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image:: https://readthedocs.org/projects/deepdish/badge/?version=latest\n    :target: https://readthedocs.org/projects/deepdish/?badge=latest\n    :alt: Documentation Status\n\n.. image:: https://travis-ci.org/uchicago-cs/deepdish.svg?branch=master\n    :target: https://travis-ci.org/uchicago-cs/deepdish/\n\n.. image:: https://img.shields.io/pypi/v/deepdish.svg\n    :target: https://pypi.python.org/pypi/deepdish\n\n.. image:: https://coveralls.io/repos/uchicago-cs/deepdish/badge.svg?branch=master\u0026service=github\n    :target: https://coveralls.io/github/uchicago-cs/deepdish?branch=master\n   \n.. image:: https://img.shields.io/badge/license-BSD%203--Clause-blue.svg?style=flat\n    :target: http://opensource.org/licenses/BSD-3-Clause \n\ndeepdish\n========\n\nFlexible HDF5 saving/loading and other data science tools from the University of Chicago. This repository also host a Deep Learning blog:\n\n* http://deepdish.io\n\nInstallation\n------------\n::\n\n    pip install deepdish\n\nAlternatively (if you have conda with the `conda-forge \u003chttps://conda-forge.github.io/\u003e`__ channel)::\n\n    conda install -c conda-forge deepdish\n\n\nMain feature\n------------\nThe primary feature of deepdish is its ability to save and load all kinds of\ndata as HDF5. It can save any Python data structure, offering the same ease of\nuse as pickling or `numpy.save \u003chttp://docs.scipy.org/doc/numpy/reference/generated/numpy.save.html\u003e`__. However, it improves by also offering:\n\n- Interoperability between languages (HDF5 is a popular standard)\n- Easy to inspect the content from the command line (using ``h5ls`` or our\n  specialized tool ``ddls``)\n- Highly compressed storage (thanks to a PyTables backend)\n- Native support for scipy sparse matrices and pandas ``DataFrame``, ``Series``\n  and ``Panel``\n- Ability to partially read files, even slices of arrays\n\nAn example:\n\n.. code:: python\n\n    import deepdish as dd\n\n    d = {\n        'foo': np.ones((10, 20)),\n        'sub': {\n            'bar': 'a string',\n            'baz': 1.23,\n        },\n    }\n    dd.io.save('test.h5', d)\n\nThis can be reconstructed using ``dd.io.load('test.h5')``, or inspected through\nthe command line using either a standard tool::\n\n    $ h5ls test.h5\n    foo                      Dataset {10, 20}\n    sub                      Group\n\nOr, better yet, our custom tool ``ddls`` (or ``python -m deepdish.io.ls``)::\n\n    $ ddls test.h5\n    /foo                       array (10, 20) [float64]\n    /sub                       dict\n    /sub/bar                   'a string' (8) [unicode]\n    /sub/baz                   1.23 [float64]\n\nRead more at `Saving and loading data \u003chttp://deepdish.readthedocs.io/en/latest/io.html\u003e`__.\n\nDocumentation\n-------------\n\n* http://deepdish.readthedocs.io/\n","funding_links":[],"categories":["Python","Data Loading \u0026 Extraction"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fuchicago-cs%2Fdeepdish","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fuchicago-cs%2Fdeepdish","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fuchicago-cs%2Fdeepdish/lists"}