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Python and HDF5by Andrew Collette
Synopses & Reviews
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, youll 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 youre familiar with the basics of Python data analysis, this is an ideal introduction to HDF5.
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.
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.
Table of Contents
PrefaceChapter 1: IntroductionChapter 2: Getting StartedChapter 3: Working with DatasetsChapter 4: How Chunking and Compression Can Help YouChapter 5: Groups, Links, and Iteration: The "H" in HDF5Chapter 6: Storing Metadata with AttributesChapter 7: More About TypesChapter 8: Organizing Data with References, Types, and Dimension ScalesChapter 9: Concurrency: Parallel HDF5, Threading, and MultiprocessingChapter 10: Next StepsIndexColophon
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