python 2-3 如何统计序列中元素的出现频度--collections.Counter
2-3 如何统计序列中元素的出现频度
1.先将需要排序的放进列表中
2.使用Counter将列表转换成为字典
3.使用counter.max_common(N)方法列出top N
方法一, 通过正常的程序来解决，建立一个空字典，然后将序列中的元素作为key一个一个放进字典
lista = [1,2,3,3,2,2,2,5,6,1,2,2]
d = {}
for key in lista:
if key in d.iterkeys():
d[key] += 1
else:
d[key] = 1
print d
方法二,通过fromkeys 函数，由序列中所有的元素作为键，默认为0 建立一个字典
help(dict.fromkeys)
Help on built-in function fromkeys:
fromkeys(...)
dict.fromkeys(S[,v]) -> New dict with keys from S and values equal to v.
v defaults to None.
lista = [1,2,3,3,2,2,2,5,6,1,2,2]
d2 = dict.fromkeys(lista,0)
for key in lista:
d2[key] += 1
print d2方法三,使用collections下的Counter,将lista直接作为Counter的参数
1.先将需要排序的放进列表中
2.使用Counter将列表转换成为字典
3.使用counter.max_common(N)方法列出top N
import collections
lista = [1,2,3,3,2,2,2,5,6,1,2,2]
d3 = collections.Counter(lista)
print d3.most_common(4)collections.Counter的帮助
help(collections.Counter)
Help on class Counter in module collections:
class Counter(__builtin__.dict)
| Dict subclass for counting hashable items. Sometimes called a bag
| or multiset. Elements are stored as dictionary keys and their counts
| are stored as dictionary values.
|
| >>> c = Counter('abcdeabcdabcaba') # count elements from a string
|
| >>> c.most_common(3) # three most common elements
| [('a', 5), ('b', 4), ('c', 3)]
| >>> sorted(c) # list all unique elements
| ['a', 'b', 'c', 'd', 'e']
| >>> ''.join(sorted(c.elements())) # list elements with repetitions
| 'aaaaabbbbcccdde'
| >>> sum(c.values()) # total of all counts
| 15
|
| >>> c['a'] # count of letter 'a'
| 5
| >>> for elem in 'shazam': # update counts from an iterable
| ... c[elem] += 1 # by adding 1 to each element's count
| >>> c['a'] # now there are seven 'a'
| 7
| >>> del c['b'] # remove all 'b'
| >>> c['b'] # now there are zero 'b'
| 0
|
| >>> d = Counter('simsalabim') # make another counter
| >>> c.update(d) # add in the second counter
| >>> c['a'] # now there are nine 'a'
| 9
|
| >>> c.clear() # empty the counter
| >>> c
| Counter()
|
| Note: If a count is set to zero or reduced to zero, it will remain
| in the counter until the entry is deleted or the counter is cleared:
|
| >>> c = Counter('aaabbc')
| >>> c['b'] -= 2 # reduce the count of 'b' by two
| >>> c.most_common() # 'b' is still in, but its count is zero
| [('a', 3), ('c', 1), ('b', 0)]
|
| Method resolution order:
| Counter
| __builtin__.dict
| __builtin__.object
|
| Methods defined here:
|
| __add__(self, other)
| Add counts from two counters.
|
| >>> Counter('abbb') + Counter('bcc')
| Counter({'b': 4, 'c': 2, 'a': 1})
|
| __and__(self, other)
| Intersection is the minimum of corresponding counts.
|
| >>> Counter('abbb') & Counter('bcc')
| Counter({'b': 1})
|
| __delitem__(self, elem)
| Like dict.__delitem__() but does not raise KeyError for missing values.
|
| __init__(self, iterable=None, **kwds)
| Create a new, empty Counter object. And if given, count elements
| from an input iterable. Or, initialize the count from another mapping
| of elements to their counts.
|
| >>> c = Counter() # a new, empty counter
| >>> c = Counter('gallahad') # a new counter from an iterable
| >>> c = Counter({'a': 4, 'b': 2}) # a new counter from a mapping
| >>> c = Counter(a=4, b=2) # a new counter from keyword args
|
| __missing__(self, key)
| The count of elements not in the Counter is zero.
|
| __or__(self, other)
| Union is the maximum of value in either of the input counters.
|
| >>> Counter('abbb') | Counter('bcc')
| Counter({'b': 3, 'c': 2, 'a': 1})
|
| __reduce__(self)
|
| __repr__(self)
|
| __sub__(self, other)
| Subtract count, but keep only results with positive counts.
|
| >>> Counter('abbbc') - Counter('bccd')
| Counter({'b': 2, 'a': 1})
|
| copy(self)
| Return a shallow copy.
|
| elements(self)
| Iterator over elements repeating each as many times as its count.
