Python and HDF5: Unlocking scientific data
By: Collette, Andrew.
Material type:
BookPublisher: Mumbai Shroff Publishers & Distributors Pvt. Ltd. 2014Description: 135.ISBN: 978-93-5110-385-1.Subject(s): PythonDDC classification: 005.133
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BSDU Knowledge Resource Center, Jaipur General Stacks | 005.133 COL (Browse shelf) | Available | 018013 |
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| 005.133 CHO Deep Learning:A practical approach (Using Python) | 005.133 CLO Programming In Prolog | 005.133 CLO Programming In Prolog | 005.133 COL Python and HDF5: Unlocking scientific data | 005.133 DTE J2EE 1.7 Projects: Black book | 005.133 GIL Efficient R Programming: A Practical Guide to smarter programming | 005.133 GOR High Performance Python: Practical performant programming for humans |
Gain hands-on experience with HDF5 for storing scientificdata in Python. This practical guide quickly gets you up to speed on thedetails, best practices, and pitfalls of using HDF5 to archive and sharenumerical datasets ranging in size from gigabytes to terabytes.
Through real-world examples and practical exercises, you’ll explore topics suchas scientific datasets, hierarchically organized groups, user-defined metadata,and interoperable files. Examples are applicable for users of both Python 2 andPython 3. If you’re familiar with the basics of Python data analysis, this isan ideal introduction to HDF5.
•Get set up with HDF5 tools and create your first HDF5file
•Work with datasets by learning the HDF5 Dataset object
•Understand advanced features like dataset chunking andcompression
•Learn how to work with HDF5’s hierarchical structure,using groups
•Create self-describing files by adding metadata with HDF5attributes
•Take advantage of HDF5’s type system to createinteroperable files
•Express relationships among data with references, namedtypes, and dimension scales
•Discover how Python mechanisms for writing parallel codeinteract with HDF5
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