Interview Prep: Python Pandas Interview Questions


Pandas is a popular Python library used for data manipulation and analysis. It provides various tools for data cleaning, shaping, merging, and grouping. Pandas can handle different types of data such as CSV files, Excel sheets, SQL databases, and more.

If you are preparing for a Python pandas interview, here are some concepts that you should be familiar with:

1. Dataframes: A dataframe is a two-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table. Dataframes are the most commonly used pandas object.

import pandas as pd

data = {'name': ['John', 'Emma', 'Peter'], 
        'age': [25, 30, 35],
        'gender': ['male', 'female', 'male']}

df = pd.DataFrame(data)


name age gender
0 John 25 male
1 Emma 30 female
2 Peter 35 male

2. Series: A series is a one-dimensional labeled array capable of holding any data type such as integers, strings, floats, and more.

import pandas as pd

data = [25, 30, 35]
s = pd.Series(data)



0 25
1 30
2 35
dtype: int64

3. Indexing and Slicing: Indexing and slicing in pandas work similarly to indexing and slicing in NumPy arrays. You can select rows or columns based on their position or label.

import pandas as pd

data = {'name': ['John', 'Emma', 'Peter'], 
        'age': [25, 30, 35],
        'gender': ['male', 'female', 'male']}

df = pd.DataFrame(data)

# Selecting a single column by name

# Selecting multiple columns by name
print(df[['name', 'age']])

# Selecting rows by index

# Selecting rows and columns by index
print(df.iloc[0:2, 0:2])


0 John
1 Emma
2 Peter
Name: name, dtype: object

name age
0 John 25
1 Emma 30
2 Peter 35

name John
age 25
gender male
Name: 0, dtype: object

name age
0 John 25
1 Emma 30

These are just some of the concepts that you should be familiar with when preparing for a Python pandas interview. Make sure to practice and understand these topics thoroughly before going into an interview.

What is Pandas?

Pandas is a popular open-source library for data manipulation and analysis in Python. It provides easy-to-use data structures and data analysis tools for handling structured and semi-structured data. Pandas is built on top of NumPy, another popular scientific computing library for Python.

Pandas introduces two primary classes to work with – Series and DataFrame. A Series is a one-dimensional labeled array that can hold any data type, while a DataFrame is a two-dimensional labeled data structure with columns of potentially different types.

With Pandas, you can easily read data from various file formats such as CSV, Excel, SQL databases, and more. Once the data is loaded into a Pandas DataFrame, you can perform various operations such as filtering, sorting, grouping, joining, and aggregating the data.

Pandas also provides powerful tools for handling missing or null values in your data. You can fill missing values using different methods such as forward-fill, backward-fill or interpolation.

Overall, Pandas is an essential tool for any data scientist or analyst who works with large datasets in Python. Being proficient in Pandas will not only help you in acing your interviews but also make your day-to-day work much easier.

Why is Pandas important in Data Science?

Pandas is a popular open-source data analysis and manipulation library for Python. It provides data structures for efficiently storing and manipulating large and complex datasets, making it an essential tool in the field of Data Science.

Pandas allows you to easily read, write, and manipulate data from various sources such as CSV files, Excel spreadsheets, SQL databases, and even web pages. With its powerful indexing and filtering capabilities, Pandas makes it easy to slice, filter and reshape datasets according to your needs.

Another important feature of Pandas is its ability to handle missing or incomplete data through methods like interpolation or filling in missing values with a default value.

In addition to data manipulation, Pandas also provides tools for data visualization, allowing you to create various plots and charts to better understand your data.

Overall, Pandas is an essential tool in any Data Scientist’s toolbox as it simplifies the process of cleaning, transforming, and analyzing large datasets. Its ease of use and flexibility make it a popular choice among Data Scientists worldwide.

Common Pandas Interview Questions

If you are preparing for a Python Pandas interview, it’s important to have a good understanding of the basics. Here are some common questions you might encounter:

What is a DataFrame in Pandas?

A DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table. It is one of the most commonly used data structures in Pandas.

Here’s an example of creating a DataFrame in Pandas:

import pandas as pd

data = {'name': ['John', 'Jane', 'Bob'], 'age': [25, 30, 35], 'city': ['New York', 'Paris', 'London']}
df = pd.DataFrame(data)


name age city
0 John 25 New York
1 Jane 30 Paris
2 Bob 35 London

How can you read data from a CSV file using Pandas?

Pandas makes it easy to read data from CSV files. You can use the `read_csv()` function to create a DataFrame from a CSV file.

import pandas as pd

df = pd.read_csv('data.csv')

What is the difference between loc and iloc in Pandas?

`loc` and `iloc` are both used to select rows and columns from a DataFrame, but they work differently.

`loc` is label-based, which means that you specify the row and column labels you want to select. For example:

df.loc[0:2, ['name', 'age']]

This would select the first three rows and the columns named `name` and `age`.

`iloc` is integer-based, which means that you specify the row and column indexes you want to select. For example:

df.iloc[0:2, 0:2]

This would select the first two rows and the first two columns.

How do you handle missing values in a DataFrame using Pandas?

