In this article we will learn how to calculate standard deviation of a Matrix using Python.
Standard deviation is used to measure the spread of values within the dataset. It indicates variations or dispersion of values in the dataset and also helps to determine the confidence in a model’s statistical conclusions. It is represented by the sigma (σ) and calculates by taking the square root of the variance. If the standard deviation is low it means most of the values are closer to the mean and if high, that means closer to the mean. In this article, we will learn what are the different ways to calculate SD in Python.
We can calculate the Standard Deviation using the following method :
- std() method in NumPy package
- stdev() method in Statistics package
Method 1:std() method in NumPy package.
Python3
# import required packages import numpy as np # Create matrix matrix = np.array([[33, 55, 66, 74], [23, 45, 65, 27], [87, 96, 34, 54]]) print("Your matrix:\n", matrix) # use std() method sd = np.std(matrix) print("Standard Deviation :\n", sd) |
Output :
Your matrix: [[33 55 66 74] [23 45 65 27] [87 96 34 54]] Standard Deviation : 22.584870796373593
Method 2: stdev() method in Statistics package.
Python3
import statistics statistics.stdev([11, 43, 56, 77, 87, 45, 67, 33]) |
Output :
24.67466890789592
