Scientific Computing with Python: High-performance scientific computing with NumPy, SciPy, and pandas, 2nd Edition 2nd ed. Edition

A broad and easy to understand introduction to scientific computing in python.

The book "Scientific Computing with Python" Second Edition by Claus Führer, Olivier Verdier, Jan Erik Solem, is perfect for both beginner-level and intermediate-level programmers, students, and python enthusiasts interested in the world of scientific computing and python. I can say that there are no pre-requisites for getting started. The book provides detailed, well-structured laid-out content making it easy for the reader to dive in with no hassle.The book provides a clear and simple introduction to the concepts in python programming like data types, variables, functions, classes, etc. This equips any reader with solid knowledge in programming by making them comfortable with python. The book also consists of necessary mathematical concepts like vectors, arrays, matrices, etc., providing the reader with a robust foundation to dive into the world of scientific computing.It provides the reader with knowledge of programming mathematical structures like arrays, vectors, etc., and doing mathematical operations on these structures in python using libraries like SciPy and NumPy. The reader is also introduced to the world of plotting using the Matplotlib library, which provides them with the knowledge of data visualization, interpretation of numbers, and data similar to the world of graphs in algebra.The authors then introduce the concepts of series and data frames using a library called Pandas, laying stepping stones to structured data, data science, and analysis. This enables the users with the knowledge of data concepts like rows, columns, filtering, aggregation, grouping, indices, etc. It also consists of input-output handling concepts providing the users with reading and writing virtual files in various formats.For any good programmer, testing is a crucial concept for understanding how their code behaves under different environments. The authors introduced the concepts of manual testing, automatic and measurement of execution time. In the end, the authors take a simplistic approach to narrate advanced concepts likes Symbolic Computations using SymPy, Operating System, Shell Commands, Parallel Computing, and other Comprehensive examples providing the readers with an overall hands-on introduction to the Scientific Computing Universe.I personally recommend this book to anyone at any level and get started by creating wonderful things with the power of python. For the students and teachers, this book would be a great resource as a primary textbook or as a reference book for any python course.

This book is very good for teaching the basics of Python scientific programming using lots of examples. However, it is poor as a reference if you want to look up a command or how to do something. The Index is minimal and pretty useless. It would have been much more useful to me if it just had a good Index and maybe an Appendix with the basic commands for quick reference.

Scientific Computing with Python is a great book for Python students aimed at an undergraduate level in university.The book covers the foundations of Python in the first few chapters which experienced programmers can easily skip over. But the heart of the content is indeed in 'scientific' programming focusing on NumPy, SciPy, and Pandas. If someone is planning to do research using Python then these three libraries are essential for getting the work done. Pandas is necessary for importing/exporting data and moving around easily in large datasets, NumPy has a lot of arithmetic and algebra operations making calculations easier, and SciPy is great for visualizing data.My favorite thing about the book is that the author uses Jupyter as the IDE which is a very easy and straightforward Python editor. The examples are easy to follow and practice and an experienced programmer can skip around the book to find code they can use in their work.I don't really have any complaints but I found that for a scientific book aimed at researchers and programmers the introductory chapters weren't really necessary but some might find them helpful.I wish I had this book when I started research in Bio Photonics as instead, I had to browse around dozens of websites to find the information that has been neatly arranged in this textbook.Overall, I would recommend this book to undergrad students in Computer Science, Mathematics, and STEM fields that could benefit from research involving Python and the Pandas, NumPy, and SciPy libraries.

Highly recommended for anyone who's interested in building their intuition for problem solving in parallel to their Python skills. The early chapters might be a bit too "light" for more experienced practitioners, but would also be a quick read / refresh. I'm looking forward to completing this book, and might be back to add the 5th star.