O'Reilly
October 31, 2013

Python and HDF5--New from O'Reilly

Sebastopol, CA—Gain hands-on experience with HDF5 for storing scientific data in Python. Python and HDF5 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.

For a review copy or more information please email reviews@oreilly.com. Please include your delivery address and contact information.

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.

View Andrew Collette's full profile page.

Additional Resources
For more information about the book, including table of contents, author bios, and cover graphic, see: http://shop.oreilly.com/product/0636920030249.do

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