Python and HDF5 Unlocking Scientific Data

Name: Python and HDF5 Unlocking Scientific Data
Price: 17.04 USD
Availability: InStock
ISBN: 9781449367831

ISBN-10: 1449367836

ISBN-13: 9781449367831

Edition: 2014

Authors: Andrew Collette

List price: $23.99

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Description:

With the rise of the Python-NumPy stack for analysis, one area which is under-documented at the moment is that of storage for large scientific datasets. When this topic is discussed, it is usually within the context of the native data-archiving features in specific Python packages, for example, pandas. While such packages may use open formats on the back end, no in-depth work currently exists covering the nuts-and-bolts, best practices, and pitfalls of dealing with gigabyte-to-terabyte-sized datasets from Python.This book aims to fill that gap in the market, by providing practical coverage of the use of HDF5 to archive and share binary data in Python.

Book details

List price: $23.99
Copyright year: 2014
Publisher: O'Reilly Media, Incorporated
Publication date: 11/1/2013
Binding: Paperback
Pages: 152
Size: 6.97" wide x 9.17" long x 0.47" tall
Weight: 0.550
Language: English

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.