Normalization using numpy.linalg.norm
The NumPy library provides a method called norm that returns one of eight different matrix norms or one of an infinite number of vector norms. It entirely depends on the ord parameter in the norm method. By default, the norm considers the Frobenius norm. The data here is normalized by dividing the given data with the returned norm by the norm method.
Python3
# import necessary packages import numpy as np # create an array data = np.array([[10, 20], [30, 40], [5, 15], [0, 10]]) normalizedData = data/np.linalg.norm(data) # normalized data using linalg.norm print(normalizedData) |
Output
[[0.17277369 0.34554737] [0.51832106 0.69109474] [0.08638684 0.25916053] [0. 0.17277369]]
How to normalize an NumPy array so the values range exactly between 0 and 1?
In this article, we will cover how to normalize a NumPy array so the values range exactly between 0 and 1.
Normalization is done on the data to transform the data to appear on the same scale across all the records. After normalization, The minimum value in the data will be normalized to 0 and the maximum value is normalized to 1. All the other values will range from 0 to 1. Normalization is necessary for the data represented in different scales. Because Machine Learning models may get over-influenced by the parameter with higher values. There are different ways to normalize the data. One of the standard procedures is the min-max value approach.