Python高手之路【三】python基础之函数
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):<br/>
"""<br/>
set() -> new empty set object<br/>
set(iterable) -> new set object
Build an unordered collection of unique elements.<br/>
"""<br/>
def add(self, *args, **kwargs): # real signature unknown<br/>
"""<br/>
Add an element to a set,添加元素
This has no effect if the element is already present.<br/>
"""<br/>
pass
def clear(self, *args, **kwargs): # real signature unknown<br/>
""" Remove all elements from this set. 清除内容"""<br/>
pass
def copy(self, *args, **kwargs): # real signature unknown<br/>
""" Return a shallow copy of a set. 浅拷贝 """<br/>
pass
def difference(self, *args, **kwargs): # real signature unknown<br/>
"""<br/>
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.)<br/>
"""<br/>
pass
def difference_update(self, *args, **kwargs): # real signature unknown<br/>
""" 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"""<br/>
pass
def discard(self, *args, **kwargs): # real signature unknown<br/>
"""<br/>
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<br/>
"""<br/>
pass
def intersection(self, *args, **kwargs): # real signature unknown<br/>
"""<br/>
Return the intersection of two sets as a new set. 交集
(i.e. all elements that are in both sets.)<br/>
"""<br/>
pass
def intersection_update(self, *args, **kwargs): # real signature unknown<br/>
""" Update a set with the intersection of itself and another. 取交集并更更新到A中 """<br/>
pass
def isdisjoint(self, *args, **kwargs): # real signature unknown<br/>
""" Return True if two sets have a null intersection. 如果没有交集,返回True,否则返回False"""<br/>
pass
def issubset(self, *args, **kwargs): # real signature unknown<br/>
""" Report whether another set contains this set. 是否是子序列"""<br/>
pass
def issuperset(self, *args, **kwargs): # real signature unknown<br/>
""" Report whether this set contains another set. 是否是父序列"""<br/>
pass
def pop(self, *args, **kwargs): # real signature unknown<br/>
"""<br/>
Remove and return an arbitrary set element.<br/>
Raises KeyError if the set is empty. 移除元素<br/>
"""<br/>
pass
def remove(self, *args, **kwargs): # real signature unknown<br/>
"""<br/>
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<br/>
"""<br/>
pass
def symmetric_difference(self, *args, **kwargs): # real signature unknown<br/>
"""<br/>
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.)<br/>
"""<br/>
pass
def symmetric_difference_update(self, *args, **kwargs): # real signature unknown<br/>
""" Update a set with the symmetric difference of itself and another. 对称差集,并更新到a中 """<br/>
pass
def union(self, *args, **kwargs): # real signature unknown<br/>
"""<br/>
Return the union of sets as a new set. 并集
(i.e. all elements that are in either set.)<br/>
"""<br/>
pass
def update(self, *args, **kwargs): # real signature unknown<br/>
""" Update a set with the union of itself and others. 更新 """<br/>
pass
1:创建
s = set()<br/>
s = {11,22,33,55}
2:转换
li = [11,22,33,44]<br/> tu = (11,22,33,44)<br/> st = ''<br/> s = set(li)
3:intersection , intersection_update方法
a = {11,22,33,44}<br/>
b = {22,66,77,88}<br/>
ret = a.intersection(b)<br/>
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}<br/>
b = {22,66,77,88}<br/>
a.intersection_update(b)<br/>
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}<br/>
b = {11,22,77,55}<br/>
ret = s.isdisjoint(b)#有交集返回False,没有交集返回True<br/>
print(ret)<br/>
## False
issubset判断是否为子集
a = {11,22,33,44}<br/>
b = {11,44}<br/>
ret = b.issubset(a)<br/>
print(ret)<br/>
##########################################<br/>
True
issuperset判断是否为父集
a = {11,22,33,44}<br/>
b = {11,44}<br/>
ret = a.issubset(b)<br/>
print(ret)<br/>
##########################################<br/>
False
5:discard , remove , pop
s = {11,22,33,44}<br/>
s.remove(11)<br/>
print(s)<br/>
s.discard(22)<br/>
print(s)<br/>
s.pop()<br/>
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}<br/>
b = {11,22,77,55}<br/>
r1 = s.difference(b)<br/>
r2 = b.difference(s)<br/>
print(r1)<br/>
print(r2)<br/>
ret = s.symmetric_difference(b)<br/>
print(ret)<br/>
## set([33, 44])<br/>
## set([77, 55])<br/>
## 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}<br/>
b = {11,22,77,55}<br/>
ret = s.union(b)<br/>
print(ret)<br/>
## set([33, 11, 44, 77, 22, 55])
union方法合并两个集合
s = {11,22,33,44}<br/>
b = {11,22,77,55}<br/>
s.update(b)<br/>
print(s)<br/>
## 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<br/>
old_dict = {<br/>
"#1":{ 'hostname':'c1', 'cpu_count': 2, 'mem_capicity': 80 },<br/>
"#2":{ 'hostname':'c1', 'cpu_count': 2, 'mem_capicity': 80 },<br/>
"#3":{ 'hostname':'c1', 'cpu_count': 2, 'mem_capicity': 80 }<br/>
}
# cmdb 新汇报的数据<br/>
new_dict = {<br/>
"#1":{ 'hostname':'c1', 'cpu_count': 2, 'mem_capicity': 800 },<br/>
"#3":{ 'hostname':'c1', 'cpu_count': 2, 'mem_capicity': 80 },<br/>
"#4":{ 'hostname':'c2', 'cpu_count': 2, 'mem_capicity': 80 }<br/>
}
old_keys = set(old_dict.keys())<br/>
new_keys = set(new_dict.keys())<br/>
#需要更新元素的键<br/>
update_keys = old_keys.intersection(new_keys)<br/>
print(update_keys)<br/>
#需要删除元素的键<br/>
del_keys = old_keys.difference(new_keys)<br/>
#需要添加元素的键<br/>
add_keys = new_keys.difference(old_keys)<br/>
print(del_keys)<br/>
print(add_keys)<br/>
update_keys = list(update_keys)<br/>
for i in update_keys :<br/>
old_dict[i] = new_dict[i]<br/>
del_keys = list(del_keys)<br/>
for j in del_keys :<br/>
del old_dict[j]<br/>
for k in list(add_keys) :<br/>
old_dict[k] = new_dict[k]<br/>
print(old_dict)<br/>
########################################<br/>
{'#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')<br/>
print c<br/>
输出:Counter({'a': 5, 'b': 4, 'c': 3, 'd': 2, 'e': 1})
########################################################################<br/>
### Counter<br/>
########################################################################
class Counter(dict):<br/>
'''Dict subclass for counting hashable items. Sometimes called a bag<br/>
or multiset. Elements are stored as dictionary keys and their counts<br/>
are stored as dictionary values.
