下面是使用itertools的另一个简洁的替代方案。Groupby也适用于无序输入:

from itertools import groupby

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

results = {value: len(list(freq)) for value, freq in groupby(sorted(items))}

结果

format: {value: num_of_occurencies}

{1: 4, 2: 4, 3: 2, 4: 1, 5: 2}

你可以这样做:

import numpy as np
a = [1,1,1,1,2,2,2,2,3,3,4,5,5]
np.unique(a, return_counts=True)

输出:

(array([1, 2, 3, 4, 5]), array([4, 4, 2, 1, 2], dtype=int64))

第一个数组是值，第二个数组是具有这些值的元素的数量。

所以如果你想要得到一个数字数组，你应该使用这个:

np.unique(a, return_counts=True)[1]

通过遍历列表并计算它们，手动计算出现的数量，使用collections.defaultdict跟踪到目前为止看到的内容:

from collections import defaultdict

appearances = defaultdict(int)

for curr in a:
    appearances[curr] += 1

简单的解决方法就是用字典。

def frequency(l):
     d = {}
     for i in l:
        if i in d.keys():
           d[i] += 1
        else:
           d[i] = 1

     for k, v in d.iteritems():
        if v ==max (d.values()):
           return k,d.keys()

print(frequency([10,10,10,10,20,20,20,20,40,40,50,50,30]))

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]

a = [1,1,1,1,2,2,2,2,3,3,4,5,5]
counts = dict.fromkeys(a, 0)
for el in a: counts[el] += 1
print(counts)
# {1: 4, 2: 4, 3: 2, 4: 1, 5: 2}