基本数据类型补充:


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
"""
Add an element to a set,添加元素 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. 清除内容"""
pass def copy(self, *args, **kwargs): # real signature unknown
""" Return a shallow copy of a set. 浅拷贝 """
pass def difference(self, *args, **kwargs): # real signature unknown
"""
Return the difference of two or more sets as a new set. A中存在,B中不存在 (i.e. all elements that are in this set but not the others.)
"""
pass def difference_update(self, *args, **kwargs): # real signature unknown
""" Remove all elements of another set from this set. 从当前集合中删除和B中相同的元素"""
pass def discard(self, *args, **kwargs): # real signature unknown
"""
Remove an element from a set if it is a member. If the element is not a member, do nothing. 移除指定元素,不存在不保错
"""
pass def intersection(self, *args, **kwargs): # real signature unknown
"""
Return the intersection of two sets as a new set. 交集 (i.e. all elements that are in both sets.)
"""
pass def intersection_update(self, *args, **kwargs): # real signature unknown
""" Update a set with the intersection of itself and another. 取交集并更更新到A中 """
pass def isdisjoint(self, *args, **kwargs): # real signature unknown
""" Return True if two sets have a null intersection. 如果没有交集,返回True,否则返回False"""
pass def issubset(self, *args, **kwargs): # real signature unknown
""" Report whether another set contains this set. 是否是子序列"""
pass def issuperset(self, *args, **kwargs): # real signature unknown
""" Report whether this set contains another set. 是否是父序列"""
pass def pop(self, *args, **kwargs): # real signature unknown
"""
Remove and return an arbitrary set element.
Raises KeyError if the set is empty. 移除元素
"""
pass 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. 移除指定元素,不存在保错
"""
pass def symmetric_difference(self, *args, **kwargs): # real signature unknown
"""
Return the symmetric difference of two sets as a new set. 对称差集 (i.e. all elements that are in exactly one of the sets.)
"""
pass def symmetric_difference_update(self, *args, **kwargs): # real signature unknown
""" Update a set with the symmetric difference of itself and another. 对称差集,并更新到a中 """
pass def union(self, *args, **kwargs): # real signature unknown
"""
Return the union of sets as a new set. 并集 (i.e. all elements that are in either set.)
"""
pass def update(self, *args, **kwargs): # real signature unknown
""" Update a set with the union of itself and others. 更新 """
pass

1:创建

 s = set()
s = {11,22,33,55}

2:转换

 li = [11,22,33,44]
tu = (11,22,33,44)
st = ''
s = set(li)

3:intersection , intersection_update方法

a = {11,22,33,44}
b = {22,66,77,88}
ret = a.intersection(b)
print(ret)

intersection取得两个集合中的交集元素,并将这些元素以一个新的集合返回给一个变量接收

a = {11,22,33,44}
b = {22,66,77,88}
a.intersection_update(b)
print(a)

intersection_update取得两个集合的交集元素,并更新a集合

4:isdisjoint , issubset , issuperset方法

 s = {11,22,33,44}
b = {11,22,77,55}
ret = s.isdisjoint(b)#有交集返回False,没有交集返回True
print(ret)
## False

issubset判断是否为子集

a = {11,22,33,44}
b = {11,44}
ret = b.issubset(a)
print(ret)
##########################################
True

issuperset判断是否为父集

a = {11,22,33,44}
b = {11,44}
ret = a.issubset(b)
print(ret)
##########################################
False

5:discard , remove , pop

 s = {11,22,33,44}
s.remove(11)
print(s)
s.discard(22)
print(s)
s.pop()
print(s)

三者都能达到移除元素的效果,区别在于remove移除集合中不存在的元素时会报错,discard移除不存在的元素是不会报错,pop无法精确控制移除哪个元素,按其自身的规则随机移除元素,返回被移除的元素,可以使用变量接收其返回值

