Getting started
Python is one of the most popular coding languages in the world—from building web apps to powering AI innovations. Its widespread usage can be attributed to its simplicity, robust community support, and versatility. As a result, proficiency in Python can give you a significant advantage in the job market as you look for your next role, whether you’re a more senior engineer or looking for your first tech job.
This guide is tailored to prepare you for Python-related interviews. It covers a wide spectrum of questions, from foundational Python concepts to advanced domain-specific challenges faced by senior developers. Detailed answers accompany each question to enhance understanding.
To prepare efficiently, it’s important for developers to accurately evaluate their own skills in Python and technical interviewing. This guide helps in this assessment by categorizing questions by difficulty. Beginners can gauge their understanding of basics, while advanced programmers can test their knowledge of more complex use cases. Recognizing your proficiency level helps you focus your preparation on areas that require the most attention.
It’s important to set specific goals for your Python interview preparation based on your skill and type of role you’re interviewing for. This guide will help you identify key areas for improvement, such as data structures, object-oriented programming, or library specific knowledge. Once you set your goals, you can create a focused and tailored practice plan that includes regular coding exercises and mock interview scenarios.
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What you will need to get started
For day-to-day coding, developers often rely on fully-featured Integrated Development Environments (IDEs) such as PyCharm, leveraging tools like debugging, auto-complete, and code navigation. However, interview coding environments are generally more lightweight, intentionally limiting available features to concentrate on assessing coding abilities. Some may only allow debugging using print statements. We’ve observed that developers accustomed to the rich debugging capabilities of IDEs can sometimes encounter challenges when transitioning to these constrained coding environments. Therefore, while full IDEs prove ideal for regular development, we strongly recommend practicing coding interviews using simpler text editors that mirror the conditions of actual interview coding platforms. Those who intentionally practice in environments resembling interviews tend to feel more at ease. If you opt for practicing in a feature-rich IDE, consider refraining from using advanced features like variable watching and instead focus on debugging using print statements. If your interview IDE does offer extra features, view them as an added bonus. Similarly, unless an interview explicitly evaluates proficiency with a framework like NumPy or TensorFlow, minimize the use of external packages and imports. Interview questions typically center around base language skills and standard library functionality. If you are accustomed to heavily relying on packages, explore alternative ways of implementing their capabilities.
How to solve our Python interview questions
Review computer science fundamentals: Familiarize yourself with basic data types (strings, lists, dictionaries) and control flow statements. Ensure comfort with the structure and syntax of Python code. Brush up on time and space complexity so you can evaluate those for all problems.
Practice common algorithm questions: Code solutions to standard algorithm questions like fizzbuzz, reversing a string, and the Fibonacci sequence. Many coding questions tend to build off the basics, and knowing some common patterns will make approaching new problems easier.
Master built-in Python features: Become adept at using built-in Python data structures and methods. Understand methods for lists, dictionaries, sets, strings, etc., and know when to apply them.
Handle input/output: Review input/output operations in Python, including reading input, opening files, and writing output. Develop proficiency in debugging with print statements.
Communication is key: Practice speaking through your code as you would during the interview; articulate the purpose of each line. After writing a solution, generate test cases after coding, considering base cases, edge cases, and invalid input.
Coding style matters: Embrace good coding style with meaningful variable names, proper indentations, and spaces for readability. Add comments where they enhance understanding. Your code should be clear enough for an interviewer to read and understand your thought process. Practice writing clear code early so it’s second nature during your interview.
Problem-solving approach: If you encounter difficulties, think aloud about how to break down the problem and try to solve smaller subproblems first. During the actual interview, you will have the interviewer to lean on—but if you’re practicing by yourself, you’ll have to work out ways to guide yourself. Consider using paper to write down ideas, write out test cases, and work out the logic step-by-step before coding.
Tips for basic level Python interview coding questions
For junior-level positions, interviewers aim to evaluate your aptitude for learning, problem-solving, and grasp of fundamental computer science concepts. While the assessment may not delve deeply into Python domain knowledge, being fluent in the language significantly enhances your coding speed and enables you to concentrate on solving the given problem effectively. When tackling a problem, prioritize developing a working solution initially, and then explore optimization possibilities. Recognize that problems often have multiple viable solutions, and experimenting with different approaches can be beneficial. At this early stage in your coding journey, gaining more practice proves to be the most advantageous strategy.
Tips for senior level Python interview coding questions
For senior-level Python interviews, anticipate a variety of challenges that extend beyond coding proficiency. As you become more specialized, the requirements for domain knowledge are likely to increase beyond fundamental understanding. After spending significant time in the industry, preparing for the more pedagogical problems that often arise in interviews can feel both challenging and unfamiliar. Consider initially practicing with beginner-level problems to reacquaint yourself with the essential skills required for interviews before delving into more domain-specific scenarios. When you transition to advanced topics, approach them with a fresh perspective, acknowledging that your prior experiences may differ from the canonical questions often posed in interviews.
Basic Python interview questions
For questions at a beginner level, interviewers may want to evaluate your computer science fundamentals more than they want to see deeper knowledge of Python functions and capabilities. They may ask you not to use built-in functions and ask you to build them from scratch, to show that you understand how these functions work. Other times, they may expect you to demonstrate enough knowledge of the language to know when to use them. For interview practice, try writing multiple solutions for each problem you encounter. If you’re unsure what to use during the actual interview, ask your interviewer for clarification.
Question 1: Adding up elements in a list
Prompt: Write a Python function that takes a list of numbers and returns the sum of all elements in the list. For example, for the list [1, 2, 3, 4], the function should return 10.
What this question evaluates: This question assesses basic list handling and the use of loops in Python. For this reason, don’t use the built-in Python sum function in your initial implementation.
Solution:
def sum_of_list(numbers):
total = 0
for number in numbers:
total += number
return total
Explanation of solution: The function iterates over each element in the list numbers using a for loop. It initializes a variable total to 0 and adds each element of the list to this variable, accumulating the sum. Finally, it returns the total sum of the elements.
Question 2: Finding the highest number in a list
Prompt: Build on your previous function to return the largest number in the list, in addition to the sum. For the list [1, 2, 3, 4], the function should return the sum 10 and the maximum number 4.
What this question evaluates: This question builds on the first question by adding an understanding of how to compare elements in a list. For this problem, don’t use the built-in max function.
Solution:
def sum_and_max_of_list(numbers):
total = 0
max_number = numbers[0] # Assume the first number is the largest initially
for number in numbers:
total += number
if number > max_number:
max_number = number
return total, max_number
Explanation of solution: The function initializes two variables: total to store the sum and max_number to store the current maximum number, initially set to the first element in the list. As it iterates through the list, it adds each element to total. Simultaneously, it checks if each element is greater than the current max_number. If so, it updates max_number. It returns both the total sum and the maximum number found in the list.
