15 Top Python Interview Questions for Data Analysts

Coding – particularly in Python – is an important skill for data analysis roles. Check out these 15 Python interview questions specifically for data analysts and get a head start on your next job interview!

Python has become one of the world’s favorite programming languages. Its robust ecosystem of data-handling libraries has made it an excellent (and in-demand) tool for data analysts. Whether you're a seasoned data enthusiast or just embarking on your journey into the field of data analysis, preparing for a data analyst interview is essential.

Python is so popular that we’ve developed learning tracks specifically for data analysts. If you’re a complete beginner in using Python, check out Python for Data Science. This track contains 5 interactive courses and more than 230 coding challenges – a perfect fit for aspiring data analysts with no IT background! For more details, read the article Python Course of the Month – Introduction to Python for Data Science by Jakub Romanowski.

As we all know, job interviews can be both exciting and intimidating. But don’t be afraid! In this article, I will delve into some of the most frequently asked Python interview questions for data analysts. From understanding fundamental Python concepts to tackling real-world data analysis scenarios, I will cover a range of data analyst interview topics and provide detailed explanations and solutions.

So, grab your favorite beverage, take notes, and prepare to shine in your next data analyst interview!

Python Interview Questions for Data Analysts

Python Core Questions

Question 1: What Is Python?

I’ve led multiple Python interviews, and I love this question. It is very open, and there is no perfect answer.

This question is principally used to demonstrate your ability to summarize high-level concepts in a few seconds. Here’s a possible short answer:

Python is a high-level and interpreted programming language that supports multi-paradigms and is used for generic purposes.

Question 2: Which Python Built-in Functions Do You Know?

Python’s built-in functions are always available for use; there’s no need to import a module. These functions are available in the official Python documentation. You don’t need to know all of them, but showing the interviewer you know a few without overthinking is a very good indicator of your coding experience.

Here are some built-in functions:

• int(): Converts a value to an integer.
• range(): Generates a sequence of numbers within a specified range.
• min(): Finds the smallest element in an iterable object (such as a list).
• max(): Finds the greatest element in an iterable object.
• sum(): Gets the sum of elements in an iterable object.
• abs(): Returns the absolute value of a number.
• round(): Returns a rounded number.
• pow(): Returns a number to a given power.
• hex(): Converts an integer number to a lowercase hexadecimal string.
• len(): Returns the length of an object.

Question 3: How Do You Import External Libraries into Python?

Importing an external library is something you’ll do multiple times a day; you should be able to do it with your eyes closed! Let’s see an example using the pandas library:

import pandas as pd

dataset = {
  'users': ["John", "Frida", "Katlyn"],
  'age': [31, 27, 42]
}

dataframe = pd.DataFrame(dataset)

This will import the entire pandas library, allowing you to use all its functions and classes, here, you use the DataFrame function. The alias pd to make it easier to reference this dataframe in the code.

You may also want to refresh your knowledge of Python Libraries Every Programming Beginner Should Know.

Question 4: How Are Errors and Exceptions Managed in Python?

As an interviewer, I usually ask this question to check something fundamental: Can the candidate write robust and reliable Python applications?

In Python, errors and exceptions are easily handled using the try-except block. This allows you to catch and handle exceptions that occur during the execution of your code, preventing them from causing your program to terminate unexpectedly.

Let’s see an example of handling a classic error: division by zero.

try:
    # Code that may raise an exception
    x = 10 / 0  # This will raise a ZeroDivisionError
except ZeroDivisionError:
    # Handle the specific exception
    print("Error: Division by zero!")
except:
    # Handle any other exception
    print("An unexpected error occurred!")
finally:
    # Cleanup code
    print("Exiting try-except block")

In the example above, the try block encloses code that may raise an exception. The except block catches and handles exceptions raised within the try block. The type of exception we want to catch can be specified; here, it’s ZeroDivisionError.

A generic except block can be used to catch any exception. The finally block is optional and is used to execute cleanup code (such as closing files or releasing resources) regardless of whether an exception occurred. The code in the finally block is always executed, even if an exception is raised and not caught.

