Python is widely used by Data Analysts for data cleaning, exploration, automation and analysis. In interviews, you may be tested on both Python fundamentals and practical Pandas workflows.

Python Basics

• What are lists, tuples, sets and dictionaries?
• What is the difference between mutable and immutable objects?
• What are functions and lambda expressions?
• What is a list comprehension?
• How does exception handling work?

Pandas Interview Questions

• What is Pandas?
• What is a DataFrame?
• How do you select rows and columns?
• What is the difference between loc and iloc?
• How do you group data?
• How do you merge DataFrames?
• How do you sort a DataFrame?

Data Cleaning Questions

• How do you handle missing values?
• How do you remove duplicates?
• How do you identify outliers?
• How do you convert data types?
• How would you standardize inconsistent categories?

Data Analysis Questions

• How would you calculate summary statistics?
• How would you analyze a trend over time?
• How would you calculate customer-level metrics?
• How would you identify an unusual observation?
• How would you visualize a distribution?

Python + SQL Workflow

Be prepared to explain how you would combine SQL and Python. A common workflow is to use SQL to extract and aggregate data close to the source, then use Python for deeper cleaning, analysis, visualization or automation.

15 Practical Questions to Practice

1. Read a CSV into Pandas.
2. Find missing values by column.
3. Remove duplicate records.
4. Filter rows based on a condition.
5. Create a calculated column.
6. Group data by category.
7. Merge two DataFrames.
8. Sort by multiple columns.
9. Calculate a rolling average.
10. Extract date components.
11. Find the top customers by revenue.
12. Calculate month-over-month change.
13. Identify outliers.
14. Create a simple visualization.
15. Explain how you would validate your analysis.

Final Tip

Don’t prepare Python as a collection of isolated syntax questions. Practice using Python to solve analytical problems and explain why you chose each step.

For a complete collection of Data Analyst interview questions across Python, SQL, Power BI, Excel, statistics and business cases, explore the Data Analyst Interview Mastery Playbook.