curr[KEY]
            curr = curr[1]                              # move to next node

    def __reversed__(self):
        'od.__reversed__() <==> reversed(od)'
        # Traverse the linked list in reverse order.
        root = self.__root
        curr = root[0]                                  # start at the last node
        while curr is not root:
            yield curr[2]                               # yield the curr[KEY]
            curr = curr[0]                              # move to previous node

    def clear(self):
        'od.clear() -> None.  Remove all items from od.'
        root = self.__root
        root[:] = [root, root, None]
        self.__map.clear()
        dict.clear(self)

    # -- the following methods do not depend on the internal structure --

    def keys(self):
        'od.keys() -> list of keys in od'
        return list(self)

    def values(self):
        'od.values() -> list of values in od'
        return [self[key] for key in self]

    def items(self):
        'od.items() -> list of (key, value) pairs in od'
        return [(key, self[key]) for key in self]

    def iterkeys(self):
        'od.iterkeys() -> an iterator over the keys in od'
        return iter(self)

    def itervalues(self):
        'od.itervalues -> an iterator over the values in od'
        for k in self:
            yield self[k]

    def iteritems(self):
        'od.iteritems -> an iterator over the (key, value) pairs in od'
        for k in self:
            yield (k, self[k])

    update = MutableMapping.update

    __update = update # let subclasses override update without breaking __init__

    __marker = object()

    def pop(self, key, default=__marker):
        '''od.pop(k[,d]) -> v, remove specified key and return the corresponding
        value.  If key is not found, d is returned if given, otherwise KeyError
        is raised.

        '''
        if key in self:
            result = self[key]
            del self[key]
            return result
        if default is self.__marker:
            raise KeyError(key)
        return default

    def setdefault(self, key, default=None):
        'od.setdefault(k[,d]) -> od.get(k,d), also set od[k]=d if k not in od'
        if key in self:
            return self[key]
        self[key] = default
        return default

    def popitem(self, last=True):
        '''od.popitem() -> (k, v), return and remove a (key, value) pair.
        Pairs are returned in LIFO order if last is true or FIFO order if false.

        '''
        if not self:
            raise KeyError('dictionary is empty')
        key = next(reversed(self) if last else iter(self))
        value = self.pop(key)
        return key, value

    def __repr__(self, _repr_running={}):
        'od.__repr__() <==> repr(od)'
        call_key = id(self), _get_ident()
        if call_key in _repr_running:
            return '...'
        _repr_running[call_key] = 1
        try:
            if not self:
                return '%s()' % (self.__class__.__name__,)
            return '%s(%r)' % (self.__class__.__name__, self.items())
        finally:
            del _repr_running[call_key]

    def __reduce__(self):
        'Return state information for pickling'
        items = [[k, self[k]] for k in self]
        inst_dict = vars(self).copy()
        for k in vars(OrderedDict()):
            inst_dict.pop(k, None)
        if inst_dict:
            return (self.__class__, (items,), inst_dict)
        return self.__class__, (items,)

    def copy(self):
        'od.copy() -> a shallow copy of od'
        return self.__class__(self)

    @classmethod
    def fromkeys(cls, iterable, value=None):
        '''OD.fromkeys(S[, v]) -> New ordered dictionary with keys from S.
        If not specified, the value defaults to None.

        '''
        self = cls()
        for key in iterable:
            self[key] = value
        return self

    def __eq__(self, other):
        '''od.__eq__(y) <==> od==y.  Comparison to another OD is order-sensitive
        while comparison to a regular mapping is order-insensitive.

        '''
        if isinstance(other, OrderedDict):
            return dict.__eq__(self, other) and all(_imap(_eq, self, other))
        return dict.__eq__(self, other)

    def __ne__(self, other):
        'od.__ne__(y) <==> od!=y'
        return not self == other

    # -- the following methods support python 3.x style dictionary views --

    def viewkeys(self):
        "od.viewkeys() -> a set-like object providing a view on od's keys"
        return KeysView(self)

    def viewvalues(self):
        "od.viewvalues() -> an object providing a view on od's values"
        return ValuesView(self)

    def viewitems(self):
        "od.viewitems() -> a set-like object providing a view on od's items"
        return ItemsView(self)

OrderedDict

3、默认字典（defaultdict）

学习需求：

'''
将一个数字列表分割成字典，'k1'的值为小于66的值，'k2'的值为大于66的值

li =  [11,22,33,44,55,66,77,88,99,90]
'''

li =  [11,22,33,44,55,66,77,88,99,90]
dic = {}
for ele in li:
    if ele < 66:
        if 'k1' in dic.keys():
            dic['k1'].append(ele)
        else:
            #创建只有一个元素的列表
            dic['k1'] = [ele,]
    else:
        if 'k2' in dic.keys():
            dic['k2'].append(ele);
        else:
            dic['k2'] = [ele,]

from collections import Counter,defaultdict;
li = [11,22,33,44,55,66,77,88,99,90,87]
my_dic = defaultdict(list)
for ele in li:
    if ele > 66:
        my_dic['k2'].append(ele)
    else:
        my_dic['k1'].append(ele)

defaultdict是对字典类型的补充，他默认给字典的值设置了一个类型，字典默认的是None，可以给字典设置一个默认类型，比如列表，元组等

class defaultdict(dict):
    """
    defaultdict(default_factory[, ...]) --> dict with default factory
    
