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.
```python
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")
```
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"description": "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.\n\nEach 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.\n\nSemantic 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.\n\n```python\nfrom sempy.fabric import FabricDataFrame\n\ndf = FabricDataFrame(\n {\"country\": [\"US\", \"AT\"],\n \"lat\": [40.7128, 47.8095],\n \"long\": [-74.0060, 13.0550]},\n column_metadata={\"lat\": {\"data_category\": \"Latitude\"}, \"long\": {\"data_category\": \"Longitude\"}},\n)\n\ndf_geo = df.to_geopandas(lat_col=\"lat\", long_col=\"long\")\n```\n\n\n",
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