NumPy var()
Summary: in this tutorial, you’ll learn how to use the var() function to calculate the variances of elements in an array.
Introduction to the NumPy var() function
The variance is a measure of the spread of a distribution. To manually calculate the variance of numbers, you follow these steps:
- First, calculate the average of all numbers.
- Second, calculate the squared difference of each number by subtracting it from the mean and square the result.
- Third, calculate the average of those squared differences.
For example, to calculate the variance of three numbers 1, 2, and 3:
First, calculate the average (or mean):
(1+2+3) / 3 = 2.0
Second, calculate the squared difference of each number with the mean:
(1-2)2 + (2-2)2 + (3-2)2 = 2
Third, calculate the average of these squared differences:
2 / 3 ~ 0.667
To calculate the variances of numbers in an array, you can use the var() function:
numpy.var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>, *, where=<no value>)Code language: Python (python)
For example:
import numpy as npa = np.array([1, 2, 3])
result = np.var(a)
print(round(result,3))
Code language: Python (python)
Output:
0.667Code language: Python (python)
Summary
- Use the numpy
var()function to calculate the variance of elements in an array.