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
View Updating and Relational Theory (Theory in Practice)
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