Tutorials on the scientific Python ecosystem: a quick introduction to central tools and techniques. The different chapters each correspond to a 1 to 2 hours course with increasing level of expertise, from beginner to expert.
Release 2024.1 (April 2024)
This is a major update from the 2017.1 release.
Highlights include:
- Python 3.10, 3.11, 3.12
- Renamed Scientific Python Lectures
- Removed old content
- Major updates to support recent packages
- Updates to the SciPy and scikit-image chapters
Online version: https://lectures.scientific-python.org/
External resources: https://github.com/scipy-lectures/scientific-python-lectures
Conditions of Use
This book is licensed under a Creative Commons License (CC BY-NC-SA). You can download the ebook Scientific Python Lectures, 2024 Edition for free.
- Title
- Scientific Python Lectures, 2024 Edition
- Subtitle
- One document to learn numerics, science, and data with Python
- Publisher
- Zenodo
- Author(s)
- aespaze, Akihiro Uchida, Bartosz Telenczuk, Christophe Combelles, Didrik Pinte, Emergency Self-Construct, Emmanuelle Gouillart, Gael Varoquaux, Gert-Ludwig Ingold, Jarrod Millman, Joris Van den Bossche, Matt Haberland, Maximilien Riehl, Michael Hartmann, Nelle Varoquaux, Nicola Masarone, Nicolas P. Rougier, Nicolas Pettiaux, Olav Vahtras, Ozan Caglayan, Pamphile Roy, Pauli Virtanen, Pierre de Buyl, Ralf Gommers, Robert Cimrman, Ross Barnowski, Stefan van der Walt, Vince Knight, Zbigniew Jędrzejewski-Szmek
- Published
- 2024-04-26
- Edition
- 2024
- Format
- eBook (pdf, epub, mobi)
- Pages
- 694
- Language
- English
- License
- CC BY-NC-SA
- Book Homepage
- Free eBook, Errata, Code, Solutions, etc.
1. Getting started with Python for science 1.1. Python scientific computing ecosystem 1.2. The Python language 1.3. NumPy: creating and manipulating numerical data 1.4. Matplotlib: plotting 1.5. SciPy : high-level scientific computing 1.6. Getting help and finding documentation 2. Advanced topics 2.1. Advanced Python Constructs 2.2. Advanced NumPy 2.3. Debugging code 2.4. Optimizing code 2.5. Sparse Arrays in SciPy 2.6. Image manipulation and processing using NumPy and SciPy 2.7. Mathematical optimization: finding minima of functions 2.8. Interfacing with C 3. Packages and applications 3.1. Statistics in Python 3.2. Sympy : Symbolic Mathematics in Python 3.3. scikit-image: image processing 3.4. scikit-learn: machine learning in Python
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