Python and HDF5: Unlocking Scientific Data

by Andrew Collette

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 fileWork with datasets by learning the HDF5 Dataset objectUnderstand advanced features like dataset chunking and compressionLearn how to work with HDF5’s hierarchical structure, using groupsCreate self-describing files by adding metadata with HDF5 attributesTake advantage of HDF5’s type system to create interoperable filesExpress relationships among data with references, named types, and dimension scalesDiscover how Python mechanisms for writing parallel code interact with HDF5

232 pages, Kindle Edition

First published October 21, 2013

Reviews

[Dan Farmer · Rating 4 out of 5]

It's a tech book. Competently written and allowed me to complete the task I was trying to do. I'd recommend this to either folks wanting to use h5py (the obvious audience; it's the subject of the book) but also to anyone wanting to use HDF5 from any language because the online resources for it are hard to put together into a coherent picture.

[TN · Rating 1 out of 5]

The book is close to a total disaster. It covers a bunch of topics on a very very very basic level. The structure of the whole book is very inconsistent. Mostly a waste of time!