>>> c = Counter('abcdeabcdabcaba') # count elements from a string
>>> c.most_common(3) # three most common elements<br/>
[('a', 5), ('b', 4), ('c', 3)]<br/>
>>> sorted(c) # list all unique elements<br/>
['a', 'b', 'c', 'd', 'e']<br/>
>>> ''.join(sorted(c.elements())) # list elements with repetitions<br/>
'aaaaabbbbcccdde'<br/>
>>> sum(c.values()) # total of all counts
>>> c['a'] # count of letter 'a'<br/>
>>> for elem in 'shazam': # update counts from an iterable<br/>
... c[elem] += 1 # by adding 1 to each element's count<br/>
>>> c['a'] # now there are seven 'a'<br/>
>>> del c['b'] # remove all 'b'<br/>
>>> c['b'] # now there are zero 'b'
>>> d = Counter('simsalabim') # make another counter<br/>
>>> c.update(d) # add in the second counter<br/>
>>> c['a'] # now there are nine 'a'
>>> c.clear() # empty the counter<br/>
>>> c<br/>
Counter()
Note: If a count is set to zero or reduced to zero, it will remain<br/>
in the counter until the entry is deleted or the counter is cleared:
>>> c = Counter('aaabbc')<br/>
>>> c['b'] -= 2 # reduce the count of 'b' by two<br/>
>>> c.most_common() # 'b' is still in, but its count is zero<br/>
[('a', 3), ('c', 1), ('b', 0)]
'''<br/>
# References:<br/>
# http://en.wikipedia.org/wiki/Multiset<br/>
# http://www.gnu.org/software/smalltalk/manual-base/html_node/Bag.html<br/>
# http://www.demo2s.com/Tutorial/Cpp/0380__set-multiset/Catalog0380__set-multiset.htm<br/>
# http://code.activestate.com/recipes/259174/<br/>
# Knuth, TAOCP Vol. II section 4.6.3
def __init__(self, iterable=None, **kwds):<br/>
'''Create a new, empty Counter object. And if given, count elements<br/>
from an input iterable. Or, initialize the count from another mapping<br/>
of elements to their counts.
>>> c = Counter() # a new, empty counter<br/>
>>> c = Counter('gallahad') # a new counter from an iterable<br/>
>>> c = Counter({'a': 4, 'b': 2}) # a new counter from a mapping<br/>
>>> c = Counter(a=4, b=2) # a new counter from keyword args
'''<br/>
super(Counter, self).__init__()<br/>
self.update(iterable, **kwds)
def __missing__(self, key):<br/>
""" 对于不存在的元素,返回计数器为0 """<br/>
'The count of elements not in the Counter is zero.'<br/>
# Needed so that self[missing_item] does not raise KeyError<br/>
return 0
def most_common(self, n=None):<br/>
""" 数量大于等n的所有元素和计数器 """<br/>
'''List the n most common elements and their counts from the most<br/>
common to the least. If n is None, then list all element counts.
>>> Counter('abcdeabcdabcaba').most_common(3)<br/>
[('a', 5), ('b', 4), ('c', 3)]
'''<br/>
# Emulate Bag.sortedByCount from Smalltalk<br/>
if n is None:<br/>
return sorted(self.iteritems(), key=_itemgetter(1), reverse=True)<br/>
return _heapq.nlargest(n, self.iteritems(), key=_itemgetter(1))
def elements(self):<br/>
""" 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 """<br/>
'''Iterator over elements repeating each as many times as its count.
>>> c = Counter('ABCABC')<br/>
>>> sorted(c.elements())<br/>
['A', 'A', 'B', 'B', 'C', 'C']
# Knuth's example for prime factors of 1836: 2**2 * 3**3 * 17**1<br/>
>>> prime_factors = Counter({2: 2, 3: 3, 17: 1})<br/>
>>> product = 1<br/>
>>> for factor in prime_factors.elements(): # loop over factors<br/>
... product *= factor # and multiply them<br/>
>>> product
Note, if an element's count has been set to zero or is a negative<br/>
number, elements() will ignore it.
'''<br/>
# Emulate Bag.do from Smalltalk and Multiset.begin from C++.<br/>
return _chain.from_iterable(_starmap(_repeat, self.iteritems()))
# Override dict methods where necessary
@classmethod<br/>
def fromkeys(cls, iterable, v=None):<br/>
# There is no equivalent method for counters because setting v=1<br/>
# means that no element can have a count greater than one.<br/>
raise NotImplementedError(<br/>
'Counter.fromkeys() is undefined. Use Counter(iterable) instead.')
def update(self, iterable=None, **kwds):<br/>
""" 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 """<br/>
'''Like dict.update() but add counts instead of replacing them.
Source can be an iterable, a dictionary, or another Counter instance.
>>> c = Counter('which')<br/>
>>> c.update('witch') # add elements from another iterable<br/>
>>> d = Counter('watch')<br/>
>>> c.update(d) # add elements from another counter<br/>
>>> c['h'] # four 'h' in which, witch, and watch
'''<br/>
# The regular dict.update() operation makes no sense here because the<br/>
# replace behavior results in the some of original untouched counts<br/>
# being mixed-in with all of the other counts for a mismash that<br/>
# doesn't have a straight-forward interpretation in most counting<br/>
# contexts. Instead, we implement straight-addition. Both the inputs<br/>
# and outputs are allowed to contain zero and negative counts.
if iterable is not None:<br/>
if isinstance(iterable, Mapping):<br/>
if self:<br/>
self_get = self.get<br/>
for elem, count in iterable.iteritems():<br/>
self[elem] = self_get(elem, 0) + count<br/>
else:<br/>
super(Counter, self).update(iterable) # fast path when counter is empty<br/>
else:<br/>
self_get = self.get<br/>
for elem in iterable:<br/>
self[elem] = self_get(elem, 0) + 1<br/>
if kwds:<br/>
self.update(kwds)
def subtract(self, iterable=None, **kwds):<br/>
""" 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 """<br/>
'''Like dict.update() but subtracts counts instead of replacing them.<br/>
Counts can be reduced below zero. Both the inputs and outputs are<br/>
allowed to contain zero and negative counts.
Source can be an iterable, a dictionary, or another Counter instance.
>>> c = Counter('which')<br/>
>>> c.subtract('witch') # subtract elements from another iterable<br/>
>>> c.subtract(Counter('watch')) # subtract elements from another counter<br/>
>>> c['h'] # 2 in which, minus 1 in witch, minus 1 in watch<br/>
>>> c['w'] # 1 in which, minus 1 in witch, minus 1 in watch<br/>
-1
'''<br/>
if iterable is not None:<br/>
self_get = self.get<br/>
if isinstance(iterable, Mapping):<br/>
for elem, count in iterable.items():<br/>
self[elem] = self_get(elem, 0) - count<br/>
else:<br/>
for elem in iterable:<br/>
self[elem] = self_get(elem, 0) - 1<br/>
if kwds:<br/>
self.subtract(kwds)
def copy(self):<br/>
""" 拷贝 """<br/>
'Return a shallow copy.'<br/>
return self.__class__(self)
def __reduce__(self):<br/>
""" 返回一个元组(类型,元组) """<br/>
return self.__class__, (dict(self),)
def __delitem__(self, elem):<br/>
""" 删除元素 """<br/>
'Like dict.__delitem__() but does not raise KeyError for missing values.'<br/>
if elem in self:<br/>
super(Counter, self).__delitem__(elem)
def __repr__(self):<br/>
if not self:<br/>
return '%s()' % self.__class__.__name__<br/>
items = ', '.join(map('%r: %r'.__mod__, self.most_common()))<br/>
return '%s({%s})' % (self.__class__.__name__, items)
# Multiset-style mathematical operations discussed in:<br/>
# Knuth TAOCP Volume II section 4.6.3 exercise 19<br/>
# and at http://en.wikipedia.org/wiki/Multiset<br/>
#<br/>
# Outputs guaranteed to only include positive counts.<br/>
#<br/>
# To strip negative and zero counts, add-in an empty counter:<br/>
# c += Counter()
def __add__(self, other):<br/>
'''Add counts from two counters.
>>> Counter('abbb') + Counter('bcc')<br/>
Counter({'b': 4, 'c': 2, 'a': 1})
'''<br/>
if not isinstance(other, Counter):<br/>
return NotImplemented<br/>
result = Counter()<br/>
for elem, count in self.items():<br/>
newcount = count + other[elem]<br/>
if newcount > 0:<br/>
result[elem] = newcount<br/>
for elem, count in other.items():<br/>
if elem not in self and count > 0:<br/>
result[elem] = count<br/>
return result
def __sub__(self, other):<br/>
''' Subtract count, but keep only results with positive counts.
