Comparison with Alternatives¶
| Feature | timeseries-qc | Pecos | SaQC | Great Expectations |
|---|---|---|---|---|
| Classification | Good / Sus / Bad | Pass / Fail | Flags | Pass / Fail |
| Timeline chart | Yes | No | No | No |
| YAML config | Yes | No | JSON | No |
| Time-series native | Yes | Yes | Yes | No |
| Custom rules | Yes | No | Yes | Yes |
| HTML report | Yes | No | No | Yes |
| Offline report | Yes | Yes | No | No |
| License | MIT | BSD-3 | LGPL | Apache-2.0 |
| Maintenance | Active | Maintenance (since 2021) | Active | Active |
Pecos (Sandia Labs)¶
Pecos offers binary pass/fail classification and has been in maintenance mode since 2021. It lacks timeline visualization and YAML-driven configuration.
SaQC (Helmholtz UFZ)¶
SaQC is a rich flagging engine designed for environmental science. It has an environmental-domain-specific API, no timeline visualization, and uses an LGPL license which may be restrictive for some commercial applications.
Great Expectations¶
Great Expectations is a general-purpose data validation framework that is not timeseries-native. It produces no visualization and requires writing expectations in Python.
Why timeseries-qc?¶
timeseries-qc is the only library that combines:
- Three-level classification (good/suspect/bad)
- Multi-tag horizontal status timeline
- YAML-driven configuration for non-Python users
- Self-contained HTML reports with no external dependencies
All in a single pip install.
Next Steps¶
- FAQ — frequently asked questions
- User Guide — walkthrough with examples