Data Analysis Questions

Question 5: Calculate a Mean in Python

The mean is undoubtedly one of the most-used functions in data analysis. Also known as the average, the mean is calculated by summing up all the values in a dataset and dividing the sum by the total number of values.

If you aspire to a data analyst job, you should know how to calculate the mean in Python. Fortunately, Python’s built-in functions allow us to do it in a very simple way. We can see this in the following example:

data = [2, 3, 4, 5, 6]
mean = sum(data) / len(data)

Thanks to the built-in functions sum() and len(), we can calculate the sum of the values of the dataset and the total number of values.

Another great way to do it is by using the function mean() from the module statistics:

import statistics

data =  [2, 3, 4, 5, 6]
mean = statistics.mean(data)

Question 6: Describe a Typical Data Analysis Process

The data analysis process involves several steps aimed at understanding, interpreting, and deriving insights from data. The general data analysis process typically includes the following main steps:

• Data Collection: Gather relevant data from various sources. In Python, you can use the requests library to retrieve data from web servers by sending HTTP GET and POST You can also use web scraping using libraries like BeautifulSoup or Selenium.
• Data Cleaning: Remove errors, duplicates, and inconsistencies from the dataset. If you need to perform complex operations as you clean your data, you can use a Python data-cleaning library like NumPy or pandas.
• Exploratory Data Analysis (EDA): Explore the dataset to understand its structure, patterns, and relationships. Python libraries such as pandas, NumPy, and Scikit-learn are excellent tools for performing EDA on a dataset.
• Modeling: Apply statistical techniques or machine learning algorithms to analyze the data. Python libraries like Scikit-learn, TensorFlow, and PyTorch provide a set of tools and algorithms for data modeling and machine learning tasks.
• Visualization: Present findings visually through charts, graphs, or dashboards. You can use Python data visualization libraries like Matplotlib or Seaborn to create plots and charts.

Question 7: What File Types Are Commonly Used in Python Data Analysis?

In data analysis, various file types are used to store and manipulate data. Your answer to this question will indicate to the interviewer how much experience you have in manipulating data. Some common file types include:

• CSV (Comma-Separated Values): CSV is a plain text format where each line represents a row of data and columns are separated by commas (or another character, such as a semi-colon). CSV files are widely used for storing tabular data and are compatible with many software tools. Read our article on How to Read CSV Files in Python or visit our course How to Read and Write CSV Files in Python for more information.
• Excel spreadsheets (.xlsx, .xls): Microsoft Excel files are commonly used for storing tabular data, performing calculations, and creating visualizations. I recommend our article How to Read Excel Files in Python and our interactive course How to Read and Write Excel Files in Python for more details.
• JSON (JavaScript Object Notation): JSON is a lightweight data-interchange format that’s easy for humans to read and write and easy for machines to parse and generate. JSON files store data in key-value pairs and are commonly used for web APIs and configuration files. To find out how to work with JSON files, I recommend our interactive course How to Read and Write JSON Files in Python.
• XML (eXtensible Markup Language): XML is a markup language that defines a set of rules for encoding documents in a human-readable and machine-readable format. An XML file is a text file that stores data in this format.
• SQL (Structured Query Language) databases: SQL databases store structured data in tables with rows and columns. Visit our article Top Python Libraries for SQL to find out more about working with SQL in Python.

String Questions

Question 8: How Do You Print a Variable in a String in Python?

There are many ways to print a variable in a Python string. The more experience with Python you have, the more ways you will share with the interviewer.

There are three ways to print a variable: concatenation, string formatting, and f-strings.

language = “Python”

print(“My favorite language is ” + language)

The syntax in the example above is called string concatenation: two strings are merged into a single string using the + operator.

print(“My favorite language is {}”.format(language))

In this example, string formatting is used. The format() method is called on a string that contains replacement fields delimited by braces {}. The replacement fields will be replaced by the parameters passed through the format() function – here, language.

print(f“My favorite language is {language}”)

The example above uses an f-string, which is a formatted string prefixed with f. The parameters we want to include in the string come between the braces {}. This is a relatively new technique to handle variables in strings; f-strings have only been available since Python 3.6.

