NumPy Tutorial: Arrays, Broadcasting & Linear Algebra

Python NumPy

NumPy is the foundation under almost every data and ML library in Python. These 5 tutorials cover array creation, indexing and slicing, broadcasting, vectorization, and linear algebra with dot products and eigenvalues. Master these and Pandas, scikit-learn, and PyTorch all start making sense.

NumPy Linear Algebra: dot, matmul, eigenvalues

Think of NumPy linear algebra as a calculator that works on whole tables of numbers at once. Instead of one value times another, you multiply a grid of inputs by a grid of weights in a single step. This numpy linear algebra guide covers the operations you will actually use in data science: dot products, matrix multiplication, the determinant, the inverse, solving linear systems, and eigenvalues. Plain English first, then the NumPy code, with every output run on real Python. ... Read More

NumPy: Array Creation with zeros, ones, arange, linspace

NumPy arrays are the building blocks of almost every data science and machine learning task in Python. This guide is your quick reference for creating one: zeros, ones, arange, linspace, eye, full, empty, the modern random generator, plus indexing basics, with tested output for every single method. “The art of programming is the art of organizing complexity.” Edsger Dijkstra Last Updated: July 2026 | Tested on: Python 3.14.6, NumPy 2.4.6 | Difficulty: Intermediate | Reading Time: 22 minutes NumPy hands ... Read More

NumPy Indexing, Slicing, and Fancy Indexing

NumPy indexing in plain words: how to pull out exactly the values you want from an array using positions, slices, boolean masks, and fancy indexing, plus np.where, with tested examples and the view vs copy trap explained. “Give me the right index and I will move your data. Give me the wrong one and I will move someone else’s.” Every NumPy user, eventually Last Updated: July 2026 | Tested on: Python 3.14.6, NumPy 2.4.6 | Difficulty: Advanced | Reading Time: ... Read More