Day 3 Python之循序渐进3

1.Python集合Set

set 是一个无序且不重复的元素集合，访问速度快，自动解决重复问题

  1 class set(object):
  2     """
  3     set() -> new empty set object
  4     set(iterable) -> new set object
  5     
  6     Build an unordered collection of unique elements.
  7     """
  8     def add(self, *args, **kwargs): # real signature unknown
  9         """ 添加 """
 10         """
 11         Add an element to a set.
 12         
 13         This has no effect if the element is already present.
 14         """
 15         pass
 16 
 17     def clear(self, *args, **kwargs): # real signature unknown
 18         """ Remove all elements from this set. """
 19         pass
 20 
 21     def copy(self, *args, **kwargs): # real signature unknown
 22         """ Return a shallow copy of a set. """
 23         pass
 24 
 25     def difference(self, *args, **kwargs): # real signature unknown
 26         """
 27         Return the difference of two or more sets as a new set.
 28         
 29         (i.e. all elements that are in this set but not the others.)
 30         """
 31         pass
 32 
 33     def difference_update(self, *args, **kwargs): # real signature unknown
 34         """ 删除当前set中的所有包含在 new set 里的元素 """
 35         """ Remove all elements of another set from this set. """
 36         pass
 37 
 38     def discard(self, *args, **kwargs): # real signature unknown
 39         """ 移除元素 """
 40         """
 41         Remove an element from a set if it is a member.
 42         
 43         If the element is not a member, do nothing.
 44         """
 45         pass
 46 
 47     def intersection(self, *args, **kwargs): # real signature unknown
 48         """ 取交集，新创建一个set """
 49         """
 50         Return the intersection of two or more sets as a new set.
 51         
 52         (i.e. elements that are common to all of the sets.)
 53         """
 54         pass
 55 
 56     def intersection_update(self, *args, **kwargs): # real signature unknown
 57         """ 取交集，修改原来set """
 58         """ Update a set with the intersection of itself and another. """
 59         pass
 60 
 61     def isdisjoint(self, *args, **kwargs): # real signature unknown
 62         """ 如果没有交集，返回true  """
 63         """ Return True if two sets have a null intersection. """
 64         pass
 65 
 66     def issubset(self, *args, **kwargs): # real signature unknown
 67         """ 是否是子集 """
 68         """ Report whether another set contains this set. """
 69         pass
 70 
 71     def issuperset(self, *args, **kwargs): # real signature unknown
 72         """ 是否是父集 """
 73         """ Report whether this set contains another set. """
 74         pass
 75 
 76     def pop(self, *args, **kwargs): # real signature unknown
 77         """ 移除 """
 78         """
 79         Remove and return an arbitrary set element.
 80         Raises KeyError if the set is empty.
 81         """
 82         pass
 83 
 84     def remove(self, *args, **kwargs): # real signature unknown
 85         """ 移除 """
 86         """
 87         Remove an element from a set; it must be a member.
 88         
 89         If the element is not a member, raise a KeyError.
 90         """
 91         pass
 92 
 93     def symmetric_difference(self, *args, **kwargs): # real signature unknown
 94         """ 差集，创建新对象"""
 95         """
 96         Return the symmetric difference of two sets as a new set.
 97         
 98         (i.e. all elements that are in exactly one of the sets.)
 99         """
100         pass
101 
102     def symmetric_difference_update(self, *args, **kwargs): # real signature unknown
103         """ 差集，改变原来 """
104         """ Update a set with the symmetric difference of itself and another. """
105         pass
106 
107     def union(self, *args, **kwargs): # real signature unknown
108         """ 并集 """
109         """
110         Return the union of sets as a new set.
111         
112         (i.e. all elements that are in either set.)
113         """
114         pass
115 
116     def update(self, *args, **kwargs): # real signature unknown
117         """ 更新 """
118         """ Update a set with the union of itself and others. """
119         pass
120 
121     def __and__(self, y): # real signature unknown; restored from __doc__
122         """ x.__and__(y) <==> x&y """
123         pass
124 
125     def __cmp__(self, y): # real signature unknown; restored from __doc__
126         """ x.__cmp__(y) <==> cmp(x,y) """
127         pass
128 
129     def __contains__(self, y): # real signature unknown; restored from __doc__
130         """ x.__contains__(y) <==> y in x. """
131         pass
132 
133     def __eq__(self, y): # real signature unknown; restored from __doc__
134         """ x.__eq__(y) <==> x==y """
135         pass
136 
137     def __getattribute__(self, name): # real signature unknown; restored from __doc__
138         """ x.__getattribute__('name') <==> x.name """
139         pass
140 
141     def __ge__(self, y): # real signature unknown; restored from __doc__
142         """ x.__ge__(y) <==> x>=y """
143         pass
144 
145     def __gt__(self, y): # real signature unknown; restored from __doc__
146         """ x.__gt__(y) <==> x>y """
147         pass
148 
149     def __iand__(self, y): # real signature unknown; restored from __doc__
150         """ x.__iand__(y) <==> x&=y """
151         pass
152 
153     def __init__(self, seq=()): # known special case of set.__init__
154         """
155         set() -> new empty set object
156         set(iterable) -> new set object
157         
158         Build an unordered collection of unique elements.
159         # (copied from class doc)
160         """
161         pass
162 
163     def __ior__(self, y): # real signature unknown; restored from __doc__
164         """ x.__ior__(y) <==> x|=y """
165         pass
166 
167     def __isub__(self, y): # real signature unknown; restored from __doc__
168         """ x.__isub__(y) <==> x-=y """
169         pass
170 
171     def __iter__(self): # real signature unknown; restored from __doc__
172         """ x.__iter__() <==> iter(x) """
173         pass
174 
175     def __ixor__(self, y): # real signature unknown; restored from __doc__
176         """ x.__ixor__(y) <==> x^=y """
177         pass
178 
179     def __len__(self): # real signature unknown; restored from __doc__
180         """ x.__len__() <==> len(x) """
181         pass
182 
183     def __le__(self, y): # real signature unknown; restored from __doc__
184         """ x.__le__(y) <==> x<=y """
185         pass
186 
187     def __lt__(self, y): # real signature unknown; restored from __doc__
188         """ x.__lt__(y) <==> x<y """
189         pass
190 
191     @staticmethod # known case of __new__
192     def __new__(S, *more): # real signature unknown; restored from __doc__
193         """ T.__new__(S, ...) -> a new object with type S, a subtype of T """
194         pass
195 
196     def __ne__(self, y): # real signature unknown; restored from __doc__
197         """ x.__ne__(y) <==> x!=y """
198         pass
199 
200     def __or__(self, y): # real signature unknown; restored from __doc__
201         """ x.__or__(y) <==> x|y """
202         pass
203 
204     def __rand__(self, y): # real signature unknown; restored from __doc__
205         """ x.__rand__(y) <==> y&x """
206         pass
207 
208     def __reduce__(self, *args, **kwargs): # real signature unknown
209         """ Return state information for pickling. """
210         pass
211 
212     def __repr__(self): # real signature unknown; restored from __doc__
213         """ x.__repr__() <==> repr(x) """
214         pass
215 
216     def __ror__(self, y): # real signature unknown; restored from __doc__
217         """ x.__ror__(y) <==> y|x """
218         pass
219 
220     def __rsub__(self, y): # real signature unknown; restored from __doc__
221         """ x.__rsub__(y) <==> y-x """
222         pass
223 
224     def __rxor__(self, y): # real signature unknown; restored from __doc__
225         """ x.__rxor__(y) <==> y^x """
226         pass
227 
228     def __sizeof__(self): # real signature unknown; restored from __doc__
229         """ S.__sizeof__() -> size of S in memory, in bytes """
230         pass
231 
232     def __sub__(self, y): # real signature unknown; restored from __doc__
233         """ x.__sub__(y) <==> x-y """
234         pass
235 
236     def __xor__(self, y): # real signature unknown; restored from __doc__
237         """ x.__xor__(y) <==> x^y """
238         pass
239 
240     __hash__ = None
241 
242 set

Set

练习测试

2.Python计数器

ounter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 + 自己类的功能

 1 #! /usr/bin/env python
 2 # -*- coding:utf-8 -*-
 3 
 4 import collections
 5 obj = collections.Counter('abdahdjkahdsa;d')
 6 print(obj)
 7 ret = obj.most_common(4)
 8 print(ret)
 9 for k in obj.elements():
10     print(k)
11 for k,v in obj.items():
12     print(k,v)
13 obj = collections.Counter(['11','22','22','22','33','44','55'])   #计数出现次数
14 print(obj)
15 obj.update(['leon','11','55','55'])                                ##更新计数出现次数
16 print(obj)
17 obj.subtract(['11','55'])                            #删除出现的次数
18 print(obj)
19 
20 输出：
21 C:\Users\Administrator\AppData\Local\Programs\Python\Python35\python.exe "D:/PythonTraining/PythonCode/Python35/Day 4/test.py"
22 Counter({'d': 4, 'a': 4, 'h': 2, 'k': 1, ';': 1, 'b': 1, 's': 1, 'j': 1})
23 [('d', 4), ('a', 4), ('h', 2), ('k', 1)]
24 h
25 h
26 d
27 d
28 d
29 d
30 k
31 ;
32 b
33 s
34 a
35 a
36 a
37 a
38 j
39 h 2
40 d 4
41 k 1
42 ; 1
43 b 1
44 s 1
45 a 4
46 j 1
47 Counter({'22': 3, '33': 1, '11': 1, '55': 1, '44': 1})
48 Counter({'55': 3, '22': 3, '11': 2, '33': 1, 'leon': 1, '44': 1})
49 Counter({'22': 3, '55': 2, '33': 1, 'leon': 1, '11': 1, '44': 1})
50 
51 Process finished with exit code 0

