83 Python data scientist interview questions to ask candidates

Python data scientists are professionals who use Python to perform complex data analysis. For this, they need a number of different programming skills to help them use database libraries efficiently. 

Finding out whether your applicants truly have the right technical expertise can be tricky – but we can help with this. Our Python skill test will help you quickly assess your candidates’ abilities; for the best results, combine it with an interview to dig deeper into their knowledge.

But which questions can help you gather more information and enhance the candidate experience in an engaging interview? 

Discover 83 Python data-scientist interview questions to check which candidates have the best data-analysis skills. This way, you’ll be able to make the best hiring choice – guaranteed.

26 frequently asked Python data-scientist interview questions

Ask your applicants some of these frequently asked Python data-scientist interview questions to assess their knowledge of libraries, data structures, and data analysis’ best practices.

1. Please explain how you would build a basic logistic regression model in Python.

2. Can you tell us how you would train and interpret linear regression models in Scikit?

3. Could you name five libraries that programmers use in Python for data analysis?

4. What is the advantage of using Seaborn for plotting in Python?

5. What is the disadvantage of using Seaborn for plotting in Python?

6. Name one disadvantage of using Matplotlib for plotting in Python.

7. How is a Pandas series different from a single-column data frame in Python?

8. Can you write us some code to arrange a data frame in descending order?

9. Please explain how you would manage duplicate dataset values for a variable.

10. Why are attention to detail and soft skills important for Python data scientists?

11. How are problem-solving skills essential for Python data scientists?

12. Please explain what the scatter_matrix method does.

13. Please tell us what the lag-plot method does in Python.

14. Is it possible for data scientists to create a data frame with several data types?

15. Can data scientists plot histograms without using Matplotlib in Pandas?

16. Please explain what numpy.loadtxt() does in Python.

17. When should data scientists avoid nested Python lists and use NumPy arrays?

18. Can you tell us the best method to check for empty arrays in NumPy?

19. Please name two evaluation metrics you can use for regression problems.

20. Can you explain what data munging is?

21. Name one library you would use to complete data munging.

22. Please write the code to sort an array by the nth column.

23. Can you explain how NumPy is related to SciPy?

24. Can you tell us a few Pandas features that you like?

25. Can you tell us a few Pandas features that you dislike?

26. Please explain what PyLab is.

5 sample answers to crucial frequently asked Python data scientist interview questions

Here are five sample answers to some of the most important and frequently asked Python data-scientist interview questions. Use the answers to assess the responses your candidates provide.

1. Could you name five libraries that programmers use in Python for data analysis?

Candidates should respond with five libraries that they can use for data analysis with Python to prove their technical knowledge. 

But it’s also crucial for them to explain what each library does and name some of the most popular features. This is important because the popular libraries’ pre-written functions and methods help programmers reduce time and effort when performing data analysis.

Some examples of libraries they might mention include:

• Matplotlib: This Python library is ideal for making static and interactive visualizations, such as 2D graphs and plots. Database analysts use Python scripts to create graphs from data.

• Seaborn: This Python library is based on Matplotlib. Its high-level interface helps data analysts draw statistical graphics.

• Pandas: This Python library provides data structures and operations for data scientists to manipulate numerical tables.

• NumPy: With its high-level mathematical functions, NumPy supports large, multi-dimensional arrays. It’s also ideal for numerical computing.

• SciKit: This library offers efficient tools ideal for machine learning tasks and statistical modeling. It’s a handy option for data scientists to do predictive data analysis.

Top candidates will be able to discuss how these Python libraries have helped them in their work, whether that’s by facilitating linear algebra tasks or data visualization. They might also know that more than half (60%) of Python data scientists use NumPy and 55% use Pandas.

Ask them for examples of projects in which they have worked with Python libraries. You can also use pre-employment tests, such as our SciKit-Learn test, to evaluate their knowledge.

2. Please tell us what the lag-plot method does in Python.

If your job opening requires you to find a candidate with strong technical Python expertise, this interview question will help you evaluate their knowledge. 

