Duke Sheets

A library for reading, writing, and manipulating Excel spreadsheets with full formula evaluation.

Includes bindings for:

• Python
• NodeJS
• WebAssembly
• Rust

Duke Sheets includes an extensive test suite:

• Formula tests covering Excel's documentation cases
• Compatibility tests against both LibreOffice and Excel
• Fuzz testing
• Performance benchmarks
• Performance regression workflow
• Corpus testing on real-world spreadsheets

Duke Sheets has a high-performance multithreaded formula engine which can evaluate millions of formulas in seconds, and has been profiled against some of the most complex financial spreadsheets in the world.

Supported file formats: .xlsx, .xlsm, .xltx, .xltm, .xls, .csv

Additional supported features:

• Styling (fonts, colors, borders, number formatting)

Node.js / TypeScript

Install from npm:

npm install @dukelib/sheets

import { Workbook } from '@dukelib/sheets';

const wb = new Workbook();
const sheet = wb.getSheet(0);

sheet.setCell('A1', 10);
sheet.setCell('A2', 20);
sheet.setFormula('A3', '=A1+A2');

wb.calculate();
console.log(sheet.getCalculatedValue('A3').asNumber()); // 30

wb.save('output.xlsx');

Open existing files from disk, bytes, or CSV strings:

const wb = Workbook.open('input.xlsx');
const wb2 = Workbook.fromBytes(buffer);
const wb3 = Workbook.fromCsvString('a,b,c\n1,2,3');

Async versions run on separate threads so the event loop stays free:

import { openAsync } from '@dukelib/sheets';

const wb = await openAsync('large-file.xlsx');
await wb.calculateAsync();
await wb.saveAsync('output.xlsx');

50+ read-only accessors for styles, comments, hyperlinks, tables, conditional formatting, data validations, merged regions, page setup, and more.

Python

I haven't publisehd to PyPI yet. You can install from a GitHub release:

pip install https://github.com/guseggert/duke-sheets/releases/download/python-v0.1.1/duke_sheets-0.1.1-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Wheels are available for Linux (x86_64, aarch64), macOS (x86_64, ARM), and Windows (x64). Pick the one matching your platform from the release assets.

Or use as an inline script dependency with uv:

