Python Interview Questions for Beginners

If you're just starting your journey in programming and preparing for your first technical interview, it's important to understand what is actually expected of you. Employers for entry-level positions evaluate not the number of terms you've memorized, but your ability to apply basic knowledge in practice, think logically, and write clean code.

The theoretical part of an interview is not an exam, but a check of the fundamental principles upon which any Python project is built: working with variables, data types, conditions, loops, functions, lists, and dictionaries.

This section contains the most frequently asked theoretical questions that actually come up in junior-level interviews. The answers are written in a way that helps you understand the concepts, not just memorize definitions. This approach gives you confidence and helps you pass the interview calmly. Many of these topics are also discussed in the practical part, so it's important not only to "know" them in theory, but also to be able to explain why your code works the way it does. Regular practice with these questions builds developer thinking and lays the foundation for the next stage—algorithms and system design.

What are the Most Common Python Interview Questions for Beginners?

This section will help you focus on the key topics that interviewers typically check for beginners. The main emphasis is on understanding basic syntax, logic, data structures, working with functions, and control flow. We've compiled only the questions that are actually asked in interviews, so you don't waste time on minor details. Each topic is presented in a way that allows you not only to answer but also to explain the core idea in your own words.

1. What is a variable in Python and how does value assignment work?

A variable is a named reference to an object in memory. In Python, a variable is created automatically when a value is first assigned. The variable's data type is determined dynamically, meaning the language tracks the type of the assigned value on its own. There's no need to declare the type beforehand.

Variables in Python are references to objects. That is, a variable points to an object, not stores it directly. This is important when working with lists, dictionaries, and other mutable data structures.

It's also worth remembering that variable names are case-sensitive (my_var and My_Var are different variables), and there are reserved keywords that cannot be used as names (class, def, return, etc.).

Assignment can be multiple:

a, b = 1, 2

Or chained:

a = b = c = 0

This approach is useful for initial value setup. A candidate should understand these principles because working with variables is the foundation of all logic in programming languages.

2. Explain the difference between a list and a tuple.

A list (list) and a tuple (tuple) are sequences in Python, but there are several key differences. The main difference is mutability.

Lists are mutable: you can add, delete, or change elements. Example:

    my_list = [1, 2, 3]
    my_list[0] = 10
  

Tuples are immutable: once created, their content cannot be changed. Example:

    my_tuple = (1, 2, 3)
    # my_tuple[0] = 10  # will raise an error
  

Lists are typically used when data needs to be modified. Tuples are suited for storing fixed sets of values, especially when data integrity matters. Tuples are also slightly faster in performance and can be used as dictionary keys (if they contain only immutable objects).

Syntax-wise: a list is created using square brackets [1, 2, 3], and a tuple with parentheses (1, 2, 3).

Conclusion: a list is a universal container, while a tuple is a compact and safe structure for fixed data.

3. How does flow control work in Python? Explain if, for, while.

Flow control in Python is implemented through conditionals and loops. The most commonly used are if, for, and while.

if / elif / else: used to execute code depending on a logical condition.

    x = 10
    if x > 0:
        print("Positive")
    elif x == 0:
        print("Zero")
    else:
        print("Negative")
  

for: used to iterate over sequences (strings, lists, range).

    for i in range(5):
      print(i)
  

while: runs a block of code while a condition is true.

    x = 0
    while x < 5:
        print(x)
        x += 1
  

Python supports break (exit the loop), continue (skip iteration), and pass (do nothing). All control structures require indentation—this is a structural element of the language.

Strong knowledge of these constructs is essential for beginners, as they are used in virtually every project—from data input to algorithm writing.

4. What is a function and why is it needed?

A function is a named block of code that can be called multiple times with different arguments. Using functions helps structure code, avoid repetition, and simplify maintenance.

Function declaration:

    def greet(name):
      print(f"Hello, {name}")
  

Function call:

greet("Anna")

Functions can return values using return, accept arbitrary arguments with *args and **kwargs.

