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(
{"country": ["US", "AT"], "date": ["2023-01-06", "2023-01-06"]},
column_metadata={"country": {"data_category": "Country"}},
)
df["date"] = pd.to_datetime(df["date"])
holiday_series = df.is_holiday(date_col="date", country_col="country")
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Release files for semantic-link-functions-holidays 0.14.2
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Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| semantic_link_functions_holidays-0.14.2-py3-none-any.whl | Python 3 | none | any | Details |
Release files / semantic_link_functions_holidays-0.14.2-py3-none-any.whl
| Download URL | semantic_link_functions_holidays-0.14.2-py3-none-any.whl |
|---|---|
| Size | 4.3 kB |
| Tags | Python 3 |
|
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