>>> Counter('abbbc') - Counter('bccd')<br/>
Counter({'b': 2, 'a': 1})
'''<br/>
if not isinstance(other, Counter):<br/>
return NotImplemented<br/>
result = Counter()<br/>
for elem, count in self.items():<br/>
newcount = count - other[elem]<br/>
if newcount > 0:<br/>
result[elem] = newcount<br/>
for elem, count in other.items():<br/>
if elem not in self and count < 0:<br/>
result[elem] = 0 - count<br/>
return result
def __or__(self, other):<br/>
'''Union is the maximum of value in either of the input counters.
>>> Counter('abbb') | Counter('bcc')<br/>
Counter({'b': 3, 'c': 2, 'a': 1})
'''<br/>
if not isinstance(other, Counter):<br/>
return NotImplemented<br/>
result = Counter()<br/>
for elem, count in self.items():<br/>
other_count = other[elem]<br/>
newcount = other_count if count < other_count else count<br/>
if newcount > 0:<br/>
result[elem] = newcount<br/>
for elem, count in other.items():<br/>
if elem not in self and count > 0:<br/>
result[elem] = count<br/>
return result
def __and__(self, other):<br/>
''' Intersection is the minimum of corresponding counts.
>>> Counter('abbb') & Counter('bcc')<br/>
Counter({'b': 1})
'''<br/>
if not isinstance(other, Counter):<br/>
return NotImplemented<br/>
result = Counter()<br/>
for elem, count in self.items():<br/>
other_count = other[elem]<br/>
newcount = count if count < other_count else other_count<br/>
if newcount > 0:<br/>
result[elem] = newcount<br/>
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):<br/>
'Dictionary that remembers insertion order'<br/>
# An inherited dict maps keys to values.<br/>
# The inherited dict provides __getitem__, __len__, __contains__, and get.<br/>
# The remaining methods are order-aware.<br/>
# 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.<br/>
# The circular doubly linked list starts and ends with a sentinel element.<br/>
# The sentinel element never gets deleted (this simplifies the algorithm).<br/>
# Each link is stored as a list of length three: [PREV, NEXT, KEY].
def __init__(self, *args, **kwds):<br/>
'''Initialize an ordered dictionary. The signature is the same as<br/>
regular dictionaries, but keyword arguments are not recommended because<br/>
their insertion order is arbitrary.
'''<br/>
if len(args) > 1:<br/>
raise TypeError('expected at most 1 arguments, got %d' % len(args))<br/>
try:<br/>
self.__root<br/>
except AttributeError:<br/>
self.__root = root = [] # sentinel node<br/>
root[:] = [root, root, None]<br/>
self.__map = {}<br/>
self.__update(*args, **kwds)
def __setitem__(self, key, value, dict_setitem=dict.__setitem__):<br/>
'od.__setitem__(i, y) <==> od[i]=y'<br/>
# Setting a new item creates a new link at the end of the linked list,<br/>
# and the inherited dictionary is updated with the new key/value pair.<br/>
if key not in self:<br/>
root = self.__root<br/>
last = root[0]<br/>
last[1] = root[0] = self.__map[key] = [last, root, key]<br/>
return dict_setitem(self, key, value)
def __delitem__(self, key, dict_delitem=dict.__delitem__):<br/>
'od.__delitem__(y) <==> del od[y]'<br/>
# Deleting an existing item uses self.__map to find the link which gets<br/>
# removed by updating the links in the predecessor and successor nodes.<br/>
dict_delitem(self, key)<br/>
link_prev, link_next, _ = self.__map.pop(key)<br/>
link_prev[1] = link_next # update link_prev[NEXT]<br/>
link_next[0] = link_prev # update link_next[PREV]
def __iter__(self):<br/>
'od.__iter__() <==> iter(od)'<br/>
# Traverse the linked list in order.<br/>
root = self.__root<br/>
curr = root[1] # start at the first node<br/>
while curr is not root:<br/>
yield curr[2] # yield the curr[KEY]<br/>
curr = curr[1] # move to next node
def __reversed__(self):<br/>
'od.__reversed__() <==> reversed(od)'<br/>
# Traverse the linked list in reverse order.<br/>
root = self.__root<br/>
curr = root[0] # start at the last node<br/>
while curr is not root:<br/>
yield curr[2] # yield the curr[KEY]<br/>
curr = curr[0] # move to previous node
def clear(self):<br/>
'od.clear() -> None. Remove all items from od.'<br/>
root = self.__root<br/>
root[:] = [root, root, None]<br/>
self.__map.clear()<br/>
dict.clear(self)
# -- the following methods do not depend on the internal structure --
def keys(self):<br/>
'od.keys() -> list of keys in od'<br/>
return list(self)
def values(self):<br/>
'od.values() -> list of values in od'<br/>
return [self[key] for key in self]
def items(self):<br/>
'od.items() -> list of (key, value) pairs in od'<br/>
return [(key, self[key]) for key in self]
def iterkeys(self):<br/>
'od.iterkeys() -> an iterator over the keys in od'<br/>
return iter(self)
def itervalues(self):<br/>
'od.itervalues -> an iterator over the values in od'<br/>
for k in self:<br/>
yield self[k]
def iteritems(self):<br/>
'od.iteritems -> an iterator over the (key, value) pairs in od'<br/>
for k in self:<br/>
yield (k, self[k])
update = MutableMapping.update
__update = update # let subclasses override update without breaking __init__
__marker = object()
def pop(self, key, default=__marker):<br/>
'''od.pop(k[,d]) -> v, remove specified key and return the corresponding<br/>
value. If key is not found, d is returned if given, otherwise KeyError<br/>
is raised.
'''<br/>
if key in self:<br/>
result = self[key]<br/>
del self[key]<br/>
return result<br/>
if default is self.__marker:<br/>
raise KeyError(key)<br/>
return default
def setdefault(self, key, default=None):<br/>
'od.setdefault(k[,d]) -> od.get(k,d), also set od[k]=d if k not in od'<br/>
if key in self:<br/>
return self[key]<br/>
self[key] = default<br/>
return default
def popitem(self, last=True):<br/>
'''od.popitem() -> (k, v), return and remove a (key, value) pair.<br/>
Pairs are returned in LIFO order if last is true or FIFO order if false.
'''<br/>
if not self:<br/>
raise KeyError('dictionary is empty')<br/>
key = next(reversed(self) if last else iter(self))<br/>
value = self.pop(key)<br/>
return key, value
def __repr__(self, _repr_running={}):<br/>
'od.__repr__() <==> repr(od)'<br/>
call_key = id(self), _get_ident()<br/>
if call_key in _repr_running:<br/>
return '...'<br/>
_repr_running[call_key] = 1<br/>
try:<br/>
if not self:<br/>
return '%s()' % (self.__class__.__name__,)<br/>
return '%s(%r)' % (self.__class__.__name__, self.items())<br/>
finally:<br/>
del _repr_running[call_key]
def __reduce__(self):<br/>
'Return state information for pickling'<br/>
items = [[k, self[k]] for k in self]<br/>
inst_dict = vars(self).copy()<br/>
for k in vars(OrderedDict()):<br/>
inst_dict.pop(k, None)<br/>
if inst_dict:<br/>
return (self.__class__, (items,), inst_dict)<br/>
return self.__class__, (items,)
def copy(self):<br/>
'od.copy() -> a shallow copy of od'<br/>
return self.__class__(self)
@classmethod<br/>
def fromkeys(cls, iterable, value=None):<br/>
'''OD.fromkeys(S[, v]) -> New ordered dictionary with keys from S.<br/>
If not specified, the value defaults to None.
'''<br/>
self = cls()<br/>
for key in iterable:<br/>
self[key] = value<br/>
return self
def __eq__(self, other):<br/>
'''od.__eq__(y) <==> od==y. Comparison to another OD is order-sensitive<br/>
while comparison to a regular mapping is order-insensitive.
