Python statistics | pvariance()

# Python 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)))

# 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)))

# 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)))

# Python code to demonstrate the 
# difference between pvariance() 
# and variance()

# importing statistics 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 population 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))))

# Python code to demonstrate StatisticsError

# importing statistics module
import statistics

# creating an empty population set
pop = ()

# will raise StatisticsError
print(statistics.pvariance(pop))