Top Python Data Structures Interview Questions and Answers for Practice
Python is a language that allows you to create dynamic programs. Programming languages rely on data structures and algorithms, which are important and difficult to master. This is why hiring managers choose Python data structure interview questions when interviewing candidates for software engineering positions.
Going through essential theoretical concepts and exercising problem-solving skills is the best method to prepare for data structures in Python interview questions. It is highly advised that you answer at least 1-2 Python data structures interview questions per day if you have a technical interview coming up.
If you’re a software engineer, coding engineer, software developer, engineering manager, or tech lead preparing for tech interviews, check out our technical interview checklist, interview questions page, and salary negotiation e-book to get interview-ready!
Having trained over 9,000 software engineers, we know what it takes to crack the most challenging tech interviews. Since 2014, Interview Kickstart alums have landed lucrative offers from FAANG and Tier-1 tech companies, with an average salary hike of 49%.
At IK, you get the unique opportunity to learn from expert instructors who are hiring managers and tech leads at Google, Facebook, Apple, and other top Silicon Valley tech companies. Our reviews will tell you how we’ve shaped the careers of thousands of professionals aspiring to take their careers to new heights.
We'll look at some Python data structures interview questions from FAANG+ interviews in this article. These questions will help you anticipate what to expect during these interviews and develop a strong plan for navigating challenging technical rounds.
Here’s what we’ll cover in this article:
- Python Data Structure Interview Questions and Answers
- Sample Data Structure Python Interview Questions
- FAQs on Python Data Structure Interviews Questions
Python Data Structure Interview Questions and Answers
What do experts say?
“Python has been an integral part of Google since the beginning. It remains so as the system grows and evolves. Today dozens of Google engineers use Python, and we're looking for more people with skills in this language." ?
- Peter Norvig
Director of search quality at Google, Inc.
Prepare for your upcoming tech interview with the 16 most frequently asked Python data structure interview questions. Continue reading to learn the most important Python concepts.
Q1. Enumerate differences between a list and a tuple in Python
This is one of the basic Python data structures interview questions. Here’s how you can answer it:
The key differences between a list and a tuple are:
Q2. How is a list different from an array?
The differences between lists and arrays are:
?Learn the concept of Arrays in Data Structures here.
Q3. Describe three advantages of NumPy arrays over Python lists
This is yet another popular Python data structure interview question. The three advantages of NumPy arrays over Python lists are:
- NumPy array is faster. The size of the NumPy arrays increases. It can become thirty times faster than Python lists.
- NumPy is more efficient and convenient. It comes with several vector and matrix operations for free, which helps avoid unnecessary work. Moreover, they can be efficiently implemented.
- Lastly, Python lists have certain limitations, like they don’t support element-wise addition, multiplication, and other vectorized operations. In addition, since lists contain heterogeneous objects, Python must store type information for every element. Contrastingly, arrays have homogeneous objects and thus escape these limitations.
Q4. Why is Python a dynamically typed language?
Dynamic type checking means data types are checked during execution. Python is an interpreted language. It executes each statement line by line. So, type-checking is done during execution, making Python a dynamically typed language.
Q5. What do you understand about inheritance in Python?
To answer this Python data structure interview question, you should know that inheritance is the property of one class to attain all the members (attributes and methods) of another class. Inheritance allows the reusability of code and makes it easier to create an application. It gives rise to two types of classes:
- Superclass is the class from which we are inheriting. It is also called the base class.
- Derived Class is the class that is inherited. It is also called the child class.
The various types of inheritance in Python are:
- Single Inheritance is when a derived class takes the members of a single superclass.
- Multi-level inheritance is when a derived class d1 is inherited from the base class- base1, and another derived class d2 is inherited from base2.
- Hierarchical inheritance allows the inheritance of a number of child classes from a single base class.
- Multiple inheritances are when a child class is inherited from more than one superclass.
Q6. What do you understand about the join method in Python?
In Python, the join method is a string method. It takes elements of an iterable data structure (array, lists, and more) and connects them together using a string connector value.
Q7. What are control flow statements in Python?
This is a common Python data structure interview question asked in tech interviews. This is how you can answer this particular question:
A program’s control flow refers to the order in which the program’s code executes. In Python, the control flow is regulated by conditional loops, statements, and function calls.
It has three main types of control structures:
- Sequential is the default mode
- Selection is used for decisions and branching
- Repetition helps in looping
Q8. Explain memory management in Python.
Python private heap space manages memory, i.e., all objects and data structures of Python are located in a private heap. The python interpreter executes this heap, and no programmer has access to it. Python's memory manager allocates heap space for Python objects. Additionally, Python has an inbuilt garbage collector. It recycles all the unused memory.
