Why should you inspect unique value counts for each column?
PICTURE THIS: HOW TO EXPLAIN IT
Simple meaning
Unique counts reveal identifiers, near-constants, high-cardinality categoricals, and unexpected duplicates.
WHY — EDA instead of guessing?
Why interviewers care about EDA:
on EDA.
the situation, the default choice, and one exception - that reads as experience.
Name the idea, why it exists, then one short example.
End with when you use it and one common pitfall.
STEPS — What happens step by step?
Before you speak the answer, walk the interviewer through these steps:
- 1Unique counts reveal identifiers,
near-constants, high-cardinality categoricals, and unexpected duplicates.
- 2A column with one
value cannot predict anything, and a column with as many uniques as rows may be an ID.
- 3This check also catches
encoding bugs such as 1000 gender labels.
- 4Give an example
One tiny concrete case you can say aloud.
- 5Common mistake
What juniors usually get wrong.
- 6Close
When you pick this over the alternative.
EXAMPLE — See it in action
Here's a short line you can speak, broken into clear beats:
Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.
Key takeaway
Unique counts reveal identifiers, near-constants, high-cardinality categoricals, and unexpected duplicates. A column with one value cannot predict anything, and a column with as many uniques as rows may be an ID.