Top 50 Python Interview Questions and Answers (2024)

Python continues to dominate as one of the most popular programming languages globally. Its versatility, simplicity, and extensive libraries make it a go-to choice for developers in web development, data science, machine learning, and more. Whether you’re preparing for a Python coding interview, Python data engineer interview, or any other Python-related role, mastering key concepts is crucial to cracking these rounds.

If you are new to programming, our guide on how to become a software developer might help a lot. If you are someone who is preparing for python coding interviews, we’ve curated the top 50 Python interview questions covering everything from basic Python interview questions to more advanced ones, including Python interview questions for data engineers. We’ve also provided detailed answers and supporting content to help you ace your next Python interview.

Metaschool also has hands-on, guided, and free courses on web3, AI, etc. that you can enroll in and improving your coding skills.

Python Basic Interview Questions

The interviewer might start with the very basic if it is an entry level developer job that you are interviewing for. Usually the interviewers skip to the coding rounds and to questions that test your understanding of this programming language.

What is Python, and what are its key features?

Python is a high-level, interpreted programming language designed for readability and ease of use. It supports multiple programming paradigms, such as procedural, object-oriented, and functional programming.

Key Features of Python:

• Object-oriented: Supports classes and objects.
• High-level language: Abstracts complex details from the programmer.
• Dynamically typed: No need to declare variable types.
• Extensive libraries: Supports modules like NumPy, Pandas, TensorFlow, etc.
• Interpreted language: Executes code line-by-line, facilitating easy debugging.

Is Python a compiled or interpreted language?

Python is both compiled and interpreted. Python code is first compiled into bytecode, which is then interpreted by the Python Virtual Machine (PVM). This makes it an interpreted language because the final execution occurs in the interpreter.

What is the difference between mutable and immutable types?

• Mutable types: Objects that can be changed after their creation (e.g., lists, dictionaries).
• Immutable types: Objects that cannot be modified after their creation (e.g., tuples, strings).

Intermediate Python Interview Questions

What are *args and **kwargs in Python?

*args and **kwargs allow a function to accept variable numbers of arguments.

• *args is used for non-keyworded variable-length arguments.
• **kwargs is used for keyworded variable-length arguments.

Example:

def example(*args, **kwargs):
    print(args)
    print(kwargs)

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

Explain list comprehension and give an example.

List comprehension provides a concise way to create lists. The syntax is:

[expression for item in iterable if condition]

Example:

squares = [x**2 for x in range(10)]
# Output: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

What is a lambda function in Python?

A: A lambda function is an anonymous function defined using the lambda keyword. It can have multiple arguments but only one expression.

Example:

add = lambda x, y: x + y
print(add(5, 3))  # Output: 8

Python Coding Interview Questions

How do you reverse a string in Python?

There are multiple ways to reverse a string. The most efficient is using slicing:

s = "Python"
reversed_s = s[::-1]
print(reversed_s)  # Output: nohtyP

Write a Python function to merge two dictionaries, adding values for common keys.

A: You can use the Counter from the collections module:

from collections import Counter

d1 = {'a': 100, 'b': 200}
d2 = {'a': 300, 'b': 200, 'c': 400}
result = Counter(d1) + Counter(d2)
print(result)  # Output: Counter({'a': 400, 'b': 400, 'c': 400})

What is the difference between deep copy and shallow copy?

• Shallow copy copies the object and references the same nested objects. Changes to nested objects affect both copies.
• Deep copy creates a new object and recursively copies all nested objects. Changes to nested objects in one copy do not affect the other.

Example using copy module:

import copy
original = [[1, 2], [3, 4]]
shallow = copy.copy(original)
deep = copy.deepcopy(original)

shallow[0][0] = 'changed'
print(original)  # Output: [['changed', 2], [3, 4]]
print(deep)      # Output: [[1, 2], [3, 4]]

Python Data Engineer Interview Questions

What is Pandas, and why is it used?

A: Pandas is a Python library used for data manipulation and analysis. It provides data structures like DataFrame and Series to work efficiently with structured data.

How do you handle missing data in a Pandas DataFrame?

A: You can use fillna() to replace missing values or dropna() to remove them.

Example:

import pandas as pd
df = pd.DataFrame({'A': [1, None, 3], 'B': [4, 5, None]})
df_filled = df.fillna(0)  # Fills missing values with 0
df_dropped = df.dropna()  # Drops rows with missing values

What is the difference between NumPy and Pandas?

• NumPy: Used for numerical operations on arrays and matrices.
• Pandas: Used for data manipulation and analysis, with support for tabular data like DataFrames.

How can you optimize the performance of large datasets in Python?

A: Use the following techniques:

• Vectorized operations with NumPy or Pandas.
• Use Dask for parallel processing on large datasets.
• Leverage Python’s built-in generators to handle large datasets without loading them into memory all at once.

Advanced Python Interview Questions

What is a Python decorator, and how do you use it?

A: A decorator is a function that takes another function and extends its behavior without explicitly modifying it.

