GeoViews is a Python library that makes it easy to explore and visualize any data that includes geographic locations. It has particularly powerful support for multidimensional meteorological and oceanographic datasets, such as those used in weather, climate, and remote sensing research, but is useful for almost anything that you would want to plot on a map! You can see lots of example notebooks at geo.holoviews.org, and a good overview is in our blog post announcement.

GeoViews is built on the HoloViews library for building flexible visualizations of multidimensional data. GeoViews adds a family of geographic plot types based on the Cartopy library, plotted using either the Matplotlib or Bokeh packages. Each of the new GeoElement plot types is a new HoloViews Element that has an associated geographic projection based on cartopy.crs. The GeoElements currently include Feature, WMTS, Tiles, Points, Contours, Image, and Text objects, each of which can easily be overlaid in the same plots. E.g. an object with temperature data can be overlaid with coastline data using an expression like gv.Image(temperature)*gv.Feature(cartopy.feature.COASTLINE). Each GeoElement can also be freely combined in layouts with any other HoloViews Element, making it simple to make even complex multi-figure layouts of overlaid objects.

You can then install GeoViews and its other dependencies using conda, many users will want iris and/or xarray as well:

conda install -c conda-forge -c ioam holoviews geoviews
# (Optional)
conda install xarray
conda install -c conda-forge iris

You can now switch to your preferred working directory, grab a copy of the notebooks to run locally, and run them using the Jupyter notebook::

cd ~
python -c 'import geoviews; geoviews.examples("geoviews-examples",include_data=True)'
cd geoviews-examples
jupyter notebook