- Paperback: 152 pages
- Publisher: O'Reilly Media; 1 edition (November 11, 2013)
- Language: English
- ISBN-10: 1449367836
- ISBN-13: 978-1449367831
- Product Dimensions: 7 x 0.3 x 9.2 inches
- Shipping Weight: 9.1 ounces (View shipping rates and policies)
- Average Customer Review: 5 customer reviews
- Amazon Best Sellers Rank: #137,333 in Books (See Top 100 in Books)
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Python and HDF5: Unlocking Scientific Data 1st Edition
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About the Author
Andrew Collette holds a Ph.D. in physics from UCLA, and works as a laboratory research scientist at the University of Colorado. He has worked with the Python-NumPy-HDF5 stack at two multimillion-dollar research facilities; the first being the Large Plasma Device at UCLA (entirely standardized on HDF5), and the second being the hypervelocity dust accelerator at the Colorado Center for Lunar Dust and Atmospheric Studies, University of Colorado at Boulder. Additionally, Dr. Collette is a leading developer of the HDF5 for Python (h5py) project.
Top customer reviews
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I want to state here that it seems to be the ONLY book on the market today on the becoming increasingly popular self contained data storage and manipulation format HDF5 that explains how to program against it in Python at an enterprise level.
Even though it is a book review, let me briefly explain that HDF5 is a database like, hierarchical file structure closely resembling the early file-based databases implementing Balanced Tree indexing for fast data retrieval. The fact the file is self contained helps keep data, attributes and even computational results together for transparent data exchange, in fact it is so inter-operating platforms exchange-ready it takes complete care of the platform differences as little-endian versa big-endian for example, and boy Andrew knows how to explain that in the book!
Actually, the book has made me aware of how important it is to use proper technologies when you have no idea where (here platform) your data will be consumed.
As a brief side note, myself I programmed hierarchical data structures for fats data retrieval in the early 90s, in C, not even knowing they are called B-Trees. And the concept has such a broad implementation.
So in short, the book is excellent, written in a concise, professional manner (between me and you, 0 volume inflating fluff).
The author has made sure the book is full of useful examples covering each nuance or an important feature so reading this book feels natural and logical. I am also glad the author devoted a significant effort to convey to a developer ( I hate the word ‘programmer’ :-) ) on the proper methods of concurrent programming, which is what a pity – a common omission in many beginners’ books.
I am sure this book will make you going or will let you start coding against HDF5 in no time. I am sure this book will be used as a table reference (or on your computer desktop).
I am giving this book a 5 out of 5 rating, kudos to O’Reilly that has delivered yet another outstanding publication.
Disclaimer: This book was given to me for free as part of the blogger review program by O'Reilly Media.
The author assumes minimal familiarity with Python and numpy; however, in the event you're coming at this cold, chapter 2 walks you through the basics. The author continues with datasets (as he writes, "the central feature of HDF5"). After that, you're off and running and free to explore the remaining sections on chunking and compression, hierarchy, external links, attributes, etc. He even includes a section on parallel HDF5 with mpi4py (a welcome surprise).
As someone who's aimlessly "Googled" his way through h5py in the past, I have to say this book is worth every penny. It's all here. Let this book and Python shape the way you think about HDF5, and maybe for the first time, you will see its simplicity.