Ebook Learning Scientific Programming with Python
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Learning Scientific Programming with Python
Ebook Learning Scientific Programming with Python
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Review
'This book is well illustrated and is supported by an extensive collection of resources online in the book's website, scipython.com. This site has code listings and solutions to exercises. I would readily recommend this book to any student (or even a colleague) who wishes to achieve a solid foundation in Python programming.' Vasudevan Lakshminarayanan, Contemporary Physics
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Book Description
Learn to master basic programming tasks from scratch with real-life scientific examples drawn from many different areas of science and engineering. This complete introduction to using Python teaches Numpy, SciPy and Matplotlib libraries and is supported by extensive online resources to provide a targeted package for students and researchers.
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Product details
Paperback: 460 pages
Publisher: Cambridge University Press; 1 edition (March 29, 2016)
Language: English
ISBN-10: 110742822X
ISBN-13: 978-1107428225
Product Dimensions:
6.8 x 1 x 9.7 inches
Shipping Weight: 2 pounds (View shipping rates and policies)
Average Customer Review:
5.0 out of 5 stars
7 customer reviews
Amazon Best Sellers Rank:
#50,228 in Books (See Top 100 in Books)
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.
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.
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.
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.
Learning Scientific Programming with Python ...
Particularly strong on graphics.
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.
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