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        "description": "Introduction\n============\n\n.. image:: https://travis-ci.org/perrette/dimarray.svg?branch=master\n   :target: https://travis-ci.org/perrette/dimarray\n\nNumpy array with dimensions\n---------------------------\ndimarray is a package to handle numpy arrays with labelled dimensions and axes. \nInspired from pandas, it includes advanced alignment and reshaping features and \nas well as missing-value (NaN) handling.\n\nThe main difference with pandas is that it is generalized to N dimensions, and behaves more closely to a numpy array. \nThe axes do not have fixed names ('index', 'columns', etc...) but are \ngiven a meaningful name by the user (e.g. 'time', 'items', 'lon' ...). \nThis is especially useful for high dimensional problems such as sensitivity analyses.\n\nA natural I/O format for such an array is netCDF, common in geophysics, which relies on \nthe netCDF4 package, and supports metadata.\n\n\nLicense\n-------\ndimarray is distributed under a 3-clause (\"Simplified\" or \"New\") BSD\nlicense. Parts of basemap which have BSD compatible licenses are included.\nSee the LICENSE file, which is distributed with the dimarray package, for details.\n\nGetting started\n---------------\n\nA **``DimArray``** can be defined just like a numpy array, with\nadditional information about its dimensions, which can be provided\nvia its `axes` and `dims` parameters:\n\n>>> from dimarray import DimArray\n>>> a = DimArray([[1.,2,3], [4,5,6]], axes=[['a', 'b'], [1950, 1960, 1970]], dims=['variable', 'time']) \n>>> a\ndimarray: 6 non-null elements (0 null)\n0 / variable (2): 'a' to 'b'\n1 / time (3): 1950 to 1970\narray([[1., 2., 3.],\n       [4., 5., 6.]])\n\nIndexing now works on axes\n\n>>> a['b', 1970]\n6.0\n\nOr can just be done **a la numpy**, via integer index:\n\n>>> a.ix[0, -1]\n3.0\n\nBasic numpy transformations are also in there:\n\n>>> a.mean(axis='time')\ndimarray: 2 non-null elements (0 null)\n0 / variable (2): 'a' to 'b'\narray([2., 5.])\n\nCan export to `pandas` for pretty printing:\n\n>>> a.to_pandas()\ntime      1950  1960  1970\nvariable                  \na          1.0   2.0   3.0\nb          4.0   5.0   6.0\n\n\n.. _links:\n\nUseful links\n------------\n================================    ====================================\nDocumentation                       http://dimarray.readthedocs.org\nCode on github (bleeding edge)      https://github.com/perrette/dimarray\nCode on pypi   (releases)           https://pypi.python.org/pypi/dimarray\nMailing List                        http://groups.google.com/group/dimarray\nIssues Tracker                      https://github.com/perrette/dimarray/issues\n================================    ====================================\n\nInstall\n-------\n\n**Requirements**:\n\n- python >= 2.7, 3\n- numpy (tested with 1.7, 1.8, 1.9, 1.10.1, 1.15)\n\n**Optional**:\n\n- netCDF4 (tested with 1.0.8, 1.2.1) (netCDF archiving) (see notes below)\n- matplotlib 1.1 (plotting)\n- pandas 0.11 (interface with pandas)\n\nDownload the latest version from github and extract from archive\nThen from the dimarray repository type (possibly preceded by sudo):\n\n.. code:: bash\n\n    python setup.py install  \n\nAlternatively, you can use pip to download and install the version from pypi (could be slightly out-of-date):\n\n.. code:: bash\n\n    pip install dimarray \n\n\nNotes on installing netCDF4\n^^^^^^^^^^^^^^^^^^^^^^^^^^^\n- On Ubuntu, using apt-get is the easiest way (as indicated at https://github.com/Unidata/netcdf4-python/blob/master/.travis.yml):\n\n\n.. code:: bash\n\n   sudo apt-get install libhdf5-serial-dev netcdf-bin libnetcdf-dev\n\n- On windows binaries are available: http://www.unidata.ucar.edu/software/netcdf/docs/winbin.html\n\n- From source. Installing the netCDF4 python module from source can be cumbersome, because \nit depends on netCDF4 and (especially) HDF5 C libraries that need to \nbe compiled with specific flags (http://unidata.github.io/netcdf4-python). \nDetailled information on Ubuntu: https://code.google.com/p/netcdf4-python/wiki/UbuntuInstall\n\nContributions\n-------------\nAll suggestions for improvement or direct contributions are very welcome.\nYou can ask a question or start a discussion on the mailing list\nor open an `issue` on github for precise requests. See `links`_.\n\n\n",
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