Release 1.3.5
Installation
pip install --no-index --find-links="https://github.com/gingermike/pytemporal/releases/download/v1.3.5" pytemporal==1.3.5Performance Documentation
📊 View Interactive Benchmark Reports
📦 Download Complete Benchmark Data: benchmarks-v1.3.5.zip (includes flamegraphs)
Features
- High-performance bitemporal timeseries processing
- Microsecond precision timestamps for audit trails
- Conflation optimization for reduced storage
- Adaptive parallelisation for large datasets
Benchmark Results (v1.3.5)
- Small Dataset (5 records): ~30-35 µs
- Medium Dataset (100 records): ~165-170 µs
- Large Dataset (500k records): ~900-950 ms
- Conflation Effectiveness: ~28 µs
Full Changelog: v1.3.4...v1.3.5