https://github.com/xoolive/cartes
Create great maps in Python 🐍 with openstreetmap 🌍
https://github.com/xoolive/cartes
altair cartopy geopandas geospatial gis maps openstreetmap
Last synced: 2 months ago
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Create great maps in Python 🐍 with openstreetmap 🌍
- Host: GitHub
- URL: https://github.com/xoolive/cartes
- Owner: xoolive
- License: mit
- Created: 2021-01-04T21:20:18.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2025-03-06T09:46:14.000Z (3 months ago)
- Last Synced: 2025-03-30T15:08:47.658Z (3 months ago)
- Topics: altair, cartopy, geopandas, geospatial, gis, maps, openstreetmap
- Language: Python
- Homepage: https://cartes-viz.github.io/
- Size: 10.1 MB
- Stars: 45
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- License: license.txt
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README
# Cartes





\



Cartes is a Python library providing facilities to produce meaningful maps.
Cartes builds on top of most common Python visualisation libraries (Matplotlib/Cartopy, Altair, ipyleaflet) and data manipulation libraries (Pandas, Geopandas) and provides mostly:
- a **comprehensive set of geographic projections**, built on top of Cartopy and Altair/d3.js;
- an **interface to OpenstreetMap Nominatim and Overpass API**. Result of requests are parsed in a convenient format for preprocessing and storing in standard formats;
- beautiful **default parameters** for quality visualisations;
- **advanced caching facilities**. Do not download twice the same content in the same day.The cartes library is a powerful asset to **publish clean, lightweight geographical datasets**; and to **produce decent geographical visualisations** in few lines of code.
## Gallery
More in the [documentation](https://cartes-viz.github.io/gallery.html)
## Installation
Latest release:
Recommended for beginners, with conda:
```sh
conda install -c conda-forge cartes
```or with pip:
```sh
pip install cartes
```Development version, with uv:
```sh
git clone https://github.com/xoolive/cartes
cd cartes
uv sync --dev
```## Documentation

Documentation available at https://cartes-viz.github.io/