semvec/pypi-packages huggingface dataset:
semantic-link-functions-validators 0.13.0
Semantic link functions for validators package. Enables validation of email addresses, credit card numbers, ... in FabricDataFrames.
License: MIT License
Requires Python: >=3.10

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()
```
semantic-link-functions-phonenumbers 0.13.0
Semantic link functions for phonenumbers package. Enables validation of phone numbers in FabricDataFrames.
License: MIT License
Requires Python: >=3.10

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(
    {"num": ["+442083661177", "ABC000-0000", "abc"], "country": ["US", "AT", "Unknown"]},
    column_metadata={"country": {"data_category": "Country"}},
)

is_phone_series = df.is_phonenumber(col_num="num", col_country="country")
```
semantic-link-functions-meteostat 0.13.0
Semantic link functions for meteostat package. Enables enrichment of FabricDataFrame with historical weather data.
License: MIT License
Requires Python: >=3.10

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({
        "lat": [40.7128, 47.8095, 47.8095, 47.8095],
        "long": [-74.0060, 13.0550, 13.0550, 13.0550],
        "date": ["2023-01-06", "2023-01-06", "2023-01-07", "2023-01-10"]
    },
    column_metadata={"lat": {"data_category": "Latitude"}, "long": {"data_category": "Longitude"}},
)

df["date"] = pd.to_datetime(df["date"])

df_weather = df.add_weather_meteostat("lat", "long", "date")

```
semantic-link-functions-holidays 0.13.0
Semantic link functions for holidays package. Enables enrichment of FabricDataFrame with public holidays.
License: MIT License
Requires Python: >=3.10

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"], "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")
```
semantic-link-functions-geopandas 0.13.0
Semantic link functions for Geopandas. Enables conversion of a FabricDataFrame to a GeoDataFrame.
License: MIT License
Requires Python: >=3.10

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")
```