wolfxl 0.3.2

Fast, openpyxl-compatible Excel I/O with Rust backend and built-in formula engine (67 functions, financial, date, time, text, lookup, conditional stats)

WolfXL

The fastest openpyxl-compatible Excel library for Python.
Drop-in replacement backed by Rust — up to 5x faster with zero code changes.

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Replaces openpyxl. One import change.

- from openpyxl import load_workbook, Workbook
- from openpyxl.styles import Font, PatternFill, Alignment, Border
+ from wolfxl import load_workbook, Workbook, Font, PatternFill, Alignment, Border

Your existing code works as-is. Same ws["A1"].value, same Font(bold=True), same wb.save().

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_{Measured with ExcelBench on Apple M1 Pro, Python 3.12, median of 3 runs.}

Install

pip install wolfxl

Quick Start

from wolfxl import load_workbook, Workbook, Font, PatternFill

# Write a styled spreadsheet
wb = Workbook()
ws = wb.active
ws["A1"].value = "Product"
ws["A1"].font = Font(bold=True, color="FFFFFF")
ws["A1"].fill = PatternFill(fill_type="solid", fgColor="336699")
ws["A2"].value = "Widget"
ws["B2"].value = 9.99
wb.save("report.xlsx")

# Read it back — styles included
wb = load_workbook("report.xlsx")
ws = wb[wb.sheetnames[0]]
for row in ws.iter_rows(values_only=False):
    for cell in row:
        print(cell.coordinate, cell.value, cell.font.bold)
wb.close()

Three Modes

Mode	Usage	Engine	What it does
Read	load_workbook(path)	calamine-styles	Parse XLSX with full style extraction
Write	Workbook()	rust_xlsxwriter	Create new XLSX files from scratch
Modify	load_workbook(path, modify=True)	XlsxPatcher	Surgical ZIP patch — only changed cells are rewritten

Modify mode preserves everything it doesn't touch: charts, macros, images, pivot tables, VBA.

Supported Features

Category	Features
Data	Cell values (string, number, date, bool), formulas, hyperlinks, comments
Styling	Font (bold, italic, underline, color, size), fills, borders, number formats, alignment
Structure	Multiple sheets, merged cells, named ranges, freeze panes, tables
Advanced	Data validation, conditional formatting

Performance at Scale

Scale	File size	WolfXL Read	openpyxl Read	WolfXL Write	openpyxl Write
100K cells	400 KB	0.11s	0.42s	0.06s	0.28s
1M cells	3 MB	1.1s	4.0s	0.9s	2.9s
5M cells	25 MB	6.0s	20.9s	3.2s	15.5s
10M cells	45 MB	13.0s	47.8s	6.7s	31.8s

Throughput stays flat as files grow — no hidden O(n^2) pathology.

How WolfXL Compares

Every Rust-backed Python Excel project picks a different slice of the problem. WolfXL is the only one that covers all three: formatting, modify mode, and openpyxl API compatibility.

Library	Read	Write	Modify	Styling	openpyxl API
fastexcel	Yes	—	—	—	—
python-calamine	Yes	—	—	—	—
FastXLSX	Yes	Yes	—	—	—
rustpy-xlsxwriter	—	Yes	—	Partial	—
WolfXL	Yes	Yes	Yes	Yes	Yes

• Styling = reads and writes fonts, fills, borders, alignment, number formats
• Modify = open an existing file, change cells, save back — without rebuilding from scratch
• openpyxl API = same load_workbook, Workbook, Cell, Font, PatternFill objects

Upstream calamine does not parse styles. WolfXL's read engine uses calamine-styles, a fork that adds Font/Fill/Border/Alignment/NumberFormat extraction from OOXML.

Batch APIs for Maximum Speed

For write-heavy workloads, use append() or write_rows() instead of cell-by-cell access. These APIs buffer rows as raw Python lists and flush them to Rust in a single call at save time, bypassing per-cell FFI overhead entirely.

from wolfxl import Workbook

wb = Workbook()
ws = wb.active

# append() — fast sequential writes (3.7x faster than cell-by-cell)
ws.append(["Name", "Amount", "Date"])
for row in data:
    ws.append(row)

# write_rows() — fast writes at arbitrary positions
ws.write_rows(header_grid, start_row=1, start_col=1)
ws.write_rows(data_grid, start_row=5, start_col=1)

wb.save("output.xlsx")

For reads, iter_rows(values_only=True) uses a fast bulk path that reads all values in a single Rust call (6.7x faster than openpyxl):

wb = load_workbook("data.xlsx")
ws = wb[wb.sheetnames[0]]
for row in ws.iter_rows(values_only=True):
    process(row)  # row is a tuple of plain Python values

API	vs openpyxl	How
ws.append(row)	3.7x faster write	Buffers rows, single Rust call at save
ws.write_rows(grid)	3.7x faster write	Same mechanism, arbitrary start position
ws.iter_rows(values_only=True)	6.7x faster read	Single Rust call, no Cell objects
ws.cell(r, c, value=v)	1.6x faster write	Per-cell FFI (compatible but slower)

Formula Engine

WolfXL includes a built-in formula evaluator with 62 functions across 7 categories. Calculate formulas without external dependencies - no need for formulas or xlcalc.

from wolfxl import Workbook
from wolfxl.calc import calculate

wb = Workbook()
ws = wb.active
ws["A1"] = 100
ws["A2"] = 200
ws["A3"] = "=SUM(A1:A2)"
ws["B1"] = "=PMT(0.05/12, 360, -300000)"  # monthly mortgage payment

results = calculate(wb)
print(results["Sheet!A3"])  # 300
print(results["Sheet!B1"])  # 1610.46...

# Recalculate after changes
ws["A1"] = 500
results = calculate(wb)
print(results["Sheet!A3"])  # 700

Category	Functions
Math (10)	SUM, ABS, ROUND, ROUNDUP, ROUNDDOWN, INT, MOD, POWER, SQRT, SIGN
Logic (5)	IF, AND, OR, NOT, IFERROR
Lookup (7)	VLOOKUP, HLOOKUP, INDEX, MATCH, OFFSET, CHOOSE, XLOOKUP
Statistical (13)	AVERAGE, AVERAGEIF, AVERAGEIFS, COUNT, COUNTA, COUNTIF, COUNTIFS, MIN, MINIFS, MAX, MAXIFS, SUMIF, SUMIFS
Financial (7)	PV, FV, PMT, NPV, IRR, SLN, DB
Text (13)	LEFT, RIGHT, MID, LEN, CONCATENATE, UPPER, LOWER, TRIM, SUBSTITUTE, TEXT, REPT, EXACT, FIND
Date (8)	TODAY, DATE, YEAR, MONTH, DAY, EDATE, EOMONTH, DAYS

Named ranges are resolved automatically. Error values (#N/A, #VALUE!, #DIV/0!, #REF!, #NUM!, #NAME?) propagate through formula chains like real Excel. Install pip install wolfxl[calc] for extended formula coverage via the formulas library fallback.

Case Study: SynthGL

SynthGL switched from openpyxl to WolfXL for their GL journal exports (14-column financial data, 1K-50K rows). Results: 4x faster writes, 9x faster reads at scale. 50K-row exports dropped from 7.6s to 1.3s. Read the full case study.

How It Works

WolfXL is a thin Python layer over compiled Rust engines, connected via PyO3. The Python side uses lazy cell proxies — opening a 10M-cell file is instant. Values and styles are fetched from Rust only when you access them. On save, dirty cells are flushed in one batch, avoiding per-cell FFI overhead.

License

MIT