View Code

  1 ########################################################################
  2 ###  Counter
  3 ########################################################################
  4 
  5 class Counter(dict):
  6     '''Dict subclass for counting hashable items.  Sometimes called a bag
  7     or multiset.  Elements are stored as dictionary keys and their counts
  8     are stored as dictionary values.
  9 
 10     >>> c = Counter('abcdeabcdabcaba')  # count elements from a string
 11 
 12     >>> c.most_common(3)                # three most common elements
 13     [('a', 5), ('b', 4), ('c', 3)]
 14     >>> sorted(c)                       # list all unique elements
 15     ['a', 'b', 'c', 'd', 'e']
 16     >>> ''.join(sorted(c.elements()))   # list elements with repetitions
 17     'aaaaabbbbcccdde'
 18     >>> sum(c.values())                 # total of all counts
 19 
 20     >>> c['a']                          # count of letter 'a'
 21     >>> for elem in 'shazam':           # update counts from an iterable
 22     ...     c[elem] += 1                # by adding 1 to each element's count
 23     >>> c['a']                          # now there are seven 'a'
 24     >>> del c['b']                      # remove all 'b'
 25     >>> c['b']                          # now there are zero 'b'
 26 
 27     >>> d = Counter('simsalabim')       # make another counter
 28     >>> c.update(d)                     # add in the second counter
 29     >>> c['a']                          # now there are nine 'a'
 30 
 31     >>> c.clear()                       # empty the counter
 32     >>> c
 33     Counter()
 34 
 35     Note:  If a count is set to zero or reduced to zero, it will remain
 36     in the counter until the entry is deleted or the counter is cleared:
 37 
 38     >>> c = Counter('aaabbc')
 39     >>> c['b'] -= 2                     # reduce the count of 'b' by two
 40     >>> c.most_common()                 # 'b' is still in, but its count is zero
 41     [('a', 3), ('c', 1), ('b', 0)]
 42 
 43     '''
 44     # References:
 45     #   http://en.wikipedia.org/wiki/Multiset
 46     #   http://www.gnu.org/software/smalltalk/manual-base/html_node/Bag.html
 47     #   http://www.demo2s.com/Tutorial/Cpp/0380__set-multiset/Catalog0380__set-multiset.htm
 48     #   http://code.activestate.com/recipes/259174/
 49     #   Knuth, TAOCP Vol. II section 4.6.3
 50 
 51     def __init__(self, iterable=None, **kwds):
 52         '''Create a new, empty Counter object.  And if given, count elements
 53         from an input iterable.  Or, initialize the count from another mapping
 54         of elements to their counts.
 55 
 56         >>> c = Counter()                           # a new, empty counter
 57         >>> c = Counter('gallahad')                 # a new counter from an iterable
 58         >>> c = Counter({'a': 4, 'b': 2})           # a new counter from a mapping
 59         >>> c = Counter(a=4, b=2)                   # a new counter from keyword args
 60 
 61         '''
 62         super(Counter, self).__init__()
 63         self.update(iterable, **kwds)
 64 
 65     def __missing__(self, key):
 66         """ 对于不存在的元素，返回计数器为0 """
 67         'The count of elements not in the Counter is zero.'
 68         # Needed so that self[missing_item] does not raise KeyError
 69         return 0
 70 
 71     def most_common(self, n=None):
 72         """ 数量大于等n的所有元素和计数器 """
 73         '''List the n most common elements and their counts from the most
 74         common to the least.  If n is None, then list all element counts.
 75 
 76         >>> Counter('abcdeabcdabcaba').most_common(3)
 77         [('a', 5), ('b', 4), ('c', 3)]
 78 
 79         '''
 80         # Emulate Bag.sortedByCount from Smalltalk
 81         if n is None:
 82             return sorted(self.iteritems(), key=_itemgetter(1), reverse=True)
 83         return _heapq.nlargest(n, self.iteritems(), key=_itemgetter(1))
 84 
 85     def elements(self):
 86         """ 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 """
 87         '''Iterator over elements repeating each as many times as its count.
 88 
 89         >>> c = Counter('ABCABC')
 90         >>> sorted(c.elements())
 91         ['A', 'A', 'B', 'B', 'C', 'C']
 92 
 93         # Knuth's example for prime factors of 1836:  2**2 * 3**3 * 17**1
 94         >>> prime_factors = Counter({2: 2, 3: 3, 17: 1})
 95         >>> product = 1
 96         >>> for factor in prime_factors.elements():     # loop over factors
 97         ...     product *= factor                       # and multiply them
 98         >>> product
 99 
100         Note, if an element's count has been set to zero or is a negative
101         number, elements() will ignore it.
102 
103         '''
104         # Emulate Bag.do from Smalltalk and Multiset.begin from C++.
105         return _chain.from_iterable(_starmap(_repeat, self.iteritems()))
106 
107     # Override dict methods where necessary
108 
109     @classmethod
110     def fromkeys(cls, iterable, v=None):
111         # There is no equivalent method for counters because setting v=1
112         # means that no element can have a count greater than one.
113         raise NotImplementedError(
114             'Counter.fromkeys() is undefined.  Use Counter(iterable) instead.')
115 
116     def update(self, iterable=None, **kwds):
117         """ 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 """
118         '''Like dict.update() but add counts instead of replacing them.
119 
120         Source can be an iterable, a dictionary, or another Counter instance.
121 
122         >>> c = Counter('which')
123         >>> c.update('witch')           # add elements from another iterable
124         >>> d = Counter('watch')
125         >>> c.update(d)                 # add elements from another counter
126         >>> c['h']                      # four 'h' in which, witch, and watch
127 
128         '''
129         # The regular dict.update() operation makes no sense here because the
130         # replace behavior results in the some of original untouched counts
131         # being mixed-in with all of the other counts for a mismash that
132         # doesn't have a straight-forward interpretation in most counting
133         # contexts.  Instead, we implement straight-addition.  Both the inputs
134         # and outputs are allowed to contain zero and negative counts.
135 
136         if iterable is not None:
137             if isinstance(iterable, Mapping):
138                 if self:
139                     self_get = self.get
140                     for elem, count in iterable.iteritems():
141                         self[elem] = self_get(elem, 0) + count
142                 else:
143                     super(Counter, self).update(iterable) # fast path when counter is empty
144             else:
145                 self_get = self.get
146                 for elem in iterable:
147                     self[elem] = self_get(elem, 0) + 1
148         if kwds:
149             self.update(kwds)
150 
151     def subtract(self, iterable=None, **kwds):
152         """ 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 """
153         '''Like dict.update() but subtracts counts instead of replacing them.
154         Counts can be reduced below zero.  Both the inputs and outputs are
155         allowed to contain zero and negative counts.
156 
157         Source can be an iterable, a dictionary, or another Counter instance.
158 
159         >>> c = Counter('which')
160         >>> c.subtract('witch')             # subtract elements from another iterable
161         >>> c.subtract(Counter('watch'))    # subtract elements from another counter
162         >>> c['h']                          # 2 in which, minus 1 in witch, minus 1 in watch
163         >>> c['w']                          # 1 in which, minus 1 in witch, minus 1 in watch
164         -1
165 
166         '''
167         if iterable is not None:
168             self_get = self.get
169             if isinstance(iterable, Mapping):
170                 for elem, count in iterable.items():
171                     self[elem] = self_get(elem, 0) - count
172             else:
173                 for elem in iterable:
174                     self[elem] = self_get(elem, 0) - 1
175         if kwds:
176             self.subtract(kwds)
177 
178     def copy(self):
179         """ 拷贝 """
180         'Return a shallow copy.'
181         return self.__class__(self)
182 
183     def __reduce__(self):
184         """ 返回一个元组（类型，元组） """
185         return self.__class__, (dict(self),)
186 
187     def __delitem__(self, elem):
188         """ 删除元素 """
189         'Like dict.__delitem__() but does not raise KeyError for missing values.'
190         if elem in self:
191             super(Counter, self).__delitem__(elem)
192 
193     def __repr__(self):
194         if not self:
195             return '%s()' % self.__class__.__name__
196         items = ', '.join(map('%r: %r'.__mod__, self.most_common()))
197         return '%s({%s})' % (self.__class__.__name__, items)
198 
199     # Multiset-style mathematical operations discussed in:
200     #       Knuth TAOCP Volume II section 4.6.3 exercise 19
201     #       and at http://en.wikipedia.org/wiki/Multiset
202     #
203     # Outputs guaranteed to only include positive counts.
204     #
205     # To strip negative and zero counts, add-in an empty counter:
206     #       c += Counter()
207 
208     def __add__(self, other):
209         '''Add counts from two counters.
210 
211         >>> Counter('abbb') + Counter('bcc')
212         Counter({'b': 4, 'c': 2, 'a': 1})
213 
214         '''
215         if not isinstance(other, Counter):
216             return NotImplemented
217         result = Counter()
218         for elem, count in self.items():
219             newcount = count + other[elem]
220             if newcount > 0:
221                 result[elem] = newcount
222         for elem, count in other.items():
223             if elem not in self and count > 0:
224                 result[elem] = count
225         return result
226 
227     def __sub__(self, other):
228         ''' Subtract count, but keep only results with positive counts.
229 
230         >>> Counter('abbbc') - Counter('bccd')
231         Counter({'b': 2, 'a': 1})
232 
233         '''
234         if not isinstance(other, Counter):
235             return NotImplemented
236         result = Counter()
237         for elem, count in self.items():
238             newcount = count - other[elem]
239             if newcount > 0:
240                 result[elem] = newcount
241         for elem, count in other.items():
242             if elem not in self and count < 0:
243                 result[elem] = 0 - count
244         return result
245 
246     def __or__(self, other):
247         '''Union is the maximum of value in either of the input counters.
248 
249         >>> Counter('abbb') | Counter('bcc')
250         Counter({'b': 3, 'c': 2, 'a': 1})
251 
252         '''
253         if not isinstance(other, Counter):
254             return NotImplemented
255         result = Counter()
256         for elem, count in self.items():
257             other_count = other[elem]
258             newcount = other_count if count < other_count else count
259             if newcount > 0:
260                 result[elem] = newcount
261         for elem, count in other.items():
262             if elem not in self and count > 0:
263                 result[elem] = count
264         return result
265 
266     def __and__(self, other):
267         ''' Intersection is the minimum of corresponding counts.
268 
269         >>> Counter('abbb') & Counter('bcc')
270         Counter({'b': 1})
271 
272         '''
273         if not isinstance(other, Counter):
274             return NotImplemented
275         result = Counter()
276         for elem, count in self.items():
277             other_count = other[elem]
278             newcount = count if count < other_count else other_count
279             if newcount > 0:
280                 result[elem] = newcount
281         return result
282 
283 Counter