Example:

def decorator_func(func):
    def wrapper():
        print("Before function execution")
        func()
        print("After function execution")
    return wrapper

@decorator_func
def say_hello():
    print("Hello!")

say_hello()
# Output: Before function execution
#         Hello!
#         After function execution

What is the Global Interpreter Lock (GIL) in Python?

A: The GIL is a mutex that protects access to Python objects, preventing multiple threads from executing Python bytecodes at once. This limits the execution of threads in a multi-threaded Python program, making it suboptimal for CPU-bound tasks.

How do you handle memory management in Python?

A: Memory management in Python is handled by the Python Memory Manager. It includes:

• Private heap space: All objects are stored in the private heap, managed by the memory manager.
• Garbage collection: Python has an inbuilt garbage collector that automatically frees memory when objects are no longer in use.

Python Coding Interview Questions

What are Python’s built-in data types?

A: Python supports several built-in data types, including:

• Numeric Types: int, float, complex.
• Sequence Types: list, tuple, range, str.
• Mapping Type: dict.
• Set Types: set, frozenset.
• Boolean Type: bool.
• Binary Types: bytes, bytearray, memoryview.

What is the difference between Python 2 and Python 3?

A: Some key differences include:

• Print: Python 2 uses print as a statement (print "Hello"), whereas Python 3 uses print() as a function (print("Hello")).
• Integer division: In Python 2, dividing two integers truncates the result (5/2 = 2), while in Python 3, it returns a float (5/2 = 2.5).
• Unicode: In Python 3, strings are Unicode by default, whereas in Python 2, they are ASCII.

What are Python modules and packages?

• A module is a single file containing Python definitions and statements.
• A package is a collection of related modules. It allows for a hierarchical structuring of the module namespace using dot notation.

How do you import a module in Python?

A: Modules can be imported using the import statement.

pythonCopy codeimport math
print(math.sqrt(16))  # Output: 4.0

You can also import specific functions:

pythonCopy codefrom math import sqrt
print(sqrt(16))  # Output: 4.0

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

A:

• == compares values to check equality.
• is compares the identity of two objects to see if they refer to the same object in memory.

Example:

pythonCopy codea = [1, 2]
b = [1, 2]
print(a == b)  # Output: True (values are the same)
print(a is b)  # Output: False (different objects)

What is a Python set and how is it different from a list?

A: A set is an unordered collection of unique elements. Unlike lists, sets do not allow duplicate entries and do not maintain order.

pythonCopy codemy_set = {1, 2, 3, 4, 4}
print(my_set)  # Output: {1, 2, 3, 4}

How does Python handle memory management?

A: Python uses a private heap to store objects and data structures. Memory management in Python involves:

• Reference counting: Keeps track of the number of references to an object in memory.
• Garbage collection: Automatically frees up memory by collecting unused objects.

Write a Python program to find the factorial of a number using recursion.

pythonCopy codedef factorial(n):
    if n == 1:
        return 1
    else:
        return n * factorial(n-1)

print(factorial(5))  # Output: 120

Write a Python program to check if a number is prime.

pythonCopy codedef is_prime(n):
    if n <= 1:
        return False
    for i in range(2, int(n**0.5) + 1):
        if n % i == 0:
            return False
    return True

print(is_prime(7))  # Output: True

How do you create a generator in Python?

A: A generator is created using a function that yields values instead of returning them. This allows you to iterate over values lazily, meaning the values are generated one at a time as needed.

Example:

pythonCopy codedef my_generator():
    yield 1
    yield 2
    yield 3

for value in my_generator():
    print(value)

What is the difference between sort() and sorted() in Python?

• sort() sorts the list in-place and returns None.
• sorted() returns a new sorted list without modifying the original list.

pythonCopy codemy_list = [3, 1, 2]
sorted_list = sorted(my_list)  # Returns a new sorted list
print(my_list)  # Output: [3, 1, 2]
print(sorted_list)  # Output: [1, 2, 3]

my_list.sort()  # Sorts in-place
print(my_list)  # Output: [1, 2, 3]

Write a program to find the maximum and minimum elements in a list.

A:

pythonCopy codemy_list = [4, 1, 8, 7, 5]
max_val = max(my_list)
min_val = min(my_list)
print(f"Max: {max_val}, Min: {min_val}")

How do you remove duplicates from a list in Python?

A: You can convert the list to a set, which automatically removes duplicates, and then convert it back to a list.

pythonCopy codemy_list = [1, 2, 2, 3, 4, 4]
unique_list = list(set(my_list))
print(unique_list)  # Output: [1, 2, 3, 4]

How do you read a CSV file in Python?

A: You can use the pandas library to read CSV files:

pythonCopy codeimport pandas as pd
df = pd.read_csv('data.csv')
print(df.head())  # Displays the first 5 rows of the CSV

What are dataframes in Pandas?

A DataFrame is a two-dimensional labeled data structure with columns of potentially different types. It is similar to a table in a database or an Excel spreadsheet.

How do you handle missing data in a DataFrame?

A: Missing data can be handled using the fillna() or dropna() functions:

pythonCopy codedf.fillna(0)  # Replaces missing values with 0
df.dropna()   # Drops rows with missing values

What is the difference between apply() and map() in Pandas?