6:symmetric_difference取差集

 s = {11,22,33,44}
b = {11,22,77,55}
r1 = s.difference(b)
r2 = b.difference(s)
print(r1)
print(r2)
ret = s.symmetric_difference(b)
print(ret)
## set([33, 44])
## set([77, 55])
## set([33, 44, 77, 55])

symmetric_difference返回两个集合中不是交集的元素

上面的代码中,将symmetric_difference换成symmetric_difference_update则表示将两个集合中不是交集的部分赋值给s

7:union , update方法

 s = {11,22,33,44}
b = {11,22,77,55}
ret = s.union(b)
print(ret)
## set([33, 11, 44, 77, 22, 55])

union方法合并两个集合

 s = {11,22,33,44}
b = {11,22,77,55}
s.update(b)
print(s)
## set([33, 11, 44, 77, 22, 55])

update方法更新s集合,将b集合中的元素添加到s集合中!update方法也可以传递一个列表,如:update([23,45,67])

练习题:有下面两个字典

要求:

1)两个字典中有相同键的,则将new_dict中的值更新到old_dict对应键的值

2)old_dict中存在的键且new_dict中没有的键,在old_dict中删除,并把new_dict中的键值更新到old_dict中

3)最后输出old_dict

 # 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 }
}
old_keys = set(old_dict.keys())
new_keys = set(new_dict.keys())
#需要更新元素的键
update_keys = old_keys.intersection(new_keys)
print(update_keys)
#需要删除元素的键
del_keys = old_keys.difference(new_keys)
#需要添加元素的键
add_keys = new_keys.difference(old_keys)
print(del_keys)
print(add_keys)
update_keys = list(update_keys)
for i in update_keys :
old_dict[i] = new_dict[i]
del_keys = list(del_keys)
for j in del_keys :
del old_dict[j]
for k in list(add_keys) :
old_dict[k] = new_dict[k]
print(old_dict)
########################################
{'#3': {'hostname': 'c1', 'cpu_count': , 'mem_capicity': }, '#1': {'hostname': 'c1', 'cpu_count': , 'mem_capicity': }, '#4': {'hostname': 'c2', 'cpu_count': , 'mem_capicity': }}

答案

collections系列

一、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

c = Counter('abcdeabcdabcaba')
print c
输出:Counter({'a': 5, 'b': 4, 'c': 3, 'd': 2, 'e': 1})
 ########################################################################
### 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

Counter

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

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

使用方法:

 import collections
dic = collections.defaultdict(list)
dic['k1'].append('alext')
print(dic)

练习:

 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}
 values = [11, 22, 33,44,55,66,77,88,99,90]

 my_dict = {}

 for value in  values:
if value>66:
if my_dict.has_key('k1'):
my_dict['k1'].append(value)
else:
my_dict['k1'] = [value]
else:
if my_dict.has_key('k2'):
my_dict['k2'].append(value)
else:
my_dict['k2'] = [value]

原生字典

 from collections import defaultdict

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

 my_dict = defaultdict(list)

 for value in  values:
if value>66:
my_dict['k1'].append(value)
else:
my_dict['k2'].append(value) defaultdict字典解决方法 默认字典

默认字典

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。

import collections
MytupleClass = collections.namedtuple('MytupleClass',['x','y','z'])
obj = MytupleClass(11,33,44)
print(obj.x)
print(obj.y)
print(obj.z)
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

Mytuple

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

注:既然有双向队列,也有单项队列(先进先出 FIFO )

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

三元运算


三元运算(三目运算),是对简单的条件语句的缩写。

 # 书写格式
result = 值1 if 条件 else 值2
# 如果条件成立,那么将 “值1” 赋值给result变量,否则,将“值2”赋值给result变量
 a = 1
name = 'poe' if a == 1 else 'jet'
print(name)

深浅拷贝


一、数字和字符串

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。

 import copy
# ######### 数字、字符串 #########
n1 = 123
# n1 = "i am alex age 10"
print(id(n1))
# ## 赋值 ##
n2 = n1
print(id(n2))
# ## 浅拷贝 ##
n2 = copy.copy(n1)
print(id(n2)) # ## 深拷贝 ##
n3 = copy.deepcopy(n1)
print(id(n3))

