xray: N-D labeled arrays and datasets in Python

https://travis-ci.org/xray/xray.svg?branch=master

xray is an open source project and Python package that aims to bring the labeled data power of pandas to the physical sciences, by providing N-dimensional variants of the core pandas data structures, Series and DataFrame: the xray DataArray and Dataset.

Our goal is to provide a pandas-like and pandas-compatible toolkit for analytics on multi-dimensional arrays, rather than the tabular data for which pandas excels. Our approach adopts the Common Data Model for self- describing scientific data in widespread use in the Earth sciences (e.g., netCDF and OPeNDAP): xray.Dataset is an in-memory representation of a netCDF file.

Important links

HTML documentation: http://xray.readthedocs.org
Issue tracker: http://github.com/xray/xray/issues
Source code: http://github.com/xray/xray
PyData talk: https://www.youtube.com/watch?v=T5CZyNwBa9c

Why xray?

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'] or x.sel(time='2014-01-01').
Mathematical operations (e.g., x - y) vectorize across multiple dimensions (known in numpy as "broadcasting") based on dimension names, not array shape.
Flexible split-apply-combine operations with groupby: x.groupby('time.dayofyear').mean().
Database like aligment based on coordinate labels that smoothly handles missing values: x, y = xray.align(x, y, join='outer').
Keep track of arbitrary metadata in the form of a Python dictionary: x.attrs.

pandas excels at working with tabular data. That suffices for many statistical analyses, but physical scientists rely on N-dimensional arrays -- which is where xray comes in.

xray 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 xray implements the same data model as the netCDF file format, xray datasets have a natural and portable serialization format. But it is also easy to robustly convert an xray 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.

Get in touch

Mailing list: https://groups.google.com/forum/#!forum/xray-dev
Twitter: http://twitter.com/shoyer

xray is an ambitious project and we have a lot of work to do make it as powerful as it should be. We would love to hear your thoughts!

History

xray is an evolution of an internal tool developed at The Climate Corporation, and was originally written by current and former Climate Corp researchers Stephan Hoyer, Alex Kleeman and Eugene Brevdo.

License

Copyright 2014, xray 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.

xray includes portions of pandas. The license for pandas is included in the LICENSES directory.

License

kirknorth/xray

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