Mastering Trigonometric Functions with NumPy in Python
When working with mathematical computations in Python, NumPy is an
essential library. It offers several powerful functions for performing
trigonometric calculations with ease.
Basic Trigonometric Function in Numpy
(ufuncs) such as sin(), cos(), and tan() which accept input in radians
and return their corresponding sine, cosine, and tangent values.
Program:
Calculate the Sine of π/2
import numpy as np
x = np.sin(np.pi / 2)
print(x)
Output:
1.0
Program:
Calculate Sine Values for Multiple Angles
import numpy as np
arr = np.array([np.pi/2, np.pi/3, np.pi/4, np.pi/5])
x = np.sin(arr)
print(x)
Output:
[1. 0.8660254 0.70710678
0.58778525]
Converting Degrees to Radians
By default, trigonometric functions in NumPy expect radian inputs. But if
you have angles in degrees, you can easily convert them using
np.deg2rad().
Formula:
radians = degrees × (Ï€ / 180)
Program:
Convert Degrees to Radians
import numpy as np
arr = np.array([90, 180, 270, 360])
x = np.deg2rad(arr)
print(x)
Output:
[1.57079633 3.14159265 4.71238898 6.28318531]
Converting Radians to Degrees
Similarly, you can convert radian values back to degrees using
np.rad2deg().
Program:
Convert Radians to Degrees
import numpy as np
arr = np.array([np.pi/2, np.pi, 1.5*np.pi, 2*np.pi])
x = np.rad2deg(arr)
print(x)
Output:
[ 90. 180. 270. 360.]
Inverse Trigonometric Functions (Finding Angles)
NumPy also provides functions for inverse trigonometric operations:
arcsin()
arccos()
arctan()
These functions return angles in radians.
Program:
Find Angle for sin⁻¹(1.0)
import numpy as np
x = np.arcsin(1.0)
print(x)
Output:
1.5707963267948966
Find Angles for Multiple Sine Values
Program:
import numpy as np
arr = np.array([1, -1, 0.1])
x = np.arcsin(arr)
print(x)
Output:
[ 1.57079633 -1.57079633 0.10016742]
Calculate Hypotenuse (Pythagorean Theorem)
Need to calculate the hypotenuse? NumPy’s hypot() function makes it
simple:
Formula:
hypotenuse = √(base² + perpendicular²)
Program:
Calculate Hypotenuse for Base=3 and Perpendicular=4
import numpy as np
base = 3
perp = 4
x = np.hypot(base, perp)
print(x)
Output:
5.0
More topic in Numpy
