Why Python keeps growing, explained

Which programming language has been around for more than three decades and continues to grow in popularity each year?

If you guessed Python, you nailed it. In the 2022 Octoverse report, we found that Python remains the second most-used programming language on GitHub. Interestingly, Python’s use grew more than 22 percent year over year with more than four million developers on GitHub using it at some point in 2022.

In this article, we’ll dive into a brief history of Python, its benefits, its use cases, and seek to answer why a program language conceived in the 1980s continues to dominate development. And, since this is GitHub, we’ll also offer a few useful tips and tricks for developers new to—and experienced in—Python.

So, what is Python? 🤔

Python is a high-level, interpreted programming language with a simple syntax, which makes it easily readable and extremely user- and beginner-friendly. Originally built to satisfy Guido Van Rossum’s desire for a programming language that was simple to use and beautiful to look at, Python was first released to the world in 1991.

Fun fact: Python was named after the BBC TV show, “Monty Python’s Flying Circus.”

Since its development, it has grown to have widespread applicability for developers, data scientists, researchers, and more. But how, you may ask, can a coding language be simple and beautiful to look at? Here’s some proof:


print("Hello world.")



public class HelloWorld {
    public static void main (String[]args) {
      System.out.println.("Hello world");

Since Python is a general-purpose language, it can be used in a variety of applications, and its uncomplicated nature makes it an excellent language for automating tasks, building websites or software, and analyzing data.

Python also has several other characteristics that make it popular amongst developers and engineers. These include:

What is Python commonly used for? 💻

Python can be used for just about anything, from web and software development to machine learning and artificial intelligence (AI). Let’s take a look at some of its most common use cases.

import antigravity

def main():

if __name__ == '__main__':

Run this command to check out an inside joke among Python developers.

Using Python for web and software development

Python is a popular language for web and software development because you can create complex, multi-protocol applications while maintaining concise, readable syntax. In fact, some of the most popular applications were built with Python. Plus, Python’s open source community provides developers with an extensive amount of reusable code, frameworks, and support. Case in point: Django is one of the most-used Python frameworks designed by experienced developers to help others accelerate their application build times and avoid issues that might balk their progress.

Using Python for task automation

One of Python’s key benefits is its ability to automate manual, repetitive tasks. With Python, you can learn how to automate just about anything by using either built-in modules or pre-written code from its robust library. Or you can write your own custom scripts to perform specific actions. For example, you can easily automate emails with the “smtplib” module or copy files with the “shutil” module. Python also has a robust set of testing frameworks, which makes it an excellent language for test automation. Frameworks such as Pytest, Behave, and Robot allow developers to write simple yet effective tests to ensure the quality of their builds.

Using Python for machine learning and data science

Here’s a fun fact: Python is the top preferred language for data science and research. Since its syntax is easily understandable and adaptable, people with little-to-no development experience can easily learn Python and use it to manipulate data for research, reporting, predictable or regression analyses, and more. Collecting and parsing data can be a time-consuming task for data scientists. Python is also one of the top languages for training machine learning (ML) models. Through specific algorithms, these models can analyze and identify patterns in data to make predictions or decisions based on that data. They also constantly evolve based on outputs of previous datasets to confront new variables. Data scientists and developers training ML models often utilize libraries, such as NumPy, Pandas, and Matplotlib, to automate functions like cleaning, data transformation, and visualization.

Using Python for financial analysis

Similar to how Python can assist data scientists with the heavy lift of large data sets, Python is widely used in the financial industry to quickly perform complex computations. Stock markets generate huge amounts of data, and Python can be used to import data on stock prices and generate strategies through algorithms to identify trading opportunities. The language can also be used for portfolio optimization, risk management, financial modeling and visualization, cryptocurrency analysis, and even fraud detection.

Using Python for and artificial intelligence

Python can also be found in some of the most complex, artificial intelligence (AI) technologies—and it’s actually one of the preferred languages for AI. Python’s concise and readable code allows developers to create consistent, reliable systems, and its vast library provides a number of frameworks like PyBrain, which offers developers powerful algorithms for machine learning tasks. Plus, Python’s visualization capabilities can help convert these large datasets for AI or ML into comprehensible graphs or reports. Interestingly enough, OpenAI, the artificial intelligence research lab, utilizes the Python framework, Pytorch, as their standard framework for deep learning, which trains its AI systems.

In addition to its relative simplicity to learn, there are a few other reasons why Python continues to consistently grow in popularity. These include:

The bottom line

Python is everywhere—and it’s been used to build a significant number of the technologies, websites, and even systems most people encounter on a daily basis. It powers everything from your favorite video streaming service to the ML algorithms that can help you make your next cryptocurrency trade. And for an even broader scope example (pun absolutely intended), NASA uses Python to power data analysis with its sophisticated James Webb Space Telescope, which makes it one of the few programming languages that is, quite literally, out of this world. 🚀

How to get started with Python 📓

A quick Google search will yield hundreds of resources out there to jumpstart your Python journey—and that can quickly get a little overwhelming. To simplify things, here are a few helpful GitHub repositories to help you get started with Python:

To get started, download the latest version of Python.

Start building on GitHub today

GitHub offers two easier ways to start working with Python: GitHub Codespaces and GitHub Copilot.

You can start building today for free with GitHub Codespaces, which every developer on GitHub gets 60 free hours of use time per month to spin up a development environment in the cloud from any device at speed. Check out the Django quick start template to begin coding right in your browser!

You can also use GitHub Copilot, GitHub’s AI pair programmer, to write your first lines of Python. Here’s how:

  1. Install the GitHub Copilot extension into your code editor.
  2. Describe the purpose of your project in a comment.
  3. Write a comment describing which libraries you may need.
  4. Start tabbing and let GitHub Copilot suggest lines of code to help you learn new techniques or methods.

From machine learning to data analysis, Python’s versatility allows it to continue its explosive growth with developers and non-developers alike. Experiment with Python through GitHub or on your local machine to be part of this growth and get started today!

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