Candidates should explain that lag-plot methods are scatter plot graphs with X- and Y-axes. On the graphs, the X-axis shows the time series data, and the Y-axis shows the lag of the time series.

Data scientists can use the lag-plot method to check whether datasets are random. They help with autocorrelation checks in time-series data; additionally, using this method allows candidates to assess whether the dataset has a specific structure. 

Your candidates should understand that if the dataset’s lag plot doesn’t show a structure and the data doesn’t have an identifiable pattern, they can conclude that the data is random. 

This method is particularly useful for data analysts who need to make predictions from data for financial projects and features in case studies such as tracking Google stock data or economics.

3. How are problem-solving skills essential for Python data scientists?

As stated by the US Bureau of Labor Statistics, problem-solving skills are essential for data scientists, enabling them to develop different statistical methods and algorithms to analyze the data. 

Look for candidates who understand how specific problem-solving skills, such as closely analyzing data, can help them predict trends or notice anomalies.

When you do a competency-based interview with data scientists, make sure they can offer specific examples of their problem-solving experience. This will enable you to learn how they work and whether they can adapt to what your organization needs.

Consider whether candidates can name other scenarios in which they have solved technical data issues to help companies make data-driven decisions. Enquire about the approaches they used, such as to:

• Analyze complex data sets

• Use data to validate their findings

• Clean data

You can evaluate your candidates’ problem-solving skills using a Problem-Solving skill test. With this aptitude-testing method, you’ll receive an objective insight into your applicants’ skills, which will help you choose the most proficient Python problem-solvers.

4. Why are attention to detail and soft skills important for Python data scientists?

To be successful, candidates need specific soft skills such as attention to detail, communication, and interpersonal skills – and be able to explain why they are important. 

Attention to detail, for example, is crucial because it can enhance your candidates’ output and ensure they don’t overlook details in their data.

Candidates should also mention that attention to detail helps data scientists in many scenarios, including when they have to:

• Identify trends in data that might not be immediately obvious

• Work with large datasets that might contain inconsistencies

• Ensure that datasets don’t have missing data

Problem-solving is also critical, ranking as third out of the top 17 soft skills for data scientists according to an empirical analysis performed by Patrick Mikalef and John Krogstie. This skill helps candidates with some of the goals above by helping them understand why datasets might contain inconsistencies or have missing data.

During the interview, ask whether your candidate’s manager would rate their soft skills and attention to detail highly. You can also easily verify your candidates’ soft skills by doing a reference check with their current employer.

You can also verify this skill with an Attention to Detail test, created by our experts.

5. Please name two evaluation metrics you can use for regression problems.

Data scientists need different evaluation metrics for regression problems to objectively measure their models’ performance, adjust data models, and make accurate predictions about new data. 

These predictions help data scientists enhance decision-making, optimize resource allocation, and reduce risks.

Candidates can prove their knowledge by responding to this interview question with two examples of evaluation metrics that they use:

• Mean squared error (MSE): This evaluation metric measures the squared difference between existing and predicted values of regression models

• R-squared: This evaluation metric measures the proportion of variance in the dependent variable that the independent variable explains in a regression model

Don’t forget to ask your candidates how these metrics have helped them adjust their models in the past and to check whether they could accurately predict the relationship between two or more variables in past projects. 

If they can also name specific cases in which this is useful, such as predicting stock prices in finance, they may be a good match for your company.

32 basic Python data scientist interview questions 

Assess your candidates’ entry-level Python knowledge by asking them some basic Python interview questions for data scientists.

