This article demonstrates how to create detailed terrain maps in Python by installing and importing rasterio, geopandas, cartopy, and related libraries, configuring map fonts, and executing a comprehensive script that reads vector and raster data, applies custom color maps, and renders the final visualization.
This article presents ten practical Python code examples for common data visualization scenarios—including line, scatter, bar, pie, box, heatmap, area, histogram, tree, and map visualizations—demonstrating how to use matplotlib, seaborn, and geopandas to effectively convey data insights.
This article introduces several Python libraries for creating geographic visualizations, demonstrates how to draw world and China maps with Bokeh, GeoPandas, Plotly, Cartopy/Basemap, Folium, and PyEcharts, and compares their ease of use, flexibility, and visual quality.
This article explains the background of China's 211 university project, lists the 116 institutions across six tiers, and provides a complete Python script that uses geopandas, matplotlib, and animation to create an interactive map showing the geographic distribution of these top universities.
This tutorial walks you through reading Chinese GeoJSON files, converting projections, preparing scatter data, and creating a detailed map with an inset of the South China Sea using Python‑geopandas, Matplotlib, and custom legends, while also covering installation tips and known limitations.
