semantic-link-functions-validators
Semantic link functions for validators package. Enables validation of email addresses, credit card numbers, ... in FabricDataFrames.
Decision gist · record as of 2026-08-14
Yes, if you work with FabricDataFrames and Power BI data. The package is actively maintained, has no security vulnerabilities, and low install friction. The permissive MIT license removes legal concerns. Install it when you need context-aware validation that adapts to your data structure rather than requiring explicit validator selection.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; designed for use with Power BI FabricDataFrames via semantic-link-sempy.
- Low friction installation with only two runtime dependencies.
- Actively maintained with a recent release within the last month and no known vulnerabilities.
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) · 473,070 downloads/mo, #6,470 on PyPI
Alternatives
Verify before relying
pip install semantic-link-functions-validators
from sempy.fabric import FabricDataFrame
df = FabricDataFrame({"contact_email": ["a@b.com", "d.com"]})
df["contact_email"].validators.is_email()- Which specific validators are included beyond email and credit card validation
- Whether validation results are integrated with Power BI's native validation UI
- Performance characteristics when applied to large FabricDataFrames
What it is and what it does
This package extends FabricDataFrames with semantic validation functions that intelligently surface only relevant validators based on your data's structure and metadata. Rather than manually selecting validators, the package examines column data types, Power BI data categories, and actual data content to determine which validators make sense—so an email validator appears in autocomplete only when you have both a string column and email-like data. The validators themselves handle common validation tasks like email addresses, credit card numbers, and international identifiers (IBAN, NIE).
It works by decorating validation functions with @semantic_function, which registers them for automatic discovery. The package integrates with semantic-link-sempy and validators, providing a bridge between Power BI's data context and Python validation logic. This is particularly useful in data preparation workflows where you want validation suggestions to adapt to the shape of your data rather than requiring explicit function calls.
Use it for
- Validate email addresses in customer contact columns with automatic discovery when both email and string types are present.
- Check credit card numbers and international payment identifiers (IBAN, NIE) in financial data columns.
- Build data quality checks in Power BI workflows where validation functions adapt to your schema.
- Prepare and clean datasets by having relevant validators surface contextually in autocomplete.
- Integrate custom domain-specific validators into FabricDataFrame workflows using the @semantic_function decorator.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with FabricDataFrames and Power BI data.
The package is actively maintained, has no security vulnerabilities, and low install friction. The permissive MIT license removes legal concerns. Install it when you need context-aware validation that adapts to your data structure rather than requiring explicit validator selection.
Install
semantic-link-functions-validators on PyPI
Before you install
Low friction installation with only two runtime dependencies. Actively maintained with a recent release within the last month and no known vulnerabilities.
Requires Python 3.10 or later; designed for use with Power BI FabricDataFrames via semantic-link-sempy.
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-validators
from sempy.fabric import FabricDataFrame
df = FabricDataFrame({"contact_email": ["a@b.com", "d.com"]})
df["contact_email"].validators.is_email()Verify before relying
- Which specific validators are included beyond email and credit card validation
- Whether validation results are integrated with Power BI's native validation UI
- Performance characteristics when applied to large FabricDataFrames
Package facts
| License | MIT License permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesvalidatorssemantic-link-sempy |
| Maintenance | Actively maintained 24 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 473,070 / month, #6,470 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_validators-0.14.2-py3-none-any.whl
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See also semantic-link-functions-phonenumbers · semantic-link-functions-holidays · semantic-link-labs · semantic-link · semantic-link-functions-geopandas · semantic-link-functions-meteostat · semantic-link-sempy · validators · airflow-powerbi-plugin · rigour