Moderate EDA Question 111 of 220

Why do duplicate rows matter, and how do you handle them?

Data Science track · Speak this in 60–90 seconds · Faridabad & Delhi NCR

PICTURE THIS: 1, 2, 2, 8

Mean3.25average

Median2middle

Mode2most often

Simple meaning

Duplicates inflate counts, double-weight users in means, and can be real repeated events or pipeline bugs.

WHY — EDA instead of guessing?

Why interviewers care about EDA:

They are checking judgment

on EDA.

A good answer names

the situation, the default choice, and one exception - that reads as experience.

Stay structured

Name the idea, why it exists, then one short example.

Close cleanly

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:

1
Duplicates inflate counts, double-weight
users in means, and can be real repeated events or pipeline bugs.
2
Decide whether the grain
should be unique on a key, then drop or aggregate accordingly.
3
Logging-level duplicates versus true
repeat purchases need different treatment.
4
Give an example
One tiny concrete case you can say aloud.
5
Common mistake
What juniors usually get wrong.
6
Close
When you pick this over the alternative.

EXAMPLE — See it in action

Here's a short line you can speak, broken into clear beats:

Say this line

“Decide whether the grain should be unique on a key, then drop or aggregate accor”

Break into beats

Decidewhetherthegrainshouldbe

Speaking order

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Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.

Key takeaway

Duplicates inflate counts, double-weight users in means, and can be real repeated events or pipeline bugs. Decide whether the grain should be unique on a key, then drop or aggregate accordingly.