000			01762nam a22002057a 4500
999			_c2459 _d2459
003			OSt
005			20200219164734.0
008			200117b ||||| |||| 00| 0 eng d
020			_a978-93-5110-385-1
028			_bAllied Informatics, Jaipur _c7084 _d13/01/2020 _q2019-20
040			_aBSDU _bEnglish _cBSDU
082			_a005.133 _bCOL
100			_aCollette, Andrew
245			_aPython and HDF5: Unlocking scientific data
260			_aMumbai _bShroff Publishers & Distributors Pvt. Ltd. _c2014
300			_a135
504			_aGain 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
650			_aPython
942			_2ddc _cBK