    The default factory is called without arguments to produce
    a new value when a key is not present, in __getitem__ only.
    A defaultdict compares equal to a dict with the same items.
    All remaining arguments are treated the same as if they were
    passed to the dict constructor, including keyword arguments.
    """
    def copy(self): # real signature unknown; restored from __doc__
        """ D.copy() -> a shallow copy of D. """
        pass

    def __copy__(self, *args, **kwargs): # real signature unknown
        """ D.copy() -> a shallow copy of D. """
        pass

    def __getattribute__(self, name): # real signature unknown; restored from __doc__
        """ x.__getattribute__('name') <==> x.name """
        pass

    def __init__(self, default_factory=None, **kwargs): # known case of _collections.defaultdict.__init__
        """
        defaultdict(default_factory[, ...]) --> dict with default factory
        
        The default factory is called without arguments to produce
        a new value when a key is not present, in __getitem__ only.
        A defaultdict compares equal to a dict with the same items.
        All remaining arguments are treated the same as if they were
        passed to the dict constructor, including keyword arguments.
        
        # (copied from class doc)
        """
        pass

    def __missing__(self, key): # real signature unknown; restored from __doc__
        """
        __missing__(key) # Called by __getitem__ for missing key; pseudo-code:
          if self.default_factory is None: raise KeyError((key,))
          self[key] = value = self.default_factory()
          return value
        """
        pass

    def __reduce__(self, *args, **kwargs): # real signature unknown
        """ Return state information for pickling. """
        pass

    def __repr__(self): # real signature unknown; restored from __doc__
        """ x.__repr__() <==> repr(x) """
        pass

    default_factory = property(lambda self: object(), lambda self, v: None, lambda self: None)  # default
    """Factory for default value called by __missing__()."""

defaultdict

4、可命名元组(namedtuple)

创建一个扩展tuple的类，Mytuple——类名

#通过可命名元组创建一个类
>>> Mytuple = collections.namedtuple('Mytuple',['x','y'])
#通过这个类创建对象
>>> new_tuple = Mytuple(1,2)
>>> new_tuple
Mytuple(x=1, y=2)
#可以通过名称来调用
>>> new_tuple.x
1
>>> new_tuple.y
2
>>> old_tuple = ([1,2])       
>>> old_tuple
[1, 2]
>>> 

class Mytuple(__builtin__.tuple)
 |  Mytuple(x, y)
 |  
 |  Method resolution order:
 |      Mytuple
 |      __builtin__.tuple
 |      __builtin__.object
 |  
 |  Methods defined here:
 |  
 |  __getnewargs__(self)
 |      Return self as a plain tuple.  Used by copy and pickle.
 |  
 |  __getstate__(self)
 |      Exclude the OrderedDict from pickling
 |  
 |  __repr__(self)
 |      Return a nicely formatted representation string
 |  
 |  _asdict(self)
 |      Return a new OrderedDict which maps field names to their values
 |  
 |  _replace(_self, **kwds)
 |      Return a new Mytuple object replacing specified fields with new values
 |  
 |  ----------------------------------------------------------------------
 |  Class methods defined here:
 |  
 |  _make(cls, iterable, new=<built-in method __new__ of type object>, len=<built-in function len>) from __builtin__.type
 |      Make a new Mytuple object from a sequence or iterable
 |  
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |  
 |  __new__(_cls, x, y)
 |      Create new instance of Mytuple(x, y)
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |  
 |  __dict__
 |      Return a new OrderedDict which maps field names to their values
 |  
 |  x
 |      Alias for field number 0
 |  
 |  y
 |      Alias for field number 1
 |  
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |  
 |  _fields = ('x', 'y')
 |  
 |  ----------------------------------------------------------------------
 |  Methods inherited from __builtin__.tuple:
 |  
 |  __add__(...)
 |      x.__add__(y) <==> x+y
 |  
 |  __contains__(...)
 |      x.__contains__(y) <==> y in x
 |  
 |  __eq__(...)
 |      x.__eq__(y) <==> x==y
 |  
 |  __ge__(...)
 |      x.__ge__(y) <==> x>=y
 |  
 |  __getattribute__(...)
 |      x.__getattribute__('name') <==> x.name
 |  
 |  __getitem__(...)
 |      x.__getitem__(y) <==> x[y]
 |  
 |  __getslice__(...)
 |      x.__getslice__(i, j) <==> x[i:j]
 |      
 |      Use of negative indices is not supported.
 |  
 |  __gt__(...)
 |      x.__gt__(y) <==> x>y
 |  
 |  __hash__(...)
 |      x.__hash__() <==> hash(x)
 |  
 |  __iter__(...)
 |      x.__iter__() <==> iter(x)
 |  
 |  __le__(...)
 |      x.__le__(y) <==> x<=y
 |  
 |  __len__(...)
 |      x.__len__() <==> len(x)
 |  
 |  __lt__(...)
 |      x.__lt__(y) <==> x<y
 |  
 |  __mul__(...)
 |      x.__mul__(n) <==> x*n
 |  
 |  __ne__(...)
 |      x.__ne__(y) <==> x!=y
 |  
 |  __rmul__(...)
 |      x.__rmul__(n) <==> n*x
 |  
 |  __sizeof__(...)
 |      T.__sizeof__() -- size of T in memory, in bytes
 |  
 |  count(...)
 |      T.count(value) -> integer -- return number of occurrences of value
 |  
 |  index(...)
 |      T.index(value, [start, [stop]]) -> integer -- return first index of value.
 |      Raises ValueError if the value is not present.

