fill_column_nulls

Replace missing values in a data column with a specified value to handle null or NaN entries during data preparation.

Instructions

Fill null/NaN values in a specific column with a specified value.

Returns: ColumnOperationResult with fill details

Examples: # Fill missing names with "Unknown" fill_column_nulls(ctx, "name", "Unknown")

# Fill missing ages with 0
fill_column_nulls(ctx, "age", 0)

# Fill missing status with default
fill_column_nulls(ctx, "status", "pending")

# Fill missing scores with -1
fill_column_nulls(ctx, "score", -1)

Input Schema

TableJSON Schema

Name	Required	Description	Default
`column`	Yes	Column name to fill null values in
`value`	Yes	Value to use for filling null/NaN values

Implementation Reference

src/databeak/servers/column_text_server.py:461-518 (handler)

The main execution logic for the 'fill_column_nulls' tool. Fills NaN/null values in the specified column of the session DataFrame with the provided value, counts affected rows, and returns a ColumnOperationResult.

async def fill_column_nulls(
    ctx: Annotated[Context, Field(description="FastMCP context for session access")],
    column: Annotated[str, Field(description="Column name to fill null values in")],
    value: Annotated[Any, Field(description="Value to use for filling null/NaN values")],
) -> ColumnOperationResult:
    """Fill null/NaN values in a specific column with a specified value.

    Returns:
        ColumnOperationResult with fill details

    Examples:
        # Fill missing names with "Unknown"
        fill_column_nulls(ctx, "name", "Unknown")

        # Fill missing ages with 0
        fill_column_nulls(ctx, "age", 0)

        # Fill missing status with default
        fill_column_nulls(ctx, "status", "pending")

        # Fill missing scores with -1
        fill_column_nulls(ctx, "score", -1)

    """
    # Get session_id from FastMCP context
    session_id = ctx.session_id
    _session, df = get_session_data(session_id)

    _validate_column_exists(column, df)

    # Count nulls before filling
    nulls_before = int(df[column].isna().sum())

    if nulls_before == 0:
        # No nulls to fill
        return ColumnOperationResult(
            operation="fill_nulls",
            rows_affected=0,
            columns_affected=[column],
        )

    # Fill null values
    # Use explicit assignment to avoid downcasting warnings
    with pd.option_context("future.no_silent_downcasting", True):  # noqa: FBT003
        filled_series = df[column].fillna(value)
        if hasattr(filled_series, "infer_objects"):
            filled_series = filled_series.infer_objects(copy=False)
        df[column] = filled_series

    # Verify fills worked
    nulls_after = int(df[column].isna().sum())
    filled_count = nulls_before - nulls_after

    return ColumnOperationResult(
        operation="fill_nulls",
        rows_affected=filled_count,
        columns_affected=[column],
    )

src/databeak/servers/column_text_server.py:536-536 (registration)
Registers the fill_column_nulls handler function as an MCP tool with the name 'fill_column_nulls' on the FastMCP server instance.
```
column_text_server.tool(name="fill_column_nulls")(fill_column_nulls)
```

src/databeak/servers/column_text_server.py:462-464 (schema)

Pydantic-based input schema defined via Annotated types with Field descriptions for the tool parameters, returning ColumnOperationResult.

ctx: Annotated[Context, Field(description="FastMCP context for session access")],
column: Annotated[str, Field(description="Column name to fill null values in")],
value: Annotated[Any, Field(description="Value to use for filling null/NaN values")],

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