二、其他基本数据类型

对于字典、元祖、列表 而言,进行赋值、浅拷贝和深拷贝时,其内存地址的变化是不同的。

1、赋值

赋值,只是创建一个变量,该变量指向原来内存地址,如:

 n1 = {"k1": "wu", "k2": 123, "k3": ["alex", 456]}

 n2 = n1

2、浅拷贝

浅拷贝,在内存中只额外创建第一层数据

 import copy

 n1 = {"k1": "wu", "k2": 123, "k3": ["alex", 456]}

 n3 = copy.copy(n1)

3、深拷贝

深拷贝,在内存中将所有的数据重新创建一份(排除最后一层,即:python内部对字符串和数字的优化)

 import copy

 n1 = {"k1": "wu", "k2": 123, "k3": ["alex", 456]}

 n4 = copy.deepcopy(n1)

函数


1:函数的定义

def 函数名(参数):

    ...
函数体
...
返回值

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。

2: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。

以上要点中,比较重要有参数和返回值:

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...

3:参数

函数有三种不同的参数:

普通参数

# ######### 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 #########
# 'wupeiqi' 叫做函数func的实际参数,简称:实参
func('poe')

默认参数

def func(name, age = 18):

    print "%s:%s" %(name,age)

# 指定参数
func('poe', 19)
# 使用默认参数
func('gin') 注:默认参数需要放在参数列表最后

动态参数

def f1(*a):
print(a,type(a))
f1(123,456,[1,2,3],'who')
## ((123, 456, [1, 2, 3], 'who'), <type 'tuple'>)
def func(**kwargs):
print args
# 执行方式一
func(name='poe',age=18) # 执行方式二
li = {'name':'poe', age:18, 'gender':'male'}
func(**li)
def f1(*a,**b) :#一个星的参数必须在前,两个星的参数必须在后
print(a,type(a))
print(b,type(b))
f1(11,22,33,k1=1234,k2=456)
## ((11, 22, 33), <type 'tuple'>)({'k2': 456, 'k1': 1234}, <type 'dict'>)

为动态参数传入列表,元组,字典:(注:这几种数据类型在函数传参的时候只有引用传递,没有值传递

def f1(*args) :
print(args,type(args))
li = [1,2,3,4]
f1(li)
f1(*li)
## (([1, 2, 3, 4],), <type 'tuple'>)
## ((1, 2, 3, 4), <type 'tuple'>)
def f2(**kwargs) :
print(kwargs,type(kwargs))
dic = {'k1':123,'k2':456}
f2(k1 = dic)
f2(**dic)
## ({'k1': {'k2': 456, 'k1': 123}}, <type 'dict'>)
## ({'k2': 456, 'k1': 123}, <type 'dict'>)

4:内置函数

注:查看详细猛击这里

数据类型转换函数

  1. chr(i) 函数返回ASCII码对应的字符串
  2. print(chr(65))
    print(chr(66))
    print(chr(65)+chr(66))
    ##########################################
    A
    B
    AB
  3. complex(real[,imaginary]) 函数可把字符串或数字转换为复数
  4. print(complex("2+1j"))
    print(complex(""))
    print(complex(2,1))
    ##########################################
    (2+1j)
    (2+0j)
    (2+1j)
  5. float(x) 函数把一个数字或字符串转换成浮点数
  6. print(float(12))
    print(float(12.2))
    ##########################################
    12.0
    12.2
  7. long(x[,base]) 函数把数字和字符串转换成长整数,base为可选的基数
  8. list(x) 函数可将序列对象转换成列表
  9. min(x[,y,z...]) 函数返回给定参数的最小值,参数可以为序列
  10. max(x[,y,z...]) 函数返回给定参数的最大值,参数可以为序列
  11. ord(x) 函数返回一个字符串参数的ASCII码或Unicode值
  12. print(ord('a'))
    print(ord(u"A"))
    ##########################################
    97
    65
  13. str(obj) 函数把对象转换成可打印字符串
  14. tuple(x) 函数把序列对象转换成tuple
  15. type(x) 可以接收任何东西作为参数――并返回它的数据类型。整型、字符串、列表、字典、元组、函数、类、模块,甚至类型对象都可以作为参数被 type 函数接受

