# 5 Best Ways to Convert Angles from Degrees to Radians in Python

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π‘ Problem Formulation: In various applications across engineering, mathematics, and computer science, converting angle measurements from degrees to radians is often necessary. This article discusses how to perform this conversion in Python. For instance, converting 180 degrees to Ο radians is a common requirement, as these are equivalent measures of an angle in different units.

## Method 1: Using the math.radians Function

The simplest way to convert an angle from degrees to radians in Python is by using the built-in `math.radians` function, which takes an angle in degrees and returns its radians equivalent.

Here’s an example:

```import math

angle_in_degrees = 180

Output:

`Radians: 3.141592653589793`

This code snippet defines a function `degrees_to_radians` that utilizes the `math.radians` method. We pass the angle in degrees to this function, which computes and returns the equivalent radians.

## Method 2: Manual Conversion Using the PI Constant

Another method of converting degrees to radians is by using the formula rad = deg * (Ο/180), with Ο as the constant from the math module.

Here’s an example:

```import math

return deg * (math.pi / 180)

angle_in_degrees = 45

Output:

`Radians: 0.7853981633974483`

This example multiplies the angle in degrees by Ο/180 for converting it to radians. The value of Ο is provided by the math module in Python.

## Method 3: Using numpy.radians Function

For those working with numpy for numerical computations, numpy provides a convenient function, `numpy.radians`, much like Python’s `math.radians` function, to perform the conversion.

Here’s an example:

```import numpy as np

angle_in_degrees = np.array([0, 30, 45, 90])

Output:

`Radians: [0.         0.52359878 0.78539816 1.57079633]`

In this snippet, we’re using numpy’s `radians` function to convert an array of angles in degrees to radians. Numpy’s advantages include vectorized operations on arrays, which is efficient for batch converting multiple angles.

## Method 4: Using Lambda Function

A lambda function in Python can also be used for a more concise code when converting degrees to radians. Lambda functions allow us to define a function in a single line of code.

Here’s an example:

```import math

# Lambda function for conversion
degrees_to_radians = lambda deg: deg * (math.pi / 180)

angle_in_degrees = 90

Output:

`Radians: 1.5707963267948966`

With the lambda function defined, the conversion process is both succinct and efficient. The anonymous function takes an angle in degrees and returns its radians counterpart.

## Bonus One-Liner Method 5: Using List Comprehension

For a quick, one-off conversion of multiple angles, list comprehensions can be combined with the math module to provide elegant and concise code.

Here’s an example:

```import math

angles_in_degrees = [0, 90, 180, 270]
angles_in_radians = [deg * math.pi / 180 for deg in angles_in_degrees]

Output:

`Radians: [0.0, 1.5707963267948966, 3.141592653589793, 4.71238898038469]`

This compact snippet uses a list comprehension to apply the conversion formula to each element in the list of angles in degrees. This method is best for simple, quick conversions of lists of degrees.

## Summary/Discussion

• Method 1: Using `math.radians`. Strengths: Simple and direct, utilizing built-in Python functions. Weaknesses: Requires importing the math module, not as straightforward for arrays.
• Method 2: Manual Conversion. Strengths: Good for understanding the underlying conversion formula. Weaknesses: More verbose than using `math.radians`.
• Method 3: Using `numpy.radians`. Strengths: Excellent for working with numpy arrays and batch processing. Weaknesses: Numpy must be installed and imported.
• Method 4: Lambda Function. Strengths: Offers a quick, one-liner function definition. Weaknesses: Might be less readable to those unfamiliar with lambda functions.
• Method 5: List Comprehension. Strengths: Elegant one-liner for list processing. Weaknesses: Not suitable for handling individual numbers or more complex data structures.