python-Day3-set 集合-counter计数器-默认字典(defaultdict) -可命名元组(namedtuple)-有序字典(orderedDict)-双向队列(deque)--Queue单项队列--深浅拷贝---函数参数
上节内容回顾:
C语言为什么比起他语言块,因为C 会把代码变异成机器码
Pyhton 的 .pyc文件是什么
python 把.py文件编译成的.pyc文件是Python的字节码,
字符串本质是 字符数组,
python 一切事物都是对象,对象是类创建的,像 增加删除更改 都存在于类里边,也可以称作类的成员
set集合
set是一个无序且不重复的元素集合
class set(object):
"""
set() -> new empty set object
set(iterable) -> new set object Build an unordered collection of unique elements.
"""
def add(self, *args, **kwargs): # real signature unknown
""" 添加 """
"""
Add an element to a set. This has no effect if the element is already present.
"""
pass def clear(self, *args, **kwargs): # real signature unknown
""" Remove all elements from this set. """
pass def copy(self, *args, **kwargs): # real signature unknown
""" Return a shallow copy of a set. """
pass def difference(self, *args, **kwargs): # real signature unknown
"""
Return the difference of two or more sets as a new set. (i.e. all elements that are in this set but not the others.)
"""
pass def difference_update(self, *args, **kwargs): # real signature unknown
""" 删除当前set中的所有包含在 new set 里的元素 """
""" Remove all elements of another set from this set. """
pass def discard(self, *args, **kwargs): # real signature unknown
""" 移除元素 """
"""
Remove an element from a set if it is a member. If the element is not a member, do nothing.
"""
pass def intersection(self, *args, **kwargs): # real signature unknown
""" 取交集，新创建一个set """
"""
Return the intersection of two or more sets as a new set. (i.e. elements that are common to all of the sets.)
"""
pass def intersection_update(self, *args, **kwargs): # real signature unknown
""" 取交集，修改原来set """
""" Update a set with the intersection of itself and another. """
pass def isdisjoint(self, *args, **kwargs): # real signature unknown
""" 如果没有交集，返回true """
""" Return True if two sets have a null intersection. """
pass def issubset(self, *args, **kwargs): # real signature unknown
""" 是否是子集 """
""" Report whether another set contains this set. """
pass def issuperset(self, *args, **kwargs): # real signature unknown
""" 是否是父集 """
""" Report whether this set contains another set. """
pass def pop(self, *args, **kwargs): # real signature unknown
""" 移除 """
"""
Remove and return an arbitrary set element.
Raises KeyError if the set is empty.
"""
pass def remove(self, *args, **kwargs): # real signature unknown
""" 移除 """
"""
Remove an element from a set; it must be a member. If the element is not a member, raise a KeyError.
"""
pass def symmetric_difference(self, *args, **kwargs): # real signature unknown
""" 差集，创建新对象"""
"""
Return the symmetric difference of two sets as a new set. (i.e. all elements that are in exactly one of the sets.)
"""
pass def symmetric_difference_update(self, *args, **kwargs): # real signature unknown
""" 差集，改变原来 """
""" Update a set with the symmetric difference of itself and another. """
pass def union(self, *args, **kwargs): # real signature unknown
""" 并集 """
"""
Return the union of sets as a new set. (i.e. all elements that are in either set.)
"""
pass def update(self, *args, **kwargs): # real signature unknown
""" 更新 """
""" Update a set with the union of itself and others. """
pass def __and__(self, y): # real signature unknown; restored from __doc__
""" x.__and__(y) <==> x&y """
pass def __cmp__(self, y): # real signature unknown; restored from __doc__
""" x.__cmp__(y) <==> cmp(x,y) """
pass def __contains__(self, y): # real signature unknown; restored from __doc__
""" x.__contains__(y) <==> y in x. """
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 __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 __iand__(self, y): # real signature unknown; restored from __doc__
""" x.__iand__(y) <==> x&=y """
pass def __init__(self, seq=()): # known special case of set.__init__
"""
set() -> new empty set object
set(iterable) -> new set object Build an unordered collection of unique elements.