# /// script
# requires-python = ">=3.9"
# dependencies = [
#     "duke-sheets @ https://github.com/guseggert/duke-sheets/releases/download/python-v0.1.1/duke_sheets-0.1.1-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
# ]
# ///

```python
import duke_sheets

wb = duke_sheets.Workbook()
sheet = wb.get_sheet(0)

sheet.set_cell("A1", 10)
sheet.set_cell("A2", 20)
sheet.set_formula("A3", "=A1+A2")

wb.calculate()
print(sheet.get_calculated_value("A3").as_number())  # 30.0

wb.save("output.xlsx")

Open existing files:

wb = duke_sheets.Workbook.open("input.xlsx")
wb = duke_sheets.Workbook.from_bytes(data)
wb = duke_sheets.Workbook.from_csv_string("a,b,c\n1,2,3")

Same 50+ read-only accessors as the Node.js API: cell styles, formatted values, comments, hyperlinks, tables, freeze panes, page setup, etc.

WebAssembly

Install from npm:

npm install @dukelib/sheets-wasm

import { Workbook } from '@dukelib/sheets-wasm';

const wb = new Workbook();
const sheet = wb.getSheet(0);

sheet.setCell('A1', 10);
sheet.setCell('A2', 20);
sheet.setFormula('A3', '=A1+A2');

const stats = wb.calculate();
console.log(sheet.getCalculatedValue('A3').asNumber()); // 30

Load files from bytes or CSV:

const wb = Workbook.fromBytes(uint8Array);
const wb2 = Workbook.loadCsvString('a,b,c\n1,2,3');

// Export back out
const xlsxBytes = wb.saveXlsxBytes();   // Uint8Array
const csvString = wb.saveCsvString();    // string

Full API parity with the Node.js bindings, including all read-only accessors (returned as plain JS objects via structured serialization).

Rust

Add to your Cargo.toml:

[dependencies]
duke-sheets = { git = "https://github.com/guseggert/duke-sheets.git", features = ["full"] }

use duke_sheets::prelude::*;

fn main() -> Result<()> {
    let mut workbook = Workbook::new();

    let sheet = workbook.worksheet_mut(0).unwrap();
    sheet.set_name("Sales Data")?;

    sheet.set_cell_value("A1", "Product")?;
    sheet.set_cell_value("B1", "Revenue")?;
    sheet.set_cell_value("A2", "Widget")?;
    sheet.set_cell_value("B2", 1500.0)?;

    sheet.set_cell_formula("B5", "=SUM(B2:B4)")?;

    let header_style = Style::new().bold(true);
    sheet.set_cell_style("A1", &header_style)?;
    sheet.set_cell_style("B1", &header_style)?;

    workbook.save("sales.xlsx")?;

    Ok(())
}

Crate Structure

Crate	Description
duke-sheets	Main API crate (re-exports all functionality)
duke-sheets-core	Core data structures
duke-sheets-formula	Formula parser and evaluator
duke-sheets-xlsx	XLSX reader/writer
duke-sheets-xls	XLS reader (legacy format)
duke-sheets-csv	CSV reader/writer
duke-sheets-chart	Chart support
duke-sheets-html	HTML table export

Benchmarks

Last updated: 2026-03-24 · commit 0c9a971

cargo bench --features full -p duke-sheets

Group	Case	Library	Time
xlsx_read/100_cells	—	calamine	110.8 µs
xlsx_read/100_cells	—	duke-sheets	208.4 µs
xlsx_read/100_cells	—	umya-spreadsheet	341.9 µs
xlsx_read/10k_cells	—	calamine	6.069 ms
xlsx_read/10k_cells	—	duke-sheets	10.68 ms
xlsx_read/10k_cells	—	umya-spreadsheet	15.31 ms
xlsx_read/1k_cells	—	calamine	665.7 µs
xlsx_read/1k_cells	—	duke-sheets	1.177 ms
xlsx_read/1k_cells	—	umya-spreadsheet	1.708 ms
xlsx_write_serialize/100_cells	—	duke-sheets	390.1 µs
xlsx_write_serialize/100_cells	—	umya-spreadsheet	463.2 µs
xlsx_write_serialize/10k_cells	—	duke-sheets	21.22 ms
xlsx_write_serialize/10k_cells	—	umya-spreadsheet	18.33 ms
xlsx_write_serialize/1k_cells	—	duke-sheets	1.985 ms
xlsx_write_serialize/1k_cells	—	umya-spreadsheet	1.884 ms
xlsx_write_full/100_cells	—	duke-sheets	407.9 µs
xlsx_write_full/100_cells	—	rust_xlsxwriter	459.7 µs
xlsx_write_full/100_cells	—	umya-spreadsheet	579 µs
xlsx_write_full/10k_cells	—	duke-sheets	21.54 ms
xlsx_write_full/10k_cells	—	rust_xlsxwriter	14.41 ms
xlsx_write_full/10k_cells	—	umya-spreadsheet	23.49 ms
xlsx_write_full/1k_cells	—	duke-sheets	2.095 ms
xlsx_write_full/1k_cells	—	rust_xlsxwriter	1.544 ms
xlsx_write_full/1k_cells	—	umya-spreadsheet	2.457 ms
csv_read/100_cells	—	duke-sheets	32.93 µs
csv_read/10k_cells	—	duke-sheets	1.096 ms
csv_read/1k_cells	—	duke-sheets	136.9 µs
csv_write/100_cells	—	duke-sheets	9.869 µs
csv_write/10k_cells	—	duke-sheets	1.097 ms
csv_write/1k_cells	—	duke-sheets	101.6 µs
formula_parse/complex	—	—	12.69 µs
formula_parse/medium	—	—	6.467 µs
formula_parse/simple	—	—	1.774 µs
formula_parse/throughput_1000	—	—	676.1 µs
calculation/linear_chain	100	—	79.03 µs
calculation/linear_chain	500	—	393.6 µs
calculation/linear_chain	1000	—	792.4 µs
calculation/fan_out	26	—	67.11 µs
calculation/fan_out	52	—	147.2 µs
calculation/fan_out	100	—	327.5 µs
calculation/fan_out	200	—	825.7 µs
calculation/cross_sheet	100	—	110 µs
calculation/cross_sheet	500	—	501.5 µs
calculation/cross_sheet	1000	—	1.003 ms
calculation/cross_sheet	5000	—	4.41 ms
calculation/mixed	100	—	161.2 µs
calculation/mixed	500	—	711.4 µs
calculation/mixed	1000	—	1.422 ms
calculation/repeated_lookups	—	repeated_lookups	283 ms

License

MIT OR Apache-2.0