Python and HDF5 / Andrew Collette.

Format:

Book

Author/Creator:

Collette, Andrew.

Language:

English

Subjects (All):

Python (Computer program language).

Scripting languages (Computer science).

Physical Description:

1 online resource (152 p.)

Edition:

1st edition

Other Title:

Python and hierarchical data format five

Place of Publication:

Beijing ; Sebastopol, Calif. : O'Reilly, [2014]

Language Note:

English

System Details:

text file

Summary:

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. Through real-world examples and practical exercises, you’ll explore topics such as scientific datasets, hierarchically organized groups, user-defined metadata, and interoperable files. 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. Get set up with HDF5 tools and create your first HDF5 file Work with datasets by learning the HDF5 Dataset object Understand advanced features like dataset chunking and 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, and dimension scales Discover how Python mechanisms for writing parallel code interact with HDF5

Contents:

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.

Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed November 25, 2013).

ISBN:

9781491945018

149194501X

9781491945001

1491945001

OCLC:

868236027