'''<br/>
if isinstance(other, OrderedDict):<br/>
return dict.__eq__(self, other) and all(_imap(_eq, self, other))<br/>
return dict.__eq__(self, other)
def __ne__(self, other):<br/>
'od.__ne__(y) <==> od!=y'<br/>
return not self == other
# -- the following methods support python 3.x style dictionary views --
def viewkeys(self):<br/>
"od.viewkeys() -> a set-like object providing a view on od's keys"<br/>
return KeysView(self)
def viewvalues(self):<br/>
"od.viewvalues() -> an object providing a view on od's values"<br/>
return ValuesView(self)
def viewitems(self):<br/>
"od.viewitems() -> a set-like object providing a view on od's items"<br/>
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):<br/>
"""<br/>
defaultdict(default_factory[, ...]) --> dict with default factory
The default factory is called without arguments to produce<br/>
a new value when a key is not present, in __getitem__ only.<br/>
A defaultdict compares equal to a dict with the same items.<br/>
All remaining arguments are treated the same as if they were<br/>
passed to the dict constructor, including keyword arguments.<br/>
"""<br/>
def copy(self): # real signature unknown; restored from __doc__<br/>
""" D.copy() -> a shallow copy of D. """<br/>
pass
def __copy__(self, *args, **kwargs): # real signature unknown<br/>
""" D.copy() -> a shallow copy of D. """<br/>
pass
def __getattribute__(self, name): # real signature unknown; restored from __doc__<br/>
""" x.__getattribute__('name') <==> x.name """<br/>
pass
def __init__(self, default_factory=None, **kwargs): # known case of _collections.defaultdict.__init__<br/>
"""<br/>
defaultdict(default_factory[, ...]) --> dict with default factory
The default factory is called without arguments to produce<br/>
a new value when a key is not present, in __getitem__ only.<br/>
A defaultdict compares equal to a dict with the same items.<br/>
All remaining arguments are treated the same as if they were<br/>
passed to the dict constructor, including keyword arguments.
# (copied from class doc)<br/>
"""<br/>
pass
def __missing__(self, key): # real signature unknown; restored from __doc__<br/>
"""<br/>
__missing__(key) # Called by __getitem__ for missing key; pseudo-code:<br/>
if self.default_factory is None: raise KeyError((key,))<br/>
self[key] = value = self.default_factory()<br/>
return value<br/>
"""<br/>
pass
def __reduce__(self, *args, **kwargs): # real signature unknown<br/>
""" Return state information for pickling. """<br/>
pass
def __repr__(self): # real signature unknown; restored from __doc__<br/>
""" x.__repr__() <==> repr(x) """<br/>
pass
default_factory = property(lambda self: object(), lambda self, v: None, lambda self: None) # default<br/>
"""Factory for default value called by __missing__()."""
defaultdict
defaultdict
使用方法:
import collections<br/>
dic = collections.defaultdict(list)<br/>
dic['k1'].append('alext')<br/>
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。<br/>
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:<br/>
if value>66:<br/>
if my_dict.has_key('k1'):<br/>
my_dict['k1'].append(value)<br/>
else:<br/>
my_dict['k1'] = [value]<br/>
else:<br/>
if my_dict.has_key('k2'):<br/>
my_dict['k2'].append(value)<br/>
else:<br/>
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:<br/>
if value>66:<br/>
my_dict['k1'].append(value)<br/>
else:<br/>
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<br/>
MytupleClass = collections.namedtuple('MytupleClass',['x','y','z'])<br/>
obj = MytupleClass(11,33,44)<br/>
print(obj.x)<br/>
print(obj.y)<br/>
print(obj.z)
class Mytuple(__builtin__.tuple)<br/>
| Mytuple(x, y)<br/>
|<br/>
| Method resolution order:<br/>
| Mytuple<br/>
| __builtin__.tuple<br/>
| __builtin__.object<br/>
|<br/>
| Methods defined here:<br/>
|<br/>
| __getnewargs__(self)<br/>
| Return self as a plain tuple. Used by copy and pickle.<br/>
|<br/>
| __getstate__(self)<br/>
| Exclude the OrderedDict from pickling<br/>
|<br/>
| __repr__(self)<br/>
| Return a nicely formatted representation string<br/>
|<br/>
| _asdict(self)<br/>
| Return a new OrderedDict which maps field names to their values<br/>
|<br/>
| _replace(_self, **kwds)<br/>
| Return a new Mytuple object replacing specified fields with new values<br/>
|<br/>
| ----------------------------------------------------------------------<br/>
| Class methods defined here:<br/>
|<br/>
| _make(cls, iterable, new=<built-in method __new__ of type object>, len=<built-in function len>) from __builtin__.type<br/>
| Make a new Mytuple object from a sequence or iterable<br/>
|<br/>
| ----------------------------------------------------------------------<br/>
| Static methods defined here:<br/>
|<br/>
| __new__(_cls, x, y)<br/>
| Create new instance of Mytuple(x, y)<br/>
|<br/>
| ----------------------------------------------------------------------<br/>
| Data descriptors defined here:<br/>
|<br/>
| __dict__<br/>
| Return a new OrderedDict which maps field names to their values<br/>
|<br/>
| x<br/>
| Alias for field number 0<br/>
|<br/>
| y<br/>
| Alias for field number 1<br/>
|<br/>
| ----------------------------------------------------------------------<br/>
| Data and other attributes defined here:<br/>
|<br/>
| _fields = ('x', 'y')<br/>
|<br/>
| ----------------------------------------------------------------------<br/>
| Methods inherited from __builtin__.tuple:<br/>
|<br/>
| __add__(...)<br/>
| x.__add__(y) <==> x+y<br/>
|<br/>
| __contains__(...)<br/>
| x.__contains__(y) <==> y in x<br/>
|<br/>
| __eq__(...)<br/>
| x.__eq__(y) <==> x==y<br/>
|<br/>
| __ge__(...)<br/>
| x.__ge__(y) <==> x>=y<br/>
|<br/>
| __getattribute__(...)<br/>
| x.__getattribute__('name') <==> x.name<br/>
|<br/>
| __getitem__(...)<br/>
| x.__getitem__(y) <==> x[y]<br/>
|<br/>
| __getslice__(...)<br/>
| x.__getslice__(i, j) <==> x[i:j]<br/>
|<br/>
| Use of negative indices is not supported.<br/>
|<br/>
| __gt__(...)<br/>
| x.__gt__(y) <==> x>y<br/>
|<br/>
| __hash__(...)<br/>
| x.__hash__() <==> hash(x)<br/>
|<br/>
| __iter__(...)<br/>
| x.__iter__() <==> iter(x)<br/>
|<br/>
| __le__(...)<br/>
| x.__le__(y) <==> x<=y<br/>
|<br/>
| __len__(...)<br/>
| x.__len__() <==> len(x)<br/>
|<br/>
| __lt__(...)<br/>
| x.__lt__(y) <==> x<y<br/>
|<br/>
| __mul__(...)<br/>
| x.__mul__(n) <==> x*n<br/>
|<br/>
| __ne__(...)<br/>
| x.__ne__(y) <==> x!=y<br/>
|<br/>
| __rmul__(...)<br/>
| x.__rmul__(n) <==> n*x<br/>
|<br/>
| __sizeof__(...)<br/>
| T.__sizeof__() -- size of T in memory, in bytes<br/>
|<br/>
| count(...)<br/>
| T.count(value) -> integer -- return number of occurrences of value<br/>
|<br/>
| index(...)<br/>
| T.index(value, [start, [stop]]) -> integer -- return first index of value.<br/>
| 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):<br/>
"""<br/>
deque([iterable[, maxlen]]) --> deque object
Build an ordered collection with optimized access from its endpoints.<br/>
"""<br/>
def append(self, *args, **kwargs): # real signature unknown<br/>
""" Add an element to the right side of the deque. """<br/>
pass
def appendleft(self, *args, **kwargs): # real signature unknown<br/>
""" Add an element to the left side of the deque. """<br/>
pass
