geopandas-ai 0.0.35

GeoPandas AI is an extension of GeoPandas allowing you to interact with your spatial data in natural language.

GeoPandas-AI

GeoPandas-AI is an open-source Python library that enhances geospatial data analysis by turning the GeoDataFrame into a conversational, intelligent assistant. It seamlessly integrates large language models (LLMs) into the geospatial workflow, enabling natural language interaction, iterative refinement, caching, and code generation directly within your Python environment.

Documentation

🌍 Motivation

Geospatial data is key to solving complex problems in urban planning, environmental science, and infrastructure development. But tools like GeoPandas require familiarity with both GIS concepts and Python-based workflows.

GeoPandas-AI lowers this barrier by:

• Embedding conversational AI directly into GeoDataFrame
• Enabling plain-language queries and refinements
• Supporting reproducible, scriptable workflows with AI-assisted code
• Caching results to avoid redundant LLM calls

This bridges human interaction with geospatial analysis in a seamless and stateful way.

---

🧠 What’s New?

Based on the arXiv preprint, GeoPandas-AI introduces:

• ✅ A stateful, LLM-augmented GeoDataFrameAI class
• ✅ .chat() and .improve() methods for language-based querying and iteration
• ✅ Built-in caching: repeated prompts reuse cached results (no extra LLM calls)
• ✅ Full compatibility with existing GeoDataFrame workflows
• ✅ Modular backends for execution, injection, caching, and LLM calls
• ✅ A vision of conversational programming for geospatial developers

Read the paper: GeoPandas-AI: A Smart Class Bringing LLM as Stateful AI Code Assistant

---

⚙️ Installation

pip install geopandas-ai

Python 3.8+ required.

---

🚀 Quick Start

Example 1: Read and visualize spatial data interactively

import geopandasai as gpdai

gdfai = gpdai.read_file("cities.geojson")
gdfai.chat("Plot the cities by population")
gdfai.improve("Add a title and a basemap")

---

Example 2: Wrap an existing GeoDataFrame

import geopandas as gpd
from geopandasai import GeoDataFrameAI

gdf = gpd.read_file("parks.geojson")
gdfai = GeoDataFrameAI(
    gdf,
    description="City parks with name, area, and geometry"
)

gdfai.chat("Show the largest 5 parks")

---

Example 3: Work with multiple dataframes

a = gpdai.read_file("zones.geojson")
b = gpdai.read_file("reference.geojson")

a.set_description("Zoning polygons for city planning")
b.set_description("Reference dataset with official labels")

a.chat(
    "Cluster the zones into 3 groups based on geometry size",
    b,
    provided_libraries=["scikit-learn", "numpy"],
    return_type=int
)

---

🔧 Configuration & Caching

GeoPandas-AI uses a flexible dependency-injection architecture (via dependency_injector) to manage:

• LiteLLM settings
• Cache backend (memoizes .chat() and .improve() calls)
• Code executor (trusted or sandboxed)
• Code injector
• Data descriptor
• Allowed return types

Built-in caching

By default, responses and generated code are cached on disk:

from geopandasai.external.cache.backend.file_system import FileSystemCacheBackend

# Default writes to `.gpd_cache/`

Any repeated prompt or improvement will reuse cached results, saving tokens and accelerating workflows.

Customizing configuration

Override defaults with update_geopandasai_config():

from geopandasai import update_geopandasai_config
from geopandasai.external.cache.backend.file_system import FileSystemCacheBackend
from geopandasai.services.inject.injectors.print_inject import PrintCodeInjector
from geopandasai.services.code.executor import TrustedCodeExecutor

update_geopandasai_config(
    cache_backend=FileSystemCacheBackend(cache_dir=".gpd_cache"),
    executor=TrustedCodeExecutor(),
    injector=PrintCodeInjector(),
    libraries=[
      "pandas",
      "matplotlib.pyplot",
      "folium",
      "geopandas",
      "contextily",
    ],
)

Forcing fresh LLM calls

To clear all memory and cache for a fresh start:

gdfai.reset()

---

📚 Learn More

• 📦 PyPI Package
• 📖 arXiv Preprint
• 📘 Example Notebooks
• 🧠 LiteLLM Docs
• 🛠 GitHub Repository
• Documentation

---

📄 Citation

If you use GeoPandas-AI in academic work, please cite:

@misc{merten2025geopandasaismartclassbringing,
  title={GeoPandas-AI: A Smart Class Bringing LLM as Stateful AI Code Assistant}, 
  author={Gaspard Merten and Gilles Dejaegere and Mahmoud Sakr},
  year={2025},
  eprint={2506.11781},
  archivePrefix={arXiv},
  primaryClass={cs.HC},
  url={https://arxiv.org/abs/2506.11781}, 
}

🖎 Authors

This project is a product of the Université Libre de Bruxelles (ULB), and most notably the Data Science and Engineering Lab. It is maintained by:

• Gaspard Merten
• Mahmoud Sakr
• Gilles Dejaegere

---

🪪 License

MIT License – see LICENSE for details.

GeoPandas-AI: Making geospatial analysis conversational, intelligent, and reproducible.