Meteostat
Open-source Python library for historical weather and climate station data — thousands of stations, MIT licensed.
About
Meteostat is an open weather-data project that collects historical observations from public interfaces — mostly national weather services like NOAA and Germany's DWD — and serves them through free bulk downloads, a web app, a terminal CLI and a Python library. The Python package, which is the project's best-known artifact, lets you pull hourly, daily and monthly series plus climate normals for thousands of stations worldwide and returns them as pandas DataFrames, with built-in station search, point interpolation and matplotlib-ready output.
It is genuinely open: MIT license, free bulk data, documented sources, and an active maintainer (repository pushing updates into 2026, roughly 660 GitHub stars). The catch is scope — this is historical and climate data, not a forecast API, and coverage follows the quality of the underlying public stations. A hosted JSON API exists for products and workflows that need one, but the bulk download plus the library is the core.
What it can do
- Hourly, daily, monthly and normals station data
- pandas DataFrame output with interpolation
- Station search by point or bounding box
- Free bulk downloads
- Terminal CLI and hosted JSON API
- MIT-licensed open source
Setup
pip install meteostat, then query stations and time series directly from Python; bulk CSV downloads need no account.
Prerequisites
Python 3; pandas and matplotlib for the documented workflows.
Use cases
Works with
Trust score — 7/10
Scored 7/10 (TIER verified, metrics-v1). Evidence: MIT license, ~660 GitHub stars and active development verified directly from the repository; data provenance (NOAA, DWD public interfaces) stated in the project docs. Strengths: genuinely free and open historical data, pandas-native workflow, honest about being observations-only. Docked for: (1) niche adoption vs commercial APIs; (2) no forecasts or realtime data; (3) coverage quality depends on public station networks. License: MIT; bulk data and library free.
★ 664 GitHub stars
How the trust score is built
Full methodology →- Adoption · stars/downloads, log-scaled vs peers
- 6.0/10
- Maintenance · recency, releases, issue load
- 7.0/10
- Openness · license + docs
- 9.0/10
- Manual review · sanity read + red-flag check
- 7.0/10
- · Open weather-data project; sources NOAA, DWD public interfaces
- · Python package ~664 GitHub stars; pandas DataFrame output
- · Hourly/daily/monthly + climate normals for thousands of stations
- · Observations only — no forecasts or realtime data
- · Hosted JSON API paid; bulk downloads free
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.