Let’s see how to calculate variance in Numpy python module.
Variance calculations
It is very easy to calculate variance in Python. With Numpy it is even easier.
There is dedicated function in Numpy module to calculate variance.
import numpy as np
my_array = np.array([1, 5, 7, 5, 43, 43, 8, 43, 6])
variance = np.var(my_array)
print("Variance equals: " + str(round(variance, 2)))
How to calculate population variance and sample variance
The NumPy var() function can be used to calculate both population variance and sample variance. The population variance is calculated using all of the data points in the population, while the sample variance is calculated using a subset of the data points (the sample).
Key Takeaways
The NumPy var() function is a powerful tool that can be used to calculate the variance of a NumPy array.
The var() function has a number of parameters that allow you to customize the way the variance is calculated, such as the ddof parameter.
The var() function can be used to calculate the variance of a variety of data types, including numeric and non-numeric data types.
FAQs
Q: What is the difference between variance and standard deviation?
The variance is a measure of how spread out the data is, while the standard deviation is a measure of how far away the data points are from the mean. The standard deviation is calculated by taking the square root of the variance.
Q: What is the ddof parameter?
The ddof parameter stands for degrees of freedom. The ddof parameter tells the var() function whether to calculate the population variance or the sample variance. If the ddof parameter is set to 0, then the population variance is calculated. If the ddof parameter is set to any other value, then the sample variance is calculated.
Check also:
how to calculate a Variance in Excel