1. Please explain how you would copy an object in Python.

2. Please tell us what Python is.

3. Can you name five critical features of Python?

4. Can you explain what experts mean when they say that Python is object-oriented?

5. Please explain what Python modules are.

6. What is a mutable object?

7. What is an immutable object?

8. What does the generator function do in Python?

9. Please explain the map function.

10. Please explain the reduce function.

11. Please explain the filter function.

12. Please explain what a tuple is in Python.

13. Can you tell us what a list is in Python?

14. Please explain what PEP 8 is.

15. Could you explain whether all memory gets freed when Python exits?

16. Please explain what _init_.py does.

17. What do you know about the range() function in Python?

18. What do you know about the xrange() function in Python?

19. Please tell us which method you would use to randomize listed items in Python.

20. Can you explain what pass means in Python?

21. How would you store the first and last names of candidates in Python?

22. Please explain what monkey patching means in Python.

23. Can you tell us what Pylint is?

24. Can you explain what Pychecker does?

25. In which situation would you use decorators in Python?

26. Please explain how you would check if a Pandas data frame is empty.

27. Can you explain what list comprehension means?

28. Is it possible for lambda forms to contain statements in Python?

29. Can you tell us what pickling means in Python?

30. Can you tell us what unpickling means in Python?

31. Why are NumPy skills important for Python data scientists?

32. Why are Python skills crucial for Python data scientists?

5 sample answers to the most essential basic Python data scientist interview questions

Here are five sample answers to the five most important basic Python data scientist interview questions from the previous section. Refer to these when evaluating your applicants’ responses.

1. Please explain how you would copy an object in Python.

This interview question will help you determine if your applicants are familiar with some crucial Python functions and blocks of code that perform specific tasks, such as copying objects.

The best answers to this question will mention two functions:

• Copy.copy()****: This function is ideal for copying copy. It produces a shallow copy from the list the data scientist requires.

• Copy.deepcopy()****: This function is ideal for copying deep copy. It produces a deep clone of the object.

Candidates should also understand that shallow copy constructs new compound objects and inserts references from the original copy, while deep copy constructs new compound objects and inserts copies of objects into them. 

This is useful and helps save time, because data scientists don’t need to create a completely new object when coding. It works for projects in industries such as finance, in which frequent backups of important data are necessary.

However, since data scientists cannot copy every Python object with these Python functions, candidates should know and be able to describe alternative technical Python methods to achieve this. This may include slicing, which data scientists can use to copy sequences in Python. 

2. Please explain what PEP 8 is.

PEP 8 is a document that offers specific coding practices for Python, helping candidates improve their code’s consistency by promoting a readable coding style. This is useful because it helps other Python developers debug, modify, and understand the codebase. 

Skilled data scientists in your applicant pool who produce quality code should know that this extensive guide offers information about the following:

• Naming conventions: Data scientists should never use the lowercase version of “L,” the uppercase “O,” or the uppercase “I,” because in some fonts, they’re indistinguishable from one and zero

• Whitespace usage: Data scientists should avoid whitespace in a few situations, including inside parentheses, between trailing commas and closing parentheses, and before a comma or semicolon 

• Code layout: Data scientists should limit their code lines to a maximum of 79 characters

Data scientists should know basic specifications in PEP 8, such as instructions related to indentation, comments, and when to use trailing commas. These recommendations also help developers add new features to their applications when maintaining their code.

According to the Python community documentation, some data scientists use auto formatters for automated code formatting. However, PEP 8 is still essential. Many data scientists use it, and it’s still popular, with engineers updating the documentation in 2023.

3. Please explain what _init_.py does.

Candidates should understand that _init_.py is a file in Python that data scientists use to mark directories as Python packages. It signals to the Python interpreter that a directory contains code for modules and ensures that Python treats directories as modules.

It’s important that candidates can name some of the advantages of _init_.py files, because they help with module importation from various parts of the code. This helps in the finance industry, when data scientists need to organize financial models, and in healthcare, when organizing medical records. 

Candidates who have good technical knowledge of Python will know that _init_.py files also help keep code organized  into reusable modules for different projects. They may be ideal for data science roles, in which strong programming skills are necessary. 

4. Why are NumPy skills important for Python data scientists?

Since NumPy is the fundamental package data scientists use for computing in Python, your candidates must know how to use it for different tasks, such as working with matrices of numerical data. 

NumPy skills are also a must for data scientists who need to manipulate large arrays using a powerful N-dimensional array object in libraries. 

The most in-depth answers might also refer to specific cases that prove how crucial NumPy is for data science projects, such as DeepLabCut’s use of NumPy’s array manipulation features for image processing and fast computation. 

They might also refer to past successful data manipulation projects and talk about how their NumPy skills helped them achieve their goals.

