Summary: In this guide, we’ll explain how to join multiple CSV files in Excel using both manual and automated methods. Whether you’re a beginner or a professional, this step-by-step article will help you merge CSV files efficiently.

Managing data in CSV format is common in businesses, marketing, finance, and IT departments. However, when you have multiple CSV files that need to be combined into one Excel sheet, the process can become confusing, especially if you’re handling large datasets.

Don’t worry, this guide will help you completely as it covers all possible methods. Let’s walk through the article.

Why Join Multiple CSV Files in Excel?

You may need to combine CSV files when:

• Exporting monthly reports into one master sheet
• Merging customer databases
• Consolidating sales records
• Combining email lists
• Preparing data for analysis

Instead of copying and pasting data manually (which is time-consuming and error-prone), Excel provides smarter ways to merge CSV files.

Common Issues While Joining Multiple CSV Files in Excel

While combining CSV files, users may face:

1. Duplicate Headers

Each CSV file may contain column headers. Remove duplicate header rows after merging.

2. Different Column Structure

Ensure all CSV files have the same column format before merging.

3. Encoding Problems

If special characters appear incorrectly, choose the correct encoding (UTF-8 recommended).

4. Large File Size

Excel has row limitations (1,048,576 rows). Large CSV datasets may require Power BI or database tools.

Method 1: Join Multiple CSV Files Using Copy & Paste

This is the simplest method and works well for small files.

Steps:

1. Open Microsoft Excel.
2. Go to File > Open and select the first CSV file.
3. Open the second CSV file in another Excel window.
4. Select all data (Ctrl + A).
5. Copy (Ctrl + C).
6. Paste it below the last row of the first CSV file.
7. Repeat for other CSV files.
8. Save the combined file as .xlsx.

Pros:

• Simple and easy
• No technical skills required

Cons:

• Not suitable for large files
• Time-consuming
• Risk of duplication and formatting issues

Method 2: Join Multiple CSV Files in Excel Using Power Query

Power Query is the most efficient way to combine multiple CSV files in Excel. It works especially well for large datasets.

Steps to Merge CSV Files Using Power Query:

1. Open Excel.
2. Go to Data tab.
3. Click Get Data > From File > From Folder.
4. Browse and select the folder containing all CSV files.
5. Click Combine & Transform Data.
6. Excel will automatically detect and merge files.
7. Click Close & Load to upload merged output data file into Excel.

That’s it! Excel will combine all CSV files into one worksheet.

If you frequently need to join multiple CSV files in Excel, Power Query is the most reliable solution. This method is ideal for professionals who regularly merge CSV files and want automation.

When Manual Excel Methods Are Not Enough

If you regularly work with hundreds of CSV files or very large datasets, then manual methods might not work or can become slow and inefficient. In such cases, we recommend using a dedicated software, which is SysTools CSV merger tool. This utility can save both time and effort and reduce errors.

Professional tools allow:

• Bulk merging of CSV files
• No size limitation
• Preservation of data integrity
• Automatic removal of duplicates
• Faster processing

These tools are useful for IT teams, marketers, and data analysts who handle bulk CSV data daily. 

Know more: If you’re having lareg csv fiel and want to divide it into multiple parts, then we recommend using SysTools CSV Splitter Software.

Tips to Merge CSV Files Smoothly

• Keep all CSV files in one folder
• Ensure identical column structure
• Remove empty rows before merging
• Use Power Query for best performance
• Backup original files before combining

Conclusion

In this article, we have discussed how to join multiple CSV files in Excel using effective solutions. As it is simple when you use the right method. For small datasets, manual copy-paste works fine. However, for large or bulk data, using automated software is the best and most efficient solution.