Python statistics variance()用法及代码示例

Statistics该模块提供了非常强大的工具，可用于计算与统计信息相关的任何信息。 variance()是这样一种函数。此函数有助于根据数据样本(样本是填充数据的子集)计算方差。

variance()仅当需要计算样本方差时才应使用该函数。还有另一个函数叫做pvariance()，用于计算整个总体的方差。

在纯统计数据中，variance是变量与其平均值的平方偏差。本质上，它根据平均值或中位数来衡量一组数据的随机分布。较低的方差值表示数据聚集在一起并且没有广泛散布，而较高的值表示给定集中的数据与平均值相比散布得多。

方差是科学中的重要工具，在科学中，对数据进行统计分析很普遍。它是给定data-set的标准偏差的平方，也被称为分布的第二中心矩。通常用 $s^{2}, \sigma ^{2}, \operatorname {Var} (X)$ 纯统计。

方差通过以下公式计算：

It’s calculated by mean of square minus square of mean

$\operatorname {Var} (X)=\operatorname {E} \left[(X-\mu )^{2}\right]$

用法： variance( [data], xbar )

Parameters:
[数据]：具有实值数字的可迭代项。
xbar (Optional):将data-set的实际平均值作为值。

Returnype:返回作为参数传递的值的实际方差。

Exceptions:
StatisticsError为data-set引发的值小于作为参数传递的2个值。
当xbar提供的值与data-set的实际均值不匹配时，将抛出不可能的值。

代码1：

# Python code to demonstrate the working of 
# variance() function of Statistics Module 
  
# Importing Statistics module 
import statistics 
  
# Creating a sample of data 
sample = [2.74, 1.23, 2.63, 2.22, 3, 1.98] 
  
# Prints variance of the sample set 
  
# Function will automatically calculate 
# it's mean and set it as xbar 
print("Variance of sample set is % s" 
      %(statistics.variance(sample)))

输出：

Variance of sample set is 0.40924

代码2：演示一系列数据类型上的variance()

# Python code to demonstrate variance()  
# function on varying range of data-types 
  
# importing statistics module 
from statistics import variance 
  
# importing fractions as parameter values 
from fractions import Fraction as fr 
  
# tuple of a set of positive integers 
# numbers are spread apart but not very much 
sample1 = (1, 2, 5, 4, 8, 9, 12) 
  
# tuple of a set of negative integers 
sample2 = (-2, -4, -3, -1, -5, -6) 
  
# tuple of a set of positive and negative numbers 
# data-points are spread apart considerably  
sample3 = (-9, -1, -0, 2, 1, 3, 4, 19) 
  
# tuple of a set of fractional numbers 
sample4 = (fr(1, 2), fr(2, 3), fr(3, 4), 
                     fr(5, 6), fr(7, 8)) 
  
# tuple of a set of floating point values 
sample5 = (1.23, 1.45, 2.1, 2.2, 1.9) 
  
# Print the variance of each samples 
print("Variance of Sample1 is % s " %(variance(sample1))) 
print("Variance of Sample2 is % s " %(variance(sample2))) 
print("Variance of Sample3 is % s " %(variance(sample3))) 
print("Variance of Sample4 is % s " %(variance(sample4))) 
print("Variance of Sample5 is % s " %(variance(sample5)))

输出：

Variance of Sample 1 is 15.80952380952381 
Variance of Sample 2 is 3.5 
Variance of Sample 3 is 61.125 
Variance of Sample 4 is 1/45 
Variance of Sample 5 is 0.17613000000000006

代码3：演示xbar参数的使用

# Python code to demonstrate  
# the use of xbar parameter 
  
# Importing statistics module 
import statistics 
  
# creating a sample list 
sample = (1, 1.3, 1.2, 1.9, 2.5, 2.2) 
  
# calculating the mean of sample set 
m = statistics.mean(sample) 
  
  
# calculating the variance of sample set 
print("Variance of Sample set is % s" 
    %(statistics.variance(sample, xbar = m)))

输出：

Variance of Sample set is 0.3656666666666667

代码4：当xbar的值与平均值/平均值不同时，显示错误

# Python code to demonstrate the error caused 
# when garbage value of xbar is entered 
  
# Importing statistics module 
import statistics 
  
# creating a sample list 
sample = (1, 1.3, 1.2, 1.9, 2.5, 2.2) 
  
# calculating the mean of sample set 
m = statistics.mean(sample) 
  
# Actual value of mean after calculation 
# comes out to 1.6833333333333333 
# But to demonstrate xbar error let's enter  
# -100 as the value for xbar parameter 
print(statistics.variance(sample, xbar = -100))

输出：

0.3656666666663053

注意：它的精度与代码3中的输出不同

代码4：展示StatisticsError

# Python code to demonstrate StatisticsError 
  
# importing Statistics module 
import statistics 
  
# creating an emoty data-srt 
sample = [] 
  
# will raise Statistics Error 
print(statistics.variance(sample))

输出：

Traceback (most recent call last):
  File "/home/64bf6d80f158b65d2b75c894d03a7779.py", line 10, in 
    print(statistics.variance(sample))
  File "/usr/lib/python3.5/statistics.py", line 555, in variance
    raise StatisticsError('variance requires at least two data points')
statistics.StatisticsError:variance requires at least two data points

应用范围：
方差是统计和处理大量数据中非常重要的工具。就像，当无所不知的均值是未知的(样本均值)时，方差被用作偏差估计量。现实世界中的观察，例如一天中公司所有股份的增减价值，不可能是所有可能的观察。因此，方差是从有限的一组数据中计算出来的，尽管考虑到总体时，方差不会匹配，但仍会为用户提供足以进行其他计算的估计值。

相关用法

注：本文由纯净天空筛选整理自retr0大神的英文原创作品 Python statistics | variance()。非经特殊声明，原始代码版权归原作者所有，本译文未经允许或授权，请勿转载或复制。