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XLS — Legacy Excel Workbook

A legacy Excel 97–2003 (.xls / BIFF8) workbook holding the same tabular data as the plain XLSX and ODS — for testing legacy-format readers and XLS↔XLSX conversion.

Rendered preview of XLS — Legacy Excel Workbook

Rendered preview of the xls file (5.5 KB). Download above for the original.

Specifications

Format
Excel 97–2003 (BIFF8 .xls)
Sheets
1
Rows
11
Twin Of
Plain XLSX

What is a .xls file?

XLS is the legacy Microsoft Excel format using the binary BIFF structure inside an OLE2 compound file, superseded by XLSX in 2007. It stores worksheets, formulas, and formatting in a binary layout rather than XML. It remains common in older systems and exports.

How to use this file

Use an example XLS to test legacy binary spreadsheet parsing, OLE2 compound-file handling, and converters that upgrade older workbooks to XLSX or CSV.

How to use this file for testing

“XLS — Legacy Excel Workbook” is a deterministic Novus Examples fixture for Spreadsheet testing, Conversion testing. Multi-sheet workbooks with documented formulas and plain sheets for testing spreadsheet parsers and importers.

Documented properties for this file: 11 rows · 1 sheets · Excel 97–2003 (BIFF8 .xls). Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.

Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.

Document fixtures list their internal structure (pages, fields, tracked changes, embedded objects) in the spec table. Test extractors, converters, and OCR against that known structure, and compare searchable↔scanned or format-twin companions when present.

Code examples

import pandas as pd  # pip install xlrd

df = pd.read_excel("legacy.xls")  # legacy .xls
print(df.head())

Generated by generation/docs_odf.py. Free for any use, no attribution required — license.