def clear(self, *args, **kwargs): # real signature unknown<br/>
""" Remove all elements from the deque. """<br/>
pass
def count(self, value): # real signature unknown; restored from __doc__<br/>
""" D.count(value) -> integer -- return number of occurrences of value """<br/>
return 0
def extend(self, *args, **kwargs): # real signature unknown<br/>
""" Extend the right side of the deque with elements from the iterable """<br/>
pass
def extendleft(self, *args, **kwargs): # real signature unknown<br/>
""" Extend the left side of the deque with elements from the iterable """<br/>
pass
def pop(self, *args, **kwargs): # real signature unknown<br/>
""" Remove and return the rightmost element. """<br/>
pass
def popleft(self, *args, **kwargs): # real signature unknown<br/>
""" Remove and return the leftmost element. """<br/>
pass
def remove(self, value): # real signature unknown; restored from __doc__<br/>
""" D.remove(value) -- remove first occurrence of value. """<br/>
pass
def reverse(self): # real signature unknown; restored from __doc__<br/>
""" D.reverse() -- reverse *IN PLACE* """<br/>
pass
def rotate(self, *args, **kwargs): # real signature unknown<br/>
""" Rotate the deque n steps to the right (default n=1). If n is negative, rotates left. """<br/>
pass
def __copy__(self, *args, **kwargs): # real signature unknown<br/>
""" Return a shallow copy of a deque. """<br/>
pass
def __delitem__(self, y): # real signature unknown; restored from __doc__<br/>
""" x.__delitem__(y) <==> del x[y] """<br/>
pass
def __eq__(self, y): # real signature unknown; restored from __doc__<br/>
""" x.__eq__(y) <==> x==y """<br/>
pass
def __getattribute__(self, name): # real signature unknown; restored from __doc__<br/>
""" x.__getattribute__('name') <==> x.name """<br/>
pass
def __getitem__(self, y): # real signature unknown; restored from __doc__<br/>
""" x.__getitem__(y) <==> x[y] """<br/>
pass
def __ge__(self, y): # real signature unknown; restored from __doc__<br/>
""" x.__ge__(y) <==> x>=y """<br/>
pass
def __gt__(self, y): # real signature unknown; restored from __doc__<br/>
""" x.__gt__(y) <==> x>y """<br/>
pass
def __iadd__(self, y): # real signature unknown; restored from __doc__<br/>
""" x.__iadd__(y) <==> x+=y """<br/>
pass
def __init__(self, iterable=(), maxlen=None): # known case of _collections.deque.__init__<br/>
"""<br/>
deque([iterable[, maxlen]]) --> deque object
Build an ordered collection with optimized access from its endpoints.<br/>
# (copied from class doc)<br/>
"""<br/>
pass
def __iter__(self): # real signature unknown; restored from __doc__<br/>
""" x.__iter__() <==> iter(x) """<br/>
pass
def __len__(self): # real signature unknown; restored from __doc__<br/>
""" x.__len__() <==> len(x) """<br/>
pass
def __le__(self, y): # real signature unknown; restored from __doc__<br/>
""" x.__le__(y) <==> x<=y """<br/>
pass
def __lt__(self, y): # real signature unknown; restored from __doc__<br/>
""" x.__lt__(y) <==> x<y """<br/>
pass
@staticmethod # known case of __new__<br/>
def __new__(S, *more): # real signature unknown; restored from __doc__<br/>
""" T.__new__(S, ...) -> a new object with type S, a subtype of T """<br/>
pass
def __ne__(self, y): # real signature unknown; restored from __doc__<br/>
""" x.__ne__(y) <==> x!=y """<br/>
pass
def __reduce__(self, *args, **kwargs): # real signature unknown<br/>
""" Return state information for pickling. """<br/>
pass
def __repr__(self): # real signature unknown; restored from __doc__<br/>
""" x.__repr__() <==> repr(x) """<br/>
pass
def __reversed__(self): # real signature unknown; restored from __doc__<br/>
""" D.__reversed__() -- return a reverse iterator over the deque """<br/>
pass
def __setitem__(self, i, y): # real signature unknown; restored from __doc__<br/>
""" x.__setitem__(i, y) <==> x[i]=y """<br/>
pass
def __sizeof__(self): # real signature unknown; restored from __doc__<br/>
""" D.__sizeof__() -- size of D in memory, in bytes """<br/>
pass
maxlen = property(lambda self: object(), lambda self, v: None, lambda self: None) # default<br/>
"""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:<br/>
"""Create a queue object with a given maximum size.
If maxsize is <= 0, the queue size is infinite.<br/>
"""<br/>
def __init__(self, maxsize=0):<br/>
self.maxsize = maxsize<br/>
self._init(maxsize)<br/>
# mutex must be held whenever the queue is mutating. All methods<br/>
# that acquire mutex must release it before returning. mutex<br/>
# is shared between the three conditions, so acquiring and<br/>
# releasing the conditions also acquires and releases mutex.<br/>
self.mutex = _threading.Lock()<br/>
# Notify not_empty whenever an item is added to the queue; a<br/>
# thread waiting to get is notified then.<br/>
self.not_empty = _threading.Condition(self.mutex)<br/>
# Notify not_full whenever an item is removed from the queue;<br/>
# a thread waiting to put is notified then.<br/>
self.not_full = _threading.Condition(self.mutex)<br/>
# Notify all_tasks_done whenever the number of unfinished tasks<br/>
# drops to zero; thread waiting to join() is notified to resume<br/>
self.all_tasks_done = _threading.Condition(self.mutex)<br/>
self.unfinished_tasks = 0
def task_done(self):<br/>
"""Indicate that a formerly enqueued task is complete.
Used by Queue consumer threads. For each get() used to fetch a task,<br/>
a subsequent call to task_done() tells the queue that the processing<br/>
on the task is complete.
If a join() is currently blocking, it will resume when all items<br/>
have been processed (meaning that a task_done() call was received<br/>
for every item that had been put() into the queue).
Raises a ValueError if called more times than there were items<br/>
placed in the queue.<br/>
"""<br/>
self.all_tasks_done.acquire()<br/>
try:<br/>
unfinished = self.unfinished_tasks - 1<br/>
if unfinished <= 0:<br/>
if unfinished < 0:<br/>
raise ValueError('task_done() called too many times')<br/>
self.all_tasks_done.notify_all()<br/>
self.unfinished_tasks = unfinished<br/>
finally:<br/>
self.all_tasks_done.release()
def join(self):<br/>
"""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<br/>
queue. The count goes down whenever a consumer thread calls task_done()<br/>
to indicate the item was retrieved and all work on it is complete.
When the count of unfinished tasks drops to zero, join() unblocks.<br/>
"""<br/>
self.all_tasks_done.acquire()<br/>
try:<br/>
while self.unfinished_tasks:<br/>
self.all_tasks_done.wait()<br/>
finally:<br/>
self.all_tasks_done.release()
def qsize(self):<br/>
"""Return the approximate size of the queue (not reliable!)."""<br/>
self.mutex.acquire()<br/>
n = self._qsize()<br/>
self.mutex.release()<br/>
return n
def empty(self):<br/>
"""Return True if the queue is empty, False otherwise (not reliable!)."""<br/>
self.mutex.acquire()<br/>
n = not self._qsize()<br/>
self.mutex.release()<br/>
return n
def full(self):<br/>
"""Return True if the queue is full, False otherwise (not reliable!)."""<br/>
self.mutex.acquire()<br/>
n = 0 < self.maxsize == self._qsize()<br/>
self.mutex.release()<br/>
return n
def put(self, item, block=True, timeout=None):<br/>
"""Put an item into the queue.
