Scientific Python Lectures

Introduction

The book "Scientific Python Lectures" (authors: Stéfan van der Walt, Gaël Varoquaux, 2023, 400 pages) is not just another Python guide, but a full-fledged educational course built on real scientific problems. The publication is a collection of lectures given at international schools on scientific programming and has been available in an updated printed form since 2023. The book is aimed at those who are already familiar with the basics of Python and want to use it for data processing, numerical calculations, and visualization.

What the book is about

The main focus is the ecosystem of scientific Python libraries: NumPy, SciPy, Matplotlib, Pandas, SymPy, scikit-learn, and others. The material is divided into logical blocks: from basic array operations to building complex machine learning models. Each lecture contains a theoretical part, code examples, and exercises for independent work. Special attention is paid to computational efficiency and proper code design.

Who this book is for

The book will be useful for:

• Students and graduate students in technical and natural sciences;
• Engineers and researchers transitioning from MATLAB or R to Python;
• Programmers wanting to delve into scientific computing;
• Teachers looking for structured material for courses.

What the reader will learn

• Efficient work with multidimensional arrays — indexing, slicing, broadcasting, linear algebra;
• Numerical methods — integration, optimization, solving differential equations;
• Data visualization — from simple plots to complex 3D scenes and animations;
• Signal and image processing — filtering, Fourier transform, morphology;
• Statistical analysis — hypothesis testing, regression, clustering;
• Machine learning — basics of scikit-learn, building pipelines, model evaluation;
• Symbolic computation — differentiation, integration, solving equations in SymPy;
• Data handling — Pandas for tables, loading/saving in various formats.

Strengths

Practical focus. Each lecture is accompanied by examples that can be immediately run and modified. Exercises help reinforce the material and develop skills for independent problem-solving.

Relevance. The book was updated in 2023, so all examples use modern versions of the libraries (NumPy 1.24, SciPy 1.10, Matplotlib 3.7, etc.).

Structure. The material is divided into modules that can be studied in any order. There are clear indications of prerequisites.

Accessibility. The authors are recognized experts in scientific programming, but they write in a simple and understandable language, avoiding excessive mathematical formalization.

Free online version. The book is available on GitHub and as Jupyter Notebooks, allowing you to immediately experiment with the code.

Who it is recommended for

The book is ideal for self-study and as supplementary material for university courses in numerical methods, data analysis, or machine learning. It will also be useful for specialists who want to systematize their knowledge of the Python scientific stack. If you already know how to write in Python but don't know how to use it effectively for scientific calculations — start with this book.