# (copied from class doc)
"""
pass def __ior__(self, y): # real signature unknown; restored from __doc__
""" x.__ior__(y) <==> x|=y """
pass def __isub__(self, y): # real signature unknown; restored from __doc__
""" x.__isub__(y) <==> x-=y """
pass def __iter__(self): # real signature unknown; restored from __doc__
""" x.__iter__() <==> iter(x) """
pass def __ixor__(self, y): # real signature unknown; restored from __doc__
""" x.__ixor__(y) <==> x^=y """
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 __or__(self, y): # real signature unknown; restored from __doc__
""" x.__or__(y) <==> x|y """
pass def __rand__(self, y): # real signature unknown; restored from __doc__
""" x.__rand__(y) <==> y&x """
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 __ror__(self, y): # real signature unknown; restored from __doc__
""" x.__ror__(y) <==> y|x """
pass def __rsub__(self, y): # real signature unknown; restored from __doc__
""" x.__rsub__(y) <==> y-x """
pass def __rxor__(self, y): # real signature unknown; restored from __doc__
""" x.__rxor__(y) <==> y^x """
pass def __sizeof__(self): # real signature unknown; restored from __doc__
""" S.__sizeof__() -> size of S in memory, in bytes """
pass def __sub__(self, y): # real signature unknown; restored from __doc__
""" x.__sub__(y) <==> x-y """
pass def __xor__(self, y): # real signature unknown; restored from __doc__
""" x.__xor__(y) <==> x^y """
pass __hash__ = None set
set集合
集合里不允许重复的元素存在
对象是由类创建的
要创建一个set
创建一个 set无序集合
列表有两种创建方法:
a1 = []
a2 = list()
set 通过类创建对象、
s1 = set() 这就是创建了一个集合的对象
现在可以往里边添加对象
用途:
#比如说在写爬虫的时候访问一个电商网站,访问第一个页面的时候收集到了一个商品名称,在访问第二个页面的时候又收集了一个商品名称,这个时候集合就起作用了,集合里不会有重复的元素
#访问速度快
#天生解决了重复问题
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
#定义一个空的集合
s1 = set()
#给集合添加对象
s1.add('amd')
#打印集合
print(s1)
#打印类型
print(type(s1))
--------------------------------------------------------------------------------
#打印添加对象后的集合
{'amd'}
#打印显示所属类型为集合
<class 'set'> --------------------------------------------------------------------------------
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
s1 = set()
#添加对象
s1.add('amd')
#添加对象
s1.add('amd')
print(s1)
print(s1)
print(type(s1))
--------------------------------------------------------------------------------
输出:
#这里表明了 集合里不允许重复的元素存在所以只打印了一个
{'amd'}
{'amd'}
<class 'set'>
创建set集合
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
s1 = set()
s1.add('amd')
s1.add('amd')
print(s1)
#打印清空数据
print(s1.clear())
-----------------------------------------------------------------------------------
输出:
#数据存在的时候
{'amd'}
#清空后会显示None
None
set清空数据clear()
'''
#找到不同的创建一个新的集合，
#注意: 而不是修改原来的集合
def difference(self, *args, **kwargs): # real signature unknown
"""
'''
#set()内可以传入一个列表,传入的参数set会自动的将列表转为集合,并且把重复的去掉
aa = set(['a','b','c','c'])
print(type(aa))
print(aa)
ww = set(['a','c'])
a1 = aa.difference(ww)
print(a1)
difference(self, *args, **kwargs):对比两个集合找到不同后生成一个新的集合
'''
#删除当前set中的所有包含在参数里的元素
#在原有的集合里删除所传入的元素
#注意: 是在原有的集合里删除,不是生成信的集合
def difference_update(self, *args, **kwargs): # real signature unknown
""" Remove all elements of another set from this set. """
pass
'''
aa = set(['a','b','c','c'])
a1 = set(['a','c'])
#difference_update 没有生成信的集合而是修改了原有的集合
a2 = aa.difference_update(a1)
print(aa)
print(a2)
difference_update(self, *args, **kwargs):删除当前set集合中的所有包含在参数里的元素
#取交集,取两个集合中相同的交集,
#注意: 并且生成一个新的集合,不是修改原来的集合
def intersection(self, *args, **kwargs): # real signature unknown
"""
Return the intersection of two sets as a new set. (i.e. all elements that are in both sets.)
"""
pass
'''
a = set(['a','c','d'])
a1 = set(['g','w','a'])
ww = a.intersection(a1)
print(ww)
print(type(ww))
-----------------------------------------------------------------------------------
输出:
{'a'}
<class 'set'>
'''
#对比两个集合取交集,
#注意: 这里是取到的交集修改原来的集合,不是生成一个新的集合
def intersection_update(self, *args, **kwargs): # real signature unknown
""" Update a set with the intersection of itself and another. """
pass
'''
a = set(['a','c','d'])
a1 = set(['g','w','a'])
ww = a.intersection_update(a1)
print(a)
print(type(a))
print(ww)
print(type(ww))
------------------------------------------------------------------------------------
输出:
{'a'}
<class 'set'>
None
<class 'NoneType'>
'''
#对比两个集合的交集,如果没有交集 返回True
def isdisjoint(self, *args, **kwargs): # real signature unknown
""" Return True if two sets have a null intersection. """
pass
'''
a = set(['a','c','d'])
a1 = set(['g','w',])
ww = a.isdisjoint(a1)
print(ww)
-----------------------------------------------------------------------------------
输出:
True
==============================================
a = set(['a','c','d',])
a1 = set(['g','w','a',])
ww = a.isdisjoint(a1)
print(ww)
----------------------------------------------------------------------------------
输出:
False
isdisjoint(self, *args, **kwargs):对比两个集合的交集,如果没有交集 返回True
'''
#是否是子集的
def issubset(self, *args, **kwargs): # real signature unknown
""" Report whether another set contains this set. """
pass
'''
a = set(['a','c','d',])
a1 = set(['a','c','d',])
#测试是否 a 中的每一个元素都在 a1 中
ww = a.issubset(a1)
print(ww)
----------------------------------------------------------------------------------
输出:
True
issubset(self, *args, **kwargs):是否是子集的
'''
#是否是父集
def issuperset(self, *args, **kwargs): # real signature unknown
""" Report whether this set contains another set. """
pass
'''
a = set(['a','c','d',])
a1 = set(['a','c','d',])
#测试是否 a1 中的每一个元素都在 a 中
ee = a.issuperset(a1)
print(ee)
-----------------------------------------------------------------------------------
输出:
True
def issuperset(self, *args, **kwargs):
'''
#pop是去一个元素里随机取一个值并且赋给一个新的变量
def pop(self, *args, **kwargs): # real signature unknown
"""
Remove and return an arbitrary set element.
Raises KeyError if the set is empty.
"""
pass
'''
a = set(['a','c','d',])
a1 = set(['a','c','d',])
w1 = a.pop()
print(w1)
def pop(self, *args, **kwargs):
'''
#移除一个元素
def remove(self, *args, **kwargs): # real signature unknown
"""
Remove an element from a set; it must be a member. If the element is not a member, raise a KeyError.
