Metadata-Version: 2.1
Name: xarray
Version: 0.16.0
Summary: N-D labeled arrays and datasets in Python
Home-page: https://github.com/pydata/xarray
Author: xarray Developers
Author-email: xarray@googlegroups.com
License: Apache
Description: 
        **xarray** (formerly **xray**) is an open source project and Python package
        that makes working with labelled multi-dimensional arrays simple,
        efficient, and fun!
        
        xarray introduces labels in the form of dimensions, coordinates and
        attributes on top of raw NumPy_-like arrays, which allows for a more
        intuitive, more concise, and less error-prone developer experience.
        The package includes a large and growing library of domain-agnostic functions
        for advanced analytics and visualization with these data structures.
        
        xarray was inspired by and borrows heavily from pandas_, the popular data
        analysis package focused on labelled tabular data.
        It is particularly tailored to working with netCDF_ files, which were the
        source of xarray's data model, and integrates tightly with dask_ for parallel
        computing.
        
        .. _NumPy: https://www.numpy.org
        .. _pandas: https://pandas.pydata.org
        .. _dask: https://dask.org
        .. _netCDF: https://www.unidata.ucar.edu/software/netcdf
        
        Why xarray?
        -----------
        Multi-dimensional (a.k.a. N-dimensional, ND) arrays (sometimes called
        "tensors") are an essential part of computational science.
        They are encountered in a wide range of fields, including physics, astronomy,
        geoscience, bioinformatics, engineering, finance, and deep learning.
        In Python, NumPy_ provides the fundamental data structure and API for
        working with raw ND arrays.
        However, real-world datasets are usually more than just raw numbers;
        they have labels which encode information about how the array values map
        to locations in space, time, etc.
        
        xarray doesn't just keep track of labels on arrays -- it uses them to provide a
        powerful and concise interface. For example:
        
        -  Apply operations over dimensions by name: ``x.sum('time')``.
        -  Select values by label instead of integer location: ``x.loc['2014-01-01']`` or ``x.sel(time='2014-01-01')``.
        -  Mathematical operations (e.g., ``x - y``) vectorize across multiple dimensions (array broadcasting) based on dimension names, not shape.
        -  Flexible split-apply-combine operations with groupby: ``x.groupby('time.dayofyear').mean()``.
        -  Database like alignment based on coordinate labels that smoothly handles missing values: ``x, y = xr.align(x, y, join='outer')``.
        -  Keep track of arbitrary metadata in the form of a Python dictionary: ``x.attrs``.
        
        Learn more
        ----------
        - Documentation: `<http://xarray.pydata.org>`_
        - Issue tracker: `<http://github.com/pydata/xarray/issues>`_
        - Source code: `<http://github.com/pydata/xarray>`_
        - SciPy2015 talk: `<https://www.youtube.com/watch?v=X0pAhJgySxk>`_
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
