{"product_id":"python-and-hdf5-unlocking-scientific-data-9781449367831","title":"Python and HDF5: Unlocking Scientific Data","description":"\u003cp\u003eGain 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. \u003c\/p\u003e\u003cp\u003e 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. \u003c\/p\u003e\u003cul\u003e \u003cli\u003eGet set up with HDF5 tools and create your first HDF5 file \u003c\/li\u003e\n\u003cli\u003eWork with datasets by learning the HDF5 Dataset object \u003c\/li\u003e\n\u003cli\u003eUnderstand advanced features like dataset chunking and compression \u003c\/li\u003e\n\u003cli\u003eLearn how to work with HDF5's hierarchical structure, using groups \u003c\/li\u003e\n\u003cli\u003eCreate self-describing files by adding metadata with HDF5 attributes \u003c\/li\u003e\n\u003cli\u003eTake advantage of HDF5's type system to create interoperable files \u003c\/li\u003e\n\u003cli\u003eExpress relationships among data with references, named types, and dimension scales \u003c\/li\u003e\n\u003cli\u003eDiscover how Python mechanisms for writing parallel code interact with HDF5 \u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Andrew Collette\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e O'Reilly Media\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 12\/10\/2013\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 148\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 0.57lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.15h x 7.09w x 0.30d\u003cbr\u003e\u003cb\u003eISBN:\u003c\/b\u003e 9781449367831\u003cbr\u003e\u003cp\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eAndrew 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.\u003c\/p\u003e\u003cbr\u003e","brand":"O'Reilly Media","offers":[{"title":"Paperback","offer_id":44923865268339,"sku":"9781449367831","price":48.09,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0555\/9255\/0515\/files\/img_be1e6fc5-b557-43ee-8d4e-604b10152d67.jpg?v=1778002562","url":"https:\/\/bookstorenmore.com\/products\/python-and-hdf5-unlocking-scientific-data-9781449367831","provider":"Bookstore N More","version":"1.0","type":"link"}