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.
cftime: recommended if you want to encode/decode datetimes for non-standard calendars or dates before year 1678 or after year 2262.
PseudoNetCDF: recommended for accessing CAMx, GEOS-Chem (bpch), NOAA ARL files, ICARTT files (ffi1001) and many other.
rasterio: for reading GeoTiffs and other gridded raster datasets. (version 1.0 or later)
iris: for conversion to and from iris’ Cube objects
cfgrib: for reading GRIB files via the ECMWF ecCodes library.
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, pynio or PseudoNetCDF:
$ conda install -c conda-forge xarray cartopy pynio pseudonetcdf
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