Python Coding Questions
Part of the Data Science Roadmap
In short
Data science coding rounds are usually about pandas and clean Python, not tricky puzzles. They check if you can turn a question into working code, calmly, while someone watches.
Live coding exercises, often involving data manipulation (Pandas) or basic algorithmic thinking — testing fluency and clean problem-solving under mild time pressure.
Think of it like this
The live cooking show
On a live cooking show, the chef talks while chopping: 'Adding salt now because the tomatoes are sour.' Even if one dish goes slightly wrong, the judges see how she thinks. Live coding rewards the same habit: think aloud, start simple, then improve.
Watch it happen
How to tackle a coding question
1/6Repeat the question in your words. Ask about input size, missing values and ties.
The big ideas
What you need to know
Think out loud
Silence makes interviewers guess. Narrate your plan so they can nudge you early.
pandas fluency
groupby, merge, sort_values, pivot_table and date handling come up again and again.
Core Python
Dicts, sets, list comprehensions and collections.Counter solve many questions in a few lines.
Vectorise, don't loop
Avoid iterrows on DataFrames. Built-in column operations are shorter and much faster.
See it in code
Walk through the code
1import pandas as pd2 3sales = pd.DataFrame({4 "category": ["snacks", "snacks", "snacks", "drinks", "drinks"],5 "product": ["chips", "namkeen", "biscuits", "lassi", "cola"],6 "revenue": [500, 800, 300, 450, 600],7})8 9top2 = (sales.sort_values("revenue", ascending=False)10 .groupby("category")11 .head(2))12print(top2)1/4Build a tiny example DataFrame so you can check the answer by eye.
A classic: 'top 2 products per category by revenue'. Sort first, then take the first rows of each group.
Save yourself the pain
Common mistakes
Oops
Coding in silence for ten minutes, then showing the result.
Do this instead
Explain your plan before typing and keep talking. A half-finished but well-explained answer can still pass.
Oops
Looping over DataFrame rows with iterrows.
Do this instead
Use groupby, merge and vectorised column maths. Mention why it's faster.
Your mission
Three timed problems
Quick check
You're asked to count how often each word appears in a list. What's the neatest Python tool?
Go deeper
- 1Practice Pandas-heavy coding questions on a platform with real interview-style problems.
- 2Talk through your approach out loud before writing code, even in practice.
- 3Practice writing clean, working code rather than optimizing for cleverness.
More coding & sql at this stop
SQL Coding Questions
Data Structures & Algorithms Basics
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