Python and HDF5

Unlocking Scientific Data

Book Description
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.
Book Details
Publisher:        O'Reilly Media
By:        Andrew Collette
ISBN:        978-1-44936-783-1
Year:        2013
Pages:        152
Language:        English
File size:        7.3 MB
File format:        PDF
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