It enables you to store huge amounts of numerical data, and easily manipulate them from NumPy. For example, you can slice into multi-terabyte datasets stored on disk, as if they were real NumPy arrays. Thousands of datasets can be stored in a single file, categorized and tagged however you want.
H5py uses straightforward NumPy and Python metaphors, like dictionary and NumPy array syntax. For example, you can iterate over datasets in a file, or check out the .shape or .dtype attributes of datasets.
In addition to the easy-to-use high level interface, h5py rests on a object-oriented Cython wrapping of the HDF5 C API. Almost anything you can do from C in HDF5, you can do from h5py.
Reference: http://www.h5py.org/
You can download h5py from Python Package Index (PyPI) or install it quickly by following instructions found here (including h5py usage Demo) or at http://docs.h5py.org.
For Ubuntu Linux, you can install h5py using conda, simply by typing
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conda install h5py
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apt-cache policy <packagename>
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dpkg -l | grep <packagename>
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apt-cache policy python-numpy
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dpkg -l | grep python-numpy
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sudo apt-get install python3-h5py