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N-D labeled arrays and datasets in Python

Project 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: http://www.numpy.org/
.. _pandas: http://pandas.pydata.org
.. _netCDF: http://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


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