cxarray
pypi i cxarray

cxarray

N-D labeled arrays and datasets in Python

by pydata

0.0.0 (see all)License:Apache
pypi i cxarray
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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>_

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