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Edité par O'Reilly Media (edition 1), 2013
ISBN 10 : 1449367836 ISBN 13 : 9781449367831
Langue: anglais
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Ajouter au panierPaperback. Etat : Fair. No Jacket. Readable copy. Pages may have considerable notes/highlighting. ~ ThriftBooks: Read More, Spend Less 0.85.
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Ajouter au panierPAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.
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Ajouter au panierPAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.
Edité par O'Reilly Media 11/11/2013, 2013
ISBN 10 : 1449367836 ISBN 13 : 9781449367831
Langue: anglais
Vendeur : BargainBookStores, Grand Rapids, MI, Etats-Unis
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Ajouter au panierPaperback or Softback. Etat : New. Python and HDF5 0.57. Book.
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Ajouter au panierPaperback. Etat : New. 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.
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EUR 31,63
Autre deviseQuantité disponible : 3 disponible(s)
Ajouter au panierPaperback. Etat : New. 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.
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Edité par O'Reilly Media, Inc, USA, 2013
ISBN 10 : 1449367836 ISBN 13 : 9781449367831
Langue: anglais
Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
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Edité par O'Reilly Media, Incorporated, 2013
ISBN 10 : 1449367836 ISBN 13 : 9781449367831
Langue: anglais
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Edité par O'Reilly Media, Incorporated, 2013
ISBN 10 : 1449367836 ISBN 13 : 9781449367831
Langue: anglais
Vendeur : Books Puddle, New York, NY, Etats-Unis
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Ajouter au panierEtat : New. pp. 152 Index.
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Edition originale
EUR 35,68
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Ajouter au panierEtat : New. 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. Num Pages: 142 pages, illustrations. BIC Classification: UMW. Category: (XV) Technical / Manuals. Dimension: 234 x 178 x 9. Weight in Grams: 276. . 2013. 1st Edition. Paperback. . . . .
EUR 42,37
Autre deviseQuantité disponible : 1 disponible(s)
Ajouter au panierEtat : New. 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. Num Pages: 142 pages, illustrations. BIC Classification: UMW. Category: (XV) Technical / Manuals. Dimension: 234 x 178 x 9. Weight in Grams: 276. . 2013. 1st Edition. Paperback. . . . . Books ship from the US and Ireland.
Edité par Oreilly & Associates Inc, 2013
ISBN 10 : 1449367836 ISBN 13 : 9781449367831
Langue: anglais
Vendeur : Revaluation Books, Exeter, Royaume-Uni
EUR 40,62
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Ajouter au panierPaperback. Etat : Brand New. 142 pages. 9.19x7.00x0.40 inches. In Stock.
EUR 32,87
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Ajouter au panierPaperback. Etat : New. 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.
EUR 26,84
Autre deviseQuantité disponible : 5 disponible(s)
Ajouter au panierEtat : New. 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 .
Edité par O'reilly Media Dez 2013, 2013
ISBN 10 : 1449367836 ISBN 13 : 9781449367831
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 31,87
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Ajouter au panierTaschenbuch. Etat : Neu. Neuware - 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.
EUR 28,39
Autre deviseQuantité disponible : 3 disponible(s)
Ajouter au panierPaperback. Etat : New. 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.
Edité par O'Reilly Media, Inc, USA, 2013
ISBN 10 : 1449367836 ISBN 13 : 9781449367831
Langue: anglais
Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
EUR 40,84
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Ajouter au panierPaperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 298.
Edité par Oreilly & Associates Inc, 2013
ISBN 10 : 1449367836 ISBN 13 : 9781449367831
Langue: anglais
Vendeur : Revaluation Books, Exeter, Royaume-Uni
EUR 32,64
Autre deviseQuantité disponible : 2 disponible(s)
Ajouter au panierPaperback. Etat : Brand New. 142 pages. 9.19x7.00x0.40 inches. In Stock. This item is printed on demand.