Python also supports lambda functions—anonymous expressions:

square = lambda x: x ** 2

Functions are the basis of modularity. Even in simple projects, it's best to extract repetitive code into separate functions. This makes testing, debugging, and reuse easier.

A good developer always considers where to use functions to make the code cleaner and more flexible.

5. Explain what a data type is and why it’s needed.

A data type defines what operations can be performed on a value. For example, numbers can be added, strings concatenated, and lists indexed. Python uses dynamic typing—variables can change their type during execution.

Primary data types:

• int — integers
• float — floating-point numbers
• str — strings
• bool — boolean values
• list, tuple, set, dict — collections

Data types help prevent errors. Trying to add a number and a string will raise an exception:

5 + "5" # error

You can check a type using type():

type(5)  # 

Working with types is also important when dealing with external sources (files, APIs), where data often comes as strings and needs conversion.

6. What are conditional operators and logical expressions in a programming language?

Conditional operators are constructs that allow executing code based on the truth value of a logical expression. They are used to control the program's execution flow.

Main conditional operators:

• if — executes a block if the condition is true.
• elif — checks an additional condition.
• else — executes code if none of the previous conditions were met.

Example:

    x = 5
    if x > 10:
        print("Greater than 10")
    elif x == 5:
        print("Equal to 5")
    else:
        print("Less than 10")
  

Logical expressions use operators and, or, not:

    if age > 18 and is_registered:
      print("Access granted")
  

Comparison operators often used:

• == — equal to
• != — not equal to
• > — greater than
• < — less than
• >=, <=

It's important to understand that logical expressions always return True or False, which determines whether the if block executes.

7. What are loops in Python and how do for and while differ?

Loops allow repeating a block of code. There are two main types of loops in Python: for and while.

The for loop is used to iterate over a sequence:

    for i in range(3):
      print(i)
  

It's especially convenient when the range or data structure is known in advance. It works with any iterable: lists, strings, dictionaries, etc.

The while loop runs while the condition is true:

    x = 0
    while x < 3:
        print(x)
        x += 1
  

while is better when the number of iterations is not known in advance.

Both loops can use break (to exit) and continue (to skip iteration). Python also allows else with loops—executed if the loop ends without break.

Understanding loops is critical in interviews, especially in tasks involving lists, strings, and nested structures.

8. What is a list and what operations can be performed on it?

A list is an ordered collection that can contain elements of different types. Lists are mutable: you can add, remove, and modify elements.

Creating a list:

numbers = [1, 2, 3]

List operations:

• Add: append(), extend(), insert()
• Remove: remove(), pop(), clear()
• Modify by index: my_list[0] = 10
• Slicing: my_list[1:3]
• Membership check: if x in my_list

Lists are one of the most commonly used structures in Python. Interviews often include tasks on filtering, sorting, removing duplicates, and merging lists. A candidate must know how list methods work and be able to write basic processing functions.

9. What are exceptions and why is try/except used?

Exceptions are used to handle errors during runtime. They prevent crashes and allow showing user-friendly messages or performing alternative actions.

try/except is used to "catch" errors and handle them safely:

    try:
        result = 10 / 0
    except ZeroDivisionError:
        print("Cannot divide by zero")
  

You can also use else (runs if no exception) and finally (runs always):

    try:
        x = int(input())
    except ValueError:
        print("Invalid input")
    else:
        print("Success")
    finally:
        print("End of block")
  

Important: using except without specifying an error type is bad practice. Always specify the exception type (ValueError, TypeError, IndexError, etc.).

Understanding exceptions is a must-have skill. It shows the developer can write robust and predictable code, especially in input handling, networking, and file operations.

10. What is OOP and how is a class implemented in Python?

Object-oriented programming (OOP) is a paradigm where code is organized around objects and classes. A class is a template for creating objects (instances), and an object is a specific instance of that class.

Creating a class:

    class Person:
    def __init__(self, name):
        self.name = name

    def greet(self):
        print(f"Hello, my name is {self.name}")
  

Creating an object:

    p = Person("Alice")
    p.greet()
  

Core OOP principles:

• Encapsulation — hiding internal implementation.
• Inheritance — class inherits properties and methods.
• Polymorphism — one interface, different behavior.
• Abstraction — showing only important features.

