Introduction

Developers consider the ability to write readable code to be a type of art. Even though I only partially agree with that assertion, producing understandable code is a skill that can be learned.

Writing more high-quality code is the only way to practice making your code more readable. As a result, I suggest reviewing code created by other developers with a reputation for producing high-quality code.

In general, readable code is an important result that becomes progressively more crucial as your code becomes more complex. Because data science applications can be somewhat difficult to understand, it could be better to introduce further complexity through poorly written code. Writing accessible code is especially crucial in data science.

I'm assuming you concur that it's crucial to write readable code. However, how can I make my code easier to read?

In this article, we'll go through a few actions you can do to write clear, excellent code.

Prepare a Structure Before You Begin Coding

Try to lay out your code structure before opening your editor and begin coding through an issue. Make a structure of your variables, functions, classes, and modules and how they interact to address the issue, being as specific as possible.

By doing that, you can implement, expand, and deploy the code more quickly. If you intend to make your code open-source, I advise you to include such a structure in the documentation or make it accessible on GitHub. With Learnbay’s data science training in Bangalore, you can easily master GitHub and become a pro coder. 

Name your Variables Descriptively

I'm sure we're all occasionally tempted to give our variables names like X, Y, and Z. But when we examine our code months later and try to understand what is saved explicitly in variable X, we become perplexed! Giving your variables meaningful names can aid future you in reading the code and anyone unfamiliar with your code.

Use exact names rather than brief ones when naming your variables. For instance, when calculating the average of a list of variables, avoid using the terms ave or av; instead, use names like average height or average time. Writing your code will take little time if you use lengthier words because many code editors now feature autocompletion.

Moreover, if your code uses an algorithm first described in a particular article or book, maintain the variable names associated with that source. Remember to put that source at the beginning of your code files.

Use Functions Wisely

The use of functions can help you write clear, well-organized code. If it's utilized correctly, that is. Use functions for operations that can be completed as a single unit, such as performing an operation on several data points or carrying out an algorithm step. Use the same reasoning we discussed when naming your variables when naming your functions.

Group functions with related functionality into a single code file and turn it into a module. The function is now simpler to locate, expand, and use a result.

Make sure the function's properties are as explicit as possible and can be expanded upon.

Target Clear and Concise Docstrings

Whether it's comprehensive or in-code documentation, documenting your code is crucial (docstrings). Docstrings are strings that explain the purpose of the code, function, or class to the reader at the beginning of a code file, following the definition of the function or class.

Docstrings are intended to give a brief explanation of the purpose and operation of your code. For instance, it should include the common types of attributes and their use in the function, the output of the function, and one or two sentences explaining how the output is calculated when used at the beginning of a function (directly beneath the function header).

Avoid creating a new wheel (unless you can do it better)

Use it rather than creating it from scratch if a function you require has already been developed by a supported package or a third-party developer. When using a package, ensure you are familiar with all its features to save time trying to implement something you can already use.

When you're new to programming and attempting to understand how everything works, or if you can implement a function better and with less complexity, these are the only two situations in which I advise doing it yourself, otherwise, using your code to leverage what has already been developed is easier for you and other people to do.

Choose longer, simpler steps rather than short, complex ones.

Aim for obvious steps when implementing a concept from a paper, book, or algorithm rather than trying to combine several phases into one to create shorter code.

Yes, shorter code may demonstrate your proficiency with a programming language's idioms. However, it can also unduly complicate your code. Read, test, debug, and expand with complexity. A code that could be more flexible will result from adding an extra layer of complexity by grouping together several processes, especially if the method you're implementing is complex in and of itself.

Stay Consistent

The readability of code benefits greatly from consistency. Choose a style to adhere to throughout your code when developing the structure of your program. This includes choosing a naming scheme for your classes, functions, and variables. How you will modulate your code, leverage pre-existing packages, address the various mathematical processes in this technique, and use comments.

Conclusion

Using code created by someone else is one of the inescapable aspects of being a data scientist. Additionally, while it will always take time to read and comprehend other people's codes, you can take a few measures to make your own codes simpler for users to understand and apply. If you want to become a skilled data scientist to write readable code, join the best data science course in Bangalore. Master programming for data science, and stay ahead of peers.