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Use Mapbox GL JS to visualize data in a Python Jupyter notebook
The main features of mapbox/mapboxgl-jupyter are: Geospatial Analysis, Python Geospatial Libraries, Visualization and Mapping.
Open-source alternatives to mapbox/mapboxgl-jupyter include: toblerity/fiona — Fiona reads and writes geographic data files. visgl/deck.gl — This project is a declarative visualization library and geospatial framework designed for rendering large-scale data… geopandas/geopandas — GeoPandas is a Python library that extends pandas with native support for geospatial data. It treats geographic… giswqs/geemap — geemap is a Python library and toolkit for interactive geospatial analysis, visualization, and satellite imagery… leaflet/leaflet.markercluster — This project is a marker clustering plugin and map visualization tool for Leaflet. It functions as a grouping library… shsarv/machine-learning-projects — This repository is a collection of practical machine learning implementations designed to demonstrate core predictive…
geemap is a Python library and toolkit for interactive geospatial analysis, visualization, and satellite imagery analysis using Google Earth Engine data and cloud computing. It provides a mapping tool for displaying geospatial datasets within Jupyter notebooks and a suite of tools for classifying imagery and calculating zonal statistics. The project includes a utility to convert geospatial analysis scripts from JavaScript into Python code to facilitate data manipulation. It also enables the generation of timelapse animations and time-series visualizations from satellite imagery catalogs. The
Fiona reads and writes geographic data files
GeoPandas is a Python library that extends pandas with native support for geospatial data. It treats geographic geometries—points, lines, and polygons—as a first-class column type within DataFrames, enabling users to store, manipulate, and analyze vector spatial data alongside traditional tabular attributes. The library is built on top of proven geospatial components: it uses Shapely for all geometric operations, Fiona and GDAL for reading and writing standard spatial file formats, PyProj for coordinate reprojection, and an R‑tree spatial index (from Shapely) to accelerate spatial queries. Wh
This project is a declarative visualization library and geospatial framework designed for rendering large-scale data sets within web browsers. It functions as a high-performance graphics engine that leverages hardware acceleration to display complex 2D and 3D visual layers, enabling the visualization of millions of data points through a structured, component-based syntax. The framework distinguishes itself through its ability to synchronize custom data visualizations with third-party mapping platforms. By managing camera states and coordinate systems, it allows developers to overlay high-perf