5、双向队列（deque）

两边都可以取，两边都可以插入，这就有一个线程安全的问题，谁先得到就会有一个线程锁，防止抢占资源

一个线程安全的双向队列

class deque(object):
    """
    deque([iterable[, maxlen]]) --> deque object
    
    Build an ordered collection with optimized access from its endpoints.
    """
    def append(self, *args, **kwargs): # real signature unknown
        """ 追加元素到队列 
        >>> q = collections.deque()
        >>> q.append(1)
        >>> q.append(11)
        >>> q.append(111)
        >>> q.append(1111)
        >>> q
        deque([1, 11, 111, 1111])
        >>>
        """
        """ Add an element to the right side of the deque. """
        pass

    def appendleft(self, *args, **kwargs): # real signature unknown
        """ 加入元素到队列的左边
        >>> q
        deque([1, 11, 111])
        >>> q.appendleft(2)
        >>> q
        deque([2, 1, 11, 111])
        >>> 
        """
        """ Add an element to the left side of the deque. """
        pass

    def clear(self, *args, **kwargs): # real signature unknown
        """ 清除队列中所有元素
        >>> q
        deque([2, 2, 1, 11, 111])
        >>> q.clear()
        >>> q
        deque([])
        >>> 
        """
        """ Remove all elements from the deque. """
        pass

    def count(self, value): # real signature unknown; restored from __doc__
        """ 计算元素的个数
        >>> q
        deque([2, 1, 11, 111])
        >>> q.count(2)
        1
        >>> q.appendleft(2)
        >>> q.count(2)     
        2
        >>> q
        deque([2, 2, 1, 11, 111])
        >>> 
        """
        """ D.count(value) -> integer -- return number of occurrences of value """
        return 0

    def extend(self, *args, **kwargs): # real signature unknown
        """ 扩展队列从一个迭代器中
        >>> q
        deque([])
        >>> li = [11,22,33,44,55]
        >>> q.extend(li)
        >>> q
        deque([11, 22, 33, 44, 55])
        >>> 
        """
        """ Extend the right side of the deque with elements from the iterable """
        pass

    def extendleft(self, *args, **kwargs): # real signature unknown
        """ 从队列左边扩展元素
        >>> q
        deque([11, 22, 33, 44, 55])
        >>> lileft = (1,2,3,4,5)
        >>> q.extendleft(lileft)
        >>> q
        deque([5, 4, 3, 2, 1, 11, 22, 33, 44, 55])
        >>> 
        """
        """ Extend the left side of the deque with elements from the iterable """
        pass

    def pop(self, *args, **kwargs): # real signature unknown
        """移除队列最后一个元素并取得
        >>> q = collections.deque()
        >>> q.append(1)
        >>> q.append(11)
        >>> q.append(111)
        >>> q.append(1111)
        >>> q
        deque([1, 11, 111, 1111])
        >>> a = q.pop()
        >>> a
        1111
        >>> q
        deque([1, 11, 111])
        >>> 
        """
        """ Remove and return the rightmost element. """
        pass

    def popleft(self, *args, **kwargs): # real signature unknown
        """ 从队列左边移除一个元素并取得
        >>> q
        deque([5, 4, 3, 2, 1, 11, 22, 33, 44, 55])
        >>> b = q.popleft()
        >>> b
        5
        >>> 
        """
        """ Remove and return the leftmost element. """
        pass

    def remove(self, value): # real signature unknown; restored from __doc__
        """ 移除第一个被找到的值，如果没有，会抛出异常
        >>> q
        deque([4, 3, 2, 1, 11, 22, 33, 44, 55])
        >>> q.remove(5)
        Traceback (most recent call last):
          File "<stdin>", line 1, in <module>
        ValueError: deque.remove(x): x not in deque
        >>> q.remove(4)
        >>> q
        deque([3, 2, 1, 11, 22, 33, 44, 55])
        >>> 
        """
        """ D.remove(value) -- remove first occurrence of value. """
        pass

    def reverse(self): # real signature unknown; restored from __doc__
        """ 反转队列
        >>> q
        deque([3, 2, 1, 11, 22, 33, 44, 55])
        >>> q.reverse()
        >>> q
        deque([55, 44, 33, 22, 11, 1, 2, 3])
        >>> 
        """
        """ D.reverse() -- reverse *IN PLACE* """
        pass

    def rotate(self, *args, **kwargs): # real signature unknown
        """ 从右边移动值到左边，默认移动一个
        >>> q
        deque([55, 44, 33, 22, 11, 1, 2, 3])
        >>> q.rotate()
        >>> q
        deque([3, 55, 44, 33, 22, 11, 1, 2])
        >>> q.rotate(2)
        >>> q
        deque([1, 2, 3, 55, 44, 33, 22, 11])
        >>> q.rotate(2)
        >>> q
        deque([22, 11, 1, 2, 3, 55, 44, 33])
        >>> 
        """
        """ Rotate the deque n steps to the right (default n=1).  If n is negative, rotates left. """
        pass

    def __copy__(self, *args, **kwargs): # real signature unknown
        """ Return a shallow copy of a deque. """
        pass

    def __delitem__(self, y): # real signature unknown; restored from __doc__
        """ x.__delitem__(y) <==> del x[y] """
        pass

    def __eq__(self, y): # real signature unknown; restored from __doc__
        """ x.__eq__(y) <==> x==y """
        pass

    def __getattribute__(self, name): # real signature unknown; restored from __doc__
        """ x.__getattribute__('name') <==> x.name """
        pass

    def __getitem__(self, y): # real signature unknown; restored from __doc__
        """ x.__getitem__(y) <==> x[y] """
        pass

    def __ge__(self, y): # real signature unknown; restored from __doc__
        """ x.__ge__(y) <==> x>=y """
        pass

    def __gt__(self, y): # real signature unknown; restored from __doc__
        """ x.__gt__(y) <==> x>y """
        pass

    def __iadd__(self, y): # real signature unknown; restored from __doc__
        """ x.__iadd__(y) <==> x+=y """
        pass

    def __init__(self, iterable=(), maxlen=None): # known case of _collections.deque.__init__
        """
        deque([iterable[, maxlen]]) --> deque object
        