Counter

3.Python有序字典OrderedDict

orderdDict是对字典类型的补充，记住了字典元素添加的顺序

  1 class OrderedDict(dict):
  2     'Dictionary that remembers insertion order'
  3     # An inherited dict maps keys to values.
  4     # The inherited dict provides __getitem__, __len__, __contains__, and get.
  5     # The remaining methods are order-aware.
  6     # Big-O running times for all methods are the same as regular dictionaries.
  7 
  8     # The internal self.__map dict maps keys to links in a doubly linked list.
  9     # The circular doubly linked list starts and ends with a sentinel element.
 10     # The sentinel element never gets deleted (this simplifies the algorithm).
 11     # Each link is stored as a list of length three:  [PREV, NEXT, KEY].
 12 
 13     def __init__(self, *args, **kwds):
 14         '''Initialize an ordered dictionary.  The signature is the same as
 15         regular dictionaries, but keyword arguments are not recommended because
 16         their insertion order is arbitrary.
 17 
 18         '''
 19         if len(args) > 1:
 20             raise TypeError('expected at most 1 arguments, got %d' % len(args))
 21         try:
 22             self.__root
 23         except AttributeError:
 24             self.__root = root = []                     # sentinel node
 25             root[:] = [root, root, None]
 26             self.__map = {}
 27         self.__update(*args, **kwds)
 28 
 29     def __setitem__(self, key, value, dict_setitem=dict.__setitem__):
 30         'od.__setitem__(i, y) <==> od[i]=y'
 31         # Setting a new item creates a new link at the end of the linked list,
 32         # and the inherited dictionary is updated with the new key/value pair.
 33         if key not in self:
 34             root = self.__root
 35             last = root[0]
 36             last[1] = root[0] = self.__map[key] = [last, root, key]
 37         return dict_setitem(self, key, value)
 38 
 39     def __delitem__(self, key, dict_delitem=dict.__delitem__):
 40         'od.__delitem__(y) <==> del od[y]'
 41         # Deleting an existing item uses self.__map to find the link which gets
 42         # removed by updating the links in the predecessor and successor nodes.
 43         dict_delitem(self, key)
 44         link_prev, link_next, _ = self.__map.pop(key)
 45         link_prev[1] = link_next                        # update link_prev[NEXT]
 46         link_next[0] = link_prev                        # update link_next[PREV]
 47 
 48     def __iter__(self):
 49         'od.__iter__() <==> iter(od)'
 50         # Traverse the linked list in order.
 51         root = self.__root
 52         curr = root[1]                                  # start at the first node
 53         while curr is not root:
 54             yield curr[2]                               # yield the curr[KEY]
 55             curr = curr[1]                              # move to next node
 56 
 57     def __reversed__(self):
 58         'od.__reversed__() <==> reversed(od)'
 59         # Traverse the linked list in reverse order.
 60         root = self.__root
 61         curr = root[0]                                  # start at the last node
 62         while curr is not root:
 63             yield curr[2]                               # yield the curr[KEY]
 64             curr = curr[0]                              # move to previous node
 65 
 66     def clear(self):
 67         'od.clear() -> None.  Remove all items from od.'
 68         root = self.__root
 69         root[:] = [root, root, None]
 70         self.__map.clear()
 71         dict.clear(self)
 72 
 73     # -- the following methods do not depend on the internal structure --
 74 
 75     def keys(self):
 76         'od.keys() -> list of keys in od'
 77         return list(self)
 78 
 79     def values(self):
 80         'od.values() -> list of values in od'
 81         return [self[key] for key in self]
 82 
 83     def items(self):
 84         'od.items() -> list of (key, value) pairs in od'
 85         return [(key, self[key]) for key in self]
 86 
 87     def iterkeys(self):
 88         'od.iterkeys() -> an iterator over the keys in od'
 89         return iter(self)
 90 
 91     def itervalues(self):
 92         'od.itervalues -> an iterator over the values in od'
 93         for k in self:
 94             yield self[k]
 95 
 96     def iteritems(self):
 97         'od.iteritems -> an iterator over the (key, value) pairs in od'
 98         for k in self:
 99             yield (k, self[k])
100 
101     update = MutableMapping.update
102 
103     __update = update # let subclasses override update without breaking __init__
104 
105     __marker = object()
106 
107     def pop(self, key, default=__marker):
108         '''od.pop(k[,d]) -> v, remove specified key and return the corresponding
109         value.  If key is not found, d is returned if given, otherwise KeyError
110         is raised.
111 
112         '''
113         if key in self:
114             result = self[key]
115             del self[key]
116             return result
117         if default is self.__marker:
118             raise KeyError(key)
119         return default
120 
121     def setdefault(self, key, default=None):
122         'od.setdefault(k[,d]) -> od.get(k,d), also set od[k]=d if k not in od'
123         if key in self:
124             return self[key]
125         self[key] = default
126         return default
127 
128     def popitem(self, last=True):
129         '''od.popitem() -> (k, v), return and remove a (key, value) pair.
130         Pairs are returned in LIFO order if last is true or FIFO order if false.
131 
132         '''
133         if not self:
134             raise KeyError('dictionary is empty')
135         key = next(reversed(self) if last else iter(self))
136         value = self.pop(key)
137         return key, value
138 
139     def __repr__(self, _repr_running={}):
140         'od.__repr__() <==> repr(od)'
141         call_key = id(self), _get_ident()
142         if call_key in _repr_running:
143             return '...'
144         _repr_running[call_key] = 1
145         try:
146             if not self:
147                 return '%s()' % (self.__class__.__name__,)
148             return '%s(%r)' % (self.__class__.__name__, self.items())
149         finally:
150             del _repr_running[call_key]
151 
152     def __reduce__(self):
153         'Return state information for pickling'
154         items = [[k, self[k]] for k in self]
155         inst_dict = vars(self).copy()
156         for k in vars(OrderedDict()):
157             inst_dict.pop(k, None)
158         if inst_dict:
159             return (self.__class__, (items,), inst_dict)
160         return self.__class__, (items,)
161 
162     def copy(self):
163         'od.copy() -> a shallow copy of od'
164         return self.__class__(self)
165 
166     @classmethod
167     def fromkeys(cls, iterable, value=None):
168         '''OD.fromkeys(S[, v]) -> New ordered dictionary with keys from S.
169         If not specified, the value defaults to None.
170 
171         '''
172         self = cls()
173         for key in iterable:
174             self[key] = value
175         return self
176 
177     def __eq__(self, other):
178         '''od.__eq__(y) <==> od==y.  Comparison to another OD is order-sensitive
179         while comparison to a regular mapping is order-insensitive.
180 
181         '''
182         if isinstance(other, OrderedDict):
183             return dict.__eq__(self, other) and all(_imap(_eq, self, other))
184         return dict.__eq__(self, other)
185 
186     def __ne__(self, other):
187         'od.__ne__(y) <==> od!=y'
188         return not self == other
189 
190     # -- the following methods support python 3.x style dictionary views --
191 
192     def viewkeys(self):
193         "od.viewkeys() -> a set-like object providing a view on od's keys"
194         return KeysView(self)
195 
196     def viewvalues(self):
197         "od.viewvalues() -> an object providing a view on od's values"
198         return ValuesView(self)
199 
200     def viewitems(self):
201         "od.viewitems() -> a set-like object providing a view on od's items"
202         return ItemsView(self)
203 
204 OrderedDict

orderdDict

有序字典：

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

import collections
"""
# 字典 dic = {'k1':'v1','k2':'v2'}
# 列表 li = [k1]

for i in li:
    print(dic[i])  #构造有序字典
"""
dic = collections.OrderedDict()
# dic = dict()  无序的字典集合
dic['k1'] = 'v1'
dic['k2'] = 'v2'
dic['k3'] = 'v3'
# print(dic)
# 输出OrderedDict([('k1', 'v1'), ('k2', 'v2'), ('k3', 'v3')])

dic.move_to_end('k1')  #把K1放到最后
print(dic)
输出：OrderedDict([('k2', 'v2'), ('k3', 'v3'), ('k1', 'v1')])

dic.popitem()
print(dic)      #内存栈，后进先出
输出：rderedDict([('k2', 'v2'), ('k3', 'v3')])

