Prerequisite: Array Arithmetic Operations in NumPy

Apart from arithmetic operations, NumPy also gives us some very useful Trigonometric functions used in Data Analysis.

Trigonometric sin(), cos(), tan() functions in NumPy

Code

import numpy as np

# pi is in radians
pi = np.pi
np.sin(pi)

Output

Code

np.cos(pi)

Output

Code

np.tan(pi)

Output

Note: The values are computed to within machine precision, which is why values that should be zero do not always hit exactly zero.

sin(), cos() & tan() in NumPy Array

The Trigonometric sin(), cos() & tan() functions work exactly the same for NumPy array as they work for a single constant value.

Code

theta = np.array([0, pi/2, 3*pi/2, 2*pi])
np.sin(theta)

Output

Code

np.cos(theta)

Output

Code

np.tan(theta)

Output

Inverse Trigonometric Functions

The arcsin(), arccos() & arctan() functions in NumPy work as inverse functions for sin(), cos() & tan() respectively.

Code

x = np.array([-1, 0, 1])
np.arcsin(x)

Output

Code

np.arccos(x) 

Output

Code

np.arctan(x)

Output