        Build an ordered collection with optimized access from its endpoints.
        # (copied from class doc)
        """
        pass

    def __iter__(self): # real signature unknown; restored from __doc__
        """ x.__iter__() <==> iter(x) """
        pass

    def __len__(self): # real signature unknown; restored from __doc__
        """ x.__len__() <==> len(x) """
        pass

    def __le__(self, y): # real signature unknown; restored from __doc__
        """ x.__le__(y) <==> x<=y """
        pass

    def __lt__(self, y): # real signature unknown; restored from __doc__
        """ x.__lt__(y) <==> x<y """
        pass

    @staticmethod # known case of __new__
    def __new__(S, *more): # real signature unknown; restored from __doc__
        """ T.__new__(S, ...) -> a new object with type S, a subtype of T """
        pass

    def __ne__(self, y): # real signature unknown; restored from __doc__
        """ x.__ne__(y) <==> x!=y """
        pass

    def __reduce__(self, *args, **kwargs): # real signature unknown
        """ Return state information for pickling. """
        pass

    def __repr__(self): # real signature unknown; restored from __doc__
        """ x.__repr__() <==> repr(x) """
        pass

    def __reversed__(self): # real signature unknown; restored from __doc__
        """ D.__reversed__() -- return a reverse iterator over the deque """
        pass

    def __setitem__(self, i, y): # real signature unknown; restored from __doc__
        """ x.__setitem__(i, y) <==> x[i]=y """
        pass

    def __sizeof__(self): # real signature unknown; restored from __doc__
        """ D.__sizeof__() -- size of D in memory, in bytes """
        pass

    maxlen = property(lambda self: object(), lambda self, v: None, lambda self: None)  # default
    """maximum size of a deque or None if unbounded"""


    __hash__ = None

6、单向队列（Queue.Queue）——先进先出（FIFO（First In First Out））

队列：先进先出 FIFO

栈：先进后出 弹夹

#创建一个最多十个内容的队列
>>> q = Queue.Queue(10)
>>> q
<Queue.Queue instance at 0x7f82028390e0>
#给队列放值，用put
>>> q.put(1)
>>> q.put(2)
>>> q.put(3) 
#取值用get，get取值是阻塞的
>>> q.get()
1
>>> q.get()
2
>>> q.get()
3
#当取完后，再取就会阻塞住，直到队列中有值才继续
>>> q.get()  

class Queue:
    """Create a queue object with a given maximum size.

    If maxsize is <= 0, the queue size is infinite.
    """
    def __init__(self, maxsize=0):
        self.maxsize = maxsize
        self._init(maxsize)
        # mutex must be held whenever the queue is mutating.  All methods
        # that acquire mutex must release it before returning.  mutex
        # is shared between the three conditions, so acquiring and
        # releasing the conditions also acquires and releases mutex.
        self.mutex = _threading.Lock()
        # Notify not_empty whenever an item is added to the queue; a
        # thread waiting to get is notified then.
        self.not_empty = _threading.Condition(self.mutex)
        # Notify not_full whenever an item is removed from the queue;
        # a thread waiting to put is notified then.
        self.not_full = _threading.Condition(self.mutex)
        # Notify all_tasks_done whenever the number of unfinished tasks
        # drops to zero; thread waiting to join() is notified to resume
        self.all_tasks_done = _threading.Condition(self.mutex)
        self.unfinished_tasks = 0

    def task_done(self):
        """Indicate that a formerly enqueued task is complete.

        Used by Queue consumer threads.  For each get() used to fetch a task,
        a subsequent call to task_done() tells the queue that the processing
        on the task is complete.

        If a join() is currently blocking, it will resume when all items
        have been processed (meaning that a task_done() call was received
        for every item that had been put() into the queue).

        Raises a ValueError if called more times than there were items
        placed in the queue.
        """
        self.all_tasks_done.acquire()
        try:
            unfinished = self.unfinished_tasks - 1
            if unfinished <= 0:
                if unfinished < 0:
                    raise ValueError('task_done() called too many times')
                self.all_tasks_done.notify_all()
            self.unfinished_tasks = unfinished
        finally:
            self.all_tasks_done.release()

    def join(self):
        """Blocks until all items in the Queue have been gotten and processed.

        The count of unfinished tasks goes up whenever an item is added to the
        queue. The count goes down whenever a consumer thread calls task_done()
        to indicate the item was retrieved and all work on it is complete.

        When the count of unfinished tasks drops to zero, join() unblocks.
        """
        self.all_tasks_done.acquire()
        try:
            while self.unfinished_tasks:
                self.all_tasks_done.wait()
        finally:
            self.all_tasks_done.release()

    def qsize(self):
        """Return the approximate size of the queue (not reliable!)."""
        self.mutex.acquire()
        n = self._qsize()
        self.mutex.release()
        return n

    def empty(self):
        """Return True if the queue is empty, False otherwise (not reliable!)."""
        self.mutex.acquire()
        n = not self._qsize()
        self.mutex.release()
        return n

    def full(self):
        """Return True if the queue is full, False otherwise (not reliable!)."""
        self.mutex.acquire()
        n = 0 < self.maxsize == self._qsize()
        self.mutex.release()
        return n

    def put(self, item, block=True, timeout=None):
        """Put an item into the queue.

