| Contents Notes |
Getting started -- Working with datasets -- How chunking and compression can help you -- Groups, links, and iteration : the "H" in HDF5 -- Storing metadata with attributes -- More about types -- Organizing data with references, types, and dimension scales -- Concurrency : parallel HDF5, threading, and multiprocessing -- Next steps. 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. 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. -- Publisher's description. |
