Openpyxl vs Calamine: Which is Better?

When working with Excel files in Python, choosing the right library is crucial. Two popular options are openpyxl and calamine (via polars). While both libraries can handle Excel files, they have different capabilities, strengths, and use cases.

openpyxl is a widely used Python library for reading and writing Excel files (.xlsx).
calamine, a Rust-based library, is optimized for fast reading of Excel and OpenDocument (.ods) files, but it does not support writing.

This article provides an in-depth comparison of both libraries, helping you decide which one is best for your needs.

Overview of openpyxl

What is openpyxl?

openpyxl is a Python library that allows you to create, modify, and read Excel (.xlsx) files. It is one of the most commonly used libraries for Excel operations in Python and is a part of many data processing workflows.

Key Features

✅ Read and write .xlsx files
✅ Create and modify workbooks, sheets, cells, and styles
✅ Support for formulas, charts, and images
✅ Ability to format cells (color, fonts, borders, etc.)
✅ Compatible with Pandas (df.to_excel())

Example Usage

Reading an Excel File

pythonCopy codefrom openpyxl import load_workbook

wb = load_workbook("data.xlsx")
sheet = wb.active

for row in sheet.iter_rows(values_only=True):
    print(row)

Writing to an Excel File

pythonCopy codefrom openpyxl import Workbook

wb = Workbook()
sheet = wb.active

sheet["A1"] = "Hello"
sheet["B1"] = "World"

wb.save("output.xlsx")

Limitations of openpyxl

Performance: Slower when working with large Excel files (e.g., 100,000+ rows).
Memory Usage: Can be high due to Python’s data handling.
Limited Multi-threading: Processing large files in parallel is inefficient.

Overview of calamine

What is calamine?

calamine is a high-performance Rust-based library designed for fast reading of Excel (.xls, .xlsx) and OpenDocument (.ods) files. It is often used with polars, a fast DataFrame library in Python, making it ideal for large datasets.

Key Features

✅ Fast reading of .xlsx, .xls, and .ods files
✅ Lower memory footprint than openpyxl
✅ Works well with Pandas alternatives like polars
❌ Read-only (does not support writing)

Example Usage with Polars

Reading an Excel File

pythonCopy codeimport polars as pl

df = pl.read_excel("data.xlsx", sheet_name="Sheet1")
print(df)

Limitations of calamine

No Writing Support: Cannot modify Excel files.
No Formatting Support: Does not support styles, charts, or images.
Requires Rust Backend: Installation may be tricky on some systems.

Performance Comparison

Feature	openpyxl	calamine
Speed	Slower for large files	Faster (Rust-based)
Memory Usage	Higher	Lower
Multi-threading	Limited	More efficient
Large File Support	Struggles with large datasets	Optimized for large datasets

For small files, openpyxl performs well, but when dealing with millions of rows, calamine is significantly faster.

Use Cases: When to Use Which?

Use `openpyxl` if you need to:

✔ Modify Excel files (writing, updating, formatting).
✔ Work with cell styling (colors, fonts, borders).
✔ Handle Excel formulas, charts, or images.
✔ Integrate with Pandas (df.to_excel()).

Use `calamine` if you need to:

✔ Read large Excel or OpenDocument (.ods) files quickly.
✔ Process big datasets with low memory usage.
✔ Use polars for data analysis instead of Pandas.

Conclusion

If you need read and write capabilities with formatting and charts, use openpyxl.
If you need fast, efficient reading of large files, use calamine with polars.

For most data processing tasks, openpyxl is the better general-purpose library, while calamine is the best choice for performance-critical applications. 🚀

ApexDelight