In digital agriculture work, data scientists combine a variety of both vector and raster datasets to produce new insights in the form of soil maps, machine learning models, and advanced visualizations. To effectively marry up vector and raster data, you need to rasterize the vector data in context-dependent ways. We have simplified this process with an open-source Python library called geocube. Now you can move data from GeoDataframes (geopandas) and other vector file formats into xarray Datasets with a variety of advanced rasterization options. Once in xarray, you can export to standard raster or multidimensional data formats.