So, It is considered a best practice while writing bigger codes for production-level projects in Data Science is to split the large Python code blocks into modules containing up to 300–400 lines of code. With the help of modules, we can organize related functions, classes, or any code block in the same file. In simple terms, we can consider a module to be the same as a code library or a file that contains a set of functions that you want to include in your application. In Python, Modules are simply files with the “.py” extension containing Python code that can be imported inside another Python Program. So, Let’s Get Started, What are Python Modules? Working with Built-in Modules in Python – Math, and Statistics Module.What are the advantages to use modules in Python?.How we can introduce variables in Python Modules?.The topics which we are going to discuss in this article are as follows: Note: If you are more interested in learning concepts in an Audio-Visual format, So to learn the basic concepts of Python Modules and some other related stuff you may see this video. So, In this article, I will help you to understand the complete intuition behind modules in Python in a detailed manner. So, to resolve these kinds of issues, Python modules help us to organize and group the content by using files and folders. This modular programming approach where we have broken the code into separate parts is where python modules come into the picture. So, keeping your code in the same file as it grows makes your code difficult to maintain and debug. When we write the codes for Production level Data Science Projects, what happens is that our Python code grows in size, and as a result most probably it becomes unorganized over time. Nowadays, the Python programming language becomes one of the most popular languages. This article was published as a part of the Data Science Blogathon Introduction
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