A 30-year-old language that refuses to slow down

Python first appeared in 1991, yet it keeps gaining ground. GitHub's 2022 Octoverse report ranked it the second most-used language on the platform, with usage up more than 22 percent year over year and over four million developers having written Python code on GitHub during 2022. That trajectory is all the more striking given how many newer languages have emerged in the same period.

The language was created by Guido van Rossum, who wanted something simple to use and readable at a glance. Python's design leans on English keywords and line breaks to define structure, making it approachable for beginners and efficient for experienced developers. (The name, incidentally, comes from "Monty Python's Flying Circus," not the snake.)

Consider the difference in verbosity between Python and Java for the same task:

Python

print("Hello world.")

Java

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

Several characteristics contribute to Python's staying power:

  • Open source. The source code is freely available to download, modify, and redistribute.
  • Portable. The same Python code runs on any platform with a Python interpreter, without platform-specific rewrites.
  • Extendable. Developers can write Python modules in languages like C++ and add them to the interpreter for customization and optimization.
  • Broad standard library. Built-in modules cover everyday programming tasks, reducing the need to write everything from scratch.

Where Python gets used

import antigravity

def main():
    antigravity.fly()

if __name__ == '__main__':
    main()

Run the command above to see a running joke among Python developers.

Python's general-purpose nature lets it span a wide range of domains, each drawing on the same readable core syntax.

Web and software development

Python supports complex, multi-protocol applications while keeping code concise. The open source community around it supplies reusable code and frameworks; Django, for example, is one of the most widely used Python frameworks and is designed to accelerate application builds and prevent common roadblocks.

Task automation

Python makes it easy to automate repetitive tasks using built-in modules or pre-written code from its library. The smtplib module handles email sending, and shutil handles file copying. For testing, frameworks like Pytest, Behave, and Robot let developers write effective tests to verify build quality.

Machine learning and data science

Python is the preferred language in data science and research. People with limited development experience can learn enough to manipulate data for analysis, reporting, and predictive modeling. Data scientists use libraries such as NumPy, Pandas, and Matplotlib to automate data cleaning, transformation, and visualization. Python is also one of the top languages for training machine learning models, which analyze patterns in data and refine their predictions as new datasets arrive.

Financial analysis

The financial sector uses Python for heavy computation on large datasets. Stock market data can be imported and analyzed with Python to generate algorithmic trading strategies. The language is also applied to portfolio optimization, risk management, financial modeling and visualization, cryptocurrency analysis, and fraud detection.

Artificial intelligence

Python's concise and readable code helps developers build consistent AI systems, and its libraries provide frameworks such as PyBrain with algorithms for machine learning tasks. Visualization capabilities help convert large AI and ML datasets into comprehensible graphs or reports. OpenAI uses the PyTorch framework as its standard for deep learning to train its AI systems.

Why Python keeps growing

Beyond ease of learning, several forces sustain Python's popularity:

  • Productivity. Compared to languages like C++, Python's syntax lets developers write more functionality with less code, saving time and effort.
  • Community support. A large user community provides documentation, tutorials, and troubleshooting help. The Python community on GitHub offers the latest version information, bug reports, and update notes.
  • Academic adoption. Python is now taught in schools from elementary levels onward and is used across STEM disciplines beyond computer science—from solving differential equations to running statistical analyses and simulating particle diffusion.
  • Corporate demand. Its broad applicability to development and data analysis makes Python a sought-after skill. Statista reported Python was the third most demanded language by recruiters worldwide in 2022.

Getting started

Several GitHub repositories can help new and experienced developers work with Python:

  • Explore pre-built Python algorithms: A repository covering everything from networking flows to physics and neural networks.
  • Learn Python in 30 days: A step-by-step guide through the language's basics.
  • Grab tips from a cheatsheet: A collection of Python scripts with code examples and explanations.
  • Sharpen your skills: A study guide for beginner and seasoned users alike.

To begin, download the latest version of Python from the official site. GitHub also offers two practical starting points: GitHub Codespaces gives every developer 60 free hours per month of cloud-based development environments, and a Django quick start template lets you code in the browser. GitHub Copilot can act as an AI pair programmer: install the extension, describe your project and required libraries in comments, and let Copilot suggest lines as you type.

Python's reach extends from everyday web services to the James Webb Space Telescope—NASA uses it for the telescope's data analysis. Whether for automation, data science, or AI, Python's versatility continues to drive its adoption across industries and skill levels.