This course teaches learners how to transition from Python to Numpy by covering topics such as the anatomy of an array, code vectorization, problem vectorization, custom vectorization, and beyond Numpy. The course aims to help students understand memory layout, views, copies, vectorization techniques, and how to work with Numpy and related libraries. The teaching method includes theoretical explanations, practical examples, and quick references for data manipulation. This course is intended for Python programmers looking to enhance their skills in numerical computing and data manipulation using Numpy.
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
