Python statistics.variance() Method

Python statistics.variance() method is “used to calculate the variance of a sample dataset.” A variance measures the spread of a dataset and is calculated as the average of the squared differences between each data point and the mean of the dataset.

Syntax

statistics.variance(data, xbar=None)

If the data has fewer than two values, StatisticsError raises.

Parameters

1. data: This parameter is required when data is an array of valid Python numbers, including Decimal and Fraction values.
2. xbar: Where xbar is the mean of data, this parameter is optional. The mean is automatically calculated if this parameter is not given(none).

Example 1: How to Use statistics.variance() Method

import statistics

dataset = [21, 19, 11, 21, 19, 46, 29]
output = statistics.variance(dataset) 

print(output)

Output

124.23809523809524

Example 2

Calculate the mean first and pass it to the variance() method as an argument.

import statistics

dataset = [21, 19, 11, 21, 19, 46, 29]
meanValue = statistics.mean(dataset)
output = statistics.variance(dataset, meanValue) 

print(output)

Output

124.23809523809524 

Example 3: Use of xbar parameter in statistics.variance() method

import statistics

# creating a sample list
sample = (1.1, 1.2, 1.9, 2.1, 1.8, 2.2)

# calculating the mean of sample set
mean = statistics.mean(sample)

# calculating the variance of sample set
print("Variance of Sample set is % s"
 % (statistics.variance(sample, xbar=mean)))

Output

Variance of Sample set is 0.2136666666666667

Example 4: statistics.StatisticsError: variance requires at least two data points

import statistics

# creating a sample list
sample = []

print(statistics.variance(sample))

Output

statistics.StatisticsError: variance requires at least two data points

That’s it.

See also

Python mean()

Python mode()

Python median()

Python stddev()

Krunal

Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Python language stands as a testament to his versatility and commitment to the craft.