Required dependencies

  • Python 2.6, 2.7, 3.3 or 3.4
  • numpy (1.7 or later)
  • pandas (0.15.0 or later)

Optional dependencies

netCDF and IO

  • netCDF4: recommended if you want to use xray for reading or writing 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

Accelerating xray

  • bottleneck: speeds up NaN-skipping aggregations by a large factor
  • cyordereddict: speeds up most internal operations with xray data structures

Parallel computing


xray itself is a pure Python package, but its dependencies are not. The easiest way to get them installed is to use conda. You can then install xray with its recommended dependencies with the conda command line tool:

$ conda install xray dask netCDF4

If you don’t use conda, be sure you have the required dependencies (numpy and pandas) installed first. Then, install xray with pip:

$ pip install xray

To run the test suite after installing xray, install nose and run nosetests xray.