Welcome to Wetterdienst, your friendly weather service library for Python.

We are a group of like-minded people trying to make access to weather data in Python feel like a warm summer breeze, similar to other projects like rdwd for the R language, which originally drew our interest in this project. Our long-term goal is to provide access to multiple weather services as well as other related agencies such as river measurements. With wetterdienst we try to use modern Python technologies all over the place. The library is based on pandas across the board, uses Poetry for package administration and GitHub Actions for all things CI. Our users are an important part of the development as we are not currently using the data we are providing and only implement what we think would be the best. Therefore contributions and feedback whether it be data related or library related are very welcome! Just hand in a PR or Issue if you think we should include a new feature or data source.


For an overview of the data we have currently made available and under which license it is published take a look at the data section. Detailed information on datasets and parameters is given at the coverage subsection. Licenses and usage requirements may differ for each provider so check this out before including the data in your project to be sure that you fulfill copyright requirements!

Here is a short glimpse on the data that is included:

DWD (Deutscher Wetterdienst / German Weather Service / Germany)
  • Historical Weather Observations
    • Historical (last ~300 years), recent (500 days to yesterday), now (yesterday up to last hour)

    • Every minute to yearly resolution

    • Time series of stations in Germany

  • Mosmix - statistical optimized scalar forecasts extracted from weather models
    • Point forecast

    • 5400 stations worldwide

    • Both MOSMIX-L and MOSMIX-S is supported

    • Up to 115 parameters

  • Radar
    • 16 locations in Germany

    • All of Composite, Radolan, Radvor, Sites and Radolan_CDC

    • Radolan: calibrated radar precipitation

    • Radvor: radar precipitation forecast

ECCC (Environnement et Changement Climatique Canada / Environment and Climate Change Canada / Canada)
  • Historical Weather Observations
    • Historical (last ~180 years)

    • Hourly, daily, monthly, (annual) resolution

    • Time series of stations in Canada

NOAA (National Oceanic And Atmospheric Administration / National Oceanic And Atmospheric Administration / United States Of America)
  • Global Historical Climatology Network
    • Historical, daily weather observations from around the globe

    • more then 100k stations

    • data for weather services which don’t publish data themselves

WSV (Wasserstraßen- und Schifffahrtsverwaltung des Bundes / Federal Waterways and Shipping Administration)
  • Pegelonline
    • data of river network of Germany

    • coverage of last 30 days

    • parameters like stage, runoff and more related to rivers

EA (Environment Agency)
  • Hydrology
    • data of river network of UK

    • parameters flow and ground water stage

NWS (NOAA National Weather Service)
  • Observation
    • recent observations (last week) of US weather stations

    • currently the list of stations is not completely right as we use a diverging source!

  • Hubeau
    • data of river network of France (continental)

    • parameters flow and stage of rivers of last 30 days

Geosphere (Geosphere Austria, formerly Central Institution for Meteorology and Geodynamics)
  • Observation
    • historical meteorological data of Austrian stations

To get better insight on which data we have currently made available and under which license those are published take a look at the data section.


  • API(s) for stations (metadata) and values

  • Get station(s) nearby a selected location

  • Define your request by arguments such as parameter, period, resolution, start date, end date

  • Command line interface

  • Web-API via FastAPI

  • Run SQL queries on the results

  • Export results to databases and other data sinks

  • Public Docker image

  • Interpolation and Summary of station values



Via PyPi (standard):

pip install wetterdienst

Via Github (most recent):

pip install git+

There are some extras available for wetterdienst. Use them like:

pip install wetterdienst[http,sql]
  • docs: Install the Sphinx documentation generator.

  • ipython: Install iPython stack.

  • export: Install openpyxl for Excel export and pyarrow for writing files in Feather- and Parquet-format.

  • http: Install HTTP API prerequisites.

  • sql: Install DuckDB for querying data using SQL.

  • duckdb: Install support for DuckDB.

  • influxdb: Install support for InfluxDB.

  • cratedb: Install support for CrateDB.

  • mysql: Install support for MySQL.

  • postgresql: Install support for PostgreSQL.

  • interpolation: Install support for station interpolation.

In order to check the installation, invoke:

wetterdienst --help


Docker images for each stable release will get pushed to GitHub Container Registry.

There are images in two variants, wetterdienst-standard and wetterdienst-full.

wetterdienst-standard will contain a minimum set of 3rd-party packages, while wetterdienst-full will try to serve a full environment, including all of the optional dependencies of Wetterdienst.

Pull the Docker image:

docker pull


Use the latest stable version of wetterdienst:

$ docker run -ti
Python 3.8.5 (default, Sep 10 2020, 16:58:22)
[GCC 8.3.0] on linux
import wetterdienst

Command line script#

The wetterdienst command is also available:

# Make an alias to use it conveniently from your shell.
alias wetterdienst='docker run -ti wetterdienst'

wetterdienst --help
wetterdienst --version
wetterdienst info


Task: Get historical climate summary for two German stations between 1990 and 2020


>>> import pandas as pd
>>> pd.options.display.max_columns = 8
>>> from wetterdienst import Settings
>>> from wetterdienst.provider.dwd.observation import DwdObservationRequest
>>> settings = Settings( # default
...     tidy=True,  # tidy data
...     humanize=True,  # humanized parameters
...     si_units=True  # convert values to SI units
... )
>>> request = DwdObservationRequest(
...    parameter=["climate_summary"],
...    resolution="daily",
...    start_date="1990-01-01",  # if not given timezone defaulted to UTC
...    end_date="2020-01-01",  # if not given timezone defaulted to UTC
...    settings=settings
... ).filter_by_station_id(station_id=(1048, 4411))
>>> request.df.head()  # station list
    station_id                 from_date                   to_date  height  \
...      01048 1934-01-01 00:00:00+00:00 ... 00:00:00+00:00   228.0
...      04411 1979-12-01 00:00:00+00:00 ... 00:00:00+00:00   155.0

     latitude  longitude                    name    state
...   51.1278    13.7543       Dresden-Klotzsche  Sachsen
...   49.9195     8.9671  Schaafheim-Schlierbach   Hessen

>>> request.values.all().df.head()  # values
  station_id          dataset      parameter                      date  value  \
0      01048  climate_summary  wind_gust_max 1990-01-01 00:00:00+00:00    NaN
1      01048  climate_summary  wind_gust_max 1990-01-02 00:00:00+00:00    NaN
2      01048  climate_summary  wind_gust_max 1990-01-03 00:00:00+00:00    5.0
3      01048  climate_summary  wind_gust_max 1990-01-04 00:00:00+00:00    9.0
4      01048  climate_summary  wind_gust_max 1990-01-05 00:00:00+00:00    7.0

0      NaN
1      NaN
2     10.0
3     10.0
4     10.0


# Get list of all stations for daily climate summary data in JSON format
wetterdienst stations --provider=dwd --network=observations --parameter=kl --resolution=daily

# Get daily climate summary data for specific stations
wetterdienst values --provider=dwd --network=observations --station=1048,4411 --parameter=kl --resolution=daily

Further examples (code samples) can be found in the examples folder.