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 file Work with datasets by learning the HDF5
Dataset object Understand advanced features like dataset chunking
and compression Learn how to work with HDF5's hierarchical
structure, using groups Create self-describing files by adding
metadata with HDF5 attributes Take advantage of HDF5's type system
to create interoperable files Express relationships among data with
references, named types, and dimension scales Discover how Python
mechanisms for writing parallel code interact with HDF5
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