semantic-link-functions-geopandas
Semantic link functions for Geopandas. Enables conversion of a FabricDataFrame to a GeoDataFrame.
Decision gist · record as of 2026-08-14
Yes, if you work with Fabric data and need geospatial operations. The package is actively maintained, has low install friction, carries a permissive MIT license, and integrates cleanly with geopandas. However, it is in beta status and has modest adoption (16 repository stars), so expect potential API changes and verify compatibility with your specific versions of geopandas and mapclassify before production use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; depends on geopandas, folium, mapclassify, and semantic-link-sempy being installed and compatible.
- Active maintenance with a recent release (24 days old).
- Low install friction with four runtime dependencies.
License · maintenance · safety
MIT License (permissive) — MIT License permits free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.
last release 2026-07-21 (24 days) · last repo commit 2026-07-16 · 16 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 473,149 downloads/mo, #6,466 on PyPI
Alternatives
Verify before relying
pip install semantic-link-functions-geopandas
from sempy.fabric import FabricDataFrame
df = FabricDataFrame(
{"country": ["US", "AT"], "lat": [40.7128, 47.8095], "long": [-74.0060, 13.0550]},
column_metadata={"lat": {"data_category": "Latitude"}, "long": {"data_category": "Longitude"}}
)
df_geo = df.to_geopandas(lat_col="lat", long_col="long")- Whether semantic function discovery works reliably across different data category metadata schemas
- Performance characteristics when working with large geospatial datasets
- Compatibility with specific versions of geopandas, folium, and mapclassify beyond the minimum Python 3.10 requirement
What it is and what it does
This package extends FabricDataFrame with geospatial semantic functions that automatically appear in autocomplete when your data contains location columns and appropriate metadata. It bridges Microsoft's Fabric data model with the geopandas geospatial ecosystem, allowing you to convert a FabricDataFrame with latitude and longitude columns into a GeoDataFrame for spatial analysis.
The package uses the @semantic_function decorator pattern to register functions that inspect data types, column metadata (such as Power BI data categories), and actual data values to determine when they should be available. This means functions like is_holiday or geospatial operations show up contextually rather than cluttering every namespace. It's designed for developers working with Fabric data who need to perform geographic operations without manual function discovery or explicit imports.
Use it for
- Convert Fabric datasets with latitude/longitude columns to GeoDataFrames for spatial analysis and mapping
- Automatically surface location-aware functions in IDE autocomplete when working with geographic data
- Integrate Power BI data categories with geopandas workflows for seamless geospatial operations
- Build data pipelines that combine Fabric's semantic metadata with folium-based map visualizations
- Classify geographic data using mapclassify within a Fabric-native data structure
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with Fabric data and need geospatial operations.
The package is actively maintained, has low install friction, carries a permissive MIT license, and integrates cleanly with geopandas. However, it is in beta status and has modest adoption (16 repository stars), so expect potential API changes and verify compatibility with your specific versions of geopandas and mapclassify before production use.
Install
semantic-link-functions-geopandas on PyPI
Before you install
Active maintenance with a recent release (24 days old). Low install friction with four runtime dependencies. Repository shows modest activity (16 stars), and the package is in beta status.
Requires Python 3.10 or later; depends on geopandas, folium, mapclassify, and semantic-link-sempy being installed and compatible.
License in practice
MIT License permits free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.
Quickstart
pip install semantic-link-functions-geopandas
from sempy.fabric import FabricDataFrame
df = FabricDataFrame(
{"country": ["US", "AT"], "lat": [40.7128, 47.8095], "long": [-74.0060, 13.0550]},
column_metadata={"lat": {"data_category": "Latitude"}, "long": {"data_category": "Longitude"}}
)
df_geo = df.to_geopandas(lat_col="lat", long_col="long")Verify before relying
- Whether semantic function discovery works reliably across different data category metadata schemas
- Performance characteristics when working with large geospatial datasets
- Compatibility with specific versions of geopandas, folium, and mapclassify beyond the minimum Python 3.10 requirement
Package facts
| License | MIT License permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesgeopandasfoliummapclassifysemantic-link-sempy |
| Maintenance | Actively maintained 24 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 473,149 / month, #6,466 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_geopandas-0.14.2-py3-none-any.whl
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See also semantic-link-functions-meteostat · semantic-link-functions-holidays · semantic-link-functions-phonenumbers · semantic-link-functions-validators · semantic-link · semantic-link-sempy · semantic-link-labs · dask-geopandas · reverse-geocode · prefixmaps