from collections import OrderedDict
a = [1,1,1,1,2,2,2,2,3,3,4,5,5]
def get_count(lists):
    dictionary = OrderedDict()
    for val in lists:
        dictionary.setdefault(val,[]).append(1)
    return [sum(val) for val in dictionary.values()]
print(get_count(a))
>>>[4, 4, 2, 1, 2]

删除副本并维持秩序:

list(dict.fromkeys(get_count(a)))
>>>[4, 2, 1]

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。

假设我们有一个列表:

fruits = ['banana', 'banana', 'apple', 'banana']

我们可以在列表中找出每种水果的数量，像这样:

import numpy as np    
(unique, counts) = np.unique(fruits, return_counts=True)
{x:y for x,y in zip(unique, counts)}

结果:

{'banana': 3, 'apple': 1}

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

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'])

def frequencyDistribution(data):
    return {i: data.count(i) for i in data}   

print frequencyDistribution([1,2,3,4])

...

 {1: 1, 2: 1, 3: 1, 4: 1}   # originalNumber: count