How to assess · For hiring teams
How to Assess Pandas Skills When Hiring
The test formats that actually work for Pandas, what a strong answer looks like, sample questions and a scoring rubric you can use as-is.
The short answer
Assess Pandas with a task, not a conversation: clean and analyse a messy file, find the bugs, ai-scored assessment (e.g. cohesyve) or performance rewrite. Score it against written criteria you fix before you see any submissions, and weight the criteria that the role actually depends on.
- Checks shapes and counts after every merge, filter and groupby
- Understands index alignment and the difference between views and copies
- Handles missing data explicitly and knows how each operation treats it
- Uses vectorised operations and knows when apply is the wrong tool
Paste a job description; Cohesyve generates a role-specific assessment and rubric. Ten candidates free, no card.
Pandas is where most Python data work happens and where most of it goes subtly wrong: a chained assignment that did not assign, a merge that silently multiplied rows, a groupby that dropped the rows with missing keys, an apply that took forty minutes because the vectorised version was not obvious. The library is easy to use and hard to use correctly. This page covers how to assess pandas for data analyst, scientist and engineering roles: data manipulation correctness, performance, handling of missing and messy data, and the habits that make analysis trustworthy.
Why Pandas is worth testing
Pandas errors do not throw. A merge that fans out, a filter that missed nulls, an index that misaligned during arithmetic — all produce a number, and the number is wrong. Testing with realistic messy data exposes whether a candidate checks shapes, understands alignment and validates results, and that separates analysts whose numbers can be trusted from those whose cannot.
What strong Pandas looks like
- Checks shapes and counts after every merge, filter and groupby
- Understands index alignment and the difference between views and copies
- Handles missing data explicitly and knows how each operation treats it
- Uses vectorised operations and knows when apply is the wrong tool
- Reshapes with melt, pivot and stack correctly
- Writes analyses as readable pipelines, not accumulated notebook cells
- Validates results against a known total before reporting
Ways to assess Pandas
Clean and analyse a messy file
Provide a CSV with mixed date formats, duplicate rows, a key with inconsistent casing and a few impossible values, plus a business question. Ask for the cleaned data, the answer, and the checks along the way. Sixty minutes.
Pros
Cons
Best for Any level.
Find the bugs
Provide a notebook with a fan-out merge, a chained-assignment that did nothing, and a groupby that dropped null keys. Ask what is wrong and what the correct numbers are.
Pros
Cons
Best for Mid and senior roles.
AI-scored assessment (e.g. Cohesyve)
Generate a pandas task from the job description — a cleaning task, a bug hunt, a performance question — with a rubric. Each candidate receives a different variant; the reasoning is scored alongside the work.
Pros
Cons
Best for Screening an applicant pool fairly before interview time is spent.
Performance rewrite
Give a slow row-wise apply and ask for a vectorised version.
Pros
Cons
Best for Roles with large data.
Cohesyve
Run a Pandas assessment on your next opening
Cohesyve generates a unique Pandas task per candidate from your job description, with the scoring rubric attached. Questions are different for every applicant, so they cannot be shared or looked up.
What to test
Correctness
Whether operations do what was intended.
Missing and messy data
Whether edge cases are handled.
Reshaping
Whether data can be shaped for the question.
Performance and structure
Whether code is efficient and readable.
Sample Pandas questions
After a merge, your row count doubled. What happened?
EntryLook for Duplicate keys on one side; check with value counts; decide on aggregation or dedup.
What is the difference between a view and a copy, and why does it matter?
EntryLook for Chained assignment may not modify the original; use loc; the warning is a real problem.
A groupby mean is missing a category. Why?
MidLook for Null keys are dropped by default; dropna parameter; check counts.
This apply takes forty minutes on a million rows. Fix it.
MidLook for Vectorised operations, numpy, or groupby transforms; measures the result.
How do you make sure a reported total is right?
SeniorLook for Reconcile against a known value, check shapes at each step, assert invariants, keep a validation cell.
Red flags
- Never checks shapes after a merge
- Ignores the chained-assignment warning
- Row-wise apply for everything
- Silent null handling
- Notebook state that cannot be rerun
Scoring rubric
| Criterion | Weight | What strong looks like |
|---|---|---|
| Correctness and validation | 35% | Checks counts and invariants; catches silent errors. |
| Messy-data handling | 25% | Nulls, duplicates and formats are handled explicitly. |
| Reshaping fluency | 20% | Shapes data for the question without hacks. |
| Performance and structure | 20% | Vectorised, readable, rerunnable. |
Mistakes hiring teams make
- Testing API recall instead of validation habits
- Using a clean dataset
- Not planting a fan-out merge — the most common silent error
- Ignoring performance
- Accepting a correct answer reached without any checks
Roles that need Pandas
Common questions
Is pandas skill the same as Python skill?
No. Pandas has its own model — alignment, views, null semantics — and strong Python developers make pandas errors. Test it with data.
What is the best single pandas question?
Describe a merge that doubled the row count and ask why. Key understanding and validation habit show together.
Should I test polars or other alternatives?
If the role uses them. The validation habits transfer; the API does not matter much.
How long should a pandas assessment take?
Sixty minutes for a clean-and-analyse task; thirty for a bug hunt.
Cohesyve · Skill assessments for hiring
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