excel-formula-analyzer

You can trace Excel calculation chains by running the provided Python script against your workbook. It builds a dependency DAG from the formulas to show how inputs propagate to outputs, making complex calculation logic transparent and auditable.

A dependency DAG is a directed acyclic graph that maps how values calculate from inputs to outputs in Excel formulas. It helps auditing by tracing calculation chains to validate logic and identify potential gaps in complex workbooks.

Yes, you can generate code from Excel formulas because this skill creates a structured blueprint of the calculation logic. This dependency DAG can be translated into reusable Python or TypeScript implementations for programmatic calculators.

Yes, the Excel formula analyzer works with specific columns by passing a column range argument to the Python script. You can target columns like 16-36 and 57-59 in a specified sheet to trace dependencies for selected calculation chains.

The best way to reverse-engineer a large Excel workbook is to build a dependency DAG that traces how inputs propagate to outputs. This maps the entire calculation flow across hundreds of interdependent cells to reproduce the logic programmatically.

Excel formula validation may show logic gaps when the dependency DAG reveals broken or unexpected calculation chains. Tracing how values calculate from inputs to outputs helps identify where the spreadsheet logic diverges from expected results.