semantic-link-functions-meteostat
Semantic link functions for meteostat package. Enables enrichment of FabricDataFrame with historical weather data.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; depends on meteostat and semantic-link-sempy.
- Active maintenance with a recent release 24 days ago.
- Low install friction with only two runtime dependencies.
License · maintenance · safety
MIT License (permissive) — MIT License permits commercial and private use with minimal restrictions; suitable for most projects.
last release 2026-07-21 (24 days) · last repo commit 2026-07-16 · 16 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 473,144 downloads/mo, #6,467 on PyPI
Alternatives
Verify before relying
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")- 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.
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 on it.
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
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.
Requires Python 3.10 or later; depends on meteostat and semantic-link-sempy.
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")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
| License | MIT License permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesmeteostatsemantic-link-sempy |
| Maintenance | Actively maintained 24 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 473,144 / month, #6,467 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3.10 |
Evidence: semantic_link_functions_meteostat-0.14.2-py3-none-any.whl
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See also meteostat · semantic-link-functions-geopandas · semantic-link-functions-holidays · semantic-link-functions-phonenumbers · semantic-link-functions-validators · semantic-link · semantic-link-sempy · semantic-link-labs · openmeteo-requests · openmeteo-sdk