python中的numpy var()和statisticsvariant()有什么区别? [英] What is the difference between numpy var() and statistics variance() in python?

问题描述

我正在尝试一个Dataquest练习，但发现这两个软件包的差异是不同的.

I was trying one Dataquest exercise and I figured out that the variance I am getting is different for the two packages..

例如[1,2,3,4]

e.g for [1,2,3,4]

from statistics import variance
import numpy as np
print(np.var([1,2,3,4]))
print(variance([1,2,3,4]))
//1.25
//1.6666666666666667

该练习的预期答案是使用np.var()

The expected answer of the exercise is calculated with np.var()

修改 我想这必须要做的是，后一个是样本方差而不是方差.有人可以解释这个差异吗?

Edit I guess it has to do that the later one is sample variance and not variance.. Anyone could explain the difference?

推荐答案

使用此

print(np.var([1,2,3,4],ddof=1))

1.66666666667

Delta自由度:计算中使用的除数为N - ddof，其中N表示元素数.默认情况下，ddof为零.

Delta Degrees of Freedom: the divisor used in the calculation is N - ddof, where N represents the number of elements. By default, ddof is zero.

通常将平均值计算为x.sum() / N，其中N = len(x).但是，如果指定了ddof，则使用除数N - ddof.

The mean is normally calculated as x.sum() / N, where N = len(x). If, however, ddof is specified, the divisor N - ddof is used instead.

在标准的统计实践中，ddof=1提供了一个假设的无限总体方差的无偏估计量. ddof=0为正态分布变量提供方差的最大似然估计.

In standard statistical practice, ddof=1 provides an unbiased estimator of the variance of a hypothetical infinite population. ddof=0 provides a maximum likelihood estimate of the variance for normally distributed variables.

诸如numpy之类的统计库使用方差 n 来表示var或方差和标准差

Statistical libraries like numpy use the variance n for what they call var or variance and the standard deviation

有关更多信息，请参阅以下文档: numpy doc

For more information refer this documentation : numpy doc

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