Required dependencies

  • Python (3.5.3 or later)

  • numpy (1.12 or later)

  • pandas (0.19.2 or later)

Optional dependencies

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

  • scipy: necessary to enable the interpolation features for xarray objects

  • bottleneck: speeds up NaN-skipping and rolling window aggregations by a large factor (1.1 or later)

  • numbagg: for exponential rolling window operations

For parallel computing

For plotting


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: Simple unit testing library

  • mock: additional testing library required for python version 2

and run py.test --pyargs xarray.

Performance Monitoring

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

and run asv run  # this will install some conda environments in ./.asv/envs