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| # xarray: N-D labeled arrays and datasets | ||
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| [](https://github.com/pydata/xarray/actions?query=workflow%3ACI) | ||
| [](https://codecov.io/gh/pydata/xarray) | ||
| [](https://docs.xarray.dev/) | ||
| [](https://pandas.pydata.org/speed/xarray/) | ||
| [](https://pypi.python.org/pypi/xarray/) | ||
| [](https://github.com/python/black) | ||
| [](https://doi.org/10.5281/zenodo.598201) | ||
| [](https://twitter.com/xarray_dev) | ||
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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! | ||
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| Xarray introduces labels in the form of dimensions, coordinates and | ||
| attributes on top of raw [NumPy](https://www.numpy.org)-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. | ||
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| Xarray was inspired by and borrows heavily from | ||
| [pandas](https://pandas.pydata.org), the popular data analysis package | ||
| focused on labelled tabular data. It is particularly tailored to working | ||
| with [netCDF](https://www.unidata.ucar.edu/software/netcdf) files, which | ||
| were the source of xarray\'s data model, and integrates tightly with | ||
| [dask](https://dask.org) for parallel computing. | ||
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| ## Why xarray? | ||
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| 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](https://www.numpy.org) 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. | ||
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| Xarray doesn\'t just keep track of labels on arrays \-- it uses them to | ||
| provide a powerful and concise interface. For example: | ||
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| - 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`. | ||
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| ## Documentation | ||
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| Learn more about xarray in its official documentation at | ||
| <https://docs.xarray.dev/>. | ||
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| Try out an [interactive Jupyter | ||
| notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/weather-data.ipynb). | ||
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| ## Contributing | ||
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| You can find information about contributing to xarray at our | ||
| [Contributing | ||
| page](https://docs.xarray.dev/en/latest/contributing.html#). | ||
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| ## Get in touch | ||
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| - Ask usage questions ("How do I?") on | ||
| [StackOverflow](https://stackoverflow.com/questions/tagged/python-xarray). | ||
| - Report bugs, suggest features or view the source code [on | ||
| GitHub](https://github.com/pydata/xarray). | ||
| - For less well defined questions or ideas, or to announce other | ||
| projects of interest to xarray users, use the [mailing | ||
| list](https://groups.google.com/forum/#!forum/xarray). | ||
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| ## NumFOCUS | ||
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| [](https://numfocus.org/) | ||
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| Xarray is a fiscally sponsored project of | ||
| [NumFOCUS](https://numfocus.org), 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](https://numfocus.salsalabs.org/donate-to-xarray/) to support | ||
| our efforts. | ||
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| ## History | ||
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| Xarray is an evolution of an internal tool developed at [The Climate | ||
| Corporation](http://climate.com/). 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](https://numfocus.org) in August 2018. | ||
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| ## License | ||
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| Copyright 2014-2019, xarray Developers | ||
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| 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 | ||
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| <https://www.apache.org/licenses/LICENSE-2.0> | ||
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| 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. | ||
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| Xarray bundles portions of pandas, NumPy and Seaborn, all of which are | ||
| available under a "3-clause BSD" license: | ||
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| - pandas: setup.py, xarray/util/print_versions.py | ||
| - NumPy: xarray/core/npcompat.py | ||
| - Seaborn: _determine_cmap_params in xarray/core/plot/utils.py | ||
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| Xarray also bundles portions of CPython, which is available under the | ||
| "Python Software Foundation License" in xarray/core/pycompat.py. | ||
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| Xarray uses icons from the icomoon package (free version), which is | ||
| available under the "CC BY 4.0" license. | ||
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| The full text of these licenses are included in the licenses directory. |
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