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Python Modules Explained: Improve Your Coding Skills

In this article, I'll simplify Python modules and highlight their importance in your project development with a little bonus at the end about the Python modules I wish I knew at the beginning of my software dev career.
Daniel Zacharias

Code Power Team

November 3, 2023

Python — a widely loved, adaptable language that’s favored by millions for its user-friendliness.

However, if you’ve been working with Python for some time, you know managing complicated projects sometimes feels like Sisyphus struggles. 

The solution for all the coffee-jugging stress? 

Python modules! They are here to revolutionize your coding process, making those once daunting tasks seamless and effortless. 

In this article, I’ll simplify Python modules and highlight their importance in your project development with a little bonus at the end about the Python modules I wish I knew at the beginning of my software dev career. 

So what are we waiting for? Let’s dive right in!

Mastering Python modules: From structure to usage

Any Python file with the .py extension filled with definitions and statements is a Python module. Every time you save a Python file, you’re creating a Python module packed with related classes, functions, and variables. 

What’s different with Python modules is that they have a well-structured layout, starting with a docstring (a concise comment explaining the purpose of the module). Then comes imports of other modules, global variables, class and function definitions, and optional “dunder” (double underscore) attributes such as __name__ and __doc__.

In the enormous universe of Python modules, there are two main types:

  • built-in modules
  • third-party modules

Think of built-in modules as the fundamental Python elements. They come pre-loaded with Python and are essential for execution. For instance, the math module provides various mathematical functions for computations.

Third-party Python modules are more fancy tools and extensions you can upgrade your run-of-the-mill modules. Besides providing extra functionality and saving tons of coding time, their main purpose is to make your code more elegant and robust.

The most famous third-party module is the “requests” HTTP library, featuring straightforwardness and code elegance. 

4 key advantages of using Python modules

The first moment you start working with Python modules, these four advantages will hit you straight in the face with the “Holy cow, I can do that with Python?!” kind of moment:

  1. Code organization and reusability — Python modules act like a well-planned filing system for your code, where you can store classes, functions, and variables. Only write them once and use them repeatedly throughout your code or even other projects. Think of it as having an organized tool shelf where everything is easily accessible and usable on any project imaginable.
  2. Modularity and concern separation — Python modules let you divide your application into smaller, manageable components, making it easier to develop, maintain, and debug. It’s like assembling LEGO blocks piece by piece. It’s also one of the ways how some of the most famous Python frameworks are built!
  3. Expanded functionality — If the built-in module doesn’t fit the bill, countless third-party Python modules are here to save the day. From advanced data analysis to sending HTTP requests, tapping into the immense power of third-party modules is at the click of a button.
  4. Time-saving — Writing entire code blocks from scratch in Python sucks. Using Python modules code you would write in an hour is finished within 10 min. 

Bonus advantage 

Did you know Python modules are also collaboration-friendly?!

Sharing is caring” is essential in the software development world. Especially, if your Python modules will improve developers’ efficiency across all skill levels and teams.

Some of the most well-known community-built Python modules you’re bound to come across are:

  • NumPy — Used for working with arrays and matrices of numerical data.
  • Beautifulsoup4 — Used for web scraping, making it easy to parse HTML and XML documents.
  • SciPy — Used for scientific and technical computing
  • Pandas — Used for data manipulation and analysis

How to create your own Python modules

Now that we’ve covered the basics, let’s get down to the business — How do you actually create a Python module?

At the beginning of your Python development career, it’s easy to feel like you’re navigating through the murky waters of Python modules. But how they are created is actually straightforward.

These are the three core steps by which every Python module is created:

  1. Writing the module — The first step is to create your module in a .py file. You can include anything from functions, variables, or classes relating to your project needs. One thing you must pay attention to is how your code is structured. Keep it well-organized and clean.
  2. Storing the module — After you’re done writing the module, save it in the same directory as the main Python script you’ll be using. Think of it as stashing your favorite novel on your bedside table, always within reach for that late-night storytelling convenience!
  3. Importing the module — Finally, import your created module into your main Python script by using the “import” command followed by the name of the module you just created. Think of it as the means of module activation.

And voila, you’ve just created and used your first Python module!

Pro Tip: When working with modules, always comment on your code. It’ll keep every function and class spotless and in check. Also, keep your modules small and focused. Don’t go with mile-long modules that’ll create runtime glitches and performance tolls.

Python modules pitfalls you must avoid

What do you do when Python modules go South?  

These are the most common Python module glitches and errors you’ll face throughout your Python development. But don’t worry, they also come with easy solutions:

  • ModuleNotFoundError: No module named X‘ — This error is prevalent and usually arises when Python isn’t able to locate the module required for importing. Check you’ve installed the right package. Also, hunt for any spelling errors in your code, and confirm file paths in your PYTHONPATH environment variable.
  • Conflicting Names — Using names of built-in modules like ‘string’ or ‘json’ can create confusion in your program’s functioning. Also, don’t name your modules with random gibberish. It’ll confuse the hell of other developers and your future self (been there, done that).
  • Circular Imports — When module A imports B while B imports A, there’s a circular import issue leading to incomplete initialization of the code. In short, you’ll confuse Python. An easy fix to this issue is refactoring your code into a hierarchy. 

Python module mastery: Achieved!

Python modules aren’t a labyrinth, but rather extremely helpful tools that enhance, organize, and simplify your codebase once you master them. 

I’ll leave you with something few developers know: Did you know that Python bootstraps itself using Python modules? Yes, you read it right! 

Python believes in its own module magic and, at startup, runs several built-in modules to kickstart processes. By using these modules yourself, you’re joining forces with the very technique that powers Python’s underlying work principle — practice what one preaches.

Embrace this virtuous cycle and continue onward with boosted efficiency!

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