        If optional args 'block' is true and 'timeout' is None (the default),
        block if necessary until a free slot is available. If 'timeout' is
        a non-negative number, it blocks at most 'timeout' seconds and raises
        the Full exception if no free slot was available within that time.
        Otherwise ('block' is false), put an item on the queue if a free slot
        is immediately available, else raise the Full exception ('timeout'
        is ignored in that case).
        """
        self.not_full.acquire()
        try:
            if self.maxsize > 0:
                if not block:
                    if self._qsize() == self.maxsize:
                        raise Full
                elif timeout is None:
                    while self._qsize() == self.maxsize:
                        self.not_full.wait()
                elif timeout < 0:
                    raise ValueError("'timeout' must be a non-negative number")
                else:
                    endtime = _time() + timeout
                    while self._qsize() == self.maxsize:
                        remaining = endtime - _time()
                        if remaining <= 0.0:
                            raise Full
                        self.not_full.wait(remaining)
            self._put(item)
            self.unfinished_tasks += 1
            self.not_empty.notify()
        finally:
            self.not_full.release()

    def put_nowait(self, item):
        """Put an item into the queue without blocking.

        Only enqueue the item if a free slot is immediately available.
        Otherwise raise the Full exception.
        """
        return self.put(item, False)

    def get(self, block=True, timeout=None):
        """Remove and return an item from the queue.

        If optional args 'block' is true and 'timeout' is None (the default),
        block if necessary until an item is available. If 'timeout' is
        a non-negative number, it blocks at most 'timeout' seconds and raises
        the Empty exception if no item was available within that time.
        Otherwise ('block' is false), return an item if one is immediately
        available, else raise the Empty exception ('timeout' is ignored
        in that case).
        """
        self.not_empty.acquire()
        try:
            if not block:
                if not self._qsize():
                    raise Empty
            elif timeout is None:
                while not self._qsize():
                    self.not_empty.wait()
            elif timeout < 0:
                raise ValueError("'timeout' must be a non-negative number")
            else:
                endtime = _time() + timeout
                while not self._qsize():
                    remaining = endtime - _time()
                    if remaining <= 0.0:
                        raise Empty
                    self.not_empty.wait(remaining)
            item = self._get()
            self.not_full.notify()
            return item
        finally:
            self.not_empty.release()

    def get_nowait(self):
        """Remove and return an item from the queue without blocking.

        Only get an item if one is immediately available. Otherwise
        raise the Empty exception.
        """
        return self.get(False)

    # Override these methods to implement other queue organizations
    # (e.g. stack or priority queue).
    # These will only be called with appropriate locks held

    # Initialize the queue representation
    def _init(self, maxsize):
        self.queue = deque()

    def _qsize(self, len=len):
        return len(self.queue)

    # Put a new item in the queue
    def _put(self, item):
        self.queue.append(item)

    # Get an item from the queue
    def _get(self):
        return self.queue.popleft()

双向队列和单向队列的区别

双向队列是两边都可以插入数据和读取数据，单向队列只能从一边取和插
双向队列是在collections模块中，单向队列是在Queue模块中
都是线程安全的

会通过help方法找到collections，大致知道计算器、有序字典、默认字典、可命名元组和双向队列

迭代器和生成器

 一、迭代器

对于python列表的for循环，他的内部原理：查看下一个元素是否存在，如果存在，则取出，如果不存在，则报异常StopIteration。（python内部对异常已处理）

>>> c
Counter({'d': 4, 's': 4, 'a': 2, 'c': 2, 'b': 2, 'e': 2, 'f': 2, 'g': 1})
>>> c.elements()
<itertools.chain object at 0x7f4424b59890>
>>> 

迭代器的内容要用循环取出来

class listiterator(object)
 |  Methods defined here:
 |  
 |  __getattribute__(...)
 |      x.__getattribute__('name') <==> x.name
 |  
 |  __iter__(...)
 |      x.__iter__() <==> iter(x)
 |  
 |  __length_hint__(...)
 |      Private method returning an estimate of len(list(it)).
 |  
 |  next(...)
 |      x.next() -> the next value, or raise StopIteration

二、生成器

range不是生成器 xrange是生成器

redlines不是生成器 xreadlines是生成器

>>> print range(10)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> print xrange(10)
xrange(10)
>>>

xrange(10)创建时只是一个对象，只有在循环或使用时才会在内存中开辟空间
生成器内部基于yield创建，即：对于生成器只有使用时才创建，从而避免内存浪费

练习：有如下列表：
    [13, 22, 6, 99, 11]
利用列表的下标进行循环

>>> li
[13, 22, 6, 99, 11]
>>> i = 0
>>> while i < len(li):
...   print li[i]
...   i += 1
... 
13
22
6
99
11

练习：有如下列表：
    [13, 22, 6, 99, 11]
 
请按照一下规则计算：
13 和 22 比较，将大的值放在右侧，即：[13, 22, 6, 99, 11]
22 和 6 比较，将大的值放在右侧，即：[13, 6, 22, 99, 11]
22 和 99 比较，将大的值放在右侧，即：[13, 6, 22, 99, 11]
99 和 42 比较，将大的值放在右侧，即：[13, 6, 22, 11, 99,]
 
13 和 6 比较，将大的值放在右侧，即：[6, 13, 22, 11, 99,]
...

