Tips To Help You Write Cleaner, More Effective Data Science Code


Developers consider the ability to write readable code to be a type of art. Although 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 reading code created by other developers with a reputation for producing high-quality code.


We’ll review a few things you can do in this article to produce clean, top-notch code. Fth

  • Indenting and blocking

The functions ought to be modest. Nobody wants to have to search through a 2000-line function for an error. Try to keep the number of lines per statement (if, else, while.) to a minimum to make the function legible.


Ideally, keep each statement to a single line. This line can be used to call a function with an explicit name to be as clear as feasible. If you must use indentation, try to keep it up to two levels to maintain the readability of the code. Refer to the Data science course in Delhi for a more thorough explanation.

  • Use a logger rather than print.

Prints help test and debug, but a logger is far more appropriate when using your code.


Prints are less readable, harder to format, and, most importantly, contain less information. However, loggers have more information, are directly formatted, and have a sufficient level of information to determine the information’s significance.

  • Be consistent.

The readability of code benefits greatly from consistency. Choose a style to adhere to throughout your code when planning the structure of your program. Choosing a naming convention for your classes, functions, and variables falls under this category. How you will modulate your code, use pre-existing packages, address the various mathematical steps in this algorithm, and use comments.

  • Focus on Short and Clear Docstrings


Whether it’s comprehensive or in-code documentation, documenting your code is very important (docstrings). Docstrings are strings that appear at the start of a code file after defining a function or class and describe the function of the code, function, or class to the reader.


Docstrings are intended to give a brief explanation of the purpose and operation of your code. For instance, when used at the start of a function, it should include the common types of attributes and their role in the function, the function’s output, and one or two sentences outlining how the output is calculated.


C for a class, the class attributes and methods should be included in the docstring.

  • Verification Control

Both backups and collaboration depend on this. It enables us to keep track of changes made to a project as it develops, which helps organize tasks and promote diligence. Git is a robust version control system that allows users to branch certain development phases, track and commit changes, push and fetch content from remote repositories, and merge code segments to resolve conflicts as needed.

  • Make tests

Try to think of what tests you can write alongside your code to verify the accuracy of your premises and reasoning. These tests can take many different forms, such as simulating the desired inputs and outputs or running many unit tests to validate the functioning of the code. A unit test, which could be a method, class, or component, often tests the functionality of the smallest possible unit of code in a repeatable manner. If you are unit testing a class, your test might determine whether the class is in the appropriate state. The unit of code is typically tested independently; your test only affects and observes changes to that unit.


Hope this article helped you in gaining insight into how to write code effectively throughout your data science journey. Visit the Data Science certification course in Delhi, to learn more about programming and practice them in your real-world data science projects. 

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