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Product details
File Size: 8979 KB
Print Length: 552 pages
Simultaneous Device Usage: Unlimited
Publisher: O'Reilly Media; 1 edition (June 25, 2015)
Publication Date: June 30, 2015
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B010ORQ8DG
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Amazon Best Sellers Rank:
#283,887 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
This book should be required (and enjoyable) reading for any student/researcher/scientist/engineer new to scientific computing. The book presents a modern account of the Python scientific computing environment from the command line to publication and everything good in between e.g. NumPy to parallel computing to version control and much more (see http://physics.codes for more). The book also helps you get quickly going with Python on your computer via the Anaconda environment which is very nice. I bought the Kindle version and plan to read it on my iPad while I try the programs (freely available for download at the above website) on my Macbook (ideal combination in my opinion) - it's never been more fun to be in scientific computing that it is these days and this book will help you get started or, like me, maintain and update your overall computational skill set. A must buy and a must read! Enjoy!
This book has been teaching me more than effective computation! I've learned about the community, the myths and the jokes so that I can laugh as well. The digital version contains color-coded python code, really awesome
Given the comments I had seen by others I had higher expectations for this book. There is a brief section early in the book titled "Who this book is not for" that states "If you have already been working as a software developer for several years, this book will help you only minimally". If I had seen that before purchasing the book I probably wouldn't have purchased it. While I wouldn't describe myself as a software developer, coding has always been an essential part of my professional toolkit. I was hoping for something that would help take my game to the next level but this wasn't it.
I’ve taught myself to code, not counting a crappy freshman C++ upteen years ago. I use MATLAB and I’m learning to dabble in Python. The reason I like this book is not for the Python parts, per se, but becuase it fills in holes in my formal training, like details on the shell as a programming tool, and how to more fully take advantage of Git for version control. As the book’s forward says, this book is for physicists (well, I’m a materials scientist) who want to learn grown-up software development, not for people who are already expert software developers.I haven’t read the whole book, so I’m sure there are things I could criticize, but having skipped around to read the parts that I know the least about, it’s spectacular so far. If you’re unsure read the Kindle preview; it overviews the book well.———Edit: now, having read the whole thing, I can say this book is almost perfect for people like me: good at math and your domain science, but lacking in computer science training.
I picked this book up because I thought the title was interesting and figured I could always improve upon my repertoire of tools and skills. Although most of the topics covered were review for me, it was a delightful text to read. I strongly believe this should be required reading for any first year graduate student in science and engineering who intendeds to rely heavily on computation for both simulation and/or analysis. The authors nicely walk you through the basics of scripting, coding, and data management. The authors then provide an overview of best practices for project management and scientific software. An easy and insightful read, strongly recommend.
I got this book from the local library and am glad that I didn't shell out the money, to be honest. Judging by the introduction, I should be the key audience for this book, a scientist interested and somewhat familiar with Python; I'm mainly using Matlab at work. Initially, I had the hope that advanced concepts would be explained by example using real-world problems as hinted at in the introduction. That's not the case at all, though. The authors lightly touch upon a multitude of random topics. There's breadth here, but only very shallow depth. To someone considering this book, try to get it from a library and judge for yourself. If you want to learn more about Python, go with 'Fluent Python', if you like to use Python for data analysis in science, I like 'Data Analysis with Open Source Tools'. For more advanced Python programming, have a look at 'Effective Python', though that one is not for the faint of heart;-)
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