"""
pass '''
a = set(['a','c','d',])
a.remove('c')
print(a)
------------------------------------------------------------------------------------
输出：
{'d', 'a'}
def remove(self, *args, **kwargs):#移除一个元素
'''
#计算两个集合的 差集
#注意: 计算两个几个的差集 并创建新的集合
def symmetric_difference(self, *args, **kwargs): # real signature unknown
"""
Return the symmetric difference of two sets as a new set. (i.e. all elements that are in exactly one of the sets.)
"""
pass '''
a = set(['a','c','d',])
b = set(['a','c','w',])
ww = a.symmetric_difference(b)
print(ww)
----------------------------------------------------------------------------------
输出:
{'w', 'd'}
def symmetric_difference(self, *args, **kwargs):计算两个集合的 差集，并生成新的集合
'''
#计算两个集合的 差集
#注意: 计算两个几个的差集 并修改原来的集合
def symmetric_difference_update(self, *args, **kwargs): # real signature unknown
""" Update a set with the symmetric difference of itself and another. """
pass '''
a = set(['a','c','d',])
b = set(['a','c','w',])
a.symmetric_difference_update(b)
#打印两个几个的差集
print(a)
----------------------------------------------------------------------------------
输出:
{'d', 'w'}
==============================================
a = set(['a','c','d',])
b = set(['a','c','w',])
a.symmetric_difference_update(b)
#打印两个集合的交集
print(b)
------------------------------------------------------------------------------
输出:
{'c', 'a', 'w'}
def symmetric_difference_update(self, *args, **kwargs):计算两个几个的差集 并修改原来的集合
'''
#取两个集合的并集
#注意:将两个集合去除重复,并合并生成一个新的变量
def union(self, *args, **kwargs): # real signature unknown
"""
Return the union of sets as a new set. (i.e. all elements that are in either set.)
"""
pass '''
a = set(['a','c','d',])
b = set(['a','c','w','wer'])
bb = a.union(b)
print(bb)
----------------------------------------------------------------------------------
输出:
{'wer', 'a', 'c', 'w', 'd'}
def union(self, *args, **kwargs):#注意:将两个集合去除重复,并合并生成一个新的变量
'''
#更新一个集合
#注意:这里更新的原有的集合,不是更新后新生成一个集合
def update(self, *args, **kwargs): # real signature unknown
""" Update a set with the union of itself and others. """
pass '''
a = set(['a','c','d',])
b = set(['a','c','w','wer'])
a.update(set(['wewewe']))
print(a)
a.update(b)
print(a)
----------------------------------------------------------------------------------
输出:
{'wewewe', 'a', 'd', 'c'}
{'a', 'c', 'wewewe', 'wer', 'd', 'w'}
def update(self, *args, **kwargs): 更新一个集合
练习：寻找差异
# 数据库中原有
old_dict = {
"#1":{ 'hostname':c1, 'cpu_count': 2, 'mem_capicity': 80 },
"#2":{ 'hostname':c1, 'cpu_count': 2, 'mem_capicity': 80 }
"#3":{ 'hostname':c1, 'cpu_count': 2, 'mem_capicity': 80 }
} # cmdb 新汇报的数据
new_dict = {
"#1":{ 'hostname':c1, 'cpu_count': 2, 'mem_capicity': 800 },
"#3":{ 'hostname':c1, 'cpu_count': 2, 'mem_capicity': 80 }
"#4":{ 'hostname':c2, 'cpu_count': 2, 'mem_capicity': 80 }
}
注意：1.无需考虑内部元素是否改变，只要原来存在，新汇报也存在，就是需要更新
2.原来的不存在就插入,新汇报的就插入
3.原来的存在,新汇报的不存在就删除
4.只需要打印出 更新的有哪些,删除的有哪些,插入的有哪些
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
old_dict = {
"#1":{ 'hostname':'c1', 'cpu_count': 2, 'mem_capicity': 80 },
"#2":{ 'hostname':'c1', 'cpu_count': 2, 'mem_capicity': 80 },
"#3":{ 'hostname':'c1', 'cpu_count': 2, 'mem_capicity': 80 },
} new_dict = {
"#1":{ 'hostname':'c1', 'cpu_count': 2, 'mem_capicity': 800 },
"#3":{ 'hostname':'c1', 'cpu_count': 2, 'mem_capicity': 80 },
"#4":{ 'hostname':'c2', 'cpu_count': 2, 'mem_capicity': 80 },
}
#用set集合中的取交集来判断字典的key是否都存在,如果都存在的生成一个新的变量,并且定义为集合
update_dict = set(old_dict.keys()).intersection(set(new_dict.keys()))
#print(update_dict)
#定义一个空的要插入的列表
new_list = []
#定义一个空的要删除的列表
delete_list = []
#这里是把原来的数据old_dict 的key赋值给i
for i in old_dict.keys():
#这里判断循环中的 i 如果不在update_dict集合里,就添加到删除列表里
if i not in update_dict:
delete_list.append(i)
#这里是把汇报上来的的数据new_dict 的key赋值给i
for i in new_dict.keys():
#这里判断 i 如果不在更新的集合中,就添加到插入的列表里
if i not in update_dict:
new_list.append(i)
#一下是前几天提到的格式化输出,下面列出了两种方法,
msg = '''
更新:%s
插入:%s
删除:%s
''' %(update_dict,new_list,delete_list)
print("更新:%s\n删除:%s\n插入:%s" %(update_dict,delete_list,new_list))
print(msg)
找差异源代码
collections系列
Counter功能在 collections模块里所有在使用 Counter功能的时候需要导入 collections 模块(import collections)
一.计数器(counter)
Counter是对字典类型的补充,用于追踪值得出现次数.