Don’t forget to use a NumPy Online test to assess your candidates’ NumPy skills, including their ability to create and manipulate NumPy arrays.

5. Why are Python skills crucial for data scientists?

Strong Python skills are essential in a data scientist’s work because they help them take advantage of the many benefits of this language, including:

• Its large active community of programmers

• Libraries like TensorFlow and scikit-learn

Using these libraries with the right skills enables data scientists to work with large datasets, and efficiently store data using the correct structure. Additionally, Python has a strong programmer community that provides a wealth of resources and support to developers.

Python was the number 1 programming language in 2022. It’s so popular because it’s relatively simple to learn and makes it easy for data scientists to understand complicated datasets when processing data – in 2021, Python was used for data analysis in 51% of all projects with this programming language.

It’s also ideal for visualizing data in graphics and charts. This makes it easier to share with non-technical teams because they can quickly make sense of it and identify patterns.

Evaluate applicants’ abilities with a data-first Python Data Structures & Objects aptitude test. This will help you determine whether their skills suit your requirements for the role of a Python data scientist. 

25 advanced Python data scientist interview questions

Evaluate senior applicants’ expertise by asking them some of these 25 advanced Python data scientist interview questions during an interview.

1. Please write the syntax to import CSV files from a URL via Pandas.

2. Which method would you use to transpose NumPy arrays?

3. Could you explain what universal functions are for n-dimensional arrays?

4. Can you tell us what Boolean arrays are?

5. Please explain what fancy indexing means.

6. Please explain what NaT means in the Pandas library.

7. Can you explain what broadcasting means for NumPy arrays?

8. Please name two conditions required for data scientists to broadcast two arrays.

9. What does the acronym PEP mean in Python?

10. Please define what overfitting a dataset means.

11. Please define what underfitting a dataset means.

12. Could you explain how test and validation sets are different?

13. What do you understand about F1-scores for binary classifiers?

14. Please explain what confounding factors are.

15. Please explain what namespace means in Python.

16. What do you know about the phrase try-except-finally in Python?

17. What does the append() function do in Python?

18. What does the extend() function do in Python?

19. Explain the difference between append() and extend() in Python.

20. What does the enumerate() function do in Python?

21. Please explain what negative indexing means in Python.

22. How would your manager rate your Matplotlib skills?

23. How would your manager rate your arrays skills?

24. How would your team rate your scikit-learn skills?

25. How would your team rate your Pandas skills?

5 sample answers to the most critical advanced Python data scientist interview questions

Check these sample answers to the five most essential advanced interview questions from the previous section. Use these when assessing the accuracy of your candidates’ responses.

1. Explain the difference between append() and extend() in Python.

Candidates should understand what these functions do if they have used Python in challenging projects, such as collecting and processing large amounts of data to build an e-commerce recommendation engine.

The best candidates will know that append() adds a single element to the end of an existing list, and extend() adds multiple elements. Candidates might also want to provide examples of how these functions work.

For instance, if a data scientist has a price purchase history list and wants to add an item, they could use append() in the syntax this_list.append(5.5) to achieve this. If a data scientist wanted to add all new elements in a different list to their existing list, they could use extend() in the syntax this_list.extend(different list).

2. Please explain what negative indexing means in Python.

Data scientists who have used Python will know that it’s one of the few languages that offers negative indexing features. 

Negative indexing in Python refers to accessing elements from the end of a sequence, which could include strings, tuples, or lists. 

There are a few reasons why data scientists may want to use negative sequencing, such as the need to:

• Remove elements from the end of sequences, which is particularly important for cleaning data in data science projects that contain irrelevant data points. It’s ideal if a data scientist wants to remove the most recent data points because they are incomplete.

• Slice a sequence from the end or extract subsets of elements, which is ideal when working with data types that are stored in reverse chronological order. It’s an option data scientists can use to specify the element’s position in the sequence before slicing them.

• Loop through sequences in reverse order, which is essential and can be more efficient because it helps data scientists access elements without reversing the order of the data.

It’s ideal if your candidates know how to access the elements at the end of a sequence and use negative indexing: This knowledge proves they can handle data of varying lengths and work with time-series data. 

