What is Python?

Python is a programming language that’s designed to be easy to read and write. It’s one of the most popular languages in the world and is used for everything from web development to data science to automation scripts.

What makes Python special is its readability. The code looks almost like plain English. Python uses indentation to organize code instead of curly braces, which makes it visually clean. Here’s a simple example:

def greet(name):
    if name:
        print(f"Hello, {name}!")
    else:
        print("Hello, stranger!")

greet("Alice")

Python was created in 1991 by Guido van Rossum. He wanted a language that was easy to learn but powerful enough for real work. The name comes from Monty Python’s Flying Circus, not the snake.

Python is everywhere. Data scientists use it with libraries like pandas and NumPy to analyze data. Machine learning engineers build AI models with TensorFlow and PyTorch. Web developers build sites with Django and Flask. DevOps engineers write automation scripts in Python. It’s the default language for teaching programming at many universities.

One reason Python became so popular for data science and machine learning is its massive ecosystem of libraries. When you need to do something, there’s probably already a well-tested library for it. The Python Package Index has over 500,000 packages you can install with a simple pip install command.

Python is interpreted, which means you don’t need to compile it before running. You can write code and run it immediately. This makes development faster but also means Python is generally slower than compiled languages like C or Rust. For most applications, the difference doesn’t matter. And when it does, you can write performance-critical parts in C and call them from Python.

The language keeps evolving. Modern Python has type hints for better tooling, async/await for concurrent programming, and pattern matching for cleaner code. But it maintains backward compatibility well, so old Python code usually still works.

I’ve never used Python professionally, but I used it for investment backtesting and built a prototype with LangChain at my current job. If I was going to write an agentic backend, I’d use Python. The ecosystem for AI and machine learning makes it the obvious choice for those kinds of projects.