PS:具备字典的所有功能 加上 自己的功能
collections 在Python里是一个文件夹 python 在导入的时候 是导入的 collections 的文件夹,导入之后 Python 会在导入的文件夹内查找Counter,找到Counter之后就可以创建对象
collections.Counter() 的功能是将元素出现的次数做一个统计
例:
__author__ = 'Administrator'
import collections
aa = collections.Counter('aabbccddeeffgg')
print(aa)
print(type(aa))
-----------------------------------------------------------------------------------
输出:
Counter({'g': 2, 'd': 2, 'b': 2, 'c': 2, 'f': 2, 'a': 2, 'e': 2})
<class 'collections.Counter'>
########################################################################
### 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 """
'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):
""" 数量大于等n的所有元素和计数器 """
'''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 """
'''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 """
'''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 """
'''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 Counter
collections方法
方法:most_common 是按照元素出现的次数 从多到少取 前4位 "we = aa.most_common(4)"
__author__ = 'Administrator'
import collections
aa = collections.Counter('aabbcccddddeeeeeffffffggggggg') we = aa.most_common(4) print(we)
print(type(aa))
----------------------------------------------------------------------------------
输出:
[('g', 7), ('f', 6), ('e', 5), ('d', 4)]
<class 'collections.Counter'>
elements
items
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
import collections
aa = collections.Counter('aabbccddeeffgg')
for item in aa.elements():
print(item)
for k,v in aa.items():
print(k,v)
---------------------------------------------------------------------------------------------
输出:
b
b
e
e
f
f
c
c
a
a
d
d
g
g
b 2
e 2
f 2
c 2
a 2
d 2
g 2
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
import collections
aa = collections.Counter(['','','',''])
print(aa)
aa.update(['aa','','ff'])
print(aa)
--------------------------------------------------------------
输出:
Counter({'': 1, '': 1, '': 1, '': 1})
Counter({'': 2, '': 1, 'aa': 1, '': 1, '': 1, 'ff': 1})
update更新计数器,增加元素出现的次数
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
import collections
aa = collections.Counter(['','','',''])
print(aa)
aa.update(['aa','','ff'])
print(aa) aa.subtract(['aa',''])
print(aa)
--------------------------------------
输出:
Counter({'': 1, '': 1, '': 1, '': 1})
#原来33 出现了两次
Counter({'': 2, 'aa': 1, '': 1, '': 1, 'ff': 1, '': 1})
#通过subtract 33变成了一次
Counter({'': 1, '': 1, 'ff': 1, '': 1, '': 1, 'aa': 0})
subtract 减少元素出现的次数
二、有序字典(orderedDict)
orderdDict是对字典类型的补充，他记住了字典元素添加的顺序
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
orderedDict
dic = collections.OrderedDict()
dic['k1'] = 'v1'
dic['k2'] = 'v2'
dic['k3'] = 'v3'
print(dic)
print(dic)
print(dic)
print(type(dic))
----------------------------------------------------------------------------------
输出:
OrderedDict([('k1', 'v1'), ('k2', 'v2'), ('k3', 'v3')])
OrderedDict([('k1', 'v1'), ('k2', 'v2'), ('k3', 'v3')])
OrderedDict([('k1', 'v1'), ('k2', 'v2'), ('k3', 'v3')])
<class 'collections.OrderedDict'> ----------------------------------------------------------------------------------
无需字典:
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
import collections
# dic = collections.OrderedDict()
dic = dict()
dic['k1'] = 'v1'
dic['k2'] = 'v2'
dic['k3'] = 'v3'
print(dic)
print(dic)
print(dic)
print(type(dic))
--------------------------------------------------------
#这里就会变成无序字典了
输出:
{'k2': 'v2', 'k3': 'v3', 'k1': 'v1'}
{'k2': 'v2', 'k3': 'v3', 'k1': 'v1'}
{'k2': 'v2', 'k3': 'v3', 'k1': 'v1'}
<class 'dict'>
OrderedDict有序字典与无序字典dict
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
import collections
dic = collections.OrderedDict()
# dic = dict()
dic['k1'] = 'v1'
dic['k2'] = 'v2'
dic['k3'] = 'v3'
print(dic)
dic.move_to_end('k1')
print(dic)
-----------------------------------------------
输出:
OrderedDict([('k1', 'v1'), ('k2', 'v2'), ('k3', 'v3')])
OrderedDict([('k2', 'v2'), ('k3', 'v3'), ('k1', 'v1')])
move_to_end:把第一个拿到最后
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
import collections
dic = collections.OrderedDict()
# dic = dict()
dic['k1'] = 'v1'
dic['k2'] = 'v2'
dic['k3'] = 'v3'
print(dic) dic.popitem()
print(dic)
dic.popitem()
print(dic)
--------------------------------------------------------------------------------
输出:
OrderedDict([('k1', 'v1'), ('k2', 'v2'), ('k3', 'v3')])
OrderedDict([('k1', 'v1'), ('k2', 'v2')])
OrderedDict([('k1', 'v1')]) 在看一组例子；
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
import collections
dic = collections.OrderedDict()
# dic = dict()
dic['k1'] = 'v1'
dic['k2'] = 'v2'
dic['k3'] = 'v3'
print(dic) a7 = dic.popitem()
print(a7)
a8 = dic.popitem()
print(a8)
a9 = dic.popitem()
print(a9)
print(dic)
---------------------------------------------------------------------
