np.sin(), along with np.cos() and np.tan(), expects its input in radians, not degrees — a common source of bugs for people used to degree-based trigonometry elsewhere. To convert degrees to radians before calling it, use np.radians(), and np.degrees() to convert a result back if needed. Sine is periodic and bounded between -1 and 1 for any real input, which makes it a common building block for generating smooth, repeating waveforms.

1Understanding np.sin()

💡

Always convert degrees to radians with np.radians() before passing angle values to np.sin()/np.cos()/np.tan() — passing raw degree values directly is one of the most common trigonometry bugs in NumPy code.

editor.html

import numpy as np

angles = np.array([0, np.pi / 2, np.pi])
print(np.sin(angles))

localhost:3000

2Practical Example

Here is a real-world application of np.sin() showing how it is used in production NumPy code.

editor.html

import numpy as np

degrees = np.array([0, 90, 180])
radians = np.radians(degrees)
print(np.sin(radians))

localhost:3000

3Best Practices

Follow these guidelines when working with np.sin():

1. Convert degree values to radians with np.radians() before calling any trigonometric function, since they all expect radians

2. Use np.linspace() to generate an evenly-spaced set of angle inputs when plotting a smooth sine curve

3. Remember floating-point precision means sin(pi) evaluates to a tiny non-zero number rather than exactly 0 — use np.isclose() rather than == when checking trigonometric results against expected exact values

⚠️

Tip: Always convert degrees to radians with np.radians() before passing angle values to np.sin()/np.cos()/np.tan() — passing raw degree values directly is one of the most common trigonometry bugs in NumPy code.

editor.html

import numpy as np

angles = np.array([0, np.pi / 2, np.pi])
print(np.sin(angles))

localhost:3000

Examples

Example 01Basic Usage

import numpy as np

angles = np.array([0, np.pi / 2, np.pi])
print(np.sin(angles))

Example 02Advanced Example

import numpy as np

degrees = np.array([0, 90, 180])
radians = np.radians(degrees)
print(np.sin(radians))

Best Practices

Convert degree values to radians with np.radians() before calling any trigonometric function, since they all expect radians
Use np.linspace() to generate an evenly-spaced set of angle inputs when plotting a smooth sine curve
Remember floating-point precision means sin(pi) evaluates to a tiny non-zero number rather than exactly 0 — use np.isclose() rather than == when checking trigonometric results against expected exact values

Interview Question

Why does np.sin(np.pi) return a tiny number like 1.2246e-16 instead of exactly 0?▼

Hint: Think about floating-point representation, not a bug in NumPy's sine implementation.

np.pi itself is only a finite-precision floating-point approximation of the true mathematical constant pi, which is irrational and can't be represented exactly in binary. Since the input to np.sin() is already a tiny bit off from the true value of pi, the mathematically exact output of 0 is also perturbed by a correspondingly tiny amount, resulting in a value extremely close to, but not exactly, zero. This is a general property of floating-point computation, not specific to NumPy's sine implementation, and it's why comparing floating-point trigonometric results to exact expected values should use a tolerance-based comparison like np.isclose() instead of ==.

Exercises

MediumPractice using np.sin() in a real scenario.

View Solution

import numpy as np

angles = np.array([0, np.pi / 2, np.pi])
print(np.sin(angles))

Frequently Asked Questions

Why does np.sin(np.pi) return a tiny number like 1.2246e-16 instead of exactly 0?▼

Related Functions

np-cosnp-tannp-isclose