Python and HDF5: Unlocking Scientific Data

By 作者: Andrew Collette

ISBN-10 书号: 1449367836

ISBN-13 书号:: 9781449367831

Edition 版本: 1

Release 出版日期: 2013-11-11

pages 页数: (152 )


$29.99


Book Description

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
Chapter 1. Introduction
Chapter 2. Getting Started
Chapter 3. Working with Datasets
Chapter 4. How Chunking and Compression Can Help You
Chapter 5. Groups, Links, and Iteration: The “H” in HDF5
Chapter 6. Storing Metadata with Attributes
Chapter 7. More About Types
Chapter 8. Organizing Data with References, Types, and Dimension Scales
Chapter 9. Concurrency: Parallel HDF5, Threading, and Multiprocessing
Chapter 10. Next Steps

下载地址:

Oreilly Python and HDF5 1449367836.epub
Oreilly Python and HDF5 1449367836.pdf

Python and HDF5: Unlocking Scientific Data

发表评论

电子邮件地址不会被公开。 必填项已用*标注