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select_columns

Extract specific columns from a CSV file for focused data analysis or manipulation. Ideal for isolating relevant data subsets in large datasets.

Instructions

Select specific columns from the dataframe.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
columnsYes
session_idYes

Implementation Reference

  • Core handler function that implements the select_columns tool logic: validates session and columns, selects columns using pandas, updates session dataframe, records operation, handles errors.
    async def select_columns( session_id: str, columns: List[str], ctx: Context = None ) -> Dict[str, Any]: """ Select specific columns from the dataframe. Args: session_id: Session identifier columns: List of column names to keep ctx: FastMCP context Returns: Dict with success status and selected columns """ try: manager = get_session_manager() session = manager.get_session(session_id) if not session or session.df is None: return {"success": False, "error": "Invalid session or no data loaded"} df = session.df # Validate columns exist missing_cols = [col for col in columns if col not in df.columns] if missing_cols: return {"success": False, "error": f"Columns not found: {missing_cols}"} session.df = df[columns].copy() session.record_operation(OperationType.SELECT, { "columns": columns, "columns_before": df.columns.tolist(), "columns_after": columns }) return { "success": True, "selected_columns": columns, "columns_removed": [col for col in df.columns if col not in columns] } except Exception as e: logger.error(f"Error selecting columns: {str(e)}") return {"success": False, "error": str(e)}
  • MCP tool registration (@mcp.tool decorator) for 'select_columns', which wraps and delegates to the core implementation imported as _select_columns from transformations.py.
    @mcp.tool async def select_columns( session_id: str, columns: List[str], ctx: Context = None ) -> Dict[str, Any]: """Select specific columns from the dataframe.""" return await _select_columns(session_id, columns, ctx)
  • Import statement that brings in the select_columns implementation from transformations.py, aliased as _select_columns for use in the server wrapper.
    from .tools.transformations import ( filter_rows as _filter_rows, sort_data as _sort_data, select_columns as _select_columns, rename_columns as _rename_columns, add_column as _add_column, remove_columns as _remove_columns, change_column_type as _change_column_type, fill_missing_values as _fill_missing_values, remove_duplicates as _remove_duplicates, update_column as _update_column )

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