Explore the transition from Python to NumPy in this comprehensive guide. Learn about array anatomy, code vectorization techniques, and problem-solving approaches using NumPy. Dive into custom vectorization methods and discover tools beyond NumPy for scientific computing. Gain practical insights through examples in path finding, fluid dynamics, and blue noise sampling. Master essential concepts like memory layout, views, copies, and broadcasting. Enhance your skills with quick references for data types, array creation, indexing, and reshaping. Ideal for Python developers looking to leverage NumPy's power for efficient numerical computations and scientific programming.
Overview
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
Taught by
Nicolas P. Rougier
