Top 30+ Python Pandas Interview Questions and Answers (2025 Guide)




Whether you're preparing for a data analyst, data scientist, or Python developer role, chances are you'll face Python Pandas interview questions. Pandas is a powerful open-source library that enables fast data manipulation and analysis using data structures like Series and DataFrame.

To help you succeed, we’ve compiled this up-to-date list of the most commonly asked Python Pandas interview questions for 2025. From basic syntax to advanced operations, this guide will help both freshers and experienced professionals confidently answer questions and stand out.

Python Pandas Interview Questions for Freshers

1. What is Pandas in Python?

Pandas is an open-source Python library used for data analysis and manipulation. It provides two primary data structures: Series (1D) and DataFrame (2D).

2. What are the key features of Pandas?

  • Easy handling of missing data

  • Powerful data alignment

  • Flexible reshaping and pivoting

  • Efficient group-by functionality

  • High-performance merging and joining

3. What is a Series in Pandas?

A Series is a one-dimensional labeled array that can store any data type (integers, floats, strings, etc.).

python
import pandas as pd
s = pd.Series([10, 20, 30])

4. What is a DataFrame?

A DataFrame is a 2-dimensional labeled data structure with columns of potentially different types, like a spreadsheet or SQL table.

5. How do you create a DataFrame in Pandas?

python
import pandas as pd  
data = {'Name': ['John', 'Alice'], 'Age': [28, 24]}  
df = pd.DataFrame(data)

6. What are common ways to read data using Pandas?

  • pd.read_csv()

  • pd.read_excel()

  • pd.read_json()

  • pd.read_sql()

  • pd.read_html()

7. How do you handle missing data in Pandas?

Use isnull(), fillna(), or dropna() to detect, replace, or remove missing values.

8. How do you filter data in a DataFrame?

python
df[df['Age'] > 25]

9. What is the difference between loc[] and iloc[]?

  • loc[] is label-based indexing.

  • iloc[] is integer position-based indexing.

10. How can you sort a DataFrame by a column?

python
df.sort_values(by='Age', ascending=False)

Intermediate Python Pandas Interview Questions

11. What does groupby() do?

groupby() splits data into groups based on column values, applies a function, and combines results.

python
df.groupby('Department').mean()

12. How do you merge two DataFrames in Pandas?

Use pd.merge() or join().

python
pd.merge(df1, df2, on='EmployeeID')

13. What is the difference between merge() and concat()?

  • merge() combines DataFrames using keys (like SQL joins).

  • concat() stacks DataFrames vertically or horizontally.

14. What is a pivot table in Pandas?

A pivot table reorganizes data by summarizing it based on column values.

python
df.pivot_table(values='Sales', index='Region', columns='Year', aggfunc='sum')

15. What is the use of apply() in Pandas?

apply() applies a function along a DataFrame axis (rows or columns).

python
df['Tax'] = df['Price'].apply(lambda x: x * 0.1)

16. What are lambda functions in Pandas?

Anonymous functions used with apply() or map() for simple transformations.

17. How to rename a column in Pandas?

python
df.rename(columns={'OldName': 'NewName'}, inplace=True)

18. How do you reset the index of a DataFrame?

python
df.reset_index(drop=True, inplace=True)

19. How do you check for duplicate rows?

Use df.duplicated() to find and df.drop_duplicates() to remove them.

20. How do you change data types of columns?

python
df['Age'] = df['Age'].astype('float')

Advanced Python Pandas Interview Questions

21. What is chaining and why is it risky?

Chaining (e.g., df[df['Age'] > 25]['Salary'] = 5000) can lead to unexpected results. Use .loc[] for safer operations.

22. How do you handle time series data in Pandas?

Pandas has support for date parsing, resampling, and time-based indexing using to_datetime(), resample(), and date_range().

23. What is multi-indexing in Pandas?

MultiIndex allows hierarchical indexing for rows and columns. It’s useful for complex datasets.

24. How to improve Pandas performance?

  • Use categorical data types

  • Drop unused columns

  • Use vectorized operations instead of loops

  • Use chunking for large files

25. How do you export a DataFrame to CSV or Excel?

python
df.to_csv('output.csv', index=False)
df.to_excel('output.xlsx', sheet_name='Sheet1')

26. How do you describe a dataset statistically?

Use df.describe() to get count, mean, std dev, min, max, etc.

Conclusion

As the demand for data roles continues to rise, strong knowledge of Pandas can set you apart. These Python Pandas interview questions are carefully chosen to reflect what companies are really asking in 2025. Whether you're preparing for your first job or aiming for a senior role, practicing these questions will help you handle any curveball thrown at you during a technical interview.

By mastering these Python Pandas interview questions, you’re not just passing interviews—you’re also building solid foundations for real-world data tasks.

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