ret = dic.pop('k2')
print(dic)
print(ret)
# 输出：v2

# dic['k4'] = None  等同于  dic.setdefault('k4','66') 如果没有66就是None

dic.update({'k1':'v111','k10':'v222'})
print(dic)
# 更新数据，没有就插入，有就更新值

4.Python默认字典defaultdict

 1 class defaultdict(dict):
 2     """
 3     defaultdict(default_factory[, ...]) --> dict with default factory
 4     
 5     The default factory is called without arguments to produce
 6     a new value when a key is not present, in __getitem__ only.
 7     A defaultdict compares equal to a dict with the same items.
 8     All remaining arguments are treated the same as if they were
 9     passed to the dict constructor, including keyword arguments.
10     """
11     def copy(self): # real signature unknown; restored from __doc__
12         """ D.copy() -> a shallow copy of D. """
13         pass
14 
15     def __copy__(self, *args, **kwargs): # real signature unknown
16         """ D.copy() -> a shallow copy of D. """
17         pass
18 
19     def __getattribute__(self, name): # real signature unknown; restored from __doc__
20         """ x.__getattribute__('name') <==> x.name """
21         pass
22 
23     def __init__(self, default_factory=None, **kwargs): # known case of _collections.defaultdict.__init__
24         """
25         defaultdict(default_factory[, ...]) --> dict with default factory
26         
27         The default factory is called without arguments to produce
28         a new value when a key is not present, in __getitem__ only.
29         A defaultdict compares equal to a dict with the same items.
30         All remaining arguments are treated the same as if they were
31         passed to the dict constructor, including keyword arguments.
32         
33         # (copied from class doc)
34         """
35         pass
36 
37     def __missing__(self, key): # real signature unknown; restored from __doc__
38         """
39         __missing__(key) # Called by __getitem__ for missing key; pseudo-code:
40           if self.default_factory is None: raise KeyError((key,))
41           self[key] = value = self.default_factory()
42           return value
43         """
44         pass
45 
46     def __reduce__(self, *args, **kwargs): # real signature unknown
47         """ Return state information for pickling. """
48         pass
49 
50     def __repr__(self): # real signature unknown; restored from __doc__
51         """ x.__repr__() <==> repr(x) """
52         pass
53 
54     default_factory = property(lambda self: object(), lambda self, v: None, lambda self: None)  # default
55     """Factory for default value called by __missing__()."""
56 
57 defaultdict

defaultdict

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

# 默认字典defaultdict  ：定义一个字典，让字典的值默认是个什么类型
import collections
# 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 = [11,22,33,44,55,66,77,88,90]
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']
******************************************************************
'''
from collections import defaultdict

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

my_dict = defaultdict(list) #设置一个默认字典，类型为列表

for value in  values:
    if value>66:
        my_dict['k1'].append(value)
    else:
        my_dict['k2'].append(value)
print(my_dict)
# 输出：defaultdict(<class 'list'>, {'k2': [11, 22, 33, 44, 55, 66], 'k1': [77, 88, 99, 90]})

# dic = {'k1':[]}  #默认让字典的值为空列表
# dic[k1].append('leon')
dic = collections.defaultdict(list) #
dic['k1'].append('leon')
print(dic)
# 输出：defaultdict(<class 'list'>, {'k1': ['leon']})

PS:orderdDict是对字典类型的补充，他记住了字典元素添加的顺序

5.Python可命名元祖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。

  1 class Mytuple(__builtin__.tuple)
  2  |  Mytuple(x, y)
  3  |  
  4  |  Method resolution order:
  5  |      Mytuple
  6  |      __builtin__.tuple
  7  |      __builtin__.object
  8  |  
  9  |  Methods defined here:
 10  |  
 11  |  __getnewargs__(self)
 12  |      Return self as a plain tuple.  Used by copy and pickle.
 13  |  
 14  |  __getstate__(self)
 15  |      Exclude the OrderedDict from pickling
 16  |  
 17  |  __repr__(self)
 18  |      Return a nicely formatted representation string
 19  |  
 20  |  _asdict(self)
 21  |      Return a new OrderedDict which maps field names to their values
 22  |  
 23  |  _replace(_self, **kwds)
 24  |      Return a new Mytuple object replacing specified fields with new values
 25  |  
 26  |  ----------------------------------------------------------------------
 27  |  Class methods defined here:
 28  |  
 29  |  _make(cls, iterable, new=<built-in method __new__ of type object>, len=<built-in function len>) from __builtin__.type
 30  |      Make a new Mytuple object from a sequence or iterable
 31  |  
 32  |  ----------------------------------------------------------------------
 33  |  Static methods defined here:
 34  |  
 35  |  __new__(_cls, x, y)
 36  |      Create new instance of Mytuple(x, y)
 37  |  
 38  |  ----------------------------------------------------------------------
 39  |  Data descriptors defined here:
 40  |  
 41  |  __dict__
 42  |      Return a new OrderedDict which maps field names to their values
 43  |  
 44  |  x
 45  |      Alias for field number 0
 46  |  
 47  |  y
 48  |      Alias for field number 1
 49  |  
 50  |  ----------------------------------------------------------------------
 51  |  Data and other attributes defined here:
 52  |  
 53  |  _fields = ('x', 'y')
 54  |  
 55  |  ----------------------------------------------------------------------
 56  |  Methods inherited from __builtin__.tuple:
 57  |  
 58  |  __add__(...)
 59  |      x.__add__(y) <==> x+y
 60  |  
 61  |  __contains__(...)
 62  |      x.__contains__(y) <==> y in x
 63  |  
 64  |  __eq__(...)
 65  |      x.__eq__(y) <==> x==y
 66  |  
 67  |  __ge__(...)
 68  |      x.__ge__(y) <==> x>=y
 69  |  
 70  |  __getattribute__(...)
 71  |      x.__getattribute__('name') <==> x.name
 72  |  
 73  |  __getitem__(...)
 74  |      x.__getitem__(y) <==> x[y]
 75  |  
 76  |  __getslice__(...)
 77  |      x.__getslice__(i, j) <==> x[i:j]
 78  |      
 79  |      Use of negative indices is not supported.
 80  |  
 81  |  __gt__(...)
 82  |      x.__gt__(y) <==> x>y
 83  |  
 84  |  __hash__(...)
 85  |      x.__hash__() <==> hash(x)
 86  |  
 87  |  __iter__(...)
 88  |      x.__iter__() <==> iter(x)
 89  |  
 90  |  __le__(...)
 91  |      x.__le__(y) <==> x<=y
 92  |  
 93  |  __len__(...)
 94  |      x.__len__() <==> len(x)
 95  |  
 96  |  __lt__(...)
 97  |      x.__lt__(y) <==> x<y
 98  |  
 99  |  __mul__(...)
100  |      x.__mul__(n) <==> x*n
101  |  
102  |  __ne__(...)
103  |      x.__ne__(y) <==> x!=y
104  |  
105  |  __rmul__(...)
106  |      x.__rmul__(n) <==> n*x
107  |  
108  |  __sizeof__(...)
109  |      T.__sizeof__() -- size of T in memory, in bytes
110  |  
111  |  count(...)
112  |      T.count(value) -> integer -- return number of occurrences of value
113  |  
114  |  index(...)
115  |      T.index(value, [start, [stop]]) -> integer -- return first index of value.
116  |      Raises ValueError if the value is not present.
117 
118 Mytuple
119 
120 Mytuple

namedtuple

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。
没有提供类，需要自己通过namedtuple 方法创建类，创建对象
'''
t = (11,22,33,44,55,566)
t =  home,age,gender,address  #给元素命名

t[0]
t[2]  
通过索引来取值

t.name
t.age
t.address
t.gender

两个元素就是X,Y轴形式

obj = (1,2)
obj.x
obj.y
'''
#! /usr/bin/env python
# -*- coding:utf-8 -*-

import collections
# 创建类，defaultdict
MytupleClass = collections.namedtuple('MytupleClass',['x','y','z'])
#print(help(MytupleClass)) 查看MytupleClass类里的方法
obj = MytupleClass(111,222,333)
print(obj.x)
print(obj.y)
print(obj.z)

6.Python双向队列deque

一个线程安全的双向队列

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

import collections

d = collections.deque()
d.append('1')      #往右添加元素
d.appendleft('10')
d.appendleft('1')  #往左添加元素
print(d)
ret = d.count('1')  #查看元素的个数
print(ret)
d.extend(['cc','aa','rr'])   #扩展元素
d.extendleft(['ff','vv'])
print(d)
d.rotate(5)  #自己从队列的右边拿数据插入到左边，执行5次操作
print(d)
'''
输出：
deque(['1', '10', '1'])
2
deque(['vv', 'ff', '1', '10', '1', 'cc', 'aa', 'rr'])
deque(['10', '1', 'cc', 'aa', 'rr', 'vv', 'ff', '1'])
'''

