**π‘ Problem Formulation:** When working with angles in mathematical computations or graphical applications in Python, you might need to convert radians to degrees. For instance, Python’s trigonometric functions in the math module such as `sin()`

and `cos()`

use radians, while you may need degrees for reporting or interfacing with other applications. Given an angle in radians, the goal is to output the same angle measured in degrees.

## Method 1: Using the math.degrees() function

Pythonβs built-in `math`

module provides the `degrees()`

function, which converts an angle from radians to degrees. It’s a straightforward and precise method preferrable for most cases when converting single values or in simple scripts.

Here’s an example:

import math angle_in_radians = 1 angle_in_degrees = math.degrees(angle_in_radians) print(angle_in_degrees)

Output: 57.29577951308232

This code snippet imports the `math`

module, converts 1 radian to degrees, and prints the result. The function `math.degrees()`

takes an angle value in radians and returns the value in degrees, utilizing the conversion factor 180 divided by Ο.

## Method 2: Manual Conversion

For understanding the mathematics behind the conversion, you can manually multiply the radian value by 180/Ο to convert it to degrees. This method reinforces the underlying conversion principle and avoids dependencies.

Here’s an example:

import math angle_in_radians = 1 angle_in_degrees = angle_in_radians * (180 / math.pi) print(angle_in_degrees)

Output: 57.29577951308232

This snippet takes advantage of the fact that one radian equals 180/Ο degrees. By manually calculating this in the code, you display the conversion factor explicitly, which could be beneficial for educational purposes or in environments where importing additional modules is not optimal.

## Method 3: Using numpy.deg2rad()

If you’re performing the conversion as part of a data processing task, using NumPy’s `deg2rad()`

function is efficient, especially when working with arrays of values. NumPy is typically faster than pure Python due to its optimizations and vectorized operations.

Here’s an example:

import numpy as np angle_in_radians = np.array([1, 2, 3]) angle_in_degrees = np.degrees(angle_in_radians) print(angle_in_degrees)

Output: [57.29577951 114.59155903 171.88733854]

This example utilizes the NumPy library to convert an array of radian values to degrees in one go. Using `np.degrees()`

can simplify your code and is particularly useful when the input is an array rather than a single number.

## Method 4: Using sympy.rad()

The SymPy library is designed for symbolic mathematics and can handle angle conversion with precise symbolic computation. Its `rad()`

function can be used when exact rational numbers are needed instead of floating-point approximations.

Here’s an example:

from sympy import rad, pi angle_in_radians = pi / 2 angle_in_degrees = rad(angle_in_radians) print(angle_in_degrees)

Output: 90

In this code, SymPy’s symbolic nature allows direct usage of Ο as `pi`

and returns the result in symbolic form. Using `rad()`

is particularly useful in educational contexts or anywhere that exact representations are more valuable than numeric approximations.

## Bonus One-Liner Method 5: Lambda Function

If you’re looking for a quick, one-time conversion without importing an entire module, a lambda function can be used as an inline converter. Itβs concise but less clear for readers unfamiliar with lambda functions.

Here’s an example:

rad2deg = lambda r: r * (180 / 3.141592653589793) print(rad2deg(1))

Output: 57.29577951308232

The lambda function defines a simple one-liner that takes a radian value and returns the equivalent in degrees. While compact, this method isn’t generally recommended for complex codebases as it can reduce readability.

## Summary/Discussion

**Method 1: math.degrees()**Simple and straight from Python’s standard library. Strengths: easy to understand, no external dependencies. Weaknesses: not ideal for processing arrays efficiently.**Method 2: Manual Conversion**Educational and dependency-free method. Strengths: reinforces understanding of the conversion principle. Weaknesses: verbose, less practical for large-scale processing.**Method 3: numpy.deg2rad()**NumPy method for array processing. Strengths: fast and convenient for numerical computations. Weaknesses: requires NumPy, which might be unnecessary for simple tasks or non-numeric inputs.**Method 4: sympy.rad()**SymPy’s symbolic computation approach. Strengths: provides exact symbolic results. Weaknesses: overkill for numerical tasks, less efficient for large arrays.**Method 5: Lambda Function**Compact one-liner solution. Strengths: quick and doesn’t require imports. Weaknesses: potentially unclear and less maintainable code.