xarray (formerly xray) is an open source project and Python package that makes working with labelled multi-dimensional arrays simple, efficient, and fun!
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.
By introducing dimensions, coordinates, and attributes on top of raw NumPy-like arrays, xarray is able to understand these labels and use them to provide a more intuitive, more concise, and less error-prone experience. Xarray also provides a large and growing library of 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. Xarray can read and write data from most common labeled ND-array storage formats and is particularly tailored to working with netCDF files, which were the source of xarray's data model.
Adding dimensions names and coordinate indexes to numpy's ndarray makes many powerful array operations possible:
- Apply operations over dimensions by name:
x.sum('time'). - Select values by label instead of integer location:
x.loc['2014-01-01']orx.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.
pandas provides many of these features, but it does not make use of dimension names, and its core data structures are fixed dimensional arrays.
pandas excels at working with tabular data. That suffices for many statistical analyses, but physical scientists rely on N-dimensional arrays -- which is where xarray comes in.
xarray aims to provide a data analysis toolkit as powerful as pandas but designed for working with homogeneous N-dimensional arrays instead of tabular data. When possible, we copy the pandas API and rely on pandas's highly optimized internals (in particular, for fast indexing).
Because xarray implements the same data model as the netCDF file format,
xarray datasets have a natural and portable serialization format. But it is also
easy to robustly convert an xarray DataArray to and from a numpy ndarray
or a pandas DataFrame or Series, providing compatibility with the full
PyData ecosystem.
Our target audience is anyone who needs N-dimensional labeled arrays, but we are particularly focused on the data analysis needs of physical scientists -- especially geoscientists who already know and love netCDF.
The official documentation is hosted on ReadTheDocs at http://xarray.pydata.org/
You can find information about contributing to xarray at our Contributing page.
- Ask usage questions ("How do I?") on StackOverflow.
- Report bugs, suggest features or view the source code on GitHub.
- For less well defined questions or ideas, or to announce other projects of interest to xarray users, use the mailing list.
Xarray is a fiscally sponsored project of NumFOCUS, a nonprofit dedicated to supporting the open source scientific computing community. If you like Xarray and want to support our mission, please consider making a donation to support our efforts.
xarray is an evolution of an internal tool developed at The Climate Corporation. It was originally written by Climate Corp researchers Stephan Hoyer, Alex Kleeman and Eugene Brevdo and was released as open source in May 2014. The project was renamed from "xray" in January 2016. Xarray became a fiscally sponsored project of NumFOCUS in August 2018.
Copyright 2014-2018, xarray Developers
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
xarray bundles portions of pandas, NumPy and Seaborn, all of which are available under a "3-clause BSD" license: - pandas: setup.py, xarray/util/print_versions.py - NumPy: xarray/core/npcompat.py - Seaborn: _determine_cmap_params in xarray/core/plot/utils.py
xarray also bundles portions of CPython, which is available under the "Python Software Foundation License" in xarray/core/pycompat.py.
The full text of these licenses are included in the licenses directory.