{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"}],"enrichment":{"capability":"Adds semantic functions to FabricDataFrame that automatically enrich data with historical weather information from meteostat based on detected latitude, longitude, and date columns.","skillfed_tags":["data-enrichment","semantic-metadata","weather-data"],"use_cases":["Enrich sales or event data with historical weather conditions for correlation analysis.","Add weather context to geographic datasets for climate or environmental studies.","Automatically populate weather columns in Power BI datasets during data preparation.","Build feature engineering pipelines that detect relevant columns and apply domain-specific enrichments.","Combine location and timestamp data with meteorological history for anomaly detection."],"what_it_does":"This package extends FabricDataFrame with semantic functions that automatically discover and apply enrichments based on column metadata. The add_weather_meteostat function examines your DataFrame's columns, detects latitude, longitude, and date columns via their data category metadata, and enriches the data with historical weather information from meteostat without requiring explicit configuration.\n\nThe package bridges Power BI's semantic metadata system with weather data APIs. It uses meteostat as its underlying data source and semantic-link-sempy as its framework for column detection and function registration. The design assumes you're working in a Fabric or Power BI context where column metadata (data categories) are already annotated; the semantic function activates only when those conditions are met.","worth_installing":"Yes, if you are working with FabricDataFrame in a Power BI or Fabric context and need to enrich location-time data with historical weather. The package is actively maintained, has low install friction, and uses a permissive MIT License. Install only if you have Python 3.10 or later and meteostat data availability meets your geographic and temporal needs."},"id":"semantic-link-functions-meteostat","links":{"html":"https://skillfed.io/packages/semantic-link-functions-meteostat","md":"https://skillfed.io/packages/semantic-link-functions-meteostat.md","pypi":"https://pypi.org/project/semantic-link-functions-meteostat/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-21","license_spdx":null,"license_treatment":"permissive","name":"semantic-link-functions-meteostat","python_support":"supports_current","summary":"Semantic link functions for meteostat package. Enables enrichment of FabricDataFrame with historical weather data."},"popularity":{"monthly_downloads":473144,"position":6467,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.14.2"}