如何在python中计算列表的方差? [英] How can I calculate the variance of a list in python?
问题描述
如果我有这样的列表:
results=[-14.82381293, -0.29423447, -13.56067979, -1.6288903, -0.31632439,
0.53459687, -1.34069996, -1.61042692, -4.03220519, -0.24332097]
我想在Python中计算此列表的方差,即与均值平方差的平均值.
I want to calculate the variance of this list in Python which is the average of the squared differences from the mean.
我该如何处理?访问列表中的元素进行计算使我困惑于平方差.
How can I go about this? Accessing the elements in the list to do the computations is confusing me for getting the square differences.
推荐答案
You can use numpy's built-in function var
:
import numpy as np
results = [-14.82381293, -0.29423447, -13.56067979, -1.6288903, -0.31632439,
0.53459687, -1.34069996, -1.61042692, -4.03220519, -0.24332097]
print(np.var(results))
这给您28.822364260579157
如果-由于某种原因-您不能使用numpy
和/或您不想为其使用内置函数,则还可以使用例如手动"计算. 列表理解:
If - for whatever reason - you cannot use numpy
and/or you don't want to use a built-in function for it, you can also calculate it "by hand" using e.g. a list comprehension:
# calculate mean
m = sum(results) / len(results)
# calculate variance using a list comprehension
var_res = sum((xi - m) ** 2 for xi in results) / len(results)
这将为您提供相同的结果.
which gives you the identical result.
If you are interested in the standard deviation, you can use numpy.std:
print(np.std(results))
5.36864640860051
@Serge Ballesta很好地解释了方差n
和n-1
之间的区别.在numpy中,您可以使用选项ddof
轻松设置此参数.它的默认值为0
,因此对于n-1
情况,您可以轻松执行以下操作:
@Serge Ballesta explained very well the difference between variance n
and n-1
. In numpy you can easily set this parameter using the option ddof
; its default is 0
, so for the n-1
case you can simply do:
np.var(results, ddof=1)
手动"解决方案在 @Serge Ballesta的答案中给出.
The "by hand" solution is given in @Serge Ballesta's answer.
两种方法都产生32.024849178421285
.
您也可以为std
设置参数:
np.std(results, ddof=1)
5.659050201086865
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