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GeoViews is a Python library that makes it easy to explore and visualize geographical, meteorological, and oceanographic datasets, such as those used in weather, climate, and remote sensing research.

Project description

Travis Coveralls Waffle


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, 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 The GeoElements currently include Feature, WMTS, Tiles, Points, Contours, Image, QuadMesh, TriMesh, RGB, HSV, Labels, Graph, HexTiles, VectorField 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 install GeoViews and its dependencies using conda:

conda install -c pyviz geoviews

Once installed you can copy the examples into the current directory using the geoviews command and run them using the Jupyter notebook:

geoviews examples 
cd geoviews-examples
jupyter notebook

(Here geoviews examples is a shorthand for geoviews copy-examples --path geoviews-examples && geoviews fetch-data --path geoviews-examples.)

To work with JupyterLab you will also need the PyViz JupyterLab extension:

conda install -c conda-forge jupyterlab
jupyter labextension install @pyviz/jupyterlab_pyviz

Once you have installed JupyterLab and the extension launch it with:


If you want to try out the latest features between releases, you can get the latest dev release by specifying -c pyviz/label/dev in place of -c pyviz.

Additional dependencies

If you need to install libraries only available from conda-forge, such as Iris (to use data stored in Iris cubes) or xesmf, you should install from conda-forge:

conda create -n env-name -c pyviz -c conda-forge geoviews iris xesmf
conda activate env-name

Note -- Do not mix conda-forge and defaults. I.e., do not install packages from conda-forge into a GeoViews environment created with defaults. If you are using the base environment of mini/anaconda, or an environment created without specifying conda-forge before defaults, and you then install from conda-forge, you will very likely have incompatibilities in underlying, low-level dependencies. These binary (ABI) incompatibilities can lead to segfaults because of differences in how non-Python packages are built between conda-forge and defaults.

GeoViews itself is also installable using pip, but to do that you will first need to have installed the dependencies of cartopy, or else have set up your system to be able to build them.

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