How to calculate absolute value using Numpy

This Python guide introduces you to calculating the absolute value using Numpy, along with several practical techniques.

Basic Absolute Value Calculation

To calculate the absolute value of a number, the abs method is your straightforward option.

import numpy as np

my_scalar = -77

my_abs_value = np.abs(my_scalar)
print(f"My absolute value equals: {my_abs_value}")

For example, the absolute value of the scalar value -77 is 77.

You can also calculate the absolute values of arrays in a similar manner:

import numpy as np

my_array = np.array([-1, -2])

my_abs_value = np.abs(my_array)
print(f"My absolute value equals: {my_abs_value}")

For instance, applying np.abs() to a NumPy array containing the elements -1 and -2 yields a new array with the absolute values [1, 2].

Regardless of the array’s size, the calculation remains the same:

import numpy as np

my_array = np.array([[-1, -2], [3, -4], [-5, 6]])

my_abs_value = np.abs(my_array)
print(f"My absolute value equals: \n{my_abs_value}")

Calculating Absolute Differences in Numpy Matrices

You can also compute the absolute difference between Numpy matrices:

import numpy as np

my_first_array = [[-2, -3, 4], [-4,  5, -6]]
my_second_array = [[-1,  2, 7], [-8,  9, -9]]

my_first_array, my_second_array = map(np.array, (my_first_array, my_second_array))

abs_difference = np.abs(my_first_array) - np.abs(my_second_array)
print(f"The absolute difference between arrays equals: \n{abs_difference}")

In this demonstration, we have applied np.abs() separately to two arrays and then computed the element-wise difference of these absolute value arrays. NumPy’s vectorized operations enable a wide range of mathematical computations on arrays and matrices in an analogous manner.

Numpy Functions for Absolute Value

Numpy provides two functions for absolute value calculations: np.abs() and np.absolute(). Both functions are functionally identical, so you can use either one based on your preference. The shorter np.abs() is commonly used for brevity.