openexcel-c 0.1.1

High-performance .xlsx reader/writer (C extension)

openexcel

A high-performance Python library for reading and writing .xlsx files, implemented as a C extension. Designed as a drop-in accelerator for workloads where openpyxl is too slow.

Why

openpyxl is pure Python. For large files (100k+ rows), the bottlenecks are ZIP extraction, XML parsing, and per-cell object allocation — all done in the Python interpreter. openexcel moves these to C:

• Streaming SAX parsing via libexpat — never loads the full XML into memory
• Flat sorted cell array — O(1) append during parse, O(1) sequential iteration
• GIL released during read and write — other threads run freely
• Vendored miniz — no external ZIP dependency

Typical speedups: 10–30× faster reads, 5–15× faster writes on 100k-row sheets.

Installation

pip install openexcel-c

Pre-built wheels are available for macOS (arm64, x86_64) and Linux (x86_64, aarch64) for Python 3.10–3.13.

Building from source

You need CMake ≥ 3.20, a C11 compiler, and libexpat development headers.

# macOS
brew install expat cmake

# Ubuntu/Debian
sudo apt-get install libexpat1-dev cmake

pip install openexcel-c --no-binary openexcel-c

Usage

Reading

import openexcel

wb = openexcel.load_workbook("data.xlsx")
ws = wb.active   # first sheet

# Iterate rows — returns tuples of Python values
for row in ws.iter_rows():
    print(row)   # e.g. (1, "hello", 3.14, True, datetime.date(2024, 6, 1))

# Slice a range
for row in ws.iter_rows(min_row=2, max_row=100, min_col=1, max_col=5):
    print(row)

Cell values map to Python types:

Excel type	Python type
Number	float
String	str
Boolean	bool
Date / datetime	datetime.date / datetime.datetime
Empty	None
Error	str (e.g. "#DIV/0!")

Writing

import openexcel
import datetime

wb = openexcel.Workbook()
ws = wb.create_sheet("Sheet1")

ws.append(["Name", "Score", "Date"])
ws.append(["Alice", 98.5, datetime.date(2024, 6, 1)])
ws.append(["Bob",   72.0, datetime.date(2024, 6, 2)])

wb.save("output.xlsx")

Multiple sheets

wb = openexcel.load_workbook("multi.xlsx")

# By index
ws = wb[0]

# By name
ws = wb["Summary"]

# Iterate all sheets
for ws in wb:
    print(ws.title, ws.max_row, ws.max_column)

Context manager

with openexcel.load_workbook("data.xlsx") as wb:
    ws = wb.active
    data = [row for row in ws.iter_rows()]
# file resources released on exit

API reference

openexcel.load_workbook(path: str) -> Workbook

Open an existing .xlsx file for reading. Parses the entire file on load; subsequent access is from memory.

openexcel.Workbook()

Create a new empty workbook.

Workbook.create_sheet(name: str) -> Worksheet

Add a new sheet and return it.

Workbook.save(path: str)

Write the workbook to an .xlsx file. GIL is released during write.

Workbook.active -> Worksheet

Returns the first sheet.

Worksheet.iter_rows(min_row=None, max_row=None, min_col=None, max_col=None)

Yields one tuple per row. Indices are 1-based and inclusive, matching openpyxl's convention.

Worksheet.append(row: list | tuple)

Append a row of values. Accepts int, float, str, bool, None, datetime.date, datetime.datetime.

Worksheet.max_row -> int

Worksheet.max_column -> int

Worksheet.title -> str

Differences from openpyxl

Feature	openexcel	openpyxl
Read speed	Fast (C, SAX)	Slow (pure Python)
Write speed	Fast (C, arena buffer)	Slow (pure Python)
Cell objects	Not exposed (values only)	Full Cell with font/fill/border
Formulas	Read result value only	Read formula string
Styles / formatting	Date detection only	Full style API
Merged cells	Not supported	Supported
Charts / images	Not supported	Supported

openexcel is optimized for data workloads — reading and writing large tables of values. If you need full formatting control or chart support, use openpyxl.

License

MIT — see LICENSE.

Vendored dependencies:

• miniz — public domain
• khash — MIT