prettymaps: Transform OpenStreetMap Data into Beautiful Custom Maps

One-Line Map Magic

Creating stunning maps has never been easier. With prettymaps, you can generate publication-ready maps with a single line of code:

import prettymaps
prettymaps.plot('Stad van de Zon, Heerhugowaard, Netherlands')

That's it! The library automatically fetches OpenStreetMap data and renders beautiful maps featuring streets, buildings, water bodies, forests, and more.

Key Features

• 10+ Built-in Presets: From minimalist to detailed artistic styles
• Full Customization: Control layers, colors, widths, and styling
• Multiple Input Types: Addresses, coordinates, or custom GeoDataFrames
• Plotter Mode: Export SVG files for pen plotters
• Multiplot Support: Combine multiple regions on one canvas
• Hillshade & Keypoints: Advanced terrain and landmark visualization

Quick Installation

pip install prettymaps

Or use Google Colab:

!pip install -e "git+https://github.com/marceloprates/prettymaps#egg=prettymaps"

Popular Presets

# List all available presets
prettymaps.presets()

# Use a specific preset
prettymaps.plot('Porto Alegre', preset='minimal')

Available presets: default, minimal, macao, tijuca, barcelona, heerhugowaard, and more.

Advanced Customization

prettymaps.plot(
    'Praça Ferreira do Amaral, Macau',
    circle=True,
    radius=1100,
    layers={
        'building': {'tags': {'building': True}},
        'water': {'tags': {'natural': ['water', 'bay']}},
        'green': {'tags': {'landuse': 'grass', 'leisure': 'park'}},
    },
    style={
        'background': {'fc': '#F2F4CB'},
        'building': {'palette': ['#FFC857', '#E9724C', '#C5283D']}
    }
)

Real-World Applications

1. Urban Planning: Visualize neighborhood layouts and infrastructure
2. Generative Art: Create unique map-based artwork
3. Data Analysis: Overlay custom data on geospatial context
4. Presentations: Professional maps for reports and publications
5. Plotter Art: Export for CNC pen plotters

Access Raw GeoDataFrames

plot = prettymaps.plot('Porto Alegre', show=False)
buildings = plot.geodataframes['building']
# Analyze 2,400+ building footprints with full OSM attributes

Why prettymaps Stands Out

• 12.2K GitHub Stars - Proven community adoption
• Hacker News Featured - Industry recognition
• AGPL-3.0 License - Commercial use allowed with source disclosure
• Streamlit Frontend - Interactive web interface
• Active Development - Regular updates and new features

Get Started Today

Install prettymaps and start creating beautiful maps in minutes. Whether you're a data scientist, cartographer, or generative artist, prettymaps delivers professional results with minimal code.

Live Demo: Google Colab

Note: Always credit OpenStreetMap and the prettymaps repository per license requirements.