Excel Data Merge

Problem

When multiple .xlsx or .xls workbooks have similar headers but live in different department files, manual copy-and-paste often creates missed rows, shifted columns, or no provenance. Excel Data Merge treats this as a scripted job: verify input files, output path, and dependencies, then use scripts/merge.py to read, align, clean, deduplicate, and write the result.

How it works

• Basic merge: --files and --output define the job; headers follow the first workbook, and blank rows are dropped.
• Source tracking: --with-source adds a source-file column for auditability across departments.
• Performance tuning: use --workers for parallel reads, --chunksize for large files to reduce peak memory, and disable parallelism or batch writes for small jobs.
• Format retention: --keep-format preserves fonts, fills, borders, number formats, alignment, column widths, and row heights for styled reports.
• Deduplication: --dedup removes identical rows or, with --dedup-cols, keeps the first occurrence based on selected columns.

Boundaries

This skill is best for similar or nearly similar tabular data. If column counts differ, the first file’s headers define alignment: missing columns are padded and extra columns truncated, so column order matters. Windows users should be cautious with parallel reads, and format retention is slower than the default mode. For a few small workbooks, the default settings are usually sufficient.