IPython Notebook Essentials: Compute scientific data and execute code interactively with NumPy and SciPy

Martins

Can$12.99 ~~Can$39.99~~

3.7 (3 Ratings)

eBook Nov 2014 190 pages 1st Edition

eBook

Can$12.99 ~~Can$39.99~~

Martins

Can$12.99 ~~Can$39.99~~

3.7 (3 Ratings)

eBook Nov 2014 190 pages 1st Edition

eBook

Can$12.99 ~~Can$39.99~~

Can$12.99 ~~Can$39.99~~

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If you are a professional, student, or educator who wants to learn to use IPython Notebook as a tool for technical and scientific computing, visualization, and data analysis, this is the book for you. This book will prove valuable for anyone that needs to do computations in an agile environment.

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IPython Interactive Computing and Visualization Cookbook

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Al Sweigart Dec 17, 2014

IPython Notebook Essentials is a solid introduction to IPython Notebook that I would recommend for scientists or others who need to learn to code for their job. IPython Notebook itself is a sort of hybrid of Excel, MATLAB, and Python IDE, and this book touches on a wide range of related topics.Don't mistake this book for simply covering just IPython on Notebook. The first two chapters teach you the ropes of the software, and the later chapters cover generating graphs with matplotlib, processing & analyzing data with Pandas (a Python software library), and doing more advanced mathematics with the SciPy, Numba, and NumbaPro libraries.IPython Notebook Essentials is a light introduction (IPython itself is fairly easy to pick up), but the strength of the book is that it brings the reader into contact with several related libraries. The examples in the book might not be exactly relevant to your field, but the skills they demonstrate will be. Following along with them on your own computer will let you get the most from this book.The main criticism I would have of the book is that it is wide but not deep. But if you understand this going into the book, you won't be disappointed.If you want to learn programming in general, this is not the book for you. If you don't mind haphazardly sorting through free tutorials on the web, then you can learn the same concepts that are presented in this book. But if you are in the particular niche of scientist or administrator who needs to do number crunching, has a small amount of previous Python programming experience, and want a light introduction to the Python ecosystem of data analysis tools, IPython Notebook Essentials is a good start.Disclosure: I received a free ebook review copy of IPython Notebook Essentials from Packt Publishing for the purposes of writing this review. I do not have any business ties to Packt Publishing.

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Mike Driscoll Dec 04, 2014

The book starts out by giving the reader a tour of the IPython Notebook in chapter one. The author recommends that you use the Anaconda distribution of Python and that you sign up for Wakari to make sharing Notebooks easier. Then you learn how to run a notebook and the author goes into an example using a coffee cooling algorithm. That may sound a little strange, but it was actually pretty interesting to learn about how to calculate how much coffee cools over time. I also thought it was a good way to demonstrate the Notebook’s capabilities. The chapter ends with a few brief exercises.In chapter two, we learn more about the Notebook’s interface. Here we learn how to edit and navigate the Notebook and IPython magics. You will also learn how to run script and load and save data. The chapter ends with a few examples showing how to embed images, Youtube videos and HTML in your Notebook. I liked this chapter quite a bit.For chapter three, we change gears dramatically. From this point on until we reach the appendices, the book is basically for scientists. This chapter is about creating plots and animations with Matplotlib inside the Notebook. The Notebook itself is rarely discussed.Chapter four is all about the pandas project, which is a powerful library for data handling and analytics. This is outside my field, so I cannot comment on its accuracy, let alone understand everything the author writes about in this chapter. Suffice it to say, I only skimmed chapter four.Chapter five is about SciPy, Numba and NumbaPro. Once again, the focus is on scientific computations (SciPy) and how to accelerate those computations (Numba/NumbaPro). I skipped this chapter too.The Appendices cover the following: An IPython Notebook Reference Card, A brief overview of the Python language, and NumPy Arrays.Going into this book, I assumed that it would be a guide to the IPython Notebook. The title certainly gives one that impression. However, the majority of the book is not about the IPython Notebook at all and instead focuses on scientific computing with Python. There are many other books that cover that topic and perhaps the IPython Notebook is used mostly by scientists. The preface states that the book is supposed to be learning about the Notebook too along with some of these other libraries. I just felt that the scientific libraries got the majority of the prose and the Notebook got short shrift.If you are looking for a guide to the IPython Notebook, I’m not sure I can recommend this book for you. It only has 2 chapters that focus exclusively on that topic plus a reference card in the appendix. On the other hand, if you are looking for a very brief overview of some of the scientific libraries you can use in Python plus learn how to use the IPython Notebook in general, then this might be the book for you.

Matteo Jan 18, 2015

The book is supposed to make you learn ipython by throwing you into a field course of examples but the explanations are not on the same level in all the examples, some are well documented and easy to read others are a bit too cryptic for a newcomer. Try using this book along with some other books if you are not well versed in the language but keep in mind that the code samples are based on python 2.7, if you want to learn or have a set of language samples/examples using the 3.x branch you should use Cyrille Rossant's books from the same publisher.

IPython Notebook Essentials: Compute scientific data and execute code interactively with NumPy and SciPy

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