df-merger

What This Skill Does

The df-merger skill is an advanced data processing utility designed specifically for the complexities of construction industry data. Construction projects frequently suffer from fragmented information across diverse platforms like Building Information Modeling (BIM), project scheduling software (Primavera/MS Project), cost estimation tools, and on-site sensor telemetry. df-merger acts as an intelligent bridge, enabling the seamless reconciliation of these disparate datasets.

Unlike standard pandas merge operations, this skill leverages a semantic mapping engine. It understands that 'guid', 'elementid', and 'globalid' often represent the same entity within a 3D model, and it automates the alignment of these columns even when naming conventions differ across files. By handling schema reconciliation, managing duplicate keys, and providing high-quality merge metrics, the tool ensures that construction managers can perform holistic data analysis without needing to manually clean or align thousands of rows of project data.

Installation

To integrate this skill into your environment, execute the following command in your terminal: clawhub install openclaw/skills/skills/datadrivenconstruction/df-merger

Use Cases

• BIM and Cost Integration: Combine your Revit model quantity takeoffs with Excel-based cost estimates to identify budget overruns in real-time.
• Schedule Performance Tracking: Merge actual sensor data from site hardware with planned task start and finish dates to analyze schedule variance.
• Data Cleaning for Reporting: Standardize column naming across multiple subcontractor reports to create a single, unified source of truth for weekly project status meetings.

Example Prompts

1. "I have a BIM quantity export and a budget report in Excel. Please use df-merger to combine them, matching rows by the element_id so I can see cost per cubic meter of concrete."
2. "Merge my construction schedule CSV with the latest sensor log. Use the 'task_id' as the key and perform an inner join to focus only on active project milestones."
3. "I have two files with different floor naming conventions—one says 'storey' and the other 'level'. Run a merge and ensure the data is aligned properly for a project-wide material summary."

Tips & Limitations

• Column Normalization: While the tool is excellent at fuzzy matching, always ensure your primary key values (e.g., GUIDs or Task IDs) are cleaned for leading/trailing whitespace before merging.
• Merge Strategy: The tool defaults to 'inner' joins for safety. If you are missing data, check your merge results report; if you see high 'left_only' or 'right_only' counts, consider switching to an 'outer' join to identify gaps in your data sources.
• Performance: For datasets exceeding 100,000 rows, consider filtering your DataFrames before merging to maintain optimal performance.