Summary
This chapter covers using an AI assistant to define custom duplicate criteria, identify and remove duplicate rows in a table based on a specific column while preserving the first instance and exporting the removed entries to a separate sheet.

General domain of usage
data cleanup

After cleaning your dataset, the final step is removing duplicates.

In many cases, duplicate rows are not identical. The same person may appear multiple times with different values, so you need to define what counts as a duplicate.




Instead of comparing entire rows, focus on a key column. In this case, use the Email Address to identify duplicates.

This ensures each person appears only once in your dataset.

Define Duplicates Correctly


Do not delete duplicates immediately. These rows may contain useful information.

First, copy all duplicate entries into a separate worksheet. After that, remove them from the main table.

This keeps your data safe and allows you to review removed entries later.

Preserve Data Before Removing


When working with multi-step tasks, write prompts in a clear sequence.

Create a new worksheet, identify duplicates based on email, copy them to the new sheet, and then remove them from the original table.

Clear instructions reduce errors and make Copilot's behavior more predictable.

Use Clear Step-By-Step Prompts


After running the task, check both sheets.

Make sure the main table contains only unique entries and that all removed rows are saved in the new worksheet.

Verify the Result

You cleaned and structured messy data using Copilot. Your dataset is now consistent, complete, and ready for analysis in the next section.

Summary

Discover how to use Excel with Copilot to solve real business problems. Explore practical workflows that turn messy data into structured, reliable systems. Learn how to build models, automate calculations, and generate insights without memorizing formulas. Understand how to guide Copilot, validate its output, and keep full control over your logic.

Explore the core setup and habits for working with Copilot in Excel. You configure your environment, learn how to write clear prompts, and apply safe workflows before using Copilot on real data. You focus on testing, verifying results, and protecting your files. These skills help you avoid mistakes and use Copilot with confidence further.

Focus on turning messy, inconsistent datasets into clean and usable data. You standardize headers, split combined fields, fix mixed formats, and remove duplicates using Copilot. Consider real-world scenarios and apply structured workflows to clean data safely.

Discover using Copilot to work with clean data and turn it into insights. You extract values, connect multiple datasets, handle errors, and build dynamic summaries. Learn to describe tasks in plain language while Copilot generates and applies the formulas.

Learn how to present data in a clear and interactive way. Create PivotTables, add slicers, build charts, and apply conditional formatting to highlight key insights. Combine all elements into a structured dashboard and use Copilot to support analysis, formatting, and presentation. 

Build a forecasting model using assumptions, pricing scenarios, and 12-month projections. Test different outcomes, guide Copilot through complex logic, and turn your analysis into a clear, decision-ready recommendation.

Turn your work into a reusable, product-like spreadsheet. Clean raw data, combine multiple sources into a single system, build automated reports, and package everything into a polished template with instructions that anyone can use.

Using Copilot to Remove Duplicates