• apply() is used to apply a function along the axis of a DataFrame (either rows or columns).
• map() is used to apply a function element-wise to a series.

pythonCopy codedf['col'] = df['col'].map(lambda x: x*2)  # Element-wise operation
df['col'] = df['col'].apply(lambda x: x*2)  # Applied to each element in the column

What is the purpose of __init__.py in Python packages?

A: __init__.py is a special file in Python that is used to mark a directory as a Python package. It can also execute initialization code for a package.

What is a context manager in Python?

A: A context manager is used to allocate and release resources precisely when needed. The most common way to implement it is using the with statement.

pythonCopy codewith open('file.txt', 'r') as f:
    data = f.read()

What is method overloading in Python?

A: Python does not support method overloading directly like Java or C++. However, you can achieve similar behavior using default arguments or variable-length arguments.

What is method overriding in Python?

A: Method overriding occurs when a child class has a method with the same name as a method in the parent class. The method in the child class overrides the method in the parent class.

pythonCopy codeclass Parent:
    def speak(self):
        print("Parent speaks")

class Child(Parent):
    def speak(self):
        print("Child speaks")

c = Child()
c.speak()  # Output: Child speaks

What is metaclass in Python?

A: A metaclass is a class of a class that defines how classes behave. A class is an instance of a metaclass.

How does Python manage memory for large objects?

A: Python uses garbage collection and reference counting to manage memory for large objects. It also has a memory pool for efficiently allocating and deallocating small objects.

What is monkey patching in Python?

A: Monkey patching refers to dynamically modifying or extending a class or module at runtime.

Write a program to find the Fibonacci sequence up to a given number.

pythonCopy codedef fibonacci(n):
    a, b = 0, 1
    while a < n:
        print(a, end=' ')

Write a Python program to check if a string is a palindrome.

A:

pythonCopy codedef is_palindrome(s):
    return s == s[::-1]

print(is_palindrome("madam"))  # Output: True
print(is_palindrome("hello"))  # Output: False

What is a virtual environment in Python, and why is it used?

A: A virtual environment is an isolated environment in Python that allows you to manage dependencies for different projects without conflicts. It ensures that the libraries used in one project do not interfere with those used in another.

You can create a virtual environment using:

bashCopy codepython -m venv myenv
source myenv/bin/activate  # Activate on Mac/Linux
myenv\Scripts\activate  # Activate on Windows

What are Python’s built-in functions?

A: Some commonly used built-in functions in Python are:

• len(): Returns the length of an object.
• type(): Returns the type of an object.
• print(): Prints the given object.
• input(): Reads input from the user.
• sum(): Returns the sum of an iterable.

What is the difference between len() and count() in Python?

A:

• len() returns the total number of elements in an object (e.g., list, string, dictionary).
• count() returns the number of occurrences of a specific element in a list or string.

Example:

pythonCopy codemy_list = [1, 2, 3, 3, 2]
print(len(my_list))    # Output: 5
print(my_list.count(2))  # Output: 2

Explain the usage of super() in Python.

A: super() is used to call a method from a parent class. This is especially useful when dealing with multiple inheritance, as it allows you to access methods of a superclass without referring to the superclass explicitly.

Example:

pythonCopy codeclass Parent:
    def show(self):
        print("Parent class")

class Child(Parent):
    def show(self):
        super().show()  # Calls the method in Parent
        print("Child class")

c = Child()
c.show()

What is exception handling in Python? Name some common exceptions.

A: Exception handling in Python is done using try, except, finally, and else blocks to catch and handle errors gracefully without crashing the program. Common exceptions include:

• ZeroDivisionError: Division by zero.
• IndexError: Accessing an index that is out of bounds.
• KeyError: Accessing a non-existent key in a dictionary.
• ValueError: Passing an argument of the wrong type.

What is the purpose of enumerate() in Python?

A: enumerate() adds a counter to an iterable and returns it as an enumerate object, which can then be used in loops to retrieve both the index and the value.

Example:

pythonCopy codemy_list = ['apple', 'banana', 'cherry']
for index, value in enumerate(my_list):
    print(index, value)

Output:

Copy code0 apple
1 banana
2 cherry

Write a Python program to find the largest and smallest elements in a list without using built-in functions.

pythonCopy codedef find_max_min(lst):
    max_val = min_val = lst[0]
    for num in lst[1:]:
        if num > max_val:
            max_val = num
        if num < min_val:
            min_val = num
    return max_val, min_val

my_list = [34, 12, 56, 78, 23]
max_val, min_val = find_max_min(my_list)
print(f"Max: {max_val}, Min: {min_val}")

What is the use of zip() in Python?

A: The zip() function takes multiple iterables and aggregates them into tuples. It stops when the shortest iterable is exhausted.

Example:

pythonCopy codelist1 = [1, 2, 3]
list2 = ['a', 'b', 'c']
zipped = zip(list1, list2)
print(list(zipped))  # Output: [(1, 'a'), (2, 'b'), (3, 'c')]

Final Tips:

1. Practice solving coding challenges on platforms like LeetCode and HackerRank.
2. Build small projects to gain hands-on experience with Python.
3. Keep up with the latest updates and changes in Python, as the language evolves regularly.

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