xlrd.biffh.xlrderror: excel xlsx file; not supported

2 min read 30-12-2024

The error "xlrd.biffh.XLRDError: Excel xlsx file; not supported" arises when you try to open an Excel XLSX file using the xlrd Python library. This is because xlrd is specifically designed to handle older Excel file formats, primarily .xls files (Excel 97-2003). XLSX files, the standard format since Excel 2007, utilize a different structure and require a different library to be processed.

Understanding the Problem: XLS vs. XLSX

Before diving into solutions, let's clarify the difference:

XLS: This is the older binary file format used by Excel 97-2003. It's simpler in structure.
XLSX: This is the newer OpenXML file format used by Excel 2007 and later. It's based on zipped XML files, making it more complex.

xlrd is not equipped to handle the complexities of the XLSX format. Attempting to use it will result in the error.

Solutions to the XLRDError

The solution is straightforward: use a library capable of handling XLSX files. The most popular and widely recommended library for this purpose is openpyxl.

1. Installing openpyxl

First, install openpyxl using pip:

pip install openpyxl

2. Reading an XLSX file with openpyxl

Here's how to read an XLSX file using openpyxl:

from openpyxl import load_workbook

def read_xlsx(filepath):
    """Reads an XLSX file using openpyxl and prints the cell values."""
    try:
        workbook = load_workbook(filepath, read_only=True) # read_only improves performance
        sheet = workbook.active  # Get the active sheet

        for row in sheet.iter_rows():
            for cell in row:
                print(cell.value, end="\t")
            print() # Newline after each row

    except FileNotFoundError:
        print(f"Error: File not found at {filepath}")
    except Exception as e:
        print(f"An error occurred: {e}")

# Example usage:
filepath = "your_excel_file.xlsx"  # Replace with your file path
read_xlsx(filepath)

Remember to replace "your_excel_file.xlsx" with the actual path to your Excel file. The read_only=True argument in load_workbook() significantly improves performance, especially for large files. This code iterates through each cell and prints its value. You can easily adapt this to store the data in a list, dictionary, or other data structure as needed for further processing.

3. Handling Potential Errors

The try...except block is crucial. It handles potential errors, such as the file not being found (FileNotFoundError) or other unexpected issues. Always include robust error handling in your code.

4. Alternative Libraries (Less Common)

While openpyxl is the recommended choice, other libraries exist, though they might be less actively maintained or have a smaller community:

xlwt and xlrd (for .xls files only): As mentioned, these are for older .xls files, not .xlsx. Use them only if you absolutely need to work with the older format.
pandas: The pandas library provides excellent data manipulation capabilities. It also supports reading Excel files (both .xls and .xlsx) using the read_excel() function. This can be very convenient if you're already using pandas for data analysis.

Choosing the Right Library

For modern Excel files (XLSX), openpyxl is the clear winner due to its active development, robust features, and ease of use. If you're also working with data analysis, integrating pandas offers a streamlined workflow. Avoid using xlrd for XLSX files – it will always produce the error we've discussed. Select the library that best fits your project's needs and dependencies.