How to Speed Up Pandas Excel Reads with the Calamine Engine

1. Introduction

The author, a Python enthusiast, received a question in a community chat about reading five Excel files (about 3 million rows total) with Pandas, which was extremely slow and caused memory issues.

2. Implementation

Two suggestions were offered:

Convert the Excel files to CSV, which reads much faster.

Use Pandas with the calamine engine to read Excel files more efficiently.

To use the Calamine engine, install the python-calamine package: pip install python-calamine Then read the Excel file with minimal code changes: pd.read_excel("demo.xlsx", engine='calamine') This approach saves time without altering the original logic.

Additional advice includes appending each Excel sheet to a CSV in append mode, handling memory constraints by processing data in smaller batches, and avoiding conversion to CSV when parsing errors occur.

Alternative file formats such as Feather and Parquet were recommended because they retain data fidelity while using less physical memory than CSV or Excel.

3. Summary

The discussion provided practical solutions for handling large Pandas data workloads: installing and using the Calamine engine, considering more efficient file formats, and processing data in smaller chunks to mitigate memory limitations.