py-maplibregl – Connecting MapLibre GL JS with Python
Data scientists often use MapLibre GL JS (GitHub link) for visualization, an open-source TypeScript library for interactive map rendering in the browser. Our package “py-maplibregl” combines the power of MapLibre GL JS with the strengths of Python and enables modern, interactive maps without any frontend setup.
Instead of switching between Python and JavaScript, spatial data can be visualized directly where it is created: in the notebook. py-maplibregl integrates seamlessly into Jupyter Notebooks as well as Marimo notebooks and supports common data structures such as Pandas and GeoPandas. This allows features, patterns, and outliers in geospatial data to be explored immediately – interactively, efficiently, and reproducibly.
A particular advantage for data-intensive applications: Rendering is handled via WebGL in the browser. This ensures that even large datasets remain smooth to navigate, while control is handled entirely in Python. This makes the library ideal for exploratory analysis, prototyping, and data-driven storytelling.
Typical use cases for data scientists:
- Exploratory analysis of geo features
- Visualization of model results (e.g., clustering, heatmaps)
- Creation of interactive reports and dashboards
- Rapid prototyping of geospatial applications
Conclusion:
py-maplibregl enables data scientists to visualize geospatial data just as intuitively as they analyze it— without leaving their familiar Python workflow. Anyone looking to integrate modern maps into their data projects will find a powerful yet accessible tool here.
Link to our repo: https://github.com/eoda-dev/py-maplibregl
Link to the documentation: https://github.com/eoda-dev/py-maplibregl
Alternative for OpenLayers: py-openlayers
OpenLayers is another alternative for interactive map visualization.
Our data scientists have developed py-openlayers to create interactive maps directly in Python—based on the proven web mapping library OpenLayers.
The package combines the functionality of OpenLayers with the Python workflow and enables visualization of complex geospatial data without JavaScript. It particularly shows its strengths in Jupyter Notebooks, Marimo, or Shiny for Python: maps can be created with just a few lines of code and explored interactively right away.
A key advantage is the broad support for data formats and sources. In addition to classic tile services (e.g., OSM, XYZ), vector data such as GeoJSON, KML, or GML can also be rendered, as well as large datasets via WebGL with high performance.
This opens up numerous use cases for data scientists:
- Exploratory analysis of spatial data directly in the notebook
- Visualization of model results (e.g., clusters, hotspots)
- Interactive selection and editing of geo features
- Integration of GeoPandas into web maps
A particular highlight is the Python-native API: maps, layers, and controls can be defined declaratively and extended flexibly—from simple base maps to complex interactive applications.
Conclusion:
py-openlayers makes the capabilities of modern web cartography accessible to data scientists—without switching to the frontend world. Anyone analyzing geospatial data while also wanting to visualize it interactively will find a versatile and production-ready tool here.
Link to the repo: https://github.com/eoda-dev/py-openlayers
Link to the documentation: https://eoda-dev.github.io/py-openlayers/
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