If optional args 'block' is true and 'timeout' is None (the default),<br/>
block if necessary until a free slot is available. If 'timeout' is<br/>
a non-negative number, it blocks at most 'timeout' seconds and raises<br/>
the Full exception if no free slot was available within that time.<br/>
Otherwise ('block' is false), put an item on the queue if a free slot<br/>
is immediately available, else raise the Full exception ('timeout'<br/>
is ignored in that case).<br/>
"""<br/>
self.not_full.acquire()<br/>
try:<br/>
if self.maxsize > 0:<br/>
if not block:<br/>
if self._qsize() == self.maxsize:<br/>
raise Full<br/>
elif timeout is None:<br/>
while self._qsize() == self.maxsize:<br/>
self.not_full.wait()<br/>
elif timeout < 0:<br/>
raise ValueError("'timeout' must be a non-negative number")<br/>
else:<br/>
endtime = _time() + timeout<br/>
while self._qsize() == self.maxsize:<br/>
remaining = endtime - _time()<br/>
if remaining <= 0.0:<br/>
raise Full<br/>
self.not_full.wait(remaining)<br/>
self._put(item)<br/>
self.unfinished_tasks += 1<br/>
self.not_empty.notify()<br/>
finally:<br/>
self.not_full.release()
def put_nowait(self, item):<br/>
"""Put an item into the queue without blocking.
Only enqueue the item if a free slot is immediately available.<br/>
Otherwise raise the Full exception.<br/>
"""<br/>
return self.put(item, False)
def get(self, block=True, timeout=None):<br/>
"""Remove and return an item from the queue.
If optional args 'block' is true and 'timeout' is None (the default),<br/>
block if necessary until an item is available. If 'timeout' is<br/>
a non-negative number, it blocks at most 'timeout' seconds and raises<br/>
the Empty exception if no item was available within that time.<br/>
Otherwise ('block' is false), return an item if one is immediately<br/>
available, else raise the Empty exception ('timeout' is ignored<br/>
in that case).<br/>
"""<br/>
self.not_empty.acquire()<br/>
try:<br/>
if not block:<br/>
if not self._qsize():<br/>
raise Empty<br/>
elif timeout is None:<br/>
while not self._qsize():<br/>
self.not_empty.wait()<br/>
elif timeout < 0:<br/>
raise ValueError("'timeout' must be a non-negative number")<br/>
else:<br/>
endtime = _time() + timeout<br/>
while not self._qsize():<br/>
remaining = endtime - _time()<br/>
if remaining <= 0.0:<br/>
raise Empty<br/>
self.not_empty.wait(remaining)<br/>
item = self._get()<br/>
self.not_full.notify()<br/>
return item<br/>
finally:<br/>
self.not_empty.release()
def get_nowait(self):<br/>
"""Remove and return an item from the queue without blocking.
Only get an item if one is immediately available. Otherwise<br/>
raise the Empty exception.<br/>
"""<br/>
return self.get(False)
# Override these methods to implement other queue organizations<br/>
# (e.g. stack or priority queue).<br/>
# These will only be called with appropriate locks held
# Initialize the queue representation<br/>
def _init(self, maxsize):<br/>
self.queue = deque()
def _qsize(self, len=len):<br/>
return len(self.queue)
# Put a new item in the queue<br/>
def _put(self, item):<br/>
self.queue.append(item)
# Get an item from the queue<br/>
def _get(self):<br/>
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<br/> result = 值1 if 条件 else 值2<br/> # 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<br/> name = 'poe' if a == 1 else 'jet'<br/> 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<br/> # ######### 数字、字符串 #########<br/> n1 = 123<br/> # n1 = "i am alex age 10"<br/> print(id(n1))<br/> # ## 赋值 ##<br/> n2 = n1<br/> print(id(n2))<br/> # ## 浅拷贝 ##<br/> n2 = copy.copy(n1)<br/> print(id(n2)) # ## 深拷贝 ##<br/> n3 = copy.deepcopy(n1)<br/> 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):
...<br/>
函数体<br/>
...<br/>
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:<br/>
return True<br/>
else:<br/>
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<br/>
# 之后,可以根据result来写日志,或重发等操作
result = 发送短信()<br/>
if result == False:<br/>
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<br/>
def func(name):<br/>
print name
# ######### 执行函数 #########<br/>
# 'wupeiqi' 叫做函数func的实际参数,简称:实参<br/>
func('poe')
默认参数
def func(name, age = 18):
print "%s:%s" %(name,age)
# 指定参数<br/>
func('poe', 19)<br/>
# 使用默认参数<br/>
func('gin')
注:默认参数需要放在参数列表最后
动态参数
def f1(*a):<br/>
print(a,type(a))<br/>
f1(123,456,[1,2,3],'who')<br/>
## ((123, 456, [1, 2, 3], 'who'), <type 'tuple'>)
def func(**kwargs):<br/>
print args<br/>
# 执行方式一<br/>
func(name='poe',age=18)
# 执行方式二<br/>
li = {'name':'poe', age:18, 'gender':'male'}<br/>
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<br/>
print(a,type(a))<br/>
print(b,type(b))<br/>
f1(11,22,33,k1=1234,k2=456)<br/>
## ((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) :<br/>
print(args,type(args))<br/>
li = [1,2,3,4]<br/>
f1(li)<br/>
f1(*li)<br/>
## (([1, 2, 3, 4],), <type 'tuple'>)<br/>
## ((1, 2, 3, 4), <type 'tuple'>)
def f2(**kwargs) :<br/>
print(kwargs,type(kwargs))<br/>
dic = {'k1':123,'k2':456}<br/>
f2(k1 = dic)<br/>
f2(**dic)<br/>
## ({'k1': {'k2': 456, 'k1': 123}}, <type 'dict'>)<br/>
## ({'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
- chr(i) 函数返回ASCII码对应的字符串
-
print(chr(65))<br/> print(chr(66))<br/> print(chr(65)+chr(66))<br/> ##########################################<br/> A<br/> B<br/> AB
- complex(real[,imaginary]) 函数可把字符串或数字转换为复数
-
print(complex("2+1j"))<br/> print(complex(""))<br/> print(complex(2,1))<br/> ##########################################<br/> (2+1j)<br/> (2+0j)<br/> (2+1j) - float(x) 函数把一个数字或字符串转换成浮点数
-
print(float(12))<br/> print(float(12.2))<br/> ##########################################<br/> 12.0<br/> 12.2
- 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
- list(x) 函数可将序列对象转换成列表
- min(x[,y,z…]) 函数返回给定参数的最小值,参数可以为序列
- max(x[,y,z…]) 函数返回给定参数的最大值,参数可以为序列
- ord(x) 函数返回一个字符串参数的ASCII码或Unicode值
-
print(ord('a'))<br/> print(ord(u"A"))<br/> ##########################################<br/> 97<br/> 65 - str(obj) 函数把对象转换成可打印字符串
- tuple(x) 函数把序列对象转换成tuple
- 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<br/> print(all(['a','b','','d']))#False<br/> #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<br/> print(any(['a',0,' ',False]))#True<br/> 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))<br/>
print(ascii('a'))<br/>
print(ascii(123))<br/>
print(ascii('中文'))#非ascii字符<br/>
##########################################<br/>
1<br/>
'a'<br/>
123<br/>
'\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<br/>
if 1 == 1:<br/>
name = 'poe'<br/>
else:<br/>
name = 'bruce'
# 三元运算<br/>
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 ######################<br/>
# 定义函数(普通方式)<br/>
def func(arg):<br/>
return arg + 1
# 执行函数<br/>
result = func(123)
# ###################### lambda ######################
# 定义函数(lambda表达式)<br/>
my_lambda = lambda arg : arg + 1
