Up to 18x Faster Excel Automation in Python.

WolfXL implements the supported subset of the openpyxl API surface (load_workbook, Workbook, Font, PatternFill, and cell access patterns), so existing code on that surface runs unchanged. Update one import line or invoke the runtime alias at startup:

import wolfxl
wolfxl.install_as_openpyxl()

The Rust core bypasses Python serialization overhead. Versus openpyxl 3.1.5 on 200,000-row workbooks:

• Bulk writes run 8-9x faster with ws.append() and ws.write_rows().
• Bulk reads run 12.7-18x faster with ws.iter_rows(values_only=True).
• Peak RAM is roughly 3x lower, easing memory pressure on servers.

Instead of rebuilding the whole workbook on save, modify mode parses the workbook, updates only the changed cells, and writes the ZIP parts back.

• Touch three cells in a 20,000-row workbook and save in 0.19 seconds, versus 10.9 seconds in openpyxl.
• Unchanged OOXML parts are preserved in supported modify-mode workflows; validate business-critical templates before migration.

Construct interactive pivot tables for supported pivot-cache workflows. Unlike libraries that output flat, static cells, WolfXL writes a pre-populated pivotCacheRecords snapshot: verified test cases open in Excel or LibreOffice with pivot data already populated, no manual refresh needed.