  1 class deque(object):
  2     """
  3     deque([iterable[, maxlen]]) --> deque object
  4     
  5     Build an ordered collection with optimized access from its endpoints.
  6     """
  7     def append(self, *args, **kwargs): # real signature unknown
  8         """ Add an element to the right side of the deque. """
  9         pass
 10 
 11     def appendleft(self, *args, **kwargs): # real signature unknown
 12         """ Add an element to the left side of the deque. """
 13         pass
 14 
 15     def clear(self, *args, **kwargs): # real signature unknown
 16         """ Remove all elements from the deque. """
 17         pass
 18 
 19     def count(self, value): # real signature unknown; restored from __doc__
 20         """ D.count(value) -> integer -- return number of occurrences of value """
 21         return 0
 22 
 23     def extend(self, *args, **kwargs): # real signature unknown
 24         """ Extend the right side of the deque with elements from the iterable """
 25         pass
 26 
 27     def extendleft(self, *args, **kwargs): # real signature unknown
 28         """ Extend the left side of the deque with elements from the iterable """
 29         pass
 30 
 31     def pop(self, *args, **kwargs): # real signature unknown
 32         """ Remove and return the rightmost element. """
 33         pass
 34 
 35     def popleft(self, *args, **kwargs): # real signature unknown
 36         """ Remove and return the leftmost element. """
 37         pass
 38 
 39     def remove(self, value): # real signature unknown; restored from __doc__
 40         """ D.remove(value) -- remove first occurrence of value. """
 41         pass
 42 
 43     def reverse(self): # real signature unknown; restored from __doc__
 44         """ D.reverse() -- reverse *IN PLACE* """
 45         pass
 46 
 47     def rotate(self, *args, **kwargs): # real signature unknown
 48         """ Rotate the deque n steps to the right (default n=1).  If n is negative, rotates left. """
 49         pass
 50 
 51     def __copy__(self, *args, **kwargs): # real signature unknown
 52         """ Return a shallow copy of a deque. """
 53         pass
 54 
 55     def __delitem__(self, y): # real signature unknown; restored from __doc__
 56         """ x.__delitem__(y) <==> del x[y] """
 57         pass
 58 
 59     def __eq__(self, y): # real signature unknown; restored from __doc__
 60         """ x.__eq__(y) <==> x==y """
 61         pass
 62 
 63     def __getattribute__(self, name): # real signature unknown; restored from __doc__
 64         """ x.__getattribute__('name') <==> x.name """
 65         pass
 66 
 67     def __getitem__(self, y): # real signature unknown; restored from __doc__
 68         """ x.__getitem__(y) <==> x[y] """
 69         pass
 70 
 71     def __ge__(self, y): # real signature unknown; restored from __doc__
 72         """ x.__ge__(y) <==> x>=y """
 73         pass
 74 
 75     def __gt__(self, y): # real signature unknown; restored from __doc__
 76         """ x.__gt__(y) <==> x>y """
 77         pass
 78 
 79     def __iadd__(self, y): # real signature unknown; restored from __doc__
 80         """ x.__iadd__(y) <==> x+=y """
 81         pass
 82 
 83     def __init__(self, iterable=(), maxlen=None): # known case of _collections.deque.__init__
 84         """
 85         deque([iterable[, maxlen]]) --> deque object
 86         
 87         Build an ordered collection with optimized access from its endpoints.
 88         # (copied from class doc)
 89         """
 90         pass
 91 
 92     def __iter__(self): # real signature unknown; restored from __doc__
 93         """ x.__iter__() <==> iter(x) """
 94         pass
 95 
 96     def __len__(self): # real signature unknown; restored from __doc__
 97         """ x.__len__() <==> len(x) """
 98         pass
 99 
100     def __le__(self, y): # real signature unknown; restored from __doc__
101         """ x.__le__(y) <==> x<=y """
102         pass
103 
104     def __lt__(self, y): # real signature unknown; restored from __doc__
105         """ x.__lt__(y) <==> x<y """
106         pass
107 
108     @staticmethod # known case of __new__
109     def __new__(S, *more): # real signature unknown; restored from __doc__
110         """ T.__new__(S, ...) -> a new object with type S, a subtype of T """
111         pass
112 
113     def __ne__(self, y): # real signature unknown; restored from __doc__
114         """ x.__ne__(y) <==> x!=y """
115         pass
116 
117     def __reduce__(self, *args, **kwargs): # real signature unknown
118         """ Return state information for pickling. """
119         pass
120 
121     def __repr__(self): # real signature unknown; restored from __doc__
122         """ x.__repr__() <==> repr(x) """
123         pass
124 
125     def __reversed__(self): # real signature unknown; restored from __doc__
126         """ D.__reversed__() -- return a reverse iterator over the deque """
127         pass
128 
129     def __setitem__(self, i, y): # real signature unknown; restored from __doc__
130         """ x.__setitem__(i, y) <==> x[i]=y """
131         pass
132 
133     def __sizeof__(self): # real signature unknown; restored from __doc__
134         """ D.__sizeof__() -- size of D in memory, in bytes """
135         pass
136 
137     maxlen = property(lambda self: object(), lambda self, v: None, lambda self: None)  # default
138     """maximum size of a deque or None if unbounded"""
139 
140 
141     __hash__ = None
142 
143 deque

deque

7.Python单向队列queue.Queue

先进先出 FIFO；

内存栈，后进先出。

练习：

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

import queue        # 含单向队列
import collections  # 含双向队列

# d = collections.deque()
q = queue.Queue()
# q.qsize() # 查看队列个数
q.put('123')
q.put('678')
q.put('ddd')
print(q.qsize())
print(q.get())
"""
输出：
3
123
"""

  1 class Queue:
  2     """Create a queue object with a given maximum size.
  3 
  4     If maxsize is <= 0, the queue size is infinite.
  5     """
  6     def __init__(self, maxsize=0):
  7         self.maxsize = maxsize
  8         self._init(maxsize)
  9         # mutex must be held whenever the queue is mutating.  All methods
 10         # that acquire mutex must release it before returning.  mutex
 11         # is shared between the three conditions, so acquiring and
 12         # releasing the conditions also acquires and releases mutex.
 13         self.mutex = _threading.Lock()
 14         # Notify not_empty whenever an item is added to the queue; a
 15         # thread waiting to get is notified then.
 16         self.not_empty = _threading.Condition(self.mutex)
 17         # Notify not_full whenever an item is removed from the queue;
 18         # a thread waiting to put is notified then.
 19         self.not_full = _threading.Condition(self.mutex)
 20         # Notify all_tasks_done whenever the number of unfinished tasks
 21         # drops to zero; thread waiting to join() is notified to resume
 22         self.all_tasks_done = _threading.Condition(self.mutex)
 23         self.unfinished_tasks = 0
 24 
 25     def task_done(self):
 26         """Indicate that a formerly enqueued task is complete.
 27 
 28         Used by Queue consumer threads.  For each get() used to fetch a task,
 29         a subsequent call to task_done() tells the queue that the processing
 30         on the task is complete.
 31 
 32         If a join() is currently blocking, it will resume when all items
 33         have been processed (meaning that a task_done() call was received
 34         for every item that had been put() into the queue).
 35 
 36         Raises a ValueError if called more times than there were items
 37         placed in the queue.
 38         """
 39         self.all_tasks_done.acquire()
 40         try:
 41             unfinished = self.unfinished_tasks - 1
 42             if unfinished <= 0:
 43                 if unfinished < 0:
 44                     raise ValueError('task_done() called too many times')
 45                 self.all_tasks_done.notify_all()
 46             self.unfinished_tasks = unfinished
 47         finally:
 48             self.all_tasks_done.release()
 49 
 50     def join(self):
 51         """Blocks until all items in the Queue have been gotten and processed.
 52 
 53         The count of unfinished tasks goes up whenever an item is added to the
 54         queue. The count goes down whenever a consumer thread calls task_done()
 55         to indicate the item was retrieved and all work on it is complete.
 56 
 57         When the count of unfinished tasks drops to zero, join() unblocks.
 58         """
 59         self.all_tasks_done.acquire()
 60         try:
 61             while self.unfinished_tasks:
 62                 self.all_tasks_done.wait()
 63         finally:
 64             self.all_tasks_done.release()
 65 
 66     def qsize(self):
 67         """Return the approximate size of the queue (not reliable!)."""
 68         self.mutex.acquire()
 69         n = self._qsize()
 70         self.mutex.release()
 71         return n
 72 
 73     def empty(self):
 74         """Return True if the queue is empty, False otherwise (not reliable!)."""
 75         self.mutex.acquire()
 76         n = not self._qsize()
 77         self.mutex.release()
 78         return n
 79 
 80     def full(self):
 81         """Return True if the queue is full, False otherwise (not reliable!)."""
 82         self.mutex.acquire()
 83         n = 0 < self.maxsize == self._qsize()
 84         self.mutex.release()
 85         return n
 86 
 87     def put(self, item, block=True, timeout=None):
 88         """Put an item into the queue.
 89 
 90         If optional args 'block' is true and 'timeout' is None (the default),
 91         block if necessary until a free slot is available. If 'timeout' is
 92         a non-negative number, it blocks at most 'timeout' seconds and raises
 93         the Full exception if no free slot was available within that time.
 94         Otherwise ('block' is false), put an item on the queue if a free slot
 95         is immediately available, else raise the Full exception ('timeout'
 96         is ignored in that case).
 97         """
 98         self.not_full.acquire()
 99         try:
100             if self.maxsize > 0:
101                 if not block:
102                     if self._qsize() == self.maxsize:
103                         raise Full
104                 elif timeout is None:
105                     while self._qsize() == self.maxsize:
106                         self.not_full.wait()
107                 elif timeout < 0:
108                     raise ValueError("'timeout' must be a non-negative number")
109                 else:
110                     endtime = _time() + timeout
111                     while self._qsize() == self.maxsize:
112                         remaining = endtime - _time()
113                         if remaining <= 0.0:
114                             raise Full
115                         self.not_full.wait(remaining)
116             self._put(item)
117             self.unfinished_tasks += 1
118             self.not_empty.notify()
119         finally:
120             self.not_full.release()
121 
122     def put_nowait(self, item):
123         """Put an item into the queue without blocking.
124 
125         Only enqueue the item if a free slot is immediately available.
126         Otherwise raise the Full exception.
127         """
128         return self.put(item, False)
129 
130     def get(self, block=True, timeout=None):
131         """Remove and return an item from the queue.
132 
133         If optional args 'block' is true and 'timeout' is None (the default),
134         block if necessary until an item is available. If 'timeout' is
135         a non-negative number, it blocks at most 'timeout' seconds and raises
136         the Empty exception if no item was available within that time.
137         Otherwise ('block' is false), return an item if one is immediately
138         available, else raise the Empty exception ('timeout' is ignored
139         in that case).
140         """
141         self.not_empty.acquire()
142         try:
143             if not block:
144                 if not self._qsize():
145                     raise Empty
146             elif timeout is None:
147                 while not self._qsize():
148                     self.not_empty.wait()
149             elif timeout < 0:
150                 raise ValueError("'timeout' must be a non-negative number")
151             else:
152                 endtime = _time() + timeout
153                 while not self._qsize():
154                     remaining = endtime - _time()
155                     if remaining <= 0.0:
156                         raise Empty
157                     self.not_empty.wait(remaining)
158             item = self._get()
159             self.not_full.notify()
160             return item
161         finally:
162             self.not_empty.release()
163 
164     def get_nowait(self):
165         """Remove and return an item from the queue without blocking.
166 
167         Only get an item if one is immediately available. Otherwise
168         raise the Empty exception.
169         """
170         return self.get(False)
171 
172     # Override these methods to implement other queue organizations
173     # (e.g. stack or priority queue).
174     # These will only be called with appropriate locks held
175 
176     # Initialize the queue representation
177     def _init(self, maxsize):
178         self.queue = deque()
179 
180     def _qsize(self, len=len):
181         return len(self.queue)
182 
183     # Put a new item in the queue
184     def _put(self, item):
185         self.queue.append(item)
186 
187     # Get an item from the queue
188     def _get(self):
189         return self.queue.popleft()
190 
191 Queue.Queue