# 执行函数<br/>
result = my_lambda(123)
生成随机数:
import random<br/>
chars = ''<br/>
for i in range(4) :<br/>
rand_num = random.randrange(0,4)<br/>
if rand_num == 3 or rand_num == 1:<br/>
rand_digit = random.randrange(0,10)<br/>
chars += str(rand_digit)<br/>
else:<br/>
rand_case = random.randrange(65,90)<br/>
case = chr(rand_case)<br/>
chars += case<br/>
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)<br/>
def is_odd(x) :<br/>
return x % 2 == 1<br/>
li = [1, 4, 6, 7, 9, 12, 17]<br/>
result = filter(is_odd,li)<br/>
print(result)<br/>
##########################################<br/>
[1, 7, 9, 17]
例2:删除 列表中的None 或者空字符串
li = ['test', None, '', 'str', ' ', 'END']<br/>
def is_not_empty(s) :<br/>
return s and len(s.strip()) > 0<br/>
print(filter(is_not_empty,li))<br/>
##########################################<br/>
['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<br/>
def is_sqr(x) :<br/>
return math.sqrt(x) % 1 == 0<br/>
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])<br/> 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]<br/>
print(li)<br/>
def f(x) :<br/>
return x*x<br/>
r = list(map(f,[1, 2, 3, 4, 5, 6, 7, 8, 9]))<br/>
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))#<br/> 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))#<br/> print(int('0o7',8))#<br/> 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:
- 打开文件
- 操作文件
一:打开文件
文件句柄 = open('文件路径', '模式')
打开文件时,需要指定文件路径和以何等方式打开文件,打开后,即可获取该文件句柄,日后通过此文件句柄对该文件操作。
打开文件的模式有:
- r ,只读模式【默认】
- w,只写模式【不可读;不存在则创建;存在则清空内容;】
- 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')<br/>
data = f.read()<br/>
f.close()<br/>
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<br/>
f = open('test.log','r+',encoding='utf-8')<br/>
print(f.tell())#打印当前指针所在的位置,此时为0<br/>
data = f.read()<br/>
print(data)<br/>
print(f.tell())#此时当前指针在文件最末尾<br/>
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<br/>
f = open('test.log','w+',encoding="utf-8")<br/>
f.write('python')#写完后,指针到了最后<br/>
f.seek(0)#移动指针到开头<br/>
data = f.read()<br/>
f.close()<br/>
print(data)
# a+模式:打开的同时,指针已经到最后,<br/>
# 写时,追加,指针到最后<br/>
f = open('test.log','a+',encoding="utf-8")<br/>
print(f.tell())#读取当前指针位置,此时指针已经到最后<br/>
f.write('c++')<br/>
print(f.tell())<br/>
#此时要读文件必须把指针移动到文件开头<br/>
f.seek(0)<br/>
data = f.read();<br/>
print(data)<br/>
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)<br/>
def close(self): # real signature unknown; restored from __doc__<br/>
关闭文件<br/>
"""<br/>
close() -> None or (perhaps) an integer. Close the file.
Sets data attribute .closed to True. A closed file cannot be used for<br/>
further I/O operations. close() may be called more than once without<br/>
error. Some kinds of file objects (for example, opened by popen())<br/>
may return an exit status upon closing.<br/>
"""
def fileno(self): # real signature unknown; restored from __doc__<br/>
文件描述符<br/>
"""<br/>
fileno() -> integer "file descriptor".
This is needed for lower-level file interfaces, such os.read().<br/>
"""<br/>
return 0
def flush(self): # real signature unknown; restored from __doc__<br/>
刷新文件内部缓冲区<br/>
""" flush() -> None. Flush the internal I/O buffer. """<br/>
pass
def isatty(self): # real signature unknown; restored from __doc__<br/>
判断文件是否是同意tty设备<br/>
""" isatty() -> true or false. True if the file is connected to a tty device. """<br/>
return False
def next(self): # real signature unknown; restored from __doc__<br/>
获取下一行数据,不存在,则报错<br/>
""" x.next() -> the next value, or raise StopIteration """<br/>
pass
def read(self, size=None): # real signature unknown; restored from __doc__<br/>
读取指定字节数据<br/>
"""<br/>
read([size]) -> read at most size bytes, returned as a string.
If the size argument is negative or omitted, read until EOF is reached.<br/>
Notice that when in non-blocking mode, less data than what was requested<br/>
may be returned, even if no size parameter was given.<br/>
"""<br/>
pass
def readinto(self): # real signature unknown; restored from __doc__<br/>
读取到缓冲区,不要用,将被遗弃<br/>
""" readinto() -> Undocumented. Don't use this; it may go away. """<br/>
pass
def readline(self, size=None): # real signature unknown; restored from __doc__<br/>
仅读取一行数据<br/>
"""<br/>
readline([size]) -> next line from the file, as a string.
Retain newline. A non-negative size argument limits the maximum<br/>
number of bytes to return (an incomplete line may be returned then).<br/>
Return an empty string at EOF.<br/>
"""<br/>
pass
def readlines(self, size=None): # real signature unknown; restored from __doc__<br/>
读取所有数据,并根据换行保存值列表<br/>
"""<br/>
readlines([size]) -> list of strings, each a line from the file.
Call readline() repeatedly and return a list of the lines so read.<br/>
The optional size argument, if given, is an approximate bound on the<br/>
total number of bytes in the lines returned.<br/>
"""<br/>
return []
def seek(self, offset, whence=None): # real signature unknown; restored from __doc__<br/>
指定文件中指针位置<br/>
"""<br/>
seek(offset[, whence]) -> None. Move to new file position.
Argument offset is a byte count. Optional argument whence defaults to<br/>
(offset from start of file, offset should be >= 0); other values are 1<br/>
(move relative to current position, positive or negative), and 2 (move<br/>
relative to end of file, usually negative, although many platforms allow<br/>
seeking beyond the end of a file). If the file is opened in text mode,<br/>
only offsets returned by tell() are legal. Use of other offsets causes<br/>
undefined behavior.<br/>
Note that not all file objects are seekable.<br/>
"""<br/>
pass
def tell(self): # real signature unknown; restored from __doc__<br/>
获取当前指针位置<br/>
""" tell() -> current file position, an integer (may be a long integer). """<br/>
pass
def truncate(self, size=None): # real signature unknown; restored from __doc__<br/>
截断数据,仅保留指定之前数据<br/>
"""<br/>
truncate([size]) -> None. Truncate the file to at most size bytes.
Size defaults to the current file position, as returned by tell().<br/>
"""<br/>
pass
def write(self, p_str): # real signature unknown; restored from __doc__<br/>
写内容<br/>
"""<br/>
write(str) -> None. Write string str to file.
Note that due to buffering, flush() or close() may be needed before<br/>
the file on disk reflects the data written.<br/>
"""<br/>
pass
def writelines(self, sequence_of_strings): # real signature unknown; restored from __doc__<br/>
将一个字符串列表写入文件<br/>
"""<br/>
writelines(sequence_of_strings) -> None. Write the strings to the file.
Note that newlines are not added. The sequence can be any iterable object<br/>
producing strings. This is equivalent to calling write() for each string.<br/>
"""<br/>
pass
def xreadlines(self): # real signature unknown; restored from __doc__<br/>
可用于逐行读取文件,非全部<br/>
"""<br/>
xreadlines() -> returns self.
