你可能不再需要这个了，但这是我对一个类似问题所做的。(因为评论，它看起来比实际要长。)

itemList = ['hi', 'hi', 'hello', 'bye']

counter = {}
maxItemCount = 0
for item in itemList:
    try:
        # Referencing this will cause a KeyError exception
        # if it doesn't already exist
        counter[item]
        # ... meaning if we get this far it didn't happen so
        # we'll increment
        counter[item] += 1
    except KeyError:
        # If we got a KeyError we need to create the
        # dictionary key
        counter[item] = 1

    # Keep overwriting maxItemCount with the latest number,
    # if it's higher than the existing itemCount
    if counter[item] > maxItemCount:
        maxItemCount = counter[item]
        mostPopularItem = item

print mostPopularItem

我在最近的一个项目中需要这样做。我承认，我无法理解Alex的回答，所以这就是我最后得到的答案。

def mostPopular(l):
    mpEl=None
    mpIndex=0
    mpCount=0
    curEl=None
    curCount=0
    for i, el in sorted(enumerate(l), key=lambda x: (x[1], x[0]), reverse=True):
        curCount=curCount+1 if el==curEl else 1
        curEl=el
        if curCount>mpCount \
        or (curCount==mpCount and i<mpIndex):
            mpEl=curEl
            mpIndex=i
            mpCount=curCount
    return mpEl, mpCount, mpIndex

我根据Alex的解决方案计时，对于短列表，它要快10-15%，但一旦超过100个或更多元素(测试多达20万个)，它就会慢20%。

numbers = [1, 3, 7, 4, 3, 0, 3, 6, 3]
max_repeat_num = max(numbers, key=numbers.count)     *# which number most* frequently
max_repeat = numbers.count(max_repeat_num)           *#how many times*
print(f" the number {max_repeat_num} is repeated{max_repeat} times")

ans  = [1, 1, 0, 0, 1, 1]
all_ans = {ans.count(ans[i]): ans[i] for i in range(len(ans))}
print(all_ans)

all_ans={4: 1, 2: 0}
max_key = max(all_ans.keys())

4

print(all_ans[max_key])

1

这是一个很简单的解，时间复杂度是线性的

L =['鹅'，'鸭'，'鸭']

def most_common (L):

current_winner = 0
max_repeated = None
for i in L:
    amount_times = L.count(i)
    if amount_times > current_winner:
        current_winner = amount_times
        max_repeated = i

return max_repeated

打印(most_common (L)

“鸭子”

number是列表中重复次数最多的元素吗

如果排序和哈希都不可行，这是一个明显的缓慢的解决方案(O(n²))，但相等比较(==)可用:

def most_common(items):
  if not items:
    raise ValueError
  fitems = [] 
  best_idx = 0
  for item in items:   
    item_missing = True
    i = 0
    for fitem in fitems:  
      if fitem[0] == item:
        fitem[1] += 1
        d = fitem[1] - fitems[best_idx][1]
        if d > 0 or (d == 0 and fitems[best_idx][2] > fitem[2]):
          best_idx = i
        item_missing = False
        break
      i += 1
    if item_missing:
      fitems.append([item, 1, i])
  return items[best_idx]

但是，如果你的列表(n)的长度很大，那么让你的项目可哈希或可排序(正如其他答案所建议的那样)几乎总是能更快地找到最常见的元素。哈希时平均为O(n)，排序时最差为O(n*log(n))。