#!/usr/bin/env python
# _*_ coding:utf-8 _*_
li = [12, 33, 6, 99, 11]
'''
方法一：第一种方法是固定一个数a与后面的数b相比，如果a大于b，就把a\b对调，再用a和下面的一个数比，全部比完后会得到一个最小的数放在最左面，然后用第二个数继续往下比，以此类推就得到了从小到大的排序
'''
for i in range(len(li) - 1):
    for j in range(i+1,len(li)):
        if li[i] > li[j]:
            temp = li[i];
            li[i] = li[j]
            li[j] = temp

print li

'''
方法二：两个相邻的数比较，如果左边的数比右边的大，就调换位置，一个循环后会把最大的数放在最右边，以此类推，所有数字都循环完后会得到从小到大的排序
'''
for x in range(len(li)):
    for i in range(len(li)-1):
        if li[i] > li[i+1]:
            t = li[i];
            li[i] = li[i+1];
            li[i+1] = t
print li;

函数

函数分为内置函数、自定义函数、导入函数。函数就是按功能划分的代码块

一、内置函数

内置函数就是python为用户提供的快捷方法

 先看几个常用的

help()函数可以得到方法的一些帮助

>>> help(list)
Help on class list in module __builtin__:

class list(object)
 |  list() -> new empty list
 |  list(iterable) -> new list initialized from iterable's items
 |  
 |  Methods defined here:
 |  
 |  __add__(...)
 |      x.__add__(y) <==> x+y
 |  
 |  __contains__(...)
 |      x.__contains__(y) <==> y in x
 |  
 |  __delitem__(...)
 |      x.__delitem__(y) <==> del x[y]
 |  
 |  __delslice__(...)
 |      x.__delslice__(i, j) <==> del x[i:j]
 |      
 |      Use of negative indices is not supported.

dir()可以把一个类的方法全部打印出来

>>> dir(list)
['__add__', '__class__', '__contains__', '__delattr__', '__delitem__', '__delslice__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getslice__', '__gt__', '__hash__', '__iadd__', '__imul__', '__init__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__reversed__', '__rmul__', '__setattr__', '__setitem__', '__setslice__', '__sizeof__', '__str__', '__subclasshook__', 'append', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse', 'sort']
>>> 

vars()可以显示出所有的变量

>>> vars()
{'a': xrange(10), 'c': Counter({'d': 4, 's': 4, 'a': 2, 'c': 2, 'b': 2, 'e': 2, 'f': 2, 'g': 1}), '__builtins__': <module '__builtin__' (built-in)>, '__package__': None, 'i': 1, 'collections': <module 'collections' from '/usr/lib/python2.7/collections.pyc'>, 'tab': <module 'tab' from '/usr/lib/python2.7/dist-packages/tab.pyc'>, '__name__': '__main__', 'li': [13, 22, 6, 99, 11], '__doc__': None}
>>> 

type()查看变量的类型

>>> type(li)
<type 'list'>
>>> 

abc()取绝对值

>>> abs(-9)
9
>>> abs(0)
0
>>> abs(33)
33
>>> 

divmod()输入两个数字，得到一个元组（商、余数）

>>> divmod(10,3)
(3, 1)
>>> divmod(10,2)
(5, 0)
>>> 

 ord()输入一个字符，返回ASCII

>>> ord('a')
97
>>> 

 chr()输入ASCII，返回字符

>>> chr(100)
'd'
>>>

 cmp()比较两个数大小，大于返回1，小于返回-1，等于返回0

>>> cmp(20,2)
1
>>> cmp(20,20)
0
>>> cmp(20,21)
-1
>>> 

 eval()可以计算一个字符串的值

>>> eval('200/4+4')
54
>>> 

 format()格式化输出字符串

>>> a = 'I am a {0},hello {1}'
>>> print a.format('techer','world')
I am a techer,hello world
>>> 

 hex(x)转换成十六进制

>>> hex(255)
'0xff'
>>> 

 id()返回对象的内存地址

>>> a = 20
>>> id(a)
22802320
>>> 

 input()输入内容

#input接收到的是输入的是什么就是什么类型，比如输入22，那么a类型就是数值类型
>>> a = input()
22
>>> type(a)
<type 'int'>
#输入一个hello，接收到的就是一个变量名，根据下面的异常提示也可以知道
>>> a = input()
hello
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<string>", line 1, in <module>
NameError: name 'hello' is not defined
#如果想得到一个字符串，输入的时候要加引号
>>> a = input()
'hello'
>>> type(a)
<type 'str'>
>>> 

int()输入一个数字，转换成整型，字符串不能是小数形式的

>>> a = '22'
>>> type(a)
<type 'str'>
>>> b = int(a)
>>> type(b)
<type 'int'>
>>> b
22
>>> 

 len()计算长度

>>> li 
[13, 22, 6, 99, 11]
>>> len(li)
5
>>> s = 'hello world'
>>> len(s)
11
>>> 