输出；
OrderedDict([('k1', 'v1'), ('k2', 'v2'), ('k3', 'v3')])
('k3', 'v3')
('k2', 'v2')
('k1', 'v1')
OrderedDict() ---------------------------
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
import collections
dic = collections.OrderedDict()
# dic = dict()
dic['k1'] = 'v1'
dic['k2'] = 'v2'
dic['k3'] = 'v3'
print(dic)
a1 = dic.pop('k1')
print(a1)
print(dic)
------------------------------------------------
输出:
OrderedDict([('k1', 'v1'), ('k2', 'v2'), ('k3', 'v3')])
v1
OrderedDict([('k2', 'v2'), ('k3', 'v3')])
popitem:是按照后进先出的方法,拿到一个值赋予新的变量
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
import collections
dic = collections.OrderedDict()
# dic = dict()
dic['k1'] = 'v1'
dic['k2'] = 'v2'
dic['k3'] = 'v3'
print(dic)
dic.update({'k1':'v111','k10':'v10'})
print(dic)
--------------------------------
OrderedDict([('k1', 'v1'), ('k2', 'v2'), ('k3', 'v3')])
OrderedDict([('k1', 'v111'), ('k2', 'v2'), ('k3', 'v3'), ('k10', 'v10')])
update:更新原来的字典,如果原来字典没有则添加
三、默认字典(defaultdict)
defaultdict是对字典的类型的补充，他默认给字典的值设置了一个类型。
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
defaultdict
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
import collections
#这里定义了一个字典输入的类型为list
dic = collections.defaultdict(list)
dic['k1'].append('asdf')
print(dic['k1']) ---------------
输出:
['asdf']
defaultdict(list):设置一个字典输入的默认类型
四、可命名元组(namedtuple)
根据nametuple可以创建一个包含tuple所有功能以及其他功能的类型。
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. Mytuple
nametuple
import collections
#创建类,等同于defaultdict
#根据类创建对象
MytupleClass = collections.namedtuple('Mytuple',['x', 'y', 'z'])
aa = MytupleClass(11,22,33)
print(aa.x,aa.y,aa.z)
-----------------------------------
输出:
11 22 33
namedtuple
五、双向队列(deque)
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
""" Add an element to the right side of the deque. """
pass def appendleft(self, *args, **kwargs): # real signature unknown
""" Add an element to the left side of the deque. """
pass def clear(self, *args, **kwargs): # real signature unknown
""" Remove all elements from the deque. """
pass def count(self, value): # real signature unknown; restored from __doc__
""" D.count(value) -> integer -- return number of occurrences of value """
return 0 def extend(self, *args, **kwargs): # real signature unknown
""" Extend the right side of the deque with elements from the iterable """
pass def extendleft(self, *args, **kwargs): # real signature unknown
""" Extend the left side of the deque with elements from the iterable """
pass def pop(self, *args, **kwargs): # real signature unknown
""" Remove and return the rightmost element. """
pass def popleft(self, *args, **kwargs): # real signature unknown
""" Remove and return the leftmost element. """
pass def remove(self, value): # real signature unknown; restored from __doc__
""" D.remove(value) -- remove first occurrence of value. """
pass def reverse(self): # real signature unknown; restored from __doc__
""" D.reverse() -- reverse *IN PLACE* """
pass def rotate(self, *args, **kwargs): # real signature unknown
""" 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 deque deque
deque
def append(self, *args, **kwargs): # real signature unknown
""" Add an element to the right side of the deque. """
pass
append--往右边添加一个
def appendleft(self, *args, **kwargs): # real signature unknown
""" Add an element to the left side of the deque. """
pass
appendleft--往左边添加
def clear(self, *args, **kwargs): # real signature unknown
""" Remove all elements from the deque. """
pass
clear--清空这个队列
def count(self, value): # real signature unknown; restored from __doc__
""" D.count(value) -> integer -- return number of occurrences of value """
return 0
count--计算队列的元素出现了多少次
import collections a = collections.deque()
#在最后插入一个队列
a.append('')
#在最左边插入一个队列
a.appendleft('')
#在最左边插入一个队列
a.appendleft('')
a.appendleft('')
#打印插入的队列
print(a)
#统计这个队列里有几个1
b= a.count('')
#打印上边count统计到的有多少个1 的结果
print("出现",b)
--------------------------------------------------------
输出:
deque(['', '', '', ''])
出现 2
import collections
#定义一个队列
a = collections.deque()
#在最后插入一个队列
a.append('')
#在最左边插入一个队列
a.appendleft('')
#在最左边插入一个队列
a.appendleft('')
a.appendleft('')
#打印插入的队列
print(a)
#统计这个队列里有几个1
b= a.count('')
#打印上边count统计到的有多少个1 的结果
print("出现",b)
#从右边扩展队列
a.extend(['yy','uu','ii'])
print(a)
#从左边扩展队列
a.extendleft(['w','ee','pp'])
print(a)
------------------------------------------------------
deque(['', '', '', ''])
出现 2
deque(['', '', '', '', 'yy', 'uu', 'ii'])
deque(['pp', 'ee', 'w', '', '', '', '', 'yy', 'uu', 'ii'])
extendleft:左边队列扩展,extend--右边队列扩展
import collections
#定义一个队列
a = collections.deque()
#在最后插入一个队列
a.append('')
#在最左边插入一个队列
a.appendleft('')
#在最左边插入一个队列
a.appendleft('')
a.appendleft('')
#打印插入的队列
#print(a)
#统计这个队列里有几个1
b= a.count('')
