For netCDF and IO¶
- netCDF4: recommended if you want to use xarray for reading or writing netCDF files
- scipy: used as a fallback for reading/writing netCDF3
- pydap: used as a fallback for accessing OPeNDAP
- h5netcdf: an alternative library for reading and writing netCDF4 files that does not use the netCDF-C libraries
- pynio: for reading GRIB and other geoscience specific file formats
- zarr: for chunked, compressed, N-dimensional arrays.
- netcdftime: recommended if you want to encode/decode datetimes for non-standard calendars.
For accelerating xarray¶
xarray itself is a pure Python package, but its dependencies are not. The easiest way to get everything installed is to use conda. To install xarray with its recommended dependencies using the conda command line tool:
$ conda install xarray dask netCDF4 bottleneck
We recommend using the community maintained conda-forge channel if you need difficult-to-build dependencies such as cartopy or pynio:
$ conda install -c conda-forge xarray cartopy pynio
New releases may also appear in conda-forge before being updated in the default channel.
If you don’t use conda, be sure you have the required dependencies (numpy and pandas) installed first. Then, install xarray with pip:
$ pip install xarray
To run the test suite after installing xarray, first install (via pypi or conda)
py.test --pyargs xarray.
A fixed-point performance monitoring of (a part of) our codes can be seen on this page.
To run these benchmark tests in a local machine, first install - airspeed-velocity: a tool for benchmarking Python packages over their lifetime.
asv run # this will install some conda environments in ./.asv/envs
Xarray plans to drop support for python 2.7 at the end of 2018. This means that new releases of xarray published after this date will only be installable on python 3+ environments, but older versions of xarray will always be available to python 2.7 users. For more information see the following references: