{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"}],"enrichment":{"capability":"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.","skillfed_tags":["weather-data","climate-analysis","geospatial"],"use_cases":["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."],"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.\n\nThe 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).","worth_installing":"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\u2014test coverage for your location of interest first."},"id":"meteostat","links":{"html":"https://skillfed.io/packages/meteostat","md":"https://skillfed.io/packages/meteostat.md","pypi":"https://pypi.org/project/meteostat/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-21","license_spdx":null,"license_treatment":"unclear","name":"meteostat","python_support":"supports_current","summary":"Access and analyze historical weather and climate data with Python."},"popularity":{"monthly_downloads":699610,"position":5295,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.1.4"}