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(
{"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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"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 {\"contact_email\": [\"a@b.com\", \"d.com\"],\n \"amex\": [\"378282246310005\", \"4242424242424242\"],\n \"iban\": [\"DE29100500001061045672\", \"123456\"],\n \"es_nie\": [\"X0095892M\", \"X0095892X\"]}\n )\n\ndf[\"contact_email\"].validators.is_email()\n```\n\n\n",
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