As part of an ongoing effort at the Unidata Program Center to bring Python-based workflows to the atmospheric science community, UPC developers have created the Unidata Jupyter Notebook Gallery to showcase tools and techniques that may be of interest to community members. The gallery is a collection of Jupyter notebooks that demonstrate ways to work with atmospheric science data using Python. The notebooks use a variety of tools available in the Python ecosystem, including several developed by Unidata.
Images in the notebook gallery link to displays of the notebook contents in the Jupyter nbviewer. While notebooks displayed in the nbviewer are not interactive, you can download any notebook in the gallery and run it locally. If you're new to Jupyter notebooks, you might want to take a look at the resources in Unidata's Online Python Training site; the materials on this site are not yet complete, but should be enough to get you started.
Our hope is that the gallery will become a useful community resource, highlighting a wide array of use cases. We encourage members of the community to submit their own notebooks for inclusion via a pull request on the gallery's GitHub repository. (We welcome improvements to the existing notebooks as well.) For more help on submissions or to ask questions, feel free to send a message to Unidata's python-users mailing list.
This resource grew out of the notebooks created for posts over on our Developers' Blog and was expanded with the help of 2016 summer development intern Kristen Pozsonyi.
