https://github.com/adpena/teadata
Unofficial Python-based toolkit for working with Texas Education Agency (TEA) public education datasets. It provides object-oriented access to rich, spatially-aware data about school districts/charters and campuses, making it easy for developers to explore, enrich, analyze, and visualize educational data.
https://github.com/adpena/teadata
public-education python python-3 texas texas-education-agency
Last synced: 4 months ago
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Unofficial Python-based toolkit for working with Texas Education Agency (TEA) public education datasets. It provides object-oriented access to rich, spatially-aware data about school districts/charters and campuses, making it easy for developers to explore, enrich, analyze, and visualize educational data.
- Host: GitHub
- URL: https://github.com/adpena/teadata
- Owner: adpena
- License: other
- Created: 2025-09-30T17:47:56.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2026-02-18T18:41:09.000Z (4 months ago)
- Last Synced: 2026-02-18T22:37:50.813Z (4 months ago)
- Topics: public-education, python, python-3, texas, texas-education-agency
- Language: Python
- Homepage: https://adpena.github.io/teadata/
- Size: 518 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Agents: AGENTS.md
Awesome Lists containing this project
README
# 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.
---
## 📚 Documentation
**[Read the full documentation here](docs/index.md)** (or run `mkdocs serve` to browse locally).
---
## Quick Start
### Installation
**Using `uv` (Recommended for development)**
```bash
git clone https://github.com/adpena/teadata.git
cd teadata
uv sync
```
**Using `pip`**
```bash
pip install teadata
```
When adding a new library or tool, update `pyproject.toml` (dependencies or
extras), refresh the lockfile, and adjust test/tooling configuration so CI and
local environments stay in sync.
**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`:
```text
teadata @ git+https://github.com/adpena/teadata.git
```
Or install it directly via CLI:
```bash
pip install git+https://github.com/adpena/teadata.git
```
### Snapshot assets (Git LFS)
Production deployments that install from GitHub often receive Git LFS pointer files instead of
the large snapshot assets. Set these URLs so the engine can download the real files on demand:
- `TEADATA_SNAPSHOT_URL` (e.g., a GitHub release asset for `repo_*.pkl.gz`)
- `TEADATA_MAP_STORE_URL` (for `map_payloads_*.sqlite`)
- `TEADATA_BOUNDARY_STORE_URL` (for `boundaries_*.sqlite`)
- Optional: `TEADATA_ASSET_CACHE_DIR` to control where downloads are cached
### Usage
```python
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)
```
## Release Policy
- Tags always use the thousandths place (e.g., `v0.0.101`, `v0.0.102`). If no tags exist, start at `v0.0.101`.
- Keep only the three most recent tags/releases; delete older tags and their GitHub release assets.
## 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](LICENSE) file for the full text.