NumPy Tutorial

NumPy is the foundation of numerical Python. Almost every data-science and ML library — Pandas, scikit-learn, TensorFlow, PyTorch, OpenCV — is built on top of it. Learn NumPy once and the rest of the Python data stack opens up.

This tutorial is beginner-friendly — every code example has a ▶ Run button so you can execute Python right in your browser, edit the code, and see the output immediately.

Topics

#	Chapter
1	Introduction — why NumPy, speed comparison
2	Creating arrays — zeros, ones, arange, linspace, random
3	Array attributes — shape, dtype, ndim, size
4	Indexing & slicing — basic, fancy, boolean
5	Reshaping — reshape, transpose, flatten
6	Math operations & universal functions
7	Broadcasting — math on different shapes
8	Aggregations — sum, mean, std, axis
9	Sorting & searching
10	Linear algebra — dot, matmul, inv, eig
11	Random sampling
12	Stacking & splitting arrays
13	Boolean masks & filtering
14	Real-world examples — feature scaling, k-means, image processing

How this tutorial is organized

Every chapter follows the same beginner-friendly format:

1. Short intro — what the concept is and why it matters.
2. Runnable code blocks — click ▶ Run to execute. Edit and re-run freely.
3. Common pitfalls — what beginners get wrong.
4. Cheatsheet — quick reference at the end.
5. Next link — pointer to the next chapter.

Prerequisites

• Python basics — lists, loops, functions.
• No math background needed (we explain everything).

What you'll be able to do after this

• Manipulate any numerical array efficiently in Python.
• Read and write code from any data science / ML library.
• Apply broadcasting tricks to avoid loops.
• Compute statistics, solve linear systems, generate random samples.
• Build the foundational skill behind Pandas, scikit-learn, and PyTorch.

Ready? → Start with Introduction