abs()函数:取绝对值

print(abs(-1.2))

all()函数与any函数:

all(iterable):如果iterable的任意一个元素为0、''、False,则返回False,否则返回True

print(all(['a','b','c','d']))#True
print(all(['a','b','','d']))#False
#注意:空元组、空列表返回值为True,这里要特别注意

any(iterable):如果iterable的所有元素都为0、''、False,则返回False,否则返回True

print(any(['a','b','c','d']))#True
print(any(['a',0,' ',False]))#True
print(any([0,'',False]))#False

ascii(object) 函数:

返回一个可打印的对象字符串方式表示,如果是非ascii字符就会输出\x,\u或\U等字符来表示。与python2版本里的repr()是等效的函数。

print(ascii(1))
print(ascii('a'))
print(ascii(123))
print(ascii('中文'))#非ascii字符
##########################################
1
'a'
123
'\u4e2d\u6587'

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 = 'poe'
else:
name = 'bruce' # 三元运算
name = 'poe' if 1 == 1 else 'bruce'

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) 

生成随机数:

import random
chars = ''
for i in range(4) :
rand_num = random.randrange(0,4)
if rand_num == 3 or rand_num == 1:
rand_digit = random.randrange(0,10)
chars += str(rand_digit)
else:
rand_case = random.randrange(65,90)
case = chr(rand_case)
chars += case
print(chars)

filter函数

filter()函数是 Python 内置的另一个有用的高阶函数,filter()函数接收一个函数 f 和一个list,这个函数 f 的作用是对每个元素进行判断,返回 True或 False,filter()根据判断结果自动过滤掉不符合条件的元素,返回由符合条件元素组成的新list。

例1,要从一个list [1, 4, 6, 7, 9, 12, 17]中删除偶数,保留奇数,首先,要编写一个判断奇数的函数:

# filter(fn,iterable)
def is_odd(x) :
return x % 2 == 1
li = [1, 4, 6, 7, 9, 12, 17]
result = filter(is_odd,li)
print(result)
##########################################
[1, 7, 9, 17] 

例2:删除 列表中的None 或者空字符串

li = ['test', None, '', 'str', '  ', 'END']
def is_not_empty(s) :
return s and len(s.strip()) > 0
print(filter(is_not_empty,li))
##########################################
['test', 'str', 'END']

例3:请利用filter()过滤出1~100中平方根是整数的数,即结果应该是:[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]

import math
def is_sqr(x) :
return math.sqrt(x) % 1 == 0
print filter(is_sqr,range(1,101))

以上三个函数都可以使用lambda表达式的写法来书写,如:

result = filter(lambda x : x % 2 == 1,[1,4,6,9,12,7,17])
print(result)

map()函数

map()是 Python 内置的高阶函数,它接收一个函数 f 和一个 list,并通过把函数 f 依次作用在 list 的每个元素上,得到一个新的 list 并返回

例如,对于list [1, 2, 3, 4, 5, 6, 7, 8, 9]如果希望把list的每个元素都作平方,就可以用map()函数

li = [1, 2, 3, 4, 5, 6, 7, 8, 9]
print(li)
def f(x) :
return x*x
r = list(map(f,[1, 2, 3, 4, 5, 6, 7, 8, 9]))
print(r)

注:在python3里面,map()的返回值已经不再是list,而是iterators, 所以想要使用,只用将iterator 转换成list 即可, 比如 list(map()) 。

进制转换函数(以下四个函数可以实现各进制间的互相转换)

bin(x) :将整数x转换为二进制字符串,如果x不为Python中int类型,x必须包含方法__index__()并且返回值为integer

oct(x):将一个整数转换成8进制字符串。如果传入浮点数或者字符串均会报错

hex(x):将一个整数转换成16进制字符串。

int():

