Duplicates can quickly pollute your Excel files, distort your calculations and complicate data analysis. Whether in a contact database, a sales table or a product list, detecting and deleting duplicates is essential to keep your files clean, reliable and usable.
In this guide, you’ll learn all the methods for removing duplicates in Excel, from the simplest to the most advanced, without losing important data. You’ll also discover practical tips for automating the process and avoiding common errors.
Why delete duplicates in Excel?
Duplicates can :
- Artificially inflate your results (sales, volumes, contacts)
- Create inconsistencies in your pivot tables
- Generate errors in formulas such as
SUM.IForCOUNTIF - slow down your file if the data is voluminous
By removing them, you’ll improve the quality of your analyses and gain in efficiency.
Method 1: Use the native “Delete duplicates” function
Excel offers a built-in tool for deleting duplicates in just a few clicks :
Steps to follow:
- Select the range containing your data (e.g. A1:C100)
- Go to the “Data” tab > click on “Delete duplicates”.
- Tick the columns to be analyzed (one or more)
- Click on OK: Excel deletes all identical rows in the selected columns.
Example:
| Name | |
|---|---|
| Alice | alice@gmail.com |
| Alice | alice@gmail.com |
| Bob | bob@gmail.com |
After deletion, only one line per duplicate remains.
Tip: remember to make a copy of your data before deleting.
Method 2: Remove duplicates with advanced filters
This method is useful if you want to extract a single list without deleting the original source.
Steps:
- Select your column
- Go to the “Data” tab > “Advanced” (in “Filter”)
- Check “Extract to another location”.
- Check the “Unique records only” box
- Choose a target cell to display the result
This method keeps the basic data intact.
Method 3: Use a formula to identify duplicates
With a formula, you can mark duplicates without immediately deleting them.
Example with NB.SI:
=NB.SI(A:A; A2) > 1
This formula checks how many times the A2 value appears in the entire A column.
If the result is > 1, it’s a duplicate.
Add a “Duplicate?” column to your table and then filter the rows concerned.
Learn more: COUNTIF in Excel with examples
Method 4: Delete duplicates in a structured table
If your data is in an Excel table (Ctrl + T), you can also :
- Click on a table cell
- Go to Table Tools > Design
- Then select “Delete duplicates”.
Excel automatically recognizes headers and guides you in the choice of columns to compare.
Delete partial duplicates (on a single column)
Sometimes you want to remove duplicates from a single column (e.g.: email), while keeping the rest of the data.
Here’s the safest way:
- Sort the data (by name, date, etc.)
- Use the formula
NB.SIin a column to identify duplicates - Filter this column to keep only one occurrence
- Manually delete identified duplicates
Practical example: Cleaning up a customer list
You have a table with the names and emails of your customers. Some of them are registered several times.
Objective:
Keep a single entry per email address.
Recommended solution:
- Use “Delete duplicates” on the Email column
- Then combine with a
SUM.SI.ENSformula if you need to group amounts by customer
Learn more about how to use the SUMIF formula in Excel
How to avoid duplicates in the future
Here are a few best practices:
- Use drop-down lists with data validation to limit errors:
-> How to create a drop-down list in Excel (step-by-step) - Apply conditional formulas to detect duplicates in real time
- Centralize your data to avoid multiple copy-paste operations
Useful links for further reading
- Understand COUNTIF with examples
- Apply COUNTIF based on another column
- Delete non-alphanumeric characters in Excel
- Creating a drop-down list in Excel
- Why don’t your COUNTIFs work? 8 reasons and solutions
- Use the SUMIF formula in Excel
Conclusion
Deleting duplicates in Excel is an essential step in ensuring the quality of your data. Thanks to the various methods described in this guide – native tools, formulas, filters, tables – you can now adapt the solution to your needs.
Clean data is the basis of reliable analysis. By applying these techniques, you will not only improve the readability of your files, but also the performance of your tables and graphs.