Opening a CSV File with XLS Format in Python

What will you learn?

In this tutorial, you will master the art of opening and working with files that have a .csv extension but are actually in XLS format. By leveraging Python libraries, you will be able to accurately read and manipulate such mislabeled files.

Introduction to the Problem and Solution

Encountering files with misleading extensions like a .csv file disguised as an XLS format can be a common challenge. However, with Python’s powerful libraries, such as Pandas, handling these files becomes straightforward. By correctly identifying the true file format and utilizing the right tools, you can seamlessly open and process these mislabeled files in Python.


import pandas as pd

# Load the Excel file despite having CSV extension
data = pd.read_excel('your_file.csv', engine='openpyxl')

# Display the loaded data

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To open a file with a .csv extension but containing an XLS format, we utilize Pandas’ pd.read_excel() function along with specifying engine=’openpyxl’. This approach ensures accurate reading of Excel files even when they are incorrectly labeled as .csv. It allows seamless access and manipulation of data stored in XLS format under misleading extensions.

    1. How do I handle column headers properly while reading this mixed-format file? To handle column headers correctly in mixed-format files, set header=0 or specify the row containing header information using header=n.

    2. Can I save changes back to this “CSV-disguised-as-XLS” file? Yes, after modifying your data within Python, you can write it back to an Excel-compatible format using Pandas’ .to_excel() function.

    3. Are there any limitations or performance considerations when working with this method? Performance may vary based on dataset size due to Pandas relying on additional libraries like OpenPyXL for Excel operations. It is recommended for efficiently managing smaller datasets.

    4. Is it possible to automate this process for multiple similar files? You can automate handling multiple mislabeled files effectively by encapsulating these steps into functions or loops tailored for batch processing.

    5. What happens if my “XLS-csv” contains multiple sheets? Pandas allows loading specific sheets by providing their names or indices as arguments within pd.read_excel(), enabling adaptation for multi-sheet scenarios.

    6. Can I manipulate cell values while maintaining integrity across formats? With Pandas’ DataFrame operations, you can control cell values granularly without compromising original formatting during conversions.

    7. Does switching engines impact compatibility with different versions of Excel files? The ‘openpyxl’ engine offers robust support for modern Excel formats; however,’ xlrd’ could be used alternatively based on legacy compatibility requirements or advanced features from older workbook versions.


Mastering the ability to handle mislabeled files is crucial for effective data manipulation in Python. By leveraging libraries like Pandas and configuring settings like ‘engine=openpyxl’, accurate interpretation of diverse file formats irrespective of their extensions is ensured.

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