 max()找出最大值

>>> a = 22
>>> b = 33
>>> c = 21
>>> max(a,b,c)
33
>>> 

 min()找出最小值

>>> a,b,c
(22, 33, 21)
>>> min(a,b,c)
21
>>> t = (11,111,1111)
>>> min(t)
11
>>> 

 oct()转换成八进制

>>> a = 100
>>> oct(a)
'0144'
>>> 

 pow()求幂

>>> pow(2,2)   
4
>>> pow(2,10)  
1024
>>> 

 range()生成一个连续的数字序列

>>> range(5)
[0, 1, 2, 3, 4]
>>> range(2,8)
[2, 3, 4, 5, 6, 7]
>>> 

 raw_input()接收终端输入的内容

>>> a = raw_input()
hello
>>> a
'hello'
>>> a = raw_input()
33
>>> a
'33'
>>> 

 reload(module)重新导入模块

如果一个模块已经导入，修改过这个模块后需要重新导入，就需要用reload重新导入

sum()计算一个数字序列的和

>>> sum([1,2,3])
6
>>> 

 all()如果里面有空值，就返回False

>>> a = [1,2,3,'']
>>> all(a)
False
>>> a = [1,2,3,'a']
>>> all(a)         
True
>>> 

 any()只要有一个是真就是真

>>> a = []
>>> any(a)
False
>>> a = [1,2,'']
>>> any(a)      
True
>>> 

 enumerate()循环序列，前面加一个序号，默认从0开始，可以指定起始值

>>> a = [11,22,33,44,55]
>>> for k,v in enumerate(a,1):
...   print k,v
... 
1 11
2 22
3 33
4 44
5 55
>>> 

 二、自定义函数

一、在学习函数之前，一直遵循：面向过程编程，即：根据业务逻辑从上到下实现功能，其往往用一长段代码来实现指定功能，开发过程中最常见的操作就是粘贴复制，也就是将之前实现的代码块复制到现需要功能处。

while True：
    if cpu利用率 > 90%:
        #发送邮件提醒
        连接邮箱服务器
        发送邮件
        关闭连接
  
    if 硬盘使用空间 > 90%:
        #发送邮件提醒
        连接邮箱服务器
        发送邮件
        关闭连接
  
    if 内存占用 > 80%:
        #发送邮件提醒
        连接邮箱服务器
        发送邮件
        关闭连接

 如上述代码，if条件语句下的内容可以被提取出来公用，如下：

def 发送邮件(内容)
    #发送邮件提醒
    连接邮箱服务器
    发送邮件
    关闭连接
  
while True：
  
    if cpu利用率 > 90%:
        发送邮件('CPU报警')
  
    if 硬盘使用空间 > 90%:
        发送邮件('硬盘报警')
  
    if 内存占用 > 80%:

 对于上述的两种实现方式，第二次必然比第一次的重用性和可读性要好，其实这就是函数式编程和面向过程编程的区别：

• 函数式：将某功能代码封装到函数中，日后便无需重复编写，仅调用函数即可
• 面向对象：对函数进行分类和封装，让开始“更快更好更强……”

函数式编程最重要的是增强代码的重用性和可读性

二、函数的定义和使用

def 函数名(参数):
     
    ...
    函数体
    ...

 函数的定义主要有如下要点：

• def：表示函数的关键字
• 函数名：函数的名称，日后根据函数名调用函数
• 函数体：函数中进行一系列的逻辑计算，如：发送邮件、计算出 [11,22,38,888,2]中的最大数等...
• 参数：为函数体提供数据
• 返回值：当函数执行完毕后，可以给调用者返回数据。

以上要点中，比较重要有参数和返回值：

1、返回值

函数是一个功能块，该功能到底执行成功与否，需要通过返回值来告知调用者。

def 发送短信():
     
    发送短信的代码...
 
    if 发送成功:
        return True
    else:
        return False
 
 
while True:
     
    # 每次执行发送短信函数，都会将返回值自动赋值给result
    # 之后，可以根据result来写日志，或重发等操作
 
    result = 发送短信()
    if result == False:
        记录日志，短信发送失败...

 2、参数

函数中有三种不同的参数

• 普通参数
• 默认参数
• 动态参数

# ######### 定义函数 ######### 

# name 叫做函数func的形式参数，简称：形参
def func(name):
    print name

# ######### 执行函数 ######### 
#  'wangwei' 叫做函数func的实际参数，简称：实参
func('wangwei')

def func(name, age = 18):
    
    print "%s:%s" %(name,age)

# 指定参数
func('wupeiqi', 19)
# 使用默认参数
func('alex')

注：默认参数需要放在参数列表最后

def func(**kwargs):

    print args


# 执行方式一
func(name＝'wupeiqi',age=18)

# 执行方式二
li = {'name':'wupeiqi', age:18, 'gender':'male'}
func(**li)
#传入字典时前面要加两个星

def func(*args, **kwargs):

    print args
    print kwargs

>>> def func(*args,**kwargs):
...   print args
...   print kwargs
... 
>>> func(11,22,33)
(11, 22, 33)
{}
>>> func(k1=123,k2=321)
()
{'k2': 321, 'k1': 123}
>>> func(1,2,3,4,k1=123,k2=321)
(1, 2, 3, 4)
{'k2': 321, 'k1': 123}
>>> func([1,2,3],k1=123,k2=321)
([1, 2, 3],)
{'k2': 321, 'k1': 123}
>>> 

文件操作

操作文件时，一般需要经历如下步骤：

• 打开文件
• 操作文件

一、打开文件

文件句柄 = file('文件路径','模式')