Missing values are a common problem in data analysis. Pandas provides several functions to handle missing values, including `dropna()`, `fillna()`, and `interpolate()`.

`dropna()` removes any rows or columns that contain missing values:


`fillna()` replaces missing values with a specified value:


`interpolate()` fills in missing values using linear interpolation:


What is groupby() function in Pandas?

The `groupby()` function is used to group data together based on one or more columns. You can then apply aggregate functions, such as `sum()` or `mean()`, to the groups to get summary statistics.

Here’s an example:


This would group the data by city and calculate the mean of each numeric column for each group.

How can you merge two DataFrames using Pandas?

You can use the `merge()` function to combine two DataFrames based on a common column.

Here’s an example:

df1 = pd.DataFrame({'name': ['John', 'Jane', 'Bob'], 'age': [25, 30, 35]})
df2 = pd.DataFrame({'name': ['John', 'Jane', 'Bob'], 'city': ['New York', 'Paris', 'London']})

merged_df = pd.merge(df1, df2, on='name')

This would merge the two DataFrames based on the `name` column.

What is pivot table and how can you create it using Pandas?

A pivot table is a way to summarize and aggregate data in a DataFrame. It allows you to group data by one or more columns, and then apply aggregate functions to the groups.

You can create a pivot table using the `pivot_table()` function in Pandas. Here’s an example:

df.pivot_table(index='city', columns='name', values='age', aggfunc='mean')

This would create a pivot table that shows the mean age of each person in each city. The `index` parameter specifies the row labels, the `columns` parameter specifies the column labels, the `values` parameter specifies the values to aggregate, and the `aggfunc` parameter specifies the aggregation function to use.

Tips for Preparing for a Pandas Interview

Pandas is a popular data manipulation library in Python that is extensively used in data science and analytics. If you are preparing for a Pandas interview, it is essential to be well-versed with the basic concepts and have a good understanding of the advanced features of Pandas. Here are some tips that can help you prepare for a Pandas interview:

Practice coding exercises on Pandas: The best way to get proficient in Pandas is to practice coding exercises related to real-world scenarios. You can start with simple exercises like cleaning and manipulating data and move on to more complex ones like data visualization, time series analysis, and machine learning with Pandas.

Read documentation and blogs on advanced topics related to Pandas: Reading official documentation and blogs related to advanced topics in Pandas can give you an edge over other candidates. Some of the advanced topics include hierarchical indexing, merging, grouping, reshaping, and pivoting data frames.

Review the basics of Python programming language: Since Pandas is a library in Python, it is essential to have a good understanding of the basics of Python programming language. You should know about data types, loops, functions, classes, and object-oriented programming concepts.

By following these tips, you can gain confidence in your skills with Pandas and be better prepared for your upcoming interview. Remember that practice makes perfect, so make sure to spend enough time practicing coding exercises related to Pandas.


In conclusion, preparing for a Python Pandas interview can be a daunting task but with the right resources and practice, it is definitely achievable. It is important to have a solid understanding of the fundamental concepts such as data structures, indexing, merging, grouping, and aggregating data in Pandas.

Additionally, being familiar with common operations like filtering, sorting, and transforming data will help you tackle any real-world problems that may arise during an interview. Remember to also brush up on your knowledge of NumPy and Matplotlib as they are often used in conjunction with Pandas.

Finally, don’t forget that practice makes perfect. Take advantage of online resources such as Kaggle and HackerRank to solve coding challenges and get hands-on experience working with Pandas. With dedication and hard work, you’ll be well on your way to acing your next Python Pandas interview.
Interested in learning more? Check out our Introduction to Python course!

How to Become a Data Scientist PDF

Your FREE Guide to Become a Data Scientist

Discover the path to becoming a data scientist with our comprehensive FREE guide! Unlock your potential in this in-demand field and access valuable resources to kickstart your journey.

Don’t wait, download now and transform your career!

Pierian Training
Pierian Training
Pierian Training is a leading provider of high-quality technology training, with a focus on data science and cloud computing. Pierian Training offers live instructor-led training, self-paced online video courses, and private group and cohort training programs to support enterprises looking to upskill their employees.

You May Also Like

Data Science, Tutorials

Guide to NLTK – Natural Language Toolkit for Python

Introduction Natural Language Processing (NLP) lies at the heart of countless applications we use every day, from voice assistants to spam filters and machine translation. It allows machines to understand, interpret, and generate human language, bridging the gap between humans and computers. Within the vast landscape of NLP tools and techniques, the Natural Language Toolkit […]

Machine Learning, Tutorials

GridSearchCV with Scikit-Learn and Python

Introduction In the world of machine learning, finding the optimal set of hyperparameters for a model can significantly impact its performance and accuracy. However, searching through all possible combinations manually can be an incredibly time-consuming and error-prone process. This is where GridSearchCV, a powerful tool provided by Scikit-Learn library in Python, comes to the rescue. […]

Python Basics, Tutorials

Plotting Time Series in Python: A Complete Guide

Introduction Time series data is a type of data that is collected over time at regular intervals. It can be used to analyze trends, patterns, and behaviors over time. In order to effectively analyze time series data, it is important to visualize it in a way that is easy to understand. This is where plotting […]