对于您的第一个问题，迭代列表并使用字典跟踪元素的存在。

对于你的第二个问题，只需使用集合操作符。

对于您的第一个问题，迭代列表并使用字典跟踪元素的存在。

对于你的第二个问题，只需使用集合操作符。

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'])

我迟到了，但这也有用，也会帮助到其他人:

a = [1,1,1,1,2,2,2,2,3,3,4,5,5]
freq_list = []
a_l = list(set(a))

for x in a_l:
    freq_list.append(a.count(x))


print 'Freq',freq_list
print 'number',a_l

会产生这个…

Freq  [4, 4, 2, 1, 2]
number[1, 2, 3, 4, 5]

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。

另一种方法是使用较重但功能强大的库——NLTK。

import nltk

fdist = nltk.FreqDist(a)
fdist.values()
fdist.most_common()