单项队列Queue.Queue

8.Python深浅拷贝原理

深浅拷贝原理：

为什么要拷贝？

1.当进行修改时，想要保留原来的数据和修改后的数据

数字字符串和集合在修改时的差异？（深浅拷贝不同的原因）

1.在修改数据时：
2.数字字符串：在内存中新建一份数据
3.集合：修改内存中的同一份数据
对于集合，如何保留其修改前和修改后的数据？

2.在内存中拷贝一份
对于集合，如何拷贝其n层元素同时拷贝？

 1 #! /usr/bin/env python
 2 # -*- coding:utf-8 -*-
 3 
 4 import copy
 5 # 浅拷贝
 6 # copy.copy()
 7 # 深拷贝
 8 # copy.deepcopy()
 9 
10 '''
11 # 字符串、数字、赋值
12 a1 = 123123
13 # a2 = 123123
14 a2 = a1
15 print(id(a1))
16 print(id(a2))
17 '''
18 '''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。
19 a1 = "adasfasdfasdf"
20 a3 = copy.copy(a1)
21 a4 = copy.deepcopy(a1)
22 print(id(a1))
23 print(id(a3))
24 print(id(a4))
25 # 输出：
26 # 10952304
27 # 10952304
28 # 10952304
29 '''
30 # 其他包括数字、元组、列表、字典
31 # 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。
32 # 赋值，只是创建一个变量，该变量指向原来内存地址，如：
33 n1 = {"k1":"wu","k2":"123","k3":["leon",456]}
34 n2 = n1 #赋值
35 n3 = copy.copy(n1)  #浅拷贝只copy内存里面的列表，但不拷贝列表里的元素，只copy一层
36 n4 = copy.deepcopy(n1) #深拷贝copy拷贝了多层，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）
37 print(id(n1))
38 print(id(n2))
39 print(id(n3["k3"]))
40 print(id(n4["k3"]))
41 '''
42 输出：
43 10470344
44 10470344
45 11318856
46 11317640
47 '''

练习代码

  1 """Generic (shallow and deep) copying operations.
  2 
  3 Interface summary:
  4 
  5         import copy
  6 
  7         x = copy.copy(y)        # make a shallow copy of y
  8         x = copy.deepcopy(y)    # make a deep copy of y
  9 
 10 For module specific errors, copy.Error is raised.
 11 
 12 The difference between shallow and deep copying is only relevant for
 13 compound objects (objects that contain other objects, like lists or
 14 class instances).
 15 
 16 - A shallow copy constructs a new compound object and then (to the
 17   extent possible) inserts *the same objects* into it that the
 18   original contains.
 19 
 20 - A deep copy constructs a new compound object and then, recursively,
 21   inserts *copies* into it of the objects found in the original.
 22 
 23 Two problems often exist with deep copy operations that don't exist
 24 with shallow copy operations:
 25 
 26  a) recursive objects (compound objects that, directly or indirectly,
 27     contain a reference to themselves) may cause a recursive loop
 28 
 29  b) because deep copy copies *everything* it may copy too much, e.g.
 30     administrative data structures that should be shared even between
 31     copies
 32 
 33 Python's deep copy operation avoids these problems by:
 34 
 35  a) keeping a table of objects already copied during the current
 36     copying pass
 37 
 38  b) letting user-defined classes override the copying operation or the
 39     set of components copied
 40 
 41 This version does not copy types like module, class, function, method,
 42 nor stack trace, stack frame, nor file, socket, window, nor array, nor
 43 any similar types.
 44 
 45 Classes can use the same interfaces to control copying that they use
 46 to control pickling: they can define methods called __getinitargs__(),
 47 __getstate__() and __setstate__().  See the documentation for module
 48 "pickle" for information on these methods.
 49 """
 50 
 51 import types
 52 import weakref
 53 from copyreg import dispatch_table
 54 import builtins
 55 
 56 class Error(Exception):
 57     pass
 58 error = Error   # backward compatibility
 59 
 60 try:
 61     from org.python.core import PyStringMap
 62 except ImportError:
 63     PyStringMap = None
 64 
 65 __all__ = ["Error", "copy", "deepcopy"]
 66 
 67 def copy(x):
 68     """Shallow copy operation on arbitrary Python objects.
 69 
 70     See the module's __doc__ string for more info.
 71     """
 72 
 73     cls = type(x)
 74 
 75     copier = _copy_dispatch.get(cls)
 76     if copier:
 77         return copier(x)
 78 
 79     try:
 80         issc = issubclass(cls, type)
 81     except TypeError: # cls is not a class
 82         issc = False
 83     if issc:
 84         # treat it as a regular class:
 85         return _copy_immutable(x)
 86 
 87     copier = getattr(cls, "__copy__", None)
 88     if copier:
 89         return copier(x)
 90 
 91     reductor = dispatch_table.get(cls)
 92     if reductor:
 93         rv = reductor(x)
 94     else:
 95         reductor = getattr(x, "__reduce_ex__", None)
 96         if reductor:
 97             rv = reductor(4)
 98         else:
 99             reductor = getattr(x, "__reduce__", None)
100             if reductor:
101                 rv = reductor()
102             else:
103                 raise Error("un(shallow)copyable object of type %s" % cls)
104 
105     return _reconstruct(x, rv, 0)
106 
107 
108 _copy_dispatch = d = {}
109 
110 def _copy_immutable(x):
111     return x
112 for t in (type(None), int, float, bool, str, tuple,
113           bytes, frozenset, type, range,
114           types.BuiltinFunctionType, type(Ellipsis),
115           types.FunctionType, weakref.ref):
116     d[t] = _copy_immutable
117 t = getattr(types, "CodeType", None)
118 if t is not None:
119     d[t] = _copy_immutable
120 for name in ("complex", "unicode"):
121     t = getattr(builtins, name, None)
122     if t is not None:
123         d[t] = _copy_immutable
124 
125 def _copy_with_constructor(x):
126     return type(x)(x)
127 for t in (list, dict, set):
128     d[t] = _copy_with_constructor
129 
130 def _copy_with_copy_method(x):
131     return x.copy()
132 if PyStringMap is not None:
133     d[PyStringMap] = _copy_with_copy_method
134 
135 del d
136 
137 def deepcopy(x, memo=None, _nil=[]):
138     """Deep copy operation on arbitrary Python objects.
139 
140     See the module's __doc__ string for more info.
141     """
142 
143     if memo is None:
144         memo = {}
145 
146     d = id(x)
147     y = memo.get(d, _nil)
148     if y is not _nil:
149         return y
150 
151     cls = type(x)
152 
153     copier = _deepcopy_dispatch.get(cls)
154     if copier:
155         y = copier(x, memo)
156     else:
157         try:
158             issc = issubclass(cls, type)
159         except TypeError: # cls is not a class (old Boost; see SF #502085)
160             issc = 0
161         if issc:
162             y = _deepcopy_atomic(x, memo)
163         else:
164             copier = getattr(x, "__deepcopy__", None)
165             if copier:
166                 y = copier(memo)
167             else:
168                 reductor = dispatch_table.get(cls)
169                 if reductor:
170                     rv = reductor(x)
171                 else:
172                     reductor = getattr(x, "__reduce_ex__", None)
173                     if reductor:
174                         rv = reductor(4)
175                     else:
176                         reductor = getattr(x, "__reduce__", None)
177                         if reductor:
178                             rv = reductor()
179                         else:
180                             raise Error(
181                                 "un(deep)copyable object of type %s" % cls)
182                 y = _reconstruct(x, rv, 1, memo)
183 
184     # If is its own copy, don't memoize.
185     if y is not x:
186         memo[d] = y
187         _keep_alive(x, memo) # Make sure x lives at least as long as d
188     return y
189 
190 _deepcopy_dispatch = d = {}
191 
192 def _deepcopy_atomic(x, memo):
193     return x
194 d[type(None)] = _deepcopy_atomic
195 d[type(Ellipsis)] = _deepcopy_atomic
196 d[int] = _deepcopy_atomic
197 d[float] = _deepcopy_atomic
198 d[bool] = _deepcopy_atomic
199 try:
200     d[complex] = _deepcopy_atomic
201 except NameError:
202     pass
203 d[bytes] = _deepcopy_atomic
204 d[str] = _deepcopy_atomic
205 try:
206     d[types.CodeType] = _deepcopy_atomic
207 except AttributeError:
208     pass
209 d[type] = _deepcopy_atomic
210 d[range] = _deepcopy_atomic
211 d[types.BuiltinFunctionType] = _deepcopy_atomic
212 d[types.FunctionType] = _deepcopy_atomic
213 d[weakref.ref] = _deepcopy_atomic
214 
215 def _deepcopy_list(x, memo):
216     y = []
217     memo[id(x)] = y
218     for a in x:
219         y.append(deepcopy(a, memo))
220     return y
221 d[list] = _deepcopy_list
222 
223 def _deepcopy_tuple(x, memo):
224     y = [deepcopy(a, memo) for a in x]
225     # We're not going to put the tuple in the memo, but it's still important we
226     # check for it, in case the tuple contains recursive mutable structures.
227     try:
228         return memo[id(x)]
229     except KeyError:
230         pass
231     for k, j in zip(x, y):
232         if k is not j:
233             y = tuple(y)
234             break
235     else:
236         y = x
237     return y
238 d[tuple] = _deepcopy_tuple
239 
240 def _deepcopy_dict(x, memo):
241     y = {}
242     memo[id(x)] = y
243     for key, value in x.items():
244         y[deepcopy(key, memo)] = deepcopy(value, memo)
245     return y
246 d[dict] = _deepcopy_dict
247 if PyStringMap is not None:
248     d[PyStringMap] = _deepcopy_dict
249 
250 def _deepcopy_method(x, memo): # Copy instance methods
251     return type(x)(x.__func__, deepcopy(x.__self__, memo))
252 _deepcopy_dispatch[types.MethodType] = _deepcopy_method
253 
254 def _keep_alive(x, memo):
255     """Keeps a reference to the object x in the memo.
256 
257     Because we remember objects by their id, we have
258     to assure that possibly temporary objects are kept
259     alive by referencing them.
260     We store a reference at the id of the memo, which should
261     normally not be used unless someone tries to deepcopy
262     the memo itself...
263     """
264     try:
265         memo[id(memo)].append(x)
266     except KeyError:
267         # aha, this is the first one :-)
268         memo[id(memo)]=[x]
269 
270 def _reconstruct(x, info, deep, memo=None):
271     if isinstance(info, str):
272         return x
273     assert isinstance(info, tuple)
274     if memo is None:
275         memo = {}
276     n = len(info)
277     assert n in (2, 3, 4, 5)
278     callable, args = info[:2]
279     if n > 2:
280         state = info[2]
281     else:
282         state = {}
283     if n > 3:
284         listiter = info[3]
285     else:
286         listiter = None
287     if n > 4:
288         dictiter = info[4]
289     else:
290         dictiter = None
291     if deep:
292         args = deepcopy(args, memo)
293     y = callable(*args)
294     memo[id(x)] = y
295 
296     if state:
297         if deep:
298             state = deepcopy(state, memo)
299         if hasattr(y, '__setstate__'):
300             y.__setstate__(state)
301         else:
302             if isinstance(state, tuple) and len(state) == 2:
303                 state, slotstate = state
304             else:
305                 slotstate = None
306             if state is not None:
307                 y.__dict__.update(state)
308             if slotstate is not None:
309                 for key, value in slotstate.items():
310                     setattr(y, key, value)
311 
312     if listiter is not None:
313         for item in listiter:
314             if deep:
315                 item = deepcopy(item, memo)
316             y.append(item)
317     if dictiter is not None:
318         for key, value in dictiter:
319             if deep:
320                 key = deepcopy(key, memo)
321                 value = deepcopy(value, memo)
322             y[key] = value
323     return y
324 
325 del d
326 
327 del types
328 
329 # Helper for instance creation without calling __init__
330 class _EmptyClass:
331     pass

