问获取列表中所有元素平均值的最有效方法，其中每个元素出现的次数至少是列表模式的一半。

EN

from __future__ import division
from collections import Counter
from itertools import islice

data = [1,2,2,3,4,4,4,4]
c = Counter(data)

# Get the mode
mode, mode_n = c.most_common(1)[0]

# Store the cumulative sum and count so we can compute the mean
# Process the most common element (the mode) first since we
# already have that data.
cumulative_sum = mode * mode_n
cumulative_n = mode_n

# Process the remaining elements. most_common returns the remaining
# elements and their counts in descending order by the number of times
# the appear in the original list.  We can skip the first element since
# we've already processed it.  As soon as an element is less numerous
# than half the mode, we can stop processing further elements.
for val, val_n in islice(c.most_common(), 1, None):
    if val_n < mode_n / 2:
        break
    cumulative_sum += val * val_n
    cumulative_n += val_n

# Compute the Mean
avg = cumulative_sum / cumulative_n

if val_n < mode_n // 2:

In [54]: x = np.array([1, 2, 2, 3, 4, 4, 4, 4])

In [55]: uniqx, counts = np.unique(x, return_counts=True)

In [56]: keep = counts >= 0.5*counts.max()

In [57]: np.average(uniqx[keep], weights=counts[keep])
Out[57]: 3.3333333333333335