  • 传入数值时,调用其__int__()方法,浮点数将向下取整
  • print(int(3))#
    print(int(3.6))#
  • 传入字符串时,默认以10进制进行转换
  • print(int(''))#
  • 字符串中允许包含"+"、"-"号,但是加减号与数值间不能有空格,数值后、符号前可出现空格
  • print(int('+36'))#
  • 传入字符串,并指定了进制,则按对应进制将字符串转换成10进制整数
  • print(int('',2))#
    print(int('0o7',8))#
    print(int('0x15',16))#

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:

  1. 打开文件
  2. 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;存在则清空内容;】
  • x, 只写模式【不可读;不存在则创建,存在则报错】
  • a, 追加模式【可读; 不存在则创建;存在则只追加内容;】
f = open('test.log','r')
data = f.read()
f.close()
print(data)

"+" 表示可以同时读写某个文件

  • r+, 读写【可读,可写】
  • w+,写读【可读,可写】
  • x+ ,写读【可读,可写】
  • a+, 写读【可读,可写】
# r+ 模式
f = open('test.log','r+',encoding='utf-8')
print(f.tell())#打印当前指针所在的位置,此时为0
data = f.read()
print(data)
print(f.tell())#此时当前指针在文件最末尾
f.close()
# w+模式:先清空文件,再写入文件,写入文件后才可以读文件
f = open('test.log','w+',encoding="utf-8")
f.write('python')#写完后,指针到了最后
f.seek(0)#移动指针到开头
data = f.read()
f.close()
print(data)
# a+模式:打开的同时,指针已经到最后,
# 写时,追加,指针到最后
f = open('test.log','a+',encoding="utf-8")
print(f.tell())#读取当前指针位置,此时指针已经到最后
f.write('c++')
print(f.tell())
#此时要读文件必须把指针移动到文件开头
f.seek(0)
data = f.read();
print(data)
f.close()

"b"表示以字节的方式操作

  • rb 或 r+b
  • wb 或 w+b
  • xb 或 w+b
  • ab 或 a+b

注:以b方式打开时,读取到的内容是字节类型,写入时也需要提供字节类型

二:文件操作

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 2.x

2.x版本

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 3.x

3.x版本

三:管理上下文

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Python 2.7 及以后,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

可使用此方法对一个文件进行读操作,同时把数据又写入到另一个打开的文件中!

read()、readline() 和 readlines()

每种方法可以接受一个变量以限制每次读取的数据量,但它们通常不使用变量。 .read() 每次读取整个文件,它通常用于将文件内容放到一个字符串变量中。然而 .read() 生成文件内容最直接的字符串表示,但对于连续的面向行的处理,它却是不必要的,并且如果文件大于可用内存,则不可能实现这种处理。

.readline() 和 .readlines() 非常相似。它们都在类似于以下的结构中使用:

fh = open('c:\\autoexec.bat')
for line in fh.readlines():
print line

.readline() 和 .readlines() 之间的差异是后者一次读取整个文件,象 .read() 一样。.readlines() 自动将文件内容分析成一个行的列表,该列表可以由 Python 的 for ... in ... 结构进行处理。另一方面,.readline() 每次只读取一行,通常比 .readlines() 慢得多。仅当没有足够内存可以一次读取整个文件时,才应该使用 .readline()。

练习题:用户名与密码的验证

首先新建一个文件,这里为test.log文件,内容为两行如下:

admin$123
ginvip$123456

1:让用户选择1或2,1为登录,2为注册

2:如果用户选择1,用户输入用户名与密码,然后与test.log文件中的用户名与密码进行验证,验证成功输出“登录成功”,否则“登录失败”