copy.py

# 监控数据更新例子：
dic = {
    "cpu":[80,],
    "mem":[80,],
    "disk":[80,],
}
print('before',dic)
# new_dic = copy.copy(dic)
new_dic = copy.deepcopy(dic)
new_dic['cpu'][0] = 50
print(dic)
# print(new_dic)
print(new_dic)
'''
copy.copy输出：会修改原来的数据
before {'disk': [80], 'cpu': [80], 'mem': [80]}
{'disk': [80], 'cpu': [50], 'mem': [80]}
{'disk': [80], 'cpu': [50], 'mem': [80]}
'''
'''
copy.deepcopy输出：不会修改原来的数据，仅update数据
before {'mem': [80], 'cpu': [80], 'disk': [80]}
{'mem': [80], 'cpu': [80], 'disk': [80]}
{'mem': [80], 'cpu': [50], 'disk': [80]}
'''

9.Python函数基本定义

9.1 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：

 1 while True：
 2     if cpu利用率 > 90%:
 3         #发送邮件提醒
 4         连接邮箱服务器
 5         发送邮件
 6         关闭连接
 7    
 8     if 硬盘使用空间 > 90%:
 9         #发送邮件提醒
10         连接邮箱服务器
11         发送邮件
12         关闭连接
13    
14     if 内存占用 > 80%:
15         #发送邮件提醒
16         连接邮箱服务器
17         发送邮件
18         关闭连接
19         
20 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：
21 
22 def 发送邮件(内容)
23     #发送邮件提醒
24     连接邮箱服务器
25     发送邮件
26     关闭连接
27    
28 while True：
29    
30     if cpu利用率 > 90%:
31         发送邮件('CPU报警')
32    
33     if 硬盘使用空间 > 90%:
34         发送邮件('硬盘报警')
35    
36     if 内存占用 > 80%:
37         发送邮件('内存报警')
38         
39 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：
40 
41 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
42 面向对象：对函数进行分类和封装，让开发“更快更好更强...”
43 函数式编程最重要的是增强代码的重用性和可读性

Teacher's 思路

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

def mail():
    n = 123
    n += 1
    print(n)

mail() #执行函数代码块，mail执行内存地址函数
f = mail
f()

"""
输出：
124
124
"""

9.2 定义和使用

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

函数的定义主要有如下要点：

def：表示函数的关键字
函数名：函数的名称，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：

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...

def show():
    print('a')
    return [11,22]  # 遇到函数、方法后return后，后面代码不执行
    print('b')
ret = show()
# So ruturn ： 1.返回值  2.中断函数操作

9.3 参数

 1 def CPU报警邮件()
 2     #发送邮件提醒
 3     连接邮箱服务器
 4     发送邮件
 5     关闭连接
 6 
 7 def 硬盘报警邮件()
 8     #发送邮件提醒
 9     连接邮箱服务器
10     发送邮件
11     关闭连接
12 
13 def 内存报警邮件()
14     #发送邮件提醒
15     连接邮箱服务器
16     发送邮件
17     关闭连接
18  
19 while True：
20  
21     if cpu利用率 > 90%:
22         CPU报警邮件（）
23  
24     if 硬盘使用空间 > 90%:
25         硬盘报警邮件（）
26  
27     if 内存占用 > 80%:
28         内存报警邮件（）
29 
30 无参数实现

无参数实现

 1 def 发送邮件(邮件内容)
 2 
 3     #发送邮件提醒
 4     连接邮箱服务器
 5     发送邮件
 6     关闭连接
 7 
 8  
 9 while True：
10  
11     if cpu利用率 > 90%:
12         发送邮件("CPU报警了。")
13  
14     if 硬盘使用空间 > 90%:
15         发送邮件("硬盘报警了。")
16  
17     if 内存占用 > 80%:
18         发送邮件("内存报警了。")
19 
20 有参数实现

有参数实现

函数的有三种不同的参数：

普通参数
默认参数
动态参数

 1 def func(name, age = 18):
 2     
 3     print "%s:%s" %(name,age)
 4 
 5 # 指定参数
 6 func('leon', 19)
 7 # 使用默认参数
 8 func('Jack')
 9 
10 注：默认参数需要放在参数列表最后
11 
12 默认参数

默认参数

1 # ######### 定义函数 ######### 
2 
3 # name 叫做函数func的形式参数，简称：形参
4 def func(name):
5     print name
6 
7 # ######### 执行函数 ######### 
8 #  'leon' 叫做函数func的实际参数，简称：实参
9 func('leon')

普通参数

 1 def func(*args):
 2 
 3     print args
 4 
 5 
 6 # 执行方式一
 7 func(11,33,4,4454,5)
 8 
 9 # 执行方式二
10 li = [11,2,2,3,3,4,54]
11 func(*li)
12 
13 动态参数-序列

动态参数-序列

 1 def func(**kwargs):
 2 
 3     print args
 4 
 5 
 6 # 执行方式一
 7 func(name＝'wupeiqi',age=18)
 8 
 9 # 执行方式二
10 li = {'name':'wupeiqi', age:18, 'gender':'male'}
11 func(**li)
12 
13 动态参数-字典

动态参数-字典

1 def func(*args, **kwargs):
2 
3     print args
4     print kwargs
5 
6 动态参数-序列和字典

动态参数-序列和字典

 1 import smtplib
 2 from email.mime.text import MIMEText
 3 from email.utils import formataddr
 4   
 5   
 6 msg = MIMEText('邮件内容', 'plain', 'utf-8')
 7 msg['From'] = formataddr(["Leon",'liyoung2008@163.com'])
 8 msg['To'] = formataddr(["test",'176487377@qq.com'])
 9 msg['Subject'] = "主题"
10   
11 server = smtplib.SMTP("smtp.163.com", 25)
12 server.login("liyoung2008@163.com", "邮箱密码")
13 server.sendmail('liyoung2008@163.com', ['176487377@qq.com',], msg.as_string())
14 server.quit()