Don’t be afraid to ask for examples of data-science projects in which candidates have used negative indexing and enquire about the outcome of their data-manipulation work.

3. How would your manager rate your Matplotlib skills?

Your candidates should have advanced Matplotlib knowledge and skills and know how to create plots, including advanced statistical plots. This enables them to create visualizations of their data, including scatter plots, line charts, box plots, and histograms. 

If they know how to create these charts and present data in a visual format, data scientists can easily share their findings with non-technical employees. This process is easier than presenting raw data because it enables non-technical co-workers to see trends in the data instantly.

Matplotlib is one of the most popular libraries for Python, and the foundation of many other visual libraries; it’s also compatible with and forms the basis of NumPy’s numerical extension. 

Your applicants need Matplotlib skills to access large amounts of data, produce graphs with unique, customizable features, and run the Matplotlib library on various platforms. If the role primarily uses Matplotlib, you should use a Matplotlib job description to find the best candidates.

When hiring a Matplotlib developer, we recommend using a data-driven pre-employment skill test, such as our Matplotlib test, to assess your candidates’ skills.

4. How would your team rate your Pandas skills?

Pandas skills, such as basic data manipulation, indexing, and data aggregation, are crucial for data scientists and enable them to select, sort, analyze, and filter data. There are many advantages of this library, such as its simple data merging feature. 

Two-dimensional data frame structures in Panda facilitate data manipulation, while its spreadsheet-like structure enables data scientists to easily analyze data. Candidates lacking Pandas skills may find it difficult to reap these advantages.

The best candidates will be able to provide examples of how they’ve used their Pandas skills in projects, such as to work with or manage missing data values or complete simple data merging when combining complex datasets. They will have the skills to use Pandas’ constant value-filling or optimal dataset-performance support features to achieve this.

In short, Pandas can be very beneficial for data scientists. To ensure your candidates have the required skills to use it, you can assess their skills with a Pandas Online pre-employment skill test and our Pandas interview questions.

5. How would your team rate your scikit-learn skills?

Scikit-learn is a library that helps with data processing, building linear models, and data cleaning, which is why it’s fundamental for experts who do data analysis. It facilitates classification and clustering, helping data scientists group similar data instances together and label data.

Suitable candidates should be able to prove their scikit-learn expertise by discussing projects in which they used this library. For example, they may have used scikit-learn to complete data computing tasks to ensure they clean the data effectively.

They will also know that scikit-learn works with many algorithms, such as regression and clustering, which are ideal for building predictive models and identifying patterns in data. Both of these algorithms are beneficial in marketing and finance and enable the close analysis of data.

If scikit-learn skills are valuable for your company, ensure your candidate has enough experience with this library by using a Scikit-learn aptitude test.

Why should you use Python-related skills tests to assess your candidates?

Skill tests are data-driven, accurate, fast, and reliable. They enable you to quickly assess precise candidate skills – including soft skills such as communication and problem-solving, and technical skills such as Python, coding, and SQL skills, or experience with specific libraries. It’s also ideal for testing your candidates’ expertise and even their personality traits and values. 

Assessments can help your company reduce its time-to-hire and increase your team’s cognitive diversity by reducing unconscious bias during the selection process.

With a Python skills test, you can easily compare candidates and select the most skilled potential employees to interview. In the interview, you can discuss your candidates’ best skills, potential concerns about skills they lack, or gain a deeper understanding of their previous experience. 

It’s also an ideal way to support your onboarding and training efforts, because you’ll automatically know what kind of training you should offer to new employees, based on their skill-assessment results.

Hire better with skill tests and the right Python data scientist interview questions

Interviewing candidates is the best way to assess your candidates’ abilities, which is why having the right Python data-scientist interview questions is crucial. 

But to improve your recruitment process and make it more efficient and objective, consider skill assessments. With them, you can easily review your applicants’ knowledge and see who truly has the skills to excel at your open position. Using a data-first method when evaluating your candidates’ skills will enable you to hire the best Python data scientists.

See how TestGorilla works in a free demo today and spot the best talent for your business with the right data.