Is openpyxl Still Relevant in 2026? Absolutely — Here's Why

Jun 5, 2026

Purpose

I saw a post on r/Python recently where someone asked: “Is openpyxl still relevant? Its description says ‘read/write Excel 2010 xlsx/xlsm files’ — isn’t that ancient?”

I get it. If you see “Excel 2010” in 2026, your brain flags it as outdated. But this is a classic case where the description describes the file format standard, not a software version limit.

After digging into it, here’s what I found — and it changed how I think about Python Excel automation entirely.

What Happened

The confusion comes straight from the library’s PyPI description. openpyxl was created in 2010 when Microsoft introduced the Office Open XML format (.xlsx) with Office 2007/2010. The description says “Excel 2010” because that’s when the format became the standard. Every version of Excel since — 2013, 2016, 2019, 2021, and Office 365 — uses the exact same .xlsx format.

The library hasn’t been sitting still either. The 3.1.x series has seen five releases between 2023 and 2024, the latest being 3.1.5. It supports Python 3.8+, includes numpy integration (openpyxl.NUMPY), defusedxml for security, and LXML parsing for better performance.

import openpyxl
print(openpyxl.__version__)  # 3.1.5

A library that gets regular updates, supports modern Python, and keeps adding features — that’s not “outdated” by any measure.

How It Works (And Why It Still Matters)

To understand why openpyxl is irreplaceable, you have to look at the things pandas cannot do with Excel.

pandas can write a DataFrame to Excel. That’s it — raw data in a grid, no formatting, no styling. The moment you need:

Cell-level formatting: fonts, colors, borders, alignment
Charts and pivot tables
Merged cells and conditional formatting
Data validation and named ranges
Image embedding and drawings
Comments and notes

…you need openpyxl. And these aren’t edge cases — they’re the daily reality of anyone building Excel reports for clients or internal teams.

from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill, Alignment

wb = Workbook()
ws = wb.active
ws.title = "Sales Report"

ws['A1'] = "Product"
ws['B1'] = "Revenue"
ws['A1'].font = Font(bold=True, size=12)
ws['B1'].font = Font(bold=True, size=12)
ws['B1'].fill = PatternFill(start_color="FFFF00", end_color="FFFF00", fill_type="solid")

data = [("Widget A", 1200), ("Widget B", 3400), ("Widget C", 890)]
for row in data:
    ws.append(row)

wb.save("report.xlsx")

This is the real pipeline used in production: pandas handles data processing and analysis, openpyxl handles the Excel output with proper formatting. They complement each other, not compete.

Why This Matters for Freelancers

If you do any kind of data automation work as a freelancer — client reporting, invoicing systems, data migration — you will encounter a requirement that pandas alone cannot meet. The client wants colored headers, a chart, or a specific layout. Every single time, openpyxl is the answer.

Knowing openpyxl isn’t a “nice to have.” It’s a practical skill that directly translates to: “yes, I can make that report look professional” instead of “here’s a CSV.”

Summary

In this post, I cleared up the “openpyxl is for old Excel” misconception. The key point is that “Excel 2010” in openpyxl’s description refers to the .xlsx file format standard that all modern Excel versions still use — the library is actively maintained, has no real competitor for native xlsx read/write in Python, and remains the go-to tool for any Excel automation task that needs formatting, charts, or cell-level control.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!