 注：python中打开文件有两种方式，即：open(...)和file(...)，本质上前者在内部会调用后者来进行文件操作，推荐使用open

打开文件时，需要指定文件路径和以何种方式打开文件，打开后，即可获取该文件句柄，日后通过此文件句柄对该文件操作。

打开文件的模式有：

• r，只读模式（默认）
• w，只写模式。——不可读，不存在则创建，存在则删除内容。
• a，追加模式。——可读，不存在则创建，存在则只追加内容。

“+”表示可以同时读写某个文件。“+”只有在r+有意思，w+和a+和w、a的效果是一样的

• r+，可读写文件——可读，可写，可追加
• w+，写读
• a+，同a

“U”表示在读取时，可以将\r \n \r\n自动转换成\n（与r或r+模式同时使用）

• rU
• r+U

“b”表示处理二进制文件（如：FTP发送上传ISO去镜像文件，linux可忽略，windows处理二进制文件时需标注）

• rb
• wb
• ab

二、操作文件

class file(object):
  
    def close(self): # real signature unknown; restored from __doc__
        #关闭文件
        """
        close() -> None or (perhaps) an integer.  Close the file.
         
        Sets data attribute .closed to True.  A closed file cannot be used for
        further I/O operations.  close() may be called more than once without
        error.  Some kinds of file objects (for example, opened by popen())
        may return an exit status upon closing.
        """
 
    def fileno(self): # real signature unknown; restored from __doc__
        #文件描述符  
         """
        fileno() -> integer "file descriptor".
         
        This is needed for lower-level file interfaces, such os.read().
        """
        return 0    
 
    def flush(self): # real signature unknown; restored from __doc__
        #刷新文件内部缓冲区
        """ flush() -> None.  Flush the internal I/O buffer. """
        pass
 
 
    def isatty(self): # real signature unknown; restored from __doc__
        #判断文件是否是同意tty设备
        """ isatty() -> true or false.  True if the file is connected to a tty device. """
        return False
 
 
    def next(self): # real signature unknown; restored from __doc__
        #获取下一行数据，不存在，则报错
        """ x.next() -> the next value, or raise StopIteration """
        pass
 
    def read(self, size=None): # real signature unknown; restored from __doc__
        #读取指定字节数据
        """
        read([size]) -> read at most size bytes, returned as a string.
         
        If the size argument is negative or omitted, read until EOF is reached.
        Notice that when in non-blocking mode, less data than what was requested
        may be returned, even if no size parameter was given.
        """
        pass
 
    def readinto(self): # real signature unknown; restored from __doc__
        #读取到缓冲区，不要用，将被遗弃
        """ readinto() -> Undocumented.  Don't use this; it may go away. """
        pass
 
    def readline(self, size=None): # real signature unknown; restored from __doc__
        #仅读取一行数据
        """
        readline([size]) -> next line from the file, as a string.
         
        Retain newline.  A non-negative size argument limits the maximum
        number of bytes to return (an incomplete line may be returned then).
        Return an empty string at EOF.
        """
        pass
 
    def readlines(self, size=None): # real signature unknown; restored from __doc__
        #读取所有数据，并根据换行保存值列表
        """
        readlines([size]) -> list of strings, each a line from the file.
         
        Call readline() repeatedly and return a list of the lines so read.
        The optional size argument, if given, is an approximate bound on the
        total number of bytes in the lines returned.
        """
        return []
 
    def seek(self, offset, whence=None): # real signature unknown; restored from __doc__
        #指定文件中指针位置
        """
        seek(offset[, whence]) -> None.  Move to new file position.
         
        Argument offset is a byte count.  Optional argument whence defaults to
        0 (offset from start of file, offset should be >= 0); other values are 1
        (move relative to current position, positive or negative), and 2 (move
        relative to end of file, usually negative, although many platforms allow
        seeking beyond the end of a file).  If the file is opened in text mode,
        only offsets returned by tell() are legal.  Use of other offsets causes
        undefined behavior.
        Note that not all file objects are seekable.
        """
        pass
 
    def tell(self): # real signature unknown; restored from __doc__
        #获取当前指针位置
        """ tell() -> current file position, an integer (may be a long integer). """
        pass
 
    def truncate(self, size=None): # real signature unknown; restored from __doc__
        #截断数据，仅保留指定之前数据
        """
        truncate([size]) -> None.  Truncate the file to at most size bytes.
         
        Size defaults to the current file position, as returned by tell().
        """
        pass
 
    def write(self, p_str): # real signature unknown; restored from __doc__
        #写内容
        """
        write(str) -> None.  Write string str to file.
         
        Note that due to buffering, flush() or close() may be needed before
        the file on disk reflects the data written.
        """
        pass
 
    def writelines(self, sequence_of_strings): # real signature unknown; restored from __doc__
        #将一个字符串列表写入文件
        """
        writelines(sequence_of_strings) -> None.  Write the strings to the file.
         
        Note that newlines are not added.  The sequence can be any iterable object
        producing strings. This is equivalent to calling write() for each string.
        """
        pass
 
    def xreadlines(self): # real signature unknown; restored from __doc__
        #可用于逐行读取文件，非全部
        """
        将弃用，可以用下面的方法
        for line in f:          
          print line.strip();
        """
        """
        xreadlines() -> returns self.
         
        For backward compatibility. File objects now include the performance
        optimizations previously implemented in the xreadlines module.
        """
        pass

 三、with

为了避免打开文件后忘记关闭，可以通过管理上下文，即：

with open('log','r') as f:
  ......

 如此方式，当with代码块执行完毕时，内部会自动关闭并释放文件资源。

在python2.7后，with又支持同时对多个文件的上下文进行管理，即：

with open('log1') as obj1,open('log2') as obj2:
    pass