xarray (formerly 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.
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:
xarray.Dataset is an in-memory representation of a netCDF file.
I added some additional functionality:
- time.doy virtual variable. Day of year for no leap calendars.
- plot.geocolormesh: automatically plots on a cartopy map
- average function (for Dataset and DataArray): weighted mean
- wrap360 to change from -180..179 to 0..359 longitude
- read_netcdfs: read a list of netcdfs and concatenate along a dimensions. The dimension does not need to exist (can be an ensemble).
- open_cesm: open a cesm netCDF file: uses time_bounds and rounds lat and lon coordinates
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/
- GitHub issue tracker: https://github.com/pydata/xarray/issues/
- Mailing list: https://groups.google.com/forum/#!forum/xarray
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.
Copyright 2014-2016, 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 includes portions of pandas, NumPy and Seaborn. Their licenses are included in the licenses directory.