For backward compatibility. File objects now include the performance<br/>
optimizations previously implemented in the xreadlines module.<br/>
"""<br/>
pass
2.x
2.x版本
class TextIOWrapper(_TextIOBase):<br/>
"""<br/>
Character and line based layer over a BufferedIOBase object, buffer.
encoding gives the name of the encoding that the stream will be<br/>
decoded or encoded with. It defaults to locale.getpreferredencoding(False).
errors determines the strictness of encoding and decoding (see<br/>
help(codecs.Codec) or the documentation for codecs.register) and<br/>
defaults to "strict".
newline controls how line endings are handled. It can be None, '',<br/>
'\n', '\r', and '\r\n'. It works as follows:
* On input, if newline is None, universal newlines mode is<br/>
enabled. Lines in the input can end in '\n', '\r', or '\r\n', and<br/>
these are translated into '\n' before being returned to the<br/>
caller. If it is '', universal newline mode is enabled, but line<br/>
endings are returned to the caller untranslated. If it has any of<br/>
the other legal values, input lines are only terminated by the given<br/>
string, and the line ending is returned to the caller untranslated.
* On output, if newline is None, any '\n' characters written are<br/>
translated to the system default line separator, os.linesep. If<br/>
newline is '' or '\n', no translation takes place. If newline is any<br/>
of the other legal values, any '\n' characters written are translated<br/>
to the given string.
If line_buffering is True, a call to flush is implied when a call to<br/>
write contains a newline character.<br/>
"""<br/>
def close(self, *args, **kwargs): # real signature unknown<br/>
关闭文件<br/>
pass
def fileno(self, *args, **kwargs): # real signature unknown<br/>
文件描述符<br/>
pass
def flush(self, *args, **kwargs): # real signature unknown<br/>
刷新文件内部缓冲区<br/>
pass
def isatty(self, *args, **kwargs): # real signature unknown<br/>
判断文件是否是同意tty设备<br/>
pass
def read(self, *args, **kwargs): # real signature unknown<br/>
读取指定字节数据<br/>
pass
def readable(self, *args, **kwargs): # real signature unknown<br/>
是否可读<br/>
pass
def readline(self, *args, **kwargs): # real signature unknown<br/>
仅读取一行数据<br/>
pass
def seek(self, *args, **kwargs): # real signature unknown<br/>
指定文件中指针位置<br/>
pass
def seekable(self, *args, **kwargs): # real signature unknown<br/>
指针是否可操作<br/>
pass
def tell(self, *args, **kwargs): # real signature unknown<br/>
获取指针位置<br/>
pass
def truncate(self, *args, **kwargs): # real signature unknown<br/>
截断数据,仅保留指定之前数据<br/>
pass
def writable(self, *args, **kwargs): # real signature unknown<br/>
是否可写<br/>
pass
def write(self, *args, **kwargs): # real signature unknown<br/>
写内容<br/>
pass
def __getstate__(self, *args, **kwargs): # real signature unknown<br/>
pass
def __init__(self, *args, **kwargs): # real signature unknown<br/>
pass
@staticmethod # known case of __new__<br/>
def __new__(*args, **kwargs): # real signature unknown<br/>
""" Create and return a new object. See help(type) for accurate signature. """<br/>
pass
def __next__(self, *args, **kwargs): # real signature unknown<br/>
""" Implement next(self). """<br/>
pass
def __repr__(self, *args, **kwargs): # real signature unknown<br/>
""" Return repr(self). """<br/>
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:<br/>
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')<br/>
for line in fh.readlines():<br/>
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<br/> ginvip$123456
1:让用户选择1或2,1为登录,2为注册
2:如果用户选择1,用户输入用户名与密码,然后与test.log文件中的用户名与密码进行验证,验证成功输出“登录成功”,否则“登录失败”
3:如果用户选择2,让用户输入用户名与密码,并与test.log文件中的用户名验证,如果test.log中用户名已经存在,则输出“该用户名已经存在”,否则将用户输入的用户与密码以上面test.log文件中的形式写入test.log文件中
def check_user(user) :<br/>
with open('test.log','r',encoding='utf-8') as f :<br/>
for line in f :<br/>
user_list = line.strip()<br/>
user_list = user_list.split('$')<br/>
if user == user_list[0] :<br/>
return True<br/>
return False<br/>
def register(user,pwd) :<br/>
with open('test.log','a',encoding='utf-8') as f :<br/>
user_info = '\n' + user + '$' + pwd<br/>
if f.write(user_info) :<br/>
return True<br/>
return False<br/>
def login(user,pwd) :<br/>
with open('test.log','r',encoding='utf-8') as f :<br/>
for line in f:<br/>
user_list = line.strip()<br/>
user_list = user_list.split('$')<br/>
if user == user_list[0] and pwd == user_list[1]:<br/>
return True<br/>
return False<br/>
def main() :<br/>
print('welcome to my website')<br/>
choice = input('1:login 2:register')<br/>
if choice == '':<br/>
user = input('input username :')<br/>
pwd = input('input password : ')<br/>
if check_user(user) :<br/>
print('the username is exist')<br/>
else:<br/>
if register(user,pwd) :<br/>
print('register success')<br/>
else:<br/>
print('register failed')<br/>
elif choice == '':<br/>
user = input('input username :')<br/>
pwd = input('input password : ')<br/>
if login(user,pwd) :<br/>
print('login success')<br/>
else:<br/>
print('login failed')<br/>
main()
冒泡排序
冒泡排序的原理:

def Bubble_sort(args) :<br/>
for i in range(len(args)-1) :<br/>
for j in range(len(args) -1):<br/>
if args[j] > args[j+1]:<br/>
temp = args[j]<br/>
args[j] = args[j+1]<br/>
args[j+1] = temp<br/>
return args<br/>
li = [33,2,10,1,9,3,8]<br/>
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<br/>
case = 0<br/>
space = 0<br/>
other = 0<br/>
def func2(s) :<br/>
global digit,case,space,other<br/>
if not isinstance(s,basestring) :<br/>
print('the data type wrong!')<br/>
return False<br/>
for i in s :<br/>
if i.isdigit() :<br/>
digit += 1<br/>
elif i.isalpha() :<br/>
case += 1<br/>
elif i.isspace() :<br/>
space += 1<br/>
else:<br/>
other += 1<br/>
s = 'I love python , is num 1 , o_k'<br/>
a = [1,2,3]<br/>
func2(s)<br/>
print(digit)<br/>
print(case)<br/>
print(space)<br/>
print(other)<br/>
########################################<br/>
1<br/>
18<br/>
8<br/>
3<br/>
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) :<br/>
if len(v) > 5 :<br/>
return True<br/>
else:<br/>
return False<br/>
a = 'I love python , is num 1 , o_k'<br/>
l = [1,2,3]<br/>
t = (5,7,9,10,45,10)<br/>
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) :<br/>
if len(lis) > 2 :<br/>
return lis[0:2]<br/>
else :<br/>
return False<br/>
li = [1,2,3]<br/>
print(func5(li))<br/>
##########################################<br/>
[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) :<br/>
new_lis = []<br/>
for k in range(len(lis)) :<br/>
if k % 2 == 1 :<br/>
new_lis.append(lis[k])<br/>
return new_lis<br/>
li = [1,2,3,8,10,44,77]<br/>
tu = ('poe','andy','jet','bruce','jacky')<br/>
print(func6(tu))<br/>
##########################################<br/>
['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) :<br/>
v = d.values()<br/>
li = []<br/>
for i in v :<br/>
if len(i) > 2:<br/>
li.append(i[0:2])<br/>
return li<br/>
print(func7(dic))<br/>
##########################################<br/>
[[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) :<br/>
if n == 0 :<br/>
return 0<br/>
elif n == 1:<br/>
return 1<br/>
else:<br/>
return fabonacci(n-1) + fabonacci(n-2)<br/>
print(fabonacci(10))
