How to calculate absolute value using Numpy

In this Python lesson you will learn how to calculate absolute value using Numpy. You will get to know several useful tricks.

Basic Absolute Value Calculation

To find the absolute value of a number, you can simply use the np.abs method.

import numpy as np

my_scalar = -77

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

For instance, the absolute value of -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 an array like (-1, -2), the absolute values become (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 example, we’ve mapped two arrays and subtracted them using the np.abs() method. You can perform various calculations on Numpy arrays or matrices in a similar fashion.

Numpy Functions for Absolute Value

Numpy provides two functions for absolute value calculations: np.abs() and np.absolute(). They are functionally identical, so choose the one that suits you best. I prefer np.abs() for its brevity, but the choice is yours.