FabricDataFrames dynamically expose semantic functions based on logic defined by each function. For example, the is_holiday function shows up in the autocomplete suggestions when you're working on a FabricDataFrame containing both a datetime column and a country column.
Each semantic function uses information about the data types, metadata (such as Power BI data categories), and the data in a FabricDataFrame or FabricSeries to determine its relevance to the particular data on which you're working.
Semantic functions are automatically discovered when annotated with the @semantic_function decorator. You can think of semantic functions as being similar to C# extension methods applied to the popular DataFrame concept.
from sempy.fabric import FabricDataFrame
df = FabricDataFrame(
{"contact_email": ["a@b.com", "d.com"],
"amex": ["378282246310005", "4242424242424242"],
"iban": ["DE29100500001061045672", "123456"],
"es_nie": ["X0095892M", "X0095892X"]}
)
df["contact_email"].validators.is_email()
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Release files for semantic-link-functions-validators 0.14.2
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| semantic_link_functions_validators-0.14.2-py3-none-any.whl | Python 3 | none | any | Details |
Release files / semantic_link_functions_validators-0.14.2-py3-none-any.whl
| Download URL | semantic_link_functions_validators-0.14.2-py3-none-any.whl |
|---|---|
| Size | 4.9 kB |
| Tags | Python 3 |
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