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Python and HDF5
Python and HDF5
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€29.99
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A01=Andrew Collete
A01=Andrew Collette
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Author_Andrew Collete
Author_Andrew Collette
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Category1=Non-Fiction
Category=UMW
COP=United States
Delivery_Delivery within 10-20 working days
eq_bestseller
eq_computing
eq_isMigrated=2
eq_nobargain
eq_non-fiction
hdf5 python data analysis science numpy pytables h5py large datasets
Inc
Language_English
PA=Available
Price_€20 to €50
PS=Active
softlaunch
USA
Product details
- ISBN 9781449367831
- Weight: 260g
- Dimensions: 178 x 234mm
- Publication Date: 10 Dec 2013
- Publisher: O'Reilly Media
- Publication City/Country: US
- Product Form: Paperback
- Language: English
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
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.
Python and HDF5
€29.99
