Compute the mean, standard deviation, and variance of a given NumPy array (original) (raw)

Last Updated : 15 Jul, 2025

In NumPy, we can compute the mean, standard deviation, and variance of a given array along the second axis by two approaches first is by using inbuilt functions and second is by the formulas of the mean, standard deviation, and variance.

Method 1: Using numpy.mean(), numpy.std(), numpy.var()

Python `

import numpy as np

Original array

array = np.arange(10) print(array)

r1 = np.mean(array) print("\nMean: ", r1)

r2 = np.std(array) print("\nstd: ", r2)

r3 = np.var(array) print("\nvariance: ", r3)

`

Output:

[0 1 2 3 4 5 6 7 8 9]

Mean: 4.5

std: 2.8722813232690143

variance: 8.25

Method 2: Using the formulas

Python3 `

import numpy as np

Original array

array = np.arange(10) print(array)

r1 = np.average(array) print("\nMean: ", r1)

r2 = np.sqrt(np.mean((array - np.mean(array)) ** 2)) print("\nstd: ", r2)

r3 = np.mean((array - np.mean(array)) ** 2) print("\nvariance: ", r3)

`

Output:

[0 1 2 3 4 5 6 7 8 9]

Mean: 4.5

std: 2.8722813232690143

variance: 8.25

Example: Comparing both inbuilt methods and formulas

Python `

import numpy as np

Original array

x = np.arange(5) print(x)

r11 = np.mean(x) r12 = np.average(x) print("\nMean: ", r11, r12)

r21 = np.std(x) r22 = np.sqrt(np.mean((x - np.mean(x)) ** 2)) print("\nstd: ", r21, r22)

r31 = np.var(x) r32 = np.mean((x - np.mean(x)) ** 2) print("\nvariance: ", r31, r32)

`

Output:

[0 1 2 3 4]

Mean: 2.0 2.0

std: 1.4142135623730951 1.4142135623730951

variance: 2.0 2.0