Python Pandas Skills: What to Assess When Hiring Data Analysts and Data Scientists

Pandas is the open source Python library most data teams use to load, clean, reshape and analyze tabular data. Python pandas skills show up on nearly every analyst, data scientist and data engineer resume, which makes them easy to claim and hard to judge. The reliable way to check is a hands on technical assessment where the candidate works with a real dataset.

Hiring managers usually discover the gap in the first week. The new analyst can write a groupby, but their notebook takes ten minutes to run because every step loops row by row, and their joins quietly duplicate records. This guide covers which pandas skills matter by level, how to test them, and what to ask in the interview.

What do data teams use pandas for?

Pandas gives Python two core structures: the Series (one column of data) and the DataFrame (a table). Day to day, teams use it to:

• Read data from CSV, Excel, SQL databases, JSON and Parquet
• Clean data: fix types, handle missing values, remove duplicates
• Reshape data with pivot, melt, stack and unstack
• Combine datasets with merge, join and concat
• Summarize with groupby, aggregation and window functions
• Prepare features for machine learning and inputs for dashboards

It sits alongside NumPy for numerical work, matplotlib or similar tools for charts, and SQL for data that lives in a warehouse.

Which Python pandas skills matter by role level?

Entry level analysts

• Loading files and inspecting data with head, info and describe
• Selecting data with loc and iloc and boolean filters
• Handling missing values with isna, fillna and dropna
• Basic groupby and aggregation
• Sorting, renaming and simple derived columns

Mid level analysts and data scientists

• Merging datasets correctly, including checking for many to many joins
• Using vectorized operations instead of apply or loops where possible
• Working with dates and time series: resampling, rolling windows, time zones
• Reshaping with pivot_table and melt
• Choosing efficient dtypes, including categoricals, to cut memory use
• Writing readable, chained transformations that other people can review

Senior data scientists and data engineers

• Profiling and speeding up slow pipelines
• Processing data too large for memory in chunks, or knowing when to move to Spark, Polars or SQL
• Avoiding chained assignment pitfalls and understanding copy versus view behavior
• Writing tests for data transformations
• Packaging pandas code for production jobs rather than leaving it in notebooks

How do pandas skills tests compare by format?

The strongest tests combine a short concept check with a hands on notebook task and one written or video explanation.

How do you assess Python pandas skills before an interview?

Give candidates a slightly messy dataset and a business question. For example: “Here are 18 months of order and customer files. Find the monthly repeat purchase rate by region, and explain any data issues you found.” A task like this tests loading, cleaning, merging, grouping and judgment in one exercise, and it is hard to complete by pattern matching.

Glider AI Technical Assessments support Jupyter Notebook tasks for data science roles, alongside SQL tasks and more than 35 question types, so you can test pandas the way the job uses it. Where a role leans heavily on software engineering, a coding simulation gives candidates a fuller project environment. AI proctoring and identity checks help confirm the work is the candidate’s own. For a wider view of the role family, see our guide to data science and analytics job roles and skills.

Pandas interview questions for hiring managers

Use these after the assessment to go deeper:

• How do you check whether a merge created duplicate rows?
• When would you avoid apply, and what would you use instead?
• Walk me through how you handle missing data in a column that drives a key metric.
• A notebook that ran in seconds now takes ten minutes on the full dataset. What do you look at first?
• When does pandas stop being the right tool, and what do you switch to?
• How do you make your analysis reproducible for someone else on the team?

Good answers mention validation steps (row counts before and after a join, for instance), specific functions and a real example. Weak answers repeat textbook definitions.

Red flags in pandas candidates

• Loops over rows for operations that pandas can do in one vectorized step
• Never checks row counts or duplicates after a merge
• Fills missing values with zero without asking what missing means
• Cannot explain loc versus iloc
• Every project lives in one long notebook with no structure

For an ETL heavy role that also uses enterprise tools, compare with our guide to Ab Initio developer skills.

How Glider AI helps you hire data talent

• Technical Assessments with Jupyter Notebook, SQL and open ended tasks for analyst and data science roles
• Rubric based scoring with human override, so hiring managers see the evidence behind every score
• Proctoring that records facts such as tab switching or pasted code for recruiter review, rather than rejecting candidates automatically
• Configurable assessments that run from 20 to 60 minutes

FAQ

Hire data talent on evidence

Pandas on a resume tells you a candidate has used Python for data. A short hands on task tells you whether they can do the work your team needs. Build your next Python pandas skills test with Glider AI Technical Assessments.