Q9. What are modules in Python? State a few benefits of modules.
This Python data structure interview question tests your basic understanding of the language. A Python module is a file containing a set of variables and functions that can be used in an application. The variables can be in the form of arrays, dictionaries, and objects.
Modules fall into two main categories:
Some key benefits of Python modules are:
- It allows structured code organization wherein code is logically grouped into a Python file. Thus, making development easier and less error-prone.
- Reusability of code as functionality in a single module can easily be reused. There is no need to recreate duplicate code.
Take a look at the Best Data Structures and Algorithms Course to crack FAANG interviews.
Q10. Explain slicing in Python.
Slicing is the mechanism to choose a range of items from sequence types such as lists, tuples, and strings. For example, slicing a list refers to selecting a specific portion or a subset of the list for some function, and the rest of the list remains unaffected. So, you remove a piece without altering the rest of the contents.
The syntax for slicing a list is: List_name[start:stop:steps]
Q11. What are decorators in Python?
In Python, decorators are essential functions. These add extension/ functionality to an existing function without altering the structure of the function itself. Instead, decorators take another function as their argument and return yet another function. This, too, is a common Python data structure interview question.
Q12. State differences between xrange and range in Python.
Although xrange() and range() are similar functions in generating a sequence of integers. The major difference is that range returns a Python list of integers while xrange returns an xrange generator object. So xrange() does not generate a static list; instead, it creates the value on the go.
Q13. What are generators in Python?
To answer this Python data structure interview question, understand that a generator is a function that returns an iterable collection of items. This happens one at a time. Thus, a generator is a special type of function that does not return a single value. Instead, they return an iterator object consisting of a sequence of values.
Q14. What are Python keywords? Give examples of keywords.
Python has certain reserved words with special meanings called keywords. They define the type of variables. However, they cannot be used for variable or function names. There are 33 keywords in Python 3.0:
Q15. What are pickling and unpickling in Python?
This is how you can answer this Python data structure interview question:
The process of pickling in Python is when the object hierarchy is converted into a byte stream. Contrastingly, the process of unpickling is the inverse operation. Unpickling involves byte stream conversion back into an object hierarchy. Pickling allows you to arrange Python objects in a serial and allows de-serializing.
Q16. What is lambda in Python? State its uses.
In Python, lambda is an anonymous function. It can accept multiple arguments but has only a single expression. Lambda functions are used in situations needing an anonymous function for a short span of time. The uses of lambda functions are:
- They are used as small, single-line functions.
- They make code easier to read.
Sample Python Data Structure Interview Questions for Practice
Here, we glance over some additional Python data structure interview questions that you can prepare for your tech interview:
- What is Scope in Python?
- Python is what type of language programming or scripting?
- State some benefits of using Python as a programming language?
- How to convert a list into a string?
- How to count the occurrences of a particular element in the list?
- What is type conversion in Python?
- What are global, protected, and private attributes in Python?
- How do you write comments in Python?
- What is the use of self in Python?
- How do you debug a Python program?
- What is a negative index in Python?
- How do you set a global variable inside a function?
- Can you write a program to find the average numbers in a Python list?
- What new features does the Python 3.9.0 version include?
These top Python data structure interview questions will help you prepare for your software developer interview and ace it. After you've finished your preparation, you can take some mock interviews for self-evaluation.
Are you preparing for a tech interview? Check out our Technical Interview Prep Checklist to plan your prep.
FAQs on Python Data Structure Interviews Questions
Q1. How to practice Python coding for tech interviews?
If you are a newbie in programming, you can attend a Python training course to understand basic and key concepts. You should practice Python coding regularly and prepare well for python coding interview questions.
Q2. What are Python data structure interview questions?
Python coding interview questions are asked to test your expertise and analytical skills. For Python data structure interview questions, you can expect questions related to keywords, architecture, frameworks, how to write a code or the program’s output, how to solve a particular scenario, etc.
Q3. What are data structures in Python?
Python includes four basic data structures: Dictionary, Lists, Set, and Tuple.
Q4. Which data structures are essential for Python interviews?
The important data structures for Python interviews are Array/List, Linked list, Hash tables, Queue, Stack, Trees (binary), and Graphs.
Q5. What is the difference between data structure and algorithm?
A data structure is concerned with effectively organizing and managing data so that a specific operation can be performed efficiently, whereas an algorithm is a step-by-step procedure to be followed to achieve the desired output.
Ace Your Next Technical Interview
If you’re looking for guidance and help with your Python data structure interview prep, sign up for our free webinar. As pioneers in the field of technical interview prep, we have trained thousands of software engineers to crack the most challenging coding interviews and land jobs at their dream companies, such as Google, Facebook, Apple, Netflix, Amazon, and more!