|
| >>> c = Counter('ABCABC')
| >>> sorted(c.elements())
| ['A', 'A', 'B', 'B', 'C', 'C']
|
| # Knuth's example for prime factors of 1836: 2**2 * 3**3 * 17**1
| >>> prime_factors = Counter({2: 2, 3: 3, 17: 1})
| >>> product = 1
| >>> for factor in prime_factors.elements(): # loop over factors
| ... product *= factor # and multiply them
| >>> product
| 1836
|
| Note, if an element's count has been set to zero or is a negative
| number, elements() will ignore it.
|
| most_common(self, n=None)
| List the n most common elements and their counts from the most
| common to the least. If n is None, then list all element counts.
|
| >>> Counter('abcdeabcdabcaba').most_common(3)
| [('a', 5), ('b', 4), ('c', 3)]
|
| subtract(self, iterable=None, **kwds)
| Like dict.update() but subtracts counts instead of replacing them.
| Counts can be reduced below zero. Both the inputs and outputs are
| allowed to contain zero and negative counts.
|
| Source can be an iterable, a dictionary, or another Counter instance.
|
| >>> c = Counter('which')
| >>> c.subtract('witch') # subtract elements from another iterable
| >>> c.subtract(Counter('watch')) # subtract elements from another counter
| >>> c['h'] # 2 in which, minus 1 in witch, minus 1 in watch
| 0
| >>> c['w'] # 1 in which, minus 1 in witch, minus 1 in watch
| -1
|
| update(self, iterable=None, **kwds)
| Like dict.update() but add counts instead of replacing them.
|
| Source can be an iterable, a dictionary, or another Counter instance.
|
| >>> c = Counter('which')
| >>> c.update('witch') # add elements from another iterable
| >>> d = Counter('watch')
| >>> c.update(d) # add elements from another counter
| >>> c['h'] # four 'h' in which, witch, and watch
| 4
|
| ----------------------------------------------------------------------
| Class methods defined here:
|
| fromkeys(cls, iterable, v=None) from __builtin__.type
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| __dict__
| dictionary for instance variables (if defined)
|
| __weakref__
| list of weak references to the object (if defined)
|
| ----------------------------------------------------------------------
| Methods inherited from __builtin__.dict:
|
| __cmp__(...)
| x.__cmp__(y) <==> cmp(x,y)
|
| __contains__(...)
| D.__contains__(k) -> True if D has a key k, else False
|
| __eq__(...)
| x.__eq__(y) <==> x==y
|
| __ge__(...)
| x.__ge__(y) <==> x>=y
|
| __getattribute__(...)
| x.__getattribute__('name') <==> x.name
|
| __getitem__(...)
| x.__getitem__(y) <==> x[y]
|
| __gt__(...)
| x.__gt__(y) <==> x>y
|
| __iter__(...)
| x.__iter__() <==> iter(x)
|
| __le__(...)
| x.__le__(y) <==> x<=y
|
| __len__(...)
| x.__len__() <==> len(x)
|
| __lt__(...)
| x.__lt__(y) <==> x
|
| __ne__(...)
| x.__ne__(y) <==> x!=y
|
| __setitem__(...)
| x.__setitem__(i, y) <==> x[i]=y
|
| __sizeof__(...)
| D.__sizeof__() -> size of D in memory, in bytes
|
| clear(...)
| D.clear() -> None. Remove all items from D.
|
| get(...)
| D.get(k[,d]) -> D[k] if k in D, else d. d defaults to None.
|
| has_key(...)
| D.has_key(k) -> True if D has a key k, else False
|
| items(...)
| D.items() -> list of D's (key, value) pairs, as 2-tuples
|
| iteritems(...)
| D.iteritems() -> an iterator over the (key, value) items of D
|
| iterkeys(...)
| D.iterkeys() -> an iterator over the keys of D
|
| itervalues(...)
| D.itervalues() -> an iterator over the values of D
|
| keys(...)
| D.keys() -> list of D's keys
|
| pop(...)
| D.pop(k[,d]) -> v, remove specified key and return the corresponding value.
| If key is not found, d is returned if given, otherwise KeyError is raised
|
| popitem(...)
| D.popitem() -> (k, v), remove and return some (key, value) pair as a
| 2-tuple; but raise KeyError if D is empty.
|
| setdefault(...)
| D.setdefault(k[,d]) -> D.get(k,d), also set D[k]=d if k not in D
|
| values(...)
| D.values() -> list of D's values
|
| viewitems(...)
| D.viewitems() -> a set-like object providing a view on D's items
|
| viewkeys(...)
| D.viewkeys() -> a set-like object providing a view on D's keys
|
| viewvalues(...)
| D.viewvalues() -> an object providing a view on D's values
|
| ----------------------------------------------------------------------
| Data and other attributes inherited from __builtin__.dict:
|
| __hash__ = None
|
| __new__ =