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Python and HDF5, by Andrew Collette
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Gain hands-on experience with HDF5 for storing scientific data in Python. This practical guide quickly gets you up to speed on the details, best practices, and pitfalls of using HDF5 to archive and share numerical datasets ranging in size from gigabytes to terabytes.
Through real-world examples and practical exercises, you’ll explore topics such as scientific datasets, hierarchically organized groups, user-defined metadata, and interoperable files. Examples are applicable for users of both Python 2 and Python 3. If you’re familiar with the basics of Python data analysis, this is an ideal introduction to HDF5.
- Get set up with HDF5 tools and create your first HDF5 file
- Work with datasets by learning the HDF5 Dataset object
- Understand advanced features like dataset chunking and compression
- Learn how to work with HDF5’s hierarchical structure, using groups
- Create self-describing files by adding metadata with HDF5 attributes
- Take advantage of HDF5’s type system to create interoperable files
- Express relationships among data with references, named types, and dimension scales
- Discover how Python mechanisms for writing parallel code interact with HDF5
- Sales Rank: #391018 in Books
- Published on: 2013-11-11
- Released on: 2013-11-11
- Original language: English
- Number of items: 1
- Dimensions: 9.19" h x .30" w x 7.00" l, .57 pounds
- Binding: Paperback
- 152 pages
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.
Most helpful customer reviews
13 of 13 people found the following review helpful.
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By Al
Andrew Collette's "Python and HDF5" is a welcome, overdue, and timely addition to the Python canon. h5py, an interface to HDF5 in Python, has become the proverbial "gateway drug" into HDF5 for most; however, h5py lacked for some time what this book now delivers--- a clear, concise, example-ridden text that teaches even the most novice of Python users how to leverage HDF5.
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.
10 of 10 people found the following review helpful.
An excellent technical read, concise, professional
By A. Zubarev
It is probably yesterday’s news that Python is the de facto programming language for anything Data Science. And the latest book on Python and HDF5 integration is a more recent proof to that.
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.
0 of 0 people found the following review helpful.
Five Stars
By LPO
Excellent discussion and demonstration of the combination of 2 big data powerhouse technologies. Very well written and accessible.
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