TEA Data Engine

A high-performance, spatially-aware Python framework for the analysis and modeling of Texas public education data.

teadata provides a robust, object-oriented interface for large-scale geographic and demographic datasets. By utilizing a high-speed, snapshot-based architecture, the engine enables rapid data integration and sophisticated spatial querying through an intuitive, Pythonic DSL.

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📚 Documentation

Read the full documentation here (or run mkdocs serve to browse locally).

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Quick Start

Installation

Using uv (Recommended for development)

git clone https://github.com/adpena/teadata.git
cd teadata
uv sync

Using pip

pip install teadata

Installing from GitHub (for Render or other CI/CD)

If you are using this in a Django application deployed on Render, add it to your requirements.txt:

teadata @ git+https://github.com/adpena/teadata.git

Or install it directly via CLI:

pip install git+https://github.com/adpena/teadata.git

Usage

from teadata import DataEngine

# Fast-path: load the latest discovered snapshot
engine = DataEngine.from_snapshot(search=True)

# Retrieve district by TEA campus number
aldine = engine.get_district("101902")
print(aldine.name)  # -> "Aldine ISD"

# Iterate campuses inside the district
for c in aldine.campuses:
    print(c.name)

Features

• Rich domain objects: District and Campus with geometry.
• Fluent query language: Chain filters like engine >> ("district", "101902") >> ("campuses_in",).
• Spatial acceleration: Nearest neighbors, containment checks.
• Enrichment: Attach external datasets (finance, accountability) easily.

License

This project is licensed under the Business Source License 1.1.

• Personal & Educational Use: You may use, copy, modify, and distribute this software for personal, educational, or non-commercial research purposes.
• Commercial & Production Use: Prohibited without obtaining a separate commercial license from the Licensor.

See the LICENSE file for the full text.