From Python to Numpy

This course explains how NumPy arrays work and how vectorization can improve Python performance. It covers memory-aware arrays, custom vectorization, and applications including path finding, fluid dynamics, and blue-noise sampling.

Syllabus

Preface
- About the author
- About this book
- License
Introduction
- Simple example
- Readability vs speed
Anatomy of an array
- Introduction
- Memory layout
- Views and copies
- Conclusion
Code vectorization
- Introduction
- Uniform vectorization
- Temporal vectorization
- Spatial vectorization
- Conclusion
Problem vectorization
- Introduction
- Path finding
- Fluid Dynamics
- Blue noise sampling
- Conclusion
Custom vectorization
- Introduction
- Typed list
- Memory aware array
- Conclusion
Beyond Numpy
- Back to Python
- Numpy & co
- Scipy & co
- Conclusion
Conclusion
Quick References
- Data type
- Creation
- Indexing
- Reshaping
- Broadcasting
Bibliography
- Tutorials
- Articles
- Books