Pandas Interview Questions and Answers

Pandas is one of the most popular data structuring and data analysis programs built on Python. If you seek a job in machine learning, data science, or data analysis, knowing how to use pandas can help you secure your desired position. Review common pandas interview questions and their answers with this guide.

Common pandas interview questions

Study the following common pandas interview questions to review pandas principles and increase your confidence going into your interview. While this isn’t a comprehensive list of everything you might be asked in an interview, you’ll be equipped with the knowledge to ace many common questions you’ll likely get.

1. What is pandas?

What they're really asking: The interviewer wants to know if you have a sense and understanding of what the pandas program is and what it does. This is a simple question to get the interview started.

The main point you have to hit is that pandas is a Python library that lets you manipulate and analyse data. Consider briefly describing an example or two of when you have used pandas so the interviewer understands your experience using the program.

Other forms this question might take:

• How would you describe pandas?

• In one sentence, could you sum up pandas?

2. What are the benefits of pandas?

What they're really asking: Now that you’ve identified the “what” and “why” of pandas, the interviewer wants to know if you can explain the practical uses of pandas.

Show your practical knowledge of pandas by discussing how using it can benefit the organisation you would like to work for:

• Python-based: Pandas was specifically crafted for Python. Since Python is a prevalent coding language, using pandas means you’ll be able to take advantage of the many other features and programs that Python offers.

• Saves time: You can save a lot of time by getting the analytical answers you need without writing down all the computations required when using Python. By using Python-based pandas libraries, you cut down the time it takes to handle data so you can analyse it faster.

• Versatile: Pandas allows you to analyse your data in many ways, making it a powerful tool.

• Specifically made to handle large data: Pandas is very helpful because it is designed to handle huge data sets.

• Ease of use: Pandas employ simple commands that are relatively easy for employees to learn.

Other forms this question might take:

• Why should our organisation use pandas?

• Name some of the advantages of using pandas.

3. What are the different types of data structures in pandas?

What they're really asking: Now that the interviewer has established that you know what pandas are and what they are designed to do, they will test your working knowledge of the program.

The two main types of data structures in pandas are series and dataframes. A third less common data structure is a panel.

Elaborate by explaining that a series is a one-dimensional structure that contains data like floating point numbers, strings, integers, and Python objects. 

A dataframe is two-dimensional, similar to a structure with rows and columns. It accepts and stores various kinds of data, such as dicts, 2-D numpy.ndarray,  series, or another dataframe.

A panel is a three-dimensional structure you can use to store heterogeneous data. It is an axis label that includes items, minor_axis, and major_axis.

Other forms this question might take:

• Pandas has two different types of data structures. What are they?

• How many types of data structures are in pandas?

4. What are the different ways to create a data frame in pandas?

What they're really asking: The interviewer wants to know if you’re familiar with terms and functions like lists, dictionaries (dicts), arrays, and zip(). This builds upon the previous question about dataframes.

Explaining the various ways to create a dataframe in pandas will show the interviewer you understand more than one or two ways to make one. Refer to your own experience whenever possible as you explain dataframes can be created by:

• Using a constructor to make a blank dataframe

• Using a single list or list of lists

• From dict of narray/lists

• Using arrays (to create an indexes dataframe)

• From lists of dicts

• Using the zip() function to merge two lists

• From a series

• Using the dicts of series

Be prepared to write code for each type of example.

Other forms this question might take:

• There are various ways to create a dataframe in pandas. What are they?

• What are a list and dictionary used for Pandas?

5. What is a time series in pandas?

What they're really asking: The interview seeks evidence that you understand the benefits of using time series in pandas.

Pandas uses time series to organise data collections into sequences to show quantity changes over time. Explain that pandas can use time series in a variety of ways, including the following:

• Creating sequences of times and dates according to pre-defined frequencies

• Using timezone information to manipulate and convert dates and times

• Using both absolute and relative time increments to calculate dates and times

• Manipulating time series data from a variety of sources and in various formats

• Converting or resampling a time series to a different frequency

Other forms this question might take:

• Give a brief description of the time series in pandas.

• What do you use to represent quantity changes over time in pandas?

Tips for acing your pandas interview

Review the various steps involved with some of the more complex and demanding questions. Also, review the code you need to write or type to answer the questions or complete the task. In addition to using resources like this guide, it’s always a good idea to get plenty of rest before your interview.

Learn more with Coursera

To learn more about pandas, Python, and other coding topics, take online courses to help you gain job-ready skills. A few of the many options on Coursera include:

• Python and pandas for Data Engineering

• Applied Data Science With Python Specialisation

• Python for Everybody Specialisation