from collections import Counter
a=["E","D","C","G","B","A","B","F","D","D","C","A","G","A","C","B","F","C","B"]

counter=Counter(a)

kk=[list(counter.keys()),list(counter.values())]

pd.DataFrame(np.array(kk).T, columns=['Letter','Count'])

在Python 2.7(或更新版本)中，可以使用集合。计数器:

>>> import collections
>>> a = [5, 1, 2, 2, 4, 3, 1, 2, 3, 1, 1, 5, 2]
>>> counter = collections.Counter(a)
>>> counter
Counter({1: 4, 2: 4, 5: 2, 3: 2, 4: 1})
>>> counter.values()
dict_values([2, 4, 4, 1, 2])
>>> counter.keys()
dict_keys([5, 1, 2, 4, 3])
>>> counter.most_common(3)
[(1, 4), (2, 4), (5, 2)]
>>> dict(counter)
{5: 2, 1: 4, 2: 4, 4: 1, 3: 2}
>>> # Get the counts in order matching the original specification,
>>> # by iterating over keys in sorted order
>>> [counter[x] for x in sorted(counter.keys())]
[4, 4, 2, 1, 2]

如果您使用的是Python 2.6或更老版本，可以在这里下载实现。

如果列表是排序的，你可以使用itertools标准库中的groupby(如果不是，你可以先排序，尽管这需要O(nlgn)时间):

from itertools import groupby

a = [5, 1, 2, 2, 4, 3, 1, 2, 3, 1, 1, 5, 2]
[len(list(group)) for key, group in groupby(sorted(a))]

输出:

[4, 4, 2, 1, 2]

str1='the cat sat on the hat hat'
list1=str1.split();
list2=str1.split();

count=0;
m=[];

for i in range(len(list1)):
    t=list1.pop(0);
    print t
    for j in range(len(list2)):
        if(t==list2[j]):
            count=count+1;
            print count
    m.append(count)
    print m
    count=0;
#print m

您可以使用python中提供的内置函数

l.count(l[i])


  d=[]
  for i in range(len(l)):
        if l[i] not in d:
             d.append(l[i])
             print(l.count(l[i])

上面的代码自动删除列表中的重复项，并打印原始列表和没有重复项的列表中每个元素的频率。

一枪双鸟!X维

我将简单地以以下方式使用scipy.stats.itemfreq:

from scipy.stats import itemfreq

a = [1,1,1,1,2,2,2,2,3,3,4,5,5]

freq = itemfreq(a)

a = freq[:,0]
b = freq[:,1]

您可以在这里查看文档:http://docs.scipy.org/doc/scipy-0.16.0/reference/generated/scipy.stats.itemfreq.html