Revision as of 23:32, 22 January 2020 by (talk | contribs)

We encourage our researchers to use Python for analysing large climate datasets. The Xarray and Dask libraries in particular make it possible to analyse very large datasets with relative ease.

At NCI we maintain a Conda environment with a wide variety of libraries for climate and weather analysis.

Learning Python

Resources for Climate Scientists

Useful Libraries

  • NumPy - Numerical Python Library
  • SciPy - Scientific Python Tools
  • Pandas - Tabular data analysis
  • Xarray - Gridded data & NetCDF analysis
  • Iris - Read, Analyse and Plot Climate Datasets (NetCDF, GRIB, UM output)
  • Cartopy - A library containing cartographic tools for python (alternative to basemap)

Creating a Local Environment

For using Python on your desktop computer, consider installing Enthought Canopy. The full version of the software is free for academic users and provides a large library of ready-made python packages

Or try Anaconda, which we use for the Conda environment at NCI