Learning Scientific Programming with Python

About 9 months ago, I had signed on to do undergraduate research in astronomy which my professor told me was going to be "heavily Python based". I had NO previous programming experience. Perusing Amazon for some Python resources, I came across this book which, upon reading the back cover, was written by a physicist, so I thought this would be a good fit for me (I'm a physics major) -- and I wasn't wrong. In the first few chapters, Hill will teach you the essentials of the Python language, including a little interlude about making plots with Pylab in Chapter 3. Throughout the rest of the book, you will learn how to use a few Python libraries which are instrumental in science work like Numpy, Matplotlib, and SciPy. In each of these chapters there are plenty of examples with all the code needed to try them for yourself. Appendix A contains solutions for the exercises at the end of the chapters (and not just every odd-numbered exercise). One quality-of-life feature that I like about this book is the index which lists page numbers for the methods used in the Python libraries (e.g. fig.addsubplot(), np.genfromtxt(), etc.). In my undergrad astronomy research, I was tasked to write a Python program which could read-in a few thousand astronomical images of a black hole X-ray binary (using the Numpy method np.genfromtxt() which is covered in this book), turn them into data that Python could read, and produce plots using all that data. During one of our observing runs, our campus observatory telescope was malfunctioning (the telescope wasn't tracking properly) and I was tasked to extract specific bits of data contained in the astronomical images (FITS file header), generate plots using that data to help both us and the telescope engineer understand the problem. This book was helpful to me in all of those cases. This book isn't an all-encompassing book on everything one could do with Python, however. (You won't be learning about machine learning and building a neural network in this book, for example). But if you're in the sciences like me, and want to learn Python in the context of science, this would be a useful resource for you as it was to me.

While there are many online Python tutorials, this book has two key advantages. First, this book is complete. It is meant to be read from cover to cover. The text, examples, exercises, and problems complement each other and highlight important features which a programmer is likely to use in practice. Unlike a cookbook, which would provide recipes for specific tasks, this book has been thoughtfully designed to teach key principles. Second, the choice of examples, exercises, and problems is outstanding. Many online tutorials provide simple examples to illustrate the syntax. But in this book, the examples solve actual problems which are interesting and useful. At the same time, the examples are neither too long nor so specialized that they would be of interest to only specialized audience. This book is also very reasonably priced.

This book makes it extremely easy to learn how to use python for scientific programming. I learned how to use python for media manipulation more than a year ago but I haven't used it since so the first 4 chapters of the book were great for getting me up to speed again and showing me new basic functions. The real meat of the book starts at chapter 6. Here I actually learned how to use python for what I wanted it for and this is something I couldn't learn at my university course. I'm a senior in applied computation and mathematics at my university but this book gave me the skills and confidence to translate the mathematics I have learned into programming. I'm looking forward to using this to boost my portfolio.

Buy this version if it isn't a high priority from the profs. It it very well writen and covers all the relavent subjects such as "Plotting with pylab" or "Metplotlib". The latest edition is very expensive.

very enjoyable volume on using Python in scientific applications. Since I am a mathematical physicist with a strong background in programming and applied work I found this book very useful.

This is the most exhaustive book on the application of Python to scientific and engineering computations. The author's exposition is clear. You will not only learn Python but scientific and engineering computation too. The author covers Linear Algebra too.

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Learning Scientific Programming with Python ...

Cabe resaltar que este libro ya lo había hojeado porque estaba disponible en mi biblioteca; sin embargo, fue tan bueno que decidí adquirirlo. El libro cubre la parte básica de Python con problemas cortos pero eficaces. Numpy y Matplotlib también son cubiertas de manera eficaz y el conocimiento adquirido te permite aplicar las bibliotecas y escribir código elegante debido a que los ejemplos y ejercicios son muy prácticos. La parte de Matplotlib cubre los tipos de gráficas más usadas, si quieres más detalles puedes consultar la guía de Matplotlib en internet. Es muy buen libro para tenerlo como referencia si es que ya lo cubriste todo, y para los principiantes en ciencias me parece que es de lo mejor, aunque recomiendo tener conocimiento básico de Python para pasar rápidamente a la parte de Numpy, Scipy y Matplotlib.

'Learning Scientific Programming with Python' is a very well thought-out introduction to Python. It would probably not suit an absolute beginner with zero programming background at all, but that's no surprise: it's often said you learn more from the second book you read on a topic, rather than the first. The chapters are well paced and cover Python from the ground up, with a large number of exercises and more intricate problems. The answers are either in the book or (more likely) on the accompanying website - that has details for both the first and second editions, which is refreshing. The problems take real scientific ideas and are on a range of topics: there is a slight bias toward the author's own background, but this is not really anything to worry about. The approach of mixing core Python skills with those specific to scientific analysis is excellent: the first introduction of pylab means one can start looking at real data within the first 100 pages of the book. I have no hesitation in recommending the book: this will be my go-to in the future.

Usado en muy buen estado

Expensive book but excellently written. A must for all scientists working with python.

I liked the problem / solution approach.