python方差的代码_Python statistics pvariance()用法及代码示例

先决条件：Python统计信息| variance()

pvariance()函数有助于计算整个方差，而不是样本方差。之间的唯一区别variance()和pvariance()是在使用variance()时，仅考虑样本均值，而在pvariance()期间，则考虑整个总体的均值。

总体方差与样本方差相似，它说明了特定总体中的数据点如何分布。它是从data-points到data-set均值的平均距离，为平方。总体方差是总体的一个参数，不依赖于研究方法或抽样方法。

用法： pvariance( [data], mu)

Parameters:

[数据]：具有实值数字的可迭代项。

mu (optional):将data-set /人口的实际平均值作为值。

Returnype:返回作为参数传递的值的实际总体方差。

Exceptions:

StatisticsError为data-set引发的值小于作为参数传递的2个值。

不可能的价值当以mu形式提供的值与data-set的实际平均值不匹配时。

代码1：

# Pythom code to demonstrate the

# use of pvariance()

# importing statistics module

import statistics

# creating a random population list

population = (1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.9, 2.2,

2.3, 2.4, 2.6, 2.9, 3.0, 3.4, 3.3, 3.2)

# Prints the population variance

print("Population variance is %s"

%(statistics.pvariance(population)))

输出：

Population variance is 0.6658984375

代码2：在不同范围的种群树上演示pvariance()。

# Python code to demonstrate pvariance()

# on various range of population sets

# importing statistics module

from statistics import pvariance

# importing fractions module as F

from fractions import Fraction as F

# Population tree for a set of positive integers

pop1 = (1, 2, 3, 5, 4, 6, 1, 2, 2, 3, 1, 3,

7, 8, 9, 1, 1, 1, 2, 6, 7, 8, 9, )

# Creating a population tree for

# a set of negative integers

pop2 = (-36, -35, -34, -32, -30, -31, -33, -33, -33,

-38, -36, -35, -34, -38, -40, -31, -32)

# Creating a population tree for

# a set of fractional numbers

pop3 = (F(1, 3), F(2, 4), F(2, 3),

F(3, 2), F(2, 5), F(2, 2),

F(1, 1), F(1, 4), F(1, 2), F(2, 1))

# Creating a population tree for

# a set of decimal values

pop4 = (3.45, 3.2, 2.5, 4.6, 5.66, 6.43,

4.32, 4.23, 6.65, 7.87, 9.87, 1.23,

1.00, 1.45, 10.12, 12.22, 19.88)

# Print the population variance for

# the created population trees

print("Population variance of set 1 is % s"

%(pvariance(pop1)))

print("Population variance of set 2 is % s"

%(pvariance(pop2)))

print("Population variance of set 3 is % s"

%(pvariance(pop3)))

print("Population variance of set 4 is % s"

%(pvariance(pop4)))

输出：

Population variance of set 1 is 7.913043478260869

Population variance of set 2 is 7.204152249134948

Population variance of set 3 is 103889/360000

Population variance of set 4 is 21.767923875432526

代码3：演示使用mu参数。

# Python code to demonstrate the use

#  of 'mu' parameter on pvariance()

# importing statistics module

import statistics

# Apparently, the Python interpreter doesn't

# even check whether the value entered for mu

# is the actual mean of data-set or not.

# Thus providing incorrect value would

# lead to impossible answers

# Creating a population tree of the

# age of kids in a locality

tree = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,

12, 12, 12, 12, 13, 1, 2, 12, 2, 2,

2, 3, 4, 5, 5, 5, 5, 6, 6, 6)

# Finding the mean of population tree

m = statistics.mean(tree)

# Using the mu parameter

# while using pvariance()

print("Population Variance is % s"

%(statistics.pvariance(tree, mu = m)))

输出：

Population Variance is 14.30385015608741

代码4：演示pvariance()和variance()之间的区别

# Pythom code to demonstrate the

# difference between pvariance()

# and variance()

# importing statistocs module

import statistics

# Population tree and extract

# a sample from it

tree = (1.1, 1.22, .23, .55, .67, 2.33, 2.81,

1.54, 1.2, 0.2, 0.1, 1.22, 1.61)

# Sample extract from population tree

sample = (1.22, .23, .55, .67, 2.33,

2.81, 1.54, 1.2, 0.2)

# Print sample variance and as

# well as population variance

print ("Variance of whole popuation is %s"

%(statistics.pvariance(tree)))

print ("Variance of sample from population is %s "

% (statistics.variance(sample)))

# Print the difference in both population

# variance and sample variance

print("n")

print("Difference in Population variance"

"and Sample variance is % s"

%(abs(statistics.pvariance(tree)

- statistics.variance(sample))))

输出：

Variance of the whole popuation is 0.6127751479289941

Variance of the sample from population is 0.8286277777777779

Difference in Population variance and Sample variance is 0.21585262984878373

注意：从上面的示例示例中可以看出，总体方差和示例方差相差不大。

代码5：展示StatisticsError

# Python code to demonstrate StatisticsError

# importing statistics module

import statistics

# creating an empty population set

pop = ()

# will raise StatisticsError

print(statistics.pvariance(pop))

输出：

Traceback (most recent call last):

File "/home/fa112e1405f09970eeddd48214318a3c.py", line 10, in

print(statistics.pvariance(pop))

File "/usr/lib/python3.5/statistics.py", line 603, in pvariance

raise StatisticsError('pvariance requires at least one data point')

statistics.StatisticsError:pvariance requires at least one data point

应用范围：

总体方差的应用与样本方差非常相似，尽管总体方差的范围比样本方差大得多。仅在要计算整个总体的方差时才使用总体方差，否则在计算样本方差时，最好使用variance()。人口差异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。