OOP is essential for structuring large projects. Even a junior developer must understand how to create classes, what self is, how methods work, and class hierarchies.

11. What is the difference between is and == in Python?

== compares values, is checks if two variables point to the same object in memory.

Example:

    a = [1, 2, 3]
    b = [1, 2, 3]
    a == b      # True, same values
    a is b      # False, different objects    
  

== invokes the __eq__() method and is used 99% of the time. is is rare—used, for example, when checking for None:

if value is None:

In an interview, it’s important to not just know the syntax, but understand why is and == are not interchangeable.

12. What are list comprehensions and why are they useful?

A list comprehension is a compact way to create a new list from an existing one. It simplifies syntax and improves readability.

Example:

squares = [x**2 for x in range(5)]

Equivalent code without comprehension:

    squares = []
    for x in range(5):
        squares.append(x**2)
  

You can use conditions in comprehensions:

even = [x for x in range(10) if x % 2 == 0]

Advantages — compactness, readability, and performance. Very useful in data processing and filtering tasks.

13. What is None and how does it differ from False, 0, and an empty string?

None is a special object meaning "nothing". It is not equal to 0, False, '', or [].

Example:

    x = None
    if x:
        print("True")
    else:
        print("False")  # This will print
  

Important: None is an object of type NoneType. It’s used as a default value, a placeholder for no result, or an explicit return of "nothing" from a function:

    def func():
      pass
    print(func())  # None
  

The candidate should clearly understand that None is not a "zero value" but a standalone object.

14. How does the in operator work?

The in operator checks whether an element is contained in a sequence or collection.

Example:

    "a" in "abc"        # True
    2 in [1, 2, 3]      # True
    "k" in {"k": 1}     # True (checks keys, not values)    
  

If an object supports the iteration protocol (__contains__() or __iter__()), in will work. This is one of the most frequently used operators, especially when working with strings, lists, and dictionaries.

15. What are *args and **kwargs?

*args — used to pass an undefined number of positional arguments.
**kwargs — used to pass named arguments (as a dictionary).

Example:

    def demo(*args, **kwargs):
      print(args)
      print(kwargs)
    demo(1, 2, 3, a=4, b=5) 
  

Output:
(1, 2, 3)
{'a': 4, 'b': 5}

This is useful for flexible functions, libraries, and frameworks. A candidate should understand how they are unpacked during a function call (*list, **dict).

16. Explain the difference between mutable and immutable data types.

Mutable — those whose values can be changed after creation. Immutable — cannot be changed.

Mutable:

• list
• dict
• set

Immutable:

• int
• float
• str
• tuple

This affects how arguments are passed to functions and caching. Knowing this difference is a basic requirement.

17. What does the built-in function zip() do?

The zip() function combines multiple iterables and returns an iterator of tuples where each tuple contains elements with the same index.

Example:

    a = [1, 2, 3]
    b = ["a", "b", "c"]
    print(list(zip(a, b)))
    # [(1, 'a'), (2, 'b'), (3, 'c')]
  

If the sequences are of different lengths — zip stops at the shortest one.

Useful when working with data pairs, dictionaries, reading CSV, and handling function inputs.

18. What is slicing in Python?

Slicing is a way to get a subset from a string, list, or other sequence type.

Syntax:

my_list[start:stop:step]

Slices do not modify the original list. This is a powerful tool for filtering, trimming, and creating new collections.

19. What does the range() function do?

range() returns a generator of numbers within a specified range. It’s used in for loops and comprehensions.

Syntax:

range(start, stop, step)

The function does not create a list in memory — it returns an iterator, which saves resources. Frequently used in algorithmic problems.

20. What are break, continue, and pass?

These are control statements that change the loop’s behavior.

• break — completely exits the loop.
• continue — skips the current iteration and proceeds to the next.
• pass — a placeholder that does nothing.

Knowing these constructs is important for writing clean and understandable loops, especially when handling conditions.