在Python中如何找到列表的中值?列表可以是任意大小的，并且数字不保证是任何特定的顺序。
如果列表包含偶数个元素，则函数应返回中间两个元素的平均值。
以下是一些例子(为了便于展示，进行了排序):
median([1]) == 1
median([1, 1]) == 1
median([1, 1, 2, 4]) == 1.5
median([0, 2, 5, 6, 8, 9, 9]) == 6
median([0, 0, 0, 0, 4, 4, 6, 8]) == 2
当前回答
更普遍的中位数(和百分位数)方法是:
def get_percentile(data, percentile):
# Get the number of observations
cnt=len(data)
# Sort the list
data=sorted(data)
# Determine the split point
i=(cnt-1)*percentile
# Find the `floor` of the split point
diff=i-int(i)
# Return the weighted average of the value above and below the split point
return data[int(i)]*(1-diff)+data[int(i)+1]*(diff)
# Data
data=[1,2,3,4,5]
# For the median
print(get_percentile(data=data, percentile=.50))
# > 3
print(get_percentile(data=data, percentile=.75))
# > 4
# Note the weighted average difference when an int is not returned by the percentile
print(get_percentile(data=data, percentile=.51))
# > 3.04
其他回答
实现它:
def median(numbers):
"""
Calculate median of a list numbers.
:param numbers: the numbers to be calculated.
:return: median value of numbers.
>>> median([1, 3, 3, 6, 7, 8, 9])
6
>>> median([1, 2, 3, 4, 5, 6, 8, 9])
4.5
>>> import statistics
>>> import random
>>> numbers = random.sample(range(-50, 50), k=100)
>>> statistics.median(numbers) == median(numbers)
True
"""
numbers = sorted(numbers)
mid_index = len(numbers) // 2
return (
(numbers[mid_index] + numbers[mid_index - 1]) / 2 if mid_index % 2 == 0
else numbers[mid_index]
)
if __name__ == "__main__":
from doctest import testmod
testmod()
来源
def median(array):
if len(array) < 1:
return(None)
if len(array) % 2 == 0:
median = (array[len(array)//2-1: len(array)//2+1])
return sum(median) / len(median)
else:
return(array[len(array)//2])
(适用于python-2.x):
def median(lst):
n = len(lst)
s = sorted(lst)
return (s[n//2-1]/2.0+s[n//2]/2.0, s[n//2])[n % 2] if n else None
>>> median([-5, -5, -3, -4, 0, -1])
-3.5
numpy.median ():
>>> from numpy import median
>>> median([1, -4, -1, -1, 1, -3])
-1.0
python 3。X，使用statistics.median:
>>> from statistics import median
>>> median([5, 2, 3, 8, 9, -2])
4.0
如果您需要关于列表分布的额外信息，百分位数方法可能会很有用。中位数对应于列表的第50个百分位数:
import numpy as np
a = np.array([1,2,3,4,5,6,7,8,9])
median_value = np.percentile(a, 50) # return 50th percentile
print median_value
这里有一个更干净的解决方案:
def median(lst):
quotient, remainder = divmod(len(lst), 2)
if remainder:
return sorted(lst)[quotient]
return sum(sorted(lst)[quotient - 1:quotient + 1]) / 2.
注:答案更改为在评论中加入建议。