发邮件实例

 1 #! /usr/bin/env python
 2 # -*- coding:utf-8 -*-
 3 """
 4 # 无参数
 5 # show():   ---> show()
 6 
 7 # 一个参数
 8 def show(arg):
 9     print(arg)
10 show('kkkkkk')
11 
12 # 两个参数
13 def show(arg,xxx):
14     print(arg,xxx)
15 show('kkkkkk','777')
16 
17 # 默认参数：
18 def show(a1,a2=999): # 指定a2 默认参数999，必须放在参数的后面
19     print(a1,a2)
20 show(111)
21 # 输出：111 999
22 
23 # 指定参数
24 def show(a1,a2):
25     print(a1,a2)
26 show(a2=123,a1=999)
27 # 输出：999 123
28 """
29 """
30 def show(arg):
31     print(arg)
32 n = [[11,22,33]]
33 show(n)
34 """
35 
36 # 动态参数：
37 
38 def show(*arg): # 带一个*将传入的参数转换成元组
39     print(arg,type(arg))
40 show(11,22,33,44,55)
41 # 输出：(11, 22, 33, 44, 55) <class 'tuple'>
42 
43 def show(**arg): # 带两个*将传入的参数转换成字典
44     print(arg,type(arg))
45 show(n1=111,n2=222,n3=333)
46 # 输出：{'n2': 222, 'n1': 111, 'n3': 333} <class 'dict'>
47 
48 def show(*args,**kwargs): #一个*的放前面，两个*放后面
49     print(args,type(args))
50     print(kwargs,type(kwargs))
51 show(11,22,33,44,n1=123,n2=321)
52 # 输出：
53 # (11, 22, 33, 44) <class 'tuple'>
54 # {'n1': 123, 'n2': 321} <class 'dict'>
55 
56 def show(*args,**kwargs): #一个*的放前面，两个*放后面
57     print(args,type(args))
58     print(kwargs,type(kwargs))
59 l = [11,22,33,44]
60 d = {"n1":"123","n2":"321"}
61 show(*l,**d)   #注意看这里的赋值

参数代码操作练习

10.使用动态参数实现字符串格式化

# 字符串的格式化
s1 = "{0} is {1}"
l = ['leon','nb']  #传入列表
# result = s1.format('leon','nb')
result = s1.format(*l)
print(result)


s1 = "{name} is {acter}"
d = {'name':'lee','acter':'nb'}    # 传入字典
# result = s1.format(name='leon',acter='nb')
result = s1.format(**d)
print(result)

11.Python lambda表达式

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

def func(a):
    a += 1
    return a
result = func(4)
print(result)

# lambda表达式：简单函数的简单表示

func = lambda a:a+1    # a是形式参数，冒号前面是参数，冒号后面是函数体，自动加return值
# 创建函数参数a
# 函数内容，a+1 并把结果返回
ret = func(99)
print(ret)

12. Python 内置函数

内置函数链接地址：请点击此处链接官网说明

内置函数二讲解：

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

class Foo:
    def __repr__(self):
        return 'aaaaa'
f = Foo()
ret = ascii(f)
print(ret)

# abs(1)  _abs_
# format(7)  = int._format_(7)
# hex(100) '0x64' 转换16进制
# max(11,22,333,4444,,5555)  可以取到最大值
# range(0,10) 进行for循环时创建0-9区间
# round(8.9)  输出9   用于四舍五入


p = bytearray('周杰伦',encoding='utf-8')
print(p)

p = bytes('周杰伦',encoding='utf-8')
print(p)

import random  # 做验证码时用到，chr（数字转换字符）和ord（字符转换数字）结合使用
f = random.randint(1,99)
print(f)

map & filter 说明：

1. map

遍历序列，对序列中每个元素进行操作，最终获取新的序列。

#每个元素增加100

li = [11, 22, 33]

new_list = map(lambda a: a + 100, li)

# 两个列表对应元素相加

li = [11, 22, 33]
sl = [1, 2, 3]
new_list = map(lambda a, b: a + b, li, sl)

2. filter

对于序列中的元素进行筛选，最终获取符合条件的序列

# 获取到大于22的值

li = [11, 22, 33]

new_list = filter(lambda arg: arg > 22, li)

#filter第一个参数为空，将获取原来序列

3. reduce (3.X中已不存在)

对于序列内所有元素进行累计操作

1 li = [11, 22, 33]
2 
3 result = reduce(lambda arg1, arg2: arg1 + arg2, li)
4 
5 # reduce的第一个参数，函数必须要有两个参数
6 # reduce的第二个参数，要循环的序列
7 # reduce的第三个参数，初始值

获取序列所有元素的和

13.Python 文件操作

13.1 文件操作：

  1 class file(object):
  2   
  3     def close(self): # real signature unknown; restored from __doc__
  4         关闭文件
  5         """
  6         close() -> None or (perhaps) an integer.  Close the file.
  7          
  8         Sets data attribute .closed to True.  A closed file cannot be used for
  9         further I/O operations.  close() may be called more than once without
 10         error.  Some kinds of file objects (for example, opened by popen())
 11         may return an exit status upon closing.
 12         """
 13  
 14     def fileno(self): # real signature unknown; restored from __doc__
 15         文件描述符  
 16          """
 17         fileno() -> integer "file descriptor".
 18          
 19         This is needed for lower-level file interfaces, such os.read().
 20         """
 21         return 0    
 22  
 23     def flush(self): # real signature unknown; restored from __doc__
 24         刷新文件内部缓冲区
 25         """ flush() -> None.  Flush the internal I/O buffer. """
 26         pass
 27  
 28  
 29     def isatty(self): # real signature unknown; restored from __doc__
 30         判断文件是否是同意tty设备
 31         """ isatty() -> true or false.  True if the file is connected to a tty device. """
 32         return False
 33  
 34  
 35     def next(self): # real signature unknown; restored from __doc__
 36         获取下一行数据，不存在，则报错
 37         """ x.next() -> the next value, or raise StopIteration """
 38         pass
 39  
 40     def read(self, size=None): # real signature unknown; restored from __doc__
 41         读取指定字节数据
 42         """
 43         read([size]) -> read at most size bytes, returned as a string.
 44          
 45         If the size argument is negative or omitted, read until EOF is reached.
 46         Notice that when in non-blocking mode, less data than what was requested
 47         may be returned, even if no size parameter was given.
 48         """
 49         pass
 50  
 51     def readinto(self): # real signature unknown; restored from __doc__
 52         读取到缓冲区，不要用，将被遗弃
 53         """ readinto() -> Undocumented.  Don't use this; it may go away. """
 54         pass
 55  
 56     def readline(self, size=None): # real signature unknown; restored from __doc__
 57         仅读取一行数据
 58         """
 59         readline([size]) -> next line from the file, as a string.
 60          
 61         Retain newline.  A non-negative size argument limits the maximum
 62         number of bytes to return (an incomplete line may be returned then).
 63         Return an empty string at EOF.
 64         """
 65         pass
 66  
 67     def readlines(self, size=None): # real signature unknown; restored from __doc__
 68         读取所有数据，并根据换行保存值列表
 69         """
 70         readlines([size]) -> list of strings, each a line from the file.
 71          
 72         Call readline() repeatedly and return a list of the lines so read.
 73         The optional size argument, if given, is an approximate bound on the
 74         total number of bytes in the lines returned.
 75         """
 76         return []
 77  
 78     def seek(self, offset, whence=None): # real signature unknown; restored from __doc__
 79         指定文件中指针位置
 80         """
 81         seek(offset[, whence]) -> None.  Move to new file position.
 82          
 83         Argument offset is a byte count.  Optional argument whence defaults to
 84         0 (offset from start of file, offset should be >= 0); other values are 1
 85         (move relative to current position, positive or negative), and 2 (move
 86         relative to end of file, usually negative, although many platforms allow
 87         seeking beyond the end of a file).  If the file is opened in text mode,
 88         only offsets returned by tell() are legal.  Use of other offsets causes
 89         undefined behavior.
 90         Note that not all file objects are seekable.
 91         """
 92         pass
 93  
 94     def tell(self): # real signature unknown; restored from __doc__
 95         获取当前指针位置
 96         """ tell() -> current file position, an integer (may be a long integer). """
 97         pass
 98  
 99     def truncate(self, size=None): # real signature unknown; restored from __doc__
100         截断数据，仅保留指定之前数据
101         """
102         truncate([size]) -> None.  Truncate the file to at most size bytes.
103          
104         Size defaults to the current file position, as returned by tell().
105         """
106         pass
107  
108     def write(self, p_str): # real signature unknown; restored from __doc__
109         写内容
110         """
111         write(str) -> None.  Write string str to file.
112          
113         Note that due to buffering, flush() or close() may be needed before
114         the file on disk reflects the data written.
115         """
116         pass
117  
118     def writelines(self, sequence_of_strings): # real signature unknown; restored from __doc__
119         将一个字符串列表写入文件
120         """
121         writelines(sequence_of_strings) -> None.  Write the strings to the file.
122          
123         Note that newlines are not added.  The sequence can be any iterable object
124         producing strings. This is equivalent to calling write() for each string.
125         """
126         pass
127  
128     def xreadlines(self): # real signature unknown; restored from __doc__
129         可用于逐行读取文件，非全部
130         """
131         xreadlines() -> returns self.
132          
133         For backward compatibility. File objects now include the performance
134         optimizations previously implemented in the xreadlines module.
135         """
136         pass

View Code

B. 基础知识点：

操作文件时，一般需要如下步骤：

打开文件 open
操作文件 r,w

read 是按照字符来读，tell按照字节来读的

f.tell #查看当前指针位置

f.seek #指定当前指针位置

13.2 打开文件

1 文件句柄 =file('文件路径', '模式')

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

打开文件时，需要指定文件路径和以何等方式打开文件，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）

"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）

13.3 with使用

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 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

部分内容引用：http://www.cnblogs.com/wupeiqi/tag/python之路（tks）

posted @ 2016-02-18 12:01 Leon2016 阅读(305) 评论(0) 编辑收藏举报

刷新页面返回顶部

Day 3 Python之循序渐进3

公告