Best Practices for Coding in Python

Python is becoming a popular programming language among both new and experienced programmers. In addition to being a powerful and versatile programming language, Python also enables you to make your code easier to read, maintain, and code more efficiently by following a few best practices.

This article will go over five such Python best practices that Python programmers can use to improve their coding experience.

Best Practices for Coding in Python

1. Writing Well-Formed Code

Whether you’re writing a simple script or a complex project, having well-structured code with proper module names, correct indentations, and documentation improves the code’s future usability.

You must include a README file to describe your project, a setup.py file to properly set up your project in a new environment, and a requirements.txt file to describe the dependencies required. Lastly, the documentation describes how all of your project’s components work. In addition, you can include some tests in your repository.

2. Values returned

When the complexity of a function grows, it is common to use multiple return statements within the function’s body. In the body of the document, there is a better way to maintain a clear intent and a sustainable reading level by refraining from returning meaningful values from many output points.

There are two main scenarios in which a function can return values: the result of the function when it has been processed normally and error cases that indicate that the function could not complete its computation or task because of an incorrect input parameter or any other reason why it was unable to execute it.

If you do not want to raise exceptions in the second case, you may need to return a value, such as None or False, indicating that the function could not be performed correctly. In this case, returning as soon as the incorrect context is detected is preferable. It will aid in flattening the function’s structure: all code following the return-because-of-error statement can assume the condition is met and compute the function’s main result. Multiple return statements are frequently required.

However, debugging the returned result becomes complicated when a function has multiple main exit points for its ordinary course, so keeping a single exit point may be preferable. This will also aid in factoring out code paths, and the multiple exit points likely indicate that such refactoring is required.

def complex_function(a, b, c):
    if not a:
        return None  # Raising an exception might be better
    if not b:
        return None  # Raising an exception might be better
    # Some complex code trying to compute x from a, b and c
    # Resist temptation to return x if succeeded
    if not x:
        # Some Plan-B computation of x
    return x  # One single exit point for the returned value x will help
              # when maintaining the code.

3. Correct Variable, Class, Function, and Module Naming

One of the most common mistakes new Python developers make is the incorrect naming of variables, classes, functions, etc. We should avoid using single-letter variable names for heavily abbreviated functions or class names.

A variable that stores the temperature of a substance, for example, should be named temperature or temp rather than t. The PEP-8 is a popular Python programming guideline that tells us how to correctly name our variables, classes, and functions.

For example:

A long variable should be separated by underscores (_), for example, a long variable name.

CamelCase should be used when writing class names, such as LongClassName.

It is recommended to use underscores when writing functions, such as function name ()

4. Developing Modular Code

Making the codebase more compact and modular is the best way to improve its quality. The module is similar to a collection of functions you can call from your code.

Python has a repository known as PyPI, or the Python Package Index, which contains many modules and libraries. It includes several models written by Python developers that you can use in your code simply by downloading them from the internet. This saves you from having to recreate all the logic and makes your script more compact and readable.

Another advantage of using modules is that programmers have written most of these existing models with years of experience, and they can provide you with the best possible solution, which would take a long time to think of from scratch.

5. Avoid using the magical wand.

Python, a powerful tool for hackers, comes with a rich set of hooks and tools that allow you to do almost any tricky trick. For example, it is possible to do the following:

• Alter the creation and instantiation of objects
• Modify the way the Python interpreter imports modules.
• It is possible (and recommended) to embed C routines in Python if necessary.

However, these options have numerous disadvantages, and taking the most straightforward path to your goal is always preferable. The main disadvantage of using these constructs is that readability could be significantly improved. Many code analysis tools, including plant and pyflakes, will fail to parse this “magic” code.

Conclusion

We conclude that Following Python programming best practices can make your code more readable, maintainable, and efficient. Some critical best practices are using meaningful variable and function names, proper indentation, docstrings, and adhering to the “PEP 8” style guide.

It is also critical to avoid using global variables, list comprehensions and generators, keep functions and methods short, use try-except blocks, keep your code simple, and thoroughly test your code.

Furthermore, adhering to these best practices aids in avoiding common pitfalls that can make debugging and troubleshooting more difficult.