
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 & pitfalls of using HDF5 to archive & share numerical datasets ranging in size from gigabytes to terabytes Through real-world examples & practical exercises you'll explore topics such as scientific datasets hierarchically organized groups user-defined metadata & interoperable files Examples are applicable for users of both Python 2 & 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 & create your first HDF5 file Work with datasets by learning the HDF5 Dataset object Understand advanced features like dataset chunking & 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 & dimension scales Discover how Python mechanisms for writing parallel code interact with HDF5