Question 9: How Do You Slice a String?

Slicing a string in Python refers to extracting a substring from a given string by specifying a range of indices. In data analysis, string slicing is essential for cleaning and preprocessing textual data. It helps handle data inconsistencies, remove unwanted characters, and standardize data formatting.

String slicing uses three optional parameters: start, end, and step. The syntax is as follows:

String[start:end:step]

Here is what each parameter does:

• start (optional): The index from which the slicing begins. It indicates the position of the first character to be included in the slice. If omitted, slicing starts from the beginning of the string (index 0).
• end (optional): The index up to which slicing occurs; however, it excludes the character at this position. If omitted, slicing extends to the end of the string.
• step (optional): The step or increment value for selecting characters within the specified range. If omitted, the default value is 1.

Below are some examples of string slicing:

s = "hello world"

print(s[:5])    # Output: "hello" (extract the first 5 characters)
print(s[6:])    # Output: "world" (extract from index 6 to the end)
print(s[::2])   # Output: "hlowrd" (extract every other character)
print(s[::-1])  # Output: "dlrow olleh" (reverse the string)

pandas Questions

Question 10: How Do You Read a CSV File with pandas?

This question seems very basic, but it demonstrates the candidate’s knowledge of the pandas library. You should be able to answer this question without hesitation!

import pandas as pd

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

With pandas, a CSV file can be easily loaded using the function read_csv(). The path of the CSV file is indicated as a parameter.

Data Structure and Algorithm Questions

Question 11: Describe Python’s Main Data Structures

Manipulating data in Python implies that you can choose the right data structure to correctly store and perform operations on your data. If you have Python programming experience, you should know the “fantastic four” data structures: list, tuple, dictionary, and set. Name them and provide a short explanation of each one in your answer:

• Lists are ordered collections of items. They are mutable – you can modify their contents after creation – and can contain elements of different data types.
• Tuples are similar to lists, but they are immutable; their contents cannot be changed after creation.
• Dictionaries are unordered collections of key-value pairs. Each key in a dictionary must be unique; it is used to quickly access its corresponding value. Dictionaries are mutable and can contain elements of different data types.
• Sets are unordered collections of unique elements. They do not allow duplicate values, but they support mathematical set operations (e.g. union, intersection, and difference).

If you mention other sub-data structures – such as OrderDict, frozensets, or namedtuple – it’s a point in your favor!

Knowing the theory of data structures is excellent, but it’s not enough; you must also be able to provide practical examples of how to use each data structure in a real-life scenario.

• Lists are widely used during data collection and data cleaning. They offer a combination of flexibility, efficiency, and ease of use that makes them suitable for storing and manipulating data.
• Tuples are lightweight data structures. Using tuples instead of lists for storing data that will never be changed is a good practice; in terms of performance, tuples are more efficient than lists.
• Dictionaries provide a powerful way to organize and access data using key-value pairs. For example, you can use a dictionary to store the total sales revenue for each product category, with product categories as keys and total revenues as values.
• Sets are generally used during data cleaning to ensure data integrity and accuracy; they are particularly useful for efficiently removing duplicate elements from a dataset. Converting a list or some other collection to a set automatically eliminates duplicates, leaving only unique elements behind.

To practice using different data structures in Python, I recommend our interactive course Python Data Structures in Practice. It’s focused on the typical usage of lists, tuples, dictionaries, and sets. You can also visit our Python practice articles that feature these data structures:

• 12 Beginner-Level Python List Exercises with Solutions
• Top 10 Python Dictionary Exercises for Beginners
• 10 Python Loop Practice Exercises for Beginners with Detailed Solutions

Question 12: How Do You Efficiently Retrieve a Dictionary Value Using a Key?

This question will test your ability to write optimized code. Suppose you have a dictionary named my_dict, and you want to retrieve the value associated with the specific key “the_key”. If the dictionary does not contain the key, it will generate a fatal error during execution. Therefore, you need to be sure that the key exists when you access it.

