Learning Scientific Programming with Python

It’s received wisdom that if you want to learn a programming language you need to sit down and program in that language. Books and videos certainly have their place, but you learn best by doing. Christian Hill’s extraordinary book bridges that gap by providing (1) a thorough description of the Python language but by also providing (2) thought-provoking questions (with answers in the text) and (3) an eclectic array of “problems”, which are intriguing programming exercises drawn from the scientific literature. If you really want to learn scientific programming in Python do at least a couple of the many problems in each section of the book, those that appeal to your specific interests. The author must have quite the imagination to provide so many interesting challenges. And the bonus is that solutions are to be found on the authors online website. Five stars plus!

Of the 'python for science' books out there, this one is very good. It covers enough of the language to be useful. For example any project of sufficient complexity will use object oriented features of the code, and this book covers enough of that to be useful. It's not exhaustive on OOP but that's not the point of the book. Otherwise, the code examples and overviews of the python packages for science is thorough enough that one should be able to immediately start using those packages productively. I highly recommend this book.

I started this book because after having completed some online courses in Python I was feeling that I wasn't actually learning. This is the best programming book I have ever read. Every chapter has decent amount of theory and references. At the end of each subsection you have some questions (with solutions on the back of the book), and of course problems that seriously require critical thinking. The solutions of these problems are on the website of the book, where the author is commenting almost line by line. Following this book you will not only learn Python, but you will also learn how to code. If you manage to solve the majority of problems of this book you will certainly not be a beginner anymore and you will have the built the foundation needed for more advanced topics. Just buy it you won't regret it.

The book is well-written and easy to follow in general except that the given examples are hard to understand not due to Python but due to the fact that they are selected from hard subjects. In the next edition, I suggest the author to give examples which will not require domain knowledge to follow them.

If you are already comfortable with C, C++ or Fortran programming in a scientific or engineering environment, this is the book for you. For books of this genre, it's even a relatively decent read. as in, you can take a cup of coffee, sit in your easy chair, and read it, and find it reasonably interesting. The Python/NumPy/SciPy environment has some interesting and unusual features that you are not accustomed to, and some important differences from what you are accustomed to. The former are fun and interesting, and the latter are important. There is very little Python cultural hype (e.g., no "Knights that Say Ni!", etc., no dwelling on how to be Pythonic, etc.) which is refreshing. All in all, highly recommended - the book I was looking for.

The book did a good job of building up base understanding of python and then tailoring for more academic needs.

I'm teaching a course in scientific computing with Python, following this book, and it is definitely a great reference on the subject. Highly recommended.

Unless you are already an intermediate to expert Python programmer, you don't want this book. Unless you have an above average math background as a scientist, engineer, etc., you don't want this book. This has to be the most condensed, monotone style book I've ever read. And you don't really read it. It's more like a reference you struggle with. I'm keeping it as a reference, but this is NOT a book to learn from. My 2 cents.

Item broadly as described by seller. A good overall textbook on the highly topical subject of programming with Python. The emphasis is on scientific computing, covering a wide range of topics, from matrices to ODEs and statistical analysis. Recommended highly.

The book content is good. I liked the introduction to the fundamentals of Python. However, the print quality could be improved. It fades in sections. Also, the font size is relatively small.

Excelente livro. Aprendendo a fazer programação científica com Python de maneira muito segura e sólida. Dá ótima base, mesmo para quem já tem alguma experiência.

Innehållet många bra tips.

This is a very structured guide to learning Python for science, mathematics and engineering. As such it also covers well the add-on functionality of Numpy, Scipy and Matplotlib. In installation it talks specifically of Windows and Mac systems and generally of Linux, though I am actually going through the book using a Raspberry Pi 4 and the built in Python IDE. The book is very well organised with a lot of exercises to reinforce the text and answers in the back. It is designed for learning and it fulfils that aim very well. It also shows the power of the language. I am a user of both MATLAB and SciLab and this book shows that Python can be a viable alternative. Bearing in mind that Python is free and supplied on a lot of platforms it makes it a worthwhile tool in the kit of students and practitioners in science and engineering and this book is an excellent guide to its application in these areas. This book does assume that the reader is mathematically literate. It is a textbook in the application of Python, not in learning mathematics, but perhaps it is time for some of the texts that use MATLAB to teach mathematical principles to be adapted to Python. All in all, I recommend this book, not just for people wishing to learn scientific Python out of curiosity, but also for those seeking to use it seriously in scientific programming and data analysis. It is an excellent guide to a powerful analytical tool.