marceloprates/prettymaps

Draw pretty maps from OpenStreetMap data! Built with osmnx +matplotlib + shapely

View on GitHub ↗Jump to charts ↓

marceloprates/prettymaps has 14k+ stars and 700+ forks on GitHub, is written in Python, and was last pushed on Jul 30, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 10 minutes ago
Added to GitGenius on August 20th, 2026
Created on March 5th, 2021
Open Issues & Pull Requests: 24 (+0)
GitHub issues: Enabled
Number of forks: 732
Total Stargazers: 14,308 (+0)
Total Subscribers: 93 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 5.2 days
Mean response time: 344.4 days
90th percentile: 1569.3 days
Tracked items: 54

Charts & Analytics

Fetching additional details & charts...

Issue Activity (beta)

Open issues: 8
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 234 days
Stale 30+ days: 7
Stale 90+ days: 0

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

No label distribution available yet.

Most active issues this week

Sign in to see which issues are moving.
Sign in

Detailed Description

Prettymaps is a Python library for drawing customized maps from OpenStreetMap data. It combines osmnx for data retrieval, matplotlib for rendering, and shapely for geometric operations to create visually styled cartographic output.

The library solves the problem of generating aesthetically appealing maps with fine-grained control over visual styling. Rather than relying on web-based mapping services, prettymaps lets users query OpenStreetMap data programmatically and render it with matplotlib's full customization capabilities, enabling everything from simple styled maps to generative art pieces. The approach leverages existing geospatial and visualization libraries rather than building mapping infrastructure from scratch.

Prettymaps suits developers and artists who want to create custom map visualizations for notebooks, scripts, or web applications. It works well for projects requiring programmatic map generation with control over colors, layers, and artistic styling. The tool is particularly useful in Jupyter environments and integrates with Google Colaboratory for cloud-based workflows. The project explicitly discourages use for NFT generation, requesting that users respect the creator and maintain proper attribution to both the repository and OpenStreetMap as required by their license.

The project maintains active development with regular updates to dependencies and features. Documentation is available through GitHub Pages alongside the core library. A Streamlit-based web interface provides an accessible entry point for users who prefer graphical interaction over code. The tool supports Python 3.11 and later versions, with installation available through standard package management channels.