36,49 €
Python and HDF5
Python and HDF5
  • Išparduota
Python and HDF5
Python and HDF5
El. knyga:
36,49 €
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 Pyth…
  • Leidėjas:
  • Metai: 2013
  • Puslapiai: 152
  • ISBN: 9781491945001
  • ISBN-10: 1491945001
  • ISBN-13: 9781491945001
  • Formatas: ACSM ?
  • Kalba: Anglų

Python and HDF5 (el. knyga) (skaityta knyga) | Andrew Collette | knygos.lt

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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.

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  • Autorius: Andrew Collette
  • Leidėjas:
  • Metai: 2013
  • Puslapiai: 152
  • ISBN: 9781491945001
  • ISBN-10: 1491945001
  • ISBN-13: 9781491945001
  • Formatas: ACSM ?
  • Kalba: Anglų

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.

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