#打印上边count统计到的有多少个1 的结果
#print("出现",b)
#从右边扩展队列
a.extend(['yy','uu','ii'])
#print(a)
#从左边扩展队列
a.extendleft(['w','ee','pp'])
#print(a)
#取这个索引值得位置
ww = a.index('ii')
print(ww)
--------------------------------------
输出:
9
index--取这个索引值在队列里的位置
import collections
#定义一个队列
a = collections.deque()
#在最后插入一个队列
a.append('')
#在最左边插入一个队列
a.appendleft('')
#在最左边插入一个队列
a.appendleft('')
print(a)
a.rotate(2)
print(a)
----------------------------------------------------------------------------------
输出:
deque(['', '', ''])
deque(['', '', ''])
rotate--转圈
注：既然有双向队列，也有单项队列（先进先出 FIFO ）
Queue.Queue单项队列
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() Queue.Queue
Queue.Queue
import queue
#创建一个单项队列
a = queue.Queue()
#插入数据
a.put('')
ww = a.put('asdf')
#qsize统计队列里有几个数据
print(a.qsize())
#到队列里通过get取数据
print(a.get())
#到队列里取数据
print(a.get())
#取数据的过程中是遵循 先进先出的规则来拿数据的
---------------------------------------------------------------------
输出:
2
999
asdf
queue--单项队列
深浅拷贝
对于 数字 和 字符串 而言，赋值、浅拷贝和深拷贝无意义，因为其永远指向同一个内存地址。
拷贝是通过copy模块的copy.copy()方法来实现的拷贝
浅拷贝:copy.copy()
深拷贝:copy.deepcopy()
赋值 =
Python分为两类:
#字符串数字 属于一类
#其他的属于一类
查看一个变量的id地址
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
import copy
a1 = 1
a2 = 1
print(id(a1))
print(id(a2))
----------------------------------------------------------------------------
输出:
1583410992
1583410992
浅拷贝
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
import copy
a1 = 'asdfasdf'
#浅拷贝
a2 = copy.copy(a1)
print(id(a1))
print(id(a2))
----------------------------------------------------------------------------
输出:
17973616
17973616 -------------------------------------------------------------------
深拷贝:__author__ = 'Administrator'
# -*- coding:utf-8 -*-
import copy
a1 = 'asdfasdf'
a2 = copy.deepcopy(a1)
print(id(a1))
print(id(a2))
------------------------------------------------------------------
17908080
17908080
PS:对于数字和字符串来说无论是赋值、深拷贝还是浅拷贝 都是使用的内存里的同一个地址,所以对于数字和字符串来说深浅拷贝是无用的.
#下面来看一下列表、元祖以及字典其他...
浅拷贝只拷贝一层
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
import copy
n1 = {"k1": "wu", "k2": 123, "k3": ["alex", 456]}
n3 = copy.copy(n1)
print(id(n1))
print(id(n3))
#下面输出的更深层次的元素的内存地址是没有变的
print(id(n1['k1']))
print(id(n3['k1']))
----------------------------------------------------
输出:
6607432
7116936
7071256
7071256
深拷贝:
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
import copy
n1 = {"k1": "wu", "k2": 123, "k3": ["alex", 456]}
n3 = copy.deepcopy(n1) print(id(n1['k3']))
print(id(n3['k3']))
----------------------------------------------------------------------
输出:
12382792
12360072 -----------------------------------------------------------------------------
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
import copy
n1 = {"k1": "wu", "k2": 123, "k3": ["alex", 456]}
n3 = copy.copy(n1) print(id(n1['k3']))
print(id(n3['k3']))
---------------------------------
输出: 18784008
18784008
小练习
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
import copy
dic = {
"CPU":[90,],
"mem":[80,],
"disk":[70,],
}
dic['CPU'][0] = 10
#浅拷贝
new_dic = copy.copy(dic)
new_dic['mem'][0] = 100
print(dic)
print(new_dic)
--------------------------
输出:
{'mem': [100], 'disk': [70], 'CPU': [10]}
{'mem': [100], 'disk': [70], 'CPU': [10]}
浅拷贝
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
import copy
dic = {
"CPU":[90,],
"mem":[80,],
"disk":[70,],
}
dic['CPU'][0] = 10
#浅拷贝
new_dic = copy.deepcopy(dic)
new_dic['mem'][0] = 100
print(dic)
print(new_dic)
----------------------------------------
输出:
{'CPU': [10], 'mem': [80], 'disk': [70]}
{'CPU': [10], 'mem': [100], 'disk': [70]}
深拷贝
函数
一、背景
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功能处，如下：
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
腚眼一看上述代码，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，让开发“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
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
#定义一个函数
def mail():
n = 123
n += 1
print(n)
#函数名mail
mail()
f = mail
f()
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：
Python函数的返回值
发送邮件:
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
#定义一个函数
import smtplib
from email.mime.text import MIMEText
from email.utils import formataddr
def mail():
ret = True
#上边ret的内容执行完了就会执行try,try 的意思是 如果发邮件的代码不出错的情况下就会继续执行下去,如果出错的话就会执行except的内容然后return ret
#如果try里的内容不出错就永远不会执行except里的内容
try:
msg = MIMEText('邮件内容', 'plain', 'utf-8')
#
msg['From'] = formataddr(["aaa",'hu_***_you@126.com'])
msg['To'] = formataddr(["没事",'352***7864@qq.com'])
msg['Subject'] = "主题啊啊啊啊啊啊"
server = smtplib.SMTP("smtp.126.com", 25)
server.login("hu_***_you@126.com", "password")
server.sendmail('hu_***_you@126.com', ['352***7864@qq.com',], msg.as_string())
server.quit()
except Exception:
ret = False
return ret #函数名mail
ret = mail()
if ret:
print("发送成功...")
else:
print("发送失败!!!")
