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Python and HDF5

by Andrew Collette | 01 November 2013
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 fileWork with datasets by learning the HDF5 Dataset objectUnderstand advanced features like dataset chunking and compressionLearn how to work with HDF5's hierarchical structure, using groupsCreate self-describing files by adding metadata with HDF5 attributesTake advantage of HDF5's type system to create interoperable filesExpress relationships among data with references, named types, and dimension scalesDiscover how Python mechanisms for writing parallel code interact with HDF5
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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 fileWork with datasets by learning the HDF5 Dataset objectUnderstand advanced features like dataset chunking and compressionLearn how to work with HDF5's hierarchical structure, using groupsCreate self-describing files by adding metadata with HDF5 attributesTake advantage of HDF5's type system to create interoperable filesExpress relationships among data with references, named types, and dimension scalesDiscover how Python mechanisms for writing parallel code interact with HDF5
Quantity:
In stock online
Delivery 5-7 Days
Eligible for free delivery
100 Reward Points

Any purchases for more than €10 are eligible for free delivery anywhere in the UK or Ireland!

€33.59
In stock online
Delivery 5-7 Days
Eligible for free delivery
Quantity:
100 Reward Points

Any purchases for more than €10 are eligible for free delivery anywhere in the UK or Ireland!

Product 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 fileWork with datasets by learning the HDF5 Dataset objectUnderstand advanced features like dataset chunking and compressionLearn how to work with HDF5's hierarchical structure, using groupsCreate self-describing files by adding metadata with HDF5 attributesTake advantage of HDF5's type system to create interoperable filesExpress relationships among data with references, named types, and dimension scalesDiscover how Python mechanisms for writing parallel code interact with HDF5

Product Details

Python and HDF5

ISBN9781449367831

Format

Publisher (01 November. 2013)

No. of Pages152

Weight282

Language English (United States)

Dimensions 233 x 178 x 8