PyWeatherEnriched

What it does

PyWeatherEnriched enriches operational datasets with historical and real-time hyperlocal weather context. Improve forecasting, analytics, logistics, retail, agriculture, healthcare, and IoT applications using weather-aware data enrichment. Try - pip install PyWeatherEnriched

Weather data enrichment for ML pipelines. Real-time forecasting, climate-aware feature engineering, global weather integration. - Mullassery/PyWeatherEnriched

Real geocoding + real historical weather, wired into a Rust core with pandas/numpy climate feature engineering on top. Given a location name and a timestamp, PyWeatherEnriched geocodes the location (OpenStreetMap Nominatim) and looks up the real, genuinely observed historical weather for that place and hour (Open-Meteo's free Archive API) — no formula-generated or fabricated numbers. The lookup/geocoding core is Rust (via PyO3) for speed and a small memory footprint; a Python layer on top adds pandas/numpy feature engineering (rolling aggregates, heating/cooling degree-days, cyclical time encoding, anomaly z-scores) useful for feeding weather into an ML pipeline. pip install…from github.com

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