Skip to main content

What Is Python?

Python might sound like a snake, but it's actually one of the most powerful tools in modern software development. Over the next five lessons, you'll discover why thousands of engineers choose Python to build AI systems, and you'll learn enough to write your own Python programs. But before we dive into code, let's understand what Python actually is and why it matters for you.

What Exactly Is Python?​

Imagine you want to tell a computer to do something. You can't speak English to it directly—computers need precise, unambiguous instructions. A programming language is a tool for writing those instructions in a way computers understand.

Python is a programming language. It's a system of words, symbols, and rules that lets you describe what you want a computer to do. Think of it like this: if English is how you communicate with people, Python is how you communicate with computers.

Here's what makes Python special: it's readable. Here's a simple Python instruction:

print("Hello, World!")

This tells the computer to display the text "Hello, World!" Notice how the code almost reads like English? That readability isn't accidental—it's a core design principle. Python's creator, Guido van Rossum, believed that code is read more often than it's written, so it should be easy to read.

Why readability matters: When you're learning, readable code is easier to understand. When you're working with AI tools, readable code is easier for AI to explain and help you with. When you're collaborating with other developers, readable code means everyone understands what's happening.

Why Python for AI Development?​

You've spent the last 12 chapters learning about AI-Driven Development—how AI is transforming the way we build software. Python is the de facto language of AI for several concrete reasons.

Real-World Examples​

ChatGPT (the AI you might already know) was trained using Python. The underlying infrastructure, training loops, and inference systems? Written in Python.

Spotify's music recommendation algorithm uses Python to process billions of songs and predict what you'll like next.

Tesla's autonomous driving system relies on Python for data processing and machine learning models that help cars navigate safely.

Instagram's feed is powered by machine learning systems written in Python that decide which posts you see.

Why Python for all of these? Three reasons:

  1. Massive ecosystem: Python has libraries purpose-built for AI and machine learning. Libraries like TensorFlow, PyTorch, and scikit-learn handle the complex math and algorithms. You don't write machine learning from scratch—you use these libraries.

  2. Readable syntax: When your team is collaborating on AI systems, readable code matters. Python forces clarity through indentation and simple syntax. This matters when you're debugging a model that's making wrong predictions.

  3. AI-friendly: Python integrates seamlessly with AI tools like the Claude Code or Gemini CLI you've been learning to use. Type hints (which you'll learn in Lesson 3) let you describe your intent so clearly that AI can generate code that matches your specifications exactly.

How Python Fits Into This Book​

You're learning Python as your first programming language, with AI as your learning partner.

This book teaches Python differently than traditional programming courses:

  • You'll learn concepts by reading clear explanations and working examples
  • You'll practice with AI help to validate your understanding and troubleshoot errors
  • You'll build real programs that demonstrate each concept
  • You'll use type hints from the start to make your code clear and self-documenting

In later chapters, you'll learn how to use Python with AI tools to build complete applications. But first, you need to understand Python itself—what it is, how it works, and how to write programs that solve problems.

The journey ahead: This chapter introduces Python and gets it running on your computer. Future chapters will teach you how to store data, make decisions, repeat operations, and organize code. Each chapter builds on the previous one, so take your time and make sure you understand before moving forward.

💬 AI Colearning Prompt​

Now that you understand Python's strengths, let's explore with your AI companion:

"Explain Python to a 10-year-old in 2-3 sentences. Focus on: What is it? Why does it matter for AI? Make it fun and relatable."

Try this with Claude Code or Gemini CLI. You're teaching your AI to explain concepts simply—and in the process, you'll validate your own understanding. If the AI's explanation makes sense, you've truly grasped the concept.

🎓 Instructor Commentary​

Here's an important insight about learning Python in the AI era:

You don't need to memorize every detail. What matters most is understanding:

  • What the code does (what problem it solves)
  • Why you'd use this approach (when is it the right tool?)
  • How to recognize when something's wrong (reading and debugging)

Your AI learning partner (Claude Code or Gemini CLI) can help with syntax details, error messages, and explaining how code works. Your job is to build understanding—to know what you want to accomplish and whether your code actually does it.

This collaborative approach—you focus on understanding, AI handles details—is how modern developers work. You're learning this from day one.

🚀 CoLearning Challenge​

Find 3 real-world AI applications built with Python. Here are some starting points:

  • ChatGPT (uses Python for training and API infrastructure)
  • Midjourney (uses Python components for image generation)
  • GitHub Copilot (uses Python in its backend)
  • Any machine learning model on Hugging Face (likely uses Python)

Ask yourself: What do these applications have in common? Why would their creators choose Python over another language?

✨ Teaching Tip​

Your AI tool (Claude Code or Gemini CLI) knows Python deeply. When you have syntax questions, ask it. Your job is to describe intent clearly; AI handles the syntax details. This partnership is what makes you powerful.

Common Mistakes to Avoid​

Mistake 1: "I need to memorize all of Python before I can code"

Reality: You'll memorize maybe 20 core concepts. Everything else is either searchable or something you'll ask your AI tool about. Focus on understanding, not memorization.

Mistake 2: "Python is only for data science and machine learning"

Reality: Python is general-purpose. Yes, it's popular for AI/ML, but it's also used for web development (Django, Flask), system administration, automation, scientific computing, and more. We're using it for AI-Driven Development, but Python is far broader.

Mistake 3: "Using AI to help understand Python means I'm not really learning"

Reality: Professional developers use AI every day. The real skill isn't "write code without help"—it's "know when and how to use AI effectively, then validate the output." That's what Chapters 1-12 taught you, and that's what we're building on here.

Try With AI​

Use Claude Code or Gemini CLI for this section. Work through each prompt in order.

Prompt 1: Recall – Python's Definition (Bloom's Level 1: Remember)

What is Python? Give a one-sentence definition suitable for a complete beginner.

Expected Outcome: Compare the AI's response to the definition from this lesson. Can you explain it in your own words? Do you understand what Python is?

Prompt 2: Understand – Why Python Matters for AI (Bloom's Level 2: Understand)

The lesson says "Python is ideal for AI because of readable syntax and library ecosystem."

Explain:
1. What "readable syntax" means and why it matters for AI development
2. Give one example of a Python library used in AI
3. Why would an AI tool prefer working with readable Python code over complex code?

Expected Outcome: After reading the AI's response, you should understand the connection between readability and AI collaboration. You'll learn about real Python libraries (pandas, TensorFlow, etc.) and why they're essential for AI work.

Prompt 3: Apply – Connecting Python to Your Goals (Bloom's Level 3: Apply)

Think about what you want to build with AI in the future (a chatbot, an analysis tool,
a content generator, a recommendation system—anything).

Explain how Python could help you build that. Ask the AI:
"Why would Python be a good choice for [your idea]? What advantages would I have?"

Expected Outcome: You'll apply Python knowledge to a real personal goal. This personalizes your learning and helps you understand Python's practical relevance to your future projects.

Prompt 4: Analyze – Python vs. Other Languages (Bloom's Level 4: Analyze)

Ask your AI: "Compare Python to JavaScript (another popular language). For building AI
applications, what are the tradeoffs? When would you choose Python? When would you
choose JavaScript? Why is this an important question for developers?"

Expected Outcome: You'll understand that language choice has real tradeoffs. Python isn't the only tool—this broader thinking will help you make informed decisions in your career. You'll also practice the kind of analytical thinking professional developers use when selecting tools.