Analysis and Solution for 'Excel file format cannot be determined' Error in Pandas

Problem Background and Error Phenomenon

When using Python's Pandas library combined with the glob module to batch read Excel files, developers often encounter a perplexing error: Excel file format cannot be determined, you must specify an engine manually. This error is characterized by intermittent occurrence - sometimes the code runs normally, while other times it throws exceptions, creating significant uncertainty in data processing workflows.

Root Cause Analysis

Through thorough investigation, we identified that the primary cause of this error lies in the temporary files automatically created by Microsoft Excel when opening documents. These temporary files typically start with a ~$ prefix, such as ~$datasheet.xlsx. When using the glob.glob("./*.xlsx") pattern matching, these temporary files are also included in the file list.

When Pandas' read_excel() function attempts to read these temporary files, it cannot automatically identify the correct Excel engine due to incomplete or corrupted file formats, thus throwing the format determination error. This issue occurs across MacOS, Windows, and Linux systems, representing a cross-platform common problem.

Solution Implementation

To completely resolve this issue, it's necessary to filter the file list before reading, excluding Excel temporary files. Here's the improved code implementation:

import pandas as pd
import glob
import os

customer_id = ["ID", "customer_id", "consumer_number", "cus_id", "client_ID"]

l = []
for f in glob.glob("./*.xlsx"):
    # Filter out Excel temporary files
    if not os.path.basename(f).startswith('~$'):
        try:
            df = pd.read_excel(f).reindex(columns=customer_id).dropna(how='all', axis=1)
            df.columns = ["ID"]
            l.append(df)
        except Exception as e:
            print(f"Error reading file {f}: {e}")

all_data = pd.concat(l, ignore_index=True)

Error Handling and Robustness Optimization

In practical applications, beyond temporary file issues, other types of file reading errors may occur. To enhance code robustness, we recommend implementing comprehensive exception handling:

import pandas as pd
import glob
import os

customer_id = ["ID", "customer_id", "consumer_number", "cus_id", "client_ID"]

l = []
for f in glob.glob("./*.xlsx"):
    filename = os.path.basename(f)
    
    # Exclude temporary and system files
    if filename.startswith('~$') or filename.startswith('.'):
        continue
    
    try:
        # Attempt automatic engine selection
        df = pd.read_excel(f).reindex(columns=customer_id).dropna(how='all', axis=1)
        df.columns = ["ID"]
        l.append(df)
        print(f"Successfully processed: {filename}")
    except Exception as e:
        print(f"Failed to process {filename}: {e}")
        # Add retry logic or alternative engine usage here
        continue

if l:
    all_data = pd.concat(l, ignore_index=True)
    print(f"Combined data shape: {all_data.shape}")
else:
    print("No valid data files found")

Best Practice Recommendations

Based on practical project experience, we summarize the following best practices:

1. File Filtering Strategy: Always filter temporary and hidden files before reading, using os.path.basename(f).startswith('~$') and filename.startswith('.') for double verification.
2. Exception Handling: Add try-except blocks for each file reading operation to ensure errors in individual files don't affect the entire batch processing pipeline.
3. Engine Selection: Although engine selection isn't the root cause, explicitly specifying engines in certain scenarios can improve code readability and stability. For .xlsx files, consider using engine='openpyxl'.
4. File Validation: Implement file size, modification time, and other validations before reading to ensure processing valid Excel files.
5. Logging: Maintain detailed records of each file's processing status for troubleshooting and progress tracking.

Environment Configuration Recommendations

Ensure dependency library version compatibility in the development environment:

• Pandas version: Recommended 1.3.0 or higher
• Python version: 3.8+
• Required dependencies: openpyxl, xlrd (for legacy .xls files)

Conclusion

Through systematic analysis of Excel temporary files' impact on Pandas reading operations, we provide complete solutions and optimization recommendations. The key lies in implementing effective file filtering before reading and adding robust exception handling mechanisms. These practices not only resolve the current error issue but also establish foundations for building more stable and reliable data processing pipelines. In practical projects, we recommend encapsulating file filtering logic into reusable functions to enhance code modularity and maintainability.