# RowDataSource

Data source for explainability analysis using a single row of data.

RowDataSource allows you to perform explainability analysis on a specific data row by providing the row data directly. This is useful when you want to explain a particular prediction or analyze feature importance for a specific instance without referencing stored data.

This data source type is ideal for real-time explanations, ad-hoc analysis, or when you have specific data points that you want to analyze independently of your stored datasets.

## Examples

Creating a row data source for a loan application:

```python
row_source = RowDataSource(
    row={
        "age": 35,
        "income": 75000,
        "credit_score": 720,
        "employment_years": 8,
        "loan_amount": 250000
    }
)
```

Creating a row data source for image classification:

```python
image_row_source = RowDataSource(
    row={
        "image_features": [0.1, 0.5, 0.3, …],
        "metadata": "product_image_001.jpg",
        "category": "electronics"
    }
)
```

## source\_type *: Literal\['ROW']*

## row *: Dict*


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