01762nam a22002057a 4500 2459 2459 OSt 20200219164734.0 200117b ||||| |||| 00| 0 eng d 978-93-5110-385-1 Allied Informatics, Jaipur 7084 13/01/2020 2019-20 BSDU English BSDU 005.133 COL Collette, Andrew Python and HDF5: Unlocking scientific data Mumbai Shroff Publishers & Distributors Pvt. Ltd. 2014 135 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 Python ddc BK 0 0 ddc 0 0 BSDU BSDU GEN 2020-01-17 275.00 0 005.133 COL 018013 2020-02-12 00:00:00 275.00 2020-01-17 BK