## validate_dataset_local()


Validate that a DataFrame is SDMX compliant and return a DataFrame of errors.


Usage

``` python
validate_dataset_local(
    df, schema=None, valid=None, sdmx_cols=None, max_errors=1000
)
```


Pass `schema` so validation info (including the expected SDMX reference columns) can be inferred. The `valid` parameter is retained as a deprecated shim for callers that previously cached the output of `~tidysdmx.utils.extract_validation_info()`.


## Parameters


`df: pd.DataFrame`  
The DataFrame to be validated.

`schema: Schema | None = None`  
The schema object (optional if `valid` is provided).

`valid: dict[str, object] | None = None`  
**Deprecated.** Precomputed validation information returned by `~tidysdmx.utils.extract_validation_info()`. This argument will be removed in a future release; pass `schema` directly instead.

`sdmx_cols: list[str] | tuple[str, …] | None = None`  
SDMX reference columns expected in the dataset, as a list or tuple. When omitted, they resolve to the standard SDMX-CSV columns `['STRUCTURE', 'STRUCTURE_ID', 'ACTION']` for every schema context; if only a `valid` dict is given, context-specific column sets cached under tidysdmx \<= 0.9.0 (e.g. `['DATAFLOW', 'DATAFLOW_ID', 'ACTION']`) are likewise normalized to the standard columns.

`max_errors: int = ``1000`  
Maximum number of individual errors to report per validation check. Defaults to `1000`.


## Returns


`pd.DataFrame`  
A DataFrame containing validation errors. Each row is one error, with

columns `Validation` and `Error`.