Module 6 Overview

Module 6 — Performance, I/O & Mini-Projects

Welcome

This is the last module of the course. The first four lessons round out your mental model of NumPy’s performance characteristics — memory layout, vectorization patterns, and I/O — and the last two put everything you’ve learned in Modules 1-5 to work in two end-to-end mini-projects.

Learning goals

• Read .strides and distinguish C order from Fortran order
• Avoid hidden costs: in-place ops with out=, preallocation vs growing
• Save/load arrays with .npy/.npz/text formats (round-tripped in memory)
• Build and sort structured arrays with named fields
• Apply the whole course to an image-as-array mini-project
• Apply the whole course to a stock-price-as-array mini-project

Setup — run this first.