Problem

Accessing weather data means wrestling with inconsistent APIs, formats, and units—slowing down data engineering and making pipelines hard to reproduce.

Solution

Wetterdienst is a Python library providing a unified, Polars-first interface to multiple open weather services (DWD, ECCC, EA, NOAA/NWS, Geosphere Austria, IMGW, Eaufrance, WSV, and more). It standardizes request patterns, returns tidy long-format data in SI units, and handles caching, timezones, and retries—so teams can focus on analysis instead of plumbing.

Core concepts:

• Polars-first — All data operations use Polars (v1.15+); pandas supported for some I/O
• Declarative request pattern — Provider → stations → values; tidy/long output by default
• Sensible defaults — UTC timestamps, SI units, humanized parameter names
• Reliability — Disk-based caching via diskcache, stamina-based retries, timezone handling
• Provider architecture — Consistent interfaces across DWD, ECCC, EA, NOAA/NWS, Geosphere, IMGW, Eaufrance, WSV, and more
• Multiple interfaces — Python API, CLI, and REST

Outline

• Introduction
• Journey — How Wetterdienst came to life
• Wetterdienst — Architecture, concepts, and request patterns
• Value — What wetterdienst offers you, me and everyone else
• Demo — Live: station discovery, timeseries retrieval, station metadata, climate stripes and more via app

Target Audience

Data engineers, scientists, and platform teams who need reliable weather data for analytics, ML, and operations.

Prerequisites

Basic Python and DataFrame experience (Polars or pandas); familiarity with ETL/ML pipelines helpful.

Key Takeaways

• A unified, Polars-first workflow to access and normalize open weather data
• Practical patterns for station discovery, timeseries retrieval, unit conversion, and caching
• How to integrate Wetterdienst via Python, CLI, and REST, and export to common formats and databases

Links

📦 Repo https://github.com/earthobservations/wetterdienst 📖 Docs https://wetterdienst.readthedocs.io/ 🌐 App https://wetterdienst.eobs.org/ 💡 Examples https://github.com/earthobservations/wetterdienst/tree/main/examples