## build_value_map_list()


Build a list of ValueMap objects from a DataFrame, with optional validity.


Usage

``` python
build_value_map_list(
    df,
    source_col="source",
    target_col="target",
    valid_from_col="valid_from",
    valid_to_col="valid_to",
    default_value=None
)
```


## Parameters


`df: pd.DataFrame`  
DataFrame where each row represents a mapping.

`source_col: str = ``"source"`  
Column name for source values.

`target_col: str = ``"target"`  
Column name for target values.

`valid_from_col: str = ``"valid_from"`  
Optional column name for validity start date. Defaults to "valid_from".

`valid_to_col: str = ``"valid_to"`  
Optional column name for validity end date. Defaults to "valid_to".

`default_value: str | None = None`  
Optional catch-all target value. When provided, a final ValueMap with source `"regex:.*"` is appended so that source values not listed in the DataFrame resolve to this value instead of remaining unmapped. Defaults to None (no catch-all).


## Returns


`list[ValueMap]`  
List of ValueMap objects created from the DataFrame.


## Raises


`ValueError`  
If DataFrame is empty or required columns are missing.

`TypeError`  
If source or target columns contain non-string values.


## Notes

- If validity columns exist and contain non-null values, they will be used.
- If validity columns are absent or contain only nulls, they are ignored.


## Examples


``` python
    >>> import pandas as pd
    >>> data = {
    ...     'source': ['BE', 'FR'],
    ...     'target': ['BEL', 'FRA'],
    ...     'valid_from': ['2020-01-01', None],
    ...     'valid_to': ['2025-12-31', None]
    ... }
    >>> df = pd.DataFrame(data)
    >>> value_maps = build_value_map_list(df, 'source', 'target')
    >>> isinstance(value_maps[0], ValueMap)
```

    True