import smtplib
from email.mime.text import MIMEText
from email.utils import formataddr msg = MIMEText('邮件内容', 'plain', 'utf-8')
msg['From'] = formataddr(["武沛齐",'wptawy@126.com'])
msg['To'] = formataddr(["走人",'424662508@qq.com'])
msg['Subject'] = "主题" server = smtplib.SMTP("smtp.126.com", 25)
server.login("wptawy@126.com", "邮箱密码")
server.sendmail('wptawy@126.com', ['424662508@qq.com',], msg.as_string())
server.quit()
Python 邮件发送实例
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
为什么要有参数？
def CPU报警邮件()
#发送邮件提醒
连接邮箱服务器
发送邮件
关闭连接 def 硬盘报警邮件()
#发送邮件提醒
连接邮箱服务器
发送邮件
关闭连接 def 内存报警邮件()
#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%:
CPU报警邮件（） if 硬盘使用空间 > 90%:
硬盘报警邮件（） if 内存占用 > 80%:
内存报警邮件（） 无参数实现
无参数
def 发送邮件(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报警了。")
if 硬盘使用空间 > 90%:
发送邮件("硬盘报警了。")
if 内存占用 > 80%:
发送邮件("内存报警了。")
有参数实现
有参数
函数的有三中不同的参数：
- 普通参数
- 默认参数
- 动态参数
__author__ = 'Administrator'
# -*- coding:utf-8 -*-
#定义一个函数
import smtplib
from email.mime.text import MIMEText
from email.utils import formataddr
#形式参数
#user = '352***7864@qq.com'
def mail(user):
ret = True
#上边ret的内容执行完了就会执行try,try 的意思是 如果发邮件的代码不出错的情况下就会继续执行下去,如果出错的话就会执行except的内容然后return ret
#如果try里的内容不出错就永远不会执行except里的内容
try:
msg = MIMEText('邮件内容', 'plain', 'utf-8')
#发件箱
msg['From'] = formataddr(["aaa",'hu_***_you@126.com'])
msg['To'] = formataddr(["没事",'352***7864@qq.com'])
msg['Subject'] = "主题啊啊啊啊啊啊"
server = smtplib.SMTP("smtp.126.com", 25)
server.login("hu_***_you@126.com", "***")
server.sendmail('hu_***_you@126.com', [user,], msg.as_string())
server.quit()
except Exception:
ret = False
return ret #函数名mail
#括号内为实际参数
ret = mail('352***7864@qq.com')
ret = mail('hu_***_you@126.com')
if ret:
print("发送成功...")
else:
print("发送失败!!!") 普通函数
普通参数
多个普通参数传参
#默认情况下a2=99,
def show(a1,a2=99):
print(a1,a2)
#执行函数的时候第一个值是赋给第一个参数,第二个值如果不定义的话函数执行的时候就会使用默认的值"99"，如果第二个值制定了的话就会使用指定的值
show(11)
----------------
输出:
11 99
===========================================
#默认情况下a2=99,
def show(a1,a2=99):
print(a1,a2)
#执行函数的时候第一个值是赋给第一个参数,第二个值如果不定义的话函数执行的时候就会使用默认的值"99"，如果第二个值制定了的话就会使用指定的值
show(11,"\n我特啊游,弄啥嘞")
--------------------------------------
输出:
11
我特啊游,弄啥嘞
默认参数
def show(a1,a2):
print(a1,a2)
show(a2=123,a1=999)
-----------------------------------------------------------------------------------
输出:
999 123
指定参数
#元祖动态参数
def show(*arg):
print(arg,type(arg))
n = [11,22,33,44]
show(n) ===========================================
#字典动态参数
def show(**arg):
print(arg,type(arg))
show(n1 = 'ww',99 = 88)
============================================== #如下例子会将传入的元素参数自动转换为元祖,传入的字典格式会自动转换为字典
def show(*args,**kwargs):
print(args,type(args),"\n",kwargs,type(kwargs))
show(11,22,33,44,55,66,77,n8 = 99) ----------------------------------
输出:
(11, 22, 33, 44, 55, 66, 77) <class 'tuple'>
{'n8': 99} <class 'dict'>
元祖,字典动态参数
ps:注意
def show(*args,**kwargs):
print(args,type(args),)
print(kwargs,type(kwargs))
l = [11,22,33,44]
d = {'n1':99,'n2':'asb'}
#show(11,22,33,44,55,66,77,n8 = 99)
show(l,d)
-----------------------------------------
输出:
([11, 22, 33, 44], {'n2': 'asb', 'n1': 99}) <class 'tuple'>
{} <class 'dict'> ============================================
def show(*args,**kwargs):
print(args,type(args),)
print(kwargs,type(kwargs))
l = [11,22,33,44]
d = {'n1':99,'n2':'asb'} show(*l,**d)
----------------------------------------------
输出:
(11, 22, 33, 44) <class 'tuple'>
{'n1': 99, 'n2': 'asb'} <class 'dict'>
指定参数格式化
s1 = "{0} is {1}"
a = ['asb','fds']
s2 = s1.format(*a)
print(s2)
------------------------
输出:
asb is fds
==================================
s1 = "{name} is {acter}"
w = s1.format(name = 'asd',acter = 'dfgh')
print(w)
---------------------------------
输出:
asd is dfgh
========================================
s1 = "{name} is {acter}"
d = {'name':'asd','acter':'dfgh'}
ret = s1.format(**d)
print(ret)
-------------------------------------------
输出:
asd is dfgh
字符串的格式化format
lambda表达式
学习条件运算时，对于简单的 if else 语句，可以使用三元运算来表示，即
# 普通条件语句
if 1 == 1:
name = 'wupeiqi'
else:
name = 'alex' # 三元运算
name = 'wupeiqi' if 1 == 1 else 'alex'
对于简单的函数，也存在一种简便的表示方式，即：lambda表达式
# ###################### 普通函数 ######################
# 定义函数（普通方式）
def func(arg):
return arg + 1 # 执行函数
result = func(123) # ###################### lambda ###################### # 定义函数（lambda表达式）
my_lambda = lambda arg : arg + 1 # 执行函数
result = my_lambda(123)
lambda存在意义就是对简单函数的简洁表示
如下表中的模块不需要任何导入都可以使用
#所有的元素为真则为True,所有的元素为假则为False
a1 = all([None,1,2,3,4])
print(a1)
-------------------------------
输出:
False ============================================
a1 = all([1,2,3,4])
print(a1)
-------------------------
输出:
True
all()
#所有元素只要有一个位真,则返回True,所有元素只要有一个位假则返回Fales
a2 = any([None,1])
print(a2)
----------------------------------------------------------
输出:
True =============================
a2 = any([None])
print(a2)
-------------------------------------------
输出:
Fales
any()-所有元素只要有一个位真,则返回True,所有元素只要有一个位假则返回Fales
#bin 是 返回一个二进制
a3 = bin(8,)
print(a3)
