meteostat

What it is and what it does

Meteostat is a Python library for fetching and analyzing historical weather and climate data from a global network of weather stations. It abstracts away the complexity of locating nearby stations, retrieving measurements, and handling missing data through interpolation, letting you work directly with pandas DataFrames of temperature, precipitation, and other meteorological variables.

The package is designed for researchers, data analysts, and developers who need historical weather records for specific locations and time ranges. It handles station discovery by geographic proximity, data aggregation from multiple sources, and spatial interpolation to fill gaps. The main runtime dependencies are pandas (for data manipulation), requests (for HTTP access to data providers), and pytz (for timezone handling).

Use it for:

• Retrieve daily temperature data for a city over a year to analyze seasonal trends or validate climate models.
• Build a dataset of precipitation records for multiple locations to study rainfall patterns across regions.
• Interpolate weather measurements from nearby stations to estimate conditions at a specific coordinate with no direct observations.
• Combine historical weather data with other datasets (e.g., crop yields, energy consumption) for correlation analysis.
• Validate or backfill gaps in your own weather station data using Meteostat's global network.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Meteostat provides access to historical weather and climate data from weather stations worldwide, allowing you to retrieve and analyze temperature, precipitation, and other meteorological measurements for specific locations and time periods.

Yes, if you need historical weather data and can work with Python 3.11+. Low install friction, active maintenance, and no known vulnerabilities make it a practical choice. Verify the actual license terms in the repository before use, since the package metadata does not formally declare them. Data accuracy and availability vary by region—test coverage for your location of interest first.

Install

meteostat on PyPI

pip install meteostat

uv add meteostat

poetry add meteostat

Installing meteostat

Before you install

Low install friction with a pure-Python wheel and only three common runtime dependencies (pandas, pytz, requests). Actively maintained with a recent release.

License in practice

License treatment is unclear in the package metadata. The description notes MIT licensing for the package itself and CC BY 4.0 for the underlying data, but this is not formally declared in the package metadata—verify the actual license terms before use.

Quickstart

pip install meteostat

from datetime import date
import meteostat as ms

point = ms.Point(50.1155, 8.6842, 113)
stations = ms.stations.nearby(point, limit=4)
ts = ms.daily(stations, date(2018, 1, 1), date(2018, 12, 31))
df = ms.interpolate(ts, point).fetch()

Requires Python 3.11 or later; data retrieval depends on network access to Meteostat's data providers.

Verify before relying

• Actual data availability and coverage for specific regions and historical periods
• Rate limits or quotas on API requests to Meteostat data providers
• Accuracy and completeness of interpolation for sparse station networks

Package facts

Evidence: meteostat-2.1.4-py3-none-any.whl

Tags

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