Most candidates write something like this:

if the_key in my_dict:
	value = my_dict[the_key]
else: 
	value = ‘default value’

The above answer is valid, but this is not the best way to do it. The condition “if key in my_dict” will iterate over each key of the dictionary, looking for the specific key. If you have this piece of code inside a loop, you’ll soon figure out how inefficient it is!

A better way to answer the question is using the dictionary get() method, which allows us to retrieve a value for a specific key in a dictionary. It accepts a default value in case the key does not exist:

value = my_dict.get(the_key, ‘default value’)

Question 13: What Data Types Can Be a Python Dictionary Key?

Hold on, this is a trick question! Most candidates will answer int or string because those are the types commonly used as a key dictionary, but this is only partially correct. To answer this question correctly, you must understand the concept of hashable.

Dictionary keys are hashable, meaning that they have a hash value that does not change over time. This allows them to be efficiently stored and retrieved. Strings, integers, floats, tuples, and booleans (among others) can be used as dictionary keys. Mutable data types like lists, sets, and dictionaries themselves cannot be used as dictionary keys because they are not hashable.

Do you need to practice your data structure skills? Our course Python Data Structures in Practice is a good start! It’s perfect for anyone wanting to gain practical Python knowledge or preparing for a coding interview.

Question 14: How Do You Sort Dataset Elements?

Sorting is always an expensive operation in terms of performance. Writing a proper sort algorithm is not as easy as it may seem. As you can probably guess, Python provides a ready-made sorting function for you. And it's pretty easy to use:

list_name.sort()

Additionally, the sort() function supports the reverse parameter. By adding reverse=True, we get the elements sorted in descending order. Note that reverse is False by default – if you don't provide the argument, you'll always get your elements sorted in ascending order.

my_list = [9, 3, 5, 1]

my_list.sort()
print(my_list) #Print 1, 3, 5, 9

my_list.sort(reverse=True)
print(my_list) #Print 9, 5, 3, 1

You may also use a custom sorting function with sort(), as described in How to Write Custom Sort Functions in Python

Do you want to go further with Python’s functions and algorithms for data science? I recommend you check out our course on Built-in Algorithms in Python.

Bonus: A Tough Python Question!

Question 15: Explain The Concept of List Comprehension in Python

The concept of list comprehension in Python is essential because it promotes clean, expressive, and efficient code. It is a fundamental tool in the Python programmer's toolkit and is widely used in various domains – including data analysis. A great answer to this question would be the following:

List comprehension is a concise and powerful way to create lists in Python. It provides a compact syntax for generating lists and applying operations or conditions to each element.

The general syntax of a list comprehension in Python is …

new_list = [expression for item in iterable if condition]

… where:

-	expression is the operation or transformation to apply to each element.
-	item is the variable representing each element in the iterable.
-	iterable is the existing sequence (e.g. list, tuple, range) over which to iterate.
-	condition is an optional filtering condition. If specified, only elements for which the condition evaluates to True will be included in the new list.

If you are asked for an example, you can provide this:

The following list comprehension squares each element of a given list, excluding the number 1:

numbers = [1, 2, 3, 4, 5]
squared_numbers = [x**2 for x in numbers if x > 1]
print(squared_numbers) #Prints 4, 9, 16, 25

After Reviewing Python Interview Questions, Don’t Forget to Practice!

Mastering Python is essential for aspiring data analysts. By preparing for common Python interview questions, data analysts can showcase their proficiency in the language and demonstrate their ability to solve real-world data problems.

Throughout this article, we've covered some of the top Python interview questions that data analysts may encounter during job interviews – and their detailed explanations and answers. Remember to not only focus on memorizing answers but also on understanding the underlying concepts. You want to be able to apply what you learn to solve new problems.

I have one final recommendation: check out our track Built-in Algorithms in Python. It will give you the fundamentals of Python that you’ll use in data analysis work.

Good luck with your interviews, and may your Python skills help you unlock exciting opportunities in the world of data!