-----------------------------------------------------------------
0b1000
bin 是 返回一个二进制
#一个汉字为三个字节,转换为数组
a4 = bytearray("猪八戒",encoding='utf-8')
print(a4)
-----------------
输出:
bytearray(b'\xe7\x8c\xaa\xe5\x85\xab\xe6\x88\x92')
bytearray:一个汉字为三个字节,转换为数组
print(ord('A'))
----------------------
打印
65
===================
print(chr(65))
---------------------------
打印:
A
# chr 是将数字转换成字符---# ord 是将字符转换成数字
#验证码
import random
print(random.randint(1,9999))
random:#验证码
open函数，该函数用于文件处理
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 = open('文件路径', '模式')
打开文件时，需要指定文件路径和以何等方式打开文件，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
- 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+ 模式同使用）
- rU
- r+U
"b"表示处理二进制文件（如：FTP发送上传ISO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
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
(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
"""
xreadlines() -> returns self. For backward compatibility. File objects now include the performance
optimizations previously implemented in the xreadlines module.
"""
pass Python 2.x
Python2.0
class TextIOWrapper(_TextIOBase):
"""
Character and line based layer over a BufferedIOBase object, buffer. encoding gives the name of the encoding that the stream will be
decoded or encoded with. It defaults to locale.getpreferredencoding(False). errors determines the strictness of encoding and decoding (see
help(codecs.Codec) or the documentation for codecs.register) and
defaults to "strict". newline controls how line endings are handled. It can be None, '',
'\n', '\r', and '\r\n'. It works as follows: * On input, if newline is None, universal newlines mode is
enabled. Lines in the input can end in '\n', '\r', or '\r\n', and
these are translated into '\n' before being returned to the
caller. If it is '', universal newline mode is enabled, but line
endings are returned to the caller untranslated. If it has any of
the other legal values, input lines are only terminated by the given
string, and the line ending is returned to the caller untranslated. * On output, if newline is None, any '\n' characters written are
translated to the system default line separator, os.linesep. If
newline is '' or '\n', no translation takes place. If newline is any
of the other legal values, any '\n' characters written are translated
to the given string. If line_buffering is True, a call to flush is implied when a call to
write contains a newline character.
"""
def close(self, *args, **kwargs): # real signature unknown
关闭文件
pass def fileno(self, *args, **kwargs): # real signature unknown
文件描述符
pass def flush(self, *args, **kwargs): # real signature unknown
刷新文件内部缓冲区
pass def isatty(self, *args, **kwargs): # real signature unknown
判断文件是否是同意tty设备
pass def read(self, *args, **kwargs): # real signature unknown
读取指定字节数据
pass def readable(self, *args, **kwargs): # real signature unknown
是否可读
pass def readline(self, *args, **kwargs): # real signature unknown
仅读取一行数据
pass def seek(self, *args, **kwargs): # real signature unknown
指定文件中指针位置
pass def seekable(self, *args, **kwargs): # real signature unknown
指针是否可操作
pass def tell(self, *args, **kwargs): # real signature unknown
获取指针位置
pass def truncate(self, *args, **kwargs): # real signature unknown
截断数据，仅保留指定之前数据
pass def writable(self, *args, **kwargs): # real signature unknown
是否可写
pass def write(self, *args, **kwargs): # real signature unknown
写内容
pass def __getstate__(self, *args, **kwargs): # real signature unknown
pass def __init__(self, *args, **kwargs): # real signature unknown
pass @staticmethod # known case of __new__
def __new__(*args, **kwargs): # real signature unknown
""" Create and return a new object. See help(type) for accurate signature. """
pass def __next__(self, *args, **kwargs): # real signature unknown
""" Implement next(self). """
pass def __repr__(self, *args, **kwargs): # real signature unknown
""" Return repr(self). """
pass buffer = property(lambda self: object(), lambda self, v: None, lambda self: None) # default closed = property(lambda self: object(), lambda self, v: None, lambda self: None) # default encoding = property(lambda self: object(), lambda self, v: None, lambda self: None) # default errors = property(lambda self: object(), lambda self, v: None, lambda self: None) # default line_buffering = property(lambda self: object(), lambda self, v: None, lambda self: None) # default name = property(lambda self: object(), lambda self, v: None, lambda self: None) # default newlines = property(lambda self: object(), lambda self, v: None, lambda self: None) # default _CHUNK_SIZE = property(lambda self: object(), lambda self, v: None, lambda self: None) # default _finalizing = property(lambda self: object(), lambda self, v: None, lambda self: None) # default Python 3.x
Python3.0
python-Day3-set 集合-counter计数器-默认字典(defaultdict) -可命名元组(namedtuple)-有序字典(orderedDict)-双向队列(deque)--Queue单项队列--深浅拷贝---函数参数的更多相关文章
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