remove_columns
Remove specified columns from a dataframe to clean data, reduce clutter, or prepare datasets for analysis by eliminating unnecessary fields.
Instructions
Remove columns from the dataframe.
Returns: ColumnOperationResult with removal details
Examples: # Remove single column remove_columns(ctx, ["temp_column"])
# Remove multiple columns
remove_columns(ctx, ["col1", "col2", "col3"])
# Clean up after analysis
remove_columns(ctx, ["_temp", "_backup", "old_value"])Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| columns | Yes | List of column names to remove from the dataframe |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| success | No | Whether operation completed successfully | |
| operation | Yes | Type of operation performed | |
| transform | No | Transform description | |
| part_index | No | Part index for split operations | |
| nulls_filled | No | Number of null values filled | |
| rows_removed | No | Number of rows removed (for remove_duplicates) | |
| rows_affected | Yes | Number of rows affected by operation | |
| values_filled | No | Number of values filled (for fill_missing_values) | |
| updated_sample | No | Sample values after operation | |
| original_sample | No | Sample values before operation | |
| columns_affected | Yes | Names of columns affected |
Implementation Reference
- The core handler function implementing the remove_columns tool. Validates column existence, drops the specified columns from the session's dataframe, and returns a ColumnOperationResult with operation details.
async def remove_columns( ctx: Annotated[Context, Field(description="FastMCP context for session access")], columns: Annotated[ list[str], Field(description="List of column names to remove from the dataframe"), ], ) -> ColumnOperationResult: """Remove columns from the dataframe. Returns: ColumnOperationResult with removal details Examples: # Remove single column remove_columns(ctx, ["temp_column"]) # Remove multiple columns remove_columns(ctx, ["col1", "col2", "col3"]) # Clean up after analysis remove_columns(ctx, ["_temp", "_backup", "old_value"]) """ # Get session_id from FastMCP context session_id = ctx.session_id session, df = get_session_data(session_id) # Validate columns exist missing_cols = [col for col in columns if col not in df.columns] if missing_cols: raise ColumnNotFoundError(str(missing_cols[0]), df.columns.tolist()) session.df = df.drop(columns=columns) # No longer recording operations (simplified MCP architecture) return ColumnOperationResult( operation="remove", rows_affected=len(df), columns_affected=columns, ) - src/databeak/servers/column_server.py:656-656 (registration)Registers the remove_columns handler as an MCP tool on the column_server FastMCP instance, specifying the tool name.
column_server.tool(name="remove_columns")(remove_columns)