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Python and HDF5

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Python and HDF5 Cover

 

Synopses & Reviews

Publisher Comments:

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, youll 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 youre 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 HDF5s hierarchical structure, using groups
  • Create self-describing files by adding metadata with HDF5 attributes
  • Take advantage of HDF5s 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

Synopsis:

With the rise of the Python-NumPy stack for analysis, one area which is under-documented at the moment is that of storage for large scientific datasets. When this topic is discussed, it is usually within the context of the native data-archiving features in specific Python packages, for example, pandas. While such packages may use open formats on the back end, no in-depth work currently exists covering the nuts-and-bolts, best practices, and pitfalls of dealing with gigabyte-to-terabyte-sized datasets from Python.

This book aims to fill that gap in the market, by providing practical coverage of the use of HDF5 to archive and share binary data in Python.

About the Author

Andrew Collette holds a Ph.D. in physics from UCLA, and works as a laboratory research scientist at the University of Colorado. He has worked with the Python-NumPy-HDF5 stack at two multimillion-dollar research facilities; the first being the Large Plasma Device at UCLA (entirely standardized on HDF5), and the second being the hypervelocity dust accelerator at the Colorado Center for Lunar Dust and Atmospheric Studies, University of Colorado at Boulder. Additionally, Dr. Collette is a leading developer of the HDF5 for Python (h5py) project.

Table of Contents

PrefaceChapter 1: IntroductionChapter 2: Getting StartedChapter 3: Working with DatasetsChapter 4: How Chunking and Compression Can Help YouChapter 5: Groups, Links, and Iteration: The "H" in HDF5Chapter 6: Storing Metadata with AttributesChapter 7: More About TypesChapter 8: Organizing Data with References, Types, and Dimension ScalesChapter 9: Concurrency: Parallel HDF5, Threading, and MultiprocessingChapter 10: Next StepsIndexColophon

Product Details

ISBN:
9781449367831
Author:
Collette, Andrew
Publisher:
O'Reilly Media
Subject:
data analysis;h5py;hdf5;large datasets;numpy;pytables;python;science
Subject:
Computer Languages - Python
Copyright:
Edition Description:
Print PDF
Publication Date:
20131131
Binding:
TRADE PAPER
Language:
English
Pages:
152
Dimensions:
9.19 x 7 in

Related Subjects

Computers and Internet » Computer Languages » Python
Computers and Internet » Database » Design
Computers and Internet » Software Engineering » Programming and Languages
Computers and Internet » Software Engineering » Software Management

Python and HDF5 New Trade Paper
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Product details 152 pages O'Reilly Media - English 9781449367831 Reviews:
"Synopsis" by ,

With the rise of the Python-NumPy stack for analysis, one area which is under-documented at the moment is that of storage for large scientific datasets. When this topic is discussed, it is usually within the context of the native data-archiving features in specific Python packages, for example, pandas. While such packages may use open formats on the back end, no in-depth work currently exists covering the nuts-and-bolts, best practices, and pitfalls of dealing with gigabyte-to-terabyte-sized datasets from Python.

This book aims to fill that gap in the market, by providing practical coverage of the use of HDF5 to archive and share binary data in Python.

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