Top 50 Pandas Interview Questions and Expert Answers

Data analysis and manipulation have become the foundation of every business and research process. Whether you are preparing for a Data Analyst, Data Scientist, or Machine Learning Engineer role, you cannot ignore Pandas, the core Python library for handling structured data.

This 2025 guide collects the top 50 Pandas interview questions asked across leading companies. Every answer is written to help you understand both the concept and its practical application.

Section 1: Core Concepts and Data Structures

1. What are the main data structures in Pandas?
Pandas primarily uses Series and DataFrame. A Series is a one-dimensional labeled array, while a DataFrame is a two-dimensional structure similar to a spreadsheet with rows and columns.

2. How do you import Pandas in Python?
Use the command import pandas as pd. In interviews, it’s worth noting that this alias has become an industry standard.

3. What is an Index in Pandas?
An Index is a label identifier for rows and columns. It enables fast lookups, data alignment, and efficient merging.

4. Explain the difference between loc and iloc.
loc selects data by labels; iloc selects data by integer position.

5. How can you create a DataFrame from a dictionary?
You can create a DataFrame by passing a dictionary where keys become column names and values become lists or arrays.

6. What is the purpose of head() and tail()?
head() displays the first few records; tail() shows the last few. They’re used for quick inspection of data.

7. How do you check for missing data?
Methods such as isna() or isnull() return a boolean mask identifying missing values.

8. How can you handle missing values?
You can fill them (fillna()), drop them (dropna()), or estimate them using interpolation techniques.

9. What is the shape attribute used for?
It returns a tuple showing the number of rows and columns — an essential check during data cleaning.

10. How do you describe numerical data quickly?
The describe() method provides statistical summaries such as count, mean, median, and standard deviation.

Section 2: Data Manipulation and Operations

11. How do you select specific columns from a DataFrame?
By referencing the column name inside brackets, e.g., df['column_name']. You can pass a list for multiple columns.

12. How do you filter rows based on conditions?
By applying logical expressions, such as df[df['Age'] > 30].

13. What is broadcasting in Pandas?
Broadcasting applies arithmetic operations across rows or columns automatically without explicit loops.

14. Explain the difference between join() and merge().
merge() performs operations like SQL joins based on column keys, while join() merges on indexes by default.

15. What is concatenation?
Combining multiple DataFrames along a particular axis. For example, appending multiple monthly sales tables.

16. What does apply() do?
It applies a custom function to rows or columns, allowing flexible transformations when built-in functions are insufficient.

17. How do you rename columns?
Use the rename method with a dictionary mapping old names to new names.

18. How do you remove duplicate rows?
Use drop_duplicates(), which keeps the first occurrence and removes others unless specified otherwise.

19. How do you sort data in Pandas?
sort_values() sorts by specific columns; sort_index() sorts by index labels.

20. What is groupby() and when is it used?
groupby() groups rows sharing the same value in one or more columns, enabling aggregate calculations such as mean or sum.

Section 3: Advanced Data Handling

21. What is a MultiIndex?
A hierarchical index with multiple levels, useful for multi-dimensional analyses.

22. How do you combine multiple aggregation functions?
Use the agg() method to apply several functions like mean, count, and max in one step.

23. How can you reshape data in Pandas?
Through operations like melt() and pivot_table(), which convert data between long and wide formats.

24. What is stacking and unstacking?
These functions rotate a DataFrame’s levels — stacking compresses columns into a row MultiIndex, while unstacking reverses it.

25. How do you create new columns from existing ones?
By assigning expressions, for example, creating a “Total” column as the sum of “Price” and “Tax.”

26. What is the difference between copy and view?
A direct assignment creates a view (linked reference), whereas copy() creates an independent object.

27. How can you iterate over rows efficiently?
Though iterrows() exists, it’s better to use vectorized operations for performance.

28. What is a lambda function in Pandas?
A short anonymous function often used with apply() to perform inline transformations.

29. How do you sample random rows from a DataFrame?
Use sample() to retrieve random subsets for testing or validation.

30. How do you drop specific rows or columns?
Use the drop() function with axis = 0 for rows and axis = 1 for columns.

Section 4: Performance, Time Series, and Optimization

31. Why are vectorized operations faster?
They execute underlying C-optimized loops via NumPy, avoiding slow Python iteration.

32. How do you optimize CSV reading for large files?
Use parameters like usecols, dtype, or chunksize to control memory usage.

33. What is the purpose of dtype?
It defines data type. Choosing efficient dtypes saves memory and improves speed.

34. When should you use Categorical dtype?
When dealing with columns containing repeated string values, such as region names.

35. What are the advantages of using at and iat?
They provide faster scalar access compared to loc and iloc when fetching single cell values.

36. What is a rolling window?
A moving window that calculates statistics like rolling mean or sum over a specific interval.

37. What is resampling?
It changes time-series frequency — for example, converting daily data into monthly averages.

38. What is the shift() method used for?
To move index values up or down, commonly used to calculate percentage changes or lag values.

39. How can you handle time-based data?
Convert columns to datetime format using pd.to_datetime() and use .dt accessor for date components.

40. What are common time-series functions in Pandas?
Functions like resample, rolling, and expanding help analyze trends and seasonality.

Section 5: Real-World Applications and Interview Scenarios

41. How do you detect outliers using Pandas?
By combining statistical summaries or boolean filters based on thresholds (like values outside 1.5×IQR).

42. What is set_index() used for?
It assigns a column as the index, enabling faster lookups and joins.

43. Why should you avoid inplace=True?
It can make debugging harder. Using assignment instead improves clarity and chaining.

44. How can you clean inconsistent text data?
Use string accessor methods like .str.lower(), .str.strip(), and .str.replace().

45. What is the explode() function?
It converts list-like values in a column into separate rows.

46. How do you merge DataFrames with overlapping columns?
Specify suffixes in the merge function to distinguish column names.

47. What are the limitations of Pandas?
It works best on moderate datasets in memory. For larger data, use frameworks such as Dask or Spark.

48. How do you export a DataFrame to Excel?
Use to_excel() with the desired file path.

49. How can you style a DataFrame for display?
The Styler object allows conditional formatting, like highlighting maximum values.

50. What makes Pandas essential in data science?
It offers simplicity, flexibility, and performance — turning raw data into analysis-ready form with minimal effort.

Practical Connection to Real-World Data Solutions

Businesses that use Pandas efficiently streamline everything—from financial forecasting and customer segmentation to AI-driven automation. For organizations building data-powered applications, seamless integration between analytics systems and mobile platforms is now a critical differentiator.

If you’re exploring how expert development teams bridge this gap between data processing and mobile deployment, check out Indi IT, a leading mobile app development company in Georgia. Their approach demonstrates how modern software architecture transforms raw data insights into intuitive, real-world mobile experiences that enhance decision-making and user engagement.

Key Takeaways

• Pandas is the heart of Python-based analytics.

• Master data cleaning, grouping, and reshaping techniques.

• Use vectorized operations and optimize dtypes for large datasets.

• Understand time-series handling and aggregation deeply.

• Practice real interview scenarios — not just syntax.

Final Thought

Pandas remains the most important library for anyone serious about data science or business analytics.
In interviews, recruiters look not just for memorized methods but for problem-solving clarity.
Use these 50 questions as checkpoints — not for rote recall, but for understanding how each function solves a real-world challenge.