teadata 0.0.118

Pythonic data engine for Texas education datasets

TEA Data Engine (teadata)

teadata is a snapshot-first Python engine for Texas education data. It provides:

• District and Campus domain models
• a fluent query DSL using >>
• geospatial lookups (nearest charter, campuses in district boundaries, private-school overlap)
• config-driven enrichment from TAPR, accountability, transfers, PEIMS financials, and closure datasets
• sidecar sqlite stores for fast boundary/map/entity lookup

Installation

PyPI

pip install teadata

Development (recommended)

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

Quick Start

from teadata import DataEngine

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

# District lookup by district number, campus number, or name.
aldine = engine.get_district("101902")
print(aldine.name)

# Campuses physically inside district boundaries.
for campus in aldine.campuses[:5]:
    print(campus.name, campus.campus_number)

Public API Surface

Primary imports:

from teadata import DataEngine, District, Campus

Core behaviors:

• DataEngine.from_snapshot(...) supports .pkl and .pkl.gz snapshots and multiple payload shapes.
• Snapshot discovery checks explicit paths, env vars, package .cache, and parent .cache directories.
• District and Campus support dynamic metadata attributes through meta.
• Campus.to_dict() always includes percent_enrollment_change (numeric when available, otherwise "N/A").

Snapshot and Asset Behavior

teadata is intentionally cache-first.

Artifacts typically used at runtime:

• repo_*.pkl / repo_*.pkl.gz (engine snapshot)
• boundaries_*.sqlite (boundary WKB sidecar)
• map_payloads_*.sqlite (map payload sidecar)
• entities_*.sqlite (entity lookup sidecar)

If snapshot/store files are Git LFS pointers or missing locally, runtime asset resolvers can fetch real files when URL env vars are provided.

Environment Variables

• TEADATA_SNAPSHOT: explicit snapshot path.
• TEADATA_SNAPSHOT_URL: URL used when snapshot candidate is missing or a Git LFS pointer.
• TEADATA_BOUNDARY_STORE: explicit boundary sqlite path.
• TEADATA_BOUNDARY_STORE_URL: URL fallback for boundary store.
• TEADATA_MAP_STORE: explicit map sqlite path.
• TEADATA_MAP_STORE_URL: URL fallback for map store.
• TEADATA_ENTITY_STORE: explicit entity sqlite path.
• TEADATA_ENTITY_STORE_URL: URL fallback for entity store.
• TEADATA_ASSET_CACHE_DIR: override cache directory used for downloaded assets.
• TEADATA_DISABLE_INDEXES: disable default spatial acceleration indexes.
• TEADATA_LOG_MEMORY: enable memory snapshot logging.

Query DSL

DataEngine and Query chains use >>.

# Resolve district then expand to district-operated campuses.
q = engine >> ("district", "ALDINE ISD") >> ("campuses_in",)

# Filter, sort, and take.
top = (
    q
    >> ("filter", lambda c: (c.enrollment or 0) > 1000)
    >> ("sort", lambda c: c.enrollment or 0, True)
    >> ("take", 10)
)

rows = top.to_df(columns=["name", "campus_number", "enrollment"])

Supported lookup semantics include:

• case-insensitive district and campus name matching
• wildcard patterns (*, ?, SQL-like %/_)
• normalized district number handling (for example "123" and "'000123")

Spatial and transfer helpers include:

• nearest-campus/nearest-charter queries
• nearest_charter_same_type(...)
• transfer graph methods such as transfers_out(...) / transfers_in(...)

Enrichment Pipeline

teadata/enrichment provides registered enrichers for district and campus datasets.

Included enrichers cover:

• district accountability and district TAPR profile data
• campus accountability, TAPR profile/historical enrollment, PEIMS financials
• planned closure overlays
• charter network augmentation

Pipeline behavior is fault-tolerant by design: dataset-level failures are generally logged and do not hard-stop the full build.

Data Build Pipeline

teadata/load_data.py builds a full DataEngine and updates cached artifacts.

uv run python -m teadata.load_data

At a high level, it:

1. resolves year-aware source paths from teadata/teadata_sources.yaml
2. warm-loads compatible snapshot cache when signatures match
3. otherwise builds districts/campuses from spatial files
4. applies enrichment datasets
5. writes snapshot + sqlite sidecars back to .cache/

Config and CLI (teadata-config)

teadata/teadata_config.py provides YAML/TOML config loading, year resolution, schema checks, and dataset joins.

CLI entrypoint:

uv run teadata-config --help

Subcommands:

• init <out.yaml>
• resolve <cfg> <section> <dataset> <year>
• report <cfg> [--json] [--min N] [--max N]
• join <cfg> <year> [--datasets a,b,c] [--parquet out.parquet] [--duckdb out.duckdb --table t]

Testing

uv run pytest

Current tests cover:

• snapshot gzip and fallback loading
• query DSL semantics and chaining
• nearest charter behavior and transfer grouping
• store discovery and asset-cache behavior
• entity serialization invariants (percent_enrollment_change)

PyPI Size Limits and Current Packaging Status

PyPI defaults currently documented at:

• per-file upload limit: 100 MB
• total project limit: 10 GB

Reference: https://docs.pypi.org/project-management/storage-limits/

Current teadata release artifacts for 0.0.118 are above the per-file limit:

• wheel: dist/teadata-0.0.118-py3-none-any.whl about 448 MB
• sdist: dist/teadata-0.0.118.tar.gz about 446 MB

These exceed the default 100 MB file cap because large .cache snapshot/store artifacts are packaged into both distributions.

Release Policy

• Versioning uses thousandths-place tags (v0.0.101, v0.0.102, ...).
• Keep only the 3 most recent release tags/assets.

License

Business Source License 1.1. See LICENSE.