If you’re preparing for a Python developer interview or a data analyst role, chances are you’ll be asked about Pandas. It’s one of the most used Python libraries for data manipulation and analysis. Whether you’re just getting started or brushing up for an interview.
This guide covers the top 30 Pandas interview questions with clear and simple answers. Let’s dive into the most commonly asked questions and get you ready to ace your interview.
Table of Contents
1. What is Pandas in Python?
Pandas is an open-source Python library used for working with structured data. It makes analyzing and cleaning data super easy with tools like DataFrames and Series.
2. What are Series and DataFrames?
- Series: A one-dimensional labeled array.
- DataFrame: A two-dimensional labeled data structure (like a table in Excel).
3. How do you create a DataFrame in Pandas?
import pandas as pd
data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)
4. How to read a CSV file using Pandas?
df = pd.read_csv('file.csv')
5. How do you write a DataFrame to a CSV file?
df.to_csv('output.csv', index=False)
6. How can you view the first few rows of a DataFrame?
df.head()
7. How do you display the last few rows?
df.tail()
8. How do you get DataFrame structure info?
df.info()
9. How to get summary statistics?
df.describe()
10. How to check for missing values in Pandas?
df.isnull().sum()
11. How to drop rows with missing values?
df.dropna(inplace=True)
12. How to fill missing values?
df.fillna(0, inplace=True)
13. How do you rename columns?
df.rename(columns={'Old': 'New'}, inplace=True)
14. How do you select a single column?
df['column_name']
15. How do you select multiple columns?
df[['col1', 'col2']]
16. How do you filter data in a DataFrame?
df[df['Age'] > 25]
17. How to sort a DataFrame?
df.sort_values(by='Age', ascending=False)
18. What does groupby() do in Pandas?
groupby() is used to group data based on column values and apply aggregate functions like sum(), mean(), etc.
df.groupby('Department')['Salary'].mean()
19. How do you reset the index of a DataFrame?
df.reset_index(drop=True, inplace=True)
20. How to set a new index?
df.set_index('column_name', inplace=True)
21. How to check data types of each column?
df.dtypes
22. How to change the data type of a column?
df['Age'] = df['Age'].astype(float)
23. How to remove duplicate rows?
df.drop_duplicates(inplace=True)
24. How to concatenate two DataFrames?
pd.concat([df1, df2])
25. How to merge two DataFrames?
pd.merge(df1, df2, on='ID', how='inner')
26. What’s the difference between loc and iloc?
- loc: Selects by labels (column names).
- iloc: Selects by index numbers.
27. How to apply a function to a column?
df['col'] = df['col'].apply(lambda x: x + 1)
28. How to get unique values from a column?
df['col'].unique()
29. How to count unique values in a column?
df['col'].nunique()
30. How to count frequency of values?
df['col'].value_counts()
Final Thoughts
Learning Python Pandas is essential if you’re working in data science, machine learning, or data analysis.
These Pandas interview questions are asked in both beginner and intermediate interviews, so make sure you understand the logic behind each one. Practice writing and playing with real datasets to boost your confidence.
Good luck with your interview!
