Python Data Analysis Interview Questions

Do you know what the buzzword is in the data analysis industry? It’s Python! Python has become the most vital tool that data analysts use daily. You require learning Python if you wish to pursue a career in data analysis. But why is Python so popular among analysts? Here are the top reasons making Python the most sought-after framework in the domain.

  • Python is a highly flexible language that remains compatible with a host of projects and platforms.
  • Python incurs no charges as it is an open-source library.
  • It is an easy-to-understand language and has shorter code snippets.
  • Python has an enormous community that helps in finding answers to all concerns.
  • You can quickly scale a data analysis project using Python.
  • Python has a myriad of libraries that can make data analysis more exciting for professionals.

Since the language is so essential, you can expect Python data analysis questions in an interview, regardless of whether you are a domain expert or pursuing a Data Analytics bootcamp course. So, here’s a questionnaire that you can consider before appearing for a data analysis interview.

Top 16 Python Data Analysis Interview Questions

Are you preparing for a data analysis interview? Get a glimpse of the following Python and data analysis questions and their answers to appear more confident on your D-day.

  1. What are some of the Python libraries used for data analysis?

Some of the Python libraries used for data analysis are:

  • Pandas
  • NumPy
  • SciPy
  • Matplotlib
  • Seaborn
  • SciKit
  1. Which is the best library in Python between Seaborn and Matplotlib for data plotting, and why?

Matplotlib is better than Seaborn as the former is easy to implement and remains at the beginner level. Also, Matplotlib produces visualizations that remain understandable to technical and non-technical team members.

You can go with the following answer if you find Seaborn better than Matplotlib.

I find Seaborn better than Matplotlib. Matplotlib is a novice library and has limited data plotting abilities. In contrast, Seaborn produces stunning visualizations, uses fewer lines of code, and provides pre-built themes to create better plots.

  1. How to create a DataFrame in Python?

We can create an empty DataFrame by writing the following lines of code.

# import pandas as pd  

import pandas as pd  

# Calling DataFrame constructor  

df = pd.DataFrame()  


  1. Which library in Python is best for Data Munging, and why?

A data analyst can use Python Pandas for Data Munging or Wrangling. It is simple, flexible, and offers better data manipulation and visualization abilities. Owing to these reasons, Pandas is ideal for data munging purposes.

  1. What are the advantages of NumPy arrays over Python lists?

The advantages of NumPy arrays over Python lists are:

  • NumPy arrays consume less memory than Python lists.
  • NumPy arrays are easy to use and convenient.
  • Also, NumPy arrays are faster than lists in Python.
  1. What is Reindexing in Python Pandas?

We use reindexing to change a DataFrame’s row and column labels. Reindexing aligns existing data with all-new axis labels and inserts no-value (NaN/NA) markers in locations having no label data.

  1. Can you create 3D plots with Python NumPy?

Yes, we can create 3D plots with Python NumPy. We would also require Matplotlib when plotting three-dimensional graphs using NumPy.

  1. What do you mean by Pylab?

Pylab is the combined package of Matplotlib, NumPy, and SciPy.

  1. Differentiate between NumPy and SciPy.

The differences between NumPy and SciPy are:

  • NumPy supports basic arithmetical operations, while SciPy can handle advanced algebraic operations.
  • We can create multidimensional arrays using NumPy. In contrast, no such provisions exist in SciPy.
  • NumPy is faster than SciPy.
  1. What are the advantages of using Python in data analysis?

Python has the following advantages in data analysis

What are the advantages of using Python in data analysis
  • It is a highly scalable programming language.
  • The presence of several libraries in Python facilitates a data analysis task.
  • Python is a straightforward language having easy-to-learn concepts.
  • A data analyst can access high-end data plotting abilities, including several libraries like Matplotlib and Seaborn.
  • Python has a massive community to solve complex issues arising in an analysis project.
  1. What are the benefits of using NumPy over Matlab and Octave?

NumPy is an extensive Python library and offers the following benefits over Matlab or Octave:

  • NumPy is open source and free to use.
  • It builds on Python, which is a general-purpose language.
  • One can connect existing C codebases with the Python interpreter effortlessly.
  1. What is the correlation between NumPy and SciPy?

The SciPy module contains all NumPy functions and builds on the latter. Also, all NumPy numerical codes stay within SciPy, making it a superset for NumPy.

  1. What is NaN, and why do we use it?

The Not-a-number or NaN is a common substitute for missing or unknown values in Python. NaN is a floating-point value and non-convertible to other data types. 

  1. What are the features of Python Pandas?

Some of the features of Python Pandas are:

  • Faster DataFrame object having customized indexing facilities.
  • Better missing data handling.
  • Pandas support multiple file formats.
  • Better data cleaning abilities.
  • Pandas offer indexing and alignment capabilities to data analysts.
  1. What do you mean by CMAP in Seaborn?

CMAP is the abbreviation for colormap in Seaborn. We use CMAP to change heatmap colors by going to the heatmap() function and altering the CMAP parameter.

  1. Explain memory management in Python.

Python uses a private heap to manage the memory. All data structures and objects are stored in the private heap. So, Python utilizes dynamic memory allocation. The language also uses a Garbage Collector to remove the allocated object spaces that no longer exist. An entity called Python memory manager caters to all distribute and deallocate requests from the API.

These were the best questions on Python that you are likely to encounter in a data analysis interview. Practicing the above questions will give you an idea of how grueling a technical round can be for you. Also, you can prepare more than half of the essential topics before answering the recruiters. So, brush up your Python in data analysis concepts and get more prepared than ever for your interview.

Best wishes!

Samuel Jim
Samuel Jim

Samuel Jim Nnamdi is the CTO of Foxstate, a platform that powers digital infrastructures for Real estate financing globally. He has over 8 years of Software Engineering and CyberSecurity expertise.

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