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 arrayarray = 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 arrayarray = 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 arrayx = 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
