semantic-link-functions-meteostat

What it is and 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.

The 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.

Use it for:

• 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.

Worth the install?

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

Adds semantic functions to FabricDataFrame that automatically enrich data with historical weather information from meteostat based on detected latitude, longitude, and date columns.

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.

Install

semantic-link-functions-meteostat on PyPI

pip install semantic-link-functions-meteostat

uv add semantic-link-functions-meteostat

poetry add semantic-link-functions-meteostat

Installing semantic-link-functions-meteostat

Before you install

Active maintenance with a recent release 24 days ago. Low install friction with only two runtime dependencies. Requires Python 3.10 or later.

License in practice

MIT License permits commercial and private use with minimal restrictions; suitable for most projects.

Quickstart

pip install semantic-link-functions-meteostat

from sempy.fabric import FabricDataFrame

df = FabricDataFrame(
    {"lat": [40.7128], "long": [-74.0060], "date": ["2023-01-06"]},
    column_metadata={"lat": {"data_category": "Latitude"}, "long": {"data_category": "Longitude"}}
)
df_weather = df.add_weather_meteostat("lat", "long", "date")

Requires Python 3.10 or later; depends on meteostat and semantic-link-sempy.

Verify before relying

• What weather variables (temperature, precipitation, etc.) are returned by add_weather_meteostat.
• Historical data coverage and geographic availability for your use case.
• Performance characteristics when enriching large DataFrames with many rows.
• Whether datetime conversion is required before calling add_weather_meteostat.

Package facts

Evidence: semantic_link_functions_meteostat-0.14.2-py3-none-any.whl

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