Duplicate Detection: Finding and Removing Repeated Rows

Duplicate rows quietly distort everything downstream: they inflate counts, over-weight repeated records in a model, and break aggregates. The chart above flags the repeats — but before deleting, you need to know what kind of duplicate you're looking at.

Note

Not every repeated value is a duplicate. Two customers can share a name; one sensor can log the same reading twice. A duplicate is a row that shouldn't exist twice — that's a judgment about your keys.

Exact vs Near-Duplicates

Exact duplicates: every field matches — usually a safe removal
Key duplicates: the same identifier repeats, other fields may differ
Near-duplicates: 'Jon Smith' vs 'John Smith' — same entity, messy text

Exact duplicates are easy. Near-duplicates need fuzzy matching or normalization first, because the text differs even though the entity is the same.

Which Copy to Keep

Once you find duplicates, deleting is only half the job — you have to decide which row survives. Keep the most recent, the most complete, or the one from the most trusted source, depending on your data.

Watch out

Dropping duplicates blindly can delete the newer, better record and keep a stale one. Decide the tie-breaker — recency, completeness, source — before you remove anything.

The Over-Deletion Risk

Deduplicating on too few columns can merge distinct records that only look alike. Two real orders on the same day for the same amount aren't necessarily one — check your key covers what truly makes a row unique.

Cleaning Duplicates in the SKARI Data Editor

The Data Editor's cleaning tools help you inspect and remove repeats: profile a column for repeated values, review the rows before acting, and apply the removal as a tracked step you can undo.

Spot repeated values through column profiling
Review flagged rows before anything is deleted
Every cleaning step recorded in the pipeline, and reversible

Takeaway

You see the duplicates and confirm the removal — with a pipeline history to fall back on if you cut too much.

Frequently Asked Questions

Should I always remove duplicates?

No — only when a row genuinely shouldn't repeat. Some repeats (repeat purchases, repeated measurements) are real data.

How do I catch near-duplicates?

Normalize the text first (case, whitespace, formatting), then match — fuzzy matching catches the rest.

Which row should win?

Usually the most recent or most complete — but decide the rule deliberately, based on your source.

Conclusion

Duplicate detection protects every count and model downstream — but only if you distinguish real duplicates from legitimate repeats, choose the surviving row on purpose, and keep the option to undo. Clean deliberately, not by reflex.

Takeaway

Find the repeats, decide which copy is truth, and remove with a trail — so cleaning fixes your data instead of quietly thinning it.

Handling Missing Data

The other half of data cleaning

Joining Datasets

Where duplicates often creep in

Column Profiling

Spotting repeats at a glance