3:如果用户选择2,让用户输入用户名与密码,并与test.log文件中的用户名验证,如果test.log中用户名已经存在,则输出“该用户名已经存在”,否则将用户输入的用户与密码以上面test.log文件中的形式写入test.log文件中

 def check_user(user) :
with open('test.log','r',encoding='utf-8') as f :
for line in f :
user_list = line.strip()
user_list = user_list.split('$')
if user == user_list[0] :
return True
return False
def register(user,pwd) :
with open('test.log','a',encoding='utf-8') as f :
user_info = '\n' + user + '$' + pwd
if f.write(user_info) :
return True
return False
def login(user,pwd) :
with open('test.log','r',encoding='utf-8') as f :
for line in f:
user_list = line.strip()
user_list = user_list.split('$')
if user == user_list[0] and pwd == user_list[1]:
return True
return False
def main() :
print('welcome to my website')
choice = input('1:login 2:register')
if choice == '':
user = input('input username :')
pwd = input('input password : ')
if check_user(user) :
print('the username is exist')
else:
if register(user,pwd) :
print('register success')
else:
print('register failed')
elif choice == '':
user = input('input username :')
pwd = input('input password : ')
if login(user,pwd) :
print('login success')
else:
print('login failed')
main()

冒泡排序


冒泡排序的原理:

def Bubble_sort(args) :
for i in range(len(args)-1) :
for j in range(len(args) -1):
if args[j] > args[j+1]:
temp = args[j]
args[j] = args[j+1]
args[j+1] = temp
return args
li = [33,2,10,1,9,3,8]
print(Bubble_sort(li))

练习题

1、简述普通参数、指定参数、默认参数、动态参数的区别

2、写函数,计算传入字符串中【数字】、【字母】、【空格] 以及 【其他】的个数

digit = 0
case = 0
space = 0
other = 0
def func2(s) :
global digit,case,space,other
if not isinstance(s,basestring) :
print('the data type wrong!')
return False
for i in s :
if i.isdigit() :
digit += 1
elif i.isalpha() :
case += 1
elif i.isspace() :
space += 1
else:
other += 1
s = 'I love python , is num 1 , o_k'
a = [1,2,3]
func2(s)
print(digit)
print(case)
print(space)
print(other)
########################################
1
18
8
3
问题:判断是不是字符串后直接退出函数,而不执行下面的代码?

第2题答案

3、写函数,判断用户传入的对象(字符串、列表、元组)长度是否大于5。

def func3(v) :
if len(v) > 5 :
return True
else:
return False
a = 'I love python , is num 1 , o_k'
l = [1,2,3]
t = (5,7,9,10,45,10)
print(func3(t))

第三题答案

4、写函数,检查用户传入的对象(字符串、列表、元组)的每一个元素是否含有空内容。

5、写函数,检查传入列表的长度,如果大于2,那么仅保留前两个长度的内容,并将新内容返回给调用者。

def func5(lis) :
if len(lis) > 2 :
return lis[0:2]
else :
return False
li = [1,2,3]
print(func5(li))
##########################################
[1, 2]

第五题答案

6、写函数,检查获取传入列表或元组对象的所有奇数位索引对应的元素,并将其作为新列表返回给调用者。

def func6(lis) :
new_lis = []
for k in range(len(lis)) :
if k % 2 == 1 :
new_lis.append(lis[k])
return new_lis
li = [1,2,3,8,10,44,77]
tu = ('poe','andy','jet','bruce','jacky')
print(func6(tu))
##########################################
['andy', 'bruce']

第六题答案

7、写函数,检查传入字典的每一个value的长度,如果大于2,那么仅保留前两个长度的内容,并将新内容返回给调用者。

dic = {"k1": "v1v1", "k2": [,,,]}

PS:字典中的value只能是字符串或列表
def func7(d) :
v = d.values()
li = []
for i in v :
if len(i) > 2:
li.append(i[0:2])
return li
print(func7(dic))
##########################################
[[11, 22], 'v1']

第七题答案

8、写函数,利用递归获取斐波那契数列中的第 10 个数,并将该值返回给调用者

def fabonacci(n) :
if n == 0 :
return 0
elif n == 1:
return 1
else